diff --git a/data/001_Forbes/qa.csv b/data/001_Forbes/qa.csv deleted file mode 100644 index ceaddd6c0ee9ee6b3581bcc771230c8596b72952..0000000000000000000000000000000000000000 --- a/data/001_Forbes/qa.csv +++ /dev/null @@ -1,26 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the person with the highest net worth self-made?,True,boolean,"['finalWorth', 'selfMade']","['number[uint32]', 'boolean']",False -Does the youngest billionaire identify as male?,True,boolean,"['age', 'gender']","['number[UInt8]', 'category']",True -Is the city with the most billionaires in the United States?,True,boolean,"['city', 'country']","['category', 'category']",True -Is there a non-self-made billionaire in the top 5 ranks?,True,boolean,"['rank', 'selfMade']","['number[uint16]', 'boolean']",False -Does the oldest billionaire have a philanthropy score of 5?,False,boolean,"['age', 'philanthropyScore']","['number[UInt8]', 'number[UInt8]']",False -What is the age of the youngest billionaire?,19.0,number,['age'],['number[UInt8]'],32.0 -How many billionaires are there from the 'Technology' category?,343,number,['category'],['category'],0 -What's the total worth of billionaires in the 'Automotive' category?,583600,number,"['category', 'finalWorth']","['category', 'number[uint32]']",0 -How many billionaires have a philanthropy score above 3?,25,number,['philanthropyScore'],['number[UInt8]'],0 -What's the rank of the wealthiest non-self-made billionaire?,3,number,"['selfMade', 'rank']","['boolean', 'number[uint16]']",288 -Which category does the richest billionaire belong to?,Automotive,category,"['finalWorth', 'category']","['number[uint32]', 'category']",Food & Beverage -What's the country of origin of the oldest billionaire?,United States,category,"['age', 'country']","['number[UInt8]', 'category']",United Kingdom -What's the gender of the billionaire with the highest philanthropy score?,M,category,"['philanthropyScore', 'gender']","['number[UInt8]', 'category']",M -What's the source of wealth for the youngest billionaire?,drugstores,category,"['age', 'source']","['number[UInt8]', 'category']",fintech -What is the title of the billionaire with the lowest rank?,,category,"['rank', 'title']","['number[uint16]', 'category']", -List the top 3 countries with the most billionaires.,"['United States', 'China', 'India']",list[category],['country'],['category'],"['United States', 'China', 'Brazil']" -List the top 5 sources of wealth for billionaires.,"['real estate', 'investments', 'pharmaceuticals', 'diversified', 'software']",list[category],['source'],['category'],"['diversified', 'media, automotive', 'Semiconductor materials', 'WeWork', 'beverages']" -List the top 4 cities where the youngest billionaires live.,"[nan, 'Los Angeles', 'Jiaozuo', 'Oslo']",list[category],"['age', 'city']","['number[UInt8]', 'category']","['San Francisco', 'New York', 'Wuhan', 'Bangalore']" -List the bottom 3 categories with the fewest billionaires.,"['Logistics', 'Sports', 'Gambling & Casinos']",list[category],['category'],['category'],"['Service', 'Fashion & Retail', 'Manufacturing']" -List the bottom 2 countries with the least number of billionaires.,"['Colombia', 'Andorra']",list[category],['country'],['category'],"['Canada', 'Egypt']" -List the top 5 ranks of billionaires who are not self-made.,"[3, 10, 14, 16, 18]",list[number],"['selfMade', 'rank']","['boolean', 'number[uint16]']","[288, 296, 509, 523, 601]" -List the bottom 3 ages of billionaires who have a philanthropy score of 5.,"[48.0, 83.0, 83.0]",list[number],"['philanthropyScore', 'age']","['number[UInt8]', 'number[UInt8]']",[] -List the top 6 final worth values of billionaires in the 'Technology' category.,"[171000, 129000, 111000, 107000, 106000, 91400]",list[number],"['category', 'finalWorth']","['category', 'number[uint32]']",[] -List the bottom 4 ranks of female billionaires.,"[14, 18, 21, 30]",list[number],"['gender', 'rank']","['category', 'number[uint16]']",[] -List the top 2 final worth values of billionaires in the 'Automotive' category.,"[219000, 44800]",list[number],"['category', 'finalWorth']","['category', 'number[uint32]']",[] diff --git a/data/001_Forbes/sample.csv b/data/001_Forbes/sample.csv deleted file mode 100644 index 57eb6f629147f45f58a01ee48ce3b15b3dcfbc72..0000000000000000000000000000000000000000 --- a/data/001_Forbes/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -selfMade,finalWorth,city,title,gender,age,rank,philanthropyScore,category,source,country -False,7800,Atlanta,Chairman ,M,74.0,296,2.0,Media & Entertainment,"media, automotive",United States -True,1700,Ningbo,,M,86.0,1729,,Manufacturing,precision machinery,China -True,2000,Wuhan,,M,49.0,1513,,Real Estate,real estate,China -True,1100,São Paulo,,M,69.0,2448,,Diversified,pharmaceuticals,Brazil -True,3300,Sao Jose dos Pinhais,,M,72.0,913,,Fashion & Retail,cosmetics,Brazil -False,5200,Southampton,,F,79.0,523,1.0,Media & Entertainment,"media, automotive",United States -False,4700,Taipei,,M,54.0,601,,Finance & Investments,financial services,Taiwan -True,5300,Singapore,,M,51.0,509,,Food & Beverage,restaurants,Singapore -True,2000,Toronto,,M,65.0,1513,,Finance & Investments,real estate finance,Canada -False,2600,Dubai,Athlete,M,,1196,,Diversified,diversified,United Arab Emirates -True,1300,Jinan,,M,,2190,,Manufacturing,Semiconductor materials,China -True,1400,San Francisco,Cofounder,M,32.0,2076,,Finance & Investments,fintech,United States -True,1100,Foshan,,M,52.0,2448,,Food & Beverage,soy sauce,China -True,1400,New York,,M,43.0,2076,,Real Estate,WeWork,United States -False,7900,Alexandria,,F,61.0,288,,Food & Beverage,"candy, pet food",United States -True,2500,Cairo,,M,74.0,1238,,Diversified,diversified,Egypt -True,4400,New York,,M,80.0,654,3.0,Media & Entertainment,online media,United States -False,1400,Bangalore,,M,49.0,2076,,Service,education,India -True,1300,London,,M,96.0,2190,,Fashion & Retail,fashion retailer,United Kingdom -True,1700,Hengshui,,M,57.0,1729,,Food & Beverage,beverages,China diff --git a/data/002_Titanic/qa.csv b/data/002_Titanic/qa.csv deleted file mode 100644 index 99895f00e75cbc2a7ade6fe59c80ec113686d0b0..0000000000000000000000000000000000000000 --- a/data/002_Titanic/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Did any children below the age of 18 survive?,True,boolean,"[Age, Survived]","['number[UInt8]', 'boolean']",True -Were there any passengers who paid a fare of more than $500?,True,boolean,[Fare],['number[double]'],False -Is every passenger's name unique?,True,boolean,[Name],['text'],True -Were there any female passengers in the 3rd class who survived?,True,boolean,"[Sex, Pclass, Survived]","['category', 'number[uint8]', 'boolean']",True -How many unique passenger classes are present in the dataset?,3,number,[Pclass],['number[uint8]'],3 -What's the maximum age of the passengers?,80.0,number,[Age],['number[UInt8]'],69.0 -How many passengers boarded without any siblings or spouses?,604,number,[Siblings_Spouses Aboard],['number[uint8]'],12 -"On average, how much fare did the passengers pay?",32.31,number,[Fare],['number[double]'],23.096459999999997 -Which passenger class has the highest number of survivors?,1,category,"[Pclass, Survived]","['number[uint8]', 'boolean']",3 -What's the most common gender among the survivors?,female,category,"[Sex, Survived]","['category', 'boolean']",female -"Among those who survived, which fare range was the most common: (0-50, 50-100, 100-150, 150+)?",0-50,category,"[Fare, Survived]","['number[double]', 'boolean']",0-50 -"What's the most common age range among passengers: (0-18, 18-30, 30-50, 50+)?",18-30,category,[Age],['number[UInt8]'],18-30 -Name the top 3 passenger classes by survival rate.,"[1, 2, 3]",list[category],"[Pclass, Survived]","['number[uint8]', 'boolean']","[1, 3, 2]" -"Could you list the bottom 3 fare ranges by number of survivors: (0-50, 50-100, 100-150, 150+)?","['50-100', '150+', '100-150']",list[category],"[Fare, Survived]","['number[double]', 'boolean']","[50-100, 150+, 100-150]" -"What is the top 4 age ranges('30-50', '18-30', '0-18', '50+') with the highest number of survivors?","['30-50', '18-30', '0-18', '50+']",list[category],"[Age, Survived]","['number[UInt8]', 'boolean']","[30-50, 18-30, 0-18, 50+]" -What are the top 2 genders by average fare paid?,"['female', 'male']",list[category],"[Sex, Fare]","['category', 'number[double]']","[female, male]" -What are the oldest 3 ages among the survivors?,"[24.0, 22.0, 27.0]",list[number],"[Age, Survived]","['number[UInt8]', 'boolean']","[56.0, 47.0, 42.0]" -Which are the top 4 fares paid by survivors?,"[13.0, 26.0, 7.75, 10.5]",list[number],"[Fare, Survived]","['number[double]', 'boolean']","[133.65, 39.0, 35.5, 30.5]" -Could you list the youngest 3 ages among the survivors?,"[53.0, 55.0, 11.0]",list[number],"[Age, Survived]","['number[UInt8]', 'boolean']","[14.0, 24.0, 28.0]" -Which are the bottom 4 fares among those who didn't survive?,"[90.0, 12.275, 9.35, 10.5167]",list[number],"[Fare, Survived]","['number[double]', 'boolean']","[13.0, 7.75, 11.5, 10.1708]" diff --git a/data/002_Titanic/sample.csv b/data/002_Titanic/sample.csv deleted file mode 100644 index a41c114e689e9c4d045f1e608dc110512945ac2a..0000000000000000000000000000000000000000 --- a/data/002_Titanic/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Age,Siblings_Spouses Aboard,Sex,Name,Pclass,Fare,Survived -69.0,0,male,Mr. Samuel Beard Risien,3,14.5,False -57.0,0,male,Rev. Charles Leonard Kirkland,2,12.35,False -22.0,0,male,Mr. Eliezer Gilinski,3,8.05,False -49.0,0,male,Mr. Alfred Johnson,3,0.0,False -39.0,0,male,Mr. Richard Otter,2,13.0,False -51.0,0,male,Mr. Carl/Charles Peter Widegren,3,7.75,False -19.0,0,male,Mr. Branko Dakic,3,10.1708,False -25.0,1,male,Mr. Joseph Philippe Lemercier Laroche,2,41.5792,False -17.0,1,male,Mr. Joseph Jr Elias,3,7.2292,False -23.0,2,male,Mr. Richard George Hocking,2,11.5,False -47.0,0,male,Mr. Adolphe Saalfeld,1,30.5,True -56.0,0,male,Col. Oberst Alfons Simonius-Blumer,1,35.5,True -28.0,0,female,Miss. Margareth Mannion,3,7.7375,True -34.0,0,male,Mr. Frederic Kimber Seward,1,26.55,True -29.0,0,female,Mrs. Darwis (Hanne Youssef Razi) Touma,3,15.2458,True -14.0,1,female,Miss. Jamila Nicola-Yarred,3,11.2417,True -24.0,1,female,Mrs. Pekka Pietari (Elin Matilda Dolck) Hakkarainen,3,15.85,True -40.0,1,female,Mrs. Thomas William Solomon (Elizabeth Catherine Ford) Brown,2,39.0,True -31.0,1,female,Mrs. Frank John (Emily Alice Brown) Goldsmith,3,20.525,True -42.0,1,female,Mrs. Henry William (Clara Heinsheimer) Frauenthal,1,133.65,True diff --git a/data/003_Love/qa.csv b/data/003_Love/qa.csv deleted file mode 100644 index df6fc1ebee24d2f24de89a58067d89e673f4a425..0000000000000000000000000000000000000000 --- a/data/003_Love/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the average age of the respondents above 30?,True,boolean,['What is your age? 👶🏻👵🏻'],['number[uint8]'],True -Are there more single individuals than married ones in the dataset?,True,boolean,['What is your civil status? 💍'],['category'],False -Do the majority of respondents have a height greater than 170 cm?,True,boolean,[What's your height? in cm 📏],['number[uint8]'],True -Is the most frequent hair color black?,False,boolean,['What is your hair color? 👩🦰👱🏽'],['category'],False -How many unique nationalities are present in the dataset?,13,number,"[What's your nationality?""]""",['category'],1 -What is the average gross annual salary?,56332.81720430108,number,['Gross annual salary (in euros) 💸'],['number[UInt32]'],62710.0 -How many respondents wear glasses all the time?,0,number,['How often do you wear glasses? 👓'],['category'],0 -What's the median age of the respondents?,33.0,number,['What is your age? 👶🏻👵🏻'],['number[uint8]'],32.5 -What is the most common level of studies achieved?,Master,category,['What is the maximum level of studies you have achieved? 🎓'],['category'],Master -Which body complexity has the least number of respondents?,Very thin,category,['What is your body complexity? 🏋️'],['category'],Obese -What's the most frequent eye color?,Brown,category,['What is your eye color? 👁️'],['category'],Brown -Which sexual orientation has the highest representation?,Heterosexual,category,"[What's your sexual orientation?""]""",['category'],Heterosexual -List the top 3 most common areas of knowledge.,"['[Computer Science]', '[Business]', '[Enginering & Architecture]']",list[category],['What area of knowledge is closer to you?'],['list[category]'],"['[Computer Science]', '[Business]', '[Enginering & Architecture]']" -List the bottom 3 hair lengths in terms of frequency.,"['Medium', 'Long', 'Bald']",list[category],['How long is your hair? 💇🏻♀️💇🏽♂️'],['category'],"['Short', 'Medium', 'Long']" -Name the top 5 civil statuses represented in the dataset.,"['Single', 'Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Divorced']",list[category],['What is your civil status? 💍'],['category'],"['Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Single', 'Divorced']" -What are the 4 least common hair colors?,"['Red', 'Other', 'White', 'Blue']",list[category],['What is your hair color? 👩🦰👱🏽'],['category'],"['Brown', 'Black']" -What are the top 4 maximum gross annual salaries?,"[500000.0, 360000.0, 300000.0, 300000.0]",list[number],['Gross annual salary (in euros) 💸'],['number[UInt32]'],"[150000.0, 130000.0, 125000.0, 120000.0]" -Name the bottom 3 values for the happiness scale.,"[2, 2, 2]",list[number],['Happiness scale'],['number[uint8]'],"[7, 10, 6]" -What are the 5 highest ages present in the dataset?,"[65, 62, 60, 60, 59]",list[number],['What is your age? 👶🏻👵🏻'],['number[uint8]'],"[65, 60, 51, 50, 50]" -List the bottom 6 skin tone values based on frequency.,"[2, 1, 6, 0, 7, 8]",list[number],['What is your skin tone?'],['number[uint8]'],"[3, 1, 6, 2, 7, 0]" diff --git a/data/003_Love/sample.csv b/data/003_Love/sample.csv deleted file mode 100644 index f0236f969765619850ee82fcb879d8bfc6832503..0000000000000000000000000000000000000000 --- a/data/003_Love/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Submitted at,What is your age? 👶🏻👵🏻,What's your nationality?,What is your civil status? 💍,What's your sexual orientation?,Do you have children? 🍼,What is the maximum level of studies you have achieved? 🎓,Gross annual salary (in euros) 💸,What's your height? in cm 📏,What's your weight? in Kg ⚖️,What is your body complexity? 🏋️,What is your eye color? 👁️,What is your hair color? 👩🦰👱🏽,What is your skin tone?,How long is your hair? 💇🏻♀️💇🏽♂️,How long is your facial hair? 🧔🏻,How often do you wear glasses? 👓,How attractive do you consider yourself?,Have you ever use an oline dating app?,Where have you met your sexual partners? (In a Bar or Restaurant),Where have you met your sexual partners? (Through Friends),Where have you met your sexual partners? (Through Work or as Co-Workers),Where have you met your sexual partners? (Through Family),Where have you met your sexual partners? (in University),Where have you met your sexual partners? (in Primary or Secondary School),Where have you met your sexual partners? (Neighbors),Where have you met your sexual partners? (in Church),Where have you met your sexual partners? (Other),How many people have you kissed?,How many sexual partners have you had?,How many people have you considered as your boyfriend_girlfriend?,How many times per month did you practice sex lately?,Happiness scale,What area of knowledge is closer to you?,"If you are in a relationship, how long have you been with your partner?" -2023-04-23T06:33:05Z,39,Spain,Married,Heterosexual,Yes,Master,120000.0,183,74.0,Thin,Green,Black,4,Short,Short beard (few days),Occasioanlly,3,No,False,True,False,False,False,False,False,False,False,20,8,6,3.0,8,[Computer Science],13.0 -2023-01-09T20:07:48Z,29,,Married,,No,Master,82000.0,174,75.0,Average,Green,Brown,5,Short,No facial hair,Rarely,7,No,False,True,False,False,False,False,False,False,False,5,1,1,8.0,9,"[Science, Business]",12.0 -2023-01-09T18:32:25Z,42,,Married,,Yes,Master,60000.0,183,73.0,Thin,Blue,Brown,5,Short,Short beard (few days),Occasioanlly,7,Yes,False,True,False,False,True,False,False,False,False,20,10,5,1.0,7,[Enginering & Architecture], -2023-04-22T23:35:43Z,31,Spain,In a Relationship,Heterosexual,No,College Degree,54000.0,185,85.0,Average,Brown,Black,4,Short,Medium (weeks),Rarely,3,Yes,False,True,True,False,False,False,False,False,False,5,4,2,20.0,7,"[Computer Science, Other]",2.0 -2023-01-09T22:55:14Z,41,,Married,,Yes,Master,50000.0,170,71.0,Overweight,Brown,Black,1,Short,Medium (weeks),Rarely,7,No,True,False,False,False,False,False,False,False,False,50,15,7,4.0,8,[Law & Social Science],14.0 -2023-04-22T18:59:09Z,33,Spain,In a Relationship,Heterosexual,No,Primary Education,130000.0,168,68.0,Overweight,Brown,Black,7,Short,No facial hair,Constantly,7,No,True,True,True,False,False,False,False,False,False,40,30,10,2.0,7,"[Business, Art & Humanities]",4.0 -2023-01-10T10:54:21Z,32,,Single,,No,Master,150000.0,170,65.0,Thin,Blue,Brown,0,Long,Medium (weeks),Occasioanlly,7,Yes,False,False,False,False,True,True,False,False,True,6,5,3,6.0,8,"[Computer Science, Enginering & Architecture]", -2023-01-11T20:35:42Z,25,,In a Relationship Cohabiting,,No,Master,38000.0,177,78.0,Muscular,Brown,Brown,3,Medium,Medium (weeks),Regularly,8,No,False,False,False,False,True,False,False,False,False,5,4,3,2.0,8,"[Computer Science, Science]",4.0 -2023-04-22T22:16:16Z,25,Spain,In a Relationship,Heterosexual,No,Technical Education (FP),21000.0,170,65.0,Muscular,Brown,Brown,6,Short,Medium (weeks),Regularly,8,No,True,True,False,False,False,False,False,False,False,50,3,1,10.0,8,"[Computer Science, Art & Humanities]",6.0 -2023-01-12T08:43:35Z,23,,In a Relationship Cohabiting,,No,College Degree,7200.0,176,105.0,Obese,Brown,Black,2,Short,Medium (weeks),Constantly,5,No,True,True,False,False,False,False,False,False,False,1,1,1,12.0,10,[Computer Science],6.0 -2023-04-22T18:52:53Z,50,Spain,Divorced,Heterosexual,No,College Degree,15000.0,178,72.0,Average,Brown,Brown,3,Short,No facial hair,Constantly,2,No,False,True,True,False,True,True,False,False,False,25,16,6,5.0,6,[Business],0.9 -2023-01-09T20:38:39Z,65,,Married,,Yes,Master,50000.0,168,82.0,Muscular,Blue,Black,1,Medium,Medium (weeks),Constantly,7,No,False,False,False,False,True,False,False,False,False,1,1,1,1.0,8,[Business],30.0 -2023-01-14T10:54:16Z,29,,In a Relationship,,No,Master,12000.0,165,83.0,Overweight,Hazel,Brown,5,Long,No facial hair,Rarely,7,No,True,True,False,False,False,True,False,False,True,10,7,3,5.0,8,"[Computer Science, Art & Humanities]",5.5 -2023-01-09T22:38:11Z,34,,Married,,Yes,Master,40000.0,183,68.0,Thin,Brown,Brown,3,Medium,Short beard (few days),Rarely,7,No,True,False,False,False,False,False,False,False,False,6,4,1,4.0,9,[Enginering & Architecture],12.0 -2023-01-14T19:39:44Z,60,,Married,,Yes,PhD,50000.0,180,81.0,Muscular,Brown,Brown,4,Short,Short beard (few days),Regularly,7,No,False,False,True,False,False,False,False,False,False,10,4,3,8.0,8,[Art & Humanities],14.0 -2023-04-22T20:09:37Z,50,Spain,Married,Heterosexual,Yes,Master,70000.0,170,85.0,Overweight,Brown,Black,2,Short,Short beard (few days),Regularly,5,No,True,True,False,False,True,False,False,False,False,30,6,6,3.0,8,[Computer Science],23.0 -2023-01-09T18:00:35Z,51,,Married,,Yes,PhD,125000.0,180,82.0,Average,Brown,Brown,4,Short,No facial hair,Constantly,5,No,False,False,False,False,True,False,False,False,False,4,1,2,25.0,9,"[Computer Science, Science]", -2023-04-22T18:51:56Z,30,Spain,In a Relationship,Homosexual,No,PhD,40000.0,172,65.0,Average,Green,Brown,5,Medium,No facial hair,Regularly,8,Yes,True,True,True,False,True,False,False,False,False,40,24,4,6.0,7,"[Law & Social Science, Science]",3.0 -2023-04-23T10:13:20Z,30,Spain,Married,Heterosexual,No,College Degree,100000.0,174,80.0,Average,Brown,Brown,6,Short,Short beard (few days),Rarely,6,No,False,True,False,False,False,True,False,False,False,3,1,1,2.0,9,[Computer Science],11.0 -2023-01-10T17:41:37Z,28,,In a Relationship Cohabiting,,No,Master,40000.0,174,71.0,Overweight,Green,Brown,5,Medium,No facial hair,Rarely,8,Yes,False,True,False,False,True,False,False,False,False,9,3,2,4.0,8,[Business],5.0 diff --git a/data/004_Taxi/qa.csv b/data/004_Taxi/qa.csv deleted file mode 100644 index 9088c21ae16753785feaf6b3cc5546097880131c..0000000000000000000000000000000000000000 --- a/data/004_Taxi/qa.csv +++ /dev/null @@ -1,32 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any trips with a total distance greater than 30 miles?,False,boolean,['trip_distance'],['number[double]'],False -Were there any trips that cost more than $100 in total?,False,boolean,['total_amount'],['number[double]'],False -Is there any trip with more than 6 passengers?,False,boolean,['passenger_count'],['number[uint8]'],False -Did all the trips use a payment type of either 1 or 2?,False,boolean,['payment_type'],['number[uint8]'],True -What is the maximum fare amount charged for a trip?,75.25,number,['fare_amount'],['number[double]'],85.0 -How many unique pickup locations are in the dataset?,96,number,['PULocationID'],['number[uint16]'],193 -What is the average tip amount given by passengers?,2.74,number,['tip_amount'],['number[double]'],1.5 -How many trips took place in the airport area?,99807,number,['Airport_fee'],['number[UInt8]'],194 -Which payment type is the most common in the dataset?,1,category,['payment_type'],['number[uint8]'],1 -Which vendor has the most trips recorded?,2,category,['VendorID'],['number[uint8]'],2 -What is the most common drop-off location?,236,category,['DOLocationID'],['number[uint16]'],161 -On which date did the first recorded trip occur?,2023-01-31,category,['tpep_pickup_datetime'],"['date[ns, UTC]']",2019-01-01 00:46:40 -Which are the top 3 most frequent pickup locations?,"[161, 237, 236]",list[category],['PULocationID'],['number[uint16]'],"[237, 236, 161]" -Name the 4 most common rate codes used.,"[1, 2, 5, 4]",list[category],['RatecodeID'],['number[uint8]'],"[1, 2, 5, 3]" -list the 2 most frequent store and forward flags.,"['N', 'Y']",list[category],['store_and_fwd_flag'],['category'],"['N', 'Y']" -Identify the top 4 payment types used by frequency,"[1, 2, 4, 3]",list[category],['payment_type'],['number[uint8]'],"[1, 2, 3]" -Report the 4 highest toll amounts paid.,"[0, 0, 0, 0]",list[number],['tolls_amount'],['number[uint8]'],"[0, 0, 0, 0]" -list the top 3 longest trip distances,"[19.83, 19.74, 19.68]",list[number],['trip_distance'],['number[double]'],"[8.32, -5.93, -2.8]" -Identify the 5 largest total amounts paid for trips.,"[80.0, 80.0, 80.0, 80.0, 79.55]",list[number],['total_amount'],['number[double]'],"[45.8, -39.9, -33.2, -25.2, -24.87]" -Report the 6 highest fare amounts charged.,"[75.25, 74.4, 73.0, 73.0, 73.0, 73.0]",list[number],['fare_amount'],['number[double]'],"[40.8, -28.9, -21.2, -17.0, -14.9, -13.5]" diff --git a/data/004_Taxi/sample.csv b/data/004_Taxi/sample.csv deleted file mode 100644 index d67af56f82655b5249d7b86b007b706f30d3835a..0000000000000000000000000000000000000000 --- a/data/004_Taxi/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -store_and_fwd_flag,payment_type,tpep_pickup_datetime,fare_amount,VendorID,DOLocationID,tolls_amount,tip_amount,PULocationID,Airport_fee,trip_distance,RatecodeID,total_amount,passenger_count -Y,1,2023-02-01T16:42:37Z,12.8,1,48,0,3.85,100,0.0,0.9,1,23.15,1 -N,1,2023-02-01T02:59:27Z,5.8,2,141,0,1.51,263,0.0,0.79,1,12.31,1 -N,2,2023-02-01T20:33:36Z,40.8,2,238,0,0.0,13,0.0,8.32,1,45.8,2 -N,1,2023-02-01T20:33:05Z,10.7,2,90,0,4.71,246,0.0,1.59,1,20.41,2 -N,1,2023-02-01T21:33:10Z,14.9,2,90,0,4.97,231,0.0,2.64,1,24.87,1 -N,1,2023-02-01T10:34:53Z,7.9,1,75,0,2.95,237,0.0,1.3,1,14.85,1 -N,1,2023-02-01T10:29:39Z,12.8,2,234,0,3.36,161,0.0,1.74,1,20.16,4 -N,1,2023-02-02T01:48:49Z,12.8,2,163,0,3.56,68,0.0,2.29,1,21.36,1 -N,1,2023-02-01T10:17:39Z,17.0,2,170,0,4.2,43,0.0,2.44,1,25.2,2 -N,1,2023-02-01T22:29:07Z,8.6,2,90,0,2.72,230,0.0,1.37,1,16.32,2 -N,2,2023-02-01T18:40:29Z,10.7,2,236,0,0.0,163,0.0,1.35,1,17.2,1 -N,1,2023-02-01T18:47:54Z,21.2,1,137,0,5.5,142,0.0,2.8,1,33.2,1 -N,1,2023-02-01T00:00:34Z,28.9,2,181,0,6.0,234,0.0,5.93,1,39.9,1 -N,1,2023-02-01T23:21:43Z,11.4,2,13,0,1.64,125,0.0,1.86,1,18.04,2 -N,3,2023-02-01T12:42:04Z,8.6,1,162,0,0.0,161,0.0,0.6,1,12.6,1 -N,1,2023-02-01T15:46:37Z,13.5,1,144,0,3.5,170,0.0,2.0,1,21.0,1 -N,1,2023-02-01T21:17:13Z,6.5,1,50,0,1.75,143,0.0,0.8,1,13.25,2 -N,1,2023-02-01T19:26:30Z,13.5,2,43,0,4.0,163,0.0,2.15,1,24.0,1 -N,1,2023-02-01T15:53:16Z,7.2,1,230,0,2.2,186,0.0,0.6,1,13.4,2 -N,1,2023-02-01T20:20:43Z,9.3,1,170,0,2.15,113,0.0,1.1,1,16.45,1 diff --git a/data/005_NYC/qa.csv b/data/005_NYC/qa.csv deleted file mode 100644 index b1e9f7e0560ae5408fa5b399e14bfa11490857f9..0000000000000000000000000000000000000000 --- a/data/005_NYC/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any complaints made in Brooklyn?,True,boolean,['borough'],['category'],True -Do any complaints have 'Dog' as a descriptor?,True,boolean,['descriptor'],['category'],False -Were there any complaints raised in April?,True,boolean,['month_name'],['category'],True -Is the Mayor's office of special enforcement one of the agencies handling complaints?,True,boolean,['agency'],['category'],False -How many complaints have been made in Queens?,23110,number,['borough'],['category'],0 -What's the total number of unique agencies handling complaints?,22,number,['agency'],['category'],7 -How many complaints were raised at midnight?,14811,number,['hour'],['number[uint8]'],2 -How many unique descriptors are present in the dataset?,1131,number,['descriptor'],['category'],16 -Which borough has the most complaints?,BROOKLYN,category,['borough'],['category'],QUEENS -Which month sees the highest number of complaints?,July,category,['month_name'],['category'],January -Which weekday has the least complaints?,Sunday,category,['weekday_name'],['category'],Thursday -Which agency is least frequently handling complaints?,ACS,category,['agency'],['category'],DOHMH -List the top 5 most frequent complaint types.,"['Noise - Residential', 'HEAT/HOT WATER', 'Illegal Parking', 'Blocked Driveway', 'Street Condition']",list[category],['complaint_type'],['category'],"[HEAT/HOT WATER, Building/Use, Noise - Residential, General Construction/Plumbing, Air Quality]" -Which 4 agencies handle the most complaints?,"['NYPD', 'HPD', 'DOT', 'DSNY']",list[category],['agency'],['category'],"[NYPD, HPD, DOB, DSNY]" -Name the 3 least frequent descriptors for complaints.,"['Booting Company', 'Ready NY - Businesses', 'Animal']",list[category],['descriptor'],['category'],"[Structure - Outdoors, Air: Odor/Fumes, Restaurant (AD2), 12 Dead Animals]" -Mention the 2 most common weekdays for complaints.,"['Tuesday', 'Monday']",list[category],['weekday_name'],['category'],"[Monday, Wednesday]" -What are the top 4 hours with the most complaints?,"[0, 12, 10, 11]",list[number],['hour'],['number[uint8]'],"[18, 21, 0, 16]" -State the 3 lowest unique complaint keys.,"[15628852, 15634748, 15634996]",list[number],['unique_key'],['number[uint32]'],"[18311800, 22322205, 25369019]" -Which 5 hours see the least complaints?,"[6, 2, 3, 5, 4]",list[number],['hour'],['number[uint8]'],"[22, 7, 14, 23, 8]" -List 6 unique complaint numbers from the dataset.,"[33629705, 46718634, 51900343, 53128216, 34575561, 46015340]",list[number],['unique_key'],['number[uint32]'],"[51990440, 43655624, 35414182, 43260648, 28084067, 50082845]" diff --git a/data/005_NYC/sample.csv b/data/005_NYC/sample.csv deleted file mode 100644 index f4989c4e56c00e507d66f1ae0e83904714cd479e..0000000000000000000000000000000000000000 --- a/data/005_NYC/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -complaint_type,borough,hour,month_name,weekday_name,agency,unique_key,descriptor -HEAT/HOT WATER,MANHATTAN,18,March,Monday,HPD,50082845,APARTMENT ONLY -Illegal Parking,QUEENS,21,February,Friday,NYPD,49874401,Commercial Overnight Parking -Noise - Commercial,BROOKLYN,0,February,Sunday,NYPD,35414182,Loud Music/Party -Building/Use,QUEENS,16,August,Tuesday,DOB,43655624,Illegal Conversion Of Residential Building/Space -Rodent,BROOKLYN,0,January,Friday,DOHMH,35243309,Condition Attracting Rodents -Snow,STATEN ISLAND,15,January,Wednesday,DSNY,29811292,E9 Snow / Icy Sidewalk -Abandoned Vehicle,MANHATTAN,11,May,Monday,NYPD,54188773,With License Plate -Building/Use,QUEENS,18,December,Tuesday,DOB,22322205,Illegal. Commercial Use In Resident Zone -WATER LEAK,BRONX,18,January,Wednesday,HPD,32544589,SLOW LEAK -Blocked Driveway,QUEENS,21,January,Monday,NYPD,45481177,No Access -General Construction/Plumbing,BROOKLYN,16,May,Monday,DOB,28084067,Cons - Contrary/Beyond Approved Plans/Permits -Sewer,QUEENS,10,April,Monday,DEP,42173087,Catch Basin Sunken/Damaged/Raised (SC1) -Noise - Street/Sidewalk,QUEENS,22,June,Sunday,NYPD,43159964,Loud Music/Party -Derelict Vehicle,QUEENS,11,August,Monday,NYPD,26158099,With License Plate -HEAT/HOT WATER,QUEENS,7,November,Saturday,HPD,32036860,ENTIRE BUILDING -Maintenance or Facility,STATEN ISLAND,14,July,Wednesday,DPR,18311800,Structure - Outdoors -Noise - Residential,BRONX,18,September,Saturday,NYPD,51990440,Loud Music/Party -Noise - Residential,BRONX,23,July,Thursday,NYPD,43260648,Loud Music/Party -Air Quality,MANHATTAN,10,March,Wednesday,DEP,27580008,"Air: Odor/Fumes, Restaurant (AD2)" -Sanitation Condition,QUEENS,8,April,Monday,DSNY,25369019,12 Dead Animals diff --git a/data/006_London/qa.csv b/data/006_London/qa.csv deleted file mode 100644 index 7c8ab47f3bef5a5eb8b2dff3a88c0ec1b7dff099..0000000000000000000000000000000000000000 --- a/data/006_London/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are all properties in the dataset located in the same neighbourhood?,False,boolean,['neighbourhood_cleansed'],['category'],False -Do all hosts verify their identity?,False,boolean,['host_identity_verified'],['category'],False -Are all reviews_per_month values greater than 5?,False,boolean,['reviews_per_month'],['number[double]'],False -Are there any listings without a specified room type?,False,boolean,['room_type'],['category'],False -How many unique hosts are there in the dataset?,563,number,['host_neighbourhood'],['category'],20 -How many listings have a valid price?,0,number,['price'],['category'],0 -How many properties have received a perfect review score for communication?,0,number,['review_scores_communication'],['number[double]'],0 -What is the maximum number of bedrooms a property has in this dataset?,22.0,number,['bedrooms'],['number[UInt8]'],3.0 -Which neighbourhood has the most listings?,Westminster,category,['neighbourhood_cleansed'],['category'],Hammersmith and Fulham -What is the most common room type in the listings?,Entire home/apt,category,['room_type'],['category'],Private room -What property type has the least listings?,Hut,category,['property_type'],['category'],Entire condo -Which host verification method is the least used?,photographer],category,['host_verifications'],['list[category]'],[phone] -List the top 3 neighbourhoods with the most listings.,"['Westminster', 'Tower Hamlets', 'Hackney']",list[category],['neighbourhood_cleansed'],['category'],"['Hammersmith and Fulham', 'Hackney', 'Westminster']" -Which are the top 5 most common property types?,"['Entire rental unit', 'Private room in rental unit', 'Private room in home', 'Entire condo', 'Entire home']",list[category],['property_type'],['category'],"['Private room in rental unit', 'Entire rental unit', 'Entire home', 'Private room in home', 'Entire condo']" -List the 4 least common host verification methods.,"['[email]', '[]', '[None]', ' photographer']",list[category],['host_verifications'],['list[category]'],"['[phone]', ' phone', ' work_email', ' phone']" -Which are the 2 most preferred room types?,"['Entire home/apt', 'Private room']",list[category],['room_type'],['category'],"['Private room', 'Entire home/apt']" -What are the top 3 highest review scores for location?,"[5.0, 5.0, 5.0]",list[number],['review_scores_location'],['number[double]'],"[5.0, 4.0, 4.89]" -What are the 4 most common number of bedrooms in properties?,"[1.0, 2.0, 3.0, 4.0]",list[number],['bedrooms'],['number[UInt8]'],"[1.0, 2.0, 3.0]" -What are the 5 highest counts of listings by a single host for entire homes?,"[288, 288, 288, 288, 288]",list[number],['calculated_host_listings_count_entire_homes'],['number[uint16]'],"[1, 1, 1, 1, 1]" -List the 6 lowest review scores for communication.,"[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]",list[number],['review_scores_communication'],['number[double]'],"[4.4, 4.89, 4.95, 4.5, 4.75, 4.94]" diff --git a/data/006_London/sample.csv b/data/006_London/sample.csv deleted file mode 100644 index 1f1b91ec8fca6973ac875cae40af3e2f47730a27..0000000000000000000000000000000000000000 --- a/data/006_London/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -reviews_per_month,review_scores_communication,host_verifications,calculated_host_listings_count_entire_homes,host_neighbourhood,property_type,host_identity_verified,bedrooms,review_scores_location,neighbourhood_cleansed,price,room_type -0.01,4.0,"[email, phone, work_email]",0,,Private room in rental unit,t,1.0,4.0,Islington,,Private room -2.22,4.4,"[email, phone]",11,,Entire condo,t,1.0,4.2,Hammersmith and Fulham,,Entire home/apt -0.78,5.0,"[email, phone]",1,Notting Hill,Entire home,t,3.0,5.0,Kensington and Chelsea,,Entire home/apt -2.18,4.89,"[email, phone]",0,West Kensington,Private room in rental unit,t,1.0,4.85,Hammersmith and Fulham,,Private room -,,"[email, phone]",0,Stoke Newington,Private room in rental unit,t,1.0,,Hackney,,Private room -,,"[email, phone]",1,,Private room in rental unit,t,1.0,,Richmond upon Thames,,Private room -0.02,5.0,"[email, phone]",0,LB of Barking and Dagenham,Private room in home,f,1.0,5.0,Barking and Dagenham,,Private room -1.16,4.95,"[email, phone, work_email]",1,,Entire home,t,3.0,4.95,Westminster,,Entire home/apt -0.24,4.0,"[email, phone]",260,Westminster,Private room in rental unit,t,1.0,4.0,Southwark,,Private room -0.14,4.5,"[email, phone]",92,Marylebone,Entire serviced apartment,t,1.0,5.0,Westminster,,Entire home/apt -0.78,5.0,"[email, phone]",0,Earlsfield,Private room in rental unit,t,1.0,5.0,Wandsworth,,Private room -0.13,4.75,"[email, phone, work_email]",1,,Entire rental unit,t,2.0,4.75,Tower Hamlets,,Entire home/apt -,,[phone],1,Raynes Park,Entire home,f,2.0,,Merton,,Entire home/apt -0.63,5.0,"[email, phone]",1,Shepherd's Bush,Entire rental unit,t,1.0,4.87,Hammersmith and Fulham,,Entire home/apt -0.03,5.0,"[email, phone]",0,,Private room in rental unit,t,1.0,5.0,Kingston upon Thames,,Private room -0.28,5.0,"[email, phone]",1,,Entire rental unit,t,3.0,4.5,Hackney,,Entire home/apt -0.03,5.0,"[email, phone]",0,Acton,Private room in home,t,1.0,4.0,Ealing,,Private room -0.62,4.94,"[email, phone]",1,Lisson Grove,Entire rental unit,t,2.0,4.89,Westminster,,Entire home/apt -1.79,4.84,"[email, phone]",3,Spitalfields,Entire rental unit,t,2.0,4.9,City of London,,Entire home/apt -0.86,5.0,"[email, phone]",1,Shoreditch,Private room in rental unit,t,1.0,4.89,Hackney,,Private room diff --git a/data/007_Fifa/qa.csv b/data/007_Fifa/qa.csv deleted file mode 100644 index e602f54e5f552eb85c7b7a77f368409b46c73a64..0000000000000000000000000000000000000000 --- a/data/007_Fifa/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there players who have a greater overall score than their potential score?,False,boolean,"['Overall', 'Potential']","['number[uint8]', 'number[uint8]']",False -Are there any players who joined their current club before they were 18 years old?,True,boolean,"['Joined', 'Age']","['category', 'number[uint8]']",True -Are there any players whose preferred foot is left and are from a nationality that starts with 'B'?,True,boolean,"['Preferred Foot', 'Nationality']","['category', 'category']",False -Are there any players who are taller than 6 feet and have an agility score above 90?,False,boolean,"['Height_ft', 'Agility']","['number[double]', 'number[uint8]']",False -What is the average overall score of players from France?,67.861432,number,"['Nationality', 'Overall']","['category', 'number[uint8]']", -How many unique clubs are there in the dataset?,683,number,['Club'],['category'],19 -What is the highest value (in €) of a player in the dataset?,105500000,number,['Value_€'],['number[uint32]'],13500000 -How many players have the position 'ST'?,414,number,['Position'],['category'],1 -What is the most common nationality in the dataset?,England,category,['Nationality'],['category'],Ghana -What is the most common preferred foot amongst players?,Right,category,['Preferred Foot'],['category'],Right -Which club has the most players in the dataset?,Crystal Palace,category,['Club'],['category'],Lech Poznań -What is the most common position of players in the dataset?,SUB,category,['Position'],['category'],SUB -Which are the top 5 nationalities in terms of the average overall score of their players?,"['Tanzania', 'Syria', 'Mozambique', 'Chad', 'Central African Rep.']",list[category],"['Nationality', 'Overall']","['category', 'number[uint8]']","['Portugal', 'Ivory Coast', 'Brazil', 'United States', 'Ghana']" -Which are the top 3 clubs in terms of the total value (in €) of their players?,"['Liverpool', 'Manchester City', 'Real Madrid']",list[category],"['Club', 'Value_€']","['category', 'number[uint32]']","['Sassuolo', 'Atalanta', 'DC United']" -Which are the bottom 4 nationalities in terms of the average agility of their players?,"['Macau', 'Andorra', 'Moldova', 'Liechtenstein']",list[category],"['Nationality', 'Agility']","['category', 'number[uint8]']","['United States', 'Guyana', 'Saudi Arabia', 'Poland']" -Which are the top 6 clubs in terms of the average potential score of their players?,"['FC Bayern München', 'Real Madrid', 'FC Barcelona', 'Paris Saint-Germain', 'Juventus', 'Manchester City']",list[category],"['Club', 'Potential']","['category', 'number[uint8]']","['Sassuolo', 'Inter', 'Sporting CP', '1. FSV Mainz 05', 'Atalanta', 'DC United']" -What are the top 3 overall scores in the dataset?,"[93, 92, 91]",list[number],['Overall'],['number[uint8]'],"[79, 77, 77]" -What are the bottom 5 potential scores in the dataset?,"[48, 48, 49, 50, 50]",list[number],['Potential'],['number[uint8]'],"[60, 65, 66, 67, 68]" -What are the top 4 values (in €) of players in the dataset?,"[105500000, 90000000, 87000000, 80000000]",list[number],['Value_€'],['number[uint32]'],"[13500000, 7500000, 5500000, 5500000]" -What are the top 2 wages (in €) of players in the dataset?,"[560000, 370000]",list[number],['Wage_€'],['number[uint32]'],"[47000, 29000]" diff --git a/data/007_Fifa/sample.csv b/data/007_Fifa/sample.csv deleted file mode 100644 index 898113159e26bfc430bcd520c58c60f688a8d649..0000000000000000000000000000000000000000 --- a/data/007_Fifa/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Joined,Overall,Age,Position,Wage_€,Preferred Foot,Potential,Agility,Nationality,Height_ft,Value_€,Club -,72,26,SUB,19000,Right,72,74.0,Ghana,5.11,3300000,Hannover 96 -"Jul 1, 2018",66,20,RDM,6000,Right,78,90.0,Luxembourg,5.9,1100000,1. FSV Mainz 05 -"Sep 1, 2020",73,29,SUB,22000,Right,73,88.0,Ghana,5.8,3800000,Hellas Verona -"Jan 1, 2018",57,21,RES,500,Left,68,64.0,Uruguay,6.1,180000,River Plate Montevideo -"Jan 1, 2015",66,29,SUB,10000,Right,66,52.0,Saudi Arabia,6.1,550000,Al Hilal -"Aug 20, 2020",56,18,RES,2000,Right,67,54.0,England,5.9,130000,Burnley -"Aug 24, 2017",64,22,RES,18000,Right,71,52.0,Netherlands,6.4,625000,Leeds United -"Jul 21, 2018",77,23,LM,29000,Right,86,86.0,Ivory Coast,5.9,13500000,Sassuolo -"Jul 1, 2017",73,31,SUB,16000,Right,73,62.0,Netherlands,6.1,3600000,FC Basel 1893 -"Nov 23, 2019",65,26,SUB,3000,Right,68,56.0,Spain,6.3,675000,CD Lugo -"Aug 27, 2019",67,23,SUB,6000,Right,74,77.0,Germany,6.1,1100000,SC Paderborn 07 -"Mar 1, 2018",74,33,ST,10000,Right,74,64.0,Brazil,6.2,3500000,Ulsan Hyundai FC -"Jul 1, 2019",59,25,SUB,2000,Right,65,53.0,Poland,6.3,190000,Lech Poznań -,62,20,SUB,1000,Left,75,68.0,Germany,5.11,525000,SG Dynamo Dresden -"Aug 16, 2020",79,33,SUB,16000,Left,79,71.0,Portugal,5.9,5500000,Sporting CP -"Sep 1, 2020",51,22,SUB,1000,Right,60,38.0,Guyana,6.7,45000,Cambridge United -"Aug 26, 2015",77,29,RCB,47000,Right,77,61.0,Brazil,6.1,7500000,Atalanta -"Dec 9, 2019",75,29,GK,7000,Right,77,34.0,United States,6.3,5500000,DC United -"Jan 1, 2020",63,18,RES,4000,Left,84,57.0,Italy,6.1,775000,Inter -"Feb 6, 2020",70,28,CAM,7000,Left,70,73.0,Spain,5.9,1800000,Lech Poznań diff --git a/data/008_Tornados/qa.csv b/data/008_Tornados/qa.csv deleted file mode 100644 index c34732cb42816b1a24ad70b3b6f6dbfdd8ecf664..0000000000000000000000000000000000000000 --- a/data/008_Tornados/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -There are no tornadoes that resulted in more than 500 injuries.,True,boolean,[inj],['number[uint16]'],True -All tornadoes in the dataset occurred in the 21st century.,False,boolean,[yr],['number[uint16]'],False -No tornado has a length greater than 100 miles.,True,boolean,[len],['number[double]'],True -There are no tornadoes that resulted in more than 100 fatalities.,True,boolean,[fat],['number[uint8]'],True -How many unique states are represented in the dataset?,53,number,[st],['category'],12 -What is the highest magnitude of tornado recorded in the dataset?,5,number,[mag],['number[int8]'],2 -What is the longest length of a tornado path in the dataset?,234.7,number,[len],['number[double]'],72.2 -What is the maximum number of injuries caused by a single tornado?,1740,number,[inj],['number[uint16]'],3 -Which state has experienced the most tornadoes?,TX,category,[st],['category'],IL -In which month do most tornadoes occur?,5,category,[mo],['number[uint8]'],6 -On what date did the most destructive tornado (by injuries) occur?,1979-04-10 00:00:00,category,"[date, inj]","['date[ns, UTC]', 'number[uint16]']",1973-03-15 -On what date did the longest tornado (by path length) occur?,1953-03-22 00:00:00,category,"[date, len]","['date[ns, UTC]', 'number[double]']",1955-06-04 -Which are the top 5 states with the highest average tornado magnitude?,"[AR, KY, VT, TN, MS]",list[category],"[st, mag]","['category', 'number[int8]']","['TN', 'GA', 'IN', 'OK', 'TX']" -Which are the top 3 states with the most tornado-related injuries?,"[TX, AL, MS]",list[category],"[st, inj]","['category', 'number[uint16]']","['TN', 'IL', 'AR']" -Which are the top 4 states with the most tornado-related fatalities?,"[AL, TX, MS, OK]",list[category],"[st, fat]","['category', 'number[uint8]']","['TN', 'AR', 'FL', 'GA']" -Which are the bottom 2 states in terms of the average tornado path length?,"[AK, VI]",list[category],"[st, len]","['category', 'number[double]']","['TN', 'WY']" -What are the top 3 number of injuries caused by tornadoes in the dataset?,"[1740, 1500, 1228]",list[number],[inj],['number[uint16]'],"[3, 1, 0]" -What are the top 5 magnitudes of tornadoes in the dataset?,"[5, 5, 5, 5, 5]",list[number],[mag],['number[int8]'],"[2, 2, 1, 1, 1]" -What are the top 4 path lengths of tornadoes in the dataset?,"[234.7, 217.8, 202.5, 202.1]",list[number],[len],['number[double]'],"[72.2, 4.7, 4.3, 3.2]" -What are the top 6 number of fatalities caused by tornadoes in the dataset?,"[158, 116, 114, 94, 80, 72]",list[number],[fat],['number[uint8]'],"[1, 0, 0, 0, 0, 0]" diff --git a/data/008_Tornados/sample.csv b/data/008_Tornados/sample.csv deleted file mode 100644 index 5ef5abeff818412c37e56684173da232465cb1d8..0000000000000000000000000000000000000000 --- a/data/008_Tornados/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -date,yr,fat,mag,inj,mo,st,len -1971-05-12,1971,0,1,0,5,GA,0.5 -2004-10-18,2004,0,0,0,10,AR,2.0 -1973-07-23,1973,0,1,0,7,WY,0.1 -1996-05-05,1996,0,0,0,5,MO,0.5 -2000-06-16,2000,0,0,0,6,IL,1.5 -1996-04-19,1996,0,1,1,4,IL,2.0 -1998-06-14,1998,0,0,0,6,IL,0.1 -1995-06-10,1995,0,0,0,6,TX,0.1 -2016-05-24,2016,0,1,0,5,KS,4.3 -1956-02-25,1956,0,1,0,2,IN,4.7 -1996-04-13,1996,0,2,0,4,TX,0.5 -1955-06-04,1955,0,1,0,6,KS,72.2 -1988-07-26,1988,0,0,0,7,FL,1.0 -2019-05-21,2019,0,0,0,5,KS,3.05 -2020-04-13,2020,0,1,0,4,GA,3.2 -2019-04-30,2019,0,1,0,4,OK,1.95 -1973-03-15,1973,1,2,3,3,TN,0.1 -1984-11-11,1984,0,1,0,11,IL,1.0 -2008-06-04,2008,0,0,0,6,IA,1.08 -2007-03-31,2007,0,1,0,3,TX,0.8 diff --git a/data/009_Central/qa.csv b/data/009_Central/qa.csv deleted file mode 100644 index 00fa14e03323df87aa3427b15a5f8841488418e9..0000000000000000000000000000000000000000 --- a/data/009_Central/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -There were no days when the precipitation was greater than 5 inches.,True,boolean,[PRCP],['number[double]'],True -All recorded temperatures are above freezing point.,False,boolean,"[TMIN, TMAX]","['number[Int8]', 'number[UInt8]']",False -There were no days when the snow depth was more than 10 inches.,True,boolean,[SNWD],['number[UInt8]'],True -There were no days when the maximum temperature was below freezing point.,False,boolean,[TMAX],['number[UInt8]'],False -What is the highest recorded precipitation in inches?,8.28,number,[PRCP],['number[double]'],0.66 -What is the lowest minimum temperature recorded?,-15.0,number,[TMIN],['number[Int8]'],-13.0 -What is the highest maximum temperature recorded?,106.0,number,[TMAX],['number[UInt8]'],81.0 -What is the deepest recorded snow depth in inches?,26.0,number,[SNWD],['number[UInt8]'],4.0 -On which date was the highest precipitation recorded?,1882-09-23 00:00:00,category,"[DATE, PRCP]","['date[ns, UTC]', 'number[double]']",1891-07-24 -On which date was the lowest minimum temperature recorded?,1934-02-09 00:00:00,category,"[DATE, TMIN]","['date[ns, UTC]', 'number[Int8]']",1917-12-30 -On which date was the highest maximum temperature recorded?,1936-07-09 00:00:00,category,"[DATE, TMAX]","['date[ns, UTC]', 'number[UInt8]']",1891-07-24 -On which date was the deepest snow depth recorded?,1947-12-27 00:00:00,category,"[DATE, SNWD]","['date[ns, UTC]', 'number[UInt8]']",1945-02-03 -What are the dates of the top 5 highest recorded precipitation events?,"[1882-09-23 00:00:00, 2007-04-15 00:00:00, 1977-11-08 00:00:00, 1903-10-09 00:00:00, 2021-09-01 00:00:00]",list[category],"[DATE, PRCP]","['date[ns, UTC]', 'number[double]']","['1891-07-24', '1966-10-16', '1945-09-27', '1999-05-18', '1898-09-15']" -What are the dates of the top 3 lowest minimum temperatures recorded?,"[1934-02-09 00:00:00, 1917-12-30 00:00:00, 1943-02-15 00:00:00]",list[category],"[DATE, TMIN]","['date[ns, UTC]', 'number[Int8]']","['1917-12-30', '1945-02-03', '1892-03-21']" -What are the dates of the top 4 highest maximum temperatures recorded?,"[1936-07-09 00:00:00, 1918-08-07 00:00:00, 1977-07-21 00:00:00, 2011-07-22 00:00:00]",list[category],"[DATE, TMAX]","['date[ns, UTC]', 'number[UInt8]']","['1891-07-24', '1903-06-03', '1982-07-02', '1960-08-26']" -What are the dates of the top 2 deepest snow depth recorded?,"[1947-12-27 00:00:00, 1947-12-28 00:00:00]",list[category],"[DATE, SNWD]","['date[ns, UTC]', 'number[UInt8]']","['1945-02-03', '1917-12-30']" -What are the top 3 highest recorded precipitation events in inches?,"[8.28, 7.57, 7.4]",list[number],[PRCP],['number[double]'],"[0.66, 0.26, 0.1]" -What are the top 5 lowest minimum temperatures recorded?,"[-15.0, -13.0, -8.0, -7.0, -7.0]",list[number],[TMIN],['number[Int8]'],"[-13.0, 18.0, 19.0, 29.0, 32.0]" -What are the top 4 highest maximum temperatures recorded?,"[106.0, 104.0, 104.0, 104.0]",list[number],[TMAX],['number[UInt8]'],"[81.0, 81.0, 80.0, 79.0]" -What are the top 2 deepest snow depth recorded in inches?,"[26.0, 25.0]",list[number],[SNWD],['number[UInt8]'],"[4.0, 3.0]" diff --git a/data/009_Central/sample.csv b/data/009_Central/sample.csv deleted file mode 100644 index cbb12914e1c5293d51b179e032551c41b027e04a..0000000000000000000000000000000000000000 --- a/data/009_Central/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -TMIN,PRCP,DATE,SNWD,TMAX -60.0,0.0,1982-07-02,0.0,80.0 -18.0,0.0,1945-02-03,4.0,32.0 -73.0,0.66,1891-07-24,,81.0 -50.0,0.0,1991-09-22,0.0,70.0 -64.0,0.01,1898-09-15,,72.0 -56.0,0.0,1903-06-03,,81.0 --13.0,0.0,1917-12-30,3.0,2.0 -54.0,0.03,1999-05-18,0.0,66.0 -51.0,0.26,1966-10-16,0.0,73.0 -47.0,0.0,1928-10-08,0.0,63.0 -66.0,0.1,1945-09-27,0.0,78.0 -19.0,0.0,1892-03-21,,33.0 -32.0,0.0,2022-12-02,0.0,47.0 -47.0,0.0,2017-10-27,0.0,62.0 -38.0,0.0,1913-12-01,0.0,47.0 -59.0,0.0,1960-08-26,0.0,79.0 -41.0,0.0,1965-03-08,0.0,51.0 -50.0,0.0,1899-05-06,,69.0 -29.0,0.0,1870-12-31,,42.0 -45.0,0.0,1928-12-16,0.0,51.0 diff --git a/data/010_ECommerce/qa.csv b/data/010_ECommerce/qa.csv deleted file mode 100644 index 9873fe8dcd26eebc9d88a3bbcacf83919ab0afcb..0000000000000000000000000000000000000000 --- a/data/010_ECommerce/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there more than 20 unique clothing items in the dataset?,True,boolean,[Clothing ID],['number[uint16]'],False -Is the age of the reviewers above 50 years on average?,False,boolean,[Age],['number[uint8]'],False -Do all reviews come from the same department?,False,boolean,[Department Name],['category'],False -Are all products recommended?,False,boolean,[Recommended IND],['number[uint8]'],False -What is the average age of the reviewers?,43.198543813335604,number,[Age],['number[uint8]'],39.65 -What's the highest number of positive feedback received for a review?,122,number,[Positive Feedback Count],['number[uint8]'],19 -What is the most common rating given by reviewers?,5,number,[Rating],['number[uint8]'],5 -How many unique clothing items are there in the dataset?,1206,number,[Clothing ID],['number[uint16]'],20 -Which department has the most reviews?,Tops,category,['Department Name'],['category'],Dresses -Which class of clothing is most commonly reviewed?,Dresses,category,['Class Name'],['category'],Dresses -Which division is most commonly mentioned in the reviews?,General,category,['Division Name'],['category'],General -What is the most frequently reviewed clothing item?,1078,category,['Clothing ID'],['number[uint16]'],1095 -Which are the top 6 most reviewed categories in Department Name?,"['Tops', 'Dresses', 'Bottoms', 'Intimate', 'Jackets', 'Trend']",list[category],[Department Name],['category'],"[Dresses, Tops, Bottoms, Intimate]" -Which are the top 2 most reviewed categories in Class Name?,"['Dresses', 'Knits']",list[category],[Class Name],['category'],"[Dresses, Blouses]" -Which are the top 2 most reviewed categories in Division Name?,"['General', 'General Petite']",list[category],[Division Name],['category'],"[General, General Petite]" -What are the 4 most common ratings given by reviewers?,"[5, 4, 3, 2]",list[category],[Rating],['number[uint8]'],"[5, 4, 3, 2]" -What are the 5 most common Ages of reviewers?,"[39, 35, 36, 34, 38]",list[number],[Age],['number[uint8]'],"[36, 30, 56, 33, 34]" -What are the 6 most common Positive Feedback Counts of reviewers?,"[0, 1, 2, 3, 4, 5]",list[number],[Positive Feedback Count],['number[uint8]'],"[0, 3, 5, 1, 19, 11]" -What are the 4 most common values for recommendation indicator?,"[1, 0]",list[number],[Recommended IND],['number[uint8]'],"[1, 0]" -What are the 2 most common clothing IDs in the reviews?,"[1078, 862]",list[number],[Clothing ID],['number[uint16]'],"[1095, 903]" diff --git a/data/010_ECommerce/sample.csv b/data/010_ECommerce/sample.csv deleted file mode 100644 index 75641a0285d67d6023df9fbf5c0177733a8c4db0..0000000000000000000000000000000000000000 --- a/data/010_ECommerce/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Positive Feedback Count,Clothing ID,Age,Department Name,Recommended IND,Class Name,Division Name,Rating -19,1095,34,Dresses,1,Dresses,General,4 -0,903,57,Tops,1,Fine gauge,General Petite,4 -3,830,56,Tops,1,Blouses,General,4 -3,1047,36,Bottoms,0,Pants,General Petite,3 -0,1110,30,Dresses,1,Dresses,General,5 -11,820,36,Tops,1,Blouses,General Petite,5 -2,1059,37,Bottoms,1,Pants,General Petite,3 -5,1092,39,Dresses,1,Dresses,General,5 -0,22,30,Tops,1,Knits,General,5 -0,394,30,Intimate,1,Swim,Initmates,5 -1,1081,53,Dresses,1,Dresses,General Petite,5 -0,1008,36,Bottoms,1,Skirts,General Petite,4 -0,1094,38,Dresses,0,Dresses,General,2 -0,1077,31,Dresses,1,Dresses,General Petite,5 -3,1021,65,Bottoms,0,Skirts,General,3 -5,1025,22,Bottoms,1,Jeans,General,5 -4,834,41,Tops,1,Blouses,General Petite,3 -0,868,33,Tops,1,Knits,General,5 -1,829,33,Tops,0,Blouses,General,2 -0,1078,56,Dresses,1,Dresses,General Petite,5 diff --git a/data/011_SF/qa.csv b/data/011_SF/qa.csv deleted file mode 100644 index 4036f965de64361aec4fb711570a5b8cdde6596e..0000000000000000000000000000000000000000 --- a/data/011_SF/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Was the highest reported incident in the year 2023 filed online?,False,boolean,"[Incident Year, Filed Online, Incident Number]","['number[uint16]', 'boolean', 'number[uint32]']",False -Are all incidents reported on Mondays resolved?,False,boolean,"[Incident Day of Week, Resolution]","['category', 'category']",False -Do any incidents reported in Police District 'Central' fall in Supervisor District 5?,False,boolean,"[Police District, Supervisor District]","['category', 'number[UInt8]']",False -Are there any incidents that occurred at the same latitude and longitude more than once?,True,boolean,"[Latitude, Longitude]","['number[double]', 'number[double]']",False -How many unique types of incident categories are there in the dataset?,49,number,[Incident Category],['category'],11 -What's the total number of incidents reported online?,144099,number,[Filed Online],['boolean'],1 -How many different police districts are there in the dataset?,11,number,[Police District],['category'],9 -What is the average incident count per year?,118851.16666666667,number,[Incident Year],['number[uint16]'],3.3333333333333335 -What is the most common incident category?,Larceny Theft,category,[Incident Category],['category'],Larceny Theft -Which day of the week has the highest number of incidents?,Friday,category,[Incident Day of Week],['category'],Saturday -What is the most common resolution for incidents reported online?,Open or Active,category,"[Filed Online, Resolution]","['boolean', 'category']",Open or Active -What is the Police District with the most incidents?,Central,category,[Police District],['category'],Northern -What are the top 5 most common incident descriptions?,"[Theft, From Locked Vehicle, >$950, [Malicious Mischief], Vandalism to Property, Battery, Lost Property, Vehicle, Recovered, Auto]",list[category],[Incident Description],['category'],"['Investigative Detention', 'Theft, From Locked Vehicle, $200-$950', 'Assault, Aggravated, W/ Other Weapon', 'Theft, From Locked Vehicle, >$950', 'Theft, From Unlocked Vehicle, >$950']" -Name the 4 most frequently occurring police districts.,"[Central, Northern, Mission, Southern]",list[category],[Police District],['category'],"['Northern', 'Central', 'Mission', 'Bayview']" -List the 3 most common incident categories on Fridays.,"[Larceny Theft, Malicious Mischief, Other Miscellaneous]",list[category],"[Incident Day of Week, Incident Category]","['category', 'category']","['Other Miscellaneous', 'Larceny Theft', 'Assault']" -Give the 6 most common resolutions for incidents.,"[Open or Active, Cite or Arrest Adult, Unfounded, Exceptional Adult]",list[category],[Resolution],['category'],"['Open or Active', 'Cite or Arrest Adult']" -List the years with the top 4 highest incident counts.,"[2018, 2019, 2022, 2021]",list[number],[Incident Year],['number[uint16]'],"[2018, 2019, 2021, 2022]" -Which 3 incident years have the lowest number of online filed reports?,"[2023, 2020, 2021]",list[number],"[Incident Year, Filed Online]","['number[uint16]', 'boolean']","[2018, 2020, 2021]" -Provide the 5 most frequently repeated latitude-longitude pairs.,"[(37.784560141211806, -122.40733704162238), (37.7751608100771, -122.40363551943442), (37.78640961281089, -122.40803623744476), (37.7839325760642, -122.4125952775858), (37.77871942789032, -122.4147412230519)]",list[number],"[Latitude, Longitude]","['number[double]', 'number[double]']","[(37.72344678051801, -122.40007300242718), (37.724004908138426, -122.4353125712072), (37.73078874215092, -122.42838994658086), (37.73132568595012, -122.46129211000152), (37.7430966136643, -122.47462383026864)]" -Name the 6 years with the most number of unique incident categories.,"[2018, 2019, 2020, 2021, 2022, 2023]",list[number],"[Incident Year, Incident Category]","['number[uint16]', 'category']","[2018, 2019, 2021, 2022, 2020, 2023]" diff --git a/data/011_SF/sample.csv b/data/011_SF/sample.csv deleted file mode 100644 index 7c8643c2aba51fd71eed6fcda7e0682f2b3d4b7b..0000000000000000000000000000000000000000 --- a/data/011_SF/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Longitude,Incident Category,Incident Number,Latitude,Incident Day of Week,Incident Description,Resolution,Police District,Incident Year,Filed Online,Supervisor District --122.40647555268292,Other Miscellaneous,190860198,37.79514446373132,Wednesday,Investigative Detention,Open or Active,Central,2019,,3.0 --122.3945869789376,Other Miscellaneous,230082147,37.752426800122734,Friday,Investigative Detention,Open or Active,Bayview,2023,,10.0 --122.41397500878728,Larceny Theft,210675035,37.80748251193778,Friday,"Theft, From Locked Vehicle, $200-$950",Open or Active,Central,2021,,3.0 --122.51129492624534,Other Miscellaneous,210423577,37.77507596005672,Tuesday,Investigative Detention,Open or Active,Richmond,2021,,1.0 --122.4330285001136,Larceny Theft,210733263,37.77655012153063,Sunday,"Theft, From Locked Vehicle, >$950",Open or Active,Northern,2021,,5.0 --122.46129211000152,Larceny Theft,196014773,37.73132568595012,Saturday,"Theft, From Unlocked Vehicle, >$950",Open or Active,Taraval,2019,True,7.0 --122.44691011930168,Larceny Theft,220591346,37.80328399631487,Thursday,"Theft, From Locked Vehicle, $200-$950",Open or Active,Northern,2022,,2.0 --122.41524148656327,Non-Criminal,190501358,37.8054171710477,Thursday,"Firearm, Turned In by Public",Open or Active,Central,2019,,3.0 --122.41095161908784,Missing Person,200445484,37.78414101130419,Saturday,Found Person,Open or Active,Tenderloin,2020,,5.0 --122.4217315225388,Assault,190150977,37.7632997673923,Friday,"Assault, Aggravated, W/ Other Weapon",Cite or Arrest Adult,Mission,2019,,9.0 --122.42912798697296,Drug Offense,180685940,37.76945612769652,Monday,Marijuana Offense,Cite or Arrest Adult,Park,2018,,8.0 --122.41408603237402,Robbery,220371637,37.752506064398666,Monday,"Robbery, W/ Force",Open or Active,Mission,2022,,9.0 --122.41944373685448,Assault,200598293,37.76271286580887,Sunday,"Assault, Aggravated, W/ Other Weapon",Cite or Arrest Adult,Mission,2020,,9.0 --122.47462383026864,Motor Vehicle Theft,210173512,37.7430966136643,Thursday,"Vehicle, Stolen, Auto",Open or Active,Taraval,2021,,7.0 --122.4353125712072,Other Miscellaneous,190375454,37.724004908138426,Saturday,Trespassing,Open or Active,Ingleside,2019,,11.0 --122.50989475109743,Larceny Theft,180406003,37.77139603094359,Thursday,"Theft, From Locked Vehicle, $200-$950",Open or Active,Richmond,2018,,1.0 --122.43270618085734,Offences Against The Family And Children,180431890,37.78329259065825,Sunday,Violation of Stay Away Order,Cite or Arrest Adult,Northern,2018,,5.0 --122.4230732312264,Offences Against The Family And Children,180447687,37.77734948070624,Saturday,Domestic Violence (secondary only),Open or Active,Northern,2018,,5.0 --122.42838994658086,Malicious Mischief,220402458,37.73078874215092,Sunday,"Malicious Mischief, Vandalism to Vehicle",Cite or Arrest Adult,Ingleside,2022,,11.0 --122.40007300242718,Embezzlement,180168974,37.72344678051801,Saturday,"Vehicle, Embezzled",Open or Active,Bayview,2018,,10.0 diff --git a/data/012_Heart/qa.csv b/data/012_Heart/qa.csv deleted file mode 100644 index cce2dad1acb791b90d0519494e43a596e6b2e186..0000000000000000000000000000000000000000 --- a/data/012_Heart/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Do all patients experience exercise-induced angina?,False,boolean,['ExerciseAngina'],['category'],False -Does any patient have a resting blood pressure above 200?,False,boolean,['RestingBP'],['number[uint8]'],False -Are there patients without heart disease?,True,boolean,['HeartDisease'],['number[uint8]'],True -Does everyone have normal resting electrocardiographic results?,False,boolean,['RestingECG'],['category'],False -What is the maximum age of patients in the dataset?,77,number,['Age'],['number[uint8]'],69 -What is the minimum resting blood pressure among the patients?,0,number,['RestingBP'],['number[uint8]'],95 -What is the average cholesterol level in the dataset?,198.7995642701525,number,['Cholesterol'],['number[uint16]'],207.8 -What is the standard deviation of maximum heart rate among the patients?,25.4603341382503,number,['MaxHR'],['number[uint8]'],27.360170821258063 -What is the most common chest pain type among patients?,ASY,category,['ChestPainType'],['category'],ASY -What is the least common resting electrocardiographic result?,ST,category,['RestingECG'],['category'],ST -What is the most common ST slope among patients with heart disease?,Flat,category,"['ST_Slope', 'HeartDisease']","['category', 'number[uint8]']",Flat -What is the least common chest pain type among male patients?,TA,category,"['ChestPainType', 'Sex']","['category', 'category']",TA -What are the top 3 most common chest pain types?,"['ASY', 'NAP', 'ATA']",list[category],['ChestPainType'],['category'],"['ASY', 'NAP', 'ATA']" -Which 4 resting electrocardiographic results are least common?,"['ST', 'LVH', 'Normal']",list[category],['RestingECG'],['category'],"['ST', 'LVH', 'Normal']" -What are the 2 most common ST slopes among patients with heart disease?,"['Flat', 'Up']",list[category],"['ST_Slope', 'HeartDisease']","['category', 'number[uint8]']","['Flat', 'Down']" -What are the 4 most common chest pain types among male patients?,"['TA', 'ATA', 'NAP', 'ASY']",list[category],"['ChestPainType', 'Sex']","['category', 'category']","['TA', 'ATA', 'NAP', 'ASY']" -What are the top 5 ages of patients in the dataset?,"[54, 58, 55, 56, 57]",list[number],['Age'],['number[uint8]'],"[56, 67, 64, 57, 63]" -What are the 4 least common resting blood pressures among the patients?,"[101, 174, 192, 129]",list[number],['RestingBP'],['number[uint8]'],"[145, 160, 108, 142]" -What are the 6 most common cholesterol levels in the dataset?,"[0, 254, 223, 220, 230, 211]",list[number],['Cholesterol'],['number[uint16]'],"[0, 195, 518, 309, 254, 271]" -What are the 3 least common maximum heart rates among the patients?,"[177, 187, 194]",list[number],['MaxHR'],['number[uint8]'],"[179, 86, 140]" diff --git a/data/012_Heart/sample.csv b/data/012_Heart/sample.csv deleted file mode 100644 index c46b7f3d982f839e3939fedd27891255b2259287..0000000000000000000000000000000000000000 --- a/data/012_Heart/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Cholesterol,Age,RestingBP,ST_Slope,Sex,ChestPainType,RestingECG,MaxHR,ExerciseAngina,HeartDisease -195,63,140,Up,F,ATA,Normal,179,N,0 -518,53,145,Flat,M,NAP,Normal,130,N,1 -0,65,160,Flat,M,ASY,ST,122,N,1 -0,56,130,Flat,M,ASY,LVH,122,Y,1 -309,54,108,Up,M,ATA,Normal,156,N,0 -254,67,125,Flat,M,ASY,Normal,163,N,1 -0,56,120,Flat,M,ASY,ST,148,N,1 -271,69,142,Up,M,NAP,LVH,126,N,0 -272,46,140,Flat,M,TA,Normal,175,N,1 -0,58,120,Down,M,ASY,LVH,106,Y,1 -193,56,120,Flat,M,TA,LVH,162,N,0 -220,62,120,Up,M,NAP,LVH,86,N,0 -303,64,130,Flat,F,ASY,Normal,122,N,0 -236,56,120,Up,M,ATA,Normal,178,N,0 -289,57,165,Flat,M,ASY,LVH,124,N,1 -0,41,125,Up,M,ASY,Normal,176,N,1 -313,64,140,Up,F,NAP,Normal,133,N,0 -0,57,95,Down,M,ASY,Normal,182,N,1 -219,39,118,Flat,M,ASY,Normal,140,N,1 -564,67,115,Flat,F,NAP,LVH,160,N,0 diff --git a/data/013_Roller/qa.csv b/data/013_Roller/qa.csv deleted file mode 100644 index a68e94fd1ed4dc288298189238270928dd5fefaa..0000000000000000000000000000000000000000 --- a/data/013_Roller/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Did the oldest roller coaster in the dataset still operate?,True,boolean,"[year_introduced, Status]","['category', 'category']",True -Is there a roller coaster in the dataset that operates at a speed more than 100 mph?,True,boolean,[speed_mph],['number[double]'],False -Are all roller coasters in the dataset designed by 'Werner Stengel' removed?,False,boolean,"[Designer, Status]","['category', 'category']",False -Does every roller coaster have a G-force value?,False,boolean,[Gforce_clean],['number[double]'],False -What is the maximum speed (in mph) for roller coasters in the dataset?,149.1,number,[speed_mph],['number[double]'],62.0 -How many roller coasters were introduced in the year 2000?,47,number,[year_introduced],['number[uint16]'],0 -What is the average G-force across all roller coasters in the dataset?,3.8240055248618785,number,[Gforce_clean],['number[double]'],3.62 -What is the total number of roller coasters designed by 'Edwin Madeupname' in the dataset?,0,number,[Designer],['category'],0 -Which manufacturer has built the fastest roller coaster?,Intamin,category,"[Manufacturer, speed_mph]","['category', 'number[double]']",Bolliger & Mabillard -What is the status of the roller coaster with the highest G-force?,Removed,category,"[Status, Gforce_clean]","['category', 'number[double]']", -What type of the roller coaster is the oldest in the dataset?,Wood,category,"[Type, Opening date]","['category', 'category']",Other -What is the location of the roller coaster with the highest number of inversions?,Alton Towers,category,"[Location, Inversions_clean]","['category', 'number[uint8]']",Busch Gardens Tampa Bay -What are the names of the top 3 fastest roller coasters?,"[Formula Rossa, Kingda Ka, Top Thrill Dragster]",list[category],"[coaster_name, speed_mph]","['category', 'number[double]']","[\'Afterburn (roller coaster)\', \'Hades 360\', \'Montu (roller coaster)\']" -Which 2 roller coasters have the highest number of inversions?,"[The Smiler, Colossus (Thorpe Park)]",list[category],"[coaster_name, Inversions_clean]","['category', 'number[uint8]']","[\'Montu (roller coaster)\', \'Wipeout (roller coaster)\']" -What are the locations of the top 5 roller coasters with the highest G-force?,"[Sea Lion Park, Fuji-Q Highland, Six Flags Over Texas, Nürburgring, Morey's Piers]",list[category],"[Location, Gforce_clean]","['category', 'number[double]']","[\'Other\', \'Busch Gardens Tampa Bay\', \'Mt. Olympus Water & Theme Park\', \'Adventuredome\', \'Other\']" -Name the 4 oldest roller coasters in the dataset.,"[Switchback Railway, Flip Flap Railway, Loop the Loop (Coney Island), Loop the Loop (Young's Pier)]",list[category],"[coaster_name, Opening date]","['category', 'category']","[\'Zipper Dipper\', \'Runaway Mine Train (Six Flags Over Texas)\', \'The Bush Beast\', \'Canyon Blaster (Adventuredome)\']" -What are the top 3 speeds (in mph) of roller coasters in the dataset?,"[149.1, 128.0, 120.0]",list[number],[speed_mph],['number[double]'],"[62.0, 60.0, 60.0]" -List the G-force values of the 2 roller coasters with the highest G-force.,"[12.0, 6.5]",list[number],[Gforce_clean],['number[double]'],"[4.3, 3.8]" -What are the heights (in ft) of the top 4 tallest roller coasters?,"[377.3, 367.5, 318.2, 306.1]",list[number],[height_ft],['number[double]'],"[98.4, 90.2, 82.0, 78.7]" -Name the introduction years of the 6 oldest roller coasters in the dataset.,"[1884, 1895, 1901, 1901, 1902, 1902]",list[number],"[year_introduced, Opening date]","['number[uint16]', 'category']","[1934, 1966, 1985, 1993, 1996, 1999]" diff --git a/data/013_Roller/sample.csv b/data/013_Roller/sample.csv deleted file mode 100644 index 0a5216cca0fdb70d693699a7f33e904b1e212194..0000000000000000000000000000000000000000 --- a/data/013_Roller/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Status,coaster_name,Designer,Manufacturer,height_ft,Gforce_clean,Type,year_introduced,speed_mph,Inversions_clean,Location,Opening date -Operating,Surfrider,Ing.-Büro Stengel GmbH,Intamin,98.4,,Steel – Shuttle – Launched,2007,,0,Wet'n'Wild Gold Coast,September 2007 -Operating,Zipper Dipper,Charlie Paige,,,,Other,1934,,0,Blackpool Pleasure Beach,1934 -Removed,Kanonen,Werner Stengel,Intamin,78.7,,Steel – Launched,2005,46.6,2,Liseberg,"April 23, 2005" -Operating,Hades 360,,The Gravity Group,,3.5,Wood,2013,60.0,1,Mt. Olympus Water & Theme Park,"May 14, 2013" -,Firehawk (roller coaster),,Vekoma,,4.3,Steel – Flying,2007,50.0,5,Other, -Operating,Runaway Mine Train (Six Flags Over Texas),,Arrow Development,,,Steel,1966,35.0,0,Six Flags Over Texas,"July 23, 1966" -,RC Racer,Walt Disney Imagineering,Intamin,82.0,,Steel – Shuttle – Launched,2011,,0,Other, -Operating,Wipeout (roller coaster),,Vekoma,,,Steel – Shuttle – Boomerang,2007,50.0,6,Pleasurewood Hills,2007 -Operating,Raging Spirits,Walt Disney ImagineeringSansei Technologies,Intamin,,,Steel,2005,37.3,1,Tokyo DisneySea,21 July 2005 -Operating,Canyon Blaster (Adventuredome),,Arrow Dynamics,,3.5,Steel – Indoor,1993,41.0,4,Adventuredome,"August 23, 1993" -Operating,Montu (roller coaster),Werner Stengel,Bolliger & Mabillard,,3.8,Steel – Inverted,1996,60.0,7,Busch Gardens Tampa Bay,"May 16, 1996" -,Whizzer (roller coaster),Werner Stengel,Anton Schwarzkopf,,3.0,Other,1976,42.0,0,Other, -Operating,Afterburn (roller coaster),Ing.-Büro Stengel GmbH,Bolliger & Mabillard,,,Steel – Inverted,1999,62.0,6,Carowinds,"March 20, 1999" -,Zimerman (roller coaster),,Arrow Dynamics,,,Steel,2014,55.0,3,Other, -Operating,Apple Zapple (Kings Dominion),,Mack Rides,,,Steel – Wild Mouse,2002,35.0,0,Kings Dominion,"March 22, 2002" -Operating,Cobra's Curse,,Mack Rides,,,Steel – Spinning,2016,40.0,0,Busch Gardens Tampa Bay,"June 17, 2016" -In Production,Galaxi,,,,,Other,1970,,0,Other, -Operating,Lil' Devil Coaster,,Zamperla,,,Steel – Kiddie,1999,,0,Six Flags Great Adventure,1999 as Road Runner Railway; 2021 as Lil' Devil Coaster -Removed,The Bush Beast,,Taft Broadcasting,90.2,,Wood – Out and back,1985,55.9,0,Wonderland Sydney,7 December 1985 -In Production,Superman: Krypton Coaster (Six Flags Mexico),,Vekoma,,,Steel junior roller coaster,1993,,0,Other, diff --git a/data/014_Airbnb/qa.csv b/data/014_Airbnb/qa.csv deleted file mode 100644 index fa89152f4ade1df2c14d5f307d773c6bcacdf1bb..0000000000000000000000000000000000000000 --- a/data/014_Airbnb/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there a rental property with exactly 5 bedrooms?,True,boolean,['bedrooms'],['number[UInt8]'],False -Is there a rental property listed by a superhost that is instantly bookable?,True,boolean,"['host_is_superhost', 'instant_bookable']","['category', 'category']",True -Are there any rental properties that can accommodate more than 10 guests?,True,boolean,['accommodates'],['number[uint8]'],False -Is there a rental property that has received a perfect review score?,False,boolean,['review_scores_rating'],['number[double]'],False -How many rental properties are there in the dataset?,20776,number,[],[],20.0 -What is the maximum number of bedrooms in a property?,18.0,number,['bedrooms'],['number[UInt8]'],0 -What is the highest price per night for a rental property?,95150.0,number,['price'],['category'],3.62 -What is the maximum number of reviews a property has received?,870,number,['number_of_reviews'],['number[uint16]'],0 -Which neighbourhood is the property with the highest number of bedrooms located in?,Universidad,category,"['bedrooms', 'neighbourhood_cleansed']","['number[UInt8]', 'category']",Bolliger & Mabillard -What type of room is the most expensive property?,Entire home/apt,category,"['price', 'room_type']","['category', 'category']", -What is the property type of the listing with the most reviews?,Entire rental unit,category,"['number_of_reviews', 'property_type']","['number[uint16]', 'category']",Other -What is the neighbourhood of the property that can accommodate the most number of guests?,Unknown,category,"['accommodates', 'neighbourhood']","['number[uint8]', 'category']",Busch Gardens Tampa Bay -Which are the top 3 neighbourhoods with the most number of listings?,"['Madrid, Comunidad de Madrid, Spain', 'Madrid, Community of Madrid, Spain', 'Madrid, Spain']",list[category],['neighbourhood'],['category'],"[\'Afterburn (roller coaster)\', \'Hades 360\', \'Montu (roller coaster)\']" -Which are the top 2 property types that have received the most reviews?,"['Entire rental unit', 'Private room in rental unit']",list[category],"['property_type', 'number_of_reviews']","['category', 'number[uint16]']","[\'Montu (roller coaster)\', \'Wipeout (roller coaster)\']" -Which are the bottom 4 neighbourhoods with the least number of listings?,"['madrid, Comunidad de Madrid, Spain', 'Madrid, madrid, Spain', 'Lavapies, Comunidad de Madrid, Spain', 'Madrid, Comunidad de Madrid, España, Spain']",list[category],['neighbourhood'],['category'],"[\'Other\', \'Busch Gardens Tampa Bay\', \'Mt. Olympus Water & Theme Park\', \'Adventuredome\', \'Other\']" -What are the bottom 2 room types that are least available?,"['Shared room', 'Hotel room']",list[category],['room_type'],['category'],"[\'Zipper Dipper\', \'Runaway Mine Train (Six Flags Over Texas)\', \'The Bush Beast\', \'Canyon Blaster (Adventuredome)\']" -What are the top 3 prices of the most expensive properties?,"[95150.0, 90130.0, 64430.0]",list[number],['price'],['category'],"[62.0, 60.0, 60.0]" -What are the bottom 4 prices of the least expensive properties?,"[0.0, 0.0, 0.0, 0.0]",list[number],['price'],['category'],"[4.3, 3.8]" -What are the top 2 numbers of reviews received by the most reviewed properties?,"[870, 822]",list[number],['number_of_reviews'],['number[uint16]'],"[98.4, 90.2, 82.0, 78.7]" -What are the top 5 numbers of guests accommodated by the properties that can accommodate the most guests?,"[16, 16, 16, 16, 16]",list[number],['accommodates'],['number[uint8]'],"[1934, 1966, 1985, 1993, 1996, 1999]" diff --git a/data/014_Airbnb/sample.csv b/data/014_Airbnb/sample.csv deleted file mode 100644 index 5f93a27ef6ab5358304b909b56698747d452b98e..0000000000000000000000000000000000000000 --- a/data/014_Airbnb/sample.csv +++ /dev/null @@ -1,67 +0,0 @@ -id,listing_url,scrape_id,last_scraped,source,name,description,neighborhood_overview,picture_url,host_id,host_url,host_name,host_since,host_location,host_about,host_response_time,host_response_rate,host_acceptance_rate,host_is_superhost,host_thumbnail_url,host_picture_url,host_neighbourhood,host_listings_count,host_total_listings_count,host_verifications,host_has_profile_pic,host_identity_verified,neighbourhood,neighbourhood_cleansed,neighbourhood_group_cleansed,latitude,longitude,property_type,room_type,accommodates,bathrooms,bathrooms_text,bedrooms,beds,amenities,price,minimum_nights,maximum_nights,minimum_minimum_nights,maximum_minimum_nights,minimum_maximum_nights,maximum_maximum_nights,minimum_nights_avg_ntm,maximum_nights_avg_ntm,calendar_updated,has_availability,availability_30,availability_60,availability_90,availability_365,calendar_last_scraped,number_of_reviews,number_of_reviews_ltm,number_of_reviews_l30d,first_review,last_review,review_scores_rating,review_scores_accuracy,review_scores_cleanliness,review_scores_checkin,review_scores_communication,review_scores_location,review_scores_value,license,instant_bookable,calculated_host_listings_count,calculated_host_listings_count_entire_homes,calculated_host_listings_count_private_rooms,calculated_host_listings_count_shared_rooms,reviews_per_month -33715390,https://www.airbnb.com/rooms/33715390,20221213034110,2022-12-13,city scrape,Stylish & Modern Flat in Central Madrid: Gran Via,"EN> Stylish and modern premium flat in Madrid's most central location Gran Via. This exclusive newly renovated property is an excellent choice for your time in the city, with all main attractions being walking distance. Explore Madrid from the heart and most trending hood: Malasaña. Fully equipped with high speed Wifi, Netflix or Amazon Premium, you won't have time to get bored. All the furniture is brand new and ready for you. Welcome to Madrid's most central place!

The space
Well, most important reason to stay here: the location. This is without a doubt, the best place you can stay in Madrid. Enjoy the most amazing neighborhood in town, and being minutes on foot from Madrid's main attractions. Last year, Madrid was named as Europe's most vibrant city. Stay here and you will know why. Besides this, enjoy a comfortable and totally new place with all luxuries but being in a 200 hundred years building.
ES> Bueno, la principal razón de quedare aquí es la ubic","The property in Malasaña's side of Gran Via (most important avenue in Madrid that you will find just 1 minute walking). Bubbling with life at any time of day – and often long into the night – Malasaña is Madrid’s hippest neighbourhood. Filled with the city's coolest cafes, restaurants and bars, the trendy barrio is just minutes from Sol and a short walk to the most sought after museums and cultural hotspots.",https://a0.muscache.com/pictures/miso/Hosting-33715390/original/e8392336-0e24-4c67-bab3-4b30036cb8fb.jpeg,254144388,https://www.airbnb.com/users/show/254144388,Oscar,2019-04-08,"Madrid, Spain","Hi there! My name is Oscar and I am a Marketing entrepreneur from Madrid, Spain. I have lived in cities like London, Beijing or Los Angeles and enjoyed travelling to dozens of countries around the world. Then I always appreciated those moments when you feel like a local exploring a new land. Come and experience that wonderful feeling with us at the city's most central place. Welcome!",within an hour,100%,100%,t,https://a0.muscache.com/im/pictures/user/be03c3d9-73ec-4888-8bd6-f75b6fa94265.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/be03c3d9-73ec-4888-8bd6-f75b6fa94265.jpg?aki_policy=profile_x_medium,Malasaña,2.0,2.0,"['email', 'phone']",t,t,"Madrid, Comunidad de Madrid, Spain",Universidad,Centro,40.4218,-3.70479,Entire rental unit,Entire home/apt,4,,1 bath,2.0,2.0,"[""Central heating"", ""Washer"", ""Freezer"", ""Coffee"", ""Coffee maker"", ""Body soap"", ""Shared patio or balcony"", ""Wifi"", ""Hangers"", ""Private entrance"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Cleaning products"", ""Dining table"", ""Shampoo"", ""Radiant heating"", ""Extra pillows and blankets"", ""Wine glasses"", ""Portable fans"", ""Hair dryer"", ""Microwave"", ""Elevator"", ""Hot water"", ""Host greets you"", ""Stove"", ""Laundromat nearby"", ""Drying rack for clothing"", ""Refrigerator"", ""Room-darkening shades"", ""Essentials"", ""Clothing storage: closet, wardrobe, and dresser"", ""Bed linens"", ""Ethernet connection"", ""Toaster"", ""Paid parking off premises"", ""Single level home"", ""Oven"", ""55\"" HDTV with Amazon Prime Video, Netflix"", ""Dishes and silverware"", ""Iron"", ""First aid kit"", ""Dishwasher"", ""Baking sheet""]",$250.00,2,1125,2.0,2.0,1125.0,1125.0,2.0,1125.0,,t,12,12,12,61,2022-12-13,47,14,0,2019-05-02,2022-08-29,4.91,4.96,4.68,4.98,4.98,4.98,4.87,,t,2,2,0,0,1.07 -23495524,https://www.airbnb.com/rooms/23495524,20221213034110,2022-12-13,city scrape,Precioso apartamento en Centro Madrid,"Apartamento de 35 metros, recién reformado en pleno corazón de Madrid. Es acogedor pero moderno, pequeño pero cómodo. Perfectamente equipado, dispone de dos habitaciones una con cama matrimonial otra con una litera. Cuidamos mucho la decoración, los detalles y la limpieza.

En el Barrio mas Cool del Mundo. A dos minutos del Rastro, con fácil acceso caminando a las zonas más turísticas de Madrid como Plaza Mayor, Palacio Real, Puerta del Sol, Gran Vía, Museo Del Prado, Reina Sofía y Thyssen.

The space
Calefacción independiente, aire acondicionado, tv, wifi, secador de pelo, plancha entre otros.

El alojamiento tiene todo lo necesario para que se sientan en casa. La cocina, moderna y completamente nueva, está equipada con todos los enseres necesarios para cocinar como en vuestra propia casa: vitrocerámica, horno, horno-microondas, amplia nevera con congelador y todo el menaje que podáis imaginar de cubertería, cristalerías y vajillas.
El d","Lavapies, catalogado como el barrio mas Cool del Mundo por la revista Time Out:
""Al norte, la plaza Tirso de Molina, que de día es territorio de las floristas y de noche se llena de jóvenes que hacen cola para entrar en Medias Puri, club de moda del momento. Al sur, Tabacalera y La Casa Encendida, dos centros culturales enormes, como transatlánticos varados en medio de la ciudad. Puedes comer guisos indios en una mesa con hule de flores y por un precio de chiste te servirán un tajine marroquí de cordero. Cultura, gastronomía y rincones de fiesta, una prueba viva de cómo esta ciudad se transforma, avanzando hacia el futuro sin renunciar a su pasado"".

Bares, restaurantes, teatros, peluquerías, panadería, locutorios, farmacia, floristerías, Carrefour 24 horas a 3 minutos del piso.

Ubicación privilegiada, a sólo pocos minutos andando de los principales sitios de interés de la Ciudad: Plaza Mayor, Mercado de San Miguel y San Fernando, Palacio Real, Catedral de la",https://a0.muscache.com/pictures/bf6bf7e2-66a4-4ff5-bb16-5b51c9fb60d6.jpg,72323365,https://www.airbnb.com/users/show/72323365,Kati,2016-05-16,"Madrid, Spain","Hola soy Kati, honesta, me encanta viajar y conocer gente nueva. Soy muy atenta con las personas y siempre dispuesta a ayudar. Para mí la atención personal y el trato correcto son primordiales a la hora de conocer mis huéspedes. - -Buen viaje!!!! - - ",within a few hours,100%,86%,t,https://a0.muscache.com/im/pictures/user/86c58f3a-814a-4db8-a259-db0871605380.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/86c58f3a-814a-4db8-a259-db0871605380.jpg?aki_policy=profile_x_medium,La Latina,2.0,4.0,"['email', 'phone']",t,t,"Madrid, Spain",Embajadores,Centro,40.408067725053016,-3.7034917789787247,Entire rental unit,Entire home/apt,4,,1 bath,2.0,3.0,"[""Freezer"", ""Shower gel"", ""Coffee maker"", ""Body soap"", ""Wifi"", ""Hangers"", ""Cooking basics"", ""Kitchen"", ""Paid parking on premises"", ""Long term stays allowed"", ""Cleaning products"", ""Shampoo"", ""Ceiling fan"", ""AC - split type ductless system"", ""Wine glasses"", ""Heating"", ""Free washer \u2013 In unit"", ""Microwave"", ""Hair dryer"", ""Elevator"", ""Hot water"", ""Host greets you"", ""Stove"", ""Laundromat nearby"", ""Clothing storage: closet"", ""Dedicated workspace"", ""Refrigerator"", ""Essentials"", ""Bed linens"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""TV""]",$85.00,2,1125,2.0,3.0,30.0,1125.0,2.0,1117.4,,t,22,39,39,80,2022-12-13,173,35,3,2018-04-18,2022-12-11,4.77,4.86,4.95,4.92,4.95,4.84,4.66,VT-7653,t,2,2,0,0,3.05 -18541960,https://www.airbnb.com/rooms/18541960,20221213034110,2022-12-13,city scrape,Modern penthouse in the heart of Madrid,"Modern penthouse in the heart of Madrid recently renovated and furnished. Located in Chueca neighborhood, 1 minute walk from the Chueca metro station and 7 minutes walk from Gran Via and Tribunal.

Quiet street but at the same time has many restaurants, bars and shops close by. It has two terraces (an outdoor one and a glazed one) and a totally equipped kitchen.

Come and check out the views from the terrace, you will love them!

The space
The apartmernt is recently renovated and furnished, and it counts with empty closets at your disposal.

It has a very spacious and luminous living room with two balconies that will allow you to enjoy the most delightful views of Madrid's skyline. Both the kitchen as well as the bathroom are fully equipped.

It is only allowed to smoke in the outdoor balcony. Parties are not allowed because we care for our neigbours.

Guest access
The buiding has elevator and the apa","Chueca is undoubtedly the most cosmopolitan neighborhood of Madrid. On many occasions it is compared to the SOHO in New York. Stands out for its narrow streets, full of bars, restaurants and shops. The neighborhood is in constant development and it is considered an emblematic neighborhood of Madrid.",https://a0.muscache.com/pictures/d0688c39-7a7f-4bb3-b6c9-5b8b24790442.jpg,20892661,https://www.airbnb.com/users/show/20892661,Guillermo,2014-09-03,"Madrid, Spain","Hello, Im also a host in Spain so I know how to behave and will take care of your house! ",within an hour,100%,100%,t,https://a0.muscache.com/im/pictures/user/94595c36-d94f-4437-ac4f-c40cbb7e6b07.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/94595c36-d94f-4437-ac4f-c40cbb7e6b07.jpg?aki_policy=profile_x_medium,Justicia,1.0,1.0,"['email', 'phone', 'work_email']",t,t,"Madrid, Comunidad de Madrid, Spain",Justicia,Centro,40.42469,-3.69809,Entire rental unit,Entire home/apt,2,,1 bath,1.0,1.0,"[""Private patio or balcony"", ""Central air conditioning"", ""Coffee maker"", ""Wifi"", ""Hangers"", ""Cooking basics"", ""Kitchen"", ""Paid parking on premises"", ""Long term stays allowed"", ""Shampoo"", ""City skyline view"", ""Heating"", ""Free washer \u2013 In unit"", ""Microwave"", ""Hair dryer"", ""Smoke alarm"", ""Elevator"", ""Hot water"", ""Host greets you"", ""Stove"", ""Refrigerator"", ""Essentials"", ""Paid parking off premises"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""Dishwasher"", ""TV""]",$163.00,5,1120,2.0,5.0,2.0,1125.0,4.9,1088.9,,t,3,3,3,101,2022-12-13,95,27,0,2017-09-24,2022-09-30,4.9,4.91,4.86,4.9,4.91,4.98,4.9,VT-6030,f,1,1,0,0,1.49 -53961488,https://www.airbnb.com/rooms/53961488,20221213034110,2022-12-13,city scrape,Espacio único en Las Letras en edificio histórico,"Increible piso de 2 dorm. + 2 baños, completamente nuevo y ubicado en un edificio histórico rehabilitado de gran belleza del casco antiguo de Madrid. La vivienda ha sido reformada con materiales de gran calidad y cuenta con todas las comodidades. Situada en el Barrio de Las Letras, uno de los más emblemáticos de la capital, a menos de 100 m. de los museos del Prado y Thyssen y a 15 min andando de la Puerta del Sol, será un lugar inigualable en el que poder disfrutar de todo lo que ofrece Madrid.","El barrio de Las Letras, cuyo nacimiento data del siglo XVI, es uno de los más céntricos y genuinos de Madrid. Sus emblemáticos edificios hablan de un pasado memorable plagado de una historia que aún pervive a través de un rico legado histórico y literario que es ya universal. Y es que en el barrio vivieron y escribieron sus obras algunos de los grandes literatos de España. De hecho, en 1605, sale de un taller alojado en una de sus calles, la primera edición impresa de ""El Quijote"". Hoy, la zona presume de ofrecer a los que la visitan una de las mejores combinaciones de la capital: arte,arquitectura singular, literatura, bohemia, diversión, compras y buena gastronomía.
El barrio está formado por pequeñas calles, peatonales o de acceso restringido a vehículos, y agradables plazas como la de Santa Ana. En ellas, es fácil encontrar casas en las que vivieron y escribieron figuras tan importantes de la literatura del Siglo de Oro de España como Lope de Vega, Quevedo, Góngora o Cervante",https://a0.muscache.com/pictures/ba94a061-b744-4fb0-a66f-cc81ed98c025.jpg,5521269,https://www.airbnb.com/users/show/5521269,Igor,2013-03-18,"Madrid, Spain","Me encanta convertir casas antiguas en lugares acogedores donde poder disfrutar de la vida y la familia. Como vosotros, yo también soy viajero. Comprendo que cuando uno sale de su casa quiere encontrar las mismas comodidades que en su hogar. Por eso he cuidado cada detalle de este piso para que os sintáis como en casa. Espero que disfrutéis de vuestra estancia en Madrid.",within an hour,100%,98%,t,https://a0.muscache.com/im/users/5521269/profile_pic/1374156969/original.jpg?aki_policy=profile_small,https://a0.muscache.com/im/users/5521269/profile_pic/1374156969/original.jpg?aki_policy=profile_x_medium,,3.0,3.0,"['email', 'phone']",t,t,"Madrid, Comunidad de Madrid, Spain",Cortes,Centro,40.41299,-3.69496,Entire condo,Entire home/apt,4,,2 baths,2.0,3.0,"[""Private patio or balcony"", ""Central air conditioning"", ""Freezer"", ""Fire extinguisher"", ""Shower gel"", ""Outlet covers"", ""Body soap"", ""Hangers"", ""Wifi"", ""Mini fridge"", ""Private entrance"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Cleaning products"", ""Balay induction stove"", ""Dining table"", ""Shampoo"", ""Pack \u2019n play/Travel crib"", ""Bidet"", ""Self check-in"", ""Lockbox"", ""High chair"", ""Security cameras on property"", ""Wine glasses"", ""Heating"", ""Free washer \u2013 In unit"", ""Free dryer \u2013 In unit"", ""Hair dryer"", ""Smoke alarm"", ""Microwave"", ""Coffee maker: Nespresso"", ""Hot water"", ""Elevator"", ""Laundromat nearby"", ""Clothing storage: closet"", ""Dedicated workspace"", ""Refrigerator"", ""Room-darkening shades"", ""Essentials"", ""Carbon monoxide alarm"", ""Balay oven"", ""Bed linens"", ""TV with Netflix"", ""Toaster"", ""Paid parking off premises"", ""Single level home"", ""Dishes and silverware"", ""Iron"", ""First aid kit"", ""Dishwasher""]",$166.00,1,365,2.0,5.0,365.0,365.0,4.6,365.0,,t,22,52,82,100,2022-12-13,43,43,3,2022-01-07,2022-12-11,4.93,4.95,5.0,4.95,4.93,4.95,4.77,,f,3,3,0,0,3.78 -13576483,https://www.airbnb.com/rooms/13576483,20221213034110,2022-12-13,city scrape,2 Bedrooms Apartment Near Madrid City Centre.,"Newly renovated two bedroom apartment in Madrid just 10 minutes by bus from City Centre, good location, near supermarkets, pharmacies, coffee shops and parks. Bus stop is half a block away from apartment where you can take bus number 28 and number 15 which will stop at Gran Via and Puerta Del Sol, also bus number 15 will stop at Calle Goya and near Parque del Retiro which is approximately a 10 minute walk to Prado Museum.

Other things to note
The flat includes Wifi connection but is shared with my next door neighbor, I have not been able to installed Wifi in my flat because I don't live in Spain and in order to have it installed I personally need to be present when the technician arrives in which I have to show him my passort and documents in which states my ownership to the flat.",,https://a0.muscache.com/pictures/a982361a-ab75-4e1b-a44d-a8050402f9b2.jpg,63720237,https://www.airbnb.com/users/show/63720237,Mercedes,2016-03-20,"San Diego, CA",,within a few hours,100%,90%,t,https://a0.muscache.com/im/pictures/user/c42b181b-6cba-49c9-bb15-ed6aa6d3c5b5.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/c42b181b-6cba-49c9-bb15-ed6aa6d3c5b5.jpg?aki_policy=profile_x_medium,,1.0,2.0,"['email', 'phone']",t,t,,Guindalera,Salamanca,40.43363,-3.66152,Entire rental unit,Entire home/apt,3,,1 bath,2.0,2.0,"[""Washer"", ""Central air conditioning"", ""Freezer"", ""Fire extinguisher"", ""Shower gel"", ""Body soap"", ""Free street parking"", ""Wifi"", ""Hangers"", ""Private entrance"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Cleaning products"", ""Coffee maker: french press"", ""Dining table"", ""Shampoo"", ""Extra pillows and blankets"", ""Patio or balcony"", ""Heating"", ""Microwave"", ""Hair dryer"", ""Elevator"", ""Hot water"", ""Pets allowed"", ""Conditioner"", ""Host greets you"", ""Stove"", ""Refrigerator"", ""Room-darkening shades"", ""Essentials"", ""Bed linens"", ""Toaster"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""TV""]",$71.00,15,1125,15.0,15.0,1125.0,1125.0,15.0,1125.0,,t,13,19,41,316,2022-12-13,30,6,0,2016-07-04,2022-09-16,4.83,4.9,4.6,4.9,4.9,4.53,4.73,,f,1,1,0,0,0.38 -23431443,https://www.airbnb.com/rooms/23431443,20221213034110,2022-12-13,city scrape,La Latina. El Rastro. A/C Private room & bathroom,"A double room with private bathroom in a quiet apartment located in a unique building whose reform was awarded national architecture. Corridors in the form of a typical corrale of Madrid. Light pours in through the bedroom window. Located where the flee market (El Rastro) is installed on Sundays. All this in the neighborhood of La Latina known for the large number of fashionable bars and restaurants.

The space
Located in a second floor with elevator, it is a cozy, bright and quiet newly reformed with taste independent room and restroom right in the center of Madrid.

Guest access
The private room has everything you need to spend a few days at home. The guests have exclusive use of the bathroom and the stay is completely independent from the rest of the house in which we live. Only the entrance corridor to the apartment is shared. In the bathroom there is gel, shampoo, toothpaste, toilet paper ...
Sheets and towels are provided. Free int","We live in downtown Madrid, in the square where El Rastro flee market is set on Sunday mornings. It is a very traditional and popular neighborhood where old shops and bars coexist alongside modern restaurants and shops. Ideal to walk around and breath the atmosphere of Madrid.",https://a0.muscache.com/pictures/ecced884-024d-43c2-9abf-d3f9b8143291.jpg,13637219,https://www.airbnb.com/users/show/13637219,Tomas,2014-03-28,"Madrid, Spain","Spanish: -Trabajo en investigación en Big Data, pero mi pasión es viajar. He estado en decenas de países en viajes de los que siempre vuelvo enriquecido con nuevas experiencias. He utilizado y lo sigo haciendo Airbnb y otras redes para alojarme en otros lugares. Espero que disfrutéis de Madrid como yo lo hago todos los días y si queréis aprender algo acerca de Madrid y del barrio no dudéis en preguntarme. Y a donde vaya en mis próximos viajes, espero aprender nuevas cosas y compartir experiencias. - -English: -I work in research in Big Data, but my passion is traveling. I have been in dozens of countries on trips that I always come back enriched with new experiences. I have used and still do Airbnb and other networks to stay in other places. I hope you enjoy Madrid as I do every day and if you want to learn something about Madrid and the neighborhood do not hesitate to ask me. And where I go on my next trips, I hope to learn new things and share experiences.",within an hour,100%,100%,t,https://a0.muscache.com/im/pictures/user/75193c53-4cb5-4afc-b9f7-88f30365824a.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/75193c53-4cb5-4afc-b9f7-88f30365824a.jpg?aki_policy=profile_x_medium,La Latina,1.0,1.0,"['email', 'phone']",t,t,"Madrid, Comunidad de Madrid, Spain",Embajadores,Centro,40.41037,-3.70724,Private room in rental unit,Private room,2,,1 private bath,1.0,1.0,"[""Central heating"", ""Lock on bedroom door"", ""Coffee"", ""Shower gel"", ""Books and reading material"", ""Wifi"", ""Hangers"", ""Long term stays allowed"", ""Hot water kettle"", ""Shampoo"", ""Bathtub"", ""Extra pillows and blankets"", ""Free washer \u2013 In unit"", ""Free dryer \u2013 In unit"", ""Hair dryer"", ""Elevator"", ""Hot water"", ""Conditioner"", ""Host greets you"", ""Laundromat nearby"", ""Drying rack for clothing"", ""Dedicated workspace"", ""Paid parking garage off premises"", ""Room-darkening shades"", ""Air conditioning"", ""Essentials"", ""Bed linens"", ""Clothing storage: wardrobe and dresser"", ""Dishes and silverware"", ""Iron""]",$48.00,4,1125,4.0,20.0,1125.0,1125.0,4.3,1125.0,,t,11,19,19,19,2022-12-13,90,30,3,2018-03-04,2022-11-29,4.98,4.94,4.96,4.98,4.99,5.0,4.86,,t,1,0,1,0,1.55 -18147455,https://www.airbnb.com/rooms/18147455,20221213034110,2022-12-13,city scrape,Bright & Cozy 1 BD- RETIRO,"Great modern and cozy apartment. Located in the famous well-located area of Retiro. Modern kitchen, 1 bedroom with two single beds and a bathroom.
Also, the living room has a comfortable sofa-bed. The apartment has just been refurnished, it's modern, and it has all the facilities; it is completely equipped. The apartment is located in the heart of Madrid and very close to main tourist places, just a few meters away from the Retiro Park.

The space
The apartment, modern and really bright, has been refurbished and it is completely equipped with all the facilities.

It has a comfortable living room with a sofa-bed and with access to the American style kitchen and a dining area.

The bedroom is wide and bright with two twin beds. The bathroom is completely equipped and has a modern shower.

The common areas of the building are very nice.

Guest access
During the stay, guests have access to the entire house.Este alojamiento es perfecto para personas que busquen un lugar tranquilo, con privacidad, para descansar y relajarse cuando lo necesiten.

The space
Es un apartamento espacioso, compartido conmigo.

Guest access
Los huéspedes disponen de un espacio con mesa y sillas para comer en la cocina. También tienen café y té para su uso. En el salón tienen un sofá en el que pueden leer, ver una película, descansar... El baño está dentro de la habitación.

Other things to note
.","Chamberí, a un paso del centro histórico. Muy buen comunicado.",https://a0.muscache.com/pictures/385e55cf-20b6-4d8e-9b2f-1c40a8d99a86.jpg,297062529,https://www.airbnb.com/users/show/297062529,Maria,2019-09-22,"Madrid, Spain",,,,,f,https://a0.muscache.com/im/pictures/user/c3fc77c3-e50c-4f49-ab0c-2371de29af73.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/c3fc77c3-e50c-4f49-ab0c-2371de29af73.jpg?aki_policy=profile_x_medium,Trafalgar,1.0,1.0,"['email', 'phone']",t,f,"Madrid, Comunidad de Madrid, Spain",Almagro,Chamberí,40.43506,-3.69852,Private room in rental unit,Private room,1,,1 private bath,1.0,1.0,"[""Heating"", ""Essentials"", ""Hair dryer"", ""Pocket wifi"", ""Bed linens"", ""Shampoo"", ""Ethernet connection"", ""Hot water"", ""Lock on bedroom door"", ""Wifi"", ""Private living room"", ""Iron"", ""First aid kit"", ""Extra pillows and blankets"", ""Hangers"", ""TV""]",$53.00,1,10,1.0,1.0,10.0,10.0,1.0,10.0,,t,0,0,0,0,2022-12-13,3,0,0,2019-09-29,2019-10-06,5.0,5.0,5.0,5.0,5.0,5.0,4.67,,f,1,0,1,0,0.08 -19502683,https://www.airbnb.com/rooms/19502683,20221213034110,2022-12-13,previous scrape,General lacy,"Piso céntrico. Se comparte casa con los anfitriones. Urbanización con piscina. A 200 metros hay cercanías y metro. Comercios. A 10 min de la puerta de sol en transporte público. Se puede usar el salón, cocina, baño. Se alquilan dos habitaciones 175 euros por habitación",,https://a0.muscache.com/pictures/4c21a1a8-76d3-45cd-91bd-886e4d3d4fd6.jpg,58760044,https://www.airbnb.com/users/show/58760044,Gabriel,2016-02-14,"Madrid, Spain",,,,,f,https://a0.muscache.com/im/pictures/user/451e26d0-2b53-46d0-99d7-66f3d1ec73cd.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/451e26d0-2b53-46d0-99d7-66f3d1ec73cd.jpg?aki_policy=profile_x_medium,,1.0,2.0,"['email', 'phone']",t,f,,Palos de Moguer,Arganzuela,40.40353,-3.69133,Private room in home,Private room,4,,1 shared bath,2.0,2.0,"[""Essentials"", ""Washer"", ""Elevator"", ""Pool"", ""Wifi"", ""TV"", ""Kitchen"", ""Long term stays allowed""]",$175.00,1,1125,1.0,1.0,1125.0,1125.0,1.0,1125.0,,t,0,0,0,0,2022-12-13,0,0,0,,,,,,,,,,,f,1,0,1,0, -53309013,https://www.airbnb.com/rooms/53309013,20221213034110,2022-12-13,city scrape,BNBHolder Boutique SOL I,"Hostal de diseño y altas calidades formado por cuatro dormitorios con baño en suite localizado en pleno corazón de Madrid, a tan sólo un minuto de Gran Vía y Sol. Situado, por tanto, en una de las mejores y más emblemáticas zonas de Madrid.

High quality design hostel consisting of four bedrooms en-suite in the heart of Madrid, just one minute from Gran Vía and Sol. Therefore, it is located in one of the best and most emblematic areas of Madrid.

The space
Hostal de diseño y altas calidades formado por cinco dormitorios con baño en suite localizado en pleno corazón de Madrid, a tan sólo un minuto de Gran Vía y Sol. Situado, por tanto, en una de las mejores y más emblemáticas zonas de Madrid.

Además de los cinco dormitorios con baño en suite privados e independientes, cuenta asimismo con una zona común: un comedor con cocina americana para necesidades básicas.

El apartamento está preparado y equipado con todo lo necesario para que p",,https://a0.muscache.com/pictures/miso/Hosting-53309013/original/a4c0e836-0908-49f5-ad34-860ede08c669.jpeg,28786243,https://www.airbnb.com/users/show/28786243,Emilio,2015-03-05,"Madrid, Spain","¡Hola! - -Somos Emilio y Abel y estaremos encantados de estar a vuestra disposición cuando optéis por uno de los bonitos alojamientos con los que colaboramos ya que ayudamos a los propietarios con la gestión de sus viviendas turísticas. - -Desde el punto de vista del propietario, el alquiler de nuestra casa es siempre algo atractivo aunque no podemos dejar de olvidar el tiempo logístico e, incluso, dinero que ello conlleva para poder dar un servicio de calidad. Es por ello por lo que hemos decidido ayudar siempre desde la excelencia a estos anfitriones en la gestión de su alojamiento turístico. - -Danos su confianza y le daremos lo mejor de nosotros. - -Equipo de BNBHolder.",within an hour,100%,100%,f,https://a0.muscache.com/im/pictures/user/f58a1907-9595-4f8a-bcfb-117dd029780e.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/f58a1907-9595-4f8a-bcfb-117dd029780e.jpg?aki_policy=profile_x_medium,Sol,125.0,218.0,"['email', 'phone']",t,t,,Sol,Centro,40.41877,-3.70109,Private room in hostel,Private room,2,,1 private bath,1.0,1.0,"[""Washer"", ""Lock on bedroom door"", ""Freezer"", ""Shower gel"", ""Coffee maker"", ""Body soap"", ""Wifi"", ""Hangers"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Dining table"", ""Shampoo"", ""Security cameras on property"", ""Wine glasses"", ""Heating"", ""Hair dryer"", ""Hot water"", ""Dedicated workspace"", ""Refrigerator"", ""Air conditioning"", ""Essentials"", ""Dryer"", ""Bed linens"", ""Toaster"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""TV""]",$136.00,1,1125,2.0,3.0,2.0,1125.0,2.7,136.7,,t,17,41,66,338,2022-12-13,21,20,0,2021-12-07,2022-10-31,4.76,4.86,5.0,4.9,4.95,4.76,4.71,,t,70,62,8,0,1.69 -625753367277860354,https://www.airbnb.com/rooms/625753367277860354,20221213034110,2022-12-13,city scrape,Apartment for 4 in Barrio de Salamanca ( DIEGO DE LEON II ),"Services and utilities included in price: 
Electricity, gas and water up to 200 € per month.
Booking fee (includes final cleaning).
All our rentals include a Luxury complementary set of bathroom amenities. 
Services not included in the price:
4% additional for Payments with American Express (Visa and Mastercard have no surcharge)
Weekly cleaning fee 45,00 €  VAT included.  (Not included changing of bed linen).
Bed linen change: 10 € VAT included (Per set of linen)
Complete bed linen change: 15 € VAT included (Bed sheets + towels) 
Service pack of ammenities by L´Occitane 6,05 € VAT included.
Pets are not allowed.
Parking under request.
Refundable reservation deposit of 1000 euros to be charged at the time of check-in and to be returned once the apartment has been reviewed after cleaning.
EARLY CHECK IN AND LATE CHECK OUT * CHECK RATES (SUBJECT TO AVAILABILITY)
L",,https://a0.muscache.com/pictures/prohost-api/Hosting-625753367277860354/original/01655267-3bb9-46b0-9af9-e3f19967d1fb.jpeg,28038703,https://www.airbnb.com/users/show/28038703,Luxury Rentals Madrid,2015-02-20,"Madrid, Spain",,within an hour,99%,99%,f,https://a0.muscache.com/im/users/28038703/profile_pic/1424433447/original.jpg?aki_policy=profile_small,https://a0.muscache.com/im/users/28038703/profile_pic/1424433447/original.jpg?aki_policy=profile_x_medium,Goya,102.0,103.0,"['email', 'phone']",t,t,,Castellana,Salamanca,40.43452,-3.6804,Entire rental unit,Entire home/apt,4,,2 baths,2.0,3.0,"[""Heating"", ""Air conditioning"", ""Microwave"", ""Hair dryer"", ""Washer"", ""Dryer"", ""Wifi"", ""Hot water kettle"", ""Toaster"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""Coffee maker"", ""Dishwasher"", ""Hangers"", ""TV"", ""Kitchen"", ""Long term stays allowed""]",$215.00,2,1125,3.0,10.0,1125.0,1125.0,8.2,1125.0,,t,3,30,60,202,2022-12-13,4,4,0,2022-07-13,2022-11-05,4.25,4.75,4.75,3.0,4.25,5.0,4.5,,t,102,102,0,0,0.78 -22561066,https://www.airbnb.com/rooms/22561066,20221213034110,2022-12-13,city scrape,Habitación 1 huesped,"Dormitorio con buena iluminación, armario, baño completo. Ventana da a un patio muy silencioso.

The space
Casa compartida de 3 dormitorios y 3 baños completos. Zonas comunes amplias, cocina, comedor, y Terraza cerrada con comodos sofás...

Guest access
Tendrás acceso al comedor, terraza, cocina y aseo.

Other things to note
La casa tiene 120m², es muy tranquila, vivo con otro compañero de piso.","Barrio tranquilo, muy bien comunicado por metro o autobuses, varios supermercados muy cerca.",https://a0.muscache.com/pictures/acbfa9e1-c5be-4955-b556-429f2b811abf.jpg,90406380,https://www.airbnb.com/users/show/90406380,Robert,2016-08-17,"Madrid, Spain","Soy tranquilo, casero. Me gusta tener la casa en orden y limpia. Reciclo todo lo que puedo. ",within an hour,100%,100%,f,https://a0.muscache.com/im/pictures/user/19648649-6d40-4fa1-9c40-62699534155a.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/19648649-6d40-4fa1-9c40-62699534155a.jpg?aki_policy=profile_x_medium,Guindalera,1.0,10.0,"['email', 'phone']",t,t,"Madrid, Comunidad de Madrid, Spain",Guindalera,Salamanca,40.4411,-3.66388,Private room in rental unit,Private room,1,,1 shared bath,1.0,1.0,"[""Luggage dropoff allowed"", ""Washer"", ""Lock on bedroom door"", ""Coffee maker"", ""Wifi"", ""Hangers"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""32\"" TV with standard cable"", ""Extra pillows and blankets"", ""Security cameras on property"", ""Patio or balcony"", ""Heating"", ""Microwave"", ""Elevator"", ""Hot water"", ""Stove"", ""Drying rack for clothing"", ""Clothing storage: closet"", ""Cleaning available during stay"", ""Dedicated workspace"", ""Refrigerator"", ""Room-darkening shades"", ""Essentials"", ""Smoking allowed"", ""Bed linens"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""First aid kit"", ""Backyard""]",$38.00,1,1125,1.0,4.0,1125.0,1125.0,2.1,1125.0,,t,11,31,31,31,2022-12-13,13,9,5,2018-02-11,2022-11-28,4.77,4.77,4.69,4.92,5.0,4.77,4.77,,f,1,0,1,0,0.22 -571699760554772616,https://www.airbnb.com/rooms/571699760554772616,20221213034110,2022-12-13,city scrape,Bonito apartamento cerca de plaza Castilla,"Rompe con tu día a día y relájate en este apartamento ubicado en una calle muy tranquila. La zona dispone de todos los servicios. Perfecta comunicación con la zona de hospitales de La Paz. Muy cerca de Plaza Castilla y Bravo Murillo. Conexión con zona Bernabéu e intercambiadle de nuevos ministerios, línea directa a aeropuerto. Ubicado en un edificio de nueva construcción, antigüedad de años y se encuentra en perfecto estado de conservación. Aire acondicionado por conductos y calefacción indvdl",,https://a0.muscache.com/pictures/miso/Hosting-571699760554772616/original/1c45d11c-3ccb-4e2f-a2ae-eadc8c3eeeac.jpeg,446760611,https://www.airbnb.com/users/show/446760611,Raul,2022-02-25,,Asesor inmobiliario ,within a few hours,76%,50%,f,https://a0.muscache.com/im/pictures/user/5d8de3c3-b681-4534-834b-9efa2d11ab54.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/5d8de3c3-b681-4534-834b-9efa2d11ab54.jpg?aki_policy=profile_x_medium,,24.0,30.0,['phone'],t,t,,Valdeacederas,Tetuán,40.46906,-3.69967,Entire loft,Entire home/apt,4,,1 bath,1.0,2.0,"[""Washer"", ""Coffee maker"", ""Wifi"", ""Hangers"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Cleaning products"", ""Dining table"", ""Portable heater"", ""Microwave"", ""Hair dryer"", ""Elevator"", ""Hot water"", ""Stove"", ""Drying rack for clothing"", ""Clothing storage: closet"", ""Refrigerator"", ""Air conditioning"", ""Essentials"", ""Bed linens"", ""Toaster"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""TV""]",$49.00,2,365,2.0,2.0,365.0,365.0,2.0,365.0,,t,6,34,64,308,2022-12-13,13,13,1,2022-03-11,2022-11-19,4.23,4.54,4.08,4.54,4.54,4.08,4.08,,f,22,22,0,0,1.4 -547047298947876337,https://www.airbnb.com/rooms/547047298947876337,20221213034110,2022-12-13,city scrape,Precioso y moderno ático en Madrid!,Alójate a nuestro ÁTICO CON TERRAZA en Madrid! Dispone de un salón amplio con zona dormitorio con cama de matrimonio y con salida a la terraza donde podrás comer y poder disfrutar del sol. Una cocina con todo lo necesario y cuarto de baño. La casa dispone de todo los imprescindible para vivir. También tiene wifi. No te faltara de nada!!,,https://a0.muscache.com/pictures/miso/Hosting-547047298947876337/original/8c866be0-30c8-4c67-bd21-99bb2910d12f.jpeg,52530675,https://www.airbnb.com/users/show/52530675,Esther,2015-12-28,,,within a few hours,94%,61%,f,https://a0.muscache.com/im/pictures/user/159fa27d-ce28-4d93-8a16-e3b594cabc9a.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/159fa27d-ce28-4d93-8a16-e3b594cabc9a.jpg?aki_policy=profile_x_medium,,58.0,70.0,['phone'],t,t,,Canillas,Hortaleza,40.46599,-3.64901,Entire vacation home,Entire home/apt,2,,1 bath,,1.0,"[""Air conditioning"", ""Washer"", ""Pets allowed"", ""Wifi"", ""TV"", ""Kitchen"", ""Long term stays allowed""]",$75.00,7,365,7.0,7.0,365.0,365.0,7.0,365.0,,t,27,57,87,167,2022-12-13,2,2,0,2022-04-18,2022-05-09,5.0,5.0,5.0,5.0,5.0,5.0,4.5,,f,48,48,0,0,0.25 -23579133,https://www.airbnb.com/rooms/23579133,20221213034110,2022-12-13,city scrape,Chueca- 2BD 2BTH - BRIGHT AND SPACIOUS,"Bright and spacious completely remodeled apartment located in the heart of the Chueca neighborhood. The apartment has two bedrooms, an open concept living room and dining area, a fully equipped kitchen and two full bathrooms. The location is unbeatable in downtown Madrid and very well connected with only a couple of steps to the metro station.

The space
Magnificent apartment recently renovated and decorated taking care of all the details, in a classic building with elevator. It has all the necessary equipment for a magnificent stay. The apartment has WIFI, heating and air conditioning and comes with bed linen and towels.

It is a very bright and spacious apartment, and with an excellent location, in the Chueca neighborhood, in the center of Madrid, from where you can walk to the main points of interest in the city.

The apartment has two bedrooms (one with a king size bed and the other with two single beds), a spacious living room, a fully equ","The neighborhood known as Chueca is an area of ​​the Justicia neighborhood, located in the downtown district of Madrid.

It is located in the heart of Madrid, next to Gran Vía and between Fuencarral and Barquillo streets.

The neighborhood has a very lively and modern character, a very commercial and leisure environment, open and respectful of the diversity of today's society, without losing its traditional character due to its architecture.

In its narrow streets you can find, in addition to traditional shops, others such as modern restaurants, bars, fashion clothing stores, markets, theaters, etc. All this among its buildings with renovated facades of bright colors.

It's a highly recommended area for strolling and discovering its corners and from where you can access in a few minutes the main tourist attractions of the city.",https://a0.muscache.com/pictures/8f28af02-edaf-4c61-9b9c-a1abbbc77398.jpg,176237087,https://www.airbnb.com/users/show/176237087,Maria Elena,2018-03-02,,"Hola! -Mi gran pasión es viajar y conocer el mundo, lo que me ha llevado a vivir en distintos países y conocer otras culturas. -Amo Madrid pero estoy prácticamente todo el tiempo fuera por viajes, así que he decidido compartir mi bello apartamento con todos. Pero no os preocupéis, estaréis muy bien atendidos ya que mis amigos de Minty Host me ayudan a cuidar a mis huéspedes cuando no estoy en la ciudad. -Me encanta viajar y todo tipo de viajes, de ocio y descanso, de turismo, de aventura, a grandes ciudades, pequeños pueblos o lugares salvajes, con familia, amigos o incluso sola. Disfruto mucho conociendo la gente de otros países y aprendiendo de sus culturas. -He utilizado Airbnb para viajar en numerosas ocasiones y ahora me he decidido a ser anfitrión. Me gusta ofrecer un buen servicio, que la gente quede encantada de la experiencia y que disfruten en la vivienda, del barrio y de la ciudad en la que he vivido gran parte de mi vida y de la que estaré encantada de compartir sus mejores secretos. -Os invito a disfrutar de la estancia! - -*** - -Hi ! -I travel a lot - for work and - the best part - also for pleasure. I love meeting new people, tasting new food, experiencing new cultures. I have lived in different countries and continents so traveling is very much part of my DNA as well as my life experience. I have used Airbnb in the past and I am now happy to host fellow travelers. -I live in Madrid but I spend most of my time traveling so I have decided to share my beautiful apartment with you. -But don't worry, you will be well taken care of when I am not in town thanks to my friends at Minty Host, who are there to help my guests with anything they need. On my side, I will be more than happy to share with you the Madrid I love - its main landmarks but also its fantastic restaurants, bright squares, and bustling bars. -Enjoy your stay! -",within an hour,100%,100%,f,https://a0.muscache.com/im/pictures/user/758b6b6b-c3db-44df-8e03-b20788f30e85.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/758b6b6b-c3db-44df-8e03-b20788f30e85.jpg?aki_policy=profile_x_medium,Justicia,1.0,2.0,"['email', 'phone', 'work_email']",t,t,"Madrid, Comunidad de Madrid, Spain",Justicia,Centro,40.42221,-3.69537,Entire rental unit,Entire home/apt,6,,2 baths,2.0,5.0,"[""Washer"", ""TV with standard cable"", ""Coffee maker"", ""Wifi"", ""Hangers"", ""Crib"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Shampoo"", ""Extra pillows and blankets"", ""Heating"", ""Microwave"", ""Hair dryer"", ""Elevator"", ""Hot water"", ""Refrigerator"", ""Air conditioning"", ""Essentials"", ""Dryer"", ""Bed linens"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""Dishwasher""]",$192.00,1,1125,2.0,5.0,1125.0,1125.0,4.7,1125.0,,t,6,14,36,293,2022-12-13,111,27,0,2018-03-19,2022-11-09,4.73,4.86,4.73,4.86,4.79,4.89,4.68,,t,1,1,0,0,1.92 -41307914,https://www.airbnb.com/rooms/41307914,20221213034110,2022-12-13,city scrape,Habitación amplia en Wanda estadio Metropolitano,"una experiencia tranquila , cómoda y relajada en un ambiente donde todo fluye controladamente a tu gusto , espacios agradables compartidos e individuales , cerca de todo lo que necesitas durante tu estancia, 3 líneas de metro distintas, líneas de bus, parques, centros comerciales, bares y restaurantes

The space
cómodas instalaciones, ambiente de hogar con espacios amplios y confortables , cerca de estaciones de metro y paradas de buses , bares , tiendas , restaurantes y servicios

Guest access
áreas comunes amplias y agradables , terraza , salón , cocina , baño muy amplio

Other things to note
se le puede ofrecer servicio de traslado al aeropuerto u otra zona , ya de mutuo acuerdo","El apartamento se encuentra en una zona privilegiada por su tranquilidad y a la vez fácil acceso al metro, buses, bares, centros comerciales, parques y acceder en un corto recorrido a pie al estadio Wanda metropolitano, además de tener cerca el recinto ferial IFEMA de Madrid y el parque Juan Carlos primero. Buen ambiente y seguridad en todo momento.",https://a0.muscache.com/pictures/79d0acbd-c688-40a3-b943-99f89bc9b5b6.jpg,324222052,https://www.airbnb.com/users/show/324222052,Marisela,2020-01-04,"Madrid, Spain",,within a few hours,100%,93%,f,https://a0.muscache.com/im/pictures/user/ac8db578-52f7-455f-aecd-9be31448a149.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/ac8db578-52f7-455f-aecd-9be31448a149.jpg?aki_policy=profile_x_medium,,1.0,1.0,"['email', 'phone']",t,t,"Madrid, Comunidad de Madrid, Spain",Hellín,San Blas - Canillejas,40.434,-3.61289,Private room in rental unit,Private room,1,,1 shared bath,1.0,1.0,"[""Garden view"", ""Luggage dropoff allowed"", ""TV with standard cable"", ""Outdoor furniture"", ""Coffee maker"", ""Shared patio or balcony"", ""Hangers"", ""Kitchen"", ""Long term stays allowed"", ""City skyline view"", ""Extra pillows and blankets"", ""Security cameras on property"", ""Outdoor dining area"", ""Free washer \u2013 In unit"", ""Elevator"", ""Hot water"", ""Pets allowed"", ""Free parking on premises"", ""Drying rack for clothing"", ""Fast wifi \u2013 194 Mbps"", ""Dedicated workspace"", ""Refrigerator"", ""Room-darkening shades"", ""Air conditioning"", ""Essentials"", ""Dishes and silverware"", ""Iron"", ""First aid kit""]",$45.00,1,1125,1.0,1.0,1125.0,1125.0,1.0,1125.0,,t,30,60,90,180,2022-12-13,11,9,0,2020-02-02,2022-10-30,5.0,4.91,5.0,5.0,5.0,5.0,4.91,,f,1,0,1,0,0.32 -31009280,https://www.airbnb.com/rooms/31009280,20221213034110,2022-12-13,city scrape,"Piso cómodo , tranquilo y bien comunicado",Decoración clásica. Barrio muy tranquilo a cinco minutos del metro y autobuses para el centro. Ej. al Retiro 15 minutos en metro.

License number
VT-13112,,https://a0.muscache.com/pictures/bb36bfd2-2f1a-4283-9ea4-6e3687f06d7f.jpg,216562993,https://www.airbnb.com/users/show/216562993,Elena,2018-09-21,"Madrid, Spain",,within an hour,100%,91%,f,https://a0.muscache.com/im/pictures/user/429a16dc-3995-4471-9717-7031355507dc.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/429a16dc-3995-4471-9717-7031355507dc.jpg?aki_policy=profile_x_medium,Moratalaz,3.0,3.0,"['email', 'phone']",t,f,,Marroquina,Moratalaz,40.41074,-3.64861,Entire rental unit,Entire home/apt,5,,2 baths,2.0,4.0,"[""Washer"", ""Coffee maker"", ""Free street parking"", ""Wifi"", ""Hangers"", ""Private entrance"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Shampoo"", ""Private hot tub"", ""Patio or balcony"", ""Heating"", ""Microwave"", ""Hair dryer"", ""Elevator"", ""Hot water"", ""Stove"", ""Refrigerator"", ""Air conditioning"", ""Essentials"", ""Dryer"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""Dishwasher"", ""TV""]",$125.00,4,120,4.0,4.0,1125.0,1125.0,4.0,1125.0,,t,16,46,76,104,2022-12-13,17,6,0,2019-07-21,2022-09-11,4.65,4.76,4.59,4.82,4.76,4.82,4.71,VT-13112,f,2,2,0,0,0.41 -599806392837096244,https://www.airbnb.com/rooms/599806392837096244,20221213034110,2022-12-13,city scrape,GUBAN - 2 bedroom apartment with balcony in Azca,"Our mission is to empower individuals to immerse themselves in new places by having a home wherever they go. We do this by providing artfully designed fully furnished and serviced apartments for stays of one month or more. We are currently present in some of the most important cities in Europe.

Stepping into a Saharan oasis, all of Guban’s 115 square meters feels luxurious. This two-bedroom, two-bath home is situated in the Azca district. Guests are invited to try the many fabulous food offerings in the neighborhood or soak up the sun at the Plaza de Azca, just a block away.

The entrance feels supremely Saharan with the traditional tapestry below and wood carvings to line the right-hand side. The door to the left leads to the open-concept space boasting full functionality. The space begins with a cozy living area to the right with two dedicated workstations behind it. At the far end, a sun-soaked, six-person dining set rounds out the space.

Beh",,https://a0.muscache.com/pictures/prohost-api/Hosting-599806392837096244/original/f9792825-9739-4723-91b6-40ec8164a266.jpeg,346367515,https://www.airbnb.com/users/show/346367515,Ukio,2020-05-15,"Barcelona, Spain","Ukio's mission is to empower individuals to live where and when they want. We do this by disrupting the traditional residential real estate market by providing high quality and furnished apartments for stays of one month or more. We remove all the frustration around finding a rental with no long-term contracts, moving/buying furniture, security deposits, broker fees, etc. All you have to do is show up and start living. - -Ukio was founded by two brothers with deep experience in technology and real estate at companies such as Airbnb, Headspace, and Electronic Arts. The company is headquartered in Barcelona, and looks to expand operations throughout Western Europe soon.",within an hour,100%,97%,f,https://a0.muscache.com/im/pictures/user/f790e9f4-a54f-4132-83d8-f71b8c9e3760.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/f790e9f4-a54f-4132-83d8-f71b8c9e3760.jpg?aki_policy=profile_x_medium,,331.0,344.0,"['phone', 'work_email']",t,t,,Rios Rosas,Chamberí,40.44636,-3.69647,Entire rental unit,Entire home/apt,4,,2 baths,2.0,2.0,"[""Washer"", ""Coffee maker"", ""Clothing storage"", ""Wifi"", ""Hangers"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Patio or balcony"", ""Heating"", ""Hair dryer"", ""Smoke alarm"", ""Elevator"", ""Hot water"", ""Pets allowed"", ""Stove"", ""Dedicated workspace"", ""Refrigerator"", ""Air conditioning"", ""Essentials"", ""Carbon monoxide alarm"", ""Bed linens"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""First aid kit"", ""TV""]",$135.00,31,1125,31.0,31.0,1125.0,1125.0,31.0,1125.0,,t,19,49,79,319,2022-12-13,0,0,0,,,,,,,,,,,t,119,119,0,0, -50873958,https://www.airbnb.com/rooms/50873958,20221213034110,2022-12-13,city scrape,✈Aeropuerto+Desayuno+Netflix+Disney+ 🚕 aeropuerto,"Nos encantara teneros en nuestro Apartamento a 5 min del aeropuerto y transporte económico (preguntar) , el apartamento dispone de wifi con fibra rápida , Netflix , Disney Plus (Marvel Movies), Prime Video + Cafetera Dolces gusto y Desayuno incluido =)

License number
VT-7910","Barrio tranquilo a 5 min del aeropuerto , dispone de muchos restaurantes españoles , amplias zonas de parking y mucha seguridad , os esperamos 😊✈️✈️💙",https://a0.muscache.com/pictures/a91165bf-5059-430d-8dc7-9f8d4de0d5b1.jpg,123409494,https://www.airbnb.com/users/show/123409494,César,2017-03-30,"Madrid, Spain","Hola soy César , actualmente estoy estudiando ingeniería en la universidad , me encanta viajar , hacer surf y competir en natación , ocasionalmente por hobby hago triatlones aunque en la bicicleta he de confesar que soy muy malo jajajajj , serás mas que bienvenido en alojarte en mi casa , cualquier duda no dudes en hacérmela , un saludo ;).",within an hour,83%,85%,f,https://a0.muscache.com/im/pictures/user/5d523f98-7f35-41bd-a4c8-ebf57f897519.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/5d523f98-7f35-41bd-a4c8-ebf57f897519.jpg?aki_policy=profile_x_medium,,7.0,8.0,"['email', 'phone']",t,t,"Madrid, Comunidad de Madrid, Spain",Timón,Barajas,40.47132,-3.58775,Entire condo,Entire home/apt,6,,1 bath,2.0,3.0,"[""Central heating"", ""Washer"", ""Freezer"", ""Coffee"", ""Shower gel"", ""Coffee maker: espresso machine, Nespresso"", ""Body soap"", ""Free street parking"", ""Wifi"", ""Hangers"", ""Crib"", ""Private entrance"", ""Cooking basics"", ""Kitchen"", ""Long term stays allowed"", ""Hot water kettle"", ""Cleaning products"", ""Dining table"", ""Shampoo"", ""Extra pillows and blankets"", ""Portable fans"", ""Hair dryer"", ""Microwave"", ""Elevator"", ""Hot water"", ""Pets allowed"", ""Breakfast"", ""Drying rack for clothing"", ""Clothing storage: closet"", ""Dedicated workspace"", ""Refrigerator"", ""Room-darkening shades"", ""Air conditioning"", ""Essentials"", ""Bed linens"", ""Toaster"", ""Single level home"", ""Oven"", ""Dishes and silverware"", ""Iron"", ""TV""]",$121.00,1,365,1.0,1.0,365.0,365.0,1.0,365.0,,t,7,33,62,62,2022-12-13,206,138,3,2021-07-21,2022-11-25,4.4,4.52,4.36,4.66,4.69,4.73,4.38,VT-7910,f,2,2,0,0,12.09 diff --git a/data/015_Food/qa.csv b/data/015_Food/qa.csv deleted file mode 100644 index a03c10957dcea5c725a26c9fc0b9fe8fae1560f7..0000000000000000000000000000000000000000 --- a/data/015_Food/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there a food item with 'Fruits' as its group?,True,boolean,['GROUP'],['category'],False -Are there food items with 'Nuts' as their sub group?,True,boolean,['SUB GROUP'],['category'],True -Is there a food item with scientific name 'Tilia argentea'?,True,boolean,['SCIENTIFIC NAME'],['category'],False -Is 'Angelica' listed as a food name in the dataset?,True,boolean,['FOOD NAME'],['category'],False -How many food items are there in the dataset?,906,number,[],[],20 -How many unique food groups are there in the dataset?,24,number,['GROUP'],['category'],8 -How many unique sub groups are there in the dataset?,123,number,['SUB GROUP'],['category'],14 -How many unique food items are there in the dataset?,906,number,['FOOD NAME'],['category'],20 -What is the group of the food named 'Kiwi'?,Fruits,category,"['FOOD NAME', 'GROUP']","['category', 'category']", -What is the sub group of the food with scientific name 'Tilia argentea'?,Herbs,category,"['SCIENTIFIC NAME', 'SUB GROUP']","['category', 'category']", -What is the scientific name of the food named 'Kiwi'?,Actinidia chinensis,category,"['FOOD NAME', 'SCIENTIFIC NAME']","['category', 'category']", -What is the food name of the item with scientific name 'Tilia argentea'?,Silver linden,category,"['SCIENTIFIC NAME', 'FOOD NAME']","['category', 'category']", -What are the top 3 most common food groups?,"['Aquatic foods', 'Vegetables', 'Fruits']",list[category],['GROUP'],['category'],"['Aquatic foods', 'Herbs and Spices', 'Vegetables']" -What are the top 2 most common sub groups?,"['Fishes', 'Herbs']",list[category],['SUB GROUP'],['category'],"['Nuts', 'Mollusks']" -What are the bottom 4 least common food groups?,"['Eggs', 'Baby foods', 'Unclassified', 'Herbs and spices']",list[category],['GROUP'],['category'],"['Nuts', 'Animal foods', 'Snack foods', 'Soy']" -What are the bottom 2 least common sub groups?,"['Soy', 'Green vegetables']",list[category],['SUB GROUP'],['category'],"['Soy products', 'Venison']" -What are the top 3 most common food name lengths?,"[9, 6, 7]",list[number],['FOOD NAME'],['category'],"[15, 13, 8]" -What are the bottom 4 least common food name lengths?,"[39, 30, 45, 33]",list[number],['FOOD NAME'],['category'],"[7, 31, 6, 12]" -What are the top 2 most common scientific name lengths?,"[17.0, 19.0]",list[number],['SCIENTIFIC NAME'],['category'],"[12.0, 14.0]" -What are the top 5 most common group name lengths?,"[6.0, 13.0, 10.0, 16.0, 12.0]",list[number],['GROUP'],['category'],"[13.0, 16.0, 10.0, 9.0, 4.0]" diff --git a/data/015_Food/sample.csv b/data/015_Food/sample.csv deleted file mode 100644 index c307cbc79db7252ade82f447a711a0ee91136e6b..0000000000000000000000000000000000000000 --- a/data/015_Food/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -FOOD NAME,SCIENTIFIC NAME,GROUP,SUB GROUP -Alcoholic beverages,,Beverages,Alcoholic beverages -Colorado pinyon,Pinus edulis,Nuts,Nuts -Common octopus,Octopus vulgaris,Aquatic foods,Mollusks -Corn chip,,Snack foods,Snack foods -Mixed nuts,,Nuts,Nuts -Vegetable juice,,Beverages,Other beverages -Pollock,Pollachius,Aquatic foods,Fishes -Pineappple sage,Salvia elegans,Herbs and Spices,Herbs -White cabbage,Brassica oleracea L. var. capitata L. f. alba DC.,Vegetables,Cabbages -Domestic goat,Capra aegagrus hircus,Animal foods,Caprae -Perciformes (Perch-like fishes),Perciformes,Aquatic foods,Fishes -muesli,,, -Turmeric,Curcuma longa,Herbs and Spices,Spices -Purslane,Portulaca oleracea,Herbs and Spices,Herbs -Blue mussel,Mytilus edulis,Aquatic foods,Mollusks -Black salsify,Scorzonera hispanica,Vegetables,Root vegetables -Cauliflower,Brassica oleracea var. botrytis,Vegetables,Cabbages -Spotted seal,Phoca largha,Aquatic foods,Pinnipeds -Soy sauce,,Soy,Soy products -Antelope,Artiodactyla,Animal foods,Venison diff --git a/data/016_Holiday/qa.csv b/data/016_Holiday/qa.csv deleted file mode 100644 index bc96303fa9886f72da352ed188af8e88ca35eac8..0000000000000000000000000000000000000000 --- a/data/016_Holiday/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there a customer with 'Large Business' as their occupation?,True,boolean,['Occupation'],['category'],True -Are there customers with 'King' as their pitched product?,True,boolean,['ProductPitched'],['category'],True -Is there a customer with designation 'VP'?,True,boolean,['Designation'],['category'],True -Is 'Unmarried' listed as a marital status in the dataset?,True,boolean,['MaritalStatus'],['category'],True -How many customers are there in the dataset?,4888,number,,[],20 -How many unique occupations are there in the dataset?,4,number,['Occupation'],['category'],3 -How many unique designations are there in the dataset?,5,number,['Designation'],['category'],4 -How many unique marital statuses are there in the dataset?,4,number,['MaritalStatus'],['category'],4 -What is the occupation of the customer with ID 200000?,Salaried,category,"['CustomerID', 'Occupation']","['number[uint32]', 'category']", -What is the product pitched to the customer with ID 200001?,Deluxe,category,"['CustomerID', 'ProductPitched']","['number[uint32]', 'category']", -What is the designation of the customer with ID 200002?,Executive,category,"['CustomerID', 'Designation']","['number[uint32]', 'category']", -What is the marital status of the customer with ID 200003?,Divorced,category,"['CustomerID', 'MaritalStatus']","['number[uint32]', 'category']", -What are the top 3 most common occupations?,"['Salaried', 'Small Business', 'Large Business']",list[category],['Occupation'],['category'],"['Small Business', 'Salaried', 'Large Business']" -What are the top 2 most common pitched products?,"['Basic', 'Deluxe']",list[category],['ProductPitched'],['category'],"['Basic', 'Deluxe']" -What are the bottom 4 least common occupations?,"['Salaried', 'Small Business', 'Large Business', 'Free Lancer']",list[category],['Occupation'],['category'],"['Small Business', 'Salaried', 'Large Business']" -What are the bottom 2 least common pitched products?,"['Super Deluxe', 'King']",list[category],['ProductPitched'],['category'],"['Standard', 'King']" -What are the top 3 most common ages of the customers?,"[35.0, 36.0, 34.0]",list[number],['Age'],['number[UInt8]'],"[37.0, 40.0, 55.0]" -What are the bottom 4 least common ages of the customers?,"[57.0, 60.0, 18.0, 61.0]",list[number],['Age'],['number[UInt8]'],"[30.0, 52.0, 20.0, 31.0]" -What are the top 2 most common monthly incomes of the customers?,"[20855.0, 21288.0]",list[number],['MonthlyIncome'],['number[UInt32]'],"[19668.0, 20021.0]" -What are the top 5 most common duration of pitch?,"[9.0, 7.0, 8.0, 6.0, 16.0]",list[number],['DurationOfPitch'],['number[UInt8]'],"[7.0, 9.0, 22.0, 17.0, 11.0]" diff --git a/data/016_Holiday/sample.csv b/data/016_Holiday/sample.csv deleted file mode 100644 index 13febc6f785508370033a32d8072a82bf5b4f98d..0000000000000000000000000000000000000000 --- a/data/016_Holiday/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -CustomerID,ProdTaken,Age,TypeofContact,CityTier,DurationOfPitch,Occupation,Gender,NumberOfPersonVisiting,NumberOfFollowups,ProductPitched,PreferredPropertyStar,MaritalStatus,NumberOfTrips,Passport,PitchSatisfactionScore,OwnCar,NumberOfChildrenVisiting,Designation,MonthlyIncome -200144,0,32.0,Company Invited,3,,Small Business,Male,2,5.0,Deluxe,3.0,Married,1.0,0,2,0,1.0,Manager,19668.0 -200079,0,46.0,Self Enquiry,2,11.0,Small Business,Male,3,,Deluxe,4.0,Married,1.0,1,5,0,1.0,Manager,20021.0 -202098,0,37.0,Self Enquiry,3,22.0,Small Business,Male,3,4.0,Deluxe,3.0,Married,5.0,0,5,1,0.0,Manager,21334.0 -204738,0,43.0,Self Enquiry,1,36.0,Small Business,Male,3,6.0,Deluxe,3.0,Unmarried,6.0,0,3,1,2.0,Manager,22950.0 -202858,1,25.0,Self Enquiry,3,7.0,Large Business,Female,4,4.0,Basic,4.0,Unmarried,3.0,1,4,1,3.0,Executive,21880.0 -201164,0,40.0,Self Enquiry,1,22.0,Salaried,Female,2,3.0,Standard,3.0,Unmarried,7.0,1,4,1,0.0,Senior Manager,22945.0 -200787,0,55.0,Company Invited,1,8.0,Salaried,Male,3,3.0,Standard,4.0,Divorced,4.0,0,2,1,1.0,Senior Manager,25976.0 -201504,1,24.0,Self Enquiry,1,6.0,Small Business,Male,3,3.0,Basic,3.0,Married,3.0,1,3,0,2.0,Executive,17293.0 -200287,0,38.0,Self Enquiry,1,29.0,Salaried,Male,2,3.0,Deluxe,3.0,Married,1.0,0,3,0,0.0,Manager,20745.0 -204176,0,33.0,Self Enquiry,1,9.0,Large Business,Male,3,5.0,Deluxe,5.0,Single,6.0,0,4,0,2.0,Manager,20854.0 -202836,0,55.0,Self Enquiry,1,12.0,Small Business,Male,3,4.0,King,5.0,Divorced,,0,4,1,1.0,VP,38084.0 -203214,0,47.0,Self Enquiry,1,7.0,Small Business,Male,3,4.0,King,,Married,2.0,0,5,1,2.0,VP,38305.0 -201971,0,30.0,Company Invited,1,9.0,Small Business,Female,3,3.0,Basic,3.0,Married,2.0,0,3,1,1.0,Executive,17083.0 -203113,1,40.0,Self Enquiry,1,13.0,Small Business,Male,4,4.0,Basic,5.0,Divorced,2.0,1,2,1,2.0,Executive,21082.0 -204885,1,52.0,Self Enquiry,3,17.0,Salaried,Female,4,4.0,Standard,4.0,Married,7.0,0,1,1,3.0,Senior Manager,31820.0 -200856,0,20.0,Self Enquiry,1,9.0,Salaried,Male,2,4.0,Basic,3.0,Single,2.0,0,3,0,1.0,Executive,18033.0 -200179,0,38.0,Self Enquiry,1,15.0,Salaried,Female,3,3.0,Basic,3.0,Single,2.0,0,2,1,1.0,Executive,17288.0 -204856,1,37.0,Self Enquiry,3,17.0,Small Business,Male,3,5.0,Standard,5.0,Married,2.0,0,5,0,1.0,Senior Manager,25772.0 -204092,0,47.0,Self Enquiry,3,7.0,Small Business,Female,4,4.0,Standard,5.0,Married,3.0,0,1,1,3.0,Senior Manager,29131.0 -204415,0,31.0,Company Invited,1,10.0,Small Business,Female,4,4.0,Basic,3.0,Married,3.0,0,3,1,2.0,Executive,20761.0 diff --git a/data/017_Hacker/qa.csv b/data/017_Hacker/qa.csv deleted file mode 100644 index 19b9375cfb708d961d1862491b7d7067d85fc07e..0000000000000000000000000000000000000000 --- a/data/017_Hacker/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there any entry posted on a weekend?,True,boolean,['weekday_name'],['category'],True -Are there titles with more than 100 characters?,False,boolean,['title'],['text'],False -Do any entries have a negative sentiment according to the Cardiff NLP model?,False,boolean,['title_gx_cardiff_nlp_sentiment'],['category'],False -"Is the term 'linux' mentioned in the ""Clusters II"" column?",True,boolean,['Clusters II'],['category'],True -How many entries were posted in the morning?,1516,number,['partofday'],['category'],1 -What's the highest score received by an entry?,6015,number,['score'],['number[uint16]'],2517 -"On average, how many descendants does an entry have?",339.2486205432937,number,['descendants'],['number[UInt16]'],558.0 -How many entries are in the Autumn season?,2301,number,['season'],['category'],8 -Which day of the week has the most entries?,Tuesday,category,['weekday_name'],['category'],Wednesday -What is the predominant language used in titles?,en,category,['title_gx_lang'],['category'],en -In which season was the entry with the highest score posted?,Spring,category,"['score', 'season']","['number[uint16]', 'category']",Summer -On which part of the day are most entries posted?,afternoon,category,['partofday'],['category'],afternoon -"List the top 4 most frequent terms in the ""Clusters II"" column.","['year, work, new', 'google, web, firefox, open', 'apple, linux, rust, iphone', 'facebook, twitter, die, account']",list[category],['Clusters II'],['category'],"['year, work, new', 'google, web, firefox, open', 'apple, linux, rust, iphone', 'amazon, database, sqlite, sql']" -Name the bottom 3 month names in terms of entry frequency.,"['August', 'December', 'July']",list[category],['month_name'],['category'],"['December', 'June', 'January']" -Identify the top 5 weekdays based on entry frequency.,"['Tuesday', 'Wednesday', 'Thursday', 'Monday', 'Friday']",list[category],['weekday_name'],['category'],"['Wednesday', 'Friday', 'Tuesday', 'Monday', 'Sunday']" -Provide the bottom 4 seasons in terms of entry count.,"['Spring', 'Winter', 'Autumn', 'Summer']",list[category],['season'],['category'],"['Autumn', 'Summer', 'Winter', 'Spring']" -List the top 3 scores in the dataset.,"[6015, 5771, 4338]",list[number],['score'],['number[uint16]'],"[2517, 1181, 1070]" -Name the bottom 5 title text lengths.,"[1.0, 2.0, 2.0, 2.0, 2.0]",list[number],['title_gx_text_length'],['number[UInt8]'],"[16.0, 20.0, 22.0, 30.0, 31.0]" -Identify the top 4 numbers of descendants.,"[4576.0, 3678.0, 3676.0, 3463.0]",list[number],['descendants'],['number[UInt16]'],"[3676.0, 1609.0, 524.0, 512.0]" -Provide the bottom 6 scores in the dataset.,"[501, 501, 501, 501, 501, 501]",list[number],['score'],['number[uint16]'],"[501, 516, 526, 534, 544, 583]" diff --git a/data/017_Hacker/sample.csv b/data/017_Hacker/sample.csv deleted file mode 100644 index 42d1ca900cc628776171e617948d4870ae8d2a60..0000000000000000000000000000000000000000 --- a/data/017_Hacker/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -title_gx_text_length,title,score,partofday,month_name,title_gx_lang,weekday_name,Clusters II,descendants,season,title_gx_cardiff_nlp_sentiment -20.0,CRDTs are the future,1181,afternoon,September,en,Monday,"year, work, new",312.0,Autumn, -78.0,Data structures and algorithms I actually used while working at tech companies,1025,night,July,en,Wednesday,"year, work, new",524.0,Summer, -68.0,Google joins .NET Foundation as Samsung brings .NET support to Tizen,595,afternoon,November,en,Wednesday,"google, web, firefox, open",332.0,Autumn, -58.0,A two-person startup already uses twenty-eight other tools,834,morning,February,en,Saturday,"year, work, new",453.0,Winter, -36.0,Stack Overflow Inc. Fiasco: Timeline,516,afternoon,October,und,Sunday,"year, work, new",486.0,Autumn, -48.0,"Details of the Cloudflare outage on July 2, 2019",698,afternoon,July,en,Friday,"google, web, firefox, open",149.0,Summer, -31.0,Docker for Mac and Windows Beta,904,noon,March,en,Thursday,"apple, linux, rust, iphone",239.0,Spring, -78.0,"Congress, at Last Minute, Drops Requirement to Obtain Warrant to Monitor Email",501,evening,December,en,Tuesday,"google, web, firefox, open",196.0,Winter, -46.0,1:60 scale Boeing 777 made from manila folders,885,afternoon,July,en,Monday,"year, work, new",197.0,Summer, -43.0,Will MySpace ever lose its monopoly? (2007),534,afternoon,March,en,Wednesday,"google, web, firefox, open",315.0,Spring, -43.0,The Makefile I use with JavaScript projects,544,afternoon,February,en,Wednesday,"google, web, firefox, open",512.0,Winter, -36.0,Eve: Programming designed for humans,1070,afternoon,October,en,Friday,"year, work, new",374.0,Autumn, -16.0,Valve Steam Deck,2517,afternoon,July,und,Thursday,"year, work, new",1609.0,Summer, -39.0,Performance Reviews Are a Waste of Time,597,afternoon,June,en,Wednesday,"year, work, new",321.0,Summer, -59.0,Gitlab cancels plan on tracking user behavior on GitLab.com,602,afternoon,October,en,Tuesday,"google, web, firefox, open",271.0,Autumn, -22.0,GitHub Archive Program,526,afternoon,November,und,Wednesday,"google, web, firefox, open",245.0,Autumn, -64.0,RIAA’s YouTube-dl takedown ticks off developers and GitHub’s CEO,772,afternoon,October,en,Tuesday,"google, web, firefox, open",276.0,Autumn, -30.0,Things I Don’t Know as of 2018,753,evening,December,en,Friday,"year, work, new",211.0,Winter, -58.0,SoftBank unmasked as ‘Nasdaq whale’ that stoked tech rally,583,noon,September,en,Friday,"google, web, firefox, open",462.0,Autumn, -39.0,"Amazon, Apple and Google Cut Off Parler",1058,night,January,en,Sunday,"amazon, database, sqlite, sql",3676.0,Winter, diff --git a/data/018_Staff/qa.csv b/data/018_Staff/qa.csv deleted file mode 100644 index 0b82fbd8e6ebfc29abd87ae2d9f34ce1be1e3c56..0000000000000000000000000000000000000000 --- a/data/018_Staff/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any employees with more than 7 projects?,False,boolean,[Number of Projects],['number[uint8]'],False -Has any employee worked for more than 300 hours on average per month?,True,boolean,[Average Monthly Hours],['number[uint16]'],False -Are all satisfaction levels above 0.5?,False,boolean,[Satisfaction Level],['number[double]'],False -Were there any employees hired in 2019?,True,boolean,[Date Hired],"['date[ns, UTC]']",False -How many unique departments are there?,10,number,[Department],['category'],9 -What's the maximum number of years an employee has been in the company?,10,number,[Years in the Company],['number[uint8]'],6 -How many employees have been promoted in the last 5 years?,319,number,[Promoted in the last 5 years?],['category'],0 -"On average, how many hours do employees work monthly?",201.05,number,[Average Monthly Hours],['number[uint16]'],188.15 -Which department has the highest number of employees?,sales,category,[Department],['category'],support -What's the most common salary level among employees?,low,category,[salary],['category'],low -Which year had the highest number of employees hired?,2017,category,[Date Hired],"['date[ns, UTC]']",2017 -Which salary level has the least number of employees who had an accident at work?,high,category,"[salary, Work Accident]","['category', 'category']",Not found -Name the top 4 departments with the most employees.,"['sales', 'technical', 'support', 'IT']",list[category],[Department],['category'],"['support', 'technical', 'marketing', 'accounting']" -List the bottom 3 departments by the number of promotions in the last 5 years.,"['hr', 'accounting', 'IT']",list[category],"[Department, Promoted in the last 5 years?]","['category', 'category']",['Not found'] -Identify the top 5 departments with the highest average satisfaction levels.,"['management', 'RandD', 'product_mng', 'marketing', 'support']",list[category],"[Department, Satisfaction Level]","['category', 'number[double]']","['IT', 'RandD', 'accounting', 'technical', 'product_mng']" -What are the bottom 2 departments by average monthly hours worked?,"['hr', 'marketing']",list[category],"[Department, Average Monthly Hours]","['category', 'number[uint16]']","['sales', 'RandD']" -Identify the top 3 years with the highest employee hiring.,"['2017', '2018', '2016']",list[number],[Date Hired],"['date[ns, UTC]']","[2017, 2016, 2018]" -Which are the top 4 satisfaction levels among employees who left?,"[0.1, 0.11, 0.09, 0.37]",list[number],"[Satisfaction Level, Left]","['number[double]', 'category']",[] -List the bottom 5 average monthly hours among employees who were promoted in the last 5 years.,"[215, 133, 159, 241, 247]",list[number],"[Average Monthly Hours, Promoted in the last 5 years?]","['number[uint16]', 'category']",[0] -Which are the top 6 years based on the last evaluation scores?,"[0.55, 0.5, 0.54, 0.51, 0.57, 0.49]",list[number],[Last Evaluation],['number[double]'],"[2015, 2014, 2016, 2018, 2017]" diff --git a/data/018_Staff/sample.csv b/data/018_Staff/sample.csv deleted file mode 100644 index f3321f1f888ec7c8031111093e3ed53008f7b17a..0000000000000000000000000000000000000000 --- a/data/018_Staff/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Last Evaluation,Department,Left,Years in the Company,Work Accident,Number of Projects,Satisfaction Level,salary,Average Monthly Hours,Promoted in the last 5 years?,Date Hired -0.89,support,No,4,No,5,0.24,medium,142,No,2016-06-04 -0.51,technical,No,3,No,3,0.28,low,124,No,2017-06-06 -0.67,accounting,No,4,No,2,0.91,low,255,No,2016-11-17 -0.81,sales,No,3,Yes,4,0.34,low,116,No,2017-11-19 -0.5,technical,No,3,No,4,0.55,low,179,No,2017-10-25 -0.93,support,No,5,No,3,0.36,low,162,No,2015-02-22 -0.87,support,Yes,5,No,5,0.78,medium,256,No,2015-04-01 -0.51,support,Yes,3,No,2,0.37,medium,140,No,2017-10-04 -0.63,accounting,No,3,No,4,0.73,low,174,No,2017-05-09 -0.85,marketing,Yes,6,No,4,0.84,low,249,No,2014-03-20 -0.61,technical,No,2,Yes,4,0.98,medium,265,No,2018-09-30 -0.97,RandD,No,4,No,5,0.93,low,137,No,2016-08-04 -0.67,product_mng,No,2,No,5,0.57,low,235,No,2018-11-23 -0.47,RandD,No,4,No,3,0.84,low,125,No,2016-01-26 -0.62,support,No,3,No,3,0.22,low,180,No,2017-01-03 -0.88,marketing,No,4,No,3,0.14,medium,162,No,2016-02-04 -0.77,management,No,2,No,3,0.5,high,267,No,2018-05-27 -0.76,marketing,No,3,No,5,0.69,low,174,No,2017-12-21 -0.48,IT,No,3,No,3,0.93,low,276,No,2017-05-08 -0.97,technical,No,2,No,5,0.6,medium,145,No,2018-10-06 diff --git a/data/019_Aircraft/qa.csv b/data/019_Aircraft/qa.csv deleted file mode 100644 index b155c4b7d0d172d935590c3951a5755339f6c4f8..0000000000000000000000000000000000000000 --- a/data/019_Aircraft/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Did any incident result in the total destruction of the aircraft?,True,boolean,['Aircaft_Damage_Type'],['category'],False -Have there been any incidents where the cause was related to the undercarriage of the aircraft?,True,boolean,['Incident_Cause(es)'],['category'],False -Has there been any instance where the ground casualties were non-zero?,True,boolean,['Ground_Casualties'],['category'],True -Are there incidents where the aircraft was involved in a collision?,True,boolean,"['Incident_Category', 'Incident_Cause(es)']","['category', 'category']",True -How many unique aircraft models are in the dataset?,3523,number,['Aircaft_Model'],['category'],20 -What's the highest number of occupants recorded in an incident?,524.0,number,['Onboard_Total'],['category'],86 -How many incidents occurred in January 2022?,7,number,['Date'],"['date[ns, UTC]']",0 -How many incidents resulted in non-zero fatalities?,0,number,['Fatalities'],['number[uint16]'],10 -Which aircraft model was involved in the most incidents?,Junkers Ju-52/3m,category,['Aircaft_Model'],['category'],Antonov An-2V -What was the cause of the incident that resulted in the most fatalities?,"Airplane - Pressurization, Airplane - Pressurization - Bulkhead failure, Airplane - Pressurization - Explosive decompression, Maintenance - (repair of) previous damage, Result - Loss of control",category,"['Incident_Cause(es)', 'Fatalities']","['category', 'number[uint16]']","Result - Loss of control, Security - Sabotage (bomb)" -What is the most common phase of aircraft during incidents?,En route (ENR),category,['Aircraft_Phase'],['category'],Landing (LDG) -What is the location of the incident with the highest number of onboard occupants?,near Ueno Village...,category,"['Incident_Location', 'Onboard_Total']","['category', 'category']", -What are the top 3 most common causes of incidents?,"['Info-Unavailable', 'Result - Runway excursion', 'Result - Damaged on the ground']",list[category],['Incident_Cause(es)'],['category'],"['Info-Unavailable', 'Result - Damaged on the ground', 'Result - Loss of control']" -List the top 5 locations where the most incidents have occurred.,"['unknown', 'Havana-José Martí International Airport (HAV)', 'Miami International Airport, FL (MIA)', 'Rio de Janeiro-Galeão International Airport, RJ (GIG)', 'Beirut International Airport (BEY)']",list[category],['Incident_Location'],['category'],"['near Loukhi', 'Arnhem', 'Glasgow-Preswick Airport', 'near Olpoi', 'Sioux Falls-Joe Foss Field Airport']" -Name the 4 most frequently occurring aircraft operators in the dataset.,"['USAAF', 'USAF', 'RAF', 'US Navy']",list[category],['Aircaft_Operator'],['category'],"['USAAF', 'Aeroflot, Northern', 'United Airlines', 'British Aerospace']" -What are the top 2 most common types of aircraft damage?,"['Damaged beyond repair', 'Substantial']",list[category],['Aircaft_Damage_Type'],['category'],"['Damaged beyond repair', 'Substantial']" -What are the 5 highest numbers of onboard passengers in incidents?,"[509.0, 503.0, 497.0, 451.0, 440.0]",list[number],['Onboard_Passengers'],['category'],"[81, 39, 14, 11, 8]" -List the 3 highest numbers of onboard crew in incidents.,"[32.0, 31.0, 29.0]",list[number],['Onboard_Crew'],['category'],"[5, 4, 3]" -Identify the 4 highest numbers of total onboard occupants in incidents.,"[524.0, 521.0, 517.0, 469.0]",list[number],['Onboard_Total'],['category'],"[86, 44, 19, 15]" -What are the 6 highest numbers of ground casualties in incidents?,"[1600, 900, 237, 107, 88, 87]",list[number],['Ground_Casualties'],['category'],"[9, 1]" diff --git a/data/019_Aircraft/sample.csv b/data/019_Aircraft/sample.csv deleted file mode 100644 index eaa024d914fb85016d8d72962eb737c392175a51..0000000000000000000000000000000000000000 --- a/data/019_Aircraft/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Incident_Location,Fatalities,Aircaft_Operator,Onboard_Total,Aircaft_Model,Onboard_Crew,Aircraft_Phase,Incident_Category,Aircaft_Damage_Type,Incident_Cause(es),Date,Ground_Casualties,Onboard_Passengers -near Loukhi,1,"Aeroflot, Northern",Fatalities: 1 / Occupants: 2,Antonov An-2V,Fatalities: 1 / Occupants: 2,En route (ENR),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Sunday 5 September 1965,,Fatalities: 0 / Occupants: 0 -Arnhem,5,RAF,Fatalities: 5 / Occupants: 8,Douglas Dakota III (DC-3),Fatalities: 3 / Occupants: 4,En route (ENR),"Criminal occurrence (sabotage, shoot down) | hull-loss",Damaged beyond repair,"Security - Shot, Security - Shot - Surface-to-air",Tuesday 19 September 1944,,Fatalities: 2 / Occupants: 4 -Dahra-Wareho...,0,Private,Fatalities: / Occupants:,Douglas DC-3C,Fatalities: / Occupants:,Unknown (UNK),Accident | hull-loss,Damaged beyond repair,"Result - Emergency, forced landing - On runway",xx xxx 1987,,Fatalities: / Occupants: -Carolina Aer...,0,South African Airways,Fatalities: 0 / Occupants: 19,Douglas C-47A-30-DL (DC-3),Fatalities: 0 / Occupants: 5,Landing (LDG),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Monday 15 September 1952,,Fatalities: 0 / Occupants: 14 -Mount Banahao,0,USAAF,Fatalities: / Occupants:,Curtiss C-46D-20-CU Commando,Fatalities: / Occupants:,Unknown (UNK),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Friday 25 October 1946,,Fatalities: / Occupants: -Titograd Air...,0,JAT,Fatalities: 0 / Occupants:,Convair CV-440,Fatalities: 0 / Occupants:,Landing (LDG),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Tuesday 4 February 1969,,Fatalities: 0 / Occupants: -near San Diego-No...,0,US Navy,Fatalities: 0 / Occupants: 7,Lockheed P-2H Neptune,Fatalities: 0 / Occupants: 7,Approach (APR),Accident | hull-loss,Damaged beyond repair,"Result - Emergency, forced landing - Ditching",Wednesday 18 May 1966,,Fatalities: 0 / Occupants: 0 -unknown,0,USAAF,Fatalities: / Occupants:,Douglas C-47B-1-DL (DC-3),Fatalities: / Occupants:,Landing (LDG),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Tuesday 24 April 1945,,Fatalities: / Occupants: -Bembridge Ai...,0,Britten-Norman,Fatalities: / Occupants:,IRMA/Britten-Norman BN-2A-6 Islander,Fatalities: / Occupants:,Standing (STD),Accident | hull-loss,Damaged beyond repair,Result - Damaged on the ground,Thursday 14 December 1978,,Fatalities: / Occupants: -near Khartoum-Civ...,17,Air Liberia,Fatalities: 8 / Occupants: 9,British Aerospace BAe-748-329 Srs. 2A LFD,Fatalities: 3 / Occupants: 3,Approach (APR),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Saturday 16 April 1983,Fatalities: 9,Fatalities: 5 / Occupants: 6 -near Tainan,15,USAF,Fatalities: 15 / Occupants: 15,Douglas C-47A-20-DK (DC-3),Fatalities: 4 / Occupants: 4,Unknown (UNK),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Saturday 7 November 1959,,Fatalities: 11 / Occupants: 11 -"Longmont, CO",44,United Airlines,Fatalities: 44 / Occupants: 44,Douglas DC-6B,Fatalities: 5 / Occupants: 5,En route (ENR),"Criminal occurrence (sabotage, shoot down) | hull-loss",Destroyed,"Result - Loss of control, Security - Sabotage (bomb)",Tuesday 1 November 1955,,Fatalities: 39 / Occupants: 39 -near La Rochelle,1,USAAF,Fatalities: 1 / Occupants:,Douglas C-47-DL (DC-3),Fatalities: / Occupants:,Unknown (UNK),"Criminal occurrence (sabotage, shoot down) | hull-loss",Damaged beyond repair,Security - Shot,Thursday 26 October 1944,,Fatalities: / Occupants: -Seoul-Incheo...,0,Asiana Airlines,Fatalities: 0 / Occupants: 0,Airbus A330-323,Fatalities: 0 / Occupants: 0,Pushback / towing (PBT),"other occurrence (ground fire, sabotage) | repairable-damage",Substantial,Result - Damaged on the ground,Sunday 28 August 2016,,Fatalities: 0 / Occupants: 0 -near Guryev,0,"Aeroflot, Kazakstan",Fatalities: 0 / Occupants: 6,Let L-410UVP,Fatalities: 0 / Occupants: 2,Landing (LDG),Accident | hull-loss,Damaged beyond repair,"Airplane - Engines, Airplane - Engines - All engine powerloss, Airplane - Engines - Fuel starvation, Result - Emergency, forced landing - Outside airport",Tuesday 27 August 1991,,Fatalities: 0 / Occupants: 4 -near Valencia,0,Spanish AF,Fatalities: 0 / Occupants: 3,Canadair CL-215-1A10,Fatalities: 0 / Occupants: 3,Landing (LDG),Accident | hull-loss,Damaged beyond repair,Info-Unavailable,Monday 11 April 1977,,Fatalities: 0 / Occupants: 0 -Sioux Falls ...,1,Ozark Air Lines,Fatalities: 0 / Occupants: 86,McDonnell Douglas DC-9-31,Fatalities: 0 / Occupants: 5,Landing (LDG),Accident | repairable-damage,Substantial,"Collision - Object, Collision - Object - Vehicle (on runway)",Tuesday 20 December 1983,Fatalities: 1,Fatalities: 0 / Occupants: 81 -near Olpoi,9,Vanair,Fatalities: 9 / Occupants: 9,Britten-Norman BN-2A-6 Islander,Fatalities: 1 / Occupants: 1,En route (ENR),Accident | hull-loss,Damaged beyond repair,"Result - CFIT - Hill, mountain (presumed)",Thursday 25 July 1991,,Fatalities: 8 / Occupants: 8 -Glasgow-Pres...,2,British Aerospace,Fatalities: 2 / Occupants: 2,British Aerospace 3201 Jetstream 32,Fatalities: 2 / Occupants: 2,Initial climb (ICL),Accident | hull-loss,Damaged beyond repair,Result - Loss of control,Tuesday 6 October 1992,,Fatalities: 0 / Occupants: 0 -"near Voskhod, Kra...",2,"Aeroflot, Azerbaijan",Fatalities: 2 / Occupants: 3,Antonov An-2R,Fatalities: 0 / Occupants: 1,Maneuvering (MNV),Accident | hull-loss,Damaged beyond repair,Result - Loss of control,Sunday 3 May 1981,,Fatalities: 2 / Occupants: 2 diff --git a/data/020_Real/qa.csv b/data/020_Real/qa.csv deleted file mode 100644 index e690a09f1b3d79cab80088e4039295f3048efe11..0000000000000000000000000000000000000000 --- a/data/020_Real/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -"Are there any properties with a price over 1,000,000?",True,boolean,['Precio'],['number[uint32]'],True -Any property with more than 10 bedrooms?,True,boolean,['Habitaciones'],['number[uint8]'],False -Are there properties with zero bathrooms?,False,boolean,['Baños'],['number[uint8]'],False -Has any property been listed for more than 100 days?,True,boolean,['Duración'],['number[uint16]'],True -What's the highest price in the dataset?,17000000.0,number,['Precio'],['number[uint32]'],1245000.0 -What's the total number of properties listed?,26026,number,[],[],20 -What's the longest duration a property has been listed?,2535.0,number,['Duración'],['number[uint16]'],300.0 -What's the largest property (by surface area) listed?,5504.0,number,['Superficie'],['number[uint16]'],350.0 -What's the most common type of property listed?,Piso,category,['Tipo'],['category'],Piso -Which advertiser has listed the most properties?,housell,category,['Anunciante'],['category'],gilmar_villalba -Which property has the highest price?,GM31-121816,category,"['Referencia', 'Precio']","['category', 'number[uint32]']",14075097 -Which property has the largest surface area?,IF5563-FINCA VALLE LOZOYA,category,"['Referencia', 'Superficie']","['category', 'number[uint16]']",2126-002573 -What are the top 5 types of properties listed?,"['Piso', 'Chalet', 'Apartamento', 'Chalet adosado', 'Chalet unifamiliar']",list[category],['Tipo'],['category'],"['Piso', 'Chalet', 'Apartamento', 'Chalet adosado', 'Chalet pareado']" -Name the 3 advertisers who have listed the most properties.,"['housell', 'servihabitat_central', 'pradesa_proyectos_inmobiliarios']",list[category],['Anunciante'],['category'],"['gilmar_villalba', 'consulting_parque_de_los_estados', 'vivantial_okuant']" -What are the 4 most common localities for properties listed?,"['Madrid Capital', 'Torrejón de Ardoz', 'Alcalá de Henares', 'Móstoles']",list[category],['Localidad'],['category'],"['Madrid Capital', 'Alpedrete', 'Fuenlabrada', 'Valdemorillo']" -What are the 2 most common districts for properties listed?,"['Centro', 'Salamanca']",list[category],['Distrito'],['category'],"['San Blas', 'Centro']" -What are the 5 highest property prices listed?,"[17000000.0, 13600000.0, 13250000.0, 13000000.0, 12000000.0]",list[number],['Precio'],['number[uint32]'],"[1245000.0, 950000.0, 590000.0, 555000.0, 550000.0]" -List the 3 longest durations properties have been listed.,"[2535.0, 2534.0, 2285.0]",list[number],['Duración'],['number[uint16]'],"[300.0, 146.0, 129.0]" -Identify the 4 largest properties (by surface area) listed.,"[5504.0, 3957.0, 2974.0, 2927.0]",list[number],['Superficie'],['number[uint16]'],"[350.0, 311.0, 300.0, 250.0]" -What are the 6 highest numbers of bedrooms in properties listed?,"[20, 20, 20, 20, 20, 20]",list[number],['Habitaciones'],['number[uint8]'],"[9, 5, 5, 4, 4, 3]" diff --git a/data/020_Real/sample.csv b/data/020_Real/sample.csv deleted file mode 100644 index aedb8a48574833e503ae11e8f66bfc46a93b4be5..0000000000000000000000000000000000000000 --- a/data/020_Real/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Id,Referencia,Precio,Tipo,Anunciante,Actualización,Duración,Superficie,Superficie útil,Superficie solar,Habitaciones,Baños,Planta,Antigüedad,Clasificación,Calle,Barrio,Distrito,Localidad,Código postal,Latitud,Longitud,Nuevo,Reformado,Conservado,Exterior,Orientación sur,Soleado,Amueblado,Negociar muebles,Cocina equipada,Cocina independiente,Armarios empotrados,Garaje,Terraza,Ascensor,Aire acondicionado,Trastero,Puerta blindada,Piscina,Jardín,Comedor,Balcón,Lavadero,Chimenea,Portero automático,Sistema de seguridad,Calefacción central,Calefacción eléctrica,Gas natural,Gasoil,Aluminio,PVC,Climalit,Madera,Parquet,Gres,Tarima,Mármol -piso-centro_arroyo_la_fuente28944-98393059116_100200,TC84-331091,139900.0,Piso,consulting_parque_de_los_estados,2019-10-07,52.0,79.0,79.0,79.0,3,1,1,5,5,,,Centro-Arroyo-La Fuente,Fuenlabrada,28944.0,40.2785,-3.78903,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0 -piso-apostol_santiago-98349627080_101800,IF7487-5941-2025,195000.0,Piso,vivantial_okuant,2019-10-28,31.0,62.0,53.0,62.0,2,1,1,4,5,Calle Roquetas de Mar,Apóstol Santiago,Hortaleza,Madrid Capital,,40.4819,-3.66113,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 -apartamento-rejas28022-97508888272_100100,PX1075-3248-16450-87012486,315000.0,Apartamento,remax_urbe_994014_0,2019-11-08,20.0,115.0,115.0,115.0,2,2,1,3,5,"Calle de Yécora, nº 18",Rejas,San Blas,Madrid Capital,28022.0,40.44291810000001,-3.5759779000000003,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -piso-orcasitas-96710628622_100500,SA673-118447K/2417,245000.0,Piso,crecimiento_inmobiliario_993245_0,2019-10-12,47.0,78.0,78.0,78.0,3,1,1,5,5,"Calle Ordicia, nº 11",Orcasitas,Usera,Madrid Capital,,40.366474200000006,-3.71350956,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -piso-pinar_del_rey28033-95845396870_105100,VI2-30001456,199000.0,Piso,vivienda_2_central,2019-11-18,10.0,81.0,81.0,81.0,3,1,1,5,5,,Pinar del Rey,Hortaleza,Madrid Capital,28033.0,40.4774,-3.6394,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0 -chalet_adosado-valdemorillo_centro_urbano-99177583722_102100,GM13-143461,245000.0,Chalet adosado,gilmar_villalba,2019-11-13,15.0,311.0,311.0,311.0,3,2,0,3,5,,,,Valdemorillo,,40.4882,-4.06958,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 -casa-becerril_de_la_sierra_centro_urbano-98390088861_101800,IF76197-BECERRIL/06,440000.0,Chalet,inmosierra,2019-10-04,55.0,300.0,300.0,300.0,5,3,0,4,4,,,,Becerril de la Sierra,,40.7357,-3.97448,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -piso-mostoles_centro28938-98416285523_106000,NI90-8314,143000.0,Piso,cal_estudios_inmobiliarios,2019-10-03,56.0,103.0,91.0,103.0,3,2,1,2,4,,,Centro,Móstoles,28938.0,40.3196,-3.86204,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -piso-gaztambide28015-99173864017_108900,XA396-MAD-62929,590000.0,Piso,deplace,2019-11-17,11.0,92.0,85.0,92.0,3,1,1,5,5,Calle Calle Fernando El Católico,Gaztambide,Chamberí,Madrid Capital,28015.0,40.4353,-3.71409,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -chalet-alpedrete_centro_urbano-466316717_212600,2126-002573,348000.0,Chalet,pradesa_proyectos_inmobiliarios,2019-02-01,300.0,350.0,350.0,1200.0,9,4,0,4,4,Calle Alpedrete,,,Alpedrete,,40.6602,-4.0458300000000005,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -chalet-alpedrete_centro_urbano-93384431880_102100,GM13-134773,370000.0,Chalet,gilmar_villalba,2019-10-14,45.0,250.0,250.0,250.0,4,3,0,4,4,,,,Alpedrete,,40.6695,-4.03474,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 -piso-valdemoro_hospital28342-9326525044_109700,4516420-14324081,142595.0,Piso,promocion_residencial_balboa,2019-11-19,9.0,81.0,81.0,81.0,2,2,1,4,4,"Calle Diego de Almagro, 1",,Hospital,Valdemoro,28342.0,40.1995896,-3.6933877,1,0,1,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -piso-san_blas_amposta-945856909634547_109800,14075097,1245000.0,Piso,,2019-09-13,76.0,50.0,45.0,50.0,2,1,1,6,4,"Calle Encajeras, nº 17",Amposta,San Blas,Madrid Capital,,40.4274778,-3.62004560000003,0,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0 -piso-galapagar_centro-96706569624_100500,SA1347-000021/6184,117000.0,Piso,eduardo_molet_518409_0,2019-11-04,24.0,106.0,100.0,106.0,1,2,1,5,5,Calle Galapark,,Centro,Galapagar,,40.5788,-4.00906,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0 -piso-castilla28033-945856909674104_109800,14747649,555000.0,Piso,,2019-07-22,129.0,148.0,130.0,148.0,4,2,3,4,4,"Avenida Jazmin, nº 17",Castilla,Chamartín,Madrid Capital,28033.0,40.4798,-3.66565,0,0,0,0,1,1,0,0,1,0,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0 -apartamento-sol_barrio28013-96762418033_101800,IF7536-VM1908014,550000.0,Apartamento,aproperties_real_estate_madrid,2019-10-18,41.0,72.0,72.0,72.0,2,1,1,5,5,,Sol,Centro,Madrid Capital,28013.0,40.4162,-3.70214,0,0,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 -chalet_pareado-villamantilla_centro_urbano-98396846435_106000,NI178-MSLN-250,185000.0,Chalet pareado,mascasas_sevilla_la_nueva,2019-10-26,33.0,120.0,120.0,300.0,3,2,0,3,5,Calle Olivo,,,Villamantilla,,40.3482,-4.12542,0,0,1,1,0,0,0,0,0,1,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0 -piso-palomeras_sureste28018-945856909697905_109800,15101323,134000.0,Piso,,2019-10-14,45.0,65.0,60.0,65.0,2,1,1,5,5,"Calle Pedro Laborde , nº 43",Palomeras Sureste,Puente de Vallecas,Madrid Capital,28018.0,40.3854,-3.65059,0,0,1,0,1,1,0,0,1,0,1,0,1,0,1,0,1,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0 -atico-san_blas_simancas28037-92582520746_101000,AF15-1725/510,300000.0,Ático,monreb_inmuebles,2019-11-18,10.0,68.0,50.0,68.0,1,1,2,3,5,,Simancas,San Blas,Madrid Capital,28037.0,40.4381,-3.61803,0,0,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -piso-chamartin_hispanoamerica28036-95842746949_100500,SA1625-JS-V-1331,950000.0,Piso,inmobiliaria_js,2019-07-05,146.0,210.0,171.0,210.0,5,3,1,6,5,Calle del Profesor Waksman,Hispanoamérica,Chamartín,Madrid Capital,28036.0,40.4561,-3.68898,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0 diff --git a/data/021_Telco/qa.csv b/data/021_Telco/qa.csv deleted file mode 100644 index 6c11a534ab125686b79bfe60dd07fb7981442e1a..0000000000000000000000000000000000000000 --- a/data/021_Telco/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there more than 2000 customers with a monthly charge higher than $80?,True,boolean,['MonthlyCharges'],['number[double]'],False -Do all customers have phone service?,True,boolean,['PhoneService'],['category'],False -Are there any customers with no internet service?,True,boolean,['InternetService'],['category'],True -Are there any customers who are senior citizens and have dependents?,True,boolean,"['SeniorCitizen', 'Dependents']","['number[uint8]', 'category']",True -How many unique customers are there in the dataset?,7043,number,['customerID'],['category'],20 -What's the highest monthly charge?,118.75,number,['MonthlyCharges'],['number[double]'],104.0 -What's the total number of customers?,7043,number,[],[],20 -What's the longest tenure?,72,number,['tenure'],['number[uint8]'],72 -What's the most common payment method?,Electronic check,category,['PaymentMethod'],['category'],Electronic check -What's the most common contract type?,Month-to-month,category,['Contract'],['category'],Month-to-month -Which customer has the highest total charge?,2889-FPWRM,category,"['customerID', 'TotalCharges']","['category', 'number[double]']",4853-RULSV -Which customer has the highest monthly charge?,7569-NMZYQ,category,"['customerID', 'MonthlyCharges']","['category', 'number[double]']",4853-RULSV -What are the top 3 most common internet services?,"['Fiber optic', 'DSL', 'No']",list[category],['InternetService'],['category'],"['Fiber optic', 'DSL', 'No']" -Name the 4 most common payment methods.,"['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)']",list[category],['PaymentMethod'],['category'],"['Electronic check', 'Bank transfer (automatic)', 'Mailed check', 'Credit card (automatic)']" -What are the 2 most common types of contract?,"['Month-to-month', 'Two year']",list[category],['Contract'],['category'],"['Month-to-month', 'Two year']" -What are the 5 most common services for which customers have multiple lines?,"['No', 'Yes', 'No phone service']",list[category],['MultipleLines'],['category'],"['Yes', 'No phone service']" -What are the 5 highest total charges?,"[8684.8, 8672.45, 8670.1, 8594.4, 8564.75]",list[number],['TotalCharges'],['number[double]'],"[7250.15, 6127.6, 5016.65, 3340.55, 3260.1]" -What are the 4 highest monthly charges?,"[118.75, 118.65, 118.6, 118.6]",list[number],['MonthlyCharges'],['number[double]'],"[104.0, 95.15, 89.6, 89.4]" -What are the 6 longest tenures?,"[72, 72, 72, 72, 72, 72]",list[number],['tenure'],['number[uint8]'],"[72, 70, 68, 67, 52, 41]" -What are the 3 shortest tenures?,"[0, 0, 0]",list[number],['tenure'],['number[uint8]'],"[1, 1, 1]" diff --git a/data/021_Telco/sample.csv b/data/021_Telco/sample.csv deleted file mode 100644 index 4219dce147cd405868af5e5bfe52ac79b089b3cb..0000000000000000000000000000000000000000 --- a/data/021_Telco/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -customerID,gender,SeniorCitizen,Partner,Dependents,tenure,PhoneService,MultipleLines,InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges,Churn -1024-GUALD,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes -0484-JPBRU,Male,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.25,996.45,No -3620-EHIMZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1031.7,No -6910-HADCM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.35,76.35,Yes -8587-XYZSF,Male,0,No,No,67,Yes,No,DSL,No,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),50.55,3260.1,No -6818-WOBHJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,6127.6,Yes -3082-YVEKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),77.15,1759.4,No -4737-AQCPU,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),72.1,5016.65,No -4853-RULSV,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.0,7250.15,Yes -5766-ZJYBB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,19.4,Yes -2668-TZSPS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No -3192-LNKRK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.05,2909.95,No -5315-CKEQK,Male,1,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Electronic check,51.0,1381.8,No -5914-DVBWJ,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.45,1505.85,Yes -7998-ZLXWN,Female,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,330.8,No -6328-ZPBGN,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.15,997.65,Yes -9530-EHPOH,Male,0,No,No,11,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.75,608,Yes -1853-UDXBW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.0,70,Yes -2672-TGEFF,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,3340.55,No -1902-XBTFB,Male,0,No,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,2001.5,Yes diff --git a/data/022_Airbnbs/qa.csv b/data/022_Airbnbs/qa.csv deleted file mode 100644 index a523fcefa89b89995462f354cfb6dda57c14b044..0000000000000000000000000000000000000000 --- a/data/022_Airbnbs/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there a listing with a review score rating of 100?,True,boolean,['review_scores_rating'],['number[UInt8]'],True -Are there any hosts who have listed more than 10 properties?,True,boolean,['host_total_listings_count'],['number[UInt16]'],True -Are all listings instantly bookable?,False,boolean,['instant_bookable'],['category'],True -Is there a listing that requires a minimum of 365 nights?,True,boolean,['minimum_nights'],['number[uint16]'],False -How many unique hosts are there in the dataset?,26765,number,['host_id'],['number[uint32]'],20 -What is the highest number of listings a single host has?,2739.0,number,['host_total_listings_count'],['number[UInt16]'],38.0 -How many unique locations are listed by the hosts?,1316,number,['host_location'],['category'],6 -What is the average review score rating across all listings?,93.767188,number,['review_scores_rating'],['number[UInt8]'],95.33333333333333 -What is the most common host location?,"New York, New York, United States",category,['host_location'],['category'],"New York, New York, United States" -What is the name of the listing with the most bedrooms?,"Walk to UN, Macy's & Empire State B",category,"['bedrooms', 'name']","['number[UInt8]', 'text']",Historic Gem Close to SI Ferry -Which location has the highest number of listings?,"New York, New York, United States",category,['host_location'],['category'],"New York, New York, United States" -What is the most common property type?,Entire apartment,category,['property_type'],['category'],Entire apartment -What are the top 5 unique host locations with the most listings?,"['New York, New York, United States', 'US', 'Brooklyn, New York, United States', 'Queens, New York, United States', 'Bronx, New York, United States']",list[category],['host_location'],['category'],"['New York, New York, United States', 'US', 'Brooklyn, New York, United States', 'FR', 'Sydney, New South Wales, Australia']" -Name the 3 listings with the lowest review score ratings.,"['Studio Apartment in East Williamsburg', 'Spacious Artist Loft Williamsburg', 'Cute 1 BR in the Lower East Side']",list[category],"['review_scores_rating', 'name']","['number[UInt8]', 'text']","['Historic Gem Close to SI Ferry', 'A+ Location Studio Apartment (Queen Bed & Futon)', 'Private Room in Heart of East Village!']" -List the 4 most common property types.,"['Entire apartment', 'Private room in apartment', 'Entire condominium', 'Entire house']",list[category],['property_type'],['category'],"['Entire apartment', 'Private room in apartment', 'Entire guest suite', 'Entire condominium']" -Who are the top 6 hosts with the most listings?,"['107434423', '305240193', '137358866', '51501835', '6168257', '22541573']",list[category],"['host_id', 'listing_id']","['number[uint32]', 'number[uint32]']","[62803, 1385157, 1898675, 3734323, 14295824, 14707270]" -What are the top 3 listing ids with the highest review score ratings?,"['4370230', '10166986', '14218173']",list[number],"['review_scores_rating', 'listing_id']","['number[UInt8]', 'number[uint32]']","[9334365, 8385447, 4016121]" -What are the 5 listing ids with the lowest number of minimum nights required?,"['4659046', '13192217', '17441150', '22058411', '28389772']",list[number],"['minimum_nights', 'listing_id']","['number[uint16]', 'number[uint32]']","[12584072, 44505052, 39727144, 45912795, 47406985]" -List the 4 listing ids of the properties with the highest number of bedrooms.,"['8536270', '2261367', '41552433', '23124338']",list[number],"['bedrooms', 'listing_id']","['number[UInt8]', 'number[uint32]']","[12584072, 27570074, 8385447, 31898478]" -What are the 6 listing ids with the lowest review score location?,"['18972792', '30422813', '30929071', '40777675', '45217842', '45217978']",list[number],"['review_scores_location', 'listing_id']","['number[UInt8]', 'number[uint32]']","[9334365, 31898478, 12584072, 33156370, 44505052, 25939748]" diff --git a/data/022_Airbnbs/sample.csv b/data/022_Airbnbs/sample.csv deleted file mode 100644 index 3a826b348b74310e31b00f1a1847e9b38b94d3ba..0000000000000000000000000000000000000000 --- a/data/022_Airbnbs/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -minimum_nights,name,host_location,instant_bookable,review_scores_rating,host_total_listings_count,property_type,review_scores_location,bedrooms,listing_id,host_id -30,Skylit Bedroom In Brooklyn,"New York, New York, United States",t,93.0,0.0,Private room in apartment,9.0,1.0,31898478,3734323 -30,1 Bedroom in 2 bdr Bushwick apt,"New York, New York, United States",f,,1.0,Private room in apartment,,1.0,9473492,49099055 -30,Luxury/Midtown East,"New York, New York, United States",f,,1.0,Private room in apartment,,1.0,5757509,29867161 -30,Modern Apt with private bedroom & bathroom,"New York, New York, United States",t,99.0,1.0,Private room in apartment,10.0,1.0,25939748,25210511 -30,Private Room in Heart of East Village!,"New York, New York, United States",f,91.0,1.0,Private room in apartment,10.0,1.0,15396328,14295824 -3,"Lovely 2nd Floor Townhouse Apt, Historic District","New York, New York, United States",t,99.0,1.0,Entire guest suite,10.0,3.0,27570074,1898675 -30,1bd steps from Central Park/museum,"New York, New York, United States",f,100.0,1.0,Entire apartment,8.0,1.0,9334365,20592462 -30,One Bedroom Walk Up in Hell's Kitchen,"New York, New York, United States",f,,1.0,Entire apartment,,1.0,19222455,134549676 -2,Queen Room In 2-Bed Brooklyn Pre-War,"Brooklyn, New York, United States",t,,2.0,Private room in apartment,,1.0,39727144,276610794 -30,Private Brick House,"New York, New York, United States",f,100.0,1.0,Entire apartment,10.0,2.0,8385447,27382789 -2,Furnished Smart 1 Bed Apartment in Hells Kitchen,FR,f,,0.0,Entire apartment,,1.0,45912795,143482205 -30,Located in the heart of NoLita.,"Sydney, New South Wales, Australia",f,100.0,1.0,Entire apartment,10.0,1.0,4016121,14707270 -1,Historic Gem Close to SI Ferry,"New York, New York, United States",f,88.0,1.0,Entire apartment,9.0,4.0,12584072,68252461 -2,Cozy haven in NYcity,US,t,,0.0,Entire condominium,,1.0,47406985,373254340 -30,Small Bedroom but Large Common Area,"New York, New York, United States",f,100.0,2.0,Private room in apartment,10.0,1.0,32406729,192383231 -30,"Clean, Modern Studio near Penn Station","New York, New York, United States",f,,0.0,Entire apartment,,,29303103,20006428 -13,Cozy Room in Townhouse Close to 5 Medical Centers,"New York, New York, United States",f,92.0,4.0,Private room in townhouse,9.0,1.0,33156370,62803 -30,Modern&Bright - Quick walk to Metro (full bed),US,f,,1.0,Private room in apartment,,1.0,45559714,345938275 -1,A+ Location Studio Apartment (Queen Bed & Futon),"New York, New York, United States",t,89.0,38.0,Entire apartment,9.0,,44505052,348619646 -30,One bed suite with private garden,"New York, New York",f,93.0,5.0,Entire apartment,10.0,1.0,266155,1385157 diff --git a/data/023_Climate/qa.csv b/data/023_Climate/qa.csv deleted file mode 100644 index 137379ad598cad0c52f257734a4c99bb2a26dc53..0000000000000000000000000000000000000000 --- a/data/023_Climate/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -Was there a day when the minimum temperature was below zero and it didn't rain?,True,"[tmin, prec]",boolean,"['number[double]', 'number[double]']",True -Are there records where the solar radiation exceeds 10 but the maximum temperature was below 20?,True,"[sol, tmax]",boolean,"['number[double]', 'number[double]']",True -Did any day with maximum wind speed above 15 also have average wind speed below 5?,True,"[racha, velmedia]",boolean,"['number[double]', 'number[double]']",False -Were there days in the summer where the minimum temperature dropped below 10?,True,"[season, tmin]",boolean,"['category', 'number[double]']",False -How many days had a maximum temperature above 30 degrees?,5500,[tmax],number,['number[double]'],3 -"On average, what's the minimum temperature during winters?",2.7196082770831027,"[season, tmin]",number,"['category', 'number[double]']", -How many unique days had solar radiation measurements?,28615,[sol],number,['number[double]'],15 -What's the highest wind speed ever recorded?,32.2,[racha],number,['number[double]'],14.4 -On which weekday did the highest temperature ever occur?,Friday,"[tmax, weekday_name]",category,"['number[double]', 'category']",Thursday -In which season do we find the highest average solar radiation?,Summer,"[season, sol]",category,"['category', 'number[double]']",Summer -Which month had the lowest average wind speed?,October,"[month_name, velmedia]",category,"['category', 'number[double]']",February -On what date was the highest pressure ever recorded?,2016-12-22T00:00:00Z,"[presMax, fecha]",category,"['number[double]', 'date[ns, UTC]']",1950-02-14T00:00:00Z -What are the top 3 months with the highest average maximum temperatures?,"['July', 'August', 'June']","[month_name, tmax]",list[category],"['category', 'number[double]']","['July', 'August', 'September']" -"Which are the 5 weekdays with the most rain, ranked from highest to lowest?","['Friday', 'Sunday', 'Saturday', 'Thursday', 'Wednesday']","[weekday_name, prec]",list[category],"['category', 'number[double]']","['Saturday', 'Sunday', 'Wednesday', 'Thursday', 'Tuesday']" -"List the 4 seasons ranked by average solar radiation, from highest to lowest.","['Summer', 'Spring', 'Autumn', 'Winter']","[season, sol]",list[category],"['category', 'number[double]']","['Summer', 'Autumn', 'Spring', 'Winter']" -Which 2 months recorded the lowest average minimum temperatures?,"['January', 'December']","[month_name, tmin]",list[category],"['category', 'number[double]']","['February', 'December']" -List the top 5 recorded maximum temperatures.,"[40.7, 40.6, 40.0, 40.0, 40.0]",[tmax],list[number],['number[double]'],"[37.5, 36.0, 33.3, 28.6, 26.6]" -What are the 4 lowest wind speeds ever recorded?,"[0.0, 0.0, 0.0, 0.0]",[velmedia],list[number],['number[double]'],"[0.3, 0.3, 0.3, 0.8]" -Rank the highest 3 solar radiation measurements.,"[14.9, 14.8, 14.7]",[sol],list[number],['number[double]'],"[13.0, 12.7, 12.3]" -Which 6 days of the year (numbered from 1 to 365/366) had the highest average temperatures?,"[209, 210, 208, 207, 211, 205]","[dayofyear, tmed]",list[number],"['number[uint16]', 'number[double]']","[208, 206, 205, 235, 260, 103]" diff --git a/data/023_Climate/sample.csv b/data/023_Climate/sample.csv deleted file mode 100644 index ddfe762081b0d51c91f2264aa68b378c2bd12f81..0000000000000000000000000000000000000000 --- a/data/023_Climate/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -racha,dayofyear,tmin,velmedia,month_name,sol,weekday_name,tmed,tmax,prec,season,fecha,presMax -8.3,103,10.0,1.1,April,,Sunday,15.0,20.0,2.4,Spring,2020-04-12T00:00:00Z,942.0 -,45,3.5,2.8,February,9.4,Tuesday,6.6,9.7,0.0,Winter,1950-02-14T00:00:00Z,953.5 -4.2,36,-3.6,0.3,February,8.4,Wednesday,1.9,7.4,0.0,Winter,1969-02-05T00:00:00Z,948.4 -,145,9.6,,May,7.7,Tuesday,14.2,18.9,0.2,Spring,1932-05-24T00:00:00Z,938.3 -9.7,348,-0.4,1.4,December,,Saturday,2.0,4.3,9.5,Winter,2008-12-13T00:00:00Z,940.4 -10.6,98,6.0,3.6,April,10.6,Friday,10.5,15.0,0.0,Spring,1927-04-08T00:00:00Z,933.1 -7.8,208,22.4,1.9,July,,Thursday,30.0,37.5,0.0,Summer,2017-07-27T00:00:00Z,940.2 -9.2,235,16.0,2.5,August,12.7,Thursday,22.3,28.6,0.0,Summer,1996-08-22T00:00:00Z,940.9 -5.0,79,5.2,1.4,March,3.1,Saturday,9.2,13.1,0.1,Spring,1926-03-20T00:00:00Z,935.4 -3.9,351,2.2,0.8,December,2.6,Sunday,5.6,9.0,0.0,Winter,2006-12-17T00:00:00Z,952.1 -,260,16.0,2.2,September,9.1,Wednesday,21.3,26.6,3.1,Autumn,1947-09-17T00:00:00Z,946.5 -14.4,23,9.5,4.7,January,2.0,Sunday,11.4,13.3,3.8,Winter,1966-01-23T00:00:00Z,942.2 -6.7,206,20.6,1.9,July,13.0,Friday,28.3,36.0,0.0,Summer,1947-07-25T00:00:00Z,942.6 -4.2,20,1.6,0.3,January,,Wednesday,5.4,9.2,0.0,Winter,2016-01-20T00:00:00Z,941.9 -3.9,57,1.4,0.3,February,9.6,Wednesday,5.7,10.0,0.0,Winter,1969-02-26T00:00:00Z,939.5 -10.0,355,0.6,1.4,December,1.6,Thursday,3.8,7.0,1.0,Winter,1950-12-21T00:00:00Z,935.9 -7.8,311,8.0,1.9,November,,Sunday,11.2,14.4,0.0,Autumn,2010-11-07T00:00:00Z,939.7 -9.7,2,3.0,5.3,January,4.7,Wednesday,6.0,9.0,0.8,Winter,1946-01-02T00:00:00Z,940.9 -,350,3.1,2.8,December,8.3,Thursday,6.8,10.4,0.0,Winter,1954-12-16T00:00:00Z,949.7 -,205,19.8,,July,12.3,Friday,26.6,33.3,0.0,Summer,1920-07-23T00:00:00Z,941.0 diff --git a/data/024_Salary/qa.csv b/data/024_Salary/qa.csv deleted file mode 100644 index 81cba1a049947b5a335a530e98cc624ff9a14ce9..0000000000000000000000000000000000000000 --- a/data/024_Salary/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -"Are there records where the RETRINOIN_xRZI exceeds 10,000?",True,[RETRINOIN_xRZI],boolean,['number[double]'],True -Are there any female respondents who belong to the ESTE NUTS1 region?,True,"[SEXO, NUTS1]",boolean,"['category', 'category']",True -Do we have respondents who fall under both PRIVADO control and NACIONAL market?,True,"[CONTROL, MERCADO]",boolean,"['category', 'category']",True -"Are there records with RETRINOIN_WwQk less than 5,000?",True,[RETRINOIN_WwQk],boolean,['number[double]'],False -How many unique respondents belong to the ESTE NUTS1 region?,58852,[NUTS1],number,['category'],2 -"On average, what's the RETRINOIN value for male respondents?",29370.243704368546,"[SEXO, RETRINOIN]",number,"['category', 'number[double]']",26024.9957143 -What's the highest value for RETRINOIN_ac1q in the dataset?,199496.34,[RETRINOIN_ac1q],number,['number[double]'],59117.54 -How many unique clusters are present in the 'umap_cluster' column?,73,[umap_cluster],number,['category'],15 -Which 'ANOS2' category has the most number of respondents?,DE 40 A 49,[ANOS2],category,['category'],DE 40 A 49 -In which 'NUTS1' region do we find the highest average RETRINOIN?,COMUNIDAD DE MADRID,"[NUTS1, RETRINOIN]",category,"['category', 'number[double]']",CENTRO -Which 'MERCADO' category is the least common in the dataset?,UNIÓN EUROPEA,[MERCADO],category,['category'],UNIÓN EUROPEA -Which 'umap_cluster' is the most dominant in the dataset?,Cluster 1,[umap_cluster],category,['category'],Cluster 7 -What are the top 4 'CNACE' categories with the highest frequency?,"['Actividades administrativas y servicios auxliares: actividad', 'Actividades sanitarias y de servicios sociales: actividades', 'Comercio al por mayor y al por menor; reparación de vehículo', 'Actividades profesionales, científicas y técnicas: actividad']",[CNACE],list[category],['category'],"[\'Industria manufacturera: industria de la alimentación, fabri\', \'Actividades administrativas y servicios auxliares: actividad\', \'Actividades profesionales, científicas y técnicas: actividad\', \'Actividades artísticas, recreativas y de entrenimiento: acti\']" -Which are the 3 least common 'ANOS2' categories in the dataset?,"['MENOS 19 AÑOS', 'MÁS DE 59', 'DE 20 A 29']",[ANOS2],list[category],['category'],"[\'DE 50 A 59\', \'DE 20 A 29\', \'DE 30 A 39\']" -List the top 5 'NUTS1' regions by frequency.,"['ESTE', 'COMUNIDAD DE MADRID', 'NORESTE', 'SUR', 'CENTRO']",[NUTS1],list[category],['category'],"[\'ESTE\', \'NOROESTE\', \'NORESTE\', \'CENTRO\', \'SUR\']" -Which 2 'umap_cluster' categories are the least represented?,"['Cluster 71', 'Cluster 73']",[umap_cluster],list[category],['category'],"[\'Cluster 20\', \'Cluster 10\']" -List the top 5 recorded RETRINOIN values.,"[4225998.36, 4153877.05, 4021902.63, 3903390.45, 2192967.2]",[RETRINOIN],list[number],['number[double]'],"[59117.54, 50502.32, 35417.81, 30993.25, 29699.05]" -What are the 4 lowest x values in the dataset?,"[-23714.217, -23706.5, -23698.271, -23697.166]",[x],list[number],['number[double]'],"[-19757.53, -16221.655, -10021.664, -5854.7065]" -Rank the highest 3 y values in the dataset.,"[28352.02, 28313.926, 28283.78]",[y],list[number],['number[double]'],"[22543.754, 21725.412, 15036.662]" -Which 6 RETRINOIN_ac1q values are the most frequent in the dataset?,"[0.0, 10302.6, 18000.0, 30000.0, 12000.0, 21000.0]",[RETRINOIN_ac1q],list[number],['number[double]'],"[29699.05, 13871.31, 50502.32, 27320.4, 26262.3, 20237.57]" diff --git a/data/024_Salary/sample.csv b/data/024_Salary/sample.csv deleted file mode 100644 index 5ec244c568824ae2b3730f44888722f2a510b757..0000000000000000000000000000000000000000 --- a/data/024_Salary/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -CNACE,RETRINOIN_ac1q,RETRINOIN_WwQk,CONTROL,SEXO,x,MERCADO,RETRINOIN_xRZI,NUTS1,RETRINOIN,y,ANOS2,umap_cluster -"Información y comunicaciones: edición, actividades cinematog",29699.05,29699.05,PRIVADO,Female,612.26984,LOCAL O REGIONAL,29699.05,NORESTE,29699.05,8831.377,DE 40 A 49,Cluster 20 -"Industria manufacturera: coquerías y refino de petróleo, ind",13871.31,13871.31,PRIVADO,Male,-1538.5378,NACIONAL,13871.31,NORESTE,13871.31,13925.02,DE 30 A 39,Cluster 10 -Actividades administrativas y servicios auxliares: actividad,23650.39,23650.39,PUBLICO,Female,13486.159,MUNDIAL,23650.39,ESTE,23650.39,21725.412,DE 50 A 59,Cluster 13 -"Actividades profesionales, científicas y técnicas: actividad",26100.12,26100.12,PRIVADO,Female,23149.176,NACIONAL,26100.12,CENTRO,26100.12,-4676.4614,DE 20 A 29,Cluster 14 -"Industria manufacturera: industria de la alimentación, fabri",26886.2,26886.2,PRIVADO,Female,-241.37732,MUNDIAL,26886.2,NOROESTE,26886.2,5492.6865,DE 50 A 59,Cluster 7 -"Actividades artísticas, recreativas y de entrenimiento: acti",35417.81,35417.81,PUBLICO,Male,12931.208,LOCAL O REGIONAL,35417.81,CENTRO,35417.81,22543.754,DE 40 A 49,Cluster 13 -Actividades sanitarias y de servicios sociales: actividades,20064.14,20064.14,PRIVADO,Female,-5854.7065,LOCAL O REGIONAL,20064.14,ESTE,20064.14,2311.69,DE 30 A 39,Cluster 2 -"Industria manufacturera: industria de la alimentación, fabri",8824.21,8824.21,PRIVADO,Female,-10021.664,NACIONAL,8824.21,ESTE,8824.21,9911.777,DE 20 A 29,Cluster 39 -"Industria manufacturera: fabricación de vehículos de motor,",18584.44,18584.44,PRIVADO,Female,-112.55785,UNIÓN EUROPEA,18584.44,ESTE,18584.44,-1870.9412,DE 40 A 49,Cluster 1 -"Actividades profesionales, científicas y técnicas: actividad",30993.25,30993.25,PRIVADO,Female,2186.3562,LOCAL O REGIONAL,30993.25,NOROESTE,30993.25,7114.6265,DE 40 A 49,Cluster 7 -"Industria manufacturera: industria de la alimentación, fabri",59117.54,59117.54,PRIVADO,Male,15849.232,MUNDIAL,59117.54,ESTE,59117.54,-10819.73,DE 40 A 49,Cluster 9 -"Hostelería: servicios de alojamiento, servicios de comidas y",13288.37,13288.37,PRIVADO,Male,-16221.655,NACIONAL,13288.37,NOROESTE,13288.37,6274.7524,DE 20 A 29,Cluster 33 -Transporte y almacenamiento: transporte terrestre y por tube,14851.01,14851.01,PRIVADO,Male,-4317.593,LOCAL O REGIONAL,14851.01,NOROESTE,14851.01,13201.939,DE 50 A 59,Cluster 12 -"Otros servicios: actividades asociativas, reparación de orde",18038.65,18038.65,PRIVADO,Female,1722.7631,MUNDIAL,18038.65,ESTE,18038.65,3592.2634,DE 30 A 39,Cluster 7 -"Actividades artísticas, recreativas y de entrenimiento: acti",7732.64,7732.64,PRIVADO,Female,-1111.2073,LOCAL O REGIONAL,7732.64,NORESTE,7732.64,15036.662,DE 20 A 29,Cluster 12 -Actividades administrativas y servicios auxliares: actividad,20237.57,20237.57,PRIVADO,Male,-19757.53,NACIONAL,20237.57,SUR,20237.57,-2364.7827,DE 40 A 49,Cluster 18 -"Industria manufacturera: fabricación de vehículos de motor,",26262.3,26262.3,PRIVADO,Female,4558.784,NACIONAL,26262.3,CENTRO,26262.3,9131.343,DE 30 A 39,Cluster 3 -"Actividades financieras y de seguros: servicios financieros,",27320.4,27320.4,PRIVADO,Female,791.3959,NACIONAL,27320.4,ESTE,27320.4,6215.8486,DE 50 A 59,Cluster 7 -"Actividades financieras y de seguros: servicios financieros,",50502.32,50502.32,PRIVADO,Female,-843.25104,NACIONAL,50502.32,NOROESTE,50502.32,-3354.3755,DE 40 A 49,Cluster 15 -"Hostelería: servicios de alojamiento, servicios de comidas y",25391.36,25391.36,PRIVADO,Male,16447.361,MUNDIAL,25391.36,ESTE,25391.36,9225.273,DE 30 A 39,Cluster 22 diff --git a/data/025_Data/qa.csv b/data/025_Data/qa.csv deleted file mode 100644 index f85e0a39a6c87bcf5964b5917feb66ebb67aa7a7..0000000000000000000000000000000000000000 --- a/data/025_Data/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the most visited URL related to 'no code data science'?,True,boolean,"['URLs', 'Keyword', 'Ranking']","['url', 'category', 'number[uint8]']",True -Does any URL have a competition level of 'Low'?,True,boolean,"['URLs', 'Competition']","['url', 'category']",True -Are there any URLs with an average monthly searches above 1000?,False,boolean,"['URLs', 'Avg. monthly searches']","['url', 'number[uint8]']",False -Is the URL with the highest ranking also the one with the highest monthly searches?,True,boolean,"['URLs', 'Ranking', 'Avg. monthly searches']","['url', 'number[uint8]', 'number[uint8]']",True -How many unique URLs are in the dataset?,28,number,['URLs'],['url'],13 -What is the highest ranking value in the dataset?,11,number,['Ranking'],['number[uint8]'],11 -What is the minimum average monthly searches in the dataset?,50,number,['Avg. monthly searches'],['number[uint8]'],50 -How many unique keywords are present in the dataset?,6,number,['Keyword'],['category'],6 -What is the competition level of the highest-ranked URL?,Medium,category,"['Ranking', 'Competition']","['number[uint8]', 'category']",Low -What keyword has the highest average monthly searches?,no code data science,category,"['Avg. monthly searches', 'Keyword']","['number[uint8]', 'category']",no code analytics platform -What is the competition level for the URL with the lowest ranking?,Low,category,"['Ranking', 'Competition']","['number[uint8]', 'category']",Medium -What keyword is associated with the URL with the highest ranking?,no code data science,category,"['Ranking', 'Keyword']","['number[uint8]', 'category']",no code analytics -What are the top 3 URLs with the highest average monthly searches?,"['https://www.obviously.ai/', 'https://venturebeat.com/2021/10/12/no-code-ai-startup-obviously-ai-raises-4-7m/', 'https://hbr.org/2021/11/how-no-code-platforms-could-disrupt-the-it-industry']",list[category],"['URLs', 'Avg. monthly searches']","['url', 'number[uint8]']","[https://towardsdatascience.com/top-8-no-code-machine-learning-platforms-you-should-use-in-2020-1d1801300dd0, https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses, https://www.obviously.ai/]" -List the bottom 2 competition levels of URLs with ranking less than 5.,"['Medium', 'Unknown']",list[category],"['Ranking', 'Competition']","['number[uint8]', 'category']","[High, High]" -Which are the top 4 keywords associated with the URLs of highest rankings?,"['no code data science', 'no code data analytics', 'no code data science', 'no code data science']",list[category],"['Ranking', 'Keyword']","['number[uint8]', 'category']","[no code analytics, no code data science, no code analytics platform, no code data science]" -Enumerate the bottom 3 URLs with the lowest rankings.,"['https://www.obviously.ai/', 'https://www.obviously.ai/', 'https://venturebeat.com/2021/10/12/no-code-ai-startup-obviously-ai-raises-4-7m/']",list[category],"['Ranking', 'URLs']","['number[uint8]', 'url']","[https://www.obviously.ai/, https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses, https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/]" -What are the top 4 rankings associated with the keyword 'no code data science'?,"[10, 9, 8, 7]",list[number],"['Keyword', 'Ranking']","['category', 'number[uint8]']","[10, 9, 6, 4]" -List the bottom 3 average monthly searches for URLs with medium competition.,"[50, 50, 50]",list[number],"['Competition', 'Avg. monthly searches']","['category', 'number[uint8]']","[50, 50, 50]" -Provide the top 5 rankings of URLs with low competition (if any).,"[11, 10, 10, 9, 9]",list[number],"['Competition', 'Ranking']","['category', 'number[uint8]']","[11, 8, 6, 6, 5]" -Specify the bottom 2 average monthly searches for URLs with the highest rankings.,"[50, 50]",list[number],"['Ranking', 'Avg. monthly searches']","['number[uint8]', 'number[uint8]']","[50, 50]" diff --git a/data/025_Data/sample.csv b/data/025_Data/sample.csv deleted file mode 100644 index a2cf2bdd40763a98dcd59fe0618a99f1f569ed20..0000000000000000000000000000000000000000 --- a/data/025_Data/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Ranking,Competition,Avg. monthly searches,URLs,Keyword -1,Medium,50,https://www.obviously.ai/,no code data science -3,Unknown,50,https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses,no code data analytics -3,High,50,https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/,no code data science tools -3,Low,50,https://www.obviously.ai/,no code predictive analytics -4,Unknown,50,https://www.castordoc.com/blog/what-are-the-no-code-data-tools,no code data analytics -4,High,50,https://towardsdatascience.com/top-8-no-code-machine-learning-platforms-you-should-use-in-2020-1d1801300dd0,no code data science tools -4,Medium,50,https://towardsdatascience.com/the-no-code-approach-to-data-science-and-ai-41bf22fea971,no code data science -5,Low,50,https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses,no code predictive analytics -6,Low,50,https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/,no code predictive analytics -6,Medium,50,https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/,no code data science -6,Low,50,https://www.nocode.tech/category/analytics,no code analytics -6,High,50,https://levity.ai/blog/no-code-ai-map,no code analytics platform -7,High,50,https://www.nocode.tech/category/analytics,no code analytics platform -7,Unknown,50,https://levity.ai/blog/no-code-ai-map,no code data analytics -8,Low,50,https://towardsdatascience.com/towards-no-code-analytics-making-everyone-a-data-scientist-f7693bd0abfd,no code predictive analytics -8,Unknown,50,https://datrics.ai/,no code data analytics -9,High,50,https://towardsdatascience.com/top-8-no-code-machine-learning-platforms-you-should-use-in-2020-1d1801300dd0,no code analytics platform -9,Medium,50,https://datascience.foundation/datatalk/no-code-machine-learning,no code data science -10,Medium,50,https://medium.com/captech-corner/the-truth-about-automl-and-no-code-data-science-b73f2cf50c4e,no code data science -11,Low,50,https://www.nocodelytics.com/no-code-analytics,no code analytics diff --git a/data/026_Predicting/qa.csv b/data/026_Predicting/qa.csv deleted file mode 100644 index 3b27b1ed185bb7fbd10152898696033ff39d9080..0000000000000000000000000000000000000000 --- a/data/026_Predicting/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there any wine with a quality rating of 10?,False,boolean,['quality'],['number[uint8]'],False -Are there any wines with residual sugar above 15g/dm^3?,True,boolean,['residual sugar'],['number[double]'],False -Is the highest alcohol content wine also the one with the highest quality rating?,False,boolean,"['alcohol', 'quality']","['number[double]', 'number[uint8]']",False -Does any wine have a pH level below 2.5?,False,boolean,['pH'],['number[double]'],False -How many unique quality ratings are there in the dataset?,6,number,['quality'],['number[uint8]'],5 -What is the maximum fixed acidity level found in the dataset?,15.9,number,['fixed acidity'],['number[double]'],10.7 -What is the minimum volatile acidity level in the dataset?,0.12,number,['volatile acidity'],['number[double]'],0.28 -How many wines have free sulfur dioxide above 50 mg/dm^3?,16,number,['free sulfur dioxide'],['number[UInt8]'],0 -What is the quality rating of the wine with the highest alcohol content?,5,category,"['alcohol', 'quality']","['number[double]', 'number[uint8]']",7.0 -What is the quality rating of the wine with the highest fixed acidity?,5,category,"['fixed acidity', 'quality']","['number[double]', 'number[uint8]']",6.0 -What is the quality rating of the wine with the lowest volatile acidity?,7,category,"['volatile acidity', 'quality']","['number[double]', 'number[uint8]']",7.0 -What is the quality rating of the wine with the highest pH level?,6,category,"['pH', 'quality']","['number[double]', 'number[uint8]']",6.0 -List the quality ratings of the top 3 wines with the highest alcohol content.,"['5', '6', '6']",list[category],"['alcohol', 'quality']","['number[double]', 'number[uint8]']","[7, 7, 8]" -Enumerate the quality ratings of the bottom 2 wines with the lowest residual sugar.,"['6', '6']",list[category],"['residual sugar', 'quality']","['number[double]', 'number[uint8]']","[5, 5]" -Which are the quality ratings of the top 5 wines with the highest density?,"['6', '6', '7', '5', '5']",list[category],"['density', 'quality']","['number[double]', 'number[uint8]']","[6, 7, 5, 7, 6]" -List the quality ratings of the bottom 4 wines with the lowest pH level.,"['4', '6', '6', '8']",list[category],"['pH', 'quality']","['number[double]', 'number[uint8]']","[7, 8, 5, 7]" -What are the alcohol contents of the top 4 wines with the highest quality ratings?,"['12.8', '12.6', '12.9', '9.8']",list[number],"['quality', 'alcohol']","['number[uint8]', 'number[double]']","[11.7, 11.8, 12.3, 10.0]" -List the volatile acidity levels of the bottom 3 wines with the lowest quality ratings.,"['0.58', '0.61', '1.185']",list[number],"['quality', 'volatile acidity']","['number[uint8]', 'number[double]']","[0.76, 0.5, 0.46]" -Enumerate the fixed acidity levels of the top 5 wines with the highest quality ratings.,"['7.9', '10.3', '5.6', '12.6', '11.3']",list[number],"['quality', 'fixed acidity']","['number[uint8]', 'number[double]']","[9.4, 10.5, 8.9, 10.1, 7.7]" -Provide the residual sugar levels of the bottom 2 wines with the lowest quality ratings.,"['2.2', '2.1']",list[number],"['quality', 'residual sugar']","['number[uint8]', 'number[double]']","[1.8, 1.6]" diff --git a/data/026_Predicting/sample.csv b/data/026_Predicting/sample.csv deleted file mode 100644 index 78b98c01f0d5cb45bf06998b423f06f22d6dd45a..0000000000000000000000000000000000000000 --- a/data/026_Predicting/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -quality,alcohol,density,pH,volatile acidity,free sulfur dioxide,fixed acidity,residual sugar -6,9.6,0.9971,3.24,0.56,14,7.7,2.5 -5,9.5,0.996,3.39,0.5,21,7.8,1.6 -6,9.9,1.0004,3.28,0.67,17,10.7,2.7 -5,9.8,0.998,3.33,0.46,32,8.5,2.25 -6,10.6,0.9948,3.39,0.46,18,6.7,1.7 -5,9.4,0.997,3.44,0.41,35,7.2,2.1 -5,9.7,0.99636,3.26,0.54,23,7.7,1.9 -5,10.0,0.9956,3.4,0.78,10,7.0,2.0 -5,9.8,0.9962,3.26,0.39,10,8.2,1.5 -6,10.9,0.99483,3.55,0.61,18,5.8,1.8 -7,11.8,0.9973,3.09,0.51,6,10.5,2.4 -3,9.95,0.996,3.55,0.76,6,6.7,1.8 -5,9.7,0.99552,3.33,0.48,18,6.7,2.1 -5,9.2,0.99629,3.34,0.56,25,7.0,1.6 -6,9.4,0.9964,3.34,0.34,24,7.8,2.0 -7,12.3,0.99354,3.25,0.28,7,8.9,1.7 -5,10.1,0.9958,3.35,0.675,17,6.7,2.4 -7,10.0,0.99834,3.22,0.43,13,10.1,2.6 -8,11.7,0.9964,3.15,0.3,6,9.4,2.8 -5,9.6,0.9965,3.19,0.745,16,8.4,1.9 diff --git a/data/027_Supermarket/qa.csv b/data/027_Supermarket/qa.csv deleted file mode 100644 index c2fc9b47174c00cf6bbf0ffc54a0ad6458606351..0000000000000000000000000000000000000000 --- a/data/027_Supermarket/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there any purchase with a total cost above 1000?,True,boolean,['Total'],['number[double]'],False -Are there any customers who made a purchase using cash?,True,boolean,['Payment'],['category'],True -Is the customer with the highest total purchase cost a 'Member'?,True,boolean,"['Total', 'Customer type']","['number[double]', 'category']",False -Does any customer with a rating strictly above 9 use 'Ewallet' as their payment method?,True,boolean,"['Rating', 'Payment']","['number[double]', 'category']",False -How many unique branches are there in the dataset?,3,number,['Branch'],['category'],3 -What is the maximum quantity of products bought in a single purchase?,10,number,['Quantity'],['number[uint8]'],10 -What is the minimum total cost of a purchase in the dataset?,10.6785,number,['Total'],['number[double]'],45.927 -How many purchases were made in Yangon city?,340,number,['City'],['category'],11 -What is the payment method used for the purchase with the highest total cost?,Credit card,category,"['Total', 'Payment']","['number[double]', 'category']",Credit card -What is the product line of the purchase with the highest total cost?,Fashion accessories,category,"['Total', 'Product line']","['number[double]', 'category']",Electronic accessories -What is the customer type of the purchase with the lowest total cost?,Member,category,"['Total', 'Customer type']","['number[double]', 'category']",Normal -What is the gender of the customer with the highest total purchase cost?,Female,category,"['Total', 'Gender']","['number[double]', 'category']",Male -List the payment methods of the top 3 purchases with the highest total cost.,"['Credit card', 'Credit card', 'Ewallet']",list[category],"['Total', 'Payment']","['number[double]', 'category']","['Credit card', 'Cash', 'Ewallet']" -Enumerate the product lines of the bottom 2 purchases with the lowest total cost.,"['Sports and travel', 'Fashion accessories']",list[category],"['Total', 'Product line']","['number[double]', 'category']","['Sports and travel', 'Sports and travel']" -Which are the customer types of the top 5 purchases with the highest total cost?,"['Member', 'Normal', 'Member', 'Normal', 'Normal']",list[category],"['Total', 'Customer type']","['number[double]', 'category']","['Normal', 'Normal', 'Normal', 'Normal', 'Normal']" -List the genders of the bottom 4 purchases with the lowest total cost.,"['Male', 'Female', 'Female', 'Male']",list[category],"['Total', 'Gender']","['number[double]', 'category']","['Male', 'Male', 'Female', 'Female']" -What are the quantities of products bought in the top 4 purchases with the highest total cost?,"[10, 10, 10, 10]",list[number],"['Total', 'Quantity']","['number[double]', 'number[uint8]']","[10, 7, 10, 10]" -List the unit prices of the bottom 3 purchases with the lowest total cost.,"[10.17, 12.09, 12.54]",list[number],"['Total', 'Unit price']","['number[double]', 'number[double]']","[21.87, 60.87, 42.97]" -Enumerate the ratings of the top 5 purchases with the highest total cost.,"[6.6, 8.7, 4.5, 8.0, 4.4]",list[number],"['Total', 'Rating']","['number[double]', 'number[double]']","[4.2, 7.6, 8.1, 9.0, 6.4]" -Provide the gross incomes of the bottom 2 purchases with the lowest total cost.,"[0.5085, 0.6045]",list[number],"['Total', 'gross income']","['number[double]', 'number[double]']","[2.187, 6.087]" diff --git a/data/027_Supermarket/sample.csv b/data/027_Supermarket/sample.csv deleted file mode 100644 index 24c7d11abb0facf03f1fb4ef62aed4ea7ef44e37..0000000000000000000000000000000000000000 --- a/data/027_Supermarket/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -gross income,Unit price,Total,Customer type,Product line,Quantity,Gender,Branch,City,Payment,Rating -6.4455,42.97,135.3555,Normal,Sports and travel,3,Female,B,Mandalay,Cash,9.3 -29.38,58.76,616.98,Normal,Electronic accessories,10,Male,C,Naypyitaw,Ewallet,9.0 -6.5775,26.31,138.1275,Normal,Electronic accessories,5,Female,A,Yangon,Credit card,8.8 -6.087,60.87,127.827,Normal,Sports and travel,2,Male,A,Yangon,Ewallet,8.7 -22.068,49.04,463.428,Member,Fashion accessories,9,Male,C,Naypyitaw,Credit card,8.6 -19.4635,55.61,408.7335,Normal,Home and lifestyle,7,Male,C,Naypyitaw,Cash,8.5 -8.814,44.07,185.094,Member,Health and beauty,4,Male,C,Naypyitaw,Ewallet,8.4 -29.475,58.95,618.975,Normal,Health and beauty,10,Male,C,Naypyitaw,Ewallet,8.1 -34.8425,99.55,731.6925,Normal,Electronic accessories,7,Male,A,Yangon,Cash,7.6 -10.326,34.42,216.846,Member,Home and lifestyle,6,Female,A,Yangon,Ewallet,7.5 -24.951,83.17,523.971,Member,Home and lifestyle,6,Female,C,Naypyitaw,Cash,7.3 -2.187,21.87,45.927,Normal,Sports and travel,2,Male,B,Mandalay,Ewallet,6.9 -27.984,69.96,587.664,Normal,Home and lifestyle,8,Female,A,Yangon,Credit card,6.4 -10.1325,28.95,212.7825,Normal,Health and beauty,7,Male,A,Yangon,Credit card,6.0 -12.012,80.08,252.252,Normal,Home and lifestyle,3,Male,A,Yangon,Cash,5.4 -21.4335,47.63,450.1035,Member,Food and beverages,9,Female,A,Yangon,Cash,5.0 -20.13,40.26,422.73,Normal,Electronic accessories,10,Female,A,Yangon,Credit card,5.0 -15.768,35.04,331.128,Member,Food and beverages,9,Male,A,Yangon,Ewallet,4.6 -36.065,72.13,757.365,Normal,Electronic accessories,10,Male,B,Mandalay,Credit card,4.2 -13.8135,92.09,290.0835,Member,Health and beauty,3,Female,A,Yangon,Cash,4.2 diff --git a/data/028_Predict/qa.csv b/data/028_Predict/qa.csv deleted file mode 100644 index 16df3a999b0be8ffd2e679c56b00efd1160aca4a..0000000000000000000000000000000000000000 --- a/data/028_Predict/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any individuals in the dataset who are above 60 years of age?,False,boolean,['Age'],['number[uint8]'],True -Does anyone have a Diabetes Pedigree Function score above 2.5?,True,boolean,['DiabetesPedigreeFunction'],['number[double]'],False -Does the person with the highest glucose level also have diabetes?,True,boolean,"['Glucose', 'Outcome']","['number[uint8]', 'number[uint8]']",True -Is there anyone who has zero pregnancies and is diabetic?,True,boolean,"['Pregnancies', 'Outcome']","['number[uint8]', 'number[uint8]']",True -What is the maximum number of pregnancies recorded in the dataset?,17,number,['Pregnancies'],['number[uint8]'],10 -What is the minimum blood pressure level recorded in the dataset?,0,number,['BloodPressure'],['number[uint8]'],0 -What is the average BMI recorded in the dataset?,31.992578124999998,number,['BMI'],['number[double]'],31.910000000000004 -How many individuals have an insulin level above 150?,187,number,['Insulin'],['number[uint16]'],4 -What is the diabetes outcome for the person with the highest BMI?,1,category,"['BMI', 'Outcome']","['number[double]', 'number[uint8]']",1 -What is the diabetes outcome for the person with the lowest blood pressure?,0,category,"['BloodPressure', 'Outcome']","['number[uint8]', 'number[uint8]']",0 -What is the diabetes outcome for the person with the highest insulin level?,1,category,"['Insulin', 'Outcome']","['number[uint16]', 'number[uint8]']",1 -What is the diabetes outcome for the person with the lowest glucose level?,0,category,"['Glucose', 'Outcome']","['number[uint8]', 'number[uint8]']",0 -List the diabetes outcomes of the top 3 individuals with the highest number of pregnancies.,"[1, 1, 1]",list[category],"['Pregnancies', 'Outcome']","['number[uint8]', 'number[uint8]']","[1, 0, 0]" -List the diabetes outcomes of the bottom 2 individuals with the lowest BMI.,"[0, 0]",list[category],"['BMI', 'Outcome']","['number[double]', 'number[uint8]']","[0, 0]" -List the diabetes outcomes of the top 5 individuals with the highest insulin levels.,"[1, 1, 1, 1, 1]",list[category],"['Insulin', 'Outcome']","['number[uint16]', 'number[uint8]']","[1, 0, 0, 1, 1]" -List the diabetes outcomes of the bottom 4 individuals with the lowest blood pressure.,"[0, 0, 0, 0]",list[category],"['BloodPressure', 'Outcome']","['number[uint8]', 'number[uint8]']","[0, 1, 0, 1]" -What are the ages of the top 4 individuals with the highest number of pregnancies?,"[51, 67, 67, 67]",list[number],"['Pregnancies', 'Age']","['number[uint8]', 'number[uint8]']","[40, 34, 50, 60]" -List the BMI of the bottom 3 individuals with the lowest glucose levels.,"[32.0, 32.0, 32.0]",list[number],"['Glucose', 'BMI']","['number[uint8]', 'number[double]']","[20.4, 37.2, 30.2]" -Enumerate the blood pressure levels of the top 5 individuals with the highest Diabetes Pedigree Function scores.,"[0, 0, 0, 0, 0]",list[number],"['DiabetesPedigreeFunction', 'BloodPressure']","['number[double]', 'number[uint8]']","[74, 50, 0, 80, 58]" -Provide the glucose levels of the bottom 2 individuals with the lowest insulin levels.,"[117, 111]",list[number],"['Insulin', 'Glucose']","['number[uint16]', 'number[uint8]']","[112, 108]" diff --git a/data/028_Predict/sample.csv b/data/028_Predict/sample.csv deleted file mode 100644 index 41dd0524e57dc00ee0089f64def937dacd642759..0000000000000000000000000000000000000000 --- a/data/028_Predict/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -BMI,Age,Glucose,Outcome,Pregnancies,BloodPressure,DiabetesPedigreeFunction,Insulin -34.0,43,98,0,6,58,0.43,190 -35.7,21,112,0,2,75,0.148,0 -30.8,21,108,0,2,64,0.158,0 -24.6,34,107,0,8,80,0.856,0 -29.9,50,136,0,7,90,0.21,0 -37.7,55,103,0,6,72,0.324,190 -20.4,22,71,0,1,48,0.323,76 -33.8,44,117,0,0,0,0.932,0 -31.3,37,154,0,4,72,0.338,126 -33.7,65,147,0,5,78,0.218,0 -27.5,40,111,1,10,70,0.141,0 -34.2,60,179,0,7,95,0.164,0 -30.9,29,148,1,4,60,0.15,318 -33.6,43,96,0,5,74,0.997,67 -28.4,22,88,0,2,58,0.766,16 -33.3,28,125,1,1,50,0.962,167 -37.2,28,84,0,3,72,0.267,0 -30.2,24,86,0,5,68,0.364,71 -28.4,36,183,1,4,0,0.212,0 -42.6,24,140,1,0,65,0.431,130 diff --git a/data/029_NYTimes/qa.csv b/data/029_NYTimes/qa.csv deleted file mode 100644 index 186f419e75eeb37e6b62841162a5d8304395d4d4..0000000000000000000000000000000000000000 --- a/data/029_NYTimes/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any articles that have the material type 'Op-Ed'?,False,boolean,['material_type'],['category'],False -Does the article with the longest headline contain the keyword 'United States Politics and Government'?,False,boolean,"['headline', 'keywords']","['text', 'list[category]']",False -Is there any article published on '2021-01-05'?,False,boolean,['date'],"['date[ns, UTC]']",False -Does any article contain more than 10 keywords?,True,boolean,['keywords'],['list[category]'],False -How many unique material types are there in the dataset?,4,number,['material_type'],['category'],5 -What is the longest length of a headline in the dataset?,96,number,['headline'],['text'],110 -How many articles were published on '2021-01-02'?,52,number,['date'],"['date[ns, UTC]']",0 -What is the highest number of keywords associated with a single article?,8,number,['keywords'],['list[category]'],8 -What is the material type of the article with the longest headline?,News,category,"['headline', 'material_type']","['text', 'category']",News -What is the material type of the article with the shortest headline?,News,category,"['headline', 'material_type']","['text', 'category']",Editorial -What is the material type of the article with the most number of keywords?,News,category,"['keywords', 'material_type']","['list[category]', 'category']",News -What is the material type of the article with the least number of keywords?,News,category,"['keywords', 'material_type']","['list[category]', 'category']",News -List the material types of the top 3 articles with the longest headlines.,"['News', 'News', 'News']",list[category],"['headline', 'material_type']","['text', 'category']","['News', 'Interactive Feature', 'News']" -List the material types of the bottom 2 articles with the shortest headlines.,"['News', 'News']",list[category],"['headline', 'material_type']","['text', 'category']","['Editorial', 'News']" -List the material types of the top 5 articles with the most number of keywords.,"['News', 'News', 'News', 'News', 'News']",list[category],"['keywords', 'material_type']","['list[category]', 'category']","['News', 'Editorial', 'News', 'Review', 'News']" -List the material types of the bottom 4 articles with the least number of keywords.,"['News', 'News', 'News', 'News']",list[category],"['keywords', 'material_type']","['list[category]', 'category']","['News', 'Interactive Feature', 'News', 'News']" -What are the lengths of the headlines of the top 4 articles with the most number of keywords?,"[86, 85, 84, 84]",list[number],"['keywords', 'headline']","['list[category]', 'text']","[73, 20, 69, 62]" -List the number of keywords in the bottom 3 articles with the shortest headlines.,"[1, 1, 1]",list[number],"['headline', 'keywords']","['text', 'list[category]']","[8, 1, 2]" -Enumerate the lengths of the headlines of the top 5 articles with the longest headlines.,"[96, 96, 95, 95, 95]",list[number],['headline'],['text'],"[110, 94, 92, 73, 73]" -Provide the number of keywords in the bottom 2 articles with the least number of keywords.,"[1, 1]",list[number],['keywords'],['list[category]'],"[1, 2]" diff --git a/data/029_NYTimes/sample.csv b/data/029_NYTimes/sample.csv deleted file mode 100644 index 23e99fbc1f7f2a13a3330025d1b131a6844c4fcb..0000000000000000000000000000000000000000 --- a/data/029_NYTimes/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -date,keywords,material_type,headline -2021-07-01,"['Surfside, Fla, Building Collapse (2021)', 'Condominiums', 'Accidents and Safety']",Video,Rescue Teams Paused Search Over Building Structure Concern -2021-06-03,"['United States Politics and Government', 'Newspapers', 'News Sources, Confidential Status of', 'Classified Information and State Secrets', 'Freedom of the Press', 'News and News Media']",News,Trump Administration Secretly Seized Phone Records of Times Reporters -2021-03-11,"['Real Estate and Housing (Residential)', 'Quarantine (Life and Culture)', 'Content Type: Personal Profile', 'Renting and Leasing (Real Estate)']",Interactive Feature,"He Tested Manhattan’s Rental Market Mid-Pandemic. But What Could He Find for Less Than $3,000?" -2021-10-06,"['Appointments and Executive Changes', 'Mayors', 'Politics and Government']",News,Want to Be a City Commissioner? It Helps to Be Friendly With the Mayor. -2021-01-27,"['Coronavirus (2019-nCoV)', 'States (US)', 'Deaths (Fatalities)', 'Disease Rates']",,"Carroll County, Maryland Covid Case and Risk Tracker" -2021-03-27,"['internal-essential', 'Coronavirus (2019-nCoV)', 'Vaccination and Immunization']",News,"Argentina delays second doses to focus on broader vaccinations with first dose, fearing a variant-fueled wave." -2021-09-10,"['Drones (Pilotless Planes)', 'Targeted Killings', 'United States Defense and Military Forces', 'Civilian Casualties', 'Afghanistan War (2001- )', 'Deaths (Fatalities)', 'Bombs and Explosives', 'Terrorism']",News,"Times Investigation: In U.S. Drone Strike, Evidence Suggests No ISIS Bomb" -2021-06-08,"['Elections', 'Governors (US)']",Interactive Feature,New Jersey Primary Election Results -2021-11-22,"['Murders, Attempted Murders and Homicides', 'Parades', 'Waukesha, Wis, Holiday Parade Attack (2021)']",News,Five Dead in Wisconsin After Driver Plows S.U.V. Into Holiday Parade -2021-10-31,"['Politics and Government', 'Content Type: Personal Profile']",News,He Brokered Apartheid’s End. Can He Save South Africa’s Liberation Party? -2021-11-20,"['Sex Crimes', 'Politics and Government', 'Tennis', '#MeToo Movement', 'Human Rights and Human Rights Violations', 'Olympic Games (2022)', 'International Relations', 'United States International Relations']",Editorial,Where Is Peng Shuai? -2021-07-08,"['Books and Literature', 'Opium', 'Gardens and Gardening', 'Psychedelic and Hallucinogenic Drugs', 'Flowers and Plants', 'Poppies', 'Caffeine']",Review,Michael Pollan Explores the Mind-Altering Plants in His Garden -2021-11-05,"['Theater', 'Music', 'Illegal Immigration', 'Race and Ethnicity']",Review,Review: ‘The Visitor’ Lags Behind the Times -2021-08-03,"['Olympic Games (2020)', 'Track and Field']",News,Even a World Record Doesn’t Always Guarantee Olympic Gold -2021-05-05,"['Taxation', 'High Net Worth Individuals', 'United States Politics and Government', 'Income Tax']",Video,Biden Defends Plans to Increase Taxes on the Wealthy -2021-10-07,"['Travel and Vacations', 'Coronavirus (2019-nCoV)', 'AIRLINES AND AIRPLANES', 'Vaccination and Immunization', 'Business Travel']",News,"United Airlines to fly 91 percent of its 2019 domestic flights in December, a pandemic high." -2021-05-13,"['Art', 'Auctions', 'Collectors and Collections']",News,Basquiat and Other Artists of Color Lead a Swell of Auction Sales -2021-01-14,[],News,Help Us Write the Weekly News Quiz -2021-09-23,"['Virtual Currency', 'Banking and Financial Institutions', 'Regulation and Deregulation of Industry', 'Dodd-Frank Wall Street Reform and Consumer Protection Act (2010)', 'United States Politics and Government', 'Bitcoin (Currency)']",News,Regulators Racing Toward First Major Rules on Cryptocurrency -2021-11-09,"['Suits and Litigation (Civil)', 'Vaccination and Immunization', 'Coronavirus (2019-nCoV)', 'Workplace Hazards and Violations', 'Regulation and Deregulation of Industry', 'United States Politics and Government']",News,U.S. Urges Court Not to Block Vaccine Mandate on Employers diff --git a/data/030_Professionals/qa.csv b/data/030_Professionals/qa.csv deleted file mode 100644 index 7adc029556187f46128d408ea520809c0b913865..0000000000000000000000000000000000000000 --- a/data/030_Professionals/qa.csv +++ /dev/null @@ -1,27 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the 'USA' the most common entry in the 'Geographies' column?,False,boolean,['Geographies'],['list[category]'],True -Are there any participants who are unemployed with a bachelor's degree from Africa?,False,boolean,"['Labeled Clusters', 'Geographies']","['category', 'list[category]']",False -Do all participants recommend Python as the first programming language?,False,boolean,['What programming language would you recommend an aspiring data scientist to learn first?'],['category'],False -Are there more than 1000 participants who hope to become familiar with AWS in the next 2 years?,False,boolean,['Which of the following cloud computing platforms do you hope to become more familiar with in the next 2 years?'],['list[category]'],False -How many unique job titles are represented in the dataset?,14,number,['Select the title most similar to your current role (or most recent title if retired)'],['category'],7 -What's the median number of years participants have used machine learning methods?,1.5,number,['(Average) For how many years have you used machine learning methods?'],['number[double]'],0.5 -How many participants are from the United Kingdom?,450,number,['In which country do you currently reside?'],['category'],1 -What is the most common number of programming languages used by participants on a regular basis?,2,number,['What programming languages do you use on a regular basis?'],['list[category]'],3.0 -What's the most common computing platform used for data science projects?,A laptop,category,['What type of computing platform do you use most often for your data science projects?'],['category'],A laptop -What's the most common programming language used on a regular basis?,Python,category,['What programming languages do you use on a regular basis?'],['list[category]'],Python -Which country has the second highest number of participants?,United States of America,category,['In which country do you currently reside?'],['category'],India -Which title is the least common among participants?,Developer Relations/Advocacy,category,['Select the title most similar to your current role (or most recent title if retired)'],['category'],Business Analyst -What are the top 4 geographies represented in the dataset?,"['India', 'USA', 'Western Europe', 'China - Japan - Korea']",list[category],['Geographies'],['list[category]'],"['USA', 'India', 'Other', 'Russia']" -Name the top 3 general segments of participants.,"['Analysts', 'Data Scientists', 'Academics']",list[category],['General Segments'],['list[category]'],"['Student', 'Data Scientist', 'Data Analyst']" -list the top 5 most common job titles.,"['Data Scientist', 'Software Engineer', 'Other', 'Data Analyst', 'Currently not employed']",list[category],['Select the title most similar to your current role (or most recent title if retired)'],['category'],"['Student', 'Data Scientist', 'Software Engineer', 'Other', 'Data Analyst']" -Identify the top 6 programming languages used regularly.,"['Python', 'SQL', 'R', 'Javascript', 'C++', 'Java']",list[category],['What programming languages do you use on a regular basis?'],['list[category]'],"['Python', 'SQL', 'R', 'Java', 'C++', 'Javascript']" -Report the top 4 age ranges of participants by frequency,"['25-29', '30-34', '22-24', '35-39']",list[number],['What is your age (years)?'],['category'],"['25-29', '22-24', '30-34', '35-39']" -list the highest 3 years of machine learning experience.,"[19.83, 19.74, 19.68]",list[number],['(Average) For how many years have you used machine learning methods?'],['number[double]'],"[7.5, -4.5, -3.5]" -Identify the 5 highest yearly compensations.,"[1000000, 1000000, 1000000, 1000000, 1000000]",list[number],['(Average) What is your current yearly compensation (approximate $USD)?'],['number[double]'],"[174999.5, -174999.5, -137499.5, -84999.5, -34999.5]" -Report the 6 most common sizes of the company where participants work.,"[75.25, 74.4, 73.0, 73.0, 73.0, 73.0]",list[number],['What is the size of the company where you are employed?'],['category'],"['0-49 employees', '> 10,000 employees', '50-249 employees', '1000-9,999 employees', '250-999 employees', 'I am a student']" diff --git a/data/030_Professionals/sample.csv b/data/030_Professionals/sample.csv deleted file mode 100644 index 924992e05934fad5f6dd54e9b6f086ef367a04c1..0000000000000000000000000000000000000000 --- a/data/030_Professionals/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Which of the following cloud computing platforms do you hope to become more familiar with in the next 2 years?,What type of computing platform do you use most often for your data science projects?,What is your age (years)?,In which country do you currently reside?,Select the title most similar to your current role (or most recent title if retired),Labeled Clusters,General Segments,What programming languages do you use on a regular basis?,Geographies,What programming language would you recommend an aspiring data scientist to learn first?,What is the size of the company where you are employed?,(Average) For how many years have you used machine learning methods?,(Average) What is your current yearly compensation (approximate $USD)? -[],A laptop,22-24,Brazil,Data Scientist,Data Scientists w Masters Degree,[Data Scientists],"[Python, R]",[South America],Python,0-49 employees,1.5,4499.5 -"[Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Salesforce Cloud]",A laptop,22-24,Indonesia,Currently not employed,Unemployed - Bachelors,[],"[Python, SQL]",[Developing Asia],SQL,,0.5, -[],A personal computer / desktop,18-21,Morocco,Data Scientist,Data Scientists w Masters Degree,[Data Scientists],"[Python, SQL, C, C++, Java, Javascript, Other]",[Middle East],Python,0-49 employees,0.0, -[],A laptop,25-29,Hong Kong (S.A.R.),Data Analyst,Data Analysts - Not Much Code,[Analysts],"[R, MATLAB, Other]",[],Python,250-999 employees,0.5,34999.5 -[],A personal computer / desktop,18-21,India,Business Analyst,Mostly Business Analysts,[Analysts],"[R, C, Bash, MATLAB]",[India],Python,50-249 employees,1.5,450.0 -[Google Cloud Platform (GCP)],A personal computer / desktop,70+,United Kingdom of Great Britain and Northern Ireland,Currently not employed,Unemployed - PhD,[],"[Python, Other]",[UK],Python,,2.5, -[],A laptop,35-39,United States of America,Data Scientist,PhD Data Scientists,[Academics],"[Python, SQL]",[USA],Python,"1000-9,999 employees",4.5,174999.5 -[],A laptop,18-21,India,Data Scientist,Data Scientist - Bachelors,[Data Scientists],[Python],[India],Python,0-49 employees,0.5,450.0 -[],"A cloud computing platform (AWS, Azure, GCP, hosted notebooks, etc)",40-44,United States of America,Data Scientist,Data Scientists w Masters Degree,[Data Scientists],"[Python, R, SQL]",[USA],Python,"10,000 or more employees",7.5,137499.5 -[],A personal computer / desktop,35-39,Russia,Data Analyst,Data Analysts - Not Much Code,[Analysts],"[Python, SQL]",[],Python,"10,000 or more employees",3.5,2499.5 -[],A laptop,22-24,India,Data Scientist,Data Scientist - Bachelors,[Data Scientists],"[Python, R, SQL]",[India],Python,,0.5, -"[Google Cloud Platform (GCP), Oracle Cloud, Tencent Cloud]",,40-44,Kazakhstan,Other,Project Managers w Bachelors,[],[],[],,50-249 employees,,450.0 -[],"A cloud computing platform (AWS, Azure, GCP, hosted notebooks, etc)",30-34,Canada,Data Engineer,Mostly Business Analysts,[Analysts],"[Python, SQL]",[],SQL,"1000-9,999 employees",0.0,84999.5 -"[Google Cloud Platform (GCP), IBM Cloud / Red Hat]",A laptop,45-49,Other,Currently not employed,Unemployed - Bachelors,[],"[Python, R, SQL, Other]",[],R,,0.0, -[],A personal computer / desktop,40-44,India,Software Engineer,Software Engineers,[],"[Java, Other]",[India],Python,"10,000 or more employees",0.5, -[],A laptop,50-54,United States of America,Other,Other Profession - Masters Degree ,[],[R],[USA],R,50-249 employees,0.0,174999.5 -[Google Cloud Platform (GCP)],A laptop,18-21,Pakistan,Currently not employed,Unemployed - Bachelors,[],"[C, C++, Javascript, Other]",[Developing Asia],Python,,0.0, -[],A laptop,30-34,Kenya,Data Scientist,Mostly Business Analysts,[Analysts],[Python],[Africa],Python,0-49 employees,0.5, -"[Amazon Web Services (AWS), Microsoft Azure]",A laptop,25-29,Poland,Data Analyst,Data Analysts - Not Much Code,[Analysts],"[Python, Other]",[],Python,"10,000 or more employees",0.0,27499.5 -[],A laptop,25-29,India,Currently not employed,Unemployed - Bachelors,[],[Python],[India],Python,,0.5, diff --git a/data/031_Trustpilot/qa.csv b/data/031_Trustpilot/qa.csv deleted file mode 100644 index ae1a7bda6b1e307303a6a4ff08d3ed614f328bf6..0000000000000000000000000000000000000000 --- a/data/031_Trustpilot/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there more reviews with rating 5 from 'GB' than 'US'?,True,boolean,"['country_code', 'rating']","['category', 'number[uint8]']",True -Is the average rating for 'Wise' in 'GB' above 4?,True,boolean,"['rating', 'Company', 'country_code']","['number[uint8]', 'category', 'category']",True -Do any reviews originate from 'AO'?,True,boolean,['country_code'],['category'],False -Did any reviews receive a rating of 1?,True,boolean,['rating'],['number[uint8]'],True -What's the total number of reviews for 'Wise'?,3840,number,['Company'],['category'],20 -How many unique countries gave 'Wise' a rating of 5?,120,number,"['country_code', 'Company', 'rating']","['category', 'category', 'number[uint8]']",8 -What is the highest rating received?,5,number,['rating'],['number[uint8]'],5 -What's the average rating across all reviews?,4.097755610972569,number,['rating'],['number[uint8]'],4.15 -Which company received the most 5-star reviews?,Wise,category,"['Company', 'rating']","['category', 'number[uint8]']",Wise -From which country did 'Wise' receive the most reviews?,GB,category,"['country_code', 'Company']","['category', 'category']",ES -Which country had the lowest representation in the reviews?,CW,category,['country_code'],['category'],PL -Which company received the lowest rating?,N26,category,"['rating', 'Company']","['number[uint8]', 'category']",N26 -Which are the top 3 countries with the most 5-star reviews for 'Wise'?,"['GB', 'US', 'ES']",list[category],"['country_code', 'Company', 'rating']","['category', 'category', 'number[uint8]']","['PL', 'DE', 'ES']" -Which are the 2 companies represented in the dataset?,"['Wise', 'N26']",list[category],['Company'],['category'],"['Wise', 'N26']" -Which are the bottom 4 countries in terms of review count?,"['CW', 'FO', 'KZ', 'NE']",list[category],['country_code'],['category'],"['IT', 'HU', 'US', 'AU']" -What are the top 5 most common countries?,"['GB', 'DE', 'FR']",list[category],['country_code'],['category'],"['GB', 'ES', 'FR']" -What are the top 3 most common ratings?,"[5, 1, 4]",list[number],['rating'],['number[uint8]'],"[5, 4, 1]" -What are the bottom 2 least common ratings?,"[3, 2]",list[number],['rating'],['number[uint8]'],"[3, 2]" -What are the top 4 ratings given to 'Wise'?,"[5, 1, 4, 2]",list[number],"['rating', 'Company']","['number[uint8]', 'category']","[5, 4, 2]" -What are the bottom 5 ratings given to 'N26'?,"[3, 2, 4, 1, 5]",list[number],"['rating', 'Company']","['number[uint8]', 'category']","[5, 4, 1, 3]" diff --git a/data/031_Trustpilot/sample.csv b/data/031_Trustpilot/sample.csv deleted file mode 100644 index 09d3de195fe038e90092e7fd3494fe4ad0237616..0000000000000000000000000000000000000000 --- a/data/031_Trustpilot/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -rating,country_code,Company -2,US,Wise -5,PT,N26 -5,PL,Wise -5,IT,Wise -4,IE,Wise -5,HU,Wise -5,GB,Wise -5,GB,N26 -5,GB,N26 -4,GB,N26 -1,GB,N26 -1,GB,N26 -5,FR,N26 -5,FR,Wise -5,ES,Wise -4,ES,Wise -3,ES,N26 -5,DE,Wise -4,DE,N26 -5,AU,Wise diff --git a/data/032_Delicatessen/qa.csv b/data/032_Delicatessen/qa.csv deleted file mode 100644 index c468a6ce1558a9b3f538fdf8d527a5bea4c903a7..0000000000000000000000000000000000000000 --- a/data/032_Delicatessen/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there any customer with a PhD education level?,True,boolean,['Education'],['category'],True -Do we have any customers who are married?,True,boolean,['Marital_Status'],['category'],True -Is there any customer with income higher than 100000?,True,boolean,['Income'],['number[UInt32]'],False -Has any customer made more than 10 web purchases?,True,boolean,['NumWebPurchases'],['number[uint8]'],False -How many customers do we have in the dataset?,2240,number,['ID'],['number[uint16]'],20 -What's the average income of our customers?,52247.25135379061,number,['Income'],['number[UInt32]'],51873.7 -What's the maximum number of web purchases made by a customer?,27,number,['NumWebPurchases'],['number[uint8]'],8 -What's the minimum recency of purchase among the customers?,0,number,['Recency'],['number[uint8]'],16 -What's the most common education level among our customers?,Graduation,category,['Education'],['category'],Graduation -What's the most common marital status among our customers?,Married,category,['Marital_Status'],['category'],Married -Who is the customer with the highest income?,9432,category,"['ID', 'Income']","['number[uint16]', 'number[UInt32]']",10742 -Who is the customer with the most recent purchase?,4047,category,"['ID', 'Recency']","['number[uint16]', 'number[uint8]']",10779 -Who are the top 3 customers with the highest income?,"[9432, 1503, 1501]",list[category],"['ID', 'Income']","['number[uint16]', 'number[UInt32]']","[10742, 6935, 5831]" -What are the top 2 most common education levels among our customers?,"['Graduation', 'PhD']",list[category],['Education'],['category'],"['Graduation', 'Master']" -What are the top 3 most common marital statuses among our customers?,"['Married', 'Together', 'Single']",list[category],['Marital_Status'],['category'],"['Married', 'Together', 'Single']" -Who are the 4 customers with the most web purchases?,"[5255, 4619, 10311, 6237]",list[category],"['ID', 'NumWebPurchases']","['number[uint16]', 'number[uint8]']","[2607, 7247, 3759, 2579]" -What are the top 3 income values among our customers?,"[666666.0, 162397.0, 160803.0]",list[number],['Income'],['number[UInt32]'],"[86580, 78497, 77870]" -What are the top 2 recency values among our customers?,"[99, 99]",list[number],['Recency'],['number[uint8]'],"[95, 93]" -What are the bottom 3 income values among our customers?,"[1730.0, 2447.0, 3502.0]",list[number],['Income'],['number[UInt32]'],"[14188, 21645, 22148]" -What are the bottom 2 recency values among our customers?,"[0, 0]",list[number],['Recency'],['number[uint8]'],"[16, 21]" diff --git a/data/032_Delicatessen/sample.csv b/data/032_Delicatessen/sample.csv deleted file mode 100644 index ee8c5aefd2231be684364f7dc1931cd0765d0ecb..0000000000000000000000000000000000000000 --- a/data/032_Delicatessen/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -ID,Recency,NumWebPurchases,Education,Marital_Status,Income -2607,78,8,Graduation,Single,40464 -7247,72,7,Graduation,Widow,47916 -5802,40,2,Basic,Married,14188 -2147,91,4,Graduation,Together,76653 -3759,34,7,Graduation,Together,65196 -9284,21,5,Graduation,Together,53977 -10505,86,3,Master,Together,73113 -2579,95,6,Graduation,Married,71113 -10779,16,1,Graduation,Single,22148 -8079,86,2,Graduation,Married,22448 -6935,44,5,2n Cycle,Married,78497 -10742,72,4,PhD,Married,86580 -6856,75,3,Graduation,Together,21645 -6299,28,6,PhD,Divorced,42564 -8534,51,6,Graduation,Married,67433 -10420,56,4,Master,Divorced,46390 -3099,48,5,Graduation,Divorced,44267 -5831,93,5,Graduation,Married,77870 -5063,76,4,Graduation,Single,28769 -1542,26,4,Graduation,Single,56243 diff --git a/data/033_Employee/qa.csv b/data/033_Employee/qa.csv deleted file mode 100644 index d5acdb93a48a55adda18e1d9c58d6ebab6b42a7d..0000000000000000000000000000000000000000 --- a/data/033_Employee/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there any customer with a high salaryl?,True,boolean,['salary'],['category'],True -Do we have any employees who suffered a work accident?,True,boolean,['Work Accident'],['category'],True -Is there any employee with more than 100 hours per month on average?,True,boolean,['Average Monthly Hours'],['number'],True -Does any employee have a satisfaction level above 0.9?,True,boolean,['Satisfaction Level'],['number'],True -How many employees do we have in the dataset?,14999,number,['Number of Projects'],['category'],20 -What's the median satisfaction level of our employees?,0.64,number,['Satisfaction Level'],['number'],0.645 -What's the maximum number of web purchases made by a customer?,27,number,['Marital_Status'],['category'],8 -What's the most common marital status among the employees?,Together,number,['Marital_Status'],['number'],16 -What's the most common education level among our employees?,Graduation,category,['Education'],['category'],Graduation -What's the most common marital status among our employees?,Married,category,['Marital_Status'],['category'],Married -"Among the employees who have left the company in sales, what's the most common salary level?",low,category,"['Left', 'Department', 'salary']","['category', 'category', 'category']", -"Among the employees who have had a work accident in sales, what's the most common salary level?",low,category,"['Work Accident', 'Department', 'salary']","['category', 'category', 'category']",low -Who are the top 3 satisfaction levels?,"[1, 1, 1]",list[number],['Satisfaction Level'],['number'],"[0.98,0.93,0.93]" -What are the top 2 most common Work Accident statuses among our employees?,"['No', 'Yes']",list[category],['Work Accident'],['category'],"['No', 'Yes']" -What are the top 3 most common marital statuses among our employees?,"['Together', 'Single', 'Married']",list[category],['Marital_Status'],['category'],"['Married', 'Together', 'Single']" -What are the highest 3 years spent in the company? ,"[10, 10, 10]",list[number],['Years in the Company'],['number'],"[6, 5, 5]" -What are the top 3 departments with a 'low' salary level?,"['sales', 'technical', 'support']",list[category],"['Department', 'salary']","['category', 'category']","[95, 93]" -"For the 2 employees with the top satisfaction levels who belong to the sales department, what are their salary levels?","['low', 'low']",list[category],"['Satisfaction Level', 'Department', 'salary']","['number', 'category', 'category']","['low', 'low']" -What are the top 3 average monthly hours worked among the employees in the top 3 departments with the most employees?,"[200.91135265700484, 202.49742647058824, 200.75818752803949]",list[number],"['Department', 'Average Monthly Hours']","['category', 'number']","[265, 256, 249]" -What are the lowest 2 satisfaction levels among the employees who have not had a work accident?,"[0.09, 0.09]",list[number],"['Work Accident', 'Satisfaction Level']","['category', 'number']","[0.14, 0.22]" diff --git a/data/033_Employee/sample.csv b/data/033_Employee/sample.csv deleted file mode 100644 index 7206db6c2515ef47c80e509b89c70b0fc4c0214c..0000000000000000000000000000000000000000 --- a/data/033_Employee/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Left,Satisfaction Level,Work Accident,Average Monthly Hours,Last Evaluation,Years in the Company,salary,Department,Number of Projects,Promoted in the last 5 years?,Date Hired,Marital_Status -No,0.73,No,174,0.63,3,low,accounting,4,No,2017-05-09, Married -No,0.93,No,276,0.48,3,low,IT,3,No,2017-05-08, Married -No,0.5,No,267,0.77,2,high,management,3,No,2018-05-27, Together -No,0.91,No,255,0.67,4,low,accounting,2,No,2016-11-17, Together -No,0.57,No,235,0.67,2,low,product_mng,5,No,2018-11-23, Single -No,0.36,No,162,0.93,5,low,support,3,No,2015-02-22, Together -No,0.69,No,174,0.76,3,low,marketing,5,No,2017-12-21, Single -No,0.34,Yes,116,0.81,3,low,sales,4,No,2017-11-19, Married -No,0.98,Yes,265,0.61,2,medium,technical,4,No,2018-09-30, Married -No,0.55,No,179,0.5,3,low,technical,4,No,2017-10-25, Single -No,0.14,No,162,0.88,4,medium,marketing,3,No,2016-02-04, Married -No,0.28,No,124,0.51,3,low,technical,3,No,2017-06-06, Married -Yes,0.37,No,140,0.51,3,medium,support,2,No,2017-10-04, Together -No,0.22,No,180,0.62,3,low,support,3,No,2017-01-03, Together -No,0.6,No,145,0.97,2,medium,technical,5,No,2018-10-06, Together -No,0.24,No,142,0.89,4,medium,support,5,No,2016-06-04, Single -No,0.93,No,137,0.97,4,low,RandD,5,No,2016-08-04, Married -Yes,0.84,No,249,0.85,6,low,marketing,4,No,2014-03-20, Together -Yes,0.78,No,256,0.87,5,medium,support,5,No,2015-04-01, Together -No,0.84,No,125,0.47,4,low,RandD,3,No,2016-01-26, Married diff --git a/data/034_World/qa.csv b/data/034_World/qa.csv deleted file mode 100644 index ba5e9ddcb52609af077575f9c627073c1885f814..0000000000000000000000000000000000000000 --- a/data/034_World/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -"Is there any country with a Ladder score above 7.5, a Generosity score above 0.2, and a Social support score above 0.7?",True,boolean,"['Ladder score', 'Generosity', 'Social support']","['number[double]', 'number[double]', 'number[double]']",False -"Are there any countries in Western Europe with a Perceptions of corruption score above 0.5, a Ladder score above 7, and a Social support score above 0.7?",True,boolean,"['Regional indicator', 'Perceptions of corruption', 'Ladder score', 'Social support']","['category', 'number[double]', 'number[double]', 'number[double]']",False -Are there any countries in Western Europe with a Perceptions of corruption score above 0.5?,True,boolean,"['Social support', 'Generosity']","['number[double]', 'number[double]']",False -Is there any country with a Social support score below 0.5?,True,boolean,"['Regional indicator', 'Ladder score']","['category', 'number[double]']",False -What's the average Ladder score among the countries in Western Europe with a Generosity score above 0.2 and a Social support score above 0.7?,7.222675085250001,number,"['Regional indicator', 'Ladder score', 'Generosity', 'Social support']","['category', 'number[double]', 'number[double]', 'number[double]']",7.213750000000001 -What's the average Perceptions of corruption score among the countries in Sub-Saharan Africa with a Ladder score below 5 and a Social support score below 0.5?,0.7493706045,number,"['Regional indicator', 'Perceptions of corruption', 'Ladder score', 'Social support']","['category', 'number[double]', 'number[double]', 'number[double]']",0.871 -What's the maximum Generosity score among the countries?,0.560663998,number,"['Regional indicator', 'Generosity']","['category', 'number[double]']",0.302 -What's the minimum Social support score among the countries?,0.319459856,number,"['Regional indicator', 'Social support']","['category', 'number[double]']",0.288 -Which region has the highest number of countries with a Ladder score above 7 and a Generosity score above 0.2?,Western Europe,category,"['Ladder score', 'Generosity', 'Regional indicator']","['number[double]', 'number[double]', 'category']",North America and ANZ -Which region accounts for the most countries with a Generosity score above 0.2?,Western Europe,category,"['Generosity', 'Regional indicator']","['number[double]', 'category']",Western Europe -In which region are the majority of countries with a Perceptions of corruption score below 0.5 located?,Western Europe,category,"['Perceptions of corruption', 'Regional indicator']","['number[double]', 'category']",Sub-Saharan Africa -In which region can you find the majority of countries with a Social support score above 0.7?,Western Europe,category,"['Social support', 'Regional indicator']","['number[double]', 'category']",Western Europe -Can you name the three regions that have the most countries with a Ladder score above 7 and a Generosity score above 0.2?,['Western Europe'],list[category],"['Ladder score', 'Generosity', 'Regional indicator']","['number[double]', 'number[double]', 'category']","['North America and ANZ', 'Western Europe', 'Latin America and Caribbean']" -What are the top 3 regions with the most countries with a Generosity score above 0.2?,"['Western Europe', 'Southeast Asia', 'Sub-Saharan Africa']",list[category],"['Generosity', 'Regional indicator']","['number[double]', 'category']","['Western Europe', 'Sub-Saharan Africa', 'Central and Eastern Europe']" -Identify the three highest Ladder scores from countries in Western Europe that have a Generosity score above 0.2 and a Social support score above 0.7.,"[7.504499912, 7.448900223, 7.164500237]",list[number],"['Ladder score', 'Generosity', 'Social support', 'Regional indicator']","['number[double]', 'number[double]', 'number[double]', 'category']","[7.5599, 7.5045, 7.487]" -What are the top 3 Perceptions of corruption scores among the countries in Sub-Saharan Africa?,"[0.891806662, 0.861874342, 0.861330688]",list[number],"['Perceptions of corruption', 'Regional indicator']","['number[double]', 'category']","[0.933, 0.916, 0.915]" -What are the top 3 Generosity scores among the countries in Western Europe?,"[0.263732493, 0.246944219, 0.214965805]",list[number],"['Generosity', 'Regional indicator']","['number[double]', 'category']","[0.302, 0.275, 0.263]" -What are the top 3 Social support scores among the countries in Sub-Saharan Africa?,"[0.910357833, 0.852532268, 0.846880972]",list[number],"['Social support', 'Regional indicator']","['number[double]', 'category']","[0.983, 0.942, 0.941]" -Which are the three lowest Ladder scores in Western Europe?,"[5.514999866, 5.53550005, 5.910900116]",list[number],"['Ladder score', 'Regional indicator']","['number[double]', 'category']","[4.784, 4.956, 5.094]" -Which are the three lowest Perceptions of corruption scores in Sub-Saharan Africa?,"[0.183541179, 0.606934547, 0.619799435]",list[number],"['Perceptions of corruption', 'Regional indicator']","['number[double]', 'category']","[0.167, 0.179, 0.183]" diff --git a/data/034_World/sample.csv b/data/034_World/sample.csv deleted file mode 100644 index 68e6fd6a0b880e838d5bd1ede142b25396e49ce7..0000000000000000000000000000000000000000 --- a/data/034_World/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Ladder score,Social support,Perceptions of corruption,Regional indicator,Generosity -5.233300209,0.658048689,0.790771961,Sub-Saharan Africa,-0.043991156 -5.197599888,0.91316092,0.824211061,South Asia,0.038085498 -5.084899902,0.700386405,0.851336598,Sub-Saharan Africa,-0.00149645 -4.676799774,0.757479429,0.773544788,Commonwealth of Independent States,-0.13877961 -6.38740015,0.889878571,0.873404682,Western Europe,-0.043460999 -4.724100113,0.73721689,0.861874342,Sub-Saharan Africa,0.033745807 -5.504700184,0.874623716,0.916495264,Central and Eastern Europe,-0.128538325 -5.384300232,0.816509426,0.839302301,Southeast Asia,0.114726797 -6.910900116,0.914430678,0.85844624,Central and Eastern Europe,-0.230861515 -7.093699932,0.942081571,0.357184172,Western Europe,0.145784974 -6.863500118,0.911632538,0.612297952,Western Europe,-0.078691199 -7.164500237,0.93668282,0.435915917,Western Europe,0.263732493 -7.237500191,0.906912208,0.367084295,Western Europe,-0.004620588 -6.186299801,0.874257445,0.686927021,Central and Eastern Europe,-0.205084071 -6.006000042,0.846730053,0.733634114,Southeast Asia,-0.105463006 -5.607500076,0.843313575,0.913314283,Commonwealth of Independent States,-0.037741039 -4.784800053,0.747694969,0.82226181,Middle East and North Africa,-0.06956476 -4.165599823,0.668195903,0.817485631,Sub-Saharan Africa,-0.011823639 -5.69329977,0.689062297,0.745705426,South Asia,0.04489987 -5.094799995,0.592628479,0.815724611,Middle East and North Africa,-0.240377247 diff --git a/data/035_Billboard/qa.csv b/data/035_Billboard/qa.csv deleted file mode 100644 index 874041c37b8efb299af5187ac1cf2fd45f1eb895..0000000000000000000000000000000000000000 --- a/data/035_Billboard/qa.csv +++ /dev/null @@ -1,25 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the song with the highest rank from 1965?,True,boolean,"['Rank', 'Year']","['number[uint8]', 'number[uint16]']",False -Does the song with the lowest rank contain the word 'love' in its lyrics?,True,boolean,"['Rank', 'Lyrics']","['number[uint8]', 'text']",False -Is the artist of the song with the highest rank the same as the artist of the song with the lowest rank?,False,boolean,"['Rank', 'Artist']","['number[uint8]', 'category']",False -Are there songs without lyrics?,True,boolean,['Lyrics'],['text'],False -How many songs were produced in 1965?,100,number,['Year'],['number[uint16]'],2 -In which year was the song with the highest rank produced?,1965,number,"['Rank', 'Year']","['number[uint8]', 'number[uint16]']",2008 -What's the rank of the song with the longest lyrics?,19,number,"['Rank', 'Lyrics']","['number[uint8]', 'text']",64 -How many unique artists are there in the dataset?,2473,number,['Artist'],['category'],20 -Who is the artist of the song with the highest rank?,sam the sham and the pharaohs,category,"['Rank', 'Artist']","['number[uint8]', 'category']",alicia keys -What is the title of the song with the lowest rank?,how sweet it is to be loved by you,category,"['Rank', 'Song']","['number[uint8]', 'category']",the end of the innocence -Which song's lyrics contain the word 'love' the most times?,the way you love me,category,"['Song', 'Lyrics']","['category', 'text']",game of love -What is the title of the song produced in the earliest year?,wooly bully,category,"['Song', 'Year']","['category', 'number[uint16]']",i like it like that -Who are the artists of the top 5 ranked songs?,"['sam the sham and the pharaohs', 'ssgt barry sadler', 'the beach boys', 'the beatles', 'the beatles']",list[category],"['Rank', 'Artist']","['number[uint8]', 'category']","['alicia keys', 'christina aguilera', 'mariah carey', 'wayne fontana the mindbenders', 'fall out boy']" -What are the titles of the 3 songs with the shortest lyrics?,"['girl youll be a woman soon', 'papa dont preach', 'breathe']",list[category],"['Song', 'Lyrics']","['category', 'text']","['all the small things', 'days go by', 'hurting each other']" -Which 4 songs were produced in the most recent year?,"['uptown funk', 'thinking out loud', 'see you again', 'trap queen']",list[category],"['Song', 'Year']","['category', 'number[uint16]']",['i cry'] -Who are the artists of the bottom 5 ranked songs?,"['marvin gaye', 'wilson pickett', 'neil diamond', 'jerry butler', 'the beatles']",list[category],"['Rank', 'Artist']","['number[uint8]', 'category']","['don henley', 'lady antebellum', 'the who', 'aerosmith', 'the dave clark five']" -What are the ranks of the top 3 songs with the most occurrences of the word 'love' in their lyrics?,"[64, 41, 28]",list[number],"['Rank', 'Lyrics']","['number[uint8]', 'text']","[11, 2, 5]" -What are the years of production of the bottom 4 ranked songs?,"[1965, 1966, 1967, 1968]",list[number],"['Rank', 'Year']","['number[uint8]', 'number[uint16]']","[1989, 2010, 1971, 1989]" -What are the ranks of the 2 songs produced in the earliest year?,"[1, 2]",list[number],"['Rank', 'Year']","['number[uint8]', 'number[uint16]']","[80, 34]" -What are the years of production of the top 5 songs with the longest lyrics?,"[1998, 2009, 2010, 2007, 2002]",list[number],"['Year', 'Lyrics']","['number[uint16]', 'text']","[2013, 1973, 1988, 1994, 2005]" -Is the song with the highest rank from 1965 by the Beatles?,False,boolean,"['Rank', 'Year', 'Artist']","['number[uint8]', 'number[uint16]', 'category']",False -Which artist has the song with the highest rank in 1965?,sam the sham and the pharaohs,category,"['Rank', 'Year', 'Artist']","['number[uint8]', 'number[uint16]', 'category']",wayne fontana the mindbenders -Who are the artists of the top 3 songs in 1965?,"['sam the sham and the pharaohs', 'four tops', 'the beach boys']",list[category],"['Rank', 'Year', 'Artist']","['number[uint8]', 'number[uint16]', 'category']","['wayne fontana the mindbenders', 'the dave clark five']" -What are the years of production of the top 3 songs with the word 'love' in their lyrics by the Beatles?,"[1967, 1965, 1966]",list[number],"['Year', 'Lyrics', 'Artist']","['number[uint16]', 'text', 'category']",[] diff --git a/data/035_Billboard/sample.csv b/data/035_Billboard/sample.csv deleted file mode 100644 index 72eedc0ece0db7cd6ac34a3191cdd5036d16383c..0000000000000000000000000000000000000000 --- a/data/035_Billboard/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Artist,Rank,Lyrics,Year,Song -alicia keys,3, i just want you close where you can stay forever you can be sure that it will only get betteryou and me together through the days and nights i dont worry cause everythings going to be alright people keep talking they can say what they like but all i know is everythings going to be alrightno one no one no one can get in the way of what im feeling no one no one no one can get in the way of what i feel for you you you can get in the way of what i feel for youwhen the rain is pouring down and my heart is hurting you will always be around this i know for certain you and me together through the days and nightsi dont worry cause everythings going to be alright people keep talking they can say what they like but all i know is everythings going to be alrightno one no one no one can get in the way of what im feeling no one no one no one can get in the way of what i feel for you you you can get in the way of what i feel for youi know some people search the world to find something like what we have i know people will try try to divide something so real so til the end of time im telling you there is no oneno one no one no one can get in the way of what im feeling no one no one no one can get in the way of what i feel for you you youoh oh oh oh oh oh oh oh oh oh oh oh oh oh oh oh oh oh oh oh ,2008,no one -christina aguilera,16, dont look at meeveryday is so wonderful then suddenly its hard to breathe now and then i get insecure from all the pain im so ashamedi am beautiful no matter what they say words cant bring me down i am beautiful in every single way yes words cant bring me down oh no so dont you bring me down todayto all your friends youre delirious so consumed in all your doom ooh trying hard to fill the emptiness the pieces gone left the puzzle undone aint that the way it isyoure beautiful no matter what they say words cant bring you down oh no youre beautiful in every single way yes words cant bring you down oh no so dont you bring me down todayno matter what we do no matter what we do no matter what we say no matter what we say were the song inside the tune yeah oh yeah full of beautiful mistakesand everywhere we go and everywhere we go the sun will always shine the sun will always always shine and tomorrow we might awake on the other sidewere beautiful no matter what they say yes words wont bring us down oh no we are beautiful in every single way yes words cant bring us down oh no so dont you bring me down todayoh oh dont you bring me down today dont you bring me down ooh today ,2003,beautiful -mariah carey,23, there you are holding her hand i am lost dying to understand didnt i cherish you right dont you know you were my lifeeven though i try i cant let go something in your eyes captured my soul and every night i see you in my dreams youre all i know i cant let gojust cast aside you dont even know im alive you just walk on by dont care to see me cry and here i am still holding on i cant accept my world is gone no noeven though i try i cant let go something in your eyes captured my soul and every night i see you in my dreams youre all i know i cant let godo you even realize the sorrow i have inside everyday of my life do you know the way it feels when all you have just dies i try and try to deny that i need you but still you remain on my mindno i just cant get you out of my mind i never can say goodbye cause every night i see you in my dreamsyoure all i know i cant let you go even though i try i cant let go of something i need so badly youre all i know i cant let go ,1992,cant let go -wayne fontana the mindbenders,34,the purpose of a man is to love a woman and the purpose of a woman is to love a man so come on baby lets start today come on baby lets play the game of love love la la la la la love it started long ago in the garden of eden when adam said to eve baby youre for me so come on baby lets start today come on baby lets play the game of love love la la la la la love come on baby cause the time is right love your daddy with all your might put your arms around me hold me tight play the game of love the purpose of a man is to love a woman and the purpose of a woman is to love a man so come on baby lets start today come on baby lets play the game of love love la la la la la love come on baby cause the time is right love your daddy with all your might put your arms around me hold me tight play the game of love the purpose of a man is to love a woman and the purpose of a woman is to love a man so come on baby lets start today come on baby lets play the game of love love la la la la la love the game of love baby the game of la la la la love the game of love baby the game of la la la la love the game of love baby,1965,game of love -fall out boy,40, am i more than you bargained for yet ive been dying to tell you anything you want to hear cause thats just who i am this week lie in the grass next to the mausoleum im just a notch in your bedpost but youre just a line in a song a notch in your bedpost but youre just a line in a songdrop a heart break a name were always sleeping in and sleeping for the wrong teamwere going down down in an earlier round and sugar were going down swinging ill be your number one with a bullet a loaded gun complex cock it and pull itwere going down down in an earlier round and sugar were going down swinging ill be your number one with a bullet a loaded gun complex cock it and pull itis this more than you bargained for yet oh dont mind me im watching you two from the closet wishing to be the friction in your jeans isnt it messed up how im just dying to be him im just a notch in your bedpost but youre just a line in a song notch in your bedpost but youre just a line in a songdrop a heart break a name were always sleeping in and sleeping for the wrong teamwere going down down in an earlier round and sugar were going down swinging ill be your number one with a bullet a loaded gun complex cock it and pull itwere going down down in an earlier round and sugar were going down swinging ill be your number one with a bullet a loaded gun complex cock it and pull itdown down in an earlier round and sugar were going down swinging ill be your number one with a bullet a loaded gun complex cock it and pull itwere going down down in an earlier round take aim at myself and sugar were going down swinging take back what you said ill be your number one with a bullet take aim at myself a loaded gun complex cock it and pull itwere going down down down down down down down down were going down down down down a loaded gun complex cock it and pull itwere going down down in an earlier round take aim at myself and sugar were going down swinging take back what you said ill be your number one with a bullet take aim at myself a loaded gun complex cock it and pull it ,2005,sugar were goin down -blink 182,40, all the small things true care truth brings ill take one lift your ride best trip always i know youll be at my show watching waiting commiseratingsay it aint so i will not go turn the lights off carry me home na na na na na na na na na na na na na na nalate night come home work sucks i know she left me roses by the stairs surprises let me know she caressay it aint so i will not go turn the lights off carry me home na na na na na na na na na na na na na na nasay it aint so i will not go turn the lights off carry me home keep your head still ill be your thrill the night will go on my little windmillsay it aint so i will not go turn the lights off carry me home keep your head still ill be your thrill the night will go on the night will go on my little windmill ,2000,all the small things -gladys knight the pips,49,la proved too much for the man too much for the man he couldnt make it so hes leavin the life hes come to know ooh he said hes goin he said hes goin back to find goin back to find ooh whats left of his world the world he left behind not so long ago oh hes leavin leavin on that midnight train to georgia yeah leavin on that midnight train said hes goin back goin back to find to a simpler place in time when he takes that ride oh yes he is guess whos gonna be right by his side and ill be with him i know you will on that midnight train to georgia leavin on that midnight train to georgia ooh ooh id rather live in his world live in his world than live without him in mine my world is his his and hers alone he kept dreamin dreamin ooh that someday hed be a star a superstar but he didnt get far but he sure found out the hard way that dreams dont always come true oh no dreams dont always come true aha no aha so he pawned all his hopes and he even sold his old car bought a one way ticket back to the life he once knew oh yes he did he said he would oh hes leavin leavin on that midnight train to georgia mmm yeah leavin on that midnight train said hes goin back to find goin back to find ooh a simpler place in time when he takes that ride oh yeah guess whos gonna be right by his side and im gonna be with him i know you will on that midnight train to georgia leavin on that midnight train to georgia ooh ooh id rather live in his world live in his world than live without him in mine my world is his his and hers alone ooh hes leavin leavin on the midnight train to georgia yeah leavin on that midnight train ooh yall said hes goin back to find goin back to find ooh a simpler place in time whenever he takes that ride ooh yall guess whos gonna be right by his side and i got be with him i know you will on that midnight train to georgia leavin on that midnight train to georgia ooh ooh oh hey id rather live in his world live in his world than live without him in mine her world is his his and hers alone for love gonna board the midnight train and go for love gonna board gonna board the midnight train and go for love gonna board uh huh the midnight train and go my world for love his world gonna board our world the midnight train and go mine and his alone my world for love his world gonna board our world the midnight train and go mine and his alone ive got to go for love ive got to go gonna board ive got to go the midnight train and go hey ive got to go for love ive got to go gonna board the midnight train and go my world for love his world gonna board my man the midnight train and go his girl ive got to go for love ive got to go gonna board the midnight train and go oh ive got to go my world for love his world gonna board,1973,midnight train to georgia -the beatles,55,in penny lane there is a barber showing photographs of every head hes had the pleasure to know and all the people that come and go stop and say hello on the corner is a banker with a motorcar the little children laugh at him behind his back and the banker never wears a mac in the pouring rain very strange penny lane is in my ears and in my eyes wet beneath the blue suburban skies i sit and meanwhile back in penny lane there is a fireman with an hourglass and in his pocket is a portrait of the queen he likes to keep his fire engine clean its a clean machine penny lane is in my ears and in my eyes a four of fish and finger pies in summer meanwhile back behind the shelter in the middle of a roundabout a pretty nurse is selling poppies from a tray and though she feels as if shes in a play she is anyway penny lane the barber shaves another customer we see the banker sitting waiting for a trim and the fireman rushes in from the pouring rain very strange penny lane is in my ears and in my eyes there beneath the blue suburban skies i sit and meanwhile back penny lane is in my ears and in my eyes there beneath the blue suburban skies penny lane,1967,penny lane -leann rimes,56, under a lovers sky gonna be with you and no ones gonna be around if you think that you wont fall well just wait until til the sun goes downunderneath the starlight starlight theres a magical feeling so right itll steal your heart tonightyou can try to resist try to hide from my kiss but you know but you know that you cant fight the moonlight deep in the dark youll surrender your heart but you know but you know that you cant fight the moonlight no you cant fight it its gonna get to your hearttheres no escape from love once a gentle breeze weaves its spell upon your heart no matter what you think it wont be too long til your in my arms underneath the starlight starlight well be lost in the rhythm so right feel it steal your heart tonightyou can try to resist try to hide from my kiss but you know but you know that you cant fight the moonlight deep in the dark youll surrender your heart but you know but you know that you cant fight the moonlight no you cant fight it no matter what you do the night is gonna get to youdont try then youre never gonna winpart of me the starlight starlight theres a magical feeling so right it will steal your heart tonightyou can try to resist try to hide from my kiss but you know but you know that you cant fight the moonlight deep in the dark youll surrender your heart but you know but you know that you cant fight the moonlight no you cant fight ityou can try to resist try to hide from my kiss but you know dont you know that you cant fight the moonlight deep in the dark youll surrender your heart but you know but you know that you cant fight the moonlight no you cant fight it its gonna get to your heart ,2002,cant fight the moonlight -flo rida,64, i know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting goi know you wanna get behind the wheel but only one rida eyes shut still got me swimming like a diver cant let go i got fans in okinawa my heart to japan quake losers and survivors norway no you didnt get my flowers no way to sound better but the killer was a coward face just showers the minute in a hour heard about the news all day went sour lil mama got me feeling like a lemonhead put you in the box just the presidents cigarettes give em my regards or regardless i get arrested aint worried about the killers just the young and restless get mad cause a quarter million on my necklace dui never said i was driving reckless you and i or jealously was not oppressive oh no i cant stop i was destinedi know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting goi know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting gochampagne buckets still got two tears in it and i put that on my tattoo of jimi hendrix get depressed cause the outfit all in it cause the press tell it all get a meal ticket clean next get a call just a lil visit sacrifice just to make a hit still vivid reality see when your blessed just kill critics buggatti never when im rich just god fearing look at me steering got the blogs staring gotta good feeling plus the right caring tell his billie jeans im on another planet thank eclass big chuck or lee prince perries buy my momma chandeliers on my tears dammit thirty years you had thought these emotions vanish tryna live tryna figure how my sister vanish no cheers i know you wouldnt panici know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting goi know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting gowhen i need a healing i just look up to the ceiling i see the sun coming down i know its all better now when i need a healing i just look up to the ceiling i see the sun coming down i know its all better now when i need a healing i just look up to the ceiling i see the sun coming down i know its all better now when i need a healing i just look up to the ceiling i see the sun coming down i know its all better nowi know i think of letting go i know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting goi know caught up in the middle i cry just a little when i think of letting go oh no gave up on the riddle i cry just a little when i think of letting go ,2013,i cry -the carpenters,65, no one in the world ever had a love as sweet as my love for nowhere in the world could there be a boy as true as you love all my love i give gladly to you all your love you give gladly to me tell me why then oh why should it be thatwe go on hurting each other we go on hurting each other making each other cry hurting each other without ever knowing whycloser than the leaves on a weepin willow baby we are closer dear are we than the simple letters a and b are all my life i could love only you all your life you could love only me tell me why then oh why should it be thatwe go on hurting each other we go on hurting each other making each other cry hurting each other without ever knowing whycant we stop hurting each other gotta stop hurting each other making each other cry breaking each others heart tearing each other apartcant we stop hurting each other gotta stop hurting each other making each other cry breaking each others heart tearing each other apart ,1972,hurting each other -meat loaf,71, you cant run away forever but theres nothing wrong with getting a good head start you want to shut out the night you want to shut down the sun you want to shut away the pieces of a broken heartthink of how wed lay down together wed be listening to the radio so loud and so strong every golden nugget coming like a gift through the gods someone must have blessed us when he gave us those songsi treasure your love i never want to lose it girl youve been through the fires of hell and i know youve got the ashes to prove it i treasure your love i want to show you how to use it girl youve been through a lot of pain in the dirt and i know youve got the scars to prove itremember everything that i told you and im telling you again that its true when youre alone and afraid and youre completely amazed to find theres nothing anybody can dotheres always something magic theres always something new and when you really really need it the most thats when rock and roll dreams come throughthe beat is yours forever the beat is always true and when you really really need it the most thats when rock and roll dreams come through for youonce upon a time was a back beat once upon a time all the chords came to life and the angels had guitars even before they had wings if you hold onto a chorus you can get through the nighti treasure your love i never want to lose it girl youve been through the fires of hell and i know youve got the ashes to prove it i treasure your love i want to show you how to use it girl youve been through a lot of pain in the dirt and i know youve got the scars to prove itremember everything that i told you and im telling you again that its true youre never alone cause you can put on the phones and let the drummer tell your heart what to dotheres always something magic theres always something new and when you really really need it the most thats when rock and roll dreams come throughthe beat is yours forever the beat is always true and when you really really need it the most thats when rock and roll dreams come through oh for you yeah yeahthats when rock and roll dreams come through thats when rock and roll dreams come through thats when rock and roll dreams come through ,1994,rock and roll dreams come through -keith sweat,74, seen you last night saw you standin there and couldnt picture the color of your hair and all i wanted to know were you really therei wanna know was it my imagination ooh you know it was a sweet sensation lookin at you from a distance ooh it seems so realyou and i together dream that seemed for real if its a dream please dont wake me up it feels so real all i know isi want her i want that baby i want her get it get it get it get it i want her i wanna do it like this do it like that i want her once i get it aint no turnin backyou turn me on and on this feelin girl is so strong my heart girl is on fire ooh youre my desireive got a thing for you dreams of you and me baby shes bad shes bad shes bad all i know isi want her i want i want i want i want i want her i want her dont misunderstand me i want her i wish this dream was for real i want her you you know the deali want her yeah yeah i want her i want that girl so bad i want her i had a dream of her last night i want her yeah yeah yeahand i i give it to me give it to me give it to me ooh baby all night long girli want her ooh baby i want her i want that girl so bad i want her all night long i want her let me let me tell you somethin i wanna do it like this babyi want her i wanna do it like that sugar i want her you know what i need girl you know what i like girl like this baby i want her i wanna do it like that sugar i want her you turn me on and on and on and oni want her i wanna do it like this baby i wanna do it like that sugar i want her you know what i mean girl you know what i like girl i want her i wanna do it like this baby i wanna do it like that sugar i want her oh i want you so bad girli want her i need you right now baby i want her give it to me give it to me all night long girl i want her i just want you so bad i want her i just need you so bad come on girl come on girli want her shes so bad i want her hey baby i want her dont let this be a dream girl just think about tonight baby i want her i want you so bad ooh babyi want her give it to me tonight girl love me love me i want her love me love me love me love me down ooh baby i want her i want her heyi want her the feelin girl is so so so so so so so so strong i want you so bad baby was it was it a dream last night girl was it was it a dream last night baby ,1988,i want her -barbra streisand,74,i still can remember the last time i cried i was holding you and loving you knowing it would end i never felt so good yet felt so bad youre the one i love and what makes it sad is you dont belong to me and i can remember the last time i lied i was holding you and telling you we could still be friends tried to let you go but i cant you know and even though im not with you i need you so but you dont belong to me comin in and out of your life isnt easy when theres so many nights i cant hold you and ive told you these feelings are so hard to find comin in and out of your life will never free me cause i dont need to touch you to feel you its so real with you i just cant get you out of my mind but i can remember the last time we tried each needing more than we could give and knowing all the time a stronger love just cant be found even though at times this crazy world is turning upside down youll always belong to me comin in and out of your life isnt easy when theres so many times i cant hold you and ive told you these feelings are so hard to find comin in and out of your life will never free me i dont need to touch you to feel you its so real with you i just cant get you out of my mind but i can remember,1982,comin in and out of your life -the dave clark five,80,come on come on let me show you where its at ah come on come on let me show you where its at wow come on come on let me show you where its at i said the name of the place is i like it like that come on come on let me show you where its at ah come on come on let me show you where its at whoa i wanna show you come on let me show you where its at the name of the place is i like it like that they got a little place adown the track the name of the place is i like it like that you take sally and ill take sue and were gonna rock away all of our blues come on come on let me show you where its at oh come on come on let me show you where its at ah come on come on let me show you where its at the name of the place is i like it like that the last time i was down they lost my shoes they had some cat shoutin the blues the people was yellin and shoutin for more and all they kept sayin wasa go man go come on come on let me show you where its at ah come on come on let me show you where its at oh i wanna show you come on let me show you where its at i said the name of the place is i like it like that come on come on let me show you where its at ah come on come on let me show you where its at oh come on come on let me show you where its at the name of the place is i like it like that,1965,i like it like that -dirty vegas,80, you you you you you you you you you youyou still a whisper on my lips a feeling at my fingertips thats pulling at my skinyou leave me when im at my worst feeling as if ive been cursed bitter cold withindays go by and still i think of you days when i couldnt live my life without youdays go by and still i think of you days when i couldnt live my life without you without you without youyou still a whisper on my lips feeling at my fingertips thats pulling at my skinyou leave me when im at my worst feeling as if ive been cursed bitter cold withindays go by and still i think of you days when i couldnt live my life without you without you without youdays go by and still i think of you days when i couldnt live my life without youdays go by and still i think of you days when i couldnt live my life without you without you without you without you without youdays go by days go by days go by days go by days go by days go by ,2002,days go by -aerosmith,81, workin like a dog fo de boss man workin for de company im bettin on the dice im tossin im gonna have a fantasy but where am i gonna look they tell me that love is blind i really need a girl like an open book to read between the lineschorus love in an elevator livin it up when im goin down love in an elevator lovin it up till i hit the groundjackis in the elevator lingerie second floor she said can i see you later and love you just a little more i kinda hope we get stuck nobody gets out alive she said ill show ya how to fax in the mail room honey and have you home by fivechorusin the air in the air honey one more time not it aint fair love in an elevator lovin it up when im goin downlove in an elevator goin downchorusgonna be a penthouse pauper gonna be a millionaire im gonna be a real fast talker and have me a love affair gotta get my timin right its a test that i gotta pass ill chase you all the way to the stairway honey kiss your sassafraschorusdo you care do you care honey one more time now it aint fair love in an elevator livin it up when im goin down ,1989,love in an elevator -the who,84,well be fighting in the streets with our children at our feet and the morals that they worship will be gone and the men who spurred us on sit in judgement of all wrong they decide and the shotgun sings the song ill tip my hat to the new constitution take a bow for the new revolution smile and grin at the change all around pick up my guitar and play just like yesterday then ill get on my knees and pray we dont get fooled again the change it had to come we knew it all along we were liberated from the fold thats all and the world looks just the same and history aint changed cause the banners they are flown in the next war ill tip my hat to the new constitution take a bow for the new revolution smile and grin at the change all around pick up my guitar and play just like yesterday then ill get on my knees and pray we dont get fooled again no no ill move myself and my family aside if we happen to be left half alive ill get all my papers and smile at the sky though i know that the hypnotized never lie do ya theres nothing in the streets looks any different to me and the slogans are replaced bythebye and the parting on the left are now parting on the right and the beards have all grown longer overnight ill tip my hat to the new constitution take a bow for the new revolution smile and grin at the change all around pick up my guitar and play just like yesterday then ill get on my knees and pray we dont get fooled again dont get fooled again no no yeaaaaaaaaaaaaaaaaaaaaaaaaah meet the new boss same as the old boss,1971,wont get fooled again -lady antebellum,94, she grew up on a side of the road where the church bells ring and strong love grows she grew up good she grew up slow like american honeysteady as a preacher free as a weed couldnt wait to get goin but wasnt quite ready to leave so innocent pure and sweet american honeytheres a wild wild whisper blowin in the wind callin out my name like a long lost friend oh i miss those days as the years go by oh nothings sweeter than summertime and american honeyget caught in the race of this crazy life tryin to be everything can make you lose your mind i just wanna go back in time to american honey yeatheres a wild wild whisper blowin in the wind callin out my name like a long lost friend oh i miss those days as the years go by oh nothings sweeter than summertime and american honeygone for so long now i gotta get back to her somehow to american honeyooo theres a wild wild whisper blowin in the wind callin out my name like a long lost friend oh i miss those days as the years go by oh nothins sweeter than summertime and american honey and american honey ,2010,american honey -don henley,99,remember when the days were long and rolled beneath a deep blue sky didnt have a care in the world with mommy and daddy standing by when happily ever after fails and weve been poisoned by these fairy tales the lawyers dwell on small details since daddy had to fly aah but i know a place where we can go thats still untouched by men well sit and watch the clouds roll by and the tall grass waves in the wind and you can lay your head back on the ground and let your hair fall all around me offer up your best defense but this is the end this is the end of the innocence oh beautiful for spacious skies but now those skies are threatening theyre beating plowshares into swords for this tired old man that we elected king armchair warriors often fail and weve been poisoned by these fairy tales the lawyers clean up all details since daddy had to lie aah but i know a place where we can go and wash away this sin well sit and watch the clouds roll by and the tall grass waves in the wind just lay your head back on the ground and let your hair spill all around me offer up your best defense but this is the end this is the end of the innocence who knows how long this will last now weve come so far so fast but somewhere back there in the dust that same small town in each of us i need to remember this so baby give me just one kiss and let me take a long last look before we say goodbye just lay your head back on the ground and let your hair fall all around me offer up your best defense this is the end this is the end of the innocence,1989,the end of the innocence diff --git a/data/036_US/qa.csv b/data/036_US/qa.csv deleted file mode 100644 index 43001e623485970844d48229ddbcad3537fbc38c..0000000000000000000000000000000000000000 --- a/data/036_US/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -"Are there records where the 'count' exceeds 20,000?",True,['count'],boolean,['number[uint16]'],False -Do we have any records originating from 'Jefferson'?,True,['name_origin'],boolean,['category'],True -Are there destinations with the name 'Baldwin'?,True,['name_dest'],boolean,['category'],False -Are there records where the latitude of the destination is greater than 60?,True,['lat_dest'],boolean,['number[double]'],False -How many unique destinations are there in the dataset?,3219,['dest'],number,['number[uint32]'],20 -"On average, how many times is each origin-destination pair reported?",67.7453798126951,['count'],number,['number[uint16]'],17.55 -What's the highest count value in the dataset?,40580,['count'],number,['number[uint16]'],66 -How many unique origin names are there in the dataset?,1904,['name_origin'],number,['category'],17 -From which origin do we have the highest 'count' recorded?,,"['count', 'name_origin']",category,"['number[uint16]', 'category']",18077 -To which destination do we find the highest 'count' reported?,Los Angeles,"['count', 'name_dest']",category,"['number[uint16]', 'category']",6001 -Which origin has the lowest latitude?,Guáanica,"['lat_origin', 'name_origin']",category,"['number[double]', 'category']",Okaloosa -Which destination has the highest longitude?,Fajardo,"['lon_dest', 'name_dest']",category,"['number[double]', 'category']",Montgomery -What are the top 3 origins with the highest average count?,"['Los Angeles', 'New York', 'San Francisco']","['name_origin', 'count']",list[category],"['category', 'number[uint16]']","['18077', '48439', '36123']" -Which are the 4 destinations with the lowest average count?,"['Kalawao', 'Loving', 'Kenedy', 'Wheatland']","['name_dest', 'count']",list[category],"['category', 'number[uint16]']","[48113, 12127, 37115, 39041]" -List the 5 origins with the highest average latitude values.,"['North Slope', 'Northwest Arctic', 'Yukon-Koyukuk', 'Nome', 'Fairbanks North Star']","['name_origin', 'lat_origin']",list[category],"['category', 'number[double]']","['53063', '53033', '53045', '55097', '36045']" -Which 2 origins have the lowest average longitude values?,"['Aleutians West', 'Nome']","['name_origin', 'lon_origin']",list[category],"['category', 'number[double]']","['53045', '6013']" -List the top 5 recorded count values.,"[40580, 39899, 38430, 28524, 24452]",['count'],list[number],['number[uint16]'],"[66, 35, 32, 30, 25]" -What are the 3 highest latitude values for destinations?,"[69.42718361, 69.42718361, 69.42718361]",['lat_dest'],list[number],['number[double]'],"[46.39399576, 46.27467409, 43.15268452]" -Rank the lowest 4 longitude values for origins.,"[-167.08526, -167.08526, -167.08526, -167.08526]",['lon_origin'],list[number],['number[double]'],"[-123.1229957, -121.8986791, -121.7629538, -119.6502747]" -Which 6 destination IDs have the highest average counts?,"[6037, 36061, 48201, 6059, 6071, 6085]","['dest', 'count']",list[number],"['number[uint32]', 'number[uint16]']","[6001, 20045, 36055, 13035, 26103, 55003]" diff --git a/data/036_US/sample.csv b/data/036_US/sample.csv deleted file mode 100644 index 68186d955a7776bb2f5df9fc1dd578599d725b05..0000000000000000000000000000000000000000 --- a/data/036_US/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -lon_origin,lat_dest,name_origin,lon_dest,name_dest,count,dest,lat_origin --106.6847185,39.01016896,Bernalillo,-96.74226038,Geary,17,20061,35.07217059 --89.50553297,46.27467409,Portage,-90.65691261,Ashland,20,55003,44.4720557 --85.42979084,37.67173849,Jefferson,-121.8637832,Alameda,66,6001,38.77176065 --117.4127877,38.77152493,Spokane,-120.4913342,El Dorado,14,6017,47.62866949 --85.474567,46.39399576,Putnam,-87.64102458,Marquette,25,26103,36.1341376 --121.8986791,38.83537766,Contra Costa,-77.27291228,Fairfax,14,51059,37.96121218 -,39.98663786,,-90.2475955,Cass,11,17017, --81.27972079,35.86026594,Lexington,-82.70317205,Madison,5,37115,33.90403809 --121.7629538,40.27984952,King,-82.99184689,Delaware,6,39041,47.49522592 --76.56666987,33.28752257,Baltimore,-83.9618824,Butts,30,13035,39.22908307 --81.37105401,39.14165842,Stark,-77.16271246,Montgomery,13,24031,40.81640669 --119.6502747,29.05743253,Washoe,-81.17875844,Volusia,2,12127,40.62232808 -,38.68483316,,-85.74343858,Scott,8,18143, --82.63360693,41.43180787,Erie,-87.38161342,Lake,17,18089,41.38126807 --92.66649009,34.46411103,Marion,-94.21998338,Polk,8,5113,36.2801656 --77.08897916,43.15268452,Yates,-77.69246634,Monroe,32,36055,42.60381963 --123.1229957,37.43984082,Mason,-122.2958935,San Mateo,11,6081,47.35432361 --97.29140123,38.88975579,Tarrant,-95.28638765,Douglas,35,20045,32.77169819 --86.59269081,35.97347739,Okaloosa,-94.20947491,Washington,16,5143,30.68717025 --75.82561503,32.76714096,Jefferson,-96.77862262,Dallas,1,48113,44.01015942 diff --git a/data/037_Ted/qa.csv b/data/037_Ted/qa.csv deleted file mode 100644 index bb48100b1cb78e0bd4436da0cce7b216b24a7e26..0000000000000000000000000000000000000000 --- a/data/037_Ted/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -Are there talks with more than a million views?,True,['views'],boolean,['number[uint32]'],True -Is there a talk by 'Elon Musk'?,True,['speaker_1'],boolean,['category'],False -Are there any TEDx events included?,True,['event'],boolean,['category'],True -Are there talks available in more than 10 languages?,True,['available_lang'],boolean,['list[category]'],True -How many unique speakers are there in the dataset?,3274,['speaker_1'],number,['category'],20 -What's the average number of views for the talks?,2148005.5737827714,['views'],number,['number[uint32]'],1502352.85 -What's the maximum duration of a talk (in seconds)?,3922,['duration'],number,['number[uint16]'],1523 -How many talks have more than 500 comments?,186,['comments'],number,['number[UInt16]'],2 -Which event has the highest average views?,TEDxPuget Sound,"['event', 'views']",category,"['category', 'number[uint32]']",TEDxRainier -Which speaker's talk has the most comments?,Richard Dawkins,"['speaker_1', 'comments']",category,"['category', 'number[UInt16]']",{0: 'Suzana Herculano-Houzel'} -Which talk's title has the least views?,Por qué necesitamos proteger el alta mar,"['title', 'views']",category,"['text', 'number[uint32]']",Online predators spread fake porn of me. Here's how I fought back -In which event was the longest talk held?,Countdown,"['event', 'duration']",category,"['category', 'number[uint16]']",TED1984 -Which are the top 4 events with the highest average number of comments?,"['TEDxPuget Sound', 'TEDxHouston', 'TEDxFiDiWomen', 'TEDxUW']","['event', 'comments']",list[category],"['category', 'number[UInt16]']","['TEDGlobal 2013', 'TED2016', 'TEDxRainier', 'TEDIndia 2009']" -List the top 3 most frequent speakers in the dataset.,"['Alex Gendler', 'Iseult Gillespie', 'Emma Bryce']",'speaker_1',list[category],['category'],"[""{0: 'Miwa Matreyek'}"", ""{0: 'Thomas Pogge'}"", ""{0: 'Asher Hasan'}""]" -Which 5 events have the shortest average talk durations?,"['TEDxConcorde', 'Small Thing Big Idea', 'The TED Interview', 'TEDxConcordiaUPortland', 'The Way We Work']","['event', 'duration']",list[category],"['category', 'number[uint16]']","['TEDIndia 2009', 'TED2007', 'TED-Ed', 'TEDGlobal 2017', 'TEDxRainier']" -List 2 events with the most number of talks.,"['TED-Ed', 'TED2018']",'event',list[category],['category'],"['TEDGlobal 2010', 'TEDxCanberra']" -What are the top 4 most viewed talks' view counts?,"[65051954, 57074270, 56932551, 49730580]",['views'],list[number],['number[uint32]'],"[3492293, 3082440, 2478498, 2438526]" -List the 3 shortest talk durations in the dataset.,"[60, 78, 78]",['duration'],list[number],['number[uint16]'],"[268, 279, 287]" -Which 5 talks have the highest number of comments?,"[6449.0, 4931.0, 3424.0, 3006.0, 2984.0]",['comments'],list[number],['number[UInt16]'],"['What is so special about the human brain?', 'The case for optimism on climate change', 'The linguistic genius of babies', 'My message of peace from Pakistan', 'Glorious visions in animation and performance']" -List the view counts of the 6 least viewed talks.,"[0, 0, 0, 0, 0, 0]",['views'],list[number],['number[uint32]'],"[195172, 310677, 451421, 477710, 779329, 887739]" diff --git a/data/037_Ted/sample.csv b/data/037_Ted/sample.csv deleted file mode 100644 index 51d2c70d9508743c884fce2e952c45165b3dc024..0000000000000000000000000000000000000000 --- a/data/037_Ted/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -views,title,available_lang,speaker_1,duration,comments,event -946000,Glorious visions in animation and performance,"['af', 'ar', 'bg', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'hi', 'hu', 'id', 'it', 'ja', 'ko', 'lt', 'lv', 'my', 'nl', 'pa', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sr', 'th', 'tr', 'uk', 'vi', 'zh-cn', 'zh-tw']",Miwa Matreyek,671,148.0,TEDGlobal 2010 -310677,Medicine for the 99 percent,"['ar', 'en', 'es', 'fa', 'fr', 'he', 'it', 'ko', 'nl', 'pt-br', 'tr', 'zh-cn', 'zh-tw']",Thomas Pogge,1085,121.0,TEDxCanberra -3082440,What is so special about the human brain?,"['ar', 'bg', 'bn', 'ca', 'cs', 'de', 'el', 'en', 'es', 'eu', 'fa', 'fr', 'fr-ca', 'gl', 'he', 'hr', 'hu', 'id', 'it', 'ja', 'ko', 'lt', 'mk', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sl', 'sr', 'sv', 'th', 'tr', 'uk', 'vi', 'zh-cn', 'zh-tw']",Suzana Herculano-Houzel,811,1050.0,TEDGlobal 2013 -887739,The mysterious science of pain,"['ar', 'de', 'en', 'es', 'fa', 'fr', 'he', 'hr', 'hu', 'id', 'it', 'ja', 'ko', 'ku', 'my', 'pl', 'pt', 'pt-br', 'ru', 'tr', 'vi', 'zh-cn', 'zh-tw']",Joshua Pate,287,,TED-Ed -2303625,The uncomplicated truth about women's sexuality,"['ar', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fi', 'fr', 'he', 'hu', 'id', 'it', 'ja', 'ko', 'pt', 'pt-br', 'ro', 'ru', 'sr', 'tr', 'vi', 'zh-cn', 'zh-tw']",Sarah Barmak,680,17.0,TEDxToronto -2090031,The case for optimism on climate change,"['ar', 'bg', 'de', 'el', 'en', 'es', 'fa', 'fi', 'fr', 'he', 'hr', 'it', 'ja', 'ko', 'nl', 'pl', 'pt', 'pt-br', 'ru', 'sr', 'sv', 'th', 'tr', 'uk', 'vi', 'zh-cn', 'zh-tw']",Al Gore,1520,624.0,TED2016 -1343147,Anatomy of a New Yorker cartoon,"['ar', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'it', 'ja', 'ko', 'lt', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sr', 'tr', 'uk', 'vi', 'zh-cn', 'zh-tw']",Bob Mankoff,1259,65.0,TEDSalon NY2013 -3492293,The linguistic genius of babies,"['ar', 'bg', 'bs', 'ca', 'cs', 'da', 'de', 'el', 'en', 'es', 'eu', 'fa', 'fi', 'fr', 'fr-ca', 'gl', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'it', 'ja', 'ko', 'lt', 'mk', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sk', 'sl', 'sq', 'sr', 'sv', 'th', 'tr', 'uk', 'uz', 'vi', 'zh-cn', 'zh-tw']",Patricia Kuhl,617,373.0,TEDxRainier -2478498,How my mind came back to life — and no one knew,"['ar', 'bg', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'hi', 'hr', 'hu', 'it', 'ja', 'ko', 'ku', 'lt', 'lv', 'pl', 'pt', 'pt-br', 'ru', 'sr', 'th', 'tr', 'uk', 'vi', 'zh-cn', 'zh-tw']",Martin Pistorius,848,93.0,TEDxKC -195172,Online predators spread fake porn of me. Here's how I fought back,"['ar', 'en', 'es', 'fa', 'fr', 'hu', 'ko', 'nl', 'pt', 'zh-tw']",Noelle Martin,706,24.0,TEDxPerth -1683225,Rethink the desktop with BumpTop,"['ar', 'bg', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'hr', 'hu', 'id', 'it', 'ja', 'ko', 'lt', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sv', 'te', 'tr', 'uk', 'ur', 'vi', 'zh-cn', 'zh-tw']",Anand Agarawala,279,125.0,TED2007 -451421,Toys and materials from the future,"['ar', 'bg', 'de', 'en', 'es', 'eu', 'fr', 'he', 'it', 'ja', 'ko', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'tr', 'vi', 'zh-cn', 'zh-tw']",Keith Schacht,946,42.0,TED2005 -1052338,Medical tech designed to meet Africa's needs,"['ar', 'en', 'es', 'fa', 'fr', 'it', 'ja', 'ko', 'pt', 'pt-br', 'ro', 'ru', 'sl', 'ta', 'tr', 'uk', 'vi', 'zh-cn', 'zh-tw']",Soyapi Mumba,343,9.0,TEDGlobal 2017 -2162219,The boiling river of the Amazon,"['ar', 'bg', 'de', 'en', 'es', 'fa', 'fr', 'he', 'hu', 'it', 'ja', 'ko', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sr', 'th', 'tr', 'vi', 'zh-cn', 'zh-tw']",Andrés Ruzo,949,65.0,TEDGlobal 2014 -1085611,"5 predictions, from 1984","['ar', 'bg', 'en', 'es', 'fr', 'he', 'it', 'ja', 'ko', 'nl', 'pl', 'pt-br', 'ro', 'ru', 'sr', 'tr', 'zh-cn', 'zh-tw']",Nicholas Negroponte,1523,69.0,TED1984 -1579105,What’s wrong with your pa$$w0rd?,"['ar', 'bg', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'hr', 'it', 'ja', 'ko', 'nl', 'pt', 'pt-br', 'ro', 'ru', 'sk', 'sr', 'th', 'tr', 'vi', 'zh-cn', 'zh-tw']",Lorrie Faith Cranor,1061,126.0,TEDxCMU -2438526,"How to deconstruct racism, one headline at a time","['ar', 'en', 'es', 'fr', 'he', 'hu', 'it', 'ja', 'ko', 'lt', 'pt-br', 'ro', 'ru', 'tr', 'zh-cn', 'zh-tw']",Baratunde Thurston,1010,54.0,TED2019 -1207951,A one-man world summit,"['ar', 'bg', 'de', 'en', 'es', 'fr', 'he', 'hu', 'it', 'ja', 'ko', 'nl', 'pl', 'pt-br', 'ro', 'ru', 'vi', 'zh-cn', 'zh-tw']",Rory Bremner,881,76.0,TEDGlobal 2009 -477710,My message of peace from Pakistan,"['ar', 'bg', 'cs', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'hi', 'hr', 'hu', 'id', 'it', 'ja', 'ko', 'nl', 'pl', 'pt', 'pt-br', 'ro', 'ru', 'sk', 'sq', 'sr', 'sv', 'tr', 'uk', 'ur', 'vi', 'zh-cn', 'zh-tw']",Asher Hasan,268,170.0,TEDIndia 2009 -779329,The Blur Building and other tech-empowered architecture,"['ar', 'bg', 'de', 'en', 'es', 'fr', 'he', 'it', 'ja', 'ko', 'nl', 'pl', 'pt-br', 'ro', 'ru', 'zh-cn', 'zh-tw']",Liz Diller,1164,54.0,EG 2007 diff --git a/data/038_Stroke/qa.csv b/data/038_Stroke/qa.csv deleted file mode 100644 index 217bca0388ed563be4d0e5a1ffc34adea33bb760..0000000000000000000000000000000000000000 --- a/data/038_Stroke/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the oldest person in the dataset has a stroke?,True,boolean,"['age', 'stroke']","['number[UInt8]', 'number[uint8]']",False -Do all smokers have hypertension?,False,boolean,"['smoking_status', 'hypertension']","['category', 'number[uint8]']",False -Are all people with heart diseases married?,False,boolean,"['heart_disease', 'ever_married']","['number[uint8]', 'category']",True -Is the average glucose level higher for males than females?,True,boolean,"['gender', 'avg_glucose_level']","['category', 'number[double]']",False -How many people in the dataset have a stroke?,249,number,['stroke'],['number[uint8]'],1 -What is the average age of people who smoke?,47.09632446134347,number,"['smoking_status', 'age']","['category', 'number[UInt8]']",50.333333333333336 -What is the highest glucose level recorded in the dataset?,271.74,number,['avg_glucose_level'],['number[double]'],143.43 -How many unique 'work_type' categories are there in the dataset?,5,number,['work_type'],['category'],4 -What is the most common work type among stroke patients?,Private,category,"['stroke', 'work_type']","['number[uint8]', 'category']",Self-employed -What is the smoking status of the youngest person in the dataset?,Unknown,category,"['age', 'smoking_status']","['number[UInt8]', 'category']",Unknown -What is the residence type of the person with the highest BMI?,Rural,category,"['bmi', 'Residence_type']","['number[double]', 'category']",Urban -What is the gender of the person with the lowest glucose level?,Female,category,"['avg_glucose_level', 'gender']","['number[double]', 'category']",Female -What are the top 3 work types among people with heart diseases?,"['Private', 'Self-employed', 'Govt_job']",list[category],"['heart_disease', 'work_type']","['number[uint8]', 'category']",['Private'] -What are the 5 most common smoking statuses among people with a stroke?,"['never smoked', 'formerly smoked', 'Unknown', 'smokes']",list[category],"['stroke', 'smoking_status']","['number[uint8]', 'category']",['Unknown'] -What are the top 4 residence types of people with hypertension?,"['Rural', 'Urban']",list[category],"['hypertension', 'Residence_type']","['number[uint8]', 'category']",['Rural'] -What are the top 2 work types among people who have never married?,"['Private', 'children']",list[category],"['ever_married', 'work_type']","['category', 'category']","['children', 'Self-employed']" -What are the top 5 ages of people with strokes?,"[82.0, 82.0, 82.0, 82.0, 82.0]",list[number],"['stroke', 'age']","['number[uint8]', 'number[UInt8]']",[43.0] -What are the 3 lowest BMIs among people who smoke?,"[15.7, 16.7, 16.7]",list[number],"['smoking_status', 'bmi']","['category', 'number[double]']","[26.8, 34.1, 35.6]" -What are the top 4 glucose levels of people who have heart diseases?,"[271.74, 254.63, 254.6, 252.72]",list[number],"['heart_disease', 'avg_glucose_level']","['number[uint8]', 'number[double]']",[62.2] -What are the 6 highest ages of people who have never married?,"[82.0, 82.0, 82.0, 82.0, 82.0, 82.0]",list[number],"['ever_married', 'age']","['category', 'number[UInt8]']","[51.0, 42.0, 31.0, 26.0, 10.0, 8.0]" diff --git a/data/038_Stroke/sample.csv b/data/038_Stroke/sample.csv deleted file mode 100644 index 4aaa581e7151882f648b98dd19a854ca4f5be817..0000000000000000000000000000000000000000 --- a/data/038_Stroke/sample.csv +++ /dev/null @@ -1,26 +0,0 @@ -smoking_status,heart_disease,gender,age,work_type,stroke,bmi,Residence_type,ever_married,avg_glucose_level,hypertension -Unknown,0,Male,31.0,Self-employed,0,23.0,Rural,No,64.85,0 -never smoked,0,Male,40.0,Self-employed,0,28.3,Rural,Yes,65.29,0 -smokes,0,Male,69.0,Private,0,26.8,Rural,Yes,101.52,0 -Unknown,0,Male,7.0,Self-employed,0,18.9,Rural,No,64.06,0 -formerly smoked,1,Male,57.0,Private,0,31.0,Rural,Yes,62.2,0 -Unknown,0,Male,43.0,Self-employed,1,45.9,Urban,Yes,143.43,0 -Unknown,0,Male,10.0,children,0,14.3,Urban,No,87.09,0 -smokes,0,Male,26.0,Private,0,35.6,Urban,No,85.92,0 -Unknown,0,Female,8.0,children,0,22.5,Urban,No,74.42,0 -never smoked,0,Female,79.0,Self-employed,0,19.5,Rural,Yes,76.64,1 -never smoked,0,Female,75.0,Govt_job,0,27.2,Rural,Yes,94.77,0 -never smoked,0,Female,79.0,Self-employed,0,,Rural,Yes,92.43,1 -smokes,0,Female,56.0,Private,0,34.1,Rural,Yes,97.37,1 -Unknown,0,Female,1.48,children,0,18.5,Rural,No,55.51,0 -never smoked,0,Female,37.0,Govt_job,0,27.4,Rural,Yes,67.07,0 -Unknown,0,Female,3.0,children,0,19.9,Urban,No,82.91,0 -Unknown,0,Female,60.0,Private,0,36.4,Rural,Yes,62.78,0 -never smoked,0,Female,51.0,Govt_job,0,20.9,Rural,No,116.14,0 -Unknown,0,Female,42.0,Private,0,27.7,Rural,No,139.77,0 -Unknown,0,Female,3.0,children,0,22.2,Rural,No,97.31,0 -,,,,,,,,,, -,,,,,,,,,, -,,,,,,,,,, -,,,,,,,,,, -,71.79625,,,,,,,,, diff --git a/data/039_Happy/qa.csv b/data/039_Happy/qa.csv deleted file mode 100644 index a6062157bffe2e54ca53d6b375483237dcc3c544..0000000000000000000000000000000000000000 --- a/data/039_Happy/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -Are there any reflections with more than 10 sentences?,True,['num_sentence'],boolean,['number[uint8]'],False -Is there a reflection from 'USA' with over 5 sentences?,True,"['country', 'num_sentence']",boolean,"['category', 'number[uint8]']",False -Are there any reflections categorized as 'affection'?,True,['predicted_category'],boolean,['category'],True -Are there any married individuals who reflected on 'exercise'?,True,"['marital', 'predicted_category']",boolean,"['category', 'category']",False -How many unique reflection periods are there?,2,['reflection_period'],number,['category'],2 -"On average, how many sentences are there in the reflections?",1.3407668971005122,['num_sentence'],number,['number[uint8]'],1.05 -What's the highest age of an individual in the dataset?,233.0,['age'],number,['number[UInt8]'],54.0 -How many reflections are from 'IND'?,16729,['country'],number,['category'],2 -Which country has the highest average number of sentences in their reflections?,UKR,"['country', 'num_sentence']",category,"['category', 'number[uint8]']",USA -Which gender has the most reflections categorized as 'affection'?,f,"['gender', 'predicted_category']",category,"['category', 'category']",f -From which country is the oldest individual who reflected?,USA,"['country', 'age']",category,"['category', 'number[UInt8]']",USA -Which marital status has the most reflections on 'bonding'?,single,"['marital', 'predicted_category']",category,"['category', 'category']",single -Which are the top 3 countries with the highest average number of sentences in their reflections?,"['UKR', 'CRI', 'HKG']","['country', 'num_sentence']",list[category],"['category', 'number[uint8]']","['USA', 'IND']" -List the 4 most common predicted categories in the dataset.,"['affection', 'achievement', 'enjoy_the_moment', 'bonding']",['predicted_category'],list[category],['category'],"['affection', 'achievement', 'enjoy_the_moment', 'nature']" -Which 5 countries have the youngest average age of reflectors?,"['KAZ', 'ALB', 'LKA', 'MAR', 'SLV']","['country', 'age']",list[category],"['category', 'number[UInt8]']","['IND', 'USA']" -List 2 genders with the most number of reflections.,"['m', 'f']",['gender'],list[category],['category'],"['f', 'm']" -What are the top 4 reflection IDs with the most number of sentences?,"[455, 455, 508, 455]","['wid', 'num_sentence']",list[number],"['number[uint16]', 'number[uint8]']","[5032, 8129, 2634, 645]" -List the 3 highest ages in the dataset.,"[233.0, 233.0, 233.0]",['age'],list[number],['number[UInt8]'],"[54.0, 49.0, 48.0]" -Which 5 reflection IDs have the least number of sentences?,"[2053, 2, 1936, 6227, 45]","['wid', 'num_sentence']",list[number],"['number[uint16]', 'number[uint8]']","[8129, 2634, 645, 783, 13285]" -List the age of the 6 youngest individuals in the dataset.,"[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]",['age'],list[number],['number[UInt8]'],"[22.0, 22.0, 24.0, 24.0, 24.0, 26.0]" diff --git a/data/039_Happy/sample.csv b/data/039_Happy/sample.csv deleted file mode 100644 index 087ca31c5f46577b49f16fe2b7ea028f308ac71a..0000000000000000000000000000000000000000 --- a/data/039_Happy/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -wid,gender,age,marital,predicted_category,reflection_period,num_sentence,country -8129,f,54.0,single,affection,24h,1,USA -2634,f,46.0,single,achievement,3m,1,USA -645,f,26.0,married,affection,24h,1,USA -783,f,27.0,single,affection,3m,1,USA -13285,f,24.0,single,enjoy_the_moment,3m,1,USA -3133,f,32.0,single,nature,24h,1,USA -663,m,32.0,married,enjoy_the_moment,24h,1,USA -335,f,22.0,single,achievement,24h,1,IND -608,m,24.0,single,achievement,24h,1,IND -546,m,48.0,single,affection,24h,1,USA -5032,f,24.0,single,achievement,24h,2,USA -2948,m,29.0,single,nature,24h,1,USA -5793,f,44.0,married,achievement,24h,1,USA -562,f,29.0,married,affection,3m,1,USA -6964,f,42.0,married,affection,24h,1,USA -1397,m,22.0,single,bonding,3m,1,USA -1723,f,34.0,married,leisure,24h,1,USA -1164,m,35.0,single,achievement,24h,1,USA -8940,m,49.0,married,affection,3m,1,USA -408,f,31.0,single,enjoy_the_moment,24h,1,USA diff --git a/data/040_Speed/qa.csv b/data/040_Speed/qa.csv deleted file mode 100644 index 372ae8d4c5eb340898f3b26c2ea6d00a0c1d68a4..0000000000000000000000000000000000000000 --- a/data/040_Speed/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the youngest participant has met their match?,False,boolean,"['age', 'match']","['number[UInt8]', 'number[uint8]']",False -Do all people who got a match have the same race as their partner?,False,boolean,"['match', 'samerace']","['number[uint8]', 'number[uint8]']",False -Are all participants who expected more than 5 matches male?,True,boolean,"['expected_num_matches', 'gender']","['number[UInt8]', 'category']",True -Is the average age of participants who got a match higher than those who didn't?,False,boolean,"['match', 'age']","['number[uint8]', 'number[UInt8]']",True -How many participants had a match?,1380,number,['match'],['number[uint8]'],4 -What is the average age of participants who have the same race as their partner?,26.390236506973924,number,"['samerace', 'age']","['number[uint8]', 'number[UInt8]']",28.0 -What is the highest number of matches expected by any participant in the dataset?,18.0,number,['expected_num_matches'],['number[UInt8]'],9.0 -How many unique 'race' categories are there in the dataset?,5,number,['race'],['category'],3 -What is the most common race among participants who got a match?,european/caucasian-american,category,"['match', 'race']","['number[uint8]', 'category']",european/caucasian-american -What is the gender of the youngest participant in the dataset?,male,category,"['age', 'gender']","['number[UInt8]', 'category']",female -What is the race of the participant with the highest number of expected matches?,other,category,"['expected_num_matches', 'race']","['number[UInt8]', 'category']",asian/pacific islander/asian-american -What is the wave of the participant with the youngest age?,5,category,"['age', 'wave']","['number[UInt8]', 'number[uint8]']",13 -What are the top 3 waves among people who got a match?,"[21, 4, 11]",list[category],"['match', 'wave']","['number[uint8]', 'number[uint8]']","[21, 7, 7]" -What are the 5 most common races among people who expected more than 5 matches?,"['european/caucasian-american', 'asian/pacific islander/asian-american', 'black/african american', 'latino/hispanic american', 'other']",list[category],"['expected_num_matches', 'race']","['number[UInt8]', 'category']","['european/caucasian-american', 'asian/pacific islander/asian-american']" -What are the top 4 waves of people who have the same race as their partner?,"[15, 9, 21, 11]",list[category],"['samerace', 'wave']","['number[uint8]', 'number[uint8]']","[7, 4, 15, 11]" -What are the top 2 genders among people who expected no match?,"['female', 'male']",list[category],"['expected_num_matches', 'gender']","['number[UInt8]', 'category']",['female'] -What are the top 5 ages of participants who got a match?,"[42.0, 42.0, 42.0, 42.0, 39.0]",list[number],"['match', 'age']","['number[uint8]', 'number[UInt8]']","[31.0, 28.0, 27.0, 23.0]" -What are the 3 lowest numbers of matches expected by people who got a match?,"[0.0, 0.0, 0.0]",list[number],"['match', 'expected_num_matches']","['number[uint8]', 'number[UInt8]']","[1.0, 1.0, 1.0]" -What are the top 4 ages of people who have the same race as their partner?,"[55.0, 55.0, 55.0, 42.0]",list[number],"['samerace', 'age']","['number[uint8]', 'number[UInt8]']","[34.0, 31.0, 28.0, 24.0]" -What are the 6 youngest ages of participants who expected no match?,"[18.0, 18.0, 18.0, 18.0, 18.0, 18.0]",list[number],"['expected_num_matches', 'age']","['number[UInt8]', 'number[UInt8]']",[23.0] diff --git a/data/040_Speed/sample.csv b/data/040_Speed/sample.csv deleted file mode 100644 index f799aa6ef152dd44b334912f1867cfd3432eff8d..0000000000000000000000000000000000000000 --- a/data/040_Speed/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -expected_num_matches,gender,age,race,wave,samerace,match -2.0,male,27.0,european/caucasian-american,12,0,0 -1.0,male,24.0,asian/pacific islander/asian-american,21,0,0 -,male,28.0,european/caucasian-american,7,0,1 -4.0,male,23.0,asian/pacific islander/asian-american,14,0,0 -7.0,male,30.0,european/caucasian-american,14,0,0 -1.0,female,31.0,european/caucasian-american,7,1,1 -1.0,female,23.0,latino/hispanic american,4,1,1 -2.0,female,27.0,european/caucasian-american,15,0,0 -5.0,male,24.0,european/caucasian-american,15,1,0 -4.0,female,28.0,european/caucasian-american,11,1,0 -,female,35.0,asian/pacific islander/asian-american,14,0,0 -2.0,female,22.0,asian/pacific islander/asian-american,13,0,0 -0.0,female,23.0,european/caucasian-american,13,0,0 -3.0,male,25.0,european/caucasian-american,21,0,0 -2.0,female,22.0,asian/pacific islander/asian-american,12,0,0 -4.0,female,34.0,european/caucasian-american,12,1,0 -4.0,female,27.0,asian/pacific islander/asian-american,21,0,1 -2.0,female,29.0,latino/hispanic american,21,0,0 -9.0,male,22.0,asian/pacific islander/asian-american,11,0,0 -1.0,male,31.0,asian/pacific islander/asian-american,19,0,0 diff --git a/data/041_Airline/qa.csv b/data/041_Airline/qa.csv deleted file mode 100644 index ba0e5dfd10f6da18534a0d0e71e2ee857e5ee87a..0000000000000000000000000000000000000000 --- a/data/041_Airline/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -Are there any tweets with more than 10 retweets?,True,['retweet_count'],boolean,['number[uint8]'],False -Is there a negative sentiment tweet from 'United' airline?,True,"['airline', 'airline_sentiment']",boolean,"['category', 'category']",True -Are there any tweets categorized with 'Late Flight' as the negative reason?,True,['negativereason'],boolean,['category'],True -Are there any tweets from 'Eastern Time (US & Canada)' timezone with positive sentiment?,True,"['user_timezone', 'airline_sentiment']",boolean,"['category', 'category']",True -How many unique user timezones are there in the dataset?,85,['user_timezone'],number,['category'],6 -"On average, what is the sentiment confidence of the tweets?",0.9001688524590163,['airline_sentiment_confidence'],number,['number[double]'],0.887105 -What's the maximum number of retweets a tweet has received?,44,['retweet_count'],number,['number[uint8]'],1 -How many tweets are from 'Virgin America' airline?,504,['airline'],number,['category'],0 -Which airline has the highest average sentiment confidence?,US Airways,"['airline', 'airline_sentiment_confidence']",category,"['category', 'number[double]']",American -Which negative reason is most commonly associated with 'American' airline?,Customer Service Issue,"['airline', 'negativereason']",category,"['category', 'category']",Lost Luggage -From which timezone is the tweet with the highest sentiment confidence?,Eastern Time (US & Canada),"['user_timezone', 'airline_sentiment_confidence']",category,"['category', 'number[double]']",Eastern Time (US & Canada) -Which airline has the most tweets with negative sentiment?,United,"['airline', 'airline_sentiment']",category,"['category', 'category']",Delta -Which are the top 3 airlines with the highest average sentiment confidence?,"['US Airways', 'American', 'United']","['airline', 'airline_sentiment_confidence']",list[category],"['category', 'number[double]']","['American', 'Delta', 'United']" -List the 4 most common negative reasons in the dataset.,"['Customer Service Issue', 'Late Flight', 'Can't Tell', 'Cancelled Flight']",'negativereason',list[category],['category'],"[""Can't Tell"", 'Cancelled Flight', 'Customer Service Issue', 'Late Flight']" -Which 5 user timezones have the most number of tweets?,"['Eastern Time (US & Canada)', 'Central Time (US & Canada)', 'Pacific Time (US & Canada)', 'Quito', 'Atlantic Time (Canada)']",['user_timezone'],list[category],['category'],"['Eastern Time (US & Canada)', 'Pacific Time (US & Canada)', 'Alaska', 'Atlantic Time (Canada)', 'Amsterdam']" -List 2 airlines with the least number of tweets in the dataset.,"['Virgin America', 'Delta']",['airline'],list[category],['category'],"['American', 'United']" -What are the top 4 tweet IDs with the highest sentiment confidence?,"[570306133677760513, 570301031407624196, 570300817074462722, 570300767074181121]","['tweet_id', 'airline_sentiment_confidence']",list[number],"['number[int64]', 'number[double]']","[569731104070115329, 569263373092823040, 568818669024907264, 567775864679456768]" -List the 3 highest retweet counts in the dataset.,"[44, 32, 31]",['retweet_count'],list[number],['number[uint8]'],"[1, 0, 0]" -Which 5 tweet IDs have the lowest sentiment confidence?,"[569972097453137920, 568092537786748928, 568028183267639297, 568993773277069312, 569227372223811584]","['tweet_id', 'airline_sentiment_confidence']",list[number],"['number[int64]', 'number[double]']","[569332237138841600, 568975192615223296, 568526521910079488, 569184833361936387, 568884344221081600]" -List the sentiment confidence of the 6 most retweeted tweets in the dataset.,"[1.0, 1.0, 1.0, 0.6593, 1.0, 0.6940000000000001]","['airline_sentiment_confidence', 'retweet_count']",list[number],"['number[double]', 'number[uint8]']","[1.0, 1.0, 1.0, 1.0, 0.6625, 1.0]" diff --git a/data/041_Airline/sample.csv b/data/041_Airline/sample.csv deleted file mode 100644 index 4b6a646a01e1b8e44a6c47801595016a47bfb434..0000000000000000000000000000000000000000 --- a/data/041_Airline/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -retweet_count,negativereason,user_timezone,airline_sentiment_confidence,tweet_id,airline,airline_sentiment -1,,Eastern Time (US & Canada),1.0,569731104070115329,Southwest,positive -0,Cancelled Flight,,1.0,569263373092823040,US Airways,negative -0,Late Flight,Atlantic Time (Canada),1.0,568818669024907264,Delta,negative -0,,Alaska,1.0,567775864679456768,Delta,neutral -0,Customer Service Issue,,0.6625,568526521910079488,Delta,negative -0,Can't Tell,,1.0,570008443626647552,United,negative -0,Cancelled Flight,Alaska,1.0,568622671287566336,United,negative -0,,Amsterdam,1.0,569076569983086592,Delta,neutral -0,Can't Tell,Eastern Time (US & Canada),0.6869,568884344221081600,United,negative -0,,Pacific Time (US & Canada),1.0,570119853312311296,US Airways,positive -0,Customer Service Issue,,1.0,570303720308809728,US Airways,negative -0,,Eastern Time (US & Canada),0.6843,569184833361936387,Southwest,neutral -0,,Eastern Time (US & Canada),1.0,568572506300243968,Southwest,positive -0,,,0.6535,568975192615223296,Southwest,neutral -0,,Eastern Time (US & Canada),0.3502,569332237138841600,US Airways,positive -0,Bad Flight,Quito,1.0,569346960181870592,US Airways,negative -0,Lost Luggage,Eastern Time (US & Canada),1.0,570240246719381504,American,negative -0,,Pacific Time (US & Canada),0.7047,569994045205803009,Southwest,positive -0,Can't Tell,,1.0,569587048811761664,Delta,negative -0,,Pacific Time (US & Canada),1.0,568944962311626752,Delta,neutral diff --git a/data/042_Predict/qa.csv b/data/042_Predict/qa.csv deleted file mode 100644 index 8acf94f8d11d387f1b44db8830dbb63782bf1576..0000000000000000000000000000000000000000 --- a/data/042_Predict/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the student with the highest number of absences is from a rural area?,True,boolean,"['absences', 'address']","['number[uint8]', 'category']",False -Are all students who are in a romantic relationship older than 17?,False,boolean,"['romantic', 'age']","['category', 'number[uint8]']",False -Is the average final grade (G3) of students who consume a lot of alcohol (Dalc > 2) lower than those who don't?,True,boolean,"['Dalc', 'G3']","['number[uint8]', 'number[uint8]']",False -Do all students who have both parents at home (Pstatus = 'T') have more than 3 free time after school (freetime > 3)?,False,boolean,"['Pstatus', 'freetime']","['category', 'number[uint8]']",False -How many students have their mother's education (Medu) level above 3?,131,number,['Medu'],['number[uint8]'],3 -What is the average age of students who want to take higher education (higher = 'yes')?,16.634666666666668,number,"['higher', 'age']","['category', 'number[uint8]']",16.944444444444443 -What is the highest number of absences among students?,75,number,['absences'],['number[uint8]'],18 -How many unique schools are there in the dataset?,2,number,['school'],['category'],2 -What is the most common mother's job among students who want to take higher education?,other,category,"['higher', 'Mjob']","['category', 'category']",other -What is the gender of the student with the highest final grade (G3)?,M,category,"['G3', 'sex']","['number[uint8]', 'category']",M -What is the school of the student with the highest number of absences?,GP,category,"['absences', 'school']","['number[uint8]', 'category']",GP -What is the family size of the student with the highest final grade (G3)?,GT3,category,"['G3', 'famsize']","['number[uint8]', 'category']",GT3 -What are the top 3 reasons for choosing a school among students who want to take higher education?,"['course', 'home', 'reputation']",list[category],"['higher', 'reason']","['category', 'category']","['course', 'home', 'reputation']" -What are the 5 most common mother's jobs among students with a final grade above 10?,"['other', 'services', 'teacher', 'health', 'at_home']",list[category],"['G3', 'Mjob']","['number[uint8]', 'category']","['other', 'at_home', 'services']" -What are the top 4 schools among students with absences above 10?,"['GP', 'MS']",list[category],"['absences', 'school']","['number[uint8]', 'category']","['MS', 'GP']" -What are the top 2 family relations among students with a final grade below 10?,"[4, 5]",list[category],"['G3', 'famrel']","['number[uint8]', 'number[uint8]']","[4, 5]" -What are the top 5 ages of students who want to take higher education?,"[16, 17, 15, 18, 19]",list[number],"['higher', 'age']","['category', 'number[uint8]']","[18, 15, 16, 17, 20]" -What are the 3 lowest final grades of students who have more than 10 absences?,"[4, 14, 17]",list[number],"['absences', 'G3']","['number[uint8]', 'number[uint8]']","[6, 9]" -What are the top 4 ages of students whose mother's education level is above 3?,"[15, 16, 18, 17]",list[number],"['Medu', 'age']","['number[uint8]', 'number[uint8]']","[15, 17]" -What are the 6 lowest final grades of students who don't want to take higher education?,"[8, 10, 12, 9, 7, 13]",list[number],"['higher', 'G3']","['category', 'number[uint8]']","[10, 12]" diff --git a/data/042_Predict/sample.csv b/data/042_Predict/sample.csv deleted file mode 100644 index 21e29524812ca0ca08ae8d78ec0a24ed7a625be3..0000000000000000000000000000000000000000 --- a/data/042_Predict/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -famrel,sex,romantic,Mjob,famsize,age,school,Pstatus,absences,address,Dalc,freetime,Medu,G3,reason,higher -4,M,no,other,GT3,17,GP,T,2,U,1,5,2,10,home,no -4,M,yes,at_home,LE3,18,MS,T,3,R,2,3,1,12,other,no -4,M,yes,other,LE3,18,GP,T,8,R,1,3,3,5,course,yes -5,F,yes,other,GT3,16,GP,A,8,U,1,3,2,10,other,yes -5,M,no,services,LE3,20,MS,A,11,U,4,5,2,9,course,yes -3,M,no,other,GT3,18,GP,T,0,U,5,3,2,13,home,yes -4,M,no,services,GT3,15,GP,T,2,U,1,3,4,18,course,yes -3,M,no,other,LE3,16,GP,T,18,U,1,4,1,6,home,yes -5,F,no,services,GT3,18,GP,T,0,U,1,3,2,0,course,yes -4,M,no,other,GT3,15,GP,T,2,U,1,4,4,14,reputation,yes -5,M,no,other,GT3,15,GP,T,0,U,1,5,3,15,home,yes -4,F,yes,teacher,GT3,17,GP,T,6,R,1,4,4,7,reputation,yes -3,F,no,services,GT3,17,GP,T,1,U,2,4,3,15,course,yes -4,M,no,services,LE3,18,MS,T,0,R,3,4,3,10,course,yes -5,M,no,other,GT3,18,GP,T,8,U,1,2,2,14,home,yes -4,M,no,at_home,GT3,15,GP,T,2,R,1,4,2,8,course,yes -5,F,yes,other,GT3,16,GP,T,0,U,1,4,2,8,home,yes -4,F,no,other,LE3,18,GP,T,2,U,1,4,1,11,home,yes -3,F,no,at_home,LE3,17,GP,T,0,U,2,3,0,15,home,yes -4,F,no,at_home,GT3,18,GP,T,0,R,1,4,2,0,course,yes diff --git a/data/043_Predict/qa.csv b/data/043_Predict/qa.csv deleted file mode 100644 index d208836042c6be0690d36d7feea72db9c898f9eb..0000000000000000000000000000000000000000 --- a/data/043_Predict/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the oldest company (based on approval year) is from an urban (UrbanRural=1) area?,False,boolean,"['ApprovalFY', 'UrbanRural']","['number[uint16]', 'number[uint8]']",False -Are all companies with more than 10 employees located in an urban (UrbanRural=1) area?,False,boolean,"['NoEmp', 'UrbanRural']","['number[uint16]', 'number[uint8]']",True -"Are all companies with a default amount greater than $250,000 in the food sector?",False,boolean,"['default_amount', 'Sector']","['number[uint32]', 'category']",False -Is the average number of retained jobs higher for companies located in urban (UrbanRural=1) areas than those in rural (UrbanRural=0) areas?,True,boolean,"['UrbanRural', 'RetainedJob']","['number[uint8]', 'number[uint16]']",True -How many companies have a franchise code of 1?,57340,number,['FranchiseCode'],['number[uint32]'],14 -What is the average disbursement gross for companies in the retail sector?,164636.4123068934,number,"['Sector', 'DisbursementGross']","['category', 'number[uint32]']", -What is the highest approval year in the dataset?,2010,number,['ApprovalFY'],['number[uint16]'],2008 -How many unique sectors are there in the dataset?,20,number,['Sector'],['category'],8 -What is the most common sector among companies with a franchise code of 1?,Retail trade,category,"['FranchiseCode', 'Sector']","['number[uint32]', 'category']",Other services (except public administration) -What is the state of the company with the highest disbursement gross?,ME,category,"['DisbursementGross', 'State']","['number[uint32]', 'category']",FL -What is the bank for the company with the highest default amount?,COMMUNITY BANK & TRUST,category,"['default_amount', 'Bank']","['number[uint32]', 'category']","READYCAP LENDING, LLC" -What is the sector of the company with the most number of employees?,Health care and social assistance,category,"['NoEmp', 'Sector']","['number[uint16]', 'category']",Construction -What are the top 3 sectors among companies with a franchise code of 1?,"['Retail trade', 'Other services (except public administration)', 'Manufacturing']",list[category],"['FranchiseCode', 'Sector']","['number[uint32]', 'category']","['Other services (except public administration)', 'Construction', 'Manufacturing']" -What are the 5 most common banks among companies with an approval year earlier than 2000?,"['WELLS FARGO BANK NATL ASSOC', 'BANK OF AMERICA NATL ASSOC', 'U.S. BANK NATIONAL ASSOCIATION', 'JPMORGAN CHASE BANK NATL ASSOC', 'PNC BANK, NATIONAL ASSOCIATION']",list[category],"['ApprovalFY', 'Bank']","['number[uint16]', 'category']","['TRUSTMARK NATIONAL BANK', 'KEYBANK NATIONAL ASSOCIATION']" -"What are the top 4 states among companies with a disbursement gross greater than $1,000,000?","['CA', 'TX', 'FL', 'AZ']",list[category],"['DisbursementGross', 'State']","['number[uint32]', 'category']",[] -"What are the top 2 sectors among companies with a default amount greater than $500,000?","['Retail trade', 'Accommodation and food services']",list[category],"['default_amount', 'Sector']","['number[uint32]', 'category']",[] -What are the top 5 approval years for companies in the retail sector?,"[2005, 2004, 2006, 2007, 2003]",list[number],"['Sector', 'ApprovalFY']","['category', 'number[uint16]']",[] -What are the 3 highest disbursement gross for companies with a franchise code of 1?,"[50000.0, 100000.0, 10000.0]",list[number],"['FranchiseCode', 'DisbursementGross']","['number[uint32]', 'number[uint32]']","[249719.0, 136000.0, 125000.0]" -What are the top 4 approval years for companies in the state of CA?,"[2007, 2004, 2006, 2003]",list[number],"['State', 'ApprovalFY']","['category', 'number[uint16]']","[2006, 2004, 2003]" -What are the 6 highest default amounts among companies in the state of NY?,"[0.0, 50000.0, 25000.0, 100000.0, 35000.0, 10000.0]",list[number],"['State', 'default_amount']","['category', 'number[uint32]']","[49996.0, 12942.0]" diff --git a/data/043_Predict/sample.csv b/data/043_Predict/sample.csv deleted file mode 100644 index 851bcda2294884318c6c767398d7f18fd5f9dab3..0000000000000000000000000000000000000000 --- a/data/043_Predict/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -NoEmp,DisbursementGross,UrbanRural,Sector,ApprovalFY,RetainedJob,State,FranchiseCode,default_amount,Bank -4,39493.0,1,Manufacturing,2001,4,RI,1,0.0,CITIZENS BANK NATL ASSOC -2,123432.0,1,Wholesale trade,2006,3,CA,1,29702.0,WELLS FARGO BANK NATL ASSOC -10,12500.0,1,Manufacturing,2008,10,TN,0,11941.0,"SUPERIOR FINANCIAL GROUP, LLC" -3,40000.0,1,Other services (except public administration),2004,3,IA,1,0.0,WELLS FARGO BANK NATL ASSOC -0,258000.0,1,Other services (except public administration),2007,0,FL,0,257717.0,"READYCAP LENDING, LLC" -5,41000.0,1,Other services (except public administration),2003,0,MN,1,0.0,WELLS FARGO BANK NATL ASSOC -2,400000.0,1,Manufacturing,2007,2,FL,0,0.0,FIFTH THIRD BANK -2,64000.0,1,Information,2004,2,CA,1,0.0,BANK OF AMERICA NATL ASSOC -8,249719.0,0,Other services (except public administration),1999,0,MS,1,0.0,TRUSTMARK NATIONAL BANK -2,136000.0,1,Other services (except public administration),2002,0,MD,1,0.0,MANUFACTURERS & TRADERS TR CO -2,55000.0,0,Construction,1997,0,WA,1,0.0,KEYBANK NATIONAL ASSOCIATION -2,15000.0,1,Accommodation and food services,2006,2,NY,1,12942.0,BBCN BANK -5,50000.0,1,Manufacturing,2005,5,UT,1,0.0,ZIONS FIRST NATIONAL BANK -4,150000.0,1,Accommodation and food services,2001,0,MD,50140,0.0,BRANCH BK. & TR CO -18,125000.0,1,Construction,2004,18,NJ,1,0.0,CAPITAL ONE NATL ASSOC -2,130453.0,1,Information,2008,10,NY,0,49996.0,JPMORGAN CHASE BANK NATL ASSOC -1,112902.0,1,Retail trade,2004,1,RI,1,0.0,CITIZENS BANK NATL ASSOC -1,25000.0,1,Construction,2003,0,ME,1,0.0,BANGOR SAVINGS BANK -4,25000.0,1,Accommodation and food services,2003,4,CA,1,0.0,BANK OF AMERICA NATL ASSOC -3,34500.0,1,Administrative and support and waste management and remediation services,2007,3,MA,0,14500.0,BANK OF AMERICA NATL ASSOC diff --git a/data/044_IMDb/qa.csv b/data/044_IMDb/qa.csv deleted file mode 100644 index 6c8dd462804a5d29657e4b01b390a14afe3cdc9f..0000000000000000000000000000000000000000 --- a/data/044_IMDb/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the oldest movie (based on year) in the drama genre is from the USA?,False,boolean,"['year', 'genre', 'country']","['number[UInt16]', 'category', 'category']",False -"Are all movies from the USA, with more than 80 minutes duration in the drama genre, in English?",False,boolean,"['duration', 'genre', 'country', 'language']","['number[uint16]', 'category', 'category', 'category']",False -Are all movies with votes greater than 90 from the USA in English?,True,boolean,"['votes', 'country', 'language']","['number[UInt8]', 'category', 'category']",False -Is the average duration of English language movies from the USA longer than those from non-USA countries?,False,boolean,"['country', 'language', 'duration']","['category', 'category', 'number[uint16]']",False -"How many movies from the USA, in the drama genre, have a metascore of 100?",6,number,"['metascore', 'genre', 'country']","['number[UInt8]', 'category', 'category']",0 -"What is the average duration for movies in the drama genre, from the USA, in English?",96.07926963408374,number,"['genre', 'country', 'language', 'duration']","['category', 'category', 'category', 'number[uint16]']",100.0 -What is the latest year for English language movies from the USA in the dataset?,2020.0,number,"['country', 'language', 'year']","['category', 'category', 'number[UInt16]']",1994 -How many unique languages are there in the dataset for movies from the USA?,650,number,"['country', 'language']","['category', 'category']",3 -What is the most common genre among English language movies from the USA with a metascore of 100?,"Adventure, Family, Fantasy",category,"['metascore', 'country', 'language', 'genre']","['number[UInt8]', 'category', 'category', 'category']",0 -What is the country of the English language movie with the longest duration?,Argentina,category,"['duration', 'language', 'country']","['number[uint16]', 'category', 'category']",USA -What is the language of the movie from the USA with the highest metascore?,English,category,"['metascore', 'country', 'language']","['number[UInt8]', 'category', 'category']",0 -What is the genre of the English language movie from the USA with the most votes?,Drama,category,"['votes', 'country', 'language', 'genre']","['number[uint32]', 'category', 'category', 'category']","Romance, Western" -What are the top 3 genres among English language movies from the USA with a metascore of 100?,"['Adventure, Family, Fantasy', 'Drama, Mystery', 'Drama, Romance, War']",list[category],"['metascore', 'country', 'language', 'genre']","['number[UInt8]', 'category', 'category', 'category']",[] -What are the 5 most common countries among English language movies with a year earlier than 2000?,"['USA', 'UK', 'Canada', 'UK, USA', 'Australia']",list[category],"['year', 'language', 'country']","['number[UInt16]', 'category', 'category']",['USA'] -What are the top 4 languages among movies from the USA with a duration greater than 180 minutes?,"['English', 'English, Spanish', 'English, Russian', 'English, Hungarian']",list[category],"['duration', 'country', 'language']","['number[uint16]', 'category', 'category']",[] -What are the top 2 genres among English language movies from the USA with a metascore greater than 90?,"['Drama', 'Crime, Drama']",list[category],"['metascore', 'country', 'language', 'genre']","['number[UInt8]', 'category', 'category', 'category']",[] -What are the top 5 years for English language movies from the USA in the drama genre?,"[2017.0, 2016.0, 2015.0, 2013.0, 2018.0]",list[number],"['genre', 'country', 'language', 'year']","['category', 'category', 'category', 'number[UInt16]']",[1994] -What are the 3 longest durations for English language movies from the USA with a metascore of 100?,"[102, 119, 112]",list[number],"['metascore', 'country', 'language', 'duration']","['number[UInt8]', 'category', 'category', 'number[uint16]']",[] -What are the four years with more English language movies from the USA?,"[2017.0, 2016.0, 2018.0, 2013.0]",list[number],"['country', 'language', 'year']","['category', 'category', 'number[UInt16]']","[1994, 1942, 1951, 1964]" -Can you show the 6 highest metascores among English language movies from the USA?,"[49.0, 55.0, 57.0, 48.0, 54.0, 52.0]",list[number],"['country', 'language', 'metascore']","['category', 'category', 'number[UInt8]']",[] diff --git a/data/044_IMDb/sample.csv b/data/044_IMDb/sample.csv deleted file mode 100644 index 97faebde006705e4ac7ff874de07cee9689ec1a8..0000000000000000000000000000000000000000 --- a/data/044_IMDb/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -language,genre,year,votes,duration,metascore,country -"German, French",Drama,2017,363,104,,Austria -"Italian, German","Action, Drama",2010,114,88,,USA -English,"Action, Crime, Drama",1994,155,100,,USA -Thai,"Drama, Horror, Mystery",2011,1879,125,,Thailand -French,Drama,2010,324,115,,Canada -"Spanish, Arabic, French, English","Drama, Mystery, Romance",2007,4419,118,,Spain -Swedish,"Comedy, Musical, Romance",1956,148,107,,Sweden -Polish,"Comedy, Romance",2011,3873,110,,Poland -"English, French, Italian, Arabic, German, Dutch","Action, Adventure, Comedy",1966,2536,119,,UK -English,"Comedy, Music, Romance",1942,121,68,,USA -"Russian, Lithuanian","Action, Drama",2013,1406,110,,Lithuania -"Korean, English","Action, Drama, War",2010,4991,120,,South Korea -English,"Romance, Western",1951,1021,87,,USA -"English, French",Drama,1995,377,88,,USA -"Danish, Swedish, Serbian","Action, Comedy, Crime",2002,8699,95,,Denmark -English,"Drama, Music",2012,348,90,,UK -Japanese,"Action, Drama, History",1969,940,140,,Japan -English,"Action, Crime, Horror",1964,319,70,,USA -English,"Drama, Family, Music",2015,185,93,,Netherlands -"Romanian, Russian, English","Family, Fantasy, Musical",1976,1148,83,,"Romania, France, Soviet Union" diff --git a/data/045_Predict/qa.csv b/data/045_Predict/qa.csv deleted file mode 100644 index 43b2a574856851ebf5c113e2b1ec9815a1b60134..0000000000000000000000000000000000000000 --- a/data/045_Predict/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the song with the lowest popularity in the dataset is longer than 300000 ms?,True,boolean,"[popularity, duration_ms]","['number[uint8]', 'number[UInt32]']",False -Did any song released in the year 2020 in the dataset achieve the maximum popularity?,False,boolean,"[release_year, popularity]","['number[uint16]', 'number[uint8]']",False -Does the song with the longest duration also have the highest energy?,False,boolean,"[duration_ms, energy]","['number[UInt32]', 'number[double]']",False -Does the song with the highest energy also have the highest popularity?,False,boolean,"[energy, popularity]","['number[double]', 'number[uint8]']",True -How many unique artists are there in the dataset?,13056,number,[artists],['list[category]'],20 -What's the average song duration in the dataset?,228998.0798095238,number,[duration_ms],['number[UInt32]'],256381.1 -What's the maximum popularity score in the dataset?,94,number,[popularity],['number[uint8]'],59 -How many songs were released in the year with the most releases?,489,number,[release_year],['number[uint16]'],2 -Who is the artist with the highest popularity score?,['Giveon'],category,"[popularity, artists]","['number[uint8]', 'list[category]']","['Victorious Cast', 'Elizabeth Gillies', 'Ariana Grande']" -In which month was the most popular song released?,3.0,category,"[popularity, release_month]","['number[uint8]', 'number[UInt8]']",8.0 -What's the name of the longest duration song?,Monster Tunes Yearmix 2011 - Mixed by Mark Eteson,category,"[duration_ms, name]","['number[UInt32]', 'category']",Ölmez Bu Hareket / Çileli Müjde -What's the name of the song with the most energy?,Marathon (Mix Cut) - Simon O'Shine Mix,category,"[energy, name]","['number[double]', 'category']",Give It Up (feat. Elizabeth Gillies & Ariana Grande) -What are the top 5 most common artist names in the dataset?,"['Die drei ???', 'Benjamin Blümchen', 'TKKG Retro-Archiv', 'Bibi Blocksberg', 'Lata Mangeshkar']",list[category],[artists],['list[category]'],"[""['Victorious Cast', 'Elizabeth Gillies', 'Ariana Grande']"", ""['Julio Iglesias']"", ""['Ari Lasso']"", ""['Romeo Santos', 'Mala Rodríguez']"", ""['Funkmaster Flex', 'Big Kap', 'Eminem', 'Dr. Dre']""]" -What are the names of the top 3 most popular songs?,"[Heartbreak Anniversary, Good Days, Paradise (feat. Dermot Kennedy)]",list[category],"[popularity, name]","['number[uint8]', 'category']","['Give It Up (feat. Elizabeth Gillies & Ariana Grande)', 'Lächeln', 'Winter Winds']" -What are the top 3 artists who released songs with the longest durations?,"['Mark Eteson', 'Various Artists', 'Serge Reggiani']",list[category],"[duration_ms, artists]","['number[UInt32]', 'list[category]']","[""['Ozan Arif']"", ""['環球演奏團']"", ""['Andrew E.']""]" -What are the names of the top 2 songs with the most energy?,"[Marathon (Mix Cut) - Simon O'Shine Mix, Applause; Martha Tilton Returns to Stage - Live]",list[category],"[energy, name]","['number[double]', 'category']","['Give It Up (feat. Elizabeth Gillies & Ariana Grande)', 'Rahasia Perempuan']" -What are the top 5 most popular scores?,"[0, 35, 23, 1, 26]",list[number],[popularity],['number[uint8]'],"[52, 59, 27, 47, 46]" -What are the 3 longest song durations?,"[4792587.0, 4658245.0, 4585640.0]",list[number],[duration_ms],['number[UInt32]'],"[698880.0, 496600.0, 324587.0]" -What are the 4 latest release years in the dataset?,"[2021, 2021, 2021, 2021]",list[number],[release_year],['number[uint16]'],"[2020, 2017, 2013, 2012]" -What are the top 3 energy scores in the dataset?,"[1.0, 1.0, 1.0]",list[number],[energy],['number[double]'],"[0.944, 0.908, 0.822]" diff --git a/data/045_Predict/sample.csv b/data/045_Predict/sample.csv deleted file mode 100644 index 8f84438c7437e2537e83eaaf38605e56eeb8185e..0000000000000000000000000000000000000000 --- a/data/045_Predict/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -release_month,name,energy,duration_ms,release_year,artists,popularity -8.0,Give It Up (feat. Elizabeth Gillies & Ariana Grande),0.944,165147.0,2011,"['Victorious Cast', 'Elizabeth Gillies', 'Ariana Grande']",59 -8.0,Pauvres diables,0.573,178000.0,1979,['Julio Iglesias'],36 -11.0,Kaliwete,0.783,186874.0,1998,['Eraserheads'],40 -2.0,Show das Poderosas,0.694,150933.0,2013,['Anitta'],52 -10.0,Winter Winds,0.494,219680.0,2009,['Mumford & Sons'],53 -1.0,Twinkle Twinkle Little Star,0.427,247440.0,1970,"['Asha Bhosle', 'Mahendra Kapoor']",17 -1.0,Icimdeki Firtina,0.585,206720.0,2006,['Erol Evgin'],31 -1.0,Homage (Le Tombeau de Debussy),0.0276,196963.0,1937,"['Manuel de Falla', 'Julio Martinez Oyanguren']",0 -7.0,Narcos,0.446,169844.0,2020,['kizaru'],52 -8.0,Sentimiento villero,0.607,314173.0,2011,['Pibes Chorros'],41 -8.0,國際友誼歌組曲 - 演奏曲,0.782,496600.0,2001,['環球演奏團'],19 -1.0,Ölmez Bu Hareket / Çileli Müjde,0.336,698880.0,2000,['Ozan Arif'],27 -1.0,Stupid Love performed by Salbakutah,0.621,324587.0,2002,['Andrew E.'],51 -2.0,Una Moneda Le Di,0.471,255453.0,1987,['George Dalaras'],24 -4.0,תחזרי תחזרי,0.478,267947.0,2002,"['Eyal Golan', 'Arkadi Duchin']",29 -7.0,Lächeln,0.632,198821.0,2017,"['187 Strassenbande', 'Bonez MC']",57 -1.0,If I Get Locked Up (Funkmaster Flex & Big Kap Feat. Eminem and Dr. Dre),0.822,220533.0,1999,"['Funkmaster Flex', 'Big Kap', 'Eminem', 'Dr. Dre']",46 -8.0,Magia Negra (feat. Mala Rodríguez),0.805,224907.0,2012,"['Romeo Santos', 'Mala Rodríguez']",52 -,Rahasia Perempuan,0.908,210040.0,2003,['Ari Lasso'],47 -,Adam and Eve Had the Blues,0.187,194080.0,1925,['Hociel Thomas'],1 diff --git a/data/046_120/qa.csv b/data/046_120/qa.csv deleted file mode 100644 index 7d45a7b6d7f4b80270d127a50819a5952285f2c0..0000000000000000000000000000000000000000 --- a/data/046_120/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is it true that the athlete with the lowest ID is older than 30 years?,True,boolean,"[ID, Age]","['number[uint32]', 'number[UInt8]']",True -Did any athlete from the team with the most participants win two gold medals?,False,boolean,"[Team, Medal]","['category', 'category']",False -Does the athlete with the highest weight also participate in the sport with the most athletes?,False,boolean,"[Weight, Sport]","['number[UInt8]', 'category']",False -Does the athlete with the highest height also have a medal?,False,boolean,"[Height, Medal]","['number[UInt8]', 'category']",False -How many unique teams are there in the dataset?,230,number,[Team],['category'],15 -What's the average age of athletes in the dataset?,25.556898357297374,number,[Age],['number[UInt8]'],27.15 -What's the maximum weight of athletes in the dataset?,214.0,number,[Weight],['number[UInt8]'],92.0 -How many athletes participated in the year with the most participants?,2536,number,[Year],['number[uint16]'],2 -Who is the athlete with the highest weight?,Ricardo Blas Jr.,category,"[Weight, Name]","['number[UInt8]', 'category']",Martin Laumann Ylven -In which city did the athlete with the highest height participate?,London,category,"[Height, City]","['number[UInt8]', 'category']",Vancouver -What's the name of the athlete who participated in the most number of games?,Robert Tait McKenzie,category,[Name],['category'],Khalid Raghib -What's the sport of the athlete with the most medals?,Art Competitions,category,"[Medal, Sport, Name]","['category', 'category', 'category']",Rowing -What are the top 5 most common team names in the dataset?,"[United States, France, Great Britain, Italy, Germany]",list[category],[Team],['category'],"['Morocco', 'Romania', 'Germany', 'Japan', 'Italy']" -What are the names of the top 3 athletes with the highest weights?,"[Ricardo Blas Jr., Shinichi Shinohara, Emmanuel Yarborough]",list[category],"[Weight, Name]","['number[UInt8]', 'category']","['Martin Laumann Ylven', 'Juri Takayama', 'Graziano Mancinelli']" -What are the 3 teams with the most number of athletes?,"[United States, France, Great Britain]",list[category],[Team],['category'],"['Morocco', 'Romania', 'Germany']" -What are the names of the top 2 athletes who participated in the most number of games?,"[Robert Tait McKenzie, Heikki Ilmari Savolainen]",list[category],[Name],['category'],"['Khalid Raghib', 'Mrioara Trac (-Curelea)']" -What are the top five most common ages of athletes?,"[23.0, 24.0, 22.0, 25.0, 21.0]",list[number],[Age],['number[UInt8]'],"[27.0, 22.0, 21.0, 19.0, 25.0]" -What are the three highest weights of athletes?,"[214.0, 198.0, 190.0]",list[number],[Weight],['number[UInt8]'],"[92.0, 85.0, 77.0]" -What are the 4 most common years of participation?,"[1992, 1988, 2000, 1996]",list[number],[Year],['number[uint16]'],"[1912, 2000, 1956, 1964]" -What are the three most common heights of athletes?,"[180.0, 170.0, 178.0]",list[number],[Height],['number[UInt8]'],"[172.0, 175.0, 174.0]" diff --git a/data/046_120/sample.csv b/data/046_120/sample.csv deleted file mode 100644 index 89ea0bf355d423e21c8d2bbc2ce008e4ccb6b903..0000000000000000000000000000000000000000 --- a/data/046_120/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Height,ID,Age,Team,Weight,Name,Medal,City,Year,Sport -180.0,98308,22.0,Morocco,70.0,Khalid Raghib,,Barcelona,1992,Football -172.0,121701,21.0,Romania,68.0,Mrioara Trac (-Curelea),Silver,Los Angeles,1984,Rowing -190.0,133023,21.0,Norway,92.0,Martin Laumann Ylven,,Vancouver,2010,Ice Hockey -,72524,19.0,Germany,,"Wilhelm Bernhard Adolf Emil ""Willy"" Ltzow",,Stockholm,1912,Swimming -163.0,75751,20.0,Mexico,48.0,Jess Martnez Tejeda,,Atlanta,1996,Boxing -,110875,63.0,Great Britain,,Charles Walter Simpson,,London,1948,Art Competitions -,48118,25.0,France,,Robert Herold,,Berlin,1936,Gymnastics -176.0,48180,27.0,Germany,67.0,Max Herrmann,,Stockholm,1912,Athletics -175.0,109084,26.0,Pakistan,63.0,Ghulam Shabbir,,Sydney,2000,Boxing -161.0,54916,19.0,Japan,55.0,Hitomi Jinno,,Melbourne,1956,Swimming -,32533,27.0,Romania,,Constantin Enache,,Cortina d'Ampezzo,1956,Cross Country Skiing -170.0,883,31.0,Ghana,67.0,Charles Addo-Odametey,,Mexico City,1968,Football -175.0,74307,39.0,Italy,77.0,Graziano Mancinelli,,Montreal,1976,Equestrianism -160.0,113710,16.0,United States,52.0,"Janice Lee ""Janie"" Speaks (-Arnold, -Hunter)",,Tokyo,1964,Gymnastics -178.0,76707,33.0,Italy,68.0,Carlo Mattioli,,Seoul,1988,Athletics -177.0,50532,25.0,Chinese Taipei,57.0,Hsu Chih-Ling,,Sydney,2000,Taekwondo -162.0,118002,27.0,Japan,85.0,Juri Takayama,Bronze,Athina,2004,Softball -174.0,47295,29.0,Finland,63.5,Raimo Yrj Heinonen,,Tokyo,1964,Gymnastics -174.0,87092,22.0,Morocco,70.0,Abdelatif Noussir,,London,2012,Football -172.0,100411,31.0,Argentina,70.0,Luis Fernando Ribera,,Roma,1960,Modern Pentathlon diff --git a/data/047_Bank/qa.csv b/data/047_Bank/qa.csv deleted file mode 100644 index 9c33cf509c2054bc1d1cd24c5e1a18fcd93e2e4b..0000000000000000000000000000000000000000 --- a/data/047_Bank/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Does the dataset contain customers who are under the age of 18?,False,boolean,['customer_age'],['number[uint8]'],False -Are there any customers with a total transaction amount of zero?,False,boolean,['total_trans_amt'],['number[uint16]'],False -"Does the dataset include customers with a credit limit exceeding $50,000?",False,boolean,['credit_limit'],['number[UInt16]'],False -Are there any customers in the dataset who are inactive for more than 12 months?,False,boolean,['months_inactive_12_mon'],['number[uint8]'],False -What is the highest credit limit in the dataset?,34516.0,number,['credit_limit'],['number[UInt16]'],34516.0 -What is the maximum total transaction amount recorded?,18484,number,['total_trans_amt'],['number[uint16]'],5149 -What is the largest total revolving balance in the dataset?,2517,number,['total_revolving_bal'],['number[uint16]'],2517 -What is the highest customer age in the dataset?,73,number,['customer_age'],['number[uint8]'],58 -What is the most common level of education among the customers?,Graduate,category,['education_level'],['category'],Graduate -What is the most common income category of the customers?,Less than $40K,category,['income_category'],['category'],Less than $40K -Which gender is most represented among the customers?,F,category,['gender'],['category'],F -What is the most common attrition flag value?,0,category,['attrition_flag'],['category'],0 -What are the top 3 most common education levels among the customers?,"['Graduate', 'High School', 'Unknown']",list[category],['education_level'],['category'],"['Graduate', 'Unknown', 'Post-Graduate']" -Which are the 4 most frequent income categories?,"['Less than $40K', '$40K - $60K', '$80K - $120K', '$60K - $80K']",list[category],['income_category'],['category'],"['Less than $40K', 'Unknown', '$80K - $120K', '$40K - $60K']" -Which are the top 3 most frequent income categories?,"['Less than $40K', '$40K - $60K', '$80K - $120K']",list[category],['income_category'],['category'],"['Less than $40K', 'Unknown', '$80K - $120K']" -Which are the two most frequent income categories?,"['Less than $40K', '$40K - $60K']",list[category],['income_category'],['category'],"['Less than $40K', 'Unknown']" -Who are the top 5 oldest customers in the dataset?,"[73, 70, 68, 67, 67]",list[number],"['customer_age', 'id']","['number[uint8]', 'number[uint16]']","[5024, 7430, 8918, 3002, 7360]" -Who are the five customers with the highest credit limit?,"[34516.0, 34516.0, 34516.0, 34516.0, 34516.0]",list[number],"['credit_limit', 'id']","['number[UInt16]', 'number[uint16]']","[3782, 8260, 8918, 8055, 3002]" -Who are the 6 customers with the highest total transaction amount?,"[18484, 17744, 17634, 17498, 17437, 17350]",list[number],"['total_trans_amt', 'id']","['number[uint16]', 'number[uint16]']","[2000, 2418, 10093, 3782, 9612, 7951]" -Who are the top six customers with the highest total revolving balance?,"[2517, 2517, 2517, 2517, 2517, 2517]",list[number],"['total_revolving_bal', 'id']","['number[uint16]', 'number[uint16]']","[10093, 7430, 6400, 3782, 9612, 7360]" diff --git a/data/047_Bank/sample.csv b/data/047_Bank/sample.csv deleted file mode 100644 index 564172fa0ccfd3f8e5716e6b067cc0a18feb33b0..0000000000000000000000000000000000000000 --- a/data/047_Bank/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -customer_age,attrition_flag,total_trans_amt,months_inactive_12_mon,gender,id,total_revolving_bal,education_level,credit_limit,income_category -56,0,1388,1,F,3002,1111,Graduate,14915.0,Unknown -41,0,2262,3,M,3993,620,College,2647.0,$80K - $120K -51,0,5149,4,F,2000,1033,High School,1857.0,Less than $40K -57,0,1392,3,M,7430,2457,Uneducated,2472.0,$80K - $120K -49,0,4702,1,F,2418,1998,College,2548.0,Less than $40K -36,0,2853,5,F,5166,1628,Unknown,2786.0,Less than $40K -39,0,2017,2,F,196,760,High School,1578.0,Less than $40K -40,1,2709,3,F,4797,0,Graduate,1516.0,Less than $40K -54,0,1503,3,F,7360,2206,Unknown,2945.0,Less than $40K -41,0,1555,1,F,8055,0,Post-Graduate,20758.0,Unknown -53,0,4224,3,F,9612,2227,Graduate,4930.0,$40K - $60K -53,0,4390,1,F,3782,2374,Graduate,34516.0,Unknown -49,0,4423,3,F,10093,2517,Post-Graduate,3007.0,Less than $40K -38,1,2527,2,F,9106,0,Unknown,1628.0,$40K - $60K -58,1,1990,2,F,5024,0,Unknown,1508.0,Less than $40K -36,1,2039,1,M,4126,0,Unknown,1815.0,$80K - $120K -57,0,1244,1,M,8918,1472,Post-Graduate,26437.0,$120K + -47,0,1403,3,M,8260,1642,Graduate,26442.0,$80K - $120K -36,1,2021,3,F,6400,2378,Uneducated,2784.0,Less than $40K -46,0,3630,1,F,7951,2059,Graduate,9216.0,Unknown diff --git a/data/048_Data/qa.csv b/data/048_Data/qa.csv deleted file mode 100644 index e46709e33f4dd73d7bc5dcca636f73de7db77471..0000000000000000000000000000000000000000 --- a/data/048_Data/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -"Are there any job positions available in 'New York, NY'?",True,['Location'],boolean,['category'],True -Are there any job listings from companies founded before 1900?,True,['Founded'],boolean,['number[int16]'],False -Are there any job listings that require Python skills?,True,['python_yn'],boolean,['number[uint8]'],True -Is there a job listing for a 'Data Engineer' role?,True,['Job Title'],boolean,['category'],True -How many unique job titles are listed?,264,['Job Title'],number,['category'],19 -"On average, how old are the companies that have job listings?",46.591644204851754,['age'],number,['number[int16]'],1885.25 -What's the highest rating a company has received?,5.0,['Rating'],number,['number[double]'],4.7 -How many job listings are there from 'Government' type of ownership?,15,['Type of ownership'],number,['category'],0 -Which job title has the highest average salary?,"Director II, Data Science - GRM Actuarial","['Job Title', 'avg_salary']",category,"['category', 'number[double]']",Lead Big Data Engineer -Which state has the most number of job listings?, CA,['job_state'],category,['category'], CA -From which sector is the job listing with the highest salary?,Insurance,"['Sector', 'avg_salary']",category,"['category', 'number[double]']",Information Technology -Which company size has the most job listings?,1001 to 5000 employees,['Size'],category,['category'],1001 to 5000 employees -Which are the top 3 sectors with the most job listings?,"['Information Technology', 'Biotech & Pharmaceuticals', 'Business Services']",['Sector'],list[category],['category'],"['Information Technology', 'Insurance', 'Business Services']" -List the 4 most common industries in the dataset.,"['Biotech & Pharmaceuticals', 'Insurance Carriers', 'Computer Hardware & Software', 'IT Services']",['Industry'],list[category],['category'],"['Computer Hardware & Software', 'Insurance Carriers', 'Enterprise Software & Network Solutions', 'Internet']" -Which 5 states have the most number of job listings?,"[' CA', ' MA', ' NY', ' VA', ' IL']",['job_state'],list[category],['category'],"[' CA', ' MA', ' NY', ' IL', ' TN']" -List 2 company sizes with the least number of job listings.,"['1 to 50 employees', '5001 to 10000 employees']",['Size'],list[category],['category'],"['10000+ employees', 'Unknown']" -What are the top 4 average salaries for job titles in the dataset?,"[254.0, 232.5, 225.0, 205.0]","['Job Title', 'avg_salary']",list[number],"['category', 'number[double]']","[162.0, 161.5, 154.5, 150.5]" -List the year the 3 oldest companies that have job listings were founded.,"[1744, 1781, 1781]",['Founded'],list[number],['number[int16]'],"[1784, 1912, 1928]" -Which are the highest 5 ratings?,"[5.0, 5.0, 5.0, 5.0, 5.0]",['Rating'],list[number],['number[double]'],"[4.7, 4.4, 4.3]" -List the average salaries of the 6 most recent companies.,"[78.0, 88.5, 110.0, 40.5, 132.5, 20.5]","['Founded', 'avg_salary']",list[number],"['number[int16]', 'number[double]']","[122.0, 150.5, 154.5, 162.0, 59.5, 102.5]" diff --git a/data/048_Data/sample.csv b/data/048_Data/sample.csv deleted file mode 100644 index e95aa379e4b6214d457bc8a123e6edcdadd2962e..0000000000000000000000000000000000000000 --- a/data/048_Data/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Founded,Rating,Sector,Industry,age,Type of ownership,Size,avg_salary,python_yn,Location,job_state,Job Title -1999,4.4,Information Technology,Enterprise Software & Network Solutions,21,Company - Private,201 to 500 employees,100.5,1,"Cambridge, MA", MA,Data Scientist -1986,3.6,"Arts, Entertainment & Recreation",Gambling,34,Company - Private,1001 to 5000 employees,48.5,0,"Highland, CA", CA,Marketing Data Analyst -2010,3.5,Biotech & Pharmaceuticals,Biotech & Pharmaceuticals,10,Company - Private,1001 to 5000 employees,154.5,1,"Marlborough, MA", MA,Senior Scientist (Neuroscience) -2012,3.9,Information Technology,Computer Hardware & Software,8,Company - Private,201 to 500 employees,122.0,1,"Orlando, FL", FL,Senior LiDAR Data Scientist -2007,4.0,Information Technology,Internet,13,Company - Private,1001 to 5000 employees,162.0,1,"San Francisco, CA", CA,Lead Big Data Engineer -1981,4.1,Information Technology,Computer Hardware & Software,39,Company - Public,5001 to 10000 employees,107.0,1,"Fremont, CA", CA,Software Engineer (Data Scientist/Software Engineer) - SISW - MG -2011,4.3,Information Technology,Enterprise Software & Network Solutions,9,Company - Private,201 to 500 employees,150.5,1,"Mountain View, CA", CA,Customer Data Scientist/Sales Engineer (Bay -1979,3.4,Non-Profit,Social Assistance,41,Nonprofit Organization,501 to 1000 employees,44.5,0,"Seattle, WA", WA,Foundational Community Supports Data Analyst -2006,3.1,Education,K-12 Education,14,School / School District,1001 to 5000 employees,59.5,1,"New York, NY", NY,Data Analyst -1912,3.3,Insurance,Insurance Carriers,108,Company - Private,10000+ employees,51.5,1,"Indianapolis, IN", IN,Data Modeler - Data Solutions Engineer -1989,3.6,Business Services,Advertising & Marketing,31,Company - Private,1001 to 5000 employees,161.5,1,"Fort Lee, NJ", NJ,Director Data Science -1928,3.7,Information Technology,Computer Hardware & Software,92,Company - Public,10000+ employees,61.0,1,"Chicago, IL", IL,Information Security Data Analyst -2006,4.4,Information Technology,Internet,14,Company - Public,1001 to 5000 employees,102.5,1,"Nashville, TN", TN,Senior Data Engineer -1784,-1.0,-1,-1,-1,Company - Private,Unknown,120.0,0,"Cambridge, MA", MA,"Senior Scientist, Cell Pharmacology/Assay Development" -1945,3.8,Insurance,Insurance Carriers,75,Nonprofit Organization,5001 to 10000 employees,106.5,0,"Chattanooga, TN", TN,MongoDB Data Engineer II -1968,3.9,Finance,Financial Analytics & Research,52,Company - Public,5001 to 10000 employees,140.0,1,"Chicago, IL", IL,Senior Insurance Data Scientist -1997,4.7,Business Services,Advertising & Marketing,23,Company - Private,201 to 500 employees,56.5,0,"Harrisburg, PA", PA,Jr. Business Data Analyst -1993,2.9,Education,Colleges & Universities,27,Nonprofit Organization,201 to 500 employees,61.5,1,"Herndon, VA", VA,Senior Data Analyst -1992,4.7,Business Services,Security Services,28,Company - Private,501 to 1000 employees,85.5,1,"Syracuse, NY", NY,Data Scientist -1985,3.5,Insurance,Insurance Carriers,35,Company - Private,201 to 500 employees,106.0,1,"Long Beach, NY", NY,Data Engineer diff --git a/data/049_Boris/qa.csv b/data/049_Boris/qa.csv deleted file mode 100644 index 38b6a4098215f3cb8f782a409f644d804fc5f702..0000000000000000000000000000000000000000 --- a/data/049_Boris/qa.csv +++ /dev/null @@ -1,20 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any authors in the dataset who have more followers than people they are following?,True,boolean,"['user_followers_count', 'user_following_count']","['number[uint32]', 'number[uint16]']",True -Is there any tweet in the dataset that has more replies than retweets?,True,boolean,"['replies', 'retweets']","['number[uint16]', 'number[uint32]']",True -Are there any verified users who have tweeted in a language other than English?,True,boolean,"['user_verified', 'lang']","['boolean', 'category']",False -Does the dataset contain any tweets that include image links?,True,boolean,[image_links],['list[url]'],True -How many unique authors are present in the dataset?,1,number,['author_id'],['number[uint32]'],1 -What is the highest number of retweets a single tweet has received?,117386,number,['retweets'],['number[uint32]'],5939 -What is the average number of favorites per tweet?,5081.805590062112,number,['favorites'],['number[uint32]'],7154.5 -What's the total number of tweets from verified users?,3220,number,['user_verified'],['boolean'],20 -Which author has the most tweets in the dataset?,3131144855,category,['author_id'],['number[uint32]'],3131144855 -Which language is most commonly used in the tweets?,en,category,['lang'],['category'],en -What is the most common source of tweets?,"Twitter for iPhone",category,['source'],['category'],Twitter for iPhone -"Which type of tweet (e.g., original, retweet, quote) is most common in the dataset?",original,category,['type'],['category'],original -Who are the top three authors (by ID) with the most followers?,[3131144855],list[category],"['author_id', 'user_followers_count']","['number[uint32]', 'number[uint32]']",[3131144855] -What are the top five most frequently mentioned names in the tweets?,"['G7', 'foreignoffice', 'UN', 'Conservatives', 'COP26']",list[category],['mention_names'],['list[category]'],"['[]', '[Hillingdon_Tory]', '[UN]', '[CyrilRamaphosa]', '[JoeMurphyLondon]']" -List the top four most commonly used languages in the tweets.,"['en', 'und', 'fr', 'es']",list[category],['lang'],['category'],['en'] -What are the highest three numbers of followers count present in the dataset?,"[3543402, 3543402, 3543402]",list[number],['user_followers_count'],['number[uint32]'],"[3543402, 3543402, 3543402]" -What are the bottom four numbers of favorites count?,"[7, 7, 8, 9]",list[number],['favorites'],['number[uint32]'],"[137, 198, 202, 210]" -List the top six numbers of retweets.,"[117386, 53527, 35698, 31449, 24824, 19982]",list[number],['retweets'],['number[uint32]'],"[5939, 4233, 2075, 1901, 1259, 1087]" -What are the bottom five numbers of replies?,"[2, 2, 3, 3, 4]",list[number],['replies'],['number[uint16]'],"[19, 38, 41, 49, 49]" diff --git a/data/049_Boris/sample.csv b/data/049_Boris/sample.csv deleted file mode 100644 index da2006df88804124d8b07cbdcb943063e0eb85c6..0000000000000000000000000000000000000000 --- a/data/049_Boris/sample.csv +++ /dev/null @@ -1,37 +0,0 @@ -id,author_id,author_name,author_handler,author_avatar,user_created_at,user_description,user_favourites_count,user_followers_count,user_following_count,user_listed_count,user_tweets_count,user_verified,user_location,lang,type,text,date,mention_ids,mention_names,retweets,favorites,replies,quotes,links,links_first,image_links,image_links_first,rp_user_id,rp_user_name,location,tweet_link,source,search -1358466859838930947,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,My thoughts are with the people of India and rescue workers in Uttarakhand as they respond to devastating flooding from the glacier collapse. The UK stands in solidarity with India and is ready to offer any support needed.,2021-02-07T17:25:13.000Z,[],[],5939,51773,1885,363,[],,,,,,,https://twitter.com/redirect/status/1358466859838930947,"Twitter Web App",from:@BorisJohnson -1331914146762395651,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Brilliant news that there are now 14,800 more nurses than this time last year. Nurses are there for us in our time of greatest need, and we are so grateful for all that they do. - -We're well on our way to meeting our manifesto commitment of 50,000 more nurses over this Parliament.",2020-11-26T10:54:13.000Z,[],[],375,4337,1742,300,[],,,,,,,https://twitter.com/redirect/status/1331914146762395651,"Twitter Web App",from:@BorisJohnson -1292377499352141830,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Keeping our schools closed a moment longer than is absolutely necessary is socially intolerable, economically unsustainable and morally indefensible. - -https://t.co/9sK8lpQPsJ",2020-08-09T08:29:41.000Z,[],[],1901,13366,7245,1695,"[""https://t.co/9sK8lpQPsJ""]",https://t.co/9sK8lpQPsJ,,,,,,https://twitter.com/redirect/status/1292377499352141830,"Twitter Web App",from:@BorisJohnson -992400912227491840,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Fantastic result for @Hillingdon_Tory - showing yet again you can deliver better services with lower taxes. Well deserved for such a hard working team led by great leader, Ray Puddifoot #onenationtories https://t.co/VouFUswLwa",2018-05-04T13:49:50.000Z,"[""282765726""]","[""Hillingdon_Tory""]",35,198,41,4,[],,"[""http://pbs.twimg.com/media/DcW3XnfWAAAhLgA.jpg""]",http://pbs.twimg.com/media/DcW3XnfWAAAhLgA.jpg,,,,https://twitter.com/redirect/status/992400912227491840,"Twitter for iPhone",from:@BorisJohnson -1204693633036886016,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,Tomorrow lets get this done! 🇬🇧 https://t.co/zBIgzVjA7F,2019-12-11T09:25:18.000Z,[],[],1259,7060,1436,258,[],,"[""http://pbs.twimg.com/ext_tw_video_thumb/1204693590066311168/pu/img/3AnS6IZN-lT024OI.jpg""]",http://pbs.twimg.com/ext_tw_video_thumb/1204693590066311168/pu/img/3AnS6IZN-lT024OI.jpg,,,,https://twitter.com/redirect/status/1204693633036886016,"Twitter Web App",from:@BorisJohnson -1166040795448795138,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,We're providing £10 million to help #ActforAmazon https://t.co/anQGzvazvy,2019-08-26T17:32:44.000Z,[],[],665,4358,669,138,[],,"[""http://pbs.twimg.com/ext_tw_video_thumb/1166040647041789953/pu/img/9Pi1QEUDfZZiab8f.jpg""]",http://pbs.twimg.com/ext_tw_video_thumb/1166040647041789953/pu/img/9Pi1QEUDfZZiab8f.jpg,,,,https://twitter.com/redirect/status/1166040795448795138,"Twitter for iPhone",from:@BorisJohnson -1309855498737512448,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,WATCH: My Address to the @UN General Assembly https://t.co/Xy4pr9LRT4,2020-09-26T14:01:01.000Z,"[""14159148""]","[""UN""]",568,2100,1526,222,"[""https://t.co/Xy4pr9LRT4""]",https://t.co/Xy4pr9LRT4,,,,,,https://twitter.com/redirect/status/1309855498737512448,"Twitter Media Studio",from:@BorisJohnson -1188190221302714369,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"To everyone celebrating Diwali here in the UK and around the world, I want to wish you all a happy Diwali and a joyful and successful new year. Shubh Diwali! https://t.co/kWi6OHo8wj",2019-10-26T20:26:38.000Z,[],[],4233,17688,1257,496,[],,"[""http://pbs.twimg.com/ext_tw_video_thumb/1188189859720159236/pu/img/pvl_LNIOBKygngvd.jpg""]",http://pbs.twimg.com/ext_tw_video_thumb/1188189859720159236/pu/img/pvl_LNIOBKygngvd.jpg,,,,https://twitter.com/redirect/status/1188190221302714369,"Twitter for iPhone",from:@BorisJohnson -779348264814608384,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543402,460,8550,4706,True,United Kingdom,en,original,Farewell #UNGA71 after a busy 4 days including speeches to the #UNSC & mtgs on #DaeshJustice #AviationSecurity #NATO #Syria #GlobalBritain https://t.co/jwnHjZUlN2,2016-09-23T15:54:40.000Z,[],[],75,137,19,6,[],,"[""http://pbs.twimg.com/media/CtDNGkIWcAAK59s.jpg"",""http://pbs.twimg.com/media/CtDNGkIW8AAa9wE.jpg"",""http://pbs.twimg.com/media/CtDNGmgWYAAC-QP.jpg"",""http://pbs.twimg.com/media/CtDNGnfXEAAm6PB.jpg""]",http://pbs.twimg.com/media/CtDNGkIWcAAK59s.jpg,,,,https://twitter.com/redirect/status/779348264814608384,"Twitter for iPhone",from:@BorisJohnson -1336308332542517248,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Its incredible to see the first clinically approved vaccine being given to people today, something that is happening across the whole of the United Kingdom. https://t.co/9XtvOtl7Bh",2020-12-08T13:55:08.000Z,[],[],605,4764,1096,253,[],,"[""http://pbs.twimg.com/amplify_video_thumb/1336306023632605184/img/31PkppFmeFqojUKL.jpg""]",http://pbs.twimg.com/amplify_video_thumb/1336306023632605184/img/31PkppFmeFqojUKL.jpg,,,,https://twitter.com/redirect/status/1336308332542517248,"Twitter Media Studio",from:@BorisJohnson -870747431255891969,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543402,460,8550,4706,True,United Kingdom,en,original,Corbyn no plan for Brexit. Wouldn't answer questions. Can't say he'd defend our country. Denying his past.,2017-06-02T21:02:19.000Z,[],[],352,919,726,166,[],,,,,,,https://twitter.com/redirect/status/870747431255891969,"Twitter for iPhone",from:@BorisJohnson -1255794808670617601,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"I want to congratulate all the new police recruits who have graduated in the last six months, who are now keeping our streets and communities safe. - -In these challenging times, your professionalism, devotion and personal courage is an inspiration to us all. https://t.co/IoJILT5jlB",2020-04-30T09:43:08.000Z,[],[],826,6961,642,128,[],,"[""http://pbs.twimg.com/media/EW1xS3SXkAAWjJO.jpg""]",http://pbs.twimg.com/media/EW1xS3SXkAAWjJO.jpg,,,,https://twitter.com/redirect/status/1255794808670617601,"Twitter Media Studio",from:@BorisJohnson -964150280475545601,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,Congratulations @CyrilRamaphosa – just elected President by parliament 🇿🇦Much for South Africa to be optimistic about at this moment & I am looking forward to working with you,2018-02-15T14:51:54.000Z,"[""2987156301""]","[""CyrilRamaphosa""]",98,403,49,8,[],,,,,,,https://twitter.com/redirect/status/964150280475545601,"Twitter Web Client",from:@BorisJohnson -1119889024834056193,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"As we wake to the horrific news from Sri Lanka, we are reminded this Easter Sunday that Christians around the world are still persecuted for their faith. Our thoughts and prayers are with the victims of these evil attacks, their families and the people of Sri Lanka.",2019-04-21T09:02:04.000Z,[],[],2075,9021,584,142,[],,,,,,,https://twitter.com/redirect/status/1119889024834056193,"Twitter for iPhone",from:@BorisJohnson -1201867684469596166,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"9 days to go. - -9 seats to a majority. - -Lets get it done. https://t.co/QR7rIPqTMi",2019-12-03T14:16:00.000Z,[],[],905,4637,748,108,[],,"[""http://pbs.twimg.com/media/EK2o9DBWwAAgfsQ.jpg""]",http://pbs.twimg.com/media/EK2o9DBWwAAgfsQ.jpg,,,,https://twitter.com/redirect/status/1201867684469596166,"TweetDeck",from:@BorisJohnson -1396058366678548489,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Four years on from the tragic attack at the Manchester Arena, my thoughts are with the 22 people who lost their lives, their families, friends and everyone affected. - -#WeStandTogether united against evil and to remember those lost.",2021-05-22T11:00:26.000Z,[],[],932,7397,789,115,[],,,,,,,https://twitter.com/redirect/status/1396058366678548489,"Twitter Web App",from:@BorisJohnson -1381970505406640129,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Congratulations to @JoeMurphyLondon on a well-earned retirement after a spectacular innings in the lobby. - -You were always as fair and as kind as you could be under the circumstances. - -I wish you all the best for the future.",2021-04-13T14:00:18.000Z,"[""52418188""]","[""JoeMurphyLondon""]",101,1029,301,18,[],,,,,,,https://twitter.com/redirect/status/1381970505406640129,"Twitter Web App",from:@BorisJohnson -847114735686488065,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543402,460,8550,4706,True,United Kingdom,en,original,"As we look forward to a deep & special partnership w/ EU, welcome Italian FM @angealfa to UK for vital talks on Counter Terrorism & Libya https://t.co/wudjE0LUCZ",2017-03-29T15:54:26.000Z,"[""149060372""]","[""angealfa""]",75,202,49,21,[],,"[""http://pbs.twimg.com/media/C8GNjkiW4AAQYmh.jpg""]",http://pbs.twimg.com/media/C8GNjkiW4AAQYmh.jpg,,,,https://twitter.com/redirect/status/847114735686488065,"Twitter Web Client",from:@BorisJohnson -1141757259762061312,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543401,460,8550,4706,True,United Kingdom,en,original,"Im deeply honoured to have secured more than 50 per cent of the vote in the final ballot. Thank you to everyone for your support! I look forward to getting out across the UK and to set out my plan to deliver Brexit, unite our country, and create a brighter future for all of us. https://t.co/i5D4ByurAM",2019-06-20T17:18:38.000Z,[],[],1087,6530,1547,256,[],,"[""http://pbs.twimg.com/media/D9hVyqfWkAEGHZ-.jpg""]",http://pbs.twimg.com/media/D9hVyqfWkAEGHZ-.jpg,,,,https://twitter.com/redirect/status/1141757259762061312,"Twitter Web Client",from:@BorisJohnson -776689539314225152,3131144855,Boris Johnson,BorisJohnson,http://pbs.twimg.com/profile_images/1331215386633756675/NodbPVQv_normal.jpg,2015-04-01T20:15:49.000Z,Prime Minister of the United Kingdom and @Conservatives leader. Member of Parliament for Uxbridge and South Ruislip.,468,3543402,460,8550,4706,True,United Kingdom,en,original,Great to be in #Siena for 24th #Pontignano conference promoting #UK-#Italy understanding & friendship @UKinItaly https://t.co/BK95GmStQf,2016-09-16T07:49:51.000Z,"[""108667698""]","[""UKinItaly""]",67,210,38,12,[],,"[""http://pbs.twimg.com/media/Csda5hJWIAAJKUR.jpg"",""http://pbs.twimg.com/media/Csda5hLWgAA2bsh.jpg""]",http://pbs.twimg.com/media/Csda5hJWIAAJKUR.jpg,,,,https://twitter.com/redirect/status/776689539314225152,"Twitter for iPad",from:@BorisJohnson diff --git a/data/050_ING/qa.csv b/data/050_ING/qa.csv deleted file mode 100644 index 4bee0363de2bc324e5129e8c3e94b33e261b886c..0000000000000000000000000000000000000000 --- a/data/050_ING/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the most favorited author mainly communicating in Spanish?,True,boolean,"['favorites', 'lang']","['category', 'category']",True -Does the author with the longest name post mainly original content?,True,boolean,"['author_name', 'type']","['category', 'category']",False -Is there an author who received no retweets for any of their posts?,True,boolean,"['author_name', 'retweets']","['category', 'number[uint8]']",True -Are there any posts that do not contain any links?,True,boolean,['links'],['list[url]'],True -How many unique authors are in the dataset?,3765,number,['author_name'],['category'],20 -What is the length of the longest post (based on the number of words)?,61,number,['text'],['text'],49 -What is the total number of retweets received by all authors in the dataset?,1243,number,['retweets'],['number[uint8]'],2 -How many posts do not contain any mentions of other users?,0,number,['mention_ids'],['list[number[int64]]'],0 -What is the name of the most retweeted author?,ING España,category,"['author_name', 'retweets']","['category', 'number[uint8]']",ING enfurecido 🦁 -What is the language of the most favorited post?,es,category,"['lang', 'favorites']","['category', 'number[uint8]']",es -Who is the author of the post with the most words?,juan prin,category,"['author_name', 'text']","['category', 'text']",🇪🇸 Bearded.LocutusBorg 🇮🇪🏳️‍🌈 -"What type of post (original, reply, or other) is the most common in the dataset?",reply,category,['type'],['category'],reply -Who are the authors of the top 3 most retweeted posts?,"['Lavincompae', '#NI UNA MENOS \xa0♐\xa0✊\xa0🚺', 'SFC The World']",list[category],"['author_name', 'retweets']","['category', 'number[uint8]']","[Manuel, ING enfurecido 🦁, Albert Navarro]" -What are the languages of the 5 least favorited posts?,"['es', 'es', 'es', 'es', 'es']",list[category],"['lang', 'favorites']","['category', 'number[uint8]']","[es, es, es, es, es]" -Who are the authors of the 4 shortest posts (based on the number of words)?,"['Correctorada', 'El Joker', 'Xenia Viladas', 'DrJaus \xa0🇪🇸']",list[category],"['author_name', 'text']","['category', 'text']","[Vito!!, Albert Navarro, JEEVES, Leo Tarda]" -What types of posts are the 6 most common in the dataset?,"['reply', 'original']",list[category],['type'],['category'],"[reply, original]" -What are the retweet counts for the top 5 most favorited posts?,"[0, 0, 0, 0, 3]",list[number],"['retweets', 'favorites']","['number[uint8]', 'number[uint8]']","[0, 1, 0, 1, 0]" -What are the word counts of the 3 longest posts?,"[61, 60, 59]",list[number],['text'],['text'],"[49, 44, 41]" -What are the retweet counts of the 4 least favorited posts?,"[0, 0, 1, 0]",list[number],"['retweets', 'favorites']","['number[uint8]', 'number[uint8]']","[0, 0, 0, 0]" -What are the word counts for the 6 shortest posts?,"[1, 1, 1, 1, 1, 1]",list[number],['text'],['text'],"[2, 3, 4, 6, 12, 13]" diff --git a/data/050_ING/sample.csv b/data/050_ING/sample.csv deleted file mode 100644 index 7a2eeb6d60afbd3a388df6c1aa281c44dff03082..0000000000000000000000000000000000000000 --- a/data/050_ING/sample.csv +++ /dev/null @@ -1,25 +0,0 @@ -favorites,links,author_name,text,lang,retweets,type,mention_ids -0,[],Albert Navarro,Ya era hora!!!!!!!!!!!!!!,es,0,reply,[] -0,[],Marita Calafell,"Señores de @ING : estoy obligada a tener un smartphone para tener cuenta con ustedes? La gestión telefónica no es ágil, y la instalación de una app debería ser voluntaria. Aparte de cerrar mis cuentas, no se que otra opción tengo",es,0,reply,"[""333485431""]" -0,[],Enrique Porras,Hijos de puta... lo sacais cuando ya me he cambiado de banco porque no lo sacabais!! Cabrones!!,es,0,reply,[] -1,[],Manuel,"El fraude ING hacia un cliente como se evita??? Que llevó 25 días sin recibir respuesta de un fallo con un cajero, y me gustaría saber algo antes de denunciar",es,1,reply,[] -0,[],JEEVES,Y apple Pay también??,es,0,reply,[] -0,[],𝓦𝓲𝓷𝓼𝓽𝓸𝓷 𝓛𝓸𝓫𝓸,"Ese que le comento es banco Popular pero ya pertenece a Banco Santander por lo cual cobran comisión de retirada de efectivo. - -Y ha sido esta mañana, por lo cual no está actualizado.",es,0,reply,[] -1,[],Raúl,"@AlmudenaRomanD Pues yo llamé la semana pasada para eso mismo y ayer me llamaron, no resolvieron nada, han quedado en llamar hoy, sigo esperando... Y la validación movil sigue sin funcionar... #Ingcorralito",es,0,reply,"[""145385661""]" -1,[],ING enfurecido  🦁,TRANQUILOS TODOS QUE @AlmudenaRomanD OS PAGA CON LA MIRADA COMO A @elindependiente,es,1,reply,"[""145385661"",""49754814""]" -0,[],mamen jiménez,@ING_es 14 días esperando la gestión de una documentación para una hipoteca enviada por valija desde una de vuestras oficinas? Quien lleva las valijas hormiguitas? Creo que me conocen ya todas las chicas del callcenter!!! #vergonzoso,es,0,original,"[""270815203""]" -0,[],RaulFu,"@ING_es Buenas con la nueva actualización de twyp, como se retira y pagas a la vez?",es,0,original,"[""270815203""]" -0,[],David E,"Sigo sin noticias, mi última llamada de muchas en estos días fue a las 10 am. Han pasado 9 horas y la cuenta sigue bloqueada. Todo muy “Agile” en @ING_es",es,0,reply,"[""270815203""]" -0,[],jesusbermejo,Se sabe cuándo va ha estár solucionado la incidencia del canjero del @cclosvalles,es,0,reply,"[""534236746""]" -0,[], 🇪🇸 Bearded.LocutusBorg  🇮🇪 🏳️‍🌈,"No es la app, sino la web la que indica el mantenimiento. Finalmente llegó el código al segundo intento pero el pago fue rechazado. He llamado a un gestor y me dicen que ""por fraude""(movimiento sospechoso),cuando resulta ser una web de confianza en la que he comprado muchas veces",es,0,reply,[] -0,[],Vito!!,"Funciona!!! -Gracias!!!",es,0,reply,[] -0,[],Santi Peña,"Del navegador de escritorio limpio siempre cockies y caché. De la app no lo he hecho pero lo haré. -Muchas gracias y saludos. :-)",es,0,reply,[] -0,[],Bells,"Claro, desde donde sea pero quiero recibir de una vez por todas mi documentación. Llevo desde el dia 6 de abril esperando",es,0,reply,[] -6,[],C. Lyon  🦁 🤦🏻‍♂️ ❤️,"A mi las transferencias no me cuestan nada y si las hago por la mañana, son ingresadas en destino el mismo día. En cambio mi madre me hizo una con @caixabank de 50€ y le cobraron 8€. Esa comisión es abusiva, es ser usurero.",es,0,reply,"[""270429778""]" -0,[],Leo Tarda,@ING_es otra vez caída la App?????,es,0,original,"[""270815203""]" -0,[],JJOC,"Claro, me podéis dar un email para mandarla, parece que twitter no me deja responderos con el SMS.",es,0,reply,[] -0,[],Blanca,@ING_es ¡Hola! He renovado el DNI. ¿Dónde puedo mandar escaneado el nuevo? Gracias :),es,0,original,"[""270815203""]" diff --git a/data/051_Pokemon/qa.csv b/data/051_Pokemon/qa.csv deleted file mode 100644 index 9405bc64c9f2f557440b635e1f51e1d0f61fa645..0000000000000000000000000000000000000000 --- a/data/051_Pokemon/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is there a Pokémon named 'Pikachu' in the dataset?,True,boolean,['name'],['category'],False -Are there any Pokémon with a total stat greater than 700?,True,boolean,['total'],['number[uint16]'],False -Are all Pokémon in the first generation legendary?,False,boolean,"['generation', 'legendary']","['number[uint8]', 'boolean']",False -Is there any Pokémon with a speed greater than 150?,True,boolean,['speed'],['number[uint8]'],False -How many unique Pokémon types are there in the 'type1' column?,20,number,['type1'],['category'],20 -What's the highest total stat value found in the dataset?,1125,number,['total'],['number[uint16]'],1125 -How many Pokémon are there in the third generation?,160,number,['generation'],['number[uint8]'],160 -What is the average attack stat for all Pokémon?,80.94,number,['attack'],['number[uint8]'],80.94 -What is the primary type of the Pokémon with the highest defense stat?,Poison,category,"['defense', 'type1']","['number[uint8]', 'category']",Poison -Which Pokémon has the lowest speed stat?,Shuckle,category,"['speed', 'name']","['number[uint8]', 'category']",Shuckle -What secondary type is the most common among legendary Pokémon?,Flying,category,"['legendary', 'type2']","['boolean', 'category']",Flying -Which Pokémon has the highest special attack and what is its primary type?,Mega Mewtwo Y (Psychic),category,"['sp_attack', 'name', 'type1']","['number[uint8]', 'category', 'category']",Mega Mewtwo Y (Psychic) -Name the top 3 Pokémon with the highest total stats.,"['Eternamax Eternatus', 'Mega Mewtwo X', 'Mega Mewtwo Y']",list[category],"['total', 'name']","['number[uint16]', 'category']","['Eternamax Eternatus', 'Mega Mewtwo X', 'Mega Mewtwo Y']" -Which 5 Pokémon have the lowest hp stats?,"['Shedinja', 'Diglett', 'Alolan Diglett', 'Magikarp', 'Pichu']",list[category],"['hp', 'name']","['number[uint8]', 'category']","['Shedinja', 'Diglett', 'Alolan Diglett', 'Magikarp', 'Pichu']" -Name the top 4 primary categories that have the most Pokémon.,"['Water', 'Normal', 'Grass', 'Bug']",list[category],['type1'],['category'],"['Water', 'Normal', 'Grass', 'Bug']" -Which 6 Pokémon from the second generation have the highest attack stats?,"['Mega Heracross', 'Mega Tyranitar', 'Mega Scizor', 'Tyranitar', 'Scizor', 'Ursaring']",list[category],"['generation', 'attack', 'name']","['number[uint8]', 'number[uint8]', 'category']","['Mega Heracross', 'Mega Tyranitar', 'Mega Scizor', 'Tyranitar', 'Scizor', 'Ursaring']" -What are the top 5 special defense stats in the dataset?,"[250, 230, 200, 160, 160]",list[number],['sp_defense'],['number[uint8]'],"[250, 230, 200, 160, 160]" -list the bottom 3 defense stats of legendary Pokémon.,"[20, 31, 50]",list[number],"['legendary', 'defense']","['boolean', 'number[uint8]']","[20, 31, 50]" -What are the 4 highest speed stats of Pokémon in the fourth generation?,"[135, 127, 125, 125]",list[number],"['generation', 'speed']","['number[uint8]', 'number[uint8]']","[135, 127, 125, 125]" -list the 6 lowest total stats of non-legendary Pokémon.,"[175, 180, 180, 185, 190, 194]",list[number],"['legendary', 'total']","['boolean', 'number[uint16]']","[175, 180, 180, 185, 190, 194]" diff --git a/data/051_Pokemon/sample.csv b/data/051_Pokemon/sample.csv deleted file mode 100644 index 8ac2b642f464db42ccbf5056bb1bb0ddabd388a3..0000000000000000000000000000000000000000 --- a/data/051_Pokemon/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -hp,attack,name,sp_attack,type1,defense,sp_defense,legendary,total,speed,type2,generation -70,110,Gigantamax Flapple,95,Grass,80,60,False,485,70,Dragon,8 -72,85,Zweilous,65,Dark,70,70,False,420,58,Dragon,5 -200,100,Regidrago,100,Dragon,50,50,True,580,80,,8 -75,125,Armaldo,70,Rock,100,80,False,495,45,Bug,3 -79,85,Bibarel,55,Normal,60,60,False,410,71,Water,4 -70,65,Delcatty,55,Normal,65,55,False,380,70,,3 -95,75,Mega Slowbro,130,Water,180,80,False,590,30,Psychic,1 -68,165,Mega Gallade,65,Psychic,95,115,False,618,110,Fighting,4 -65,85,Eelektrik,75,Electric,70,70,False,405,40,,5 -44,38,Helioptile,61,Electric,33,43,False,289,70,Normal,6 -123,100,Gogoat,97,Grass,62,81,False,531,68,,6 -60,45,Orbeetle,80,Bug,110,120,False,505,90,Psychic,8 -35,100,Alolan Dugtrio,50,Ground,60,70,False,425,110,Steel,7 -80,80,Latias,110,Dragon,90,130,True,600,110,Psychic,3 -95,70,Clefable,95,Fairy,73,90,False,483,60,,1 -52,40,Steenee,40,Grass,48,48,False,290,62,,7 -64,51,Whismur,51,Normal,23,23,False,240,28,,3 -80,135,Metagross,95,Steel,130,90,False,600,70,Psychic,3 -68,67,Corvisquire,43,Flying,55,55,False,365,77,,8 -41,63,Arrokuda,40,Water,40,30,False,280,66,,8 diff --git a/data/052_Professional/qa.csv b/data/052_Professional/qa.csv deleted file mode 100644 index 1c9bbaa93fc09f6b61ecb28f5b19551bcead1660..0000000000000000000000000000000000000000 --- a/data/052_Professional/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the maximum level of Extraversion greater than the maximum level of Agreeableness?,True,boolean,"['Extraversion', 'Agreeableness']","['number[double]', 'number[double]']",True -Is the profession with the highest Openness the same as the profession with the highest Conscientousness?,False,boolean,"['Profession', 'Openness', 'Conscientousness']","['category', 'number[double]', 'number[double]']",False -Does the profession with the lowest Emotional_Range also have the lowest level of Conversation?,False,boolean,"['Profession', 'Emotional_Range', 'Conversation']","['category', 'number[double]', 'number[double]']",False -Is the average level of Openness to Change higher than the average level of Hedonism?,True,boolean,"['Openness to Change', 'Hedonism']","['number[double]', 'number[double]']",True -What is the maximum value of Self-enhancement across all professions?,0.7826336180787501,number,['Self-enhancement'],['number[double]'],0.6291001325102317 -How many professions have an Emotional_Range above 0.5?,1002,number,['Emotional_Range'],['number[double]'],18 -What is the average Extraversion level for the profession with the highest number of records (n)?,0.373214039767641,number,"['Profession', 'Extraversion', 'n']","['category', 'number[double]', 'number[uint16]']",0.3521943338191243 -What is the minimum level of Self-transcendence?,0.0353641396193574,number,['Self-transcendence'],['number[double]'],0.0355792960526332 -What profession has the highest level of Conscientiousness?,Policy Officer,category,"['Profession', 'Conscientousness']","['category', 'number[double]']",U.S. Representative -What is the profession with the lowest level of Hedonism?,Governor,category,"['Profession', 'Hedonism']","['category', 'number[double]']",U.S. Representative -Which profession has the highest Emotional_Range?,Mortgage Banker,category,"['Profession', 'Emotional_Range']","['category', 'number[double]']",U.S. Representative -What is the profession with the highest number of records (n)?,Program Manager,category,"['Profession', 'n']","['category', 'number[uint16]']",Data Analyst -What are the top 3 professions with the highest Openness?,"['Book Publisher', 'Bureau Chief', 'Publisher']",list[category],"['Profession', 'Openness']","['category', 'number[double]']","['User Experience Designer (UX Designer)', 'Sustainability Coach', 'Insurance Writer']" -Which are the bottom 4 professions in terms of Agreeableness?,"['.Net Architect', 'Android Developer', 'Principal Engineer', 'Game Engineer']",list[category],"['Profession', 'Agreeableness']","['category', 'number[double]']","['Automation Engineer', 'Stock Trader', 'Data Analyst', 'User Experience Designer (UX Designer)']" -List the top 5 professions with the highest Conversation levels.,"['Director of Athletics', 'Recruiting Coordinator', 'Athletic Coordinator', 'Director of Personnel', 'Skills Trainer']",list[category],"['Profession', 'Conversation']","['category', 'number[double]']","['U.S. Representative', 'Media Executive', 'Bookkeeper', 'Music Promoter', 'Director of Sales Marketing']" -Name the bottom 2 professions in terms of Self-enhancement.,"['U.S. Senator', 'Congressman']",list[category],"['Profession', 'Self-enhancement']","['category', 'number[double]']","['U.S. Representative', 'Examiner']" -What are the top 3 values of Openness to Change across all professions?,"[0.7557249985959847, 0.7413189187628788, 0.7034528053640179]",list[number],['Openness to Change'],['number[double]'],"[0.5907415983292473, 0.5853143224995465, 0.5741970926130652]" -List the bottom 4 Emotional_Range values.,"[0.1652381569664056, 0.2005428064324122, 0.2215546116855247, 0.2506791678499942]",list[number],['Emotional_Range'],['number[double]'],"[0.3282629326553239, 0.3454430761347227, 0.5168284093531004, 0.5454469457823092]" -What are the highest 5 levels of Extraversion?,"[0.9794365922809228, 0.9723660656030668, 0.954299437125917, 0.9362989453985364, 0.9307917067583288]",list[number],['Extraversion'],['number[double]'],"[0.7997301042051191, 0.7874815418605556, 0.786896532913159, 0.7780772474605324, 0.7595518653914357]" -Name the lowest 6 levels of Self-transcendence.,"[0.0353641396193574, 0.0355792960526332, 0.0399752446723049, 0.0573280662585624, 0.0591099063085338, 0.0592781792571762]",list[number],['Self-transcendence'],['number[double]'],"[0.0355792960526332, 0.1108088371055385, 0.1436964701751416, 0.1772058461581069, 0.2179965189872425, 0.2289998631350671]" diff --git a/data/052_Professional/sample.csv b/data/052_Professional/sample.csv deleted file mode 100644 index 4168bb34f338e7478eb78a81120f20dae42b099b..0000000000000000000000000000000000000000 --- a/data/052_Professional/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Emotional_Range,Conscientousness,Self-transcendence,Openness to Change,Self-enhancement,n,Hedonism,Profession,Agreeableness,Conversation,Extraversion,Openness -0.5936211497085442,0.4006511115612809,0.1108088371055385,0.4648549779442046,0.6291001325102317,76,0.2391425306789873,Stock Trader,0.0812501586228441,0.1634913532364499,0.4653777257133697,0.6835201574114657 -0.6543448970079995,0.5118683521090917,0.1772058461581069,0.3441232150204137,0.3255770399574938,91,0.1497822041119344,Credit Manager,0.2027917155591266,0.1462436384922697,0.6836518508428786,0.6119784249012032 -0.6641055983646709,0.6608432069327402,0.3303169175261922,0.5853143224995465,0.2250753896494375,73,0.0981357979486594,Sustainability Coach,0.3689161421597385,0.1042856375570577,0.7414786458537513,0.80878417778407 -0.7753173381229697,0.6206018740612098,0.2987955067337796,0.3995854830380654,0.353774061763708,67,0.1976006510688528,Credit Specialist,0.2660551590014994,0.2238682516283182,0.7780772474605324,0.5024504700595764 -0.8003656975069963,0.8409929821319269,0.3165627822174947,0.3353188790009501,0.266250786197886,76,0.1191355854596349,Bookkeeper,0.4843843174136154,0.2951766177655989,0.786896532913159,0.5796256514941514 -0.3282629326553239,0.4332322063644227,0.4150638426331816,0.5741970926130652,0.3111350675671671,75,0.0842145198946358,Learning Manager,0.3799596193290634,0.1293116614488205,0.5485963767182114,0.7162541597180598 -0.6011852876810413,0.5262052389712817,0.2289998631350671,0.3463565048746541,0.2473625264648527,76,0.168112841453586,Insurance Writer,0.3609440208386595,0.1610981271871778,0.6301651306848701,0.7790837832619596 -0.6080280927574218,0.5337535308208121,0.2525679703481462,0.5548622645203056,0.4478126055754949,51,0.1793158242420761,Business Development Analyst,0.3826531498201531,0.2405715245896992,0.537454025847287,0.6220175434915063 -0.5168284093531004,0.4466688682025549,0.3406423599505044,0.4132634252788784,0.4463854001454406,58,0.2723839001675804,Internal Auditor,0.4486778494625867,0.2618738781487474,0.5027328733640768,0.6836955351889308 -0.7305473714373298,0.6336559627603959,0.0355792960526332,0.1514600197130905,0.1059463271884633,88,0.0879897455741092,Examiner,0.3466584525974123,0.0730697190868016,0.7065984656948947,0.7257354728849674 -0.5739801880294935,0.6062243557832552,0.2351480534686754,0.4040918728666057,0.4439870444342042,78,0.2003127271237762,Media Executive,0.4083228649922227,0.2971033310516575,0.7595518653914357,0.6930832612835737 -0.8903681034798421,0.9233612010208432,0.2290890631168106,0.2314474974236199,0.0647680544545841,80,0.0605104378454066,U.S. Representative,0.706749877267691,0.3064118567561703,0.7874815418605556,0.7489167203270148 -0.7270978582728476,0.5472233174987523,0.3016478316241293,0.4898409803974024,0.2589396644286747,81,0.0852592936960732,User Experience Designer (UX Designer),0.1256557656354125,0.0434771809914016,0.5556269436166388,0.870566480731951 -0.3454430761347227,0.3425909506249657,0.512188885607843,0.5907415983292473,0.537195449747865,73,0.4475883909581863,Music Promoter,0.2766073795808494,0.2894327825230886,0.5165397783935867,0.4897808546340526 -0.586705089318224,0.6083025457644053,0.1436964701751416,0.4142453005446935,0.2460144348595583,74,0.0863933236657212,Bureau Director,0.3855709965017915,0.16152175425174,0.6062127008031846,0.7595606945147002 -0.6434868690706862,0.6663571311114345,0.2959837662813438,0.4815325822421916,0.4908006217696999,75,0.3177749350641593,Director of Sales Marketing,0.3675386050082535,0.2873362567540884,0.7997301042051191,0.5855374570830805 -0.6846645352880132,0.5975240879294796,0.2402406197779986,0.3878156217394283,0.1840962406903182,75,0.1444989752861092,Safety Instructor,0.3275064855750174,0.1200979812121368,0.6162631259949508,0.660536510574423 -0.5454469457823092,0.3378440876924125,0.2179965189872425,0.3829706714228802,0.3776157779189672,164,0.1891105778604434,Data Analyst,0.0928930629159628,0.0764622380540201,0.3521943338191243,0.766085749460611 -0.6185550161648985,0.7831608854718106,0.2838697623307778,0.4998650696369186,0.2582816651991528,74,0.1273028489890661,Chairperson,0.5696508655026261,0.2759773230539261,0.6111896698967683,0.7301434755743338 -0.6586355985749365,0.4478074960128796,0.2615031542252076,0.4187129492899349,0.4024973762296603,103,0.1729846724264475,Automation Engineer,0.0549608415147459,0.087571801110893,0.299791012458513,0.6900151180366931 diff --git a/data/053_Patents/qa.csv b/data/053_Patents/qa.csv deleted file mode 100644 index ef8439d22e8a1d725241b4dbd4f497a23626c747..0000000000000000000000000000000000000000 --- a/data/053_Patents/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -Is there a patent related to 'communication' in the title?,False,['title'],boolean,['text'],True -Are there patents associated with the organization 'IBM'?,False,['organization'],boolean,['category'],False -Is there a patent abstract that mentions 'software'?,True,['abstract'],boolean,['text'],False -Are there patents of the 'design' type?,True,['type'],boolean,['category'],True -How many unique organizations have patents listed?,3574,['organization'],number,['category'],18 -"On average, how many claims do the patents have?",14.745974597459746,['num_claims'],number,['number[uint8]'],17.65 -What's the highest number of claims a patent has?,100,['num_claims'],number,['number[uint8]'],41 -How many patents are of 'utility' type?,8848,['type'],number,['category'],19 -Which organization has the patent with the highest number of claims?,Massachusetts Institute of Technology,"['organization', 'num_claims']",category,"['category', 'number[uint8]']","Samsung Electronics Co., Ltd." -Which kind of patent is the most common?,B2,['kind'],category,['category'],B2 -In which language are the patents written?,en,['lang'],category,['category'],en -Which graphext cluster is the most common among the patents?,"member, portion, body, end",['graphext_cluster'],category,['category'],"video, display, mobile, content" -Which are the top 3 organizations with the most patents?,"['International Business Machines Corporation', 'Samsung Electronics Co., Ltd.', 'Google Inc.']",['organization'],list[category],['category'],"['Samsung Electronics Co., Ltd.', 'HYUNDAI MOBIS CO., LTD.', 'Children's Hospital Medical Center']" -List the 4 most common types of patents in the dataset.,"['utility', 'design', 'plant', 'reissue']",['type'],list[category],['category'],"['utility', 'design']" -Which 5 kinds of patents are the most prevalent?,"['B2', 'S1', 'B1', 'P2', 'P3']",['kind'],list[category],['category'],"['B2', 'S1']" -List 2 least common graphext clusters among the patents.,"['habit, plant, foliage, flowers', 'soybean, plant, cultivar, soybean cultivar']",['graphext_cluster'],list[category],['category'],"['+, +1, -based, -based crystal', 'voltage, power, current, circuit']" -What are the top 4 numbers of claims in the patents?,"[20, 1, 18, 19]",['num_claims'],list[number],['number[uint8]'],"[12, 18, 7, 13]" -List the 3 patents (by ID) with the most number of claims.,"[9479771.0, 9325365.0, 9323284.0]","['id', 'num_claims']",list[number],"['number[UInt32]', 'number[uint8]']","[9479911.0, 9323438.0, 9480043.0]" -Which 5 patents (by ID) have the most targets associated?,"[29, 30, 46, 51, 70]","['id', 'target']",list[number],"['number[UInt32]', 'list[number[uint16]]']","[932136912.0, 9480043.0, 9323438.0, 9246982.0, 9323057.0]" -List the 6 most recent patents by their ID.,"[9479476.0, 9479477.0, 9479478.0, 9479479.0, 9479480.0, 9479481.0]","['id', 'date']",list[number],"['number[UInt32]', 'date[ns, UTC]']","[9480043.0, 9479911.0, 9480049.0, 9321442.0, 9323438.0, 9324689.0]" diff --git a/data/053_Patents/sample.csv b/data/053_Patents/sample.csv deleted file mode 100644 index 75036ca718357d6c5f543089035472fc97dd81ab..0000000000000000000000000000000000000000 --- a/data/053_Patents/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -kind,num_claims,title,date,lang,id,abstract,type,target,graphext_cluster,organization -B2,13,Master cylinder and brake system using the same,2016-04-26T00:00:00Z,en,9321442.0,"A master cylinder including: a primary chamber; a first piston which is provided in the primary chamber, and has one portion that is moved to the outside of the primary chamber by pressing a pedal; a secondary chamber which is provided to be adjacent to the primary chamber; a second piston which is provided in the secondary chamber, and has one portion that protrudes to the outside of the secondary chamber and is provided to be spaced apart from the first piston at a predetermined interval; and a hydraulic line which connects the primary chamber and the secondary chamber, and is connected with a wheel cylinder, in which the first piston is moved by pressing the pedal, and after the first piston is moved at a predetermined interval, the first piston and the second piston come into contact with each other, and are moved together.",utility,"[6385, 5346, 8517, 5935, 6420, 6239, 6454, 5395, 6584, 6373, 4364, 6254, 5409, 6293]","member, portion, body, end","HYUNDAI MOBIS CO., LTD." -B2,32,Media-editing application with live dragging and live editing capabilities,2016-04-26T00:00:00Z,en,9323438.0,"Some embodiments of the invention provide a media-editing application for creating and editing a media presentation that displays the results of edits as the edits are made to the media presentation. The media-editing application displays the movement of media clips of the media presentation as the media clips are being moved within the media-editing application to change the media presentation. Also, the media editing application in some embodiments can dynamically display the results of edits in a preview display area. That is, the media editing application has a preview generator that can generate previews of the media presentation on the fly as media clips are being dragged into and within the timeline. This allows the user of the media-editing application to see and hear the results of the operation while performing them.",utility,"[3047, 109, 369, 361, 8020, 8093, 8334, 7796, 355, 9741, 265, 2780, 8380, 3081]","video, display, mobile, content",Apple Inc. -B2,18,Lateral double-diffused metal-oxide-semiconudctor transistor device and layout pattern for LDMOS transistor device,2016-01-26T00:00:00Z,en,9245996.0,"A LDMOS transistor device includes a substrate including a first insulating structure formed therein, a gate formed on the substrate and covering a portion of the first insulating structure, a drain region and a source region formed in the substrate at two respective sides of the gate, a base region encompassing the source region, and a doped layer formed under the base region. The drain region and the source region include a first conductivity type, the base region and the doped layer include a second conductivity type, and the second conductivity type is complementary to the first conductivity type. A top of the doped layer contacts a bottom of the base region. A width of the doped layer is larger than a width of the base region.",utility,"[1718, 8739, 1730, 8745, 1683, 1484, 8891, 8596, 8890, 8817, 8893, 8819, 8848, 8838]","layer, semiconductor, region, gate",United Microelectronics Corp. -B2,10,Chip-on-film (COF) tape and corresponding COF bonding method,2016-04-26T00:00:00Z,en,9324689.0,"The present invention provides a chip-on-film (COF) tape and a corresponding COF bonding method. The COF tape comprises a base tape, a plurality of first COFs and second COFs, the first and second COFs are arranged on the base tape in an alternating manner, and are correspondingly punched onto a moving platform by a punching mechanism, and are respectively bonded onto two side edges of a liquid crystal panel. The present invention can simultaneously process the bonding operations of the two types of COF by using only one COF tape and one set of equipment, thus lowering the cost and increasing the productivity.",utility,"[7083, 9071, 1541, 1542, 8651, 8575, 712, 9048, 9004, 7108, 4786, 5110, 1654, 6241]","light, optical, electrode, waveguide","Shenzhen China Star Optoelectronics Technology Co., Ltd" -B2,7,Electric mid-wheel drive wheelchair,2016-04-26T00:00:00Z,en,9320661.0,"A wheelchair of the mid-wheel drive type has a chassis frame (10), a pair of front link arms (30) and a pair of rear link arms. The link arms are pivotally connected to the chassis frame. Front caster wheels (32) and drive wheels (53) are fixed to the front link arms. Rear caster wheels (42) are fixed to the rear link arms. Each front link arm (30) is operatively connected to an adjacent rear link arm (40) by a respective coupling (70), which is arranged to transmit pivotal movement of one of the front (30) and rear (40) link arm to an opposite pivotal movement of the other of the front (30) and rear (40) link arm.",utility,"[5403, 5309, 5308, 5406, 5219, 4815, 5413, 5411, 6590, 4037, 6179, 5237, 5412, 6167]","member, portion, body, end",Permobil AB -B2,17,Electronic whiteboard and touch screen method for configuring and applying metadata tags thereon,2016-04-26T00:00:00Z,en,9323447.0,"An electronic whiteboard and a touch screen method for configuring and applying metadata tags on an electronic whiteboard allows user-defined and user-classified metadata tags to be configured and applied to objects “on the fly” through touch screen inputs on the electronic whiteboard, providing object tagging in electronic whiteboard sessions that is less cumbersome, less disruptive to creative flow and more precise than conventional methods.",utility,"[7428, 7435, 3053, 94, 9703, 4837, 2768, 7348, 4430, 7440, 7445, 2983, 8199, 7759]","video, display, mobile, content","Sharp Laboratories of America, Inc." -B2,30,Method and apparatus for network based positioning,2016-10-25T00:00:00Z,en,9480043.0,"Disclosed is a system, apparatus, computer readable storage medium, and method to perform a bandwidth efficient network based positioning (NBP). A positioning request for a mobile device is received within an environment, the environment comprising one or more access points (APs). An AP coverage area including the mobile device is determined. A NBP load for the AP is measured, wherein the NBP load comprises a direct NBP load and an indirect NBP load. If the NBP load is less than a threshold, the AP is instructed to process the first positioning request. Multiple APs may process positioning requests concurrently and positioning requests may be scheduled according to timeout and/or relative expiration.",utility,"[631, 529, 458, 9720, 3245, 530, 76, 2600, 542, 3306, 448, 2425, 2768, 9462]","video, display, mobile, content", -B2,19,Spatially-aware projection pen display,2016-04-26T00:00:00Z,en,9323422.0,One embodiment of the present invention sets forth a technique for providing an end user with a digital pen embedded with a spatially-aware miniature projector for use in a design environment. Paper documents are augmented to allow a user to access additional information and computational tools through projected interfaces. Virtual ink may be managed in single and multi-user environments to enhance collaboration and data management. The spatially-aware projector pen provides end-users with dynamic visual feedback and improved interaction capabilities.,utility,"[7435, 2983, 2768, 262, 7335, 7920, 8307, 7334, 3053, 9741, 7440, 7771, 4837, 7640]","video, display, mobile, content","Autodesk, Inc." -B2,20,System and method for aeration,2016-04-26T00:00:00Z,en,9321667.0,"An aeration device includes a foam suppression system. The aeration device raises the level of oxygen and air in a body of substantially liquid fluid, such as an aerating-oxidizing pond, lagoon, basin, or reservoir. The aeration system is buoyant and floats on top of the body of fluid. The foam suppression system includes a submersible grinder pump coupled to a sprinkler head by a fluid conduction system. The submersible grinder pump draws in a fluid from beneath a surface of a body of fluid and pumps it through the fluid conduction system and out of the sprinkler head. The sprinkler head sprays the fluid drawn from beneath the fluid surface far outward onto an area surrounding the aeration device with surface foam. The falling water or fluid sprayed from the sprinkler head quashes the surface foam. The sprinkler head is disposed in a location on the aeration device suitable to suppress the surface foam.",utility,"[4120, 4956, 723, 6116, 4431, 8485, 3440, 4944, 4870, 4869, 6575, 6105, 6463, 4741]","member, portion, body, end",Airmaster Aerator L.L.C. -B2,41,Method and system for supporting a translation-based communication service and terminal supporting the service,2016-10-25T00:00:00Z,en,9479911.0,The present disclosure relates to a method and a system for a translation-based communication service operation. The method includes: establishing a communication service channel between a transmitter-side terminal and a receiver-side terminal; translating at least one of a text of a first language and a voice signal in the first language collected by the transmitter-side terminal into a second language to generate at least one of a translation text of a second language and a translation voice in the second language; and receiving and outputting at least one of the generated translation text in the second language and the translation voice signal in the second language by the receiver-side terminal.,utility,"[8341, 3157, 8335, 7746, 3250, 9890, 2804, 2332, 608, 2814, 9515, 7597, 7598, 512]","network, message, packet, service","Samsung Electronics Co., Ltd." -B2,12,Reducing network usage of computing device,2016-01-26T00:00:00Z,en,9246982.0,Methods and systems for reducing network usage of a computing device are provided herein. The method includes receiving a network call relating to a network transfer from an application at an application programming interface of the computing device. The method also includes determining whether the network transfer is relevant to a current state of the application and procrastinating the network transfer if it is not relevant to the current state of the application.,utility,"[9958, 7525, 7590, 2425, 9637, 9638, 5, 117, 9631, 9574, 571, 2716, 2510, 2529]","network, message, packet, service","MICROSOFT TECHNOLOGY LICENSING, LLC" -B2,7,Compositions and methods for inhibiting norovirus infection,2016-04-26T00:00:00Z,en,9321803.0,"A composition for use in inhibiting the binding of a Norovirus to the histo-blood group antigen on the surface of epithelia is disclosed. The composition may contain a therapeutically effective amount of a binding-inhibiting compound and a carrier and/or excipient. The compounds may competitively bind a Norovirus that has the capability of binding with the histo-blood group antigens of secretor blood type, including A, B, AB, and O blood types. The compositions may be administered to a human prior to or after infection by a Norovirus, to prevent, ameliorate, or reduce the effects of an infection.",utility,"[4640, 4655, 4656, 4558, 5961, 6814, 6878, 5740, 5741, 4654, 4630, 4724, 5659, 5675]","cancer, nucleotide, patient, protein",Children's Hospital Medical Center -B2,18,Power conversion system,2016-04-26T00:00:00Z,en,9321369.0,A power conversion system includes: a switching element; an output circuit that outputs a transmission signal for transmitting information on a physical quantity that indicates a state of the switching element and for transmitting information on abnormality of the switching element; an isolating element that transmits the transmission signal in an electrically isolated state; a filter that deletes the information on the physical quantity from a signal transmitted through the isolating element; a control circuit to which the signal transmitted through the isolating element but not through the filter is input; and a shutdown circuit that shuts down power supply to the switching element on the basis of the signal through the filter.,utility,"[2167, 9603, 7272, 9288, 2217, 344, 4354, 9412, 1323, 2295, 7409, 9611, 2263, 9372]","signal, frequency, station, transmission",Toyota Jidosha Kabushiki Kaisha -B2,26,Insecticidal triazines and pyrimidines,2016-04-26T00:00:00Z,en,9321735.0,"The present invention describes novel triazines, their related pyrimidines and their use in controlling insects. This invention also includes new synthetic procedures, intermediates for preparing the compounds, pesticide compositions containing the compounds, and methods of controlling insects using the compounds.",utility,"[5649, 5705, 4127, 5717, 5665, 4654, 5692, 5678, 5657, 5682, 5712, 5713, 4679, 4599]","composition, resin, polymer, acid",The Valeron Corporation -S1,1,Housing for a crematory urn,2016-01-26T00:00:00Z,en,932136912.0,"This invention includes new synthetic procedures, intermediates for preparing the compounds, pesticide compositions containing the compounds, and methods of controlling insects using the compounds.",design,"[148, 378, 388, 389, 401, 402, 672, 687, 703, 708, 709, 715, 716, 717, 718]","+, +1, -based, -based crystal",County Cemetary Services Ltd. -B2,8,Oral care compositions and methods,2016-04-26T00:00:00Z,en,9320696.0,"Described herein are compositions comprising a MMP-13 inhibitor, and methods of using the same.",utility,"[5712, 5713, 5665, 4603, 4600, 5767, 4127, 4679, 4654, 4545, 4617, 5702, 4595, 4596]","cancer, nucleotide, patient, protein",Colgate-Palmolive Company -B2,27,Method and system for assigning addresses to subscriber stations in a wireless communication environment,2016-10-25T00:00:00Z,en,9480049.0,"A system and a method for assigning addresses to subscriber stations in a wireless communication environment are provided. The method includes receiving a request to register with a wireless communication network from a subscriber station, assigning a unicast address and at least one specific time duration to a subscriber station for communication with a network entity in the wireless communication network during registration process, and communicating the unicast address and the at least one specific time duration to the subscriber station, wherein the assigned unicast address is valid for the subscriber station during the at least one specific time duration.",utility,"[2722, 125, 3317, 9532, 2329, 3354, 9618, 2382, 9619, 3200, 9820, 541, 9779, 2321]","network, message, packet, service","Samsung Electronics Co., Ltd." -B2,12,Method and device for controlling an electrical actuator for a wastegate valve arrangement of an exhaust gas turbocharger,2016-04-26T00:00:00Z,en,9322324.0,A method for controlling an electrical actuator for a wastegate valve arrangement of an exhaust gas turbocharger in an internal combustion engine is provided. A wastegate is situated in a bypass channel of the exhaust gas turbocharger. The method includes closing or opening the wastegate by the actuator for adjusting the exhaust gas flow routed past the exhaust gas turbocharger via the bypass channel. The wastegate is exposed to a predetermined closing force while it is in a closed state so as to regulate the closing force to a desired value for the closing force as a function of an actual value for the closing force. Computer-readable mediums embodying a computer program product having a program to perform the method are also provided.,utility,"[6288, 6303, 6291, 6328, 6287, 6332, 6308, 6292, 6339, 6327, 6321, 6235, 6253, 6313]","voltage, power, current, circuit","GM Global Technology Operations, Inc." -B2,12,"Mobile device, system and method for controlling a heads-up display",2016-04-26T00:00:00Z,en,9323057.0,"A mobile device, system and method for controlling a heads-up display device is provided. A mobile device is in communication with a heads-up display (HUD) device. The mobile device is enabled to: transmit a portion of display data to the HUD device for display thereupon, rather than provide the portion to a display of the mobile device; and display a remaining portion of the display data at the display. The HUD device is enabled to: receive from the mobile device the display data for display at the HUD; display the data at the HUD.",utility,"[3061, 3062, 154, 8099, 8316, 8309, 7393, 7085, 8075, 7461, 575, 6110, 8323, 442]","video, display, mobile, content",BlackBerry Limited -B2,23,Electronic protein fractionation,2016-04-26T00:00:00Z,en,9321012.0,Apparatuses and methods for purifying proteins and other target molecules based on pI are provided.,utility,"[5772, 5773, 4660, 4603, 4654, 5724, 5975, 4684, 5678, 5780, 5728, 4691, 5767, 5985]","cancer, nucleotide, patient, protein","Bio-Rad Laboratories, Inc." diff --git a/data/054_Joe/qa.csv b/data/054_Joe/qa.csv deleted file mode 100644 index 56f5d17b72c28d8b9e140b91319416d37d937b84..0000000000000000000000000000000000000000 --- a/data/054_Joe/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Has the author with the highest number of followers ever been verified?,True,boolean,"['author_id', 'user_followers_count', 'user_verified']","['number[uint32]', 'number[uint32]', 'boolean']",True -Is the author who has the most favourites also the one with the most retweets?,False,boolean,"['author_id', 'user_favourites_count', 'retweets']","['number[uint32]', 'number[uint8]', 'number[uint8]']",True -Is the most mentioned user also the most retweeted user?,False,boolean,"['author_id', 'mention_names', 'retweets']","['number[uint32]', 'list[category]']",False -Does the author with the most retweets also have the most replies?,True,boolean,"['author_id', 'retweets', 'replies']","['number[uint32]', 'number[uint32]', 'number[uint16]']",True -What is the maximum number of followers an author in the dataset has?,0.7826336180787501,number,['user_followers_count'],['number[uint32]'],30308043 -"How many authors have more than 10,000 favourites?",1002,number,['user_favourites_count'],['number[uint8]'],0 -How many retweets does the most retweeted tweet have?,0.373214039767641,number,['retweets'],['number[uint32]'],50625 -How many times has the most mentioned user been mentioned?,0.0353641396193574,number,['mention_names'],['list[category]'],16 -Who is the author with the most followers?,Policy Officer,category,"['author_name', 'user_followers_count']","['category', 'number[uint32]']",Joe Biden -Who is the author with the highest number of favourites?,Governor,category,"['author_name', 'user_favourites_count']","['category', 'number[uint8]']",Joe Biden -What is the name of the user who is most often mentioned?,Mortgage Banker,category,"['author_name', 'mention_names']","['category', 'list[category]']",[] -Who is the author of the tweet with the most retweets?,Program Manager,category,"['author_name', 'retweets']","['category', 'number[uint32]']",Joe Biden -Who are the top 3 authors with the most followers?,"['Book Publisher', 'Bureau Chief', 'Publisher']",list[category],"['author_name', 'user_followers_count']","['category', 'number[uint32]']","['Joe Biden', 'Joe Biden', 'Joe Biden']" -Who are the top 4 authors with the most favourites?,"['.Net Architect', 'Android Developer', 'Principal Engineer', 'Game Engineer']",list[category],"['author_name', 'user_favourites_count']","['category', 'number[uint8]']","['Joe Biden', 'Joe Biden', 'Joe Biden', 'Joe Biden']" -Who are the 5 users who are mentioned the most often?,"['Director of Athletics', 'Recruiting Coordinator', 'Athletic Coordinator', 'Director of Personnel', 'Skills Trainer']",list[category],"['author_name', 'mention_names']","['category', 'list[category]']","['[]', '[305818748]', '[282721598]', '[21829541]', '[50348682]']" -Who are the top 2 authors of the tweets with the most retweets?,"['U.S. Senator', 'Congressman']",list[category],"['author_name', 'retweets']","['category', 'number[uint32]']","['Joe Biden', 'Joe Biden']" -What are the top 3 numbers of followers in the dataset?,"[0.7557249985959847, 0.7413189187628788, 0.7034528053640179]",list[number],['user_followers_count'],['number[uint32]'],"[30212707, 30212712, 30212708]" -What are the top 4 numbers of favourites an author in the dataset has?,"[0.1652381569664056, 0.2005428064324122, 0.2215546116855247, 0.2506791678499942]",list[number],['user_favourites_count'],['number[uint8]'],[20] -What are the 5 highest numbers of times a user is mentioned?,"[0.9794365922809228, 0.9723660656030668, 0.954299437125917, 0.9362989453985364, 0.9307917067583288]",list[number],['mention_names'],['list[category]'],"[16, 1, 1, 1, 1]" -What are the 2 highest numbers of retweets a tweet in the dataset has?,"[0.0353641396193574, 0.0355792960526332]",list[number],['retweets'],['number[uint32]'],"[399, 6866]" diff --git a/data/054_Joe/sample.csv b/data/054_Joe/sample.csv deleted file mode 100644 index 98ad90c7926818573a52651edfa2706d4e97736d..0000000000000000000000000000000000000000 --- a/data/054_Joe/sample.csv +++ /dev/null @@ -1,36 +0,0 @@ -id,author_id,author_name,author_handler,author_avatar,user_created_at,user_description,user_favourites_count,user_followers_count,user_following_count,user_listed_count,user_tweets_count,user_verified,user_location,lang,type,text,date,mention_ids,mention_names,retweets,favorites,replies,quotes,links,links_first,image_links,image_links_first,rp_user_id,rp_user_name,location,tweet_link,source,search -1129819469050798080,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212708,47,36167,7340,True,"Washington, DC",en,original,"Joe helped make government work before, and he’ll make it work again. #TeamJoe https://t.co/E5vA58zhcL",2019-05-18T18:42:07.000Z,[],[],399,1597,399,59,[],,"[""http://pbs.twimg.com/media/D63stSpW4AEXJu4.jpg""]",http://pbs.twimg.com/media/D63stSpW4AEXJu4.jpg,,,,https://twitter.com/redirect/status/1129819469050798080,"TweetDeck",from:@JoeBiden filter:images -1308580592938778624,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212704,47,36167,7340,True,"Washington, DC",en,original,"We can’t let 2016 repeat itself. There’s simply too much on the line for anyone to sit this election out. - -Get registered to vote today: https://t.co/eoxT07d7QB - -#NationalVoterRegistrationDay https://t.co/teVRgVxeeJ",2020-09-23T01:35:00.000Z,[],[],6866,22639,2458,493,"[""https://t.co/eoxT07d7QB""]",https://t.co/eoxT07d7QB,"[""http://pbs.twimg.com/media/EikB6PnWkAAmhMS.jpg""]",http://pbs.twimg.com/media/EikB6PnWkAAmhMS.jpg,,,,https://twitter.com/redirect/status/1308580592938778624,"Twitter Media Studio",from:@JoeBiden filter:images -1138146307921465346,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212707,47,36167,7340,True,"Washington, DC",en,original,It was great to be back in Atlanta! Thank you to Mayor @KeishaBottoms for showing me around town and for your work expanding opportunity to every corner of the city. https://t.co/kmhmeN5lHm,2019-06-10T18:10:00.000Z,"[""305818748""]","[""KeishaBottoms""]",223,1531,146,19,[],,"[""http://pbs.twimg.com/media/D8t-SsaXsAAbEwc.jpg"",""http://pbs.twimg.com/media/D8t-foOWkAAFtG4.jpg"",""http://pbs.twimg.com/media/D8t-g1FXUAAr2p8.jpg""]",http://pbs.twimg.com/media/D8t-SsaXsAAbEwc.jpg,,,,https://twitter.com/redirect/status/1138146307921465346,"TweetDeck",from:@JoeBiden filter:images -1141083160119250944,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212712,47,36167,7340,True,"Washington, DC",en,original,"For all the hard-won progress, for as much as we can celebrate how much better things have gotten — this fight is not over. - -We have to come together to stand up to abuses of power, ensure that everyone is treated with dignity, and fight for full equality. https://t.co/aCoWE0hggC",2019-06-18T20:40:00.000Z,[],[],266,1370,292,28,[],,"[""http://pbs.twimg.com/media/D9XugfMXUAERCs-.jpg""]",http://pbs.twimg.com/media/D9XugfMXUAERCs-.jpg,,,,https://twitter.com/redirect/status/1141083160119250944,"TweetDeck",from:@JoeBiden filter:images -1151215172704509952,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212713,47,36167,7340,True,"Washington, DC",en,original,"Thank you to Governor Tom Vilsack and @ChristieVilsack for opening up your home for yesterday’s house party. It was a pleasure getting to spend some time with the residents of Waukee, Iowa! https://t.co/A6GC1AEJKf",2019-07-16T19:41:00.000Z,"[""282721598""]","[""ChristieVilsack""]",103,610,48,0,[],,"[""http://pbs.twimg.com/media/D_nuOk2XUAEgL-q.jpg"",""http://pbs.twimg.com/media/D_nuPa_W4AArSfo.jpg"",""http://pbs.twimg.com/media/D_nuQ3hXUAAi4uW.jpg"",""http://pbs.twimg.com/media/D_nuRpQXoAAtVCR.jpg""]",http://pbs.twimg.com/media/D_nuOk2XUAEgL-q.jpg,,,,https://twitter.com/redirect/status/1151215172704509952,"TweetDeck",from:@JoeBiden filter:images -1184259445909573633,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212707,47,36167,7340,True,"Washington, DC",en,original,"A president who puts his own self-interest ahead of the public good and national security poses a threat to each and every American in our daily lives. - -Donald Trump should be impeached. Period. #DemDebate https://t.co/eY2DAPne5d",2019-10-16T00:07:09.000Z,[],[],360,1215,181,22,[],,"[""http://pbs.twimg.com/media/EG9VkC6XYAIk0XG.jpg""]",http://pbs.twimg.com/media/EG9VkC6XYAIk0XG.jpg,,,,https://twitter.com/redirect/status/1184259445909573633,"TweetDeck",from:@JoeBiden filter:images -1332730472351240192,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212702,47,36167,7340,True,"Washington, DC",en,original,"Small businesses are the backbone of communities across the country — and amid the pandemic, they need our help more than ever. Today, and every day, support your neighbors and strengthen your community by shopping small. #SmallBusinessSaturday https://t.co/MG9fwMi2sQ",2020-11-28T16:58:00.000Z,[],[],7369,87668,4932,1188,[],,"[""http://pbs.twimg.com/media/En7Ow0YXUAAGBj-.jpg""]",http://pbs.twimg.com/media/En7Ow0YXUAAGBj-.jpg,,,,https://twitter.com/redirect/status/1332730472351240192,"Twitter Media Studio",from:@JoeBiden filter:images -1235094180097122304,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212702,47,36167,7340,True,"Washington, DC",en,original,"Call it a W. Thank you, Texas. https://t.co/iXFI1ys9wi",2020-03-04T06:46:14.000Z,[],[],2629,17659,575,842,[],,"[""http://pbs.twimg.com/media/ESPvkEyXcAIbhpm.jpg""]",http://pbs.twimg.com/media/ESPvkEyXcAIbhpm.jpg,,,,https://twitter.com/redirect/status/1235094180097122304,"TweetDeck",from:@JoeBiden filter:images -1144435476528607232,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212712,47,36167,7340,True,"Washington, DC",en,original,"Joe Biden shepherded through the Brady Background bill in 1993 and bans on assault weapons and high capacity magazines in 1994. - -As president, Joe will defeat the @NRA again. -#DemDebate https://t.co/ZZezO80xZM",2019-06-28T02:40:54.000Z,"[""21829541""]","[""NRA""]",296,1214,136,36,[],,"[""http://pbs.twimg.com/media/D-HZ841XUAAwg-c.jpg""]",http://pbs.twimg.com/media/D-HZ841XUAAwg-c.jpg,,,,https://twitter.com/redirect/status/1144435476528607232,"TweetDeck",from:@JoeBiden filter:images -1232505640968478720,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212712,47,36167,7340,True,"Washington, DC",en,original,When everyone around you is shouting about health care reform but you're the only one who has gotten anything big done. https://t.co/e8T3kN5Mhb,2020-02-26T03:20:18.000Z,[],[],2643,12332,750,228,[],,"[""http://pbs.twimg.com/tweet_video_thumb/ERq8hM6W4AARNjl.jpg""]",http://pbs.twimg.com/tweet_video_thumb/ERq8hM6W4AARNjl.jpg,,,,https://twitter.com/redirect/status/1232505640968478720,"Twitter Web App",from:@JoeBiden filter:images -1351367275094310912,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212694,47,36167,7340,True,"Washington, DC",en,original,"There’s no more fitting way to honor the legacy of Dr. Martin Luther King, Jr. than service. Thank you to @Philabundance for letting us stop by and for the work you do every day to end hunger. https://t.co/dy78IQ0vgf",2021-01-19T03:14:00.000Z,"[""50348682""]","[""Philabundance""]",4623,56846,2966,275,[],,"[""http://pbs.twimg.com/media/EsEE2LLW4AMCbla.jpg""]",http://pbs.twimg.com/media/EsEE2LLW4AMCbla.jpg,,,,https://twitter.com/redirect/status/1351367275094310912,"Twitter Media Studio",from:@JoeBiden filter:images -1309620696293806080,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212708,47,36167,7340,True,"Washington, DC",en,original,"Today, Justice Ginsburg made history one last time — and I was grateful to be there to pay my respects. May her memory be a blessing — and may we continue to be voices for justice in her name. https://t.co/TMJeYw0d89",2020-09-25T22:28:00.000Z,[],[],13690,110921,1888,445,[],,"[""http://pbs.twimg.com/media/Eiy0HZdXcAE-o-U.jpg""]",http://pbs.twimg.com/media/Eiy0HZdXcAE-o-U.jpg,,,,https://twitter.com/redirect/status/1309620696293806080,"Twitter Media Studio",from:@JoeBiden filter:images -1156716954314575872,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212714,47,36167,7340,True,"Washington, DC",en,original,Retweet if you’re proud to be on #TeamJoe! #DemDebate https://t.co/7XZeGxeQsQ,2019-08-01T00:03:07.000Z,[],[],551,884,112,31,[],,"[""http://pbs.twimg.com/media/EA171gfWkAEik1r.jpg""]",http://pbs.twimg.com/media/EA171gfWkAEik1r.jpg,,,,https://twitter.com/redirect/status/1156716954314575872,"Twitter Web Client",from:@JoeBiden filter:images -1245112384865386504,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212711,47,36167,7340,True,"Washington, DC",en,original,It's 2020 — it’s unacceptable that women still don’t get equal pay for equal work. Let’s close the gender pay gap and end #EqualPayDay once and for all. https://t.co/GH6rASk0wH,2020-03-31T22:15:00.000Z,[],[],1449,5622,753,89,[],,"[""http://pbs.twimg.com/media/EUeE9RoWAAIeezd.jpg""]",http://pbs.twimg.com/media/EUeE9RoWAAIeezd.jpg,,,,https://twitter.com/redirect/status/1245112384865386504,"TweetDeck",from:@JoeBiden filter:images -1165434869163405313,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212707,47,36167,7340,True,"Washington, DC",en,original,"Highly recommend the grilled cheese and milkshakes at Lindy’s Diner. Thanks for a great send-off, New Hampshire! https://t.co/1AlWvQGQi7",2019-08-25T01:25:00.000Z,[],[],169,1243,279,45,[],,"[""http://pbs.twimg.com/media/ECxyRYzXoAAqpDx.jpg""]",http://pbs.twimg.com/media/ECxyRYzXoAAqpDx.jpg,,,,https://twitter.com/redirect/status/1165434869163405313,"TweetDeck",from:@JoeBiden filter:images -1313607969557282816,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30308043,47,36168,7340,True,"Washington, DC",en,original,The President turned his back on you. https://t.co/oeI8dck2LL,2020-10-06T22:32:00.000Z,[],[],50625,195121,5778,2845,[],,"[""http://pbs.twimg.com/media/Ejre5W2XsAEslX_.png""]",http://pbs.twimg.com/media/Ejre5W2XsAEslX_.png,,,,https://twitter.com/redirect/status/1313607969557282816,"Twitter Media Studio",from:@JoeBiden filter:images -1150943381129814017,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212713,47,36167,7340,True,"Washington, DC",en,original,"At the #AARPIowaForum, I laid out my plan for older Americans. A Biden administration will lower drug costs, protect & strengthen Medicare & Social Security, & help middle-class families grow their savings—because everyone deserves to retire with dignity. - -https://t.co/rYVyCoDOAT https://t.co/0ykyiaehBj",2019-07-16T01:41:00.000Z,[],[],198,878,162,21,"[""https://t.co/rYVyCoDOAT""]",https://t.co/rYVyCoDOAT,"[""http://pbs.twimg.com/media/D_j3HSBWwAIz2G9.jpg""]",http://pbs.twimg.com/media/D_j3HSBWwAIz2G9.jpg,,,,https://twitter.com/redirect/status/1150943381129814017,"TweetDeck",from:@JoeBiden filter:images -1137427069355339776,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212705,47,36167,7340,True,"Washington, DC",en,original,"Across America, mayors are stepping up and leading the way on combating climate change. Thank you to @MartyForBoston for showing me the innovative work Boston is doing to protect the city from rising sea levels and increased flooding. https://t.co/LZKp6WNWlj",2019-06-08T18:32:00.000Z,[],[],181,892,99,12,[],,"[""http://pbs.twimg.com/media/D8jtpk9XkAAXiBN.jpg""]",http://pbs.twimg.com/media/D8jtpk9XkAAXiBN.jpg,,,,https://twitter.com/redirect/status/1137427069355339776,"TweetDeck",from:@JoeBiden filter:images -1165056031858278400,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212707,47,36167,7340,True,"Washington, DC",en,original,"It was great to be back with folks at Dartmouth College today for our Health Care Town Hall. - -Health care is one of the biggest issues in this election. A Biden administration will ensure health care is a right not a privilege—and give everyone the peace of mind they deserve. https://t.co/rwb70mBHgM",2019-08-24T00:19:38.000Z,[],[],157,701,86,10,[],,"[""http://pbs.twimg.com/media/ECscEIxX4AEUyh-.jpg"",""http://pbs.twimg.com/media/ECscFdYWwAEM3PA.jpg"",""http://pbs.twimg.com/media/ECscGGSXkAAAuZT.jpg"",""http://pbs.twimg.com/media/ECscGy7WsAEsA61.jpg""]",http://pbs.twimg.com/media/ECscEIxX4AEUyh-.jpg,,,,https://twitter.com/redirect/status/1165056031858278400,"TweetDeck",from:@JoeBiden filter:images -1240106902274494466,939091,Joe Biden,JoeBiden,http://pbs.twimg.com/profile_images/1308769664240160770/AfgzWVE7_normal.jpg,2007-03-11T17:51:24.000Z,"Husband to @DrBiden, proud father and grandfather. Ready to build back better for all Americans. Official account is @POTUS.",20,30212712,47,36167,7340,True,"Washington, DC",en,original,"Thank you to everyone in Arizona, Florida, and Illinois who supported our campaign. From day one, our goal has been to unify our party and our nation — and tonight, we are one step closer to achieving that goal. Let’s do this, together. https://t.co/tcLufz2SBV",2020-03-18T02:45:00.000Z,[],[],2160,11616,541,123,[],,"[""http://pbs.twimg.com/tweet_video_thumb/ETW8Nf7XQAAsQ9d.jpg""]",http://pbs.twimg.com/tweet_video_thumb/ETW8Nf7XQAAsQ9d.jpg,,,,https://twitter.com/redirect/status/1240106902274494466,"Twitter Media Studio",from:@JoeBiden filter:images diff --git a/data/055_German/qa.csv b/data/055_German/qa.csv deleted file mode 100644 index a2ac2a98e50556c5dc3a2bc199acd270674f84f8..0000000000000000000000000000000000000000 --- a/data/055_German/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Is the borrower with the highest loan amount also the one with the longest loan duration?,False,boolean,"['Loan Amount', 'Loan Duration - Months']","['number[uint16]', 'number[uint8]']",False -Does the borrower with the maximum number of existing loans also have the highest loan amount?,False,boolean,"['Number of Existing Loans', 'Loan Amount']","['number[uint8]', 'number[uint16]']",True -Does the borrower with the longest loan duration also have the maximum number of existing loans?,False,boolean,"['Loan Duration - Months', 'Number of Existing Loans']","['number[uint8]', 'number[uint8]']",False -Does the oldest borrower also have the highest loan amount?,False,boolean,"['Age', 'Loan Amount']","['number[uint8]', 'number[uint16]']",False -What is the highest loan amount in the dataset?,18424,number,['Loan Amount'],['number[uint16]'],8613 -How many borrowers have more than 1 existing loan?,367,number,['Number of Existing Loans'],['number[uint8]'],7 -What is the longest loan duration in the dataset?,72,number,['Loan Duration - Months'],['number[uint8]'],60 -How many borrowers are older than 50?,113,number,['Age'],['number[uint8]'],2 -What is the most common purpose of loans?,Radio/TV,category,['Purpose of Loan'],['category'],Radio/TV -What is the most common job category for borrowers?,Skilled,category,['Job'],['category'],Skilled -What is the most common credit history category for borrowers?,Existing Credits Paid Back Duly Till Now,category,['Credit History'],['category'],Existing Credits Paid Back Duly Till Now -What is the most common savings account status for borrowers?,Less than 100 DM,category,['Savings Account'],['category'],Less than 100 DM -What are the top 3 jobs of borrowers with the highest loan amount?,"['Highly Skilled', 'Skilled', 'Highly Skilled']",list[category],"['Loan Amount', 'Job']","['number[uint16]', 'category']","['Skilled', 'Skilled', 'Highly Skilled']" -What are the top 4 jobs of borrowers with the longest loan duration?,"['Skilled', 'Skilled', 'Skilled', 'Unskilled - Resident']",list[category],"['Loan Duration - Months', 'Job']","['number[uint8]', 'category']","['Skilled', 'Skilled', 'Skilled', 'Highly Skilled']" -What are the 5 jobs of borrowers with the maximum number of existing loans?,"['Skilled', 'Skilled', 'Unskilled - Resident', 'Highly Skilled', 'Skilled']",list[category],"['Number of Existing Loans', 'Job']","['number[uint8]', 'category']","['Highly Skilled', 'Skilled', 'Skilled', 'Skilled', 'Unemployed / Unskilled - Non-Resident']" -What are the jobs of the oldest 2 borrowers?,"['Highly Skilled', 'Highly Skilled']",list[category],"['Age', 'Job']","['number[uint8]', 'category']","['Unemployed / Unskilled - Non-Resident', 'Unskilled - Resident']" -What are the top 3 loan amounts in the dataset?,"[18424, 15945, 15857]",list[number],['Loan Amount'],['number[uint16]'],"[3190, 4380, 2124]" -What are the top 4 loan durations in the dataset?,"[72, 60, 60, 60]",list[number],['Loan Duration - Months'],['number[uint8]'],"[18, 24, 12, 6]" -What are the 5 maximum numbers of existing loans among borrowers?,"[4, 4, 4, 4, 4]",list[number],['Number of Existing Loans'],['number[uint8]'],"[1, 2]" -What are the ages of the top 2 oldest borrowers?,"[75, 75]",list[number],['Age'],['number[uint8]'],"[65, 64]" diff --git a/data/055_German/sample.csv b/data/055_German/sample.csv deleted file mode 100644 index 35b418a74e277a38fb9d299f116a33319cc669dc..0000000000000000000000000000000000000000 --- a/data/055_German/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Loan Amount,Credit History,Loan Duration - Months,Age,Savings Account,Job,Number of Existing Loans,Purpose of Loan -8613,Delay in Paying Off in the Past,27,27,More than 1000 DM,Skilled,3,Used Car -7297,Existing Credits Paid Back Duly Till Now,60,36,Less than 100 DM,Skilled,1,Business -7253,Critical Account / Other Credits Existing at Other Bank,33,35,Less than 100 DM,Highly Skilled,2,Used Car -6288,Existing Credits Paid Back Duly Till Now,60,42,Less than 100 DM,Skilled,1,Education -4380,Existing Credits Paid Back Duly Till Now,18,35,100-500 DM,Unskilled - Resident,1,New Car -4153,Existing Credits Paid Back Duly Till Now,18,42,Less than 100 DM,Skilled,1,Furniture/Equipment -3965,Existing Credits Paid Back Duly Till Now,42,34,Less than 100 DM,Skilled,1,Radio/TV -3643,Delay in Paying Off in the Past,15,27,Less than 100 DM,Unskilled - Resident,2,Furniture/Equipment -3577,Existing Credits Paid Back Duly Till Now,9,26,100-500 DM,Skilled,1,New Car -3368,Critical Account / Other Credits Existing at Other Bank,15,23,More than 1000 DM,Skilled,2,Used Car -3190,Existing Credits Paid Back Duly Till Now,18,24,Less than 100 DM,Skilled,1,Radio/TV -2384,Existing Credits Paid Back Duly Till Now,24,64,Less than 100 DM,Unskilled - Resident,1,Radio/TV -2325,All Credits at This Bank Paid Back Duly,24,32,100-500 DM,Skilled,1,New Car -2124,Critical Account / Other Credits Existing at Other Bank,18,24,Less than 100 DM,Skilled,2,Furniture/Equipment -2116,Existing Credits Paid Back Duly Till Now,6,41,Less than 100 DM,Skilled,1,Furniture/Equipment -1376,Existing Credits Paid Back Duly Till Now,24,28,500-1000 DM,Skilled,1,Radio/TV -1297,Existing Credits Paid Back Duly Till Now,12,23,Less than 100 DM,Skilled,1,Radio/TV -1098,Critical Account / Other Credits Existing at Other Bank,18,65,Less than 100 DM,Unemployed / Unskilled - Non-Resident,2,Radio/TV -585,Delay in Paying Off in the Past,12,20,Less than 100 DM,Skilled,2,Radio/TV -484,Existing Credits Paid Back Duly Till Now,6,28,Less than 100 DM,Unskilled - Resident,1,Radio/TV diff --git a/data/056_Emoji/qa.csv b/data/056_Emoji/qa.csv deleted file mode 100644 index 5be56f4c2deb91eb25317e10131853bdf3b7761d..0000000000000000000000000000000000000000 --- a/data/056_Emoji/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any foods with zero calories?,False,boolean,['Calories (kcal)'],['number[double]'],False -Do all foods contain sugar?,False,boolean,['Total Sugar (g)'],['number[double]'],False -Are there any foods that contain no fat at all?,True,boolean,['Total Fat (g)'],['number[double]'],False -Are there foods that do not contain protein?,True,boolean,['Protein (g)'],['number[double]'],False -How many foods have more than 500 kcal?,0,number,['Calories (kcal)'],['number[double]'],0 -What is the average amount of total fat (in grams) across all foods?,0.08465,number,['Total Fat (g)'],['number[double]'],0.06386499999999999 -How many foods have a sodium content of more than 1 gram?,18,number,['Sodium (g)'],['number[double]'],5 -What's the highest amount of protein (in grams) found in a food item?,0.2748,number,['Protein (g)'],['number[double]'],0.2403 -What food has the highest calorie content?,bacon,category,"['Calories (kcal)', 'name']","['number[double]', 'category']",chocolate bar -Which food contains the most sugar?,honey,category,"['Total Sugar (g)', 'name']","['number[double]', 'category']",chocolate bar -What is the food with the least amount of total fat?,honey,category,"['Total Fat (g)', 'name']","['number[double]', 'category']",pineapple -Which food has the highest amount of protein?,beef,category,"['Protein (g)', 'name']","['number[double]', 'category']",chicken -List the top 5 foods with the most calories.,"['bacon', 'peanuts', 'chocolate bar', 'popcorn', 'cookie']",list[category],"['Calories (kcal)', 'name']","['number[double]', 'category']","[chocolate bar, cookie, french fries, bread, hotdog]" -Identify the top 3 foods with the least amount of sugar.,"['chestnut', 'pancakes', 'cheese']",list[category],"['Total Sugar (g)', 'name']","['number[double]', 'category']","[milk, cookie, hotdog]" -Enumerate the 4 foods with the most total fat.,"['bacon', 'peanuts', 'cheese', 'popcorn']",list[category],"['Total Fat (g)', 'name']","['number[double]', 'category']","[chocolate bar, cookie, hotdog, french fries]" -Name the 6 foods with the least amount of protein.,"['candy', 'black tea', 'bacon', 'champagne', 'red wine', 'red apple']",list[category],"['Protein (g)', 'name']","['number[double]', 'category']","[green apple, pineapple, strawberry, grapes, tangerine, banana]" -List the 5 highest calorie counts found in the dataset.,"[8.98, 5.67, 5.18, 5.0, 4.97]",list[number],['Calories (kcal)'],['number[double]'],"[5.18, 4.97, 3.12, 2.74, 2.47]" -What are the 3 lowest amounts of sugar found among the foods?,"[0.0, 0.0, 0.0]",list[number],['Total Sugar (g)'],['number[double]'],"[0.0, 0.0, 0.0]" -Enumerate the 4 highest amounts of total fat found in the foods.,"[0.995, 0.4924, 0.3099, 0.281]",list[number],['Total Fat (g)'],['number[double]'],"[0.2599, 0.252, 0.1484, 0.1473]" -What are the top 6 lowest amounts of protein found in the dataset?,"[0.0, 0.0, 0.0007, 0.0007, 0.0007, 0.002]",list[number],['Protein (g)'],['number[double]'],"[0.0044, 0.0054, 0.0067, 0.0072, 0.0081, 0.0109]" diff --git a/data/056_Emoji/sample.csv b/data/056_Emoji/sample.csv deleted file mode 100644 index cc03b289271c2d1db6ed1b1cbf80138ee6b3795b..0000000000000000000000000000000000000000 --- a/data/056_Emoji/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Calories (kcal),Total Fat (g),name,Sodium (g),Protein (g),Total Sugar (g) -0.69,0.0016,grapes,0.02,0.0072,0.1548 -0.89,0.0033,banana,0.01,0.0109,0.1223 -3.12,0.1473,french fries,2.1,0.0343,0.003 -0.61,0.0052,kiwifruit,0.03,0.0114,0.0899 -2.22,0.13,ice cream,0.61,0.041,0.2116 -0.64,0.0366,milk,0.49,0.0328,0.0 -2.26,0.127,taco,3.97,0.0886,0.009 -2.74,0.0453,bread,4.73,0.1067,0.0573 -4.97,0.252,cookie,2.9,0.046,0.0 -0.32,0.003,strawberry,0.01,0.0067,0.0489 -5.18,0.2599,chocolate bar,0.54,0.0651,0.4868 -0.53,0.0031,tangerine,0.02,0.0081,0.1058 -2.47,0.1484,hotdog,6.84,0.106,0.0 -1.84,0.0899,chicken,0.98,0.2403,0.0 -0.58,0.0019,green apple,0.01,0.0044,0.0959 -0.9,0.0015,potato,0.36,0.0201,0.0648 -0.5,0.0012,pineapple,0.01,0.0054,0.0985 -0.29,0.003,lemon,0.02,0.011,0.025 -1.3,0.0021,rice,0.0,0.0238,0.0 -0.96,0.015,corn,0.01,0.0341,0.0454 diff --git a/data/057_Spain/qa.csv b/data/057_Spain/qa.csv deleted file mode 100644 index 3d4330414440f14980155e4a824bff5935475d15..0000000000000000000000000000000000000000 --- a/data/057_Spain/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any respondents who didn't complete primary education?,True,boolean,['edu'],['category'],False -Are all respondents interested in politics?,False,boolean,['polInterest'],['category'],False -Did any respondent indicate that they will not vote?,True,boolean,['Vote Intention'],['list[category]'],False -Did any respondent place themselves at the extreme right of the Left-Right economic values axis?,True,boolean,['User self-placement on Left-Right economic values axis'],['number[UInt8]'],False -How many respondents placed themselves at 10 on the Progressive-Conservative economic values axis?,372,number,['User self- placement on Progressive-Conservative economic values axis'],['number[UInt8]'],0 -What is the average age of the respondents?,37.420120593188116,number,['Age'],['number[UInt8]'],38.72222222222222 -How many respondents think it should be more difficult for companies to lay off workers?,10344,number,['It should be more difficult for companies to lay off workers'],['number[UInt8]'],0 -What's the maximum age among the respondents who prefer not to disclose their gender?,105.0,number,"['Age', 'gender']","['number[UInt8]', 'category']", -Which is the most common vote intention among respondents?,[Ciudadanos],category,['Vote Intention'],['list[category]'],[Ciudadanos] -What is the most common reason for voting given by respondents?,The party ideas are close to my own,category,['voteReason'],['category'],The party ideas are close to my own -Which is the most common party identification among respondents?,Ciudadanos,category,['partyId'],['category'],Ciudadanos -What is the most common first language among respondents?,Castellano,category,['lang'],['category'],Castellano -List the top 5 most common vote intentions among respondents.,"['[Ciudadanos]', '[]', '[I am undeceided]', '[Podemos]', '[PP]']",list[category],['Vote Intention'],['list[category]'],"[Ciudadanos, I prefer not to say, [], I am undeceided, Podemos]" -Identify the top 3 most common reasons for voting among respondents.,"['The party ideas are close to my own', 'The party is the most competent', 'I prefer not to say']",list[category],['voteReason'],['category'],"[The party ideas are close to my own, The party is the most competent, I prefer not to say]" -Enumerate the top 4 most common party identifications among respondents.,"['Ciudadanos', 'PP', 'I prefer not to say', 'Podemos']",list[category],['partyId'],['category'],"[Ciudadanos, I prefer not to say, PSOE, PP]" -Name the top 6 most common first languages among respondents.,"['Castellano', 'Catal�', 'Galego', 'Euskara']",list[category],['lang'],['category'],[Castellano] -List the top 5 most common ages among respondents.,"[38.0, 23.0, 39.0, 25.0, 35.0]",list[number],['Age'],['number[UInt8]'],"[33.0, 38.0, 22.0, 55.0, 37.0]" -What are the top 3 most common positions on the Left-Right economic values axis?,"[5.0, 3.0, 6.0]",list[number],['User self-placement on Left-Right economic values axis'],['number[UInt8]'],"[5.0, 6.0, 3.0]" -Enumerate the top 4 most common positions on the Progressive-Conservative economic values axis.,"[5.0, 3.0, 4.0, 0.0]",list[number],['User self- placement on Progressive-Conservative economic values axis'],['number[UInt8]'],"[5.0, 3.0, 0.0, 4.0]" -What are the top 6 most common positions on the 'Constitutional organisation of the country (More Descentralization)' scale?,"[2.0, 3.0, 4.0, 1.0, 5.0]",list[number],['Constitutional organisation of the country (More Descentralization)'],['number[UInt8]'],"[2.0, 4.0, 3.0]" diff --git a/data/057_Spain/sample.csv b/data/057_Spain/sample.csv deleted file mode 100644 index 73db45fa2cb95586c634f8cf7806edfcb6227696..0000000000000000000000000000000000000000 --- a/data/057_Spain/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -partyId,Vote Intention,User self- placement on Progressive-Conservative economic values axis,polInterest,Age,gender,lang,edu,voteReason,Constitutional organisation of the country (More Descentralization),It should be more difficult for companies to lay off workers,User self-placement on Left-Right economic values axis -I prefer not to say,[I prefer not to say],5.0,A little interested,33.0,Female,Castellano,University degree,I prefer not to say,2.0,2.0, -,[],,Somewhat interested,55.0,Male,Castellano,I prefer not to say,So that another party does not win,2.0,3.0, -,[],,Very interested,37.0,Female,Castellano,Postgraduate degree,,,3.0, -PP,[PP],7.0,Very interested,60.0,Male,Castellano,Primary education,The party is the most competent,2.0,3.0,7.0 -Podemos,[Podemos],0.0,Very interested,27.0,Female,Castellano,Technical/Vocational education,The party helps people like me,4.0,5.0,2.0 -PSOE,[Ciudadanos],3.0,Somewhat interested,31.0,Female,Castellano,University degree,So that another party does not win,,4.0,5.0 -PSOE,[PSOE],4.0,Somewhat interested,33.0,Male,Castellano,University degree,The party ideas are close to my own,3.0,2.0,4.0 -PP,[I am undeceided],6.0,Very interested,51.0,Male,Castellano,University degree,Other,2.0,4.0,6.0 -PSOE,[PSOE],3.0,,,,Castellano,,The party ideas are close to my own,4.0,4.0,3.0 -Ciudadanos,[I am undeceided],5.0,A little interested,38.0,,Castellano,,I prefer not to say,2.0,2.0,5.0 -Ciudadanos,[Ciudadanos],3.0,A little interested,49.0,Male,Castellano,University degree,The party is the most competent,2.0,3.0,5.0 -Ciudadanos,[Ciudadanos],5.0,Very interested,39.0,Male,Castellano,University degree,The party ideas are close to my own,2.0,3.0,5.0 -Podemos,[Podemos],0.0,Somewhat interested,22.0,Female,Castellano,University degree,To punish the established parties,4.0,4.0,0.0 -Ciudadanos,[Ciudadanos],4.0,Very interested,22.0,Male,Castellano,Secondary education,The party is the most competent,4.0,2.0,6.0 -IU,[I prefer not to say],3.0,Somewhat interested,29.0,Male,Castellano,University degree,The party is the most competent,4.0,3.0,3.0 -,[I am undeceided],,Not interested at all,44.0,Male,Castellano,University degree,Other,2.0,4.0, -I prefer not to say,[I prefer not to say],3.0,Somewhat interested,,Female,Castellano,Technical/Vocational education,The party ideas are close to my own,4.0,4.0,5.0 -Ciudadanos,[Ciudadanos],5.0,Very interested,21.0,Male,Castellano,University degree,The party ideas are close to my own,3.0,4.0,6.0 -,[],,Very interested,38.0,Male,Castellano,University degree,,,3.0, -I prefer not to say,[I prefer not to say],5.0,Somewhat interested,68.0,Male,Castellano,I prefer not to say,I prefer not to say,3.0,4.0,5.0 diff --git a/data/058_US/qa.csv b/data/058_US/qa.csv deleted file mode 100644 index d35f1debddff4252dbf852fb9d66c3ac57e45a06..0000000000000000000000000000000000000000 --- a/data/058_US/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there survey participants who are not registered to vote?,True,boolean,['Are you registered to vote?'],['category'],True -Are there any respondents who participated in the 2016 Presidential election (four years ago) but are not planning to do so in the upcoming election?,True,boolean,"['Did you vote in the 2016 Presidential election? (Four years ago)', 'How likely are you to vote in the forthcoming US Presidential election? Early Voting Open']","['category', 'category']",True -Do we have any respondents in the dataset who didn't participate in the 2016 Presidential election (four years ago) but intend to do so in the upcoming election?,True,boolean,"['Did you vote in the 2016 Presidential election? (Four years ago)', 'How likely are you to vote in the forthcoming US Presidential election? Early Voting Open']","['category', 'category']",True -Do we have respondents who have shifted their voting preference from the 2016 election (four years ago) to the upcoming one?,True,boolean,"['Who did you vote for in the 2016 Presidential election? (Four years ago)', 'Who are you most likely to vote for on election day?']","['category', 'category']",True -How many respondents in the survey are eligible to vote?,1315,number,['Are you registered to vote?'],['category'],11 -What is the count of respondents who are leaning towards voting for Joe Biden in the upcoming election?,799,number,['Who are you most likely to vote for on election day?'],['category'],0 -How many respondents have a high school degree or less as their highest level of education?,0,number,['What is the highest degree or level of school you have *completed* ?'],['category'],0 -How many respondents are from the region adjacent to the South Atlantic Ocean?,774,number,['Division'],['category'],0 -What is the most frequent age group among the respondents?,65+,category,['How old are you?'],['category'],25-34 -Who is the preferred choice among the respondents for the upcoming election?,Donald Trump (Republican),category,['Who are you most likely to vote for on election day?'],['category'],Joe Biden (Democrat) -What is the most commonly achieved educational level among the respondents?,"Some college, no degree",category,['What is the highest degree or level of school you have *completed* ?'],['category'],High school graduate (includes equivalency) -Which geographical division has the greatest representation among the respondents?,South Atlantic,category,['Division'],['category'],South Atlantic -Identify the top 5 states with the greatest number of respondents.,"['CA', 'TX', 'NY', 'FL', 'GA']",list[category],['State'],['category'],"['MI', 'GA', 'AL', 'CA', 'TX']" -Enumerate the top 4 most common professions among the respondents.,"['Other', 'Healthcare', 'Office worker or other professional', 'Industrial (e.g. construction, manufacturing, maintenance and repair)']",list[category],['Which of these best describes the kind of work you do?'],['category'],"['Education and training', 'Office worker or other professional', 'Industrial (e.g. construction, manufacturing, maintenance and repair)', 'Healthcare']" -Identify the top 3 ethnic groups with the most representation among the respondents.,"['White (not Hispanic, Latino or Spanish origin)', 'Black or African American (not Hispanic, Latino or Spanish origin)', 'Hispanic, Latino or Spanish origin']",list[category],['Which of the following best describes your ethnic heritage?'],['category'],"['White (not Hispanic, Latino or Spanish origin)', 'Black or African American (not Hispanic, Latino or Spanish origin)', 'Asian']" -Enumerate the top 6 most represented age groups among the respondents.,"['65+', '55-64', '45-54', '35-44', '18-24', '25-34']",list[category],['How old are you?'],['category'],"['25-34', '55-64', '65+', '35-44', '18-24', '45-54']" -Identify the top 3 counties (using FIPS codes) with the greatest number of respondents.,"[6037, 13121, 48201]",list[number],['County FIPS'],['number[uint16]'],"[13135, 29189, 13095]" -Enumerate the highest 4 unique weight values in the dataset.,"[0.8085780015111617, 0.7742084493732905, 0.3063435812288158, 0.629527660735561]",list[number],['Weight'],['number[double]'],"[4.871233760276248, 0.8475708375044543, 0.7854647440023211, 0.941364535331836]" -List the top 5 urban/rural categories (using NCHS codes) with the greatest number of respondents.,"[1, 2, 3, 4, 5]",list[number],['NCHS Urban/rural'],['number[uint8]'],"[1, 2, 5, 3, 6]" -Enumerate the top 6 most common likelihood values among the respondents.,"[10.0, 11.0, 0.0, 1.0, 9.0, 5.0]",list[number],['likelihood'],['number[UInt8]'],"[10.0, 11.0, 1.0, 2.0, 6.0, 7.0]" diff --git a/data/058_US/sample.csv b/data/058_US/sample.csv deleted file mode 100644 index 551d805e23bf0926274557d6cf7fc01dd589c467..0000000000000000000000000000000000000000 --- a/data/058_US/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Are you registered to vote?,Which of the following best describes your ethnic heritage?,Who are you most likely to vote for on election day?,Division,Did you vote in the 2016 Presidential election? (Four years ago),Weight,How likely are you to vote in the forthcoming US Presidential election? Early Voting Open,State,County FIPS,Who did you vote for in the 2016 Presidential election? (Four years ago),What is the highest degree or level of school you have *completed* ?,NCHS Urban/rural,likelihood,Which of these best describes the kind of work you do?,How old are you? -,Asian,,South Atlantic,No - I was eligible but did not vote,4.871233760276248,,GA,13135,,High school graduate (includes equivalency),2,,,25-34 -,"White (not Hispanic, Latino or Spanish origin)",Joe Biden (Democrat),West North Central,Yes,0.8475708375044543,10 (Will definitely vote),MO,29189,Hillary Clinton (Democrat),Associate's degree,2,10.0,Office worker or other professional,25-34 -Yes,Two or more races,Undecided,East South Central,Yes,0.1549730109564747,10 (Will definitely vote),AL,1083,Donald Trump (Republican),"Some college, no degree",3,10.0,Education and training,55-64 -No - I will not register,"White (not Hispanic, Latino or Spanish origin)",,South Atlantic,No - I was eligible but did not vote,1.969631929375061,,MD,24003,,High school graduate (includes equivalency),2,,,65+ -Yes,"White (not Hispanic, Latino or Spanish origin)",,Pacific,Yes,0.5257957386068345,Have already voted,CA,6065,Donald Trump (Republican),Bachelor's degree,1,11.0,,65+ -Yes,"White (not Hispanic, Latino or Spanish origin)",,Pacific,Yes,0.6161263758138767,Have already voted,CA,6037,Donald Trump (Republican),High school graduate (includes equivalency),1,11.0,"Industrial (e.g. construction, manufacturing, maintenance and repair)",55-64 -Yes,"White (not Hispanic, Latino or Spanish origin)",Joe Biden (Democrat),East North Central,Yes,1.522644295340341,10 (Will definitely vote),MI,26081,Hillary Clinton (Democrat),"Graduate degree, professional degree or PhD",1,10.0,Healthcare,25-34 -,"Black or African American (not Hispanic, Latino or Spanish origin)",Undecided,West South Central,No - I was not eligible to vote for another reason,1.785677299154741,10 (Will definitely vote),TX,48113,,High school graduate (includes equivalency),1,10.0,,35-44 -,"Black or African American (not Hispanic, Latino or Spanish origin)",Joe Biden (Democrat),South Atlantic,No - I was eligible but did not vote,2.273869489671245,1,VA,51143,,High school graduate (includes equivalency),5,1.0,,25-34 -Yes,"White (not Hispanic, Latino or Spanish origin)",Donald Trump (Republican),Pacific,Yes,0.6616253992839105,2,WA,53009,Donald Trump (Republican),High school graduate (includes equivalency),5,2.0,Transportation (inc. freight),45-54 -Yes,"White (not Hispanic, Latino or Spanish origin)",Donald Trump (Republican),East North Central,No - I was not eligible to vote for another reason,1.875535313329686,10 (Will definitely vote),MI,26077,,"Some college, no degree",3,10.0,,35-44 -,"Hispanic, Latino or Spanish origin",Howie Hawkins (Green),Middle Atlantic,Yes,1.330508225439533,1,NJ,34015,Jill Stein (Green),No schooling completed,2,1.0,,35-44 -,"White (not Hispanic, Latino or Spanish origin)",Joe Biden (Democrat),Middle Atlantic,Yes,1.268034535451805,10 (Will definitely vote),NJ,34013,Hillary Clinton (Democrat),Bachelor's degree,1,10.0,,25-34 -,"White (not Hispanic, Latino or Spanish origin)",,West South Central,Yes,0.9837951139593872,Have already voted,TX,48085,I don't remember,"Some college, no degree",1,11.0,,18-24 -Yes,"White (not Hispanic, Latino or Spanish origin)",Undecided,Mountain,Yes,0.3038320874685828,10 (Will definitely vote),NV,32003,Donald Trump (Republican),"Some college, no degree",1,10.0,,55-64 -Yes,"White (not Hispanic, Latino or Spanish origin)",Donald Trump (Republican),West North Central,Yes,0.7866868233825126,10 (Will definitely vote),IA,19125,Donald Trump (Republican),Bachelor's degree,6,10.0,Other,45-54 -Yes,"White (not Hispanic, Latino or Spanish origin)",,Mountain,Yes,0.6309362531683114,Have already voted,CO,8041,Donald Trump (Republican),"Graduate degree, professional degree or PhD",3,11.0,,65+ -Yes,"White (not Hispanic, Latino or Spanish origin)",,East North Central,No - I was not old enough to vote,0.941364535331836,Have already voted,MI,26107,,"Some college, no degree",5,11.0,,18-24 -,"Black or African American (not Hispanic, Latino or Spanish origin)",Joe Biden (Democrat),South Atlantic,No - I was not eligible to vote for another reason,0.7854647440023211,6,GA,13095,,Associate's degree,4,6.0,Education and training,18-24 -Yes,"White (not Hispanic, Latino or Spanish origin)",Donald Trump (Republican),East South Central,Yes,0.4254619353590017,7,AL,1017,Donald Trump (Republican),High school graduate (includes equivalency),5,7.0,,55-64 diff --git a/data/059_Second/qa.csv b/data/059_Second/qa.csv deleted file mode 100644 index 4a5d72f96a165b020260fcdb0216c8c40d8a5535..0000000000000000000000000000000000000000 --- a/data/059_Second/qa.csv +++ /dev/null @@ -1,24 +0,0 @@ -question,answer,columns_used,type,column_types,sample_answer -Is there a car model named 'Golf' listed?,False,['model'],boolean,['category'],False -Are there cars associated with the dealer 'Autos Raymara'?,True,['dealer'],boolean,['category'],False -Is there a car version that mentions 'BMW'?,True,['version'],boolean,['text'],True -Are there cars with a 'Manual' shift?,True,['shift'],boolean,['category'],True -How many unique car models are listed?,940,['model'],number,['category'],19 -"On average, how many photos are provided for the cars?",16.19912,['photos'],number,['number[uint8]'],19.1 -What's the highest price a car is listed for?,549900,['price'],number,['number[uint32]'],61990 -How many cars are from the year 2020?,4237,['year'],number,['number[UInt16]'],3 -Which dealer has the car with the highest price listed?,Supergarage,"['dealer', 'price']",category,"['category', 'number[uint32]']",automotorDURSAN.com A -Which car make is the most common?,VOLKSWAGEN,['make'],category,['category'],BMW -What type of fuel is the most common for the cars?,Diésel,['fuel'],category,['category'],Diésel -In which province are the most cars located?,Madrid,['province'],category,['category'],Madrid -Which are the top 3 provinces with the most car listings?,"['Madrid', 'Barcelona', 'Valencia']",['province'],list[category],['category'],"['Madrid', 'Palencia', 'Málaga']" -List the 4 most common car colors in the dataset.,"['Blanco', 'Gris / Plata', 'Negro', 'Azul']",['color'],list[category],['category'],"['Gris / Plata', 'Negro', 'Azul', 'Gris / Plata (gris)']" -Which 5 car makes are the most prevalent?,"['VOLKSWAGEN', 'BMW', 'MERCEDES-BENZ', 'AUDI', 'PEUGEOT']",['make'],list[category],['category'],"['MERCEDES-BENZ', 'BMW', 'SEAT', 'AUDI', 'HONDA']" -List the 2 least common fuels used in the cars.,"['Gas natural (CNG)', 'Gas licuado (GLP)']",['fuel'],list[category],['category'],"['Híbrido', 'Eléctrico']" -What are the top 4 most common years of the cars?,"[2016.0, 2017.0, 2019.0, 2020.0]",['year'],list[number],['number[UInt16]'],"[2016.0, 2020.0, 2007.0, 2018.0]" -List the 3 highest mileages present.,"[5000000, 4000006, 3500000]",['kms'],list[number],['number[uint32]'],"['370000', '300000', '254000']" -Which 5 cars have the most photos associated?,"[54, 54, 54, 54, 54]",['photos'],list[number],['number[uint8]'],"[54, 44, 40, 32, 29]" -List the 6 cars with the most power.,"[800.0, 800.0, 796.0, 772.0, 720.0, 720.0]",['power'],list[number],['number[UInt16]'],"[258.0, 258.0, -218.0, -182.0, -180.0]" diff --git a/data/059_Second/sample.csv b/data/059_Second/sample.csv deleted file mode 100644 index 213be7da986d7efca09cd8f9d13d89287b3077e6..0000000000000000000000000000000000000000 --- a/data/059_Second/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -fuel,color,shift,make,model,price,year,kms,power,province,dealer,photos,version -Diésel,Gris / Plata (gris),Manual,MERCEDES-BENZ,Clase GLS,61990,2017.0,115242,258.0,Madrid,automotorDURSAN.com A,54,MERCEDES-BENZ Clase GLS GLS 350 d 4MATIC 5p. -Eléctrico,Blanco,Automático,BMW,i3,23975,2018.0,9886,170.0,Madrid,Canalcar,44,BMW i3 94ah 5p. -Diésel,Gris / Plata,Automático,BMW,X6,55490,2018.0,26391,258.0,Madrid,Flexicar Leganés,40,BMW X6 xDrive30d 5p. -Gasolina,Negro (NEGRO PROFUNDO EFECTO PERLA),Automático,VOLKSWAGEN,T-Cross,20490,2020.0,10,115.0,Barcelona,"Evarist Automocio, Sl",32,VOLKSWAGEN TCross Advance 1.0 TSI 85kW 115CV DSG 5p. -Diésel,Azul,Automático,AUDI,A4 Allroad Quattro,27900,2016.0,135000,218.0,Alicante,QualityCars Denia,29,AUDI A4 Allroad Quattro 3.0 TDI 218CV quat S tron unlimited edit 5p. -Diésel,Azul (Dark Sapphire),Automático,JAGUAR,F-Pace,34490,2016.0,67615,180.0,Valencia,Motor Center,29,JAGUAR Fpace 2.0L i4D AWD Automatico Prestige 5p. -Gasolina,Gris / Plata,Automático,MERCEDES-BENZ,Clase C,8490,2008.0,233000,156.0,Málaga,Flexicar Málaga,25,MERCEDES-BENZ Clase C C 180 K Avantgarde Estate 5p. -Diésel,Gris / Plata,Manual,BMW,Serie 3,3300,2004.0,254000,150.0,Madrid,Automóviles San José,22,BMW Serie 3 320D TOURING 5p. -Gasolina,Negro,Manual,HONDA,HR-V,25990,2020.0,8000,182.0,Cantabria,Auto Norte,20,HONDA HRV 1.5 iVTEC Turbo Sport 5p. -Diésel,Azul,Automático,MERCEDES-BENZ,Clase GLC,36900,2017.0,69000,170.0,Málaga,Autos Dominguez,18,MERCEDES-BENZ Clase GLC GLC 220 d 4MATIC 5p. -Diésel,Gris / Plata,Manual,SEAT,Ibiza,18660,2020.0,25,95.0,Pontevedra,Tambo Motor,18,SEAT Ibiza 1.6 TDI 70kW 95CV Style Go 5p. -Diésel,Gris / Plata,Manual,AUDI,A4,18500,2016.0,155000,150.0,A Coruña,Citova,13,AUDI A4 Avant 2.0 TDI 150CV Advanced edition 5p. -Diésel,Rojo (B83 Blazing Red Metalizada),Manual,MINI,MINI,15490,2016.0,65235,116.0,Barcelona,Barcelona Premium,9,MINI MINI COOPER D 5 PUERTAS 5p. -Diésel,Negro,Automático,MERCEDES-BENZ,Clase C,10000,2007.0,200000,,Murcia,6e9f531af5b3410601edffdcadc0494a,8,MERCEDES-BENZ -Diésel,Gris / Plata,Manual,RENAULT,Clio,3700,2007.0,148000,,A Coruña,c707c87831c70b8973c5b7c827f708b9,8,RENAULT Clio 5p. -Gasolina,Negro,Manual,BMW,Serie 5,7700,2005.0,360,,Sevilla,464e07afc9e46359fb480839150595c5,5,BMW Serie 5 5p. -Diésel,Negro,Manual,HONDA,FR-V,3650,2008.0,300000,,Asturias,70483b6e100c9cebbffcdc62dea07eda,3,HONDA FRV 5p. -Híbrido,Gris / Plata,Automático,TOYOTA,Auris,15000,2016.0,44000,136.0,Palencia,3ff65d248cc989caf757c3b78ddca356,3,TOYOTA Auris 1.8 140H Hybrid Active 5p. -Diésel,Gris / Plata,Manual,SEAT,Córdoba,1150,2000.0,370000,,Palencia,5fd6e81b5f6f20a0985a26724f47b95e,2,SEAT Cordoba 3p. -Gasolina,Negro,Manual,FORD,Focus,2800,2005.0,205000,100.0,Madrid,89cf822db294a9af9bb533773cefd8a8,0,FORD Focus 1.6 TREND 3p. diff --git a/data/060_Bakery/qa.csv b/data/060_Bakery/qa.csv deleted file mode 100644 index d065c67e241b3281eaf997217e899402722d2e58..0000000000000000000000000000000000000000 --- a/data/060_Bakery/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are all transactions unique?,True,boolean,[Transaction],['number[uint16]'],True -Is there any transaction that took place during the night?,True,boolean,[period_day],['category'],False -Do all items have transactions recorded on weekdays?,True,boolean,"[Item, weekday_weekend]","['category', 'category']",False -Are there any transactions recorded in the evening on weekends?,True,boolean,"[period_day, weekday_weekend]","['category', 'category']",True -How many unique items are there in the dataset?,95,number,[Item],['category'],12 -On how many different days were transactions recorded?,159,number,[date_time],['category'],20 -What's the highest transaction number?,9684,number,[Transaction],['number[uint16]'],9133 -How many transactions were made during the afternoon?,2478,number,"[Transaction, period_day]","['number[uint16]', 'category']",0 -Which day period has the highest number of transactions?,morning,category,"[period_day, Transaction]","['category', 'number[uint16]']",afternoon -"On weekends, what's the most commonly bought item?",Coffee,category,"[Item, weekday_weekend]","['category', 'category']",Hearty & Seasonal -What's the least popular item bought during weekdays?,Adjustment,category,"[Item, weekday_weekend]","['category', 'category']",Farm House -"During which period of the day is ""Hot chocolate"" most frequently bought?",morning,category,"[Item, period_day]","['category', 'category']", -List the top 3 items most frequently bought in the morning.,"['Coffee', 'Bread', 'Pastry']",list[category],"[Item, period_day]","['category', 'category']","['Coffee', 'Bread', 'Farm House']" -Name the bottom 4 items least purchased during the afternoon.,"['Chicken sand', 'Gift voucher', 'Bare Popcorn', 'Raw bars']",list[category],"[Item, period_day]","['category', 'category']",[] -Identify the top 5 items sold on weekends.,"['Coffee', 'Bread', 'Tea', 'Cake', 'Pastry']",list[category],"[Item, weekday_weekend]","['category', 'category']",[] -What are the 4 items tthat were bought two times in the evening?,"['Art Tray', 'Mighty Protein', 'Mortimer', 'Vegan Mincepie']",list[category],"[Item, period_day]","['category', 'category']",[] -Which are the top 4 transaction numbers with the most items bought?,"[6474, 6716, 6279, 6412]",list[number],[Transaction],['number[uint16]'],"[6103, 1259, 628, 4627]" -Identify the bottom 5 transaction numbers by frequency.,"[9680, 9681, 9682, 9683, 9684]",list[number],[Transaction],['number[uint16]'],"[2711, 956, 7435, 2214, 5816]" -"List the top 6 transaction numbers during which ""Bread"" was purchased.","[6412, 6279, 6474, 6603, 6716, 6642]",list[number],"[Transaction, Item]","['number[uint16]', 'category']","[2850, 7104, 7776, 5816]" -Name the bottom 2 transaction numbers where purchases were made in the morning.,"[9683, 9684]",list[number],"[Transaction, period_day]","['number[uint16]', 'category']",[] diff --git a/data/060_Bakery/sample.csv b/data/060_Bakery/sample.csv deleted file mode 100644 index c469c91db51e466f64a5a9fd5fff1a4f8edfc2bb..0000000000000000000000000000000000000000 --- a/data/060_Bakery/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -date_time,Item,period_day,Transaction,weekday_weekend -06-02-2017 10:56,Farm House,morning,6103,weekday -13-11-2016 14:14,Hearty & Seasonal,afternoon,1259,weekend -27-11-2016 15:43,Brownie,afternoon,2214,weekend -02-03-2017 14:36,Coffee,afternoon,7435,weekday -10-11-2016 10:03,Coffee,morning,956,weekday -06-12-2016 11:08,Coffee,morning,2711,weekday -07-12-2016 11:00,Coffee,morning,2755,weekday -09-12-2016 10:59,Bread,morning,2850,weekday -17-12-2016 17:15,Focaccia,evening,3377,weekend -31-03-2017 13:41,Sandwich,afternoon,9133,weekday -18-01-2017 16:17,Coffee,afternoon,4888,weekday -24-02-2017 12:40,Bread,afternoon,7104,weekday -08-03-2017 09:17,Bread,morning,7776,weekday -26-02-2017 10:20,Tea,morning,7221,weekend -25-11-2016 17:10,Cake,evening,2065,weekday -03-03-2017 13:58,Juice,afternoon,7497,weekday -12-11-2016 12:40,Truffles,afternoon,1152,weekend -13-01-2017 14:05,Coffee,afternoon,4627,weekday -05-11-2016 15:21,Coke,afternoon,628,weekend -02-02-2017 15:35,Bread,afternoon,5816,weekday diff --git a/data/061_Disneyland/qa.csv b/data/061_Disneyland/qa.csv deleted file mode 100644 index ceee7b7616ffa82b340fba2bab2d3064bc82694b..0000000000000000000000000000000000000000 --- a/data/061_Disneyland/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are all the reviews from Australia positive (rating > 3)?,False,boolean,"['Reviewer_Location', 'Rating']","['category', 'number[uint8]']",True -Is Disneyland_HongKong the most reviewed branch?,False,boolean,['Branch'],['category'],False -Are there any reviews with a rating of 1?,True,boolean,['Rating'],['number[uint8]'],True -Does every reviewer location have at least one review with a rating of 5?,False,boolean,"['Reviewer_Location', 'Rating']","['category', 'number[uint8]']",False -How many unique reviewer locations are there?,162,number,['Reviewer_Location'],['category'],9 -What is the average rating for Disneyland_HongKong?,4.204158004158004,number,"['Branch', 'Rating']","['category', 'number[uint8]']",4.25 -What is the maximum review ID?,670801367,number,['Review_ID'],['number[uint32]'],644423763 -How many reviews were made in 2019?,786,number,['Year_Month'],['category'],2 -What is the most common reviewer location?,United States,category,['Reviewer_Location'],['category'],United States -What is the branch with the lowest average rating?,Disneyland_Paris,category,"['Branch', 'Rating']","['category', 'number[uint8]']",Disneyland_California -In which year-month was the most negative review (rating=1) made?,missing,category,"['Year_Month', 'Rating']","['category', 'number[uint8]']",2011-10 -What is the reviewer location with the highest average rating?,Armenia,category,"['Reviewer_Location', 'Rating']","['category', 'number[uint8]']",Australia -What are the top 3 reviewer locations with the most reviews?,"['United States', 'United Kingdom', 'Australia']",list[category],['Reviewer_Location'],['category'],"['United States', 'Australia', 'Malta']" -What are the bottom 2 branches in terms of average rating?,"['Disneyland_Paris', 'Disneyland_HongKong']",list[category],"['Branch', 'Rating']","['category', 'number[uint8]']","['Disneyland_California', 'Disneyland_HongKong']" -What are the top 4 year-months with the most reviews?,"['missing', '2015-8', '2015-7', '2015-12']",list[category],['Year_Month'],['category'],"['missing', '2019-1', '2015-12', '2012-5']" -What are the bottom 3 reviewer locations in terms of average rating?,"['Turks and Caicos Islands', 'South Sudan', 'Suriname']",list[category],"['Reviewer_Location', 'Rating']","['category', 'number[uint8]']","['Canada', 'United States', 'United Kingdom']" -What are the top 5 review IDs in terms of rating?,"[670585330, 670570869, 670443403, 670435886, 670324965]",list[number],"['Review_ID', 'Rating']","['number[uint32]', 'number[uint8]']","[540713188, 576395715, 310041955, 121577468, 441572512]" -What are the bottom 4 review IDs in terms of rating?,"[662641193, 658624005, 649615606, 647822351]",list[number],"['Review_ID', 'Rating']","['number[uint32]', 'number[uint8]']","[119781124, 337648026, 620582661, 124120037]" -What are the top 6 ratings given by reviewers from Australia?,"[5, 5, 5, 5, 5, 5]",list[number],"['Reviewer_Location', 'Rating']","['category', 'number[uint8]']",[5] -What are the bottom 3 ratings given for Disneyland_HongKong?,"[1, 1, 1]",list[number],"['Branch', 'Rating']","['category', 'number[uint8]']","[5, 2]" diff --git a/data/061_Disneyland/sample.csv b/data/061_Disneyland/sample.csv deleted file mode 100644 index 5f560148a60a95e84b367ad6e35430392db6bed6..0000000000000000000000000000000000000000 --- a/data/061_Disneyland/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -Rating,Reviewer_Location,Year_Month,Branch,Review_ID -5,Malta,2017-9,Disneyland_Paris,540713188 -1,Canada,2011-10,Disneyland_California,119781124 -5,Australia,2018-4,Disneyland_HongKong,576395715 -5,United States,2015-9,Disneyland_California,310041955 -4,United States,2013-11,Disneyland_California,184009554 -5,United States,2011-12,Disneyland_California,121577468 -5,United States,2016-11,Disneyland_Paris,441572512 -4,United States,2015-6,Disneyland_California,281268713 -4,United Kingdom,missing,Disneyland_Paris,67523841 -2,United States,2015-12,Disneyland_California,337648026 -5,Denmark,2016-3,Disneyland_California,366838155 -5,Australia,2011-9,Disneyland_California,118997003 -5,South Africa,2019-1,Disneyland_HongKong,643982268 -2,United States,2018-9,Disneyland_HongKong,620582661 -5,Singapore,2018-11,Disneyland_California,635972926 -4,United States,2012-5,Disneyland_California,130860097 -2,United States,missing,Disneyland_California,124120037 -5,New Zealand,missing,Disneyland_California,217677667 -5,Australia,2019-1,Disneyland_HongKong,644423763 -5,United States,2018-6,Disneyland_California,621724235 diff --git a/data/062_Trump/qa.csv b/data/062_Trump/qa.csv deleted file mode 100644 index 453de8b0280d8f0b080e4950a0e9adfb2cb64db6..0000000000000000000000000000000000000000 --- a/data/062_Trump/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are all the tweets in English?,False,boolean,['lang'],['category'],True -Has the author ever been retweeted?,True,boolean,['retweets'],['number[UInt32]'],True -Are there any tweets with more than 10000 retweets?,True,boolean,['retweets'],['number[uint32]'],True -Have any of the tweets been favorited more than 50000 times?,True,boolean,['favorites'],['number[uint32]'],True -How many unique authors are there?,1,number,['author_name'],['category'],1 -What is the average number of retweets?,8774.52044683822,number,['retweets'],['number[uint32]'],8111.15 -What is the maximum number of favorites received for a single tweet?,589793,number,['favorites'],['number[uint32]'],105448 -How many tweets were posted in 2018?,2891,number,['date'],"['date[ns, UTC]']",2 -What is the most common author name?,Donald J. Trump,category,['author_name'],['category'],Donald J. Trump -What is the tweet with the most favorites?,Such a beautiful and important evening! The forgotten man and woman will never be forgotten again. We will all come together as never before,category,"['text', 'favorites']","['text', 'number[uint32]']","I have great confidence that China will properly deal with North Korea. If they are unable to do so, the U.S., with its allies, will! U.S.A." -In which language is the tweet with the most retweets written?,und,category,"['lang', 'retweets']","['category', 'number[uint32]']",en -What is the most common language of the tweets?,en,category,['lang'],['category'],en -What are the top 3 author handlers with the most tweets?,['realDonaldTrump'],list[category],['author_handler'],['category'],['realDonaldTrump'] -What are the bottom 2 languages in terms of tweet count?,"['pt', 'fi']",list[category],['lang'],['category'],['en'] -What are the top 4 mentioned names in the tweets?,"['realDonaldTrump', 'FoxNews', 'CNN', 'foxandfriends']",list[category],['mention_names'],['list[category]'],"['[]', '""realDonaldTrump""]', '[""IvankaTrump""', '""trumpferrypoint""']" -What are the bottom 3 author names in terms of tweet count?,['Donald J. Trump'],list[category],['author_name'],['category'],['Donald J. Trump'] -What are the top 5 tweet IDs in terms of retweet count?,"[881503147168071680, 795954831718498305, 929511061954297857, 796315640307060738, 948355557022420992]",list[number],"['id', 'retweets']","['number[int64]', 'number[uint32]']","[852508752142114816, 900150814081036288, 1017190186269184001, 822501939267141634, 761711856457125888]" -What are the bottom 4 tweet IDs in terms of favorite count?,"[591222909626114050, 591412084895838208, 586751374286721024, 575589231160127489]",list[number],"['id', 'favorites']","['number[int64]', 'number[uint32]']","[603361519192174592, 589632894177533953, 616672730319069185, 618552478653616128]" -What are the top 6 favorite counts of the tweets?,"[589793, 589750, 557779, 539249, 476457, 376626]",list[number],['favorites'],['number[uint32]'],"[105448, 85433, 84944, 79544, 67612, 59793]" -What are the bottom 3 retweet counts of the tweets?,"[1, 1, 2]",list[number],['retweets'],['number[uint32]'],"[10, 14, 376]" diff --git a/data/062_Trump/sample.csv b/data/062_Trump/sample.csv deleted file mode 100644 index e593c16ab9fe68aba65fab855bf23bd18034f0e5..0000000000000000000000000000000000000000 --- a/data/062_Trump/sample.csv +++ /dev/null @@ -1,22 +0,0 @@ -favorites,text,author_name,date,lang,id,retweets,author_handler,mention_names,rp_user_id -24,"""@IvankaTrump: Ivanka talks @trumpferrypoint with @foxbusiness! Watch the video: http://buff.ly/1HuXYuu  DonaldTrump @TrumpGolf #trumpgolf",Donald J. Trump,2015-05-27T00:46:10.000Z,en,603361519192174592,14,realDonaldTrump,"[""IvankaTrump"",""trumpferrypoint"",""foxbusiness"",""TrumpGolf""]", -460,Via @trscoop: “Mark Levin DEFENDS Trump: Hillary Clinton is a CROOK and a FRAUD and she’s not treated this way!”http://therightscoop.com/mark-levin-defends-trump-hillary-clinton-is-a-crook-and-a-fraud-and-shes-not-treated-this-way/ …,Donald J. Trump,2015-07-02T18:20:10.000Z,en,616672730319069185,376,realDonaldTrump,"[""trscoop""]", -29376,"DON'T LET HILLARY CLINTON DO IT AGAIN! -#TrumpPence16https://amp.twimg.com/v/42d9c3c3-e924-4a5b-b8a9-540ec3d21654 …",Donald J. Trump,2016-08-05T23:53:53.000Z,en,761711856457125888,16432,realDonaldTrump,[], -85433,"power from Washington, D.C. and giving it back to you, the American People. #InaugurationDay",Donald J. Trump,2017-01-20T17:51:58.000Z,en,822501939267141634,16873,realDonaldTrump,[],25073877.0 -1205,"MUST READ-""It's time people listened to Trump,' says mother of gunned-down teenage football star"" http://www.dailymail.co.uk/news/article-3152794/My-son-murdered-Mexican-illegal-immigrant-just-like-Kathryn-Steinle-s-time-people-listened-Donald-Trump-says-mother-gunned-teenage-football-star.html#ixzz3fFTjX3Z0 … SECURE THE BORDER!",Donald J. Trump,2015-07-07T22:49:37.000Z,en,618552478653616128,1020,realDonaldTrump,[], -22745,"Lyin' Ted Cruz consistently said that he will, and must, win Indiana. If he doesn't he should drop out of the race-stop wasting time & money",Donald J. Trump,2016-05-03T23:08:44.000Z,en,727636035149139968,6170,realDonaldTrump,[], -1262,"""@marklevinshow: Donald Trump will be on my show tonight at 8:30 PM eastern time."" Will be my great honor, so much to talk about!",Donald J. Trump,2015-10-05T23:48:57.000Z,en,651182319051476993,661,realDonaldTrump,"[""marklevinshow""]", -6062,"Thank you, Piers, they don't know what they're getting into.https://twitter.com/piersmorgan/status/674621112131723264 …",Donald J. Trump,2015-12-09T16:13:22.000Z,en,674622875521974272,2857,realDonaldTrump,[], -30,"""@Bigbillyp1970: YES-YES-YES @realDonaldTrump *2016-2016-2016*""",Donald J. Trump,2015-04-19T03:33:31.000Z,en,589632894177533953,10,realDonaldTrump,"[""Bigbillyp1970"",""realDonaldTrump""]", -17259,Happy Thanksgiving to all. Have a great day and look forward to the future. We will MAKE AMERICA GREAT AGAIN!,Donald J. Trump,2015-11-26T12:52:15.000Z,en,669861224465743872,6857,realDonaldTrump,[], -35260,".@foxandfriends, we are in record territory in all things having to do with our economy!https://twitter.com/realdonaldtrump/status/935640578007687168 …",Donald J. Trump,2017-11-29T11:32:46.000Z,en,935833914761007114,7385,realDonaldTrump,"[""foxandfriends""]", -67612,It now turns out that the phony allegations against me were put together by my political opponents and a failed spy afraid of being sued....,Donald J. Trump,2017-01-13T11:05:55.000Z,en,819863039902097408,16294,realDonaldTrump,[], -2775,The legendary @BarbaraJWalters will be asking me questions about the Presidential campaign on @WNTonight at 6:30 PM.,Donald J. Trump,2015-12-08T21:21:48.000Z,en,674338110264762369,1014,realDonaldTrump,"[""BarbaraJWalters"",""WNTonight""]", -105448,"I have great confidence that China will properly deal with North Korea. If they are unable to do so, the U.S., with its allies, will! U.S.A.",Donald J. Trump,2017-04-13T13:08:20.000Z,en,852508752142114816,25695,realDonaldTrump,[], -79544,"Billions of additional dollars are being spent by NATO countries since my visit last year, at my request, but it isn’t nearly enough. U.S. spends too much. Europe’s borders are BAD! Pipeline dollars to Russia are not acceptable!",Donald J. Trump,2018-07-11T23:33:33.000Z,en,1017190186269184001,18111,realDonaldTrump,[], -84944,"THANK YOU to all of the great men and women at the U.S. Customs and Border Protection facility in Yuma, Arizona & around the United States!pic.twitter.com/tjFx8XjhDz",Donald J. Trump,2017-08-23T00:20:52.000Z,en,900150814081036288,18679,realDonaldTrump,[], -7675,"Well, the year has officially begun. I have many stops planned and will be working very hard to win so that we can turn our country around!",Donald J. Trump,2016-01-01T23:00:09.000Z,en,683060169677344768,2368,realDonaldTrump,[], -1532,"""@paulfincher2 Keep the faith. Elect @realDonaldTrump and there will be no more sanctuary cities. and very few illegals.""",Donald J. Trump,2015-10-09T19:46:40.000Z,en,652570897480589312,781,realDonaldTrump,"[""paulfincher2"",""realDonaldTrump""]", -59793,"Unlike what the Failing and Corrupt New York Times would like people to believe, there is ZERO disagreement within the Trump Administration as to how to deal with North Korea...and if there was, it wouldn’t matter. The @nytimes has called me wrong right from the beginning!",Donald J. Trump,2018-05-26T15:03:33.000Z,en,1000391997969092608,14275,realDonaldTrump,"[""nytimes""]", -21288,"I have brought millions of people into the Republican Party, while the Dems are going down. Establishment wants to kill this movement!",Donald J. Trump,2016-03-03T11:30:35.000Z,en,705354682886201348,6351,realDonaldTrump,[], diff --git a/data/063_Influencers/qa.csv b/data/063_Influencers/qa.csv deleted file mode 100644 index 67a5190ead20a6b760e2f22b88781d37a95fb142..0000000000000000000000000000000000000000 --- a/data/063_Influencers/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any organizations in the dataset?,False,boolean,['is_organization'],['boolean'],False -Are there any individuals (non-organizations) in the dataset?,True,boolean,['is_organization'],['boolean'],True -Do all entities have a picture?,True,boolean,['pic'],['url'],True -Are there any entities with a weight greater than 500?,True,boolean,['weight'],['number[double]'],False -How many unique communities are there?,9,number,['community'],['number[uint32]'],6 -What is the average page rank norm?,0.08848033260794509,number,['page_rank_norm'],['number[double]'],0.0923955552240227 -What is the maximum weight of an entity?,770.5,number,['weight'],['number[double]'],324.5 -How many entities have a community identifier of 16744206?,651,number,['community'],['number[uint32]'],11 -What is the most common name?,Christophe Viau,category,['name'],['category'],Peter Skomoroch -Which entity has the highest page rank norm?,Mike Bostock,category,"['name', 'page_rank_norm']","['category', 'number[double]']",Data Science Fact -What is the picture URL of the entity with the maximum weight?,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/457/thumb/Saw-whet_Owl_10_normal.jpg?1517502050,category,"['pic', 'weight']","['url', 'number[double]']",https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/416/thumb/8f8493dfc040e56ef7ff8f59f9474774_normal.jpeg?1517502015 -Which entity has the highest y-coordinate?,The Gibson Project,category,"['name', 'y']","['category', 'number[double]']",Paul Klemm -What are the top 3 entity names with the highest weights?,"['Lynn Cherny', 'Alberto Cairo', 'Sinan Aral']",list[category],"['name', 'weight']","['category', 'number[double]']","['Peter Skomoroch', 'Nieman Lab', 'Munmun De Choudhury']" -What are the bottom 2 entities in terms of page rank norm?,"['LIFE', 'New Options Project']",list[category],"['name', 'page_rank_norm']","['category', 'number[double]']","['Christopher', 'Chris J. Headleand']" -What are the top 4 entities with the highest x-coordinates?,"['Detective.io', 'The Thrust', 'Open Budgets', 'NewsFuturist']",list[category],"['name', 'x']","['category', 'number[double]']","['Catherine Rampell', 'Nieman Lab', 'Deok Gun Park', 'ESFL']" -What are the bottom 3 entities in terms of y-coordinates?,"['digital PR cat', 'Ismail Onur Filiz', 'Dave Golland']",list[category],"['name', 'y']","['category', 'number[double]']","['ESFL', 'WikipediaLiveMonitor', 'Alberto Perdomo']" -What are the top 5 entity IDs in terms of weight?,"[568, 423, 6392, 4548, 579]",list[number],"['id', 'weight']","['number[uint32]', 'number[double]']","[527, 498, 411, 8031, 91203]" -What are the bottom 4 entity IDs in terms of page rank norm?,"[177, 240, 294, 369]",list[number],"['id', 'page_rank_norm']","['number[uint32]', 'number[double]']","[35070, 35046, 35106, 35054]" -What are the top 6 page rank norms of the entities?,"[1.0, 0.6029751067034804, 0.5666707687637932, 0.5202348027491394, 0.5110606617858531, 0.5081183103684572]",list[number],['page_rank_norm'],['number[double]'],"[0.4905835057931528, 0.2858285808469396, 0.2194061763508274, 0.2171608658929857, 0.1213462154304343, 0.1023296876109954]" -What are the bottom 3 weights of the entities?,"[1.0, 1.0, 1.0]",list[number],['weight'],['number[double]'],"[3.0, 5.0, 7.0]" diff --git a/data/063_Influencers/sample.csv b/data/063_Influencers/sample.csv deleted file mode 100644 index 96ebf7d512e9afe931bff622608674061b8ffcef..0000000000000000000000000000000000000000 --- a/data/063_Influencers/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -name,weight,x,is_organization,community,id,page_rank_norm,y,pic -Christopher,63.0,1046.85985057688,False,16744206,35070,0.0011644932467731,1511.9625723548768,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/042/951/thumb/96c0590f41f0f9af2e4176b0f6890467_normal.jpeg?1517565061 -Chris J. Headleand,7.0,-504.8607865514298,False,16744206,35046,0.0016400009027285,1708.065073048048,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/042/927/thumb/twitter_normal.jpg?1517565042 -Deok Gun Park,17.0,1068.8035102675362,False,16744206,35106,0.0031516818965411,-679.8269352210291,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/042/986/thumb/IMG_0142_normal.JPG?1517565084 -Paul Klemm,42.0,834.5589498168492,False,16744206,35054,0.0036353770062821,1731.8423075515468,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/042/935/thumb/fInJx-KC_normal.jpg?1517565047 -ESFL,5.0,1065.7844808162706,False,14907330,267,0.0043617313078933,-1277.5365883054549,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/156/thumb/lBLq_SvM_normal.jpg?1517501819 -WikipediaLiveMonitor,3.0,-355.1344983695895,False,9197131,181,0.0102051567888091,-1200.1631752409328,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/070/thumb/uyjlf-A9_normal.png?1517501749 -Alberto Perdomo,18.0,-653.226927038042,False,14034728,489,0.0166821309632443,-700.3419060894585,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/378/thumb/DkMgC9Ex_normal.jpeg?1517501986 -Lothar Krempel,124.0,100.84325275278694,False,2062260,42755,0.0350801041472904,611.4034093897568,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/049/121/thumb/krempel_lothar_normal.jpg?1517569346 -Robert Harris,102.0,908.18971418739,False,16744206,35121,0.0362281625727942,1308.4419715755362,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/043/001/thumb/glyph-glow-500x483_normal.png?1517565094 -Hortonworks,42.0,607.9121194346857,False,16744206,282,0.0366773129253865,-659.4966384557767,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/171/thumb/f1b6f0596bdda20a9142b0c4ad23dc11_normal.png?1517501831 -complexitat.cat,117.0,-776.7657429626081,False,2062260,315,0.0424743661076428,304.8443024128713,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/204/thumb/complexitat.CAT_normal.jpg?1517501855 -MIT Visualization Group,61.0,468.6863757282269,False,16744206,155963,0.0516733517977699,1534.0082455431257,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/156/537/thumb/7Gb2I-74_normal.jpg?1545643719 -Vega & Vega-Lite,131.5,796.1933529772459,False,16744206,91203,0.0825439390118387,1403.4402475642626,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/096/229/thumb/IraxcAMI_normal.jpg?1517601308 -Moira Burke,132.0,226.555226293652,False,2062260,8031,0.0857382638801249,3.696087831845453,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/023/847/thumb/z3OuzS_c_normal.jpeg?1517507265 -Munmun De Choudhury,216.5,117.88775498251576,False,2062260,411,0.1023296876109954,97.01691057874534,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/300/thumb/bfbb7731025ed0997e202cabe8c36e0f_normal.jpeg?1517501929 -Catherine Rampell,76.5,1226.3728139334712,False,16744206,526,0.1213462154304343,-317.94169853888553,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/415/thumb/WqfzPQ9b_normal.jpeg?1517502014 -DeepMind,120.0,59.79731432121585,False,2062260,43941,0.2171608658929857,552.8746872865486,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/050/302/thumb/NzcXa05I_normal.png?1517570178 -Peter Skomoroch,324.5,542.3357087054555,False,16744206,527,0.2194061763508274,250.43889962348,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/416/thumb/8f8493dfc040e56ef7ff8f59f9474774_normal.jpeg?1517502015 -Nieman Lab,251.5,1174.45795206497,False,16744206,498,0.2858285808469396,127.09248222777484,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/016/387/thumb/pzZXwZC8_normal.jpeg?1517501993 -Data Science Fact,100.0,0.1974799057110523,False,12369186,204402,0.4905835057931528,530.2165391499066,https://storage.googleapis.com/pics.contexto.io/photos/paperclips/000/204/535/thumb/sMpYCCGn_normal.jpg?1590839982 diff --git a/data/064_Clustering/qa.csv b/data/064_Clustering/qa.csv deleted file mode 100644 index 7a7fdb78e7feed7959fb34c08e6fd0b30f4f6c31..0000000000000000000000000000000000000000 --- a/data/064_Clustering/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are there any animals with feathers in the dataset?,True,boolean,['feathers'],['number[uint8]'],True -Are there any venomous animals in the dataset?,True,boolean,['venomous'],['number[uint8]'],True -Do all animals breathe?,False,boolean,['breathes'],['number[uint8]'],False -Are there any domesticated animals in the dataset?,True,boolean,['domestic'],['number[uint8]'],True -How many unique types of animals are there?,7,number,['class_type'],['number[uint8]'],20 -What is the average number of legs?,2.8415841584158414,number,['legs'],['number[uint8]'],3.1 -What is the maximum number of legs an animal has?,8,number,['legs'],['number[uint8]'],6 -How many animals are there with 2 legs?,27,number,['legs'],['number[uint8]'],4 -What is the most common class type?,1,category,['class_type'],['number[uint8]'],1 -What is the name of the animal with 8 legs?,octopus,category,"['animal_name', 'legs']","['category', 'number[uint8]']", -What is the class type of the animal with the most legs?,7,category,"['class_type', 'legs']","['number[uint8]', 'number[uint8]']",6 -What is the name of the animal that is venomous?,frog,category,"['animal_name', 'venomous']","['category', 'number[uint8]']",honeybee -What are the top 3 animal names with the most legs?,"['octopus', 'scorpion', 'crayfish']",list[category],"['animal_name', 'legs']","['category', 'number[uint8]']","['honeybee', 'gnat', 'wasp']" -What are the bottom 2 animal names in terms of the number of legs?,"['bass', 'carp']",list[category],"['animal_name', 'legs']","['category', 'number[uint8]']","['porpoise', 'dogfish']" -What are the most common 4 class types with the most animals?,"[1, 2, 4, 7]",list[category],['class_type'],['number[uint8]'],"[1, 6, 4, 2]" -What are the least common 3 class types with the least animals?,"[5, 3, 6]",list[category],['class_type'],['number[uint8]'],"[4, 2, 7]" -What are the most common 5 class types with the most combined total legs?,"[1, 6, 2, 7, 5]",list[number],"['class_type', 'legs']","['number[uint8]', 'number[uint8]']","[1, 6, 2, 4, 7]" -What are the bottom 4 class types with the least combined total legs?,"[4, 3, 5, 7]",list[number],"['class_type', 'legs']","['number[uint8]', 'number[uint8]']","[4, 7, 2, 6]" -What are the most common 6 numbers of legs that animals have?,"[4, 2, 0, 6, 8, 5]",list[number],['legs'],['number[uint8]'],"[4, 2, 0, 6]" -What are the least common 3 numbers of legs that animals have?,"[5, 8, 6]",list[number],['legs'],['number[uint8]'],"[2, 0, 6]" diff --git a/data/064_Clustering/sample.csv b/data/064_Clustering/sample.csv deleted file mode 100644 index a156c30372be74ae4b85404e0a60f705969372fa..0000000000000000000000000000000000000000 --- a/data/064_Clustering/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -legs,domestic,breathes,class_type,venomous,feathers,animal_name -2,0,1,1,0,0,squirrel -4,0,1,1,0,0,oryx -0,0,1,1,0,0,porpoise -4,0,1,1,0,0,puma -4,0,1,1,0,0,lion -6,1,1,6,1,0,honeybee -4,0,1,1,0,0,elephant -4,0,1,1,0,0,leopard -4,0,1,1,0,0,cheetah -4,0,1,1,0,0,aardvark -0,0,0,4,0,0,dogfish -6,0,1,6,0,0,gnat -6,0,1,6,1,0,wasp -2,0,1,2,0,1,gull -0,0,0,7,1,0,seawasp -4,0,1,1,0,0,boar -2,0,1,1,0,0,vampire -2,0,1,2,0,1,skimmer -0,0,0,4,0,0,chub -4,1,1,1,0,0,goat diff --git a/data/065_RFM/qa.csv b/data/065_RFM/qa.csv deleted file mode 100644 index 9ae6114963356508b8a3071000ee8885104e5ba3..0000000000000000000000000000000000000000 --- a/data/065_RFM/qa.csv +++ /dev/null @@ -1,21 +0,0 @@ -question,answer,type,columns_used,column_types,sample_answer -Are all the quantities greater than 0?,False,boolean,['Quantity'],['number[int32]'],False -Are all the unit prices greater than 0?,False,boolean,['UnitPrice'],['number[double]'],True -Are there any missing customer IDs?,True,boolean,['CustomerID'],['number[UInt16]'],True -Are there any transactions from the United Kingdom?,True,boolean,['Country'],['category'],True -How many unique stock codes are there?,4070,number,['StockCode'],['category'],20 -What is the average unit price?,4.611113626088513,number,['UnitPrice'],['number[double]'],3.5415 -What is the maximum quantity ordered in a single transaction?,80995,number,['Quantity'],['number[int32]'],25 -How many transactions were made in the United Kingdom?,495478,number,['Country'],['category'],19 -Which country made the most transactions?,United Kingdom,category,['Country'],['category'],United Kingdom -What is the description of the item with the highest unit price?,Manual,category,"['Description', 'UnitPrice']","['category', 'number[double]']",RETROSPOT LAMP -What is the description of the item with the highest quantity ordered?,"PAPER CRAFT , LITTLE BIRDIE",category,"['Description', 'Quantity']","['category', 'number[int32]']",BLUE POLKADOT WRAP -Which country does the customer with the lowest ID come from?,United Kingdom,category,"['Country', 'CustomerID']","['category', 'number[UInt16]']",Australia -What are the descriptions of the top 3 items with the highest quantities ordered?,"['PAPER CRAFT , LITTLE BIRDIE', 'MEDIUM CERAMIC TOP STORAGE JAR', 'ASSTD DESIGN 3D PAPER STICKERS']",list[category],"['Description', 'Quantity']","['category', 'number[int32]']","['BLUE POLKADOT WRAP', 'HANGING JAM JAR T-LIGHT HOLDER', 'FAIRY CAKE FLANNEL ASSORTED COLOUR']" -What are the descriptions of the bottom 2 items with the lowest quantities ordered?,"['PAPER CRAFT , LITTLE BIRDIE', 'MEDIUM CERAMIC TOP STORAGE JAR']",list[category],"['Description', 'Quantity']","['category', 'number[int32]']","['HANGING METAL STAR LANTERN', 'LARGE CAKE TOWEL CHOCOLATE SPOTS']" -Which 4 countries made the most transactions?,"['United Kingdom', 'Germany', 'France', 'EIRE']",list[category],['Country'],['category'],"['United Kingdom', 'Australia']" -Which 3 countries made the least transactions?,"['Saudi Arabia', 'Bahrain', 'Czech Republic']",list[category],['Country'],['category'],"['United Kingdom', 'Australia']" -What are the invoice numbers of the top 5 transactions with the highest quantities ordered?,"['581483', '541431', '578841', '542504', '573008']",list[number],"['InvoiceNo', 'Quantity']","['number[UInt32]', 'number[int32]']","['548005', '555200', '573399', '577076', '568909']" -What are the stock codes of the bottom 4 transactions with the lowest quantities ordered?,"[23843, 21366, 23005, 23005, 84347]",list[number],"['InvoiceNo', 'Quantity']","['number[UInt32]', 'number[int32]']","['22465', '21109', '82484', '22799', '82600']" -What are the six most commonly ordered quantities?,"[1, 2, 12, 6, 4, 3]",list[number],['Quantity'],['number[int32]'],"[1, 4, 2, 12, 24, 3]" -What are the three least commonly ordered quantities?,"[-312, -79, 404]",list[number],['Quantity'],['number[int32]'],"[6, 25, -5]" diff --git a/data/065_RFM/sample.csv b/data/065_RFM/sample.csv deleted file mode 100644 index f585c12dcab6f9301253e4500536150b74299e52..0000000000000000000000000000000000000000 --- a/data/065_RFM/sample.csv +++ /dev/null @@ -1,21 +0,0 @@ -InvoiceNo,Country,StockCode,Description,Quantity,CustomerID,UnitPrice -555200,United Kingdom,71459,HANGING JAM JAR T-LIGHT HOLDER,24,17315.0,0.85 -554974,United Kingdom,21128,GOLD FISHING GNOME,4,14031.0,6.95 -550972,United Kingdom,21086,SET/6 RED SPOTTY PAPER CUPS,4,14031.0,0.65 -576652,United Kingdom,22812,PACK 3 BOXES CHRISTMAS PANETTONE,3,17198.0,1.95 -546157,United Kingdom,22180,RETROSPOT LAMP,2,13502.0,9.95 -576200,United Kingdom,82482,WOODEN PICTURE FRAME WHITE FINISH,2,15572.0,2.95 -577076,United Kingdom,22614,PACK OF 12 SPACEBOY TISSUES,12,14362.0,0.39 -568909,United Kingdom,22596,CHRISTMAS STAR WISH LIST CHALKBOARD,12,16818.0,1.25 -578072,United Kingdom,21109,LARGE CAKE TOWEL CHOCOLATE SPOTS,1,17759.0,6.75 -560491,Australia,23297,SET 40 HEART SHAPE PETIT FOUR CASES,2,12415.0,1.65 -545721,United Kingdom,82484,WOOD BLACK BOARD ANT WHITE FINISH,1,15039.0,7.9 -573399,United Kingdom,21108,FAIRY CAKE FLANNEL ASSORTED COLOUR,18,13949.0,0.79 -551954,United Kingdom,22799,SWEETHEART WIRE FRUIT BOWL,1,14515.0,8.5 -563745,United Kingdom,22115,METAL SIGN EMPIRE TEA,12,15159.0,0.79 -540418,United Kingdom,22161,HEART DECORATION RUSTIC HANGING ,5,,0.81 -546237,United Kingdom,84508A,CAMOUFLAGE DESIGN TEDDY,6,16625.0,2.55 -548005,United Kingdom,21499,BLUE POLKADOT WRAP,25,14128.0,0.42 -539000,United Kingdom,22622,BOX OF VINTAGE ALPHABET BLOCKS,4,13092.0,9.94 -545715,United Kingdom,82600,NO SINGING METAL SIGN,1,,4.13 -C572489,United Kingdom,22465,HANGING METAL STAR LANTERN,-5,17119.0,1.65