remove old csv

data/001_Forbes/qa.csv

@@ -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]']",[]

data/001_Forbes/sample.csv

@@ -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

data/002_Titanic/qa.csv

@@ -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]"

data/002_Titanic/sample.csv

@@ -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

data/003_Love/qa.csv

@@ -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]"

data/003_Love/sample.csv

@@ -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

data/004_Taxi/qa.csv

@@ -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]"

data/004_Taxi/sample.csv

@@ -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

data/005_NYC/qa.csv

@@ -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]"

data/005_NYC/sample.csv

@@ -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

data/006_London/qa.csv

@@ -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]"

data/006_London/sample.csv

@@ -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

data/007_Fifa/qa.csv

@@ -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<gx:number>', 'Potential<gx:number>']","['number[uint8]', 'number[uint8]']",False
-Are there any players who joined their current club before they were 18 years old?,True,boolean,"['Joined<gx:date>', 'Age<gx:number>']","['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<gx:category>', 'Nationality<gx:category>']","['category', 'category']",False
-Are there any players who are taller than 6 feet and have an agility score above 90?,False,boolean,"['Height_ft<gx:number>', 'Agility<gx:number>']","['number[double]', 'number[uint8]']",False
-What is the average overall score of players from France?,67.861432,number,"['Nationality<gx:category>', 'Overall<gx:number>']","['category', 'number[uint8]']",
-How many unique clubs are there in the dataset?,683,number,['Club<gx:category>'],['category'],19
-What is the highest value (in €) of a player in the dataset?,105500000,number,['Value_€<gx:currency>'],['number[uint32]'],13500000
-How many players have the position 'ST'?,414,number,['Position<gx:category>'],['category'],1
-What is the most common nationality in the dataset?,England,category,['Nationality<gx:category>'],['category'],Ghana
-What is the most common preferred foot amongst players?,Right,category,['Preferred Foot<gx:category>'],['category'],Right
-Which club has the most players in the dataset?,Crystal Palace,category,['Club<gx:category>'],['category'],Lech Poznań
-What is the most common position of players in the dataset?,SUB,category,['Position<gx:category>'],['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<gx:category>', 'Overall<gx:number>']","['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<gx:category>', 'Value_€<gx:currency>']","['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<gx:category>', 'Agility<gx:number>']","['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<gx:category>', 'Potential<gx:number>']","['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<gx:number>'],['number[uint8]'],"[79, 77, 77]"
-What are the bottom 5 potential scores in the dataset?,"[48, 48, 49, 50, 50]",list[number],['Potential<gx:number>'],['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_€<gx:currency>'],['number[uint32]'],"[13500000, 7500000, 5500000, 5500000]"
-What are the top 2 wages (in €) of players in the dataset?,"[560000, 370000]",list[number],['Wage_€<gx:currency>'],['number[uint32]'],"[47000, 29000]"

data/007_Fifa/sample.csv

@@ -1,21 +0,0 @@
-Joined<gx:date>,Overall<gx:number>,Age<gx:number>,Position<gx:category>,Wage_€<gx:currency>,Preferred Foot<gx:category>,Potential<gx:number>,Agility<gx:number>,Nationality<gx:category>,Height_ft<gx:number>,Value_€<gx:currency>,Club<gx:category>
-,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

data/008_Tornados/qa.csv

@@ -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]"

data/008_Tornados/sample.csv

@@ -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

data/009_Central/qa.csv

@@ -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]"

data/009_Central/sample.csv

@@ -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

data/010_ECommerce/qa.csv

@@ -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]"

data/010_ECommerce/sample.csv

@@ -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

data/011_SF/qa.csv

@@ -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]"

data/011_SF/sample.csv

@@ -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

data/012_Heart/qa.csv

@@ -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]"

data/012_Heart/sample.csv

@@ -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

data/013_Roller/qa.csv

@@ -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]"

data/013_Roller/sample.csv

@@ -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,

data/014_Airbnb/qa.csv

@@ -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]"

data/014_Airbnb/sample.csv

@@ -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!<br /><br /><b>The space</b><br />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.  <br />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. <br /><br />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.<br /><br /><b>The space</b><br />Calefacción independiente, aire acondicionado, tv, wifi, secador de pelo, plancha entre otros.<br /><br />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.<br />El d","Lavapies, catalogado como el barrio mas Cool del Mundo por la revista Time Out: <br />""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"".<br /><br />Bares, restaurantes, teatros, peluquerías, panadería, locutorios, farmacia, floristerías, Carrefour 24 horas a 3 minutos del piso.<br /><br />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.  <br /><br />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.<br /><br />Come and check out the views from the terrace, you will love them!<br /><br /><b>The space</b><br />The apartmernt is recently renovated and furnished, and it counts with empty closets at your disposal.<br /><br />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.<br /><br />It is only allowed to smoke in the outdoor balcony. Parties are not allowed because we care for our neigbours.<br /><br /><b>Guest access</b><br />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.<br />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.<br /><br /><b>Other things to note</b><br />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.<br /><br /><b>The space</b><br />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.<br /><br /><b>Guest access</b><br />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 ...<br />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.  
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-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.<br />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.<br /><br /><b>The space</b><br />The apartment, modern and really bright, has been refurbished and it is completely equipped with all the facilities. <br /><br />It has a comfortable living room with a sofa-bed and with access to the American style kitchen and a dining area. <br /><br />The bedroom is wide and bright with two twin beds. The bathroom is completely equipped and has a modern shower. <br /><br />The common areas of the building are very nice.<br /><br /><b>Guest access</b><br />During the stay, guests have access to the entire house.<b",,https://a0.muscache.com/pictures/f76bd423-cb77-4919-9bc9-209aa67b026c.jpg,125126426,https://www.airbnb.com/users/show/125126426,Jelisaveta Elizabeth Y Javier,2017-04-10,"Madrid, Spain","Hola soy Jelisaveta!
-Soy una persona curiosa y es lo que me ha llevado a vivir en distintos países, conocer culturas y viajar mucho.
-Vivo en Madrid pero estoy prácticamente todo el tiempo fuera por viajes, así que he decidido compartir este increíble 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 gente 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!
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-*** 
-
-Hi I'm Jelisaveta!
-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/8918ca6e-9aaa-445f-bf53-7bc750bd7660.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/8918ca6e-9aaa-445f-bf53-7bc750bd7660.jpg?aki_policy=profile_x_medium,Recoletos,1.0,3.0,"['email', 'phone', 'work_email']",t,t,,Jerónimos,Retiro,40.42061,-3.6843,Entire rental unit,Entire home/apt,4,,1 bath,1.0,3.0,"[""Heating"", ""Air conditioning"", ""Essentials"", ""Hair dryer"", ""Washer"", ""Dryer"", ""Shampoo"", ""Wifi"", ""Elevator"", ""Dishes and silverware"", ""Iron"", ""Coffee maker"", ""Cooking basics"", ""Dishwasher"", ""Hangers"", ""TV"", ""Refrigerator"", ""Kitchen"", ""Long term stays allowed""]",$104.00,1,1125,2.0,5.0,1125.0,1125.0,4.7,1125.0,,t,7,15,31,283,2022-12-13,87,7,0,2017-04-16,2022-11-08,4.47,4.52,4.63,4.79,4.81,4.95,4.35,,f,1,1,0,0,1.26
-672918689302503497,https://www.airbnb.com/rooms/672918689302503497,20221213034110,2022-12-13,city scrape,NEW Suite ONE Madrid centro con aire acondicionado,"Disfruta de este espacio amplio, moderno con baño privado y aire acondicionado en este verano tan caluroso",,https://a0.muscache.com/pictures/b0647929-5112-4cab-a7c9-eede115aca80.jpg,436119926,https://www.airbnb.com/users/show/436119926,Joe,2021-12-15,"Madrid, Spain",,within an hour,99%,100%,f,https://a0.muscache.com/im/pictures/user/dda1d38f-7dcc-4b30-8bf1-099b83947bbf.jpg?aki_policy=profile_small,https://a0.muscache.com/im/pictures/user/dda1d38f-7dcc-4b30-8bf1-099b83947bbf.jpg?aki_policy=profile_x_medium,,15.0,18.0,"['email', 'phone']",t,t,,Sol,Centro,40.4180985,-3.7075094,Private room in rental unit,Private room,1,,1 private bath,1.0,1.0,"[""Heating"", ""Air conditioning"", ""Smoke alarm"", ""Lock on bedroom door"", ""Fire extinguisher"", ""Dedicated workspace"", ""Wifi"", ""TV"", ""Paid parking on premises"", ""Long term stays allowed""]",$48.00,1,365,1.0,1.0,1125.0,1125.0,1.0,1125.0,,t,16,16,23,298,2022-12-13,29,29,3,2022-07-30,2022-11-28,4.38,4.66,4.45,4.62,4.83,4.97,4.31,,t,15,2,13,0,6.35
-38820946,https://www.airbnb.com/rooms/38820946,20221213034110,2022-12-13,previous scrape,Habitación con baño privado,"Habitación luminosa con ventana exterior.  Dispone de una cama doble para uso individual y un baño doble dentro de la habitación con total privacidad. El Wifi es fibra óptica y funciona muy bien. El piso está bien cuidado y limpio.<br />Este alojamiento es perfecto para personas que busquen un lugar tranquilo, con privacidad, para descansar y relajarse cuando lo necesiten.<br /><br /><b>The space</b><br />Es un apartamento espacioso, compartido conmigo.<br /><br /><b>Guest access</b><br />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.<br /><br /><b>Other things to note</b><br />.","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.<br /><br />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.<br /><br /><b>The space</b><br />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.<br /><br />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. <br /><br />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!
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-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:&nbsp;<br />Electricity, gas and water up to 200 &euro; per month.<br />Booking fee (includes final cleaning).<br />All our rentals include a Luxury complementary set of bathroom amenities.&nbsp;<br />Services not included in the price:<br />4% additional for Payments with American Express (Visa and Mastercard have no surcharge)<br />Weekly cleaning fee 45,00 &euro; &nbsp;VAT included. &nbsp;(Not included changing of bed linen).<br />Bed linen change: 10 &euro; VAT included (Per set of linen)<br />Complete bed linen change: 15 &euro; VAT included (Bed sheets + towels)&nbsp;<br />Service pack of ammenities by L&acute;Occitane 6,05 &euro; VAT included.<br />Pets are not allowed.<br />Parking under request.<br />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.<br />EARLY CHECK IN AND LATE CHECK OUT * CHECK RATES (SUBJECT TO AVAILABILITY)<br />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.<br /><br /><b>The space</b><br />Casa compartida de 3 dormitorios y 3 baños completos. Zonas comunes amplias, cocina, comedor, y Terraza cerrada con comodos sofás...<br /><br /><b>Guest access</b><br />Tendrás acceso al comedor, terraza, cocina y aseo.<br /><br /><b>Other things to note</b><br />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.<br /><br /><b>The space</b><br />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.<br /><br />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.<br /><br />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.<br /><br />It is located in the heart of Madrid, next to Gran Vía and between Fuencarral and Barquillo streets.<br /><br />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.<br /><br />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.<br /><br />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<br /><br /><b>The space</b><br />cómodas instalaciones, ambiente de hogar con espacios amplios y confortables , cerca de estaciones de metro y paradas de buses , bares , tiendas , restaurantes y servicios<br /><br /><b>Guest access</b><br />áreas comunes amplias y agradables , terraza , salón , cocina , baño muy amplio<br /><br /><b>Other things to note</b><br />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.<br /><br /><b>License number</b><br />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. <br /> <br />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.  <br /> <br />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.  <br /> <br />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 =)<br /><br /><b>License number</b><br />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

data/015_Food/qa.csv

@@ -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]"

data/015_Food/sample.csv

@@ -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

data/016_Holiday/qa.csv

@@ -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]"

data/016_Holiday/sample.csv

@@ -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

data/017_Hacker/qa.csv

@@ -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]"

data/017_Hacker/sample.csv

@@ -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,

data/018_Staff/qa.csv

@@ -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]"

data/018_Staff/sample.csv

@@ -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

data/019_Aircraft/qa.csv

@@ -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]"

data/019_Aircraft/sample.csv

@@ -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

data/020_Real/qa.csv

@@ -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]"

data/020_Real/sample.csv

@@ -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

data/021_Telco/qa.csv

@@ -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]"

data/021_Telco/sample.csv

@@ -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

data/022_Airbnbs/qa.csv

@@ -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]"

data/022_Airbnbs/sample.csv

@@ -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

data/023_Climate/qa.csv

@@ -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]"

data/023_Climate/sample.csv

@@ -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

data/024_Salary/qa.csv

@@ -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]"

data/024_Salary/sample.csv

@@ -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

data/025_Data/qa.csv

@@ -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]"

data/025_Data/sample.csv

@@ -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