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Datasets:
cardiffnlp
/
databench

Tasks:
Table Question Answering
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Spanish
Size:
1K - 10K
Tags:
table-question-answering
table
qa
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
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DataBench v1

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  1. .DS_Store +0 -0
  2. 032_Employee_Attrition/sample.csv +0 -21
  3. README.md +107 -0
  4. {000_Forbes โ†’ data/001_Forbes}/all.parquet +0 -0
  5. {000_Forbes โ†’ data/001_Forbes}/info.yml +0 -0
  6. data/001_Forbes/qa.csv +26 -0
  7. {000_Forbes โ†’ data/001_Forbes}/sample.csv +0 -0
  8. {001_Titanic โ†’ data/002_Titanic}/all.parquet +0 -0
  9. {001_Titanic โ†’ data/002_Titanic}/info.yml +0 -0
  10. data/002_Titanic/qa.csv +21 -0
  11. {001_Titanic โ†’ data/002_Titanic}/sample.csv +0 -0
  12. {002_Love โ†’ data/003_Love}/all.parquet +0 -0
  13. {002_Love โ†’ data/003_Love}/info.yml +0 -0
  14. data/003_Love/qa.csv +21 -0
  15. {002_Love โ†’ data/003_Love}/sample.csv +0 -0
  16. {003_Taxi โ†’ data/004_Taxi}/all.parquet +0 -0
  17. {003_Taxi โ†’ data/004_Taxi}/info.yml +0 -0
  18. data/004_Taxi/qa.csv +32 -0
  19. {003_Taxi โ†’ data/004_Taxi}/sample.csv +13 -13
  20. {004_NYC_Calls โ†’ data/005_NYC}/all.parquet +0 -0
  21. {004_NYC_Calls โ†’ data/005_NYC}/info.yml +0 -0
  22. data/005_NYC/qa.csv +21 -0
  23. {004_NYC_Calls โ†’ data/005_NYC}/sample.csv +0 -0
  24. {005_London_Airbnbs โ†’ data/006_London}/all.parquet +0 -0
  25. {005_London_Airbnbs โ†’ data/006_London}/info.yml +0 -0
  26. data/006_London/qa.csv +21 -0
  27. {005_London_Airbnbs โ†’ data/006_London}/sample.csv +0 -0
  28. {006_Fifa โ†’ data/007_Fifa}/all.parquet +0 -0
  29. {006_Fifa โ†’ data/007_Fifa}/info.yml +0 -0
  30. data/007_Fifa/qa.csv +21 -0
  31. {006_Fifa โ†’ data/007_Fifa}/sample.csv +0 -0
  32. {007_Tornados โ†’ data/008_Tornados}/all.parquet +0 -0
  33. {007_Tornados โ†’ data/008_Tornados}/info.yml +0 -0
  34. data/008_Tornados/qa.csv +21 -0
  35. {007_Tornados โ†’ data/008_Tornados}/sample.csv +0 -0
  36. {008_Central_Park โ†’ data/009_Central}/all.parquet +0 -0
  37. {008_Central_Park โ†’ data/009_Central}/info.yml +0 -0
  38. data/009_Central/qa.csv +21 -0
  39. {008_Central_Park โ†’ data/009_Central}/sample.csv +0 -0
  40. {009_ECommerce_Reviews โ†’ data/010_ECommerce}/all.parquet +0 -0
  41. {009_ECommerce_Reviews โ†’ data/010_ECommerce}/info.yml +0 -0
  42. data/010_ECommerce/qa.csv +21 -0
  43. {009_ECommerce_Reviews โ†’ data/010_ECommerce}/sample.csv +0 -0
  44. {010_SF_Police โ†’ data/011_SF}/all.parquet +0 -0
  45. {010_SF_Police โ†’ data/011_SF}/info.yml +0 -0
  46. data/011_SF/qa.csv +21 -0
  47. {010_SF_Police โ†’ data/011_SF}/sample.csv +0 -0
  48. {011_Heart_Failure โ†’ data/012_Heart}/all.parquet +0 -0
  49. {011_Heart_Failure โ†’ data/012_Heart}/info.yml +0 -0
  50. data/012_Heart/qa.csv +21 -0
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032_Employee_Attrition/sample.csv DELETED
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- Left,Satisfaction Level,Work Accident,Average Monthly Hours,Last Evaluation,Years in the Company,salary,Department,Number of Projects,Promoted in the last 5 years?,Date Hired
2
- No,0.24,No,142,0.89,4,medium,support,5,No,2016-06-04
3
- No,0.28,No,124,0.51,3,low,technical,3,No,2017-06-06
4
- No,0.91,No,255,0.67,4,low,accounting,2,No,2016-11-17
5
- No,0.34,Yes,116,0.81,3,low,sales,4,No,2017-11-19
6
- No,0.55,No,179,0.5,3,low,technical,4,No,2017-10-25
7
- No,0.36,No,162,0.93,5,low,support,3,No,2015-02-22
8
- Yes,0.78,No,256,0.87,5,medium,support,5,No,2015-04-01
9
- Yes,0.37,No,140,0.51,3,medium,support,2,No,2017-10-04
10
- No,0.73,No,174,0.63,3,low,accounting,4,No,2017-05-09
11
- Yes,0.84,No,249,0.85,6,low,marketing,4,No,2014-03-20
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- No,0.98,Yes,265,0.61,2,medium,technical,4,No,2018-09-30
13
- No,0.93,No,137,0.97,4,low,RandD,5,No,2016-08-04
14
- No,0.57,No,235,0.67,2,low,product_mng,5,No,2018-11-23
15
- No,0.84,No,125,0.47,4,low,RandD,3,No,2016-01-26
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- No,0.22,No,180,0.62,3,low,support,3,No,2017-01-03
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- No,0.14,No,162,0.88,4,medium,marketing,3,No,2016-02-04
18
- No,0.5,No,267,0.77,2,high,management,3,No,2018-05-27
19
- No,0.69,No,174,0.76,3,low,marketing,5,No,2017-12-21
20
- No,0.93,No,276,0.48,3,low,IT,3,No,2017-05-08
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- No,0.6,No,145,0.97,2,medium,technical,5,No,2018-10-06
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
README.md CHANGED
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1
  ---
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  license: mit
3
  ---
 
1
+ # ๐Ÿ’พ๐Ÿ‹๏ธ๐Ÿ’พ DataBench ๐Ÿ’พ๐Ÿ‹๏ธ๐Ÿ’พ
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+
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+ This repository contains the original 65 datasets used for the paper Question Answering over Tabular Data with DataBench:
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+ A Large-Scale Empirical Evaluation of LLMs.
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+
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+ Large Language Models (LLMs) are showing emerging abilities, and one of the latest recognized ones is tabular
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+ reasoning in question answering on tabular data. Although there are some available datasets to assess question
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+ answering systems on tabular data, they are not large and diverse enough to evaluate this new ability of LLMs.
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+ To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.
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+ By clicking on each in the table below, you will be able to explore each dataset.
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+
12
+ | | Name | Rows | Cols | Domain | Source (Reference) |
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+ |---:|:-------------------------------|-------:|-------:|:---------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
14
+ | 1 | [Forbes](https://public.graphext.com/0b211530c7e213d3/index.html?section=data) | 2668 | 17 | Business | [Forbes](https://www.forbes.com/billionaires/)|
15
+ | 2 | [Titanic](https://public.graphext.com/8577225c5ffd88fd/index.html) | 887 | 8 | Travel and Locations | [Kaggle](https://www.kaggle.com/competitions/titanic/data)|
16
+ | 3 | [Love](https://public.graphext.com/be7a566b0c485916/index.html) | 373 | 35 | Social Networks and Surveys | [Graphext](https://public.graphext.com/1de78f6820cfd5ba/index.html) |
17
+ | 4 | [Taxi](https://public.graphext.com/bcee13c23070f333/index.html) | 100000 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/competitions/nyc-taxi-trip-duration/overview) |
18
+ | 5 | [NYC Calls](https://public.graphext.com/1ce2f5fae408621e/index.html) | 100000 | 46 | Business | [City of New York](https://data.cityofnewyork.us/Social-Services/NYC-311-Data/jrb2-thup) |
19
+ | 6 | [London Airbnbs](https://public.graphext.com/6bbf4bbd3ff279c0/index.html) | 75241 | 74 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/labdmitriy/airbnb) |
20
+ | 7 | [Fifa](https://public.graphext.com/37bca51494c10a79/index.html) | 14620 | 59 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/stefanoleone992/fifa-21-complete-player-dataset) |
21
+ | 8 | [Tornados](https://public.graphext.com/4be9872e031199c3/index.html) | 67558 | 14 | Health | [Kaggle](https://www.kaggle.com/datasets/danbraswell/us-tornado-dataset-1950-2021) |
22
+ | 9 | [Central Park](https://public.graphext.com/7b3d3a4d7bf1e9b5/index.html) | 56245 | 6 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/danbraswell/new-york-city-weather-18692022) |
23
+ | 10 | [ECommerce Reviews](https://public.graphext.com/a5b8911b215958ad/index.html) | 23486 | 10 | Business | [Kaggle](https://www.kaggle.com/datasets/nicapotato/womens-ecommerce-clothing-reviews) |
24
+ | 11 | [SF Police](https://public.graphext.com/ab815ab14f88115c/index.html) | 713107 | 35 | Social Networks and Surveys | [US Gov](https://catalog.data.gov/dataset/police-department-incident-reports-2018-to-present) |
25
+ | 12 | [Heart Failure](https://public.graphext.com/245cec64075f5542/index.html) | 918 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) |
26
+ | 13 | [Roller Coasters](https://public.graphext.com/1e550e6c24fc1930/index.html) | 1087 | 56 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/robikscube/rollercoaster-database) |
27
+ | 14 | [Madrid Airbnbs](https://public.graphext.com/77265ea3a63e650f/index.html) | 20776 | 75 | Travel and Locations | [Inside Airbnb](http://data.insideairbnb.com/spain/comunidad-de-madrid/madrid/2023-09-07/data/listings.csv.gz) |
28
+ | 15 | [Food Names](https://public.graphext.com/5aad4c5d6ef140b3/index.html) | 906 | 4 | Business | [Data World](https://data.world/alexandra/generic-food-database) |
29
+ | 16 | [Holiday Package Sales](https://public.graphext.com/fbc34d3f24282e46/index.html) | 4888 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/susant4learning/holiday-package-purchase-prediction) |
30
+ | 17 | [Hacker News](https://public.graphext.com/f20501a9d616b5a5/index.html) | 9429 | 20 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/hacker-news/hacker-news) |
31
+ | 18 | [Staff Satisfaction](https://public.graphext.com/6822ac1ce6307fec/index.html) | 14999 | 11 | Business | [Kaggle](https://www.kaggle.com/datasets/mohamedharris/employee-satisfaction-index-dataset) |
32
+ | 19 | [Aircraft Accidents](https://public.graphext.com/1802117b1b14f5c5/index.html) | 23519 | 23 | Health | [Kaggle](https://www.kaggle.com/datasets/ramjasmaurya/aviation-accidents-history1919-april-2022) |
33
+ | 20 | [Real Estate Madrid](https://public.graphext.com/5f83ec219a7ea84f/index.html) | 26026 | 59 | Business | [Idealista](https://public.graphext.com/5f83ec219a7ea84f/index.html) |
34
+ | 21 | [Telco Customer Churn](https://public.graphext.com/362cd8e3e96f70d4/index.html) | 7043 | 21 | Business | [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn) |
35
+ | 22 | [Airbnbs Listings NY](https://public.graphext.com/77265ea3a63e650f/index.html) | 37012 | 33 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data) |
36
+ | 23 | [Climate in Madrid](https://public.graphext.com/83a75b4f1cea8df4/index.html?section=data) | 36858 | 26 | Travel and Locations | [AEMET](https://public.graphext.com/83a75b4f1cea8df4/index.html?section=data) |
37
+ | 24 | [Salary Survey Spain 2018](https://public.graphext.com/24d1e717ba01aa3d/index.html) | 216726 | 29 | Business | [INE](ine.es) |
38
+ | 25 | [Data Driven SEO ](https://public.graphext.com/4e5b1cac9ebdfa44/index.html) | 62 | 5 | Business | [Graphext](https://www.graphext.com/post/data-driven-seo-a-keyword-optimization-guide-using-web-scraping-co-occurrence-analysis-graphext-deepnote-adwords) |
39
+ | 26 | [Predicting Wine Quality](https://public.graphext.com/de04acf5d18a9aea/index.html) | 1599 | 12 | Business | [Kaggle](https://www.kaggle.com/datasets/yasserh/wine-quality-dataset) |
40
+ | 27 | [Supermarket Sales](https://public.graphext.com/9a6742da6a8d8f7f/index.html) | 1000 | 17 | Business | [Kaggle](https://www.kaggle.com/datasets/aungpyaeap/supermarket-sales) |
41
+ | 28 | [Predict Diabetes](https://public.graphext.com/def4bada27af324c/index.html) | 768 | 9 | Health | [Kaggle](https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset) |
42
+ | 29 | [NYTimes World In 2021](https://public.graphext.com/af4c8eef1757973c/index.html?section=data) | 52588 | 5 | Travel and Locations | [New York Times](https://public.graphext.com/af4c8eef1757973c/index.html) |
43
+ | 30 | [Professionals Kaggle Survey](https://public.graphext.com/3a2e87f90363a85d/index.html) | 19169 | 64 | Business | [Kaggle](https://www.kaggle.com/c/kaggle-survey-2021/data) |
44
+ | 31 | [Trustpilot Reviews](https://public.graphext.com/367e29432331fbfd/index.html?section=data) | 8020 | 6 | Business | [TrustPilot](https://public.graphext.com/367e29432331fbfd/index.html?section=data) |
45
+ | 32 | [Delicatessen Customers](https://public.graphext.com/a1687589fbde07bc/index.html) | 2240 | 29 | Business | [Kaggle](https://www.kaggle.com/datasets/rodsaldanha/arketing-campaign) |
46
+ | 33 | [Employee Attrition](https://public.graphext.com/07a91a15ecf2b8f6/index.html) | 14999 | 11 | Business | [Kaggle(modified)](https://www.kaggle.com/datasets/pavan9065/predicting-employee-attrition) |
47
+ | 34 | [World Happiness Report 2020](https://public.graphext.com/754c83ff0a7ba087/index.html) | 153 | 20 | Social Networks and Surveys | [World Happiness](https://worldhappiness.report/data/) |
48
+ | 35 | [Billboard Lyrics](https://public.graphext.com/7e0b009e8d0af719/index.html) | 5100 | 6 | Sports and Entertainment | [Brown University](https://cs.brown.edu/courses/cs100/students/project11/) |
49
+ | 36 | [US Migrations 2012-2016](https://public.graphext.com/dbdadf87a5c21695/index.html) | 288300 | 9 | Social Networks and Surveys | [US Census](https://www.census.gov/topics/population/migration/guidance/county-to-county-migration-flows.html) |
50
+ | 37 | [Ted Talks](https://public.graphext.com/07e48466fb670904/index.html) | 4005 | 19 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ashishjangra27/ted-talks) |
51
+ | 38 | [Stroke Likelihood](https://public.graphext.com/20ccfee9e84948e3/index.html) | 5110 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease) |
52
+ | 39 | [Happy Moments](https://public.graphext.com/9b86efff48989701/index.html) | 100535 | 11 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ritresearch/happydb) |
53
+ | 40 | [Speed Dating](https://public.graphext.com/f1912daad7870be0/index.html) | 8378 | 123 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/ulrikthygepedersen/speed-dating) |
54
+ | 41 | [Airline Mentions X (former Twitter)](https://public.graphext.com/29cb7f73f6e17a38/index.html) | 14640 | 15 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/7e6999327d1f83fd/index.html) |
55
+ | 42 | [Predict Student Performance](https://public.graphext.com/def4bada27af324c/index.html) | 395 | 33 | Business | [Kaggle](https://www.kaggle.com/datasets/impapan/student-performance-data-set) |
56
+ | 43 | [Loan Defaults](https://public.graphext.com/0c7fb68ab8071a1f/index.html) | 83656 | 20 | Business | [SBA](https://www.kaggle.com/datasets/mirbektoktogaraev/should-this-loan-be-approved-or-denied) |
57
+ | 44 | [IMDb Movies](https://public.graphext.com/e23e33774872c496/index.html) | 85855 | 22 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows) |
58
+ | 45 | [Spotify Song Popularity](https://public.graphext.com/def4bada27af324c/index.html) | 21000 | 19 | Sports and Entertainment | [Spotify](https://www.kaggle.com/datasets/tomigelo/spotify-audio-features) |
59
+ | 46 | [120 Years Olympics](https://public.graphext.com/e57d5e2f172c9a99/index.html) | 271116 | 15 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results) |
60
+ | 47 | [Bank Customer Churn](https://public.graphext.com/e8f7aeacd209f74a/index.html) | 7088 | 15 | Business | [Kaggle](https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers) |
61
+ | 48 | [Data Science Salary Data](https://public.graphext.com/4e5b1cac9ebdfa44/index.html) | 742 | 28 | Business | [Kaggle](https://www.kaggle.com/datasets/ruchi798/data-science-job-salaries) |
62
+ | 49 | [Boris Johnson UK PM Tweets](https://public.graphext.com/f6623a1ca0f41c8e/index.html) | 3220 | 34 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/f6623a1ca0f41c8e/index.html) |
63
+ | 50 | [ING 2019 X Mentions](https://public.graphext.com/075030310aa702c6/index.html) | 7244 | 22 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/075030310aa702c6/index.html) |
64
+ | 51 | [Pokemon Features](https://public.graphext.com/f30d4d863a2e6b01/index.html) | 1072 | 13 | Business | [Kaggle](https://www.kaggle.com/datasets/rounakbanik/pokemon) |
65
+ | 52 | [Professional Map](https://public.graphext.com/70af2240cb751968/index.html) | 1227 | 12 | Business | [Kern et al, PNAS'20](https://github.com/behavioral-ds/VocationMap) |
66
+ | 53 | [Google Patents](https://public.graphext.com/a262300e31874716/index.html) | 9999 | 20 | Business | [BigQuery](https://www.kaggle.com/datasets/bigquery/patents/data) |
67
+ | 54 | [Joe Biden Tweets](https://public.graphext.com/33fa2efa41541ab1/index.html) | 491 | 34 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/339cee259f0a9b32/index.html?section=data) |
68
+ 55 | [German Loans](https://public.graphext.com/d3f5e425e9d4b0a1/index.html) | 1000 | 18 | Business | [Kaggle](https://www.kaggle.com/datasets/uciml/german-credit/data) |
69
+ | 56 | [Emoji Diet](https://public.graphext.com/e721cc7d790c06d4/index.html) | 58 | 35 | Health | [Kaggle](https://www.kaggle.com/datasets/ofrancisco/emoji-diet-nutritional-data-sr28) |
70
+ | 57 | [Spain Survey 2015](https://public.graphext.com/90ca7539b160fdfa/index.html?section=data) | 20000 | 45 | Social Networks and Surveys | [CIS](https://public.graphext.com/90ca7539b160fdfa/index.html?section=data) |
71
+ | 58 | [US Polls 2020](https://public.graphext.com/dbdadf87a5c21695/index.html) | 3523 | 52 | Social Networks and Surveys | [Brandwatch](https://www.brandwatch.com/p/us-election-raw-polling-data/) |
72
+ | 59 | [Second Hand Cars](https://public.graphext.com/543d0c49d7120ca0/index.html) | 50000 | 21 | Business | [DataMarket](https://www.kaggle.com/datasets/datamarket/venta-de-coches) |
73
+ | 60 | [Bakery Purchases](https://public.graphext.com/6f2102e80f47a192/index.html) | 20507 | 5 | Business | [Kaggle](https://www.kaggle.com/code/xvivancos/market-basket-analysis/report) |
74
+ | 61 | [Disneyland Customer Reviews](https://public.graphext.com/b1037bb566b7b316/index.html) | 42656 | 6 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/arushchillar/disneyland-reviews) |
75
+ | 62 | [Trump Tweets](https://public.graphext.com/7aff94c3b7f159fc/index.html) | 15039 | 20 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/be903c098a90e46f/index.html?section=data) |
76
+ | 63 | [Influencers](https://public.graphext.com/e097f1ea03d761a9/index.html) | 1039 | 14 | Social Networks and Surveys | [X (former Twitter)](https://public.graphext.com/e097f1ea03d761a9/index.html) |
77
+ | 64 | [Clustering Zoo Animals](https://public.graphext.com/d1b66902e46a712a/index.html) | 101 | 18 | Health | [Kaggle](https://www.kaggle.com/datasets/jirkadaberger/zoo-animals) |
78
+ | 65 | [RFM Analysis](https://public.graphext.com/4db2e54e29006a21/index.html) | 541909 | 8 | Business | [UCI ML](https://www.kaggle.com/datasets/carrie1/ecommerce-data) |
79
+
80
+ ## Folder structure
81
+ Each folder represents one dataset. You will find the following files within:
82
+
83
+ * all.parquet: the processed data, with each column tagged with our typing system, in [parquet](https://arrow.apache.org/docs/python/parquet.html).
84
+ * qa.csv: contains the human-made set of questions, tagged by type and columns used, for the dataset.
85
+ * sample.csv: sample containing 20 rows of the original dataset
86
+ * info.yml: additional information about the dataset
87
+
88
+ ## Column typing system
89
+ In an effort to map the stage for later analysis, we have categorized the columns by type. This information allows us to segment different kinds of data so that we can subsequently analyze the model's behavior on each column type separately. All parquet files have been casted to their smallest viable data type using the open source [Lector](https://github.com/graphext/lector) reader.
90
+
91
+ What this means is that in the data types we have more granular information that allows us to know if the column contains NaNs or not (following pandaโ€™s convention of Int vs int), as well as whether small numerical values contain negatives (Uint vs int) and their range. We also have dates with potential timezone information (although for now theyโ€™re all UTC), as well as information about categoriesโ€™ cardinality coming from the arrow types.
92
+
93
+ In the table below you can see all the data types assigned to each column, as well as the number of columns for each type. The most common data types are numbers and categories with 1336 columns of the total of 1615 included in DataBench. These are followed by some other more rare types as urls, booleans, dates or lists of elements.
94
+
95
+ | Type | Columns | Example |
96
+ | -------------- | ------- | ----------------------- |
97
+ | number | 788 | 55 |
98
+ | category | 548 | apple |
99
+ | date | 50 | 1970-01-01 |
100
+ | text | 46 | A red fox ran... |
101
+ | url | 31 | google.com |
102
+ | boolean | 18 | True |
103
+ | list[number] | 14 | [1,2,3] |
104
+ | list[category] | 112 | [apple, orange, banana] |
105
+ | list[url] | 8 | [google.com, apple.com] |
106
+
107
+
108
  ---
109
  license: mit
110
  ---
{000_Forbes โ†’ data/001_Forbes}/all.parquet RENAMED
File without changes
{000_Forbes โ†’ data/001_Forbes}/info.yml RENAMED
File without changes
data/001_Forbes/qa.csv ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Is the person with the highest net worth self-made?,True,boolean,"['finalWorth', 'selfMade']","['number[uint32]', 'boolean']",False
3
+ Does the youngest billionaire identify as male?,True,boolean,"['age', 'gender']","['number[UInt8]', 'category']",True
4
+ Is the city with the most billionaires in the United States?,True,boolean,"['city', 'country']","['category', 'category']",True
5
+ Is there a non-self-made billionaire in the top 5 ranks?,True,boolean,"['rank', 'selfMade']","['number[uint16]', 'boolean']",False
6
+ Does the oldest billionaire have a philanthropy score of 5?,False,boolean,"['age', 'philanthropyScore']","['number[UInt8]', 'number[UInt8]']",False
7
+ What is the age of the youngest billionaire?,19.0,number,['age'],['number[UInt8]'],32.0
8
+ How many billionaires are there from the 'Technology' category?,343,number,['category'],['category'],0
9
+ What's the total worth of billionaires in the 'Automotive' category?,583600,number,"['category', 'finalWorth']","['category', 'number[uint32]']",0
10
+ How many billionaires have a philanthropy score above 3?,25,number,['philanthropyScore'],['number[UInt8]'],0
11
+ What's the rank of the wealthiest non-self-made billionaire?,3,number,"['selfMade', 'rank']","['boolean', 'number[uint16]']",288
12
+ Which category does the richest billionaire belong to?,Automotive,category,"['finalWorth', 'category']","['number[uint32]', 'category']",Food & Beverage
13
+ What's the country of origin of the oldest billionaire?,United States,category,"['age', 'country']","['number[UInt8]', 'category']",United Kingdom
14
+ What's the gender of the billionaire with the highest philanthropy score?,M,category,"['philanthropyScore', 'gender']","['number[UInt8]', 'category']",M
15
+ What's the source of wealth for the youngest billionaire?,drugstores,category,"['age', 'source']","['number[UInt8]', 'category']",fintech
16
+ What is the title of the billionaire with the lowest rank?,,category,"['rank', 'title']","['number[uint16]', 'category']",
17
+ List the top 3 countries with the most billionaires.,"['United States', 'China', 'India']",list[category],['country'],['category'],"['United States', 'China', 'Brazil']"
18
+ 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']"
19
+ 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']"
20
+ List the bottom 3 categories with the fewest billionaires.,"['Logistics', 'Sports', 'Gambling & Casinos']",list[category],['category'],['category'],"['Service', 'Fashion & Retail', 'Manufacturing']"
21
+ List the bottom 2 countries with the least number of billionaires.,"['Colombia', 'Andorra']",list[category],['country'],['category'],"['Canada', 'Egypt']"
22
+ 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]"
23
+ 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]']",[]
24
+ 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]']",[]
25
+ List the bottom 4 ranks of female billionaires.,"[14, 18, 21, 30]",list[number],"['gender', 'rank']","['category', 'number[uint16]']",[]
26
+ List the top 2 final worth values of billionaires in the 'Automotive' category.,"[219000, 44800]",list[number],"['category', 'finalWorth']","['category', 'number[uint32]']",[]
{000_Forbes โ†’ data/001_Forbes}/sample.csv RENAMED
File without changes
{001_Titanic โ†’ data/002_Titanic}/all.parquet RENAMED
File without changes
{001_Titanic โ†’ data/002_Titanic}/info.yml RENAMED
File without changes
data/002_Titanic/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Did any children below the age of 18 survive?,True,boolean,"[Age, Survived]","['number[UInt8]', 'boolean']",True
3
+ Were there any passengers who paid a fare of more than $500?,True,boolean,[Fare],['number[double]'],False
4
+ Is every passenger's name unique?,True,boolean,[Name],['text'],True
5
+ Were there any female passengers in the 3rd class who survived?,True,boolean,"[Sex, Pclass, Survived]","['category', 'number[uint8]', 'boolean']",True
6
+ How many unique passenger classes are present in the dataset?,3,number,[Pclass],['number[uint8]'],3
7
+ What's the maximum age of the passengers?,80.0,number,[Age],['number[UInt8]'],69.0
8
+ How many passengers boarded without any siblings or spouses?,604,number,[Siblings_Spouses Aboard],['number[uint8]'],12
9
+ "On average, how much fare did the passengers pay?",32.31,number,[Fare],['number[double]'],23.096459999999997
10
+ Which passenger class has the highest number of survivors?,1,category,"[Pclass, Survived]","['number[uint8]', 'boolean']",3
11
+ What's the most common gender among the survivors?,female,category,"[Sex, Survived]","['category', 'boolean']",female
12
+ "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
13
+ "What's the most common age range among passengers: (0-18, 18-30, 30-50, 50+)?",18-30,category,[Age],['number[UInt8]'],18-30
14
+ Name the top 3 passenger classes by survival rate.,"[1, 2, 3]",list[category],"[Pclass, Survived]","['number[uint8]', 'boolean']","[1, 3, 2]"
15
+ "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]"
16
+ "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+]"
17
+ What are the top 2 genders by average fare paid?,"['female', 'male']",list[category],"[Sex, Fare]","['category', 'number[double]']","[female, male]"
18
+ 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]"
19
+ 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]"
20
+ 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]"
21
+ 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]"
{001_Titanic โ†’ data/002_Titanic}/sample.csv RENAMED
File without changes
{002_Love โ†’ data/003_Love}/all.parquet RENAMED
File without changes
{002_Love โ†’ data/003_Love}/info.yml RENAMED
File without changes
data/003_Love/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Is the average age of the respondents above 30?,True,boolean,['What is your age? ๐Ÿ‘ถ๐Ÿป๐Ÿ‘ต๐Ÿป'],['number[uint8]'],True
3
+ Are there more single individuals than married ones in the dataset?,True,boolean,['What is your civil status? ๐Ÿ’'],['category'],False
4
+ Do the majority of respondents have a height greater than 170 cm?,True,boolean,[What's your height? in cm ๐Ÿ“],['number[uint8]'],True
5
+ Is the most frequent hair color black?,False,boolean,['What is your hair color? ๐Ÿ‘ฉ๐Ÿฆฐ๐Ÿ‘ฑ๐Ÿฝ'],['category'],False
6
+ How many unique nationalities are present in the dataset?,13,number,"[What's your nationality?""]""",['category'],1
7
+ What is the average gross annual salary?,56332.81720430108,number,['Gross annual salary (in euros) ๐Ÿ’ธ'],['number[UInt32]'],62710.0
8
+ How many respondents wear glasses all the time?,0,number,['How often do you wear glasses? ๐Ÿ‘“'],['category'],0
9
+ What's the median age of the respondents?,33.0,number,['What is your age? ๐Ÿ‘ถ๐Ÿป๐Ÿ‘ต๐Ÿป'],['number[uint8]'],32.5
10
+ What is the most common level of studies achieved?,Master,category,['What is the maximum level of studies you have achieved? ๐ŸŽ“'],['category'],Master
11
+ Which body complexity has the least number of respondents?,Very thin,category,['What is your body complexity? ๐Ÿ‹๏ธ'],['category'],Obese
12
+ What's the most frequent eye color?,Brown,category,['What is your eye color? ๐Ÿ‘๏ธ'],['category'],Brown
13
+ Which sexual orientation has the highest representation?,Heterosexual,category,"[What's your sexual orientation?""]""",['category'],Heterosexual
14
+ 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]']"
15
+ List the bottom 3 hair lengths in terms of frequency.,"['Medium', 'Long', 'Bald']",list[category],['How long is your hair? ๐Ÿ’‡๐Ÿปโ™€๏ธ๐Ÿ’‡๐Ÿฝโ™‚๏ธ'],['category'],"['Short', 'Medium', 'Long']"
16
+ 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? ๐Ÿ’'],['category'],"['Married', 'In a Relationship', 'In a Relationship Cohabiting', 'Single', 'Divorced']"
17
+ What are the 4 least common hair colors?,"['Red', 'Other', 'White', 'Blue']",list[category],['What is your hair color? ๐Ÿ‘ฉ๐Ÿฆฐ๐Ÿ‘ฑ๐Ÿฝ'],['category'],"['Brown', 'Black']"
18
+ What are the top 4 maximum gross annual salaries?,"[500000.0, 360000.0, 300000.0, 300000.0]",list[number],['Gross annual salary (in euros) ๐Ÿ’ธ'],['number[UInt32]'],"[150000.0, 130000.0, 125000.0, 120000.0]"
19
+ Name the bottom 3 values for the happiness scale.,"[2, 2, 2]",list[number],['Happiness scale'],['number[uint8]'],"[7, 10, 6]"
20
+ What are the 5 highest ages present in the dataset?,"[65, 62, 60, 60, 59]",list[number],['What is your age? ๐Ÿ‘ถ๐Ÿป๐Ÿ‘ต๐Ÿป'],['number[uint8]'],"[65, 60, 51, 50, 50]"
21
+ 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]"
{002_Love โ†’ data/003_Love}/sample.csv RENAMED
File without changes
{003_Taxi โ†’ data/004_Taxi}/all.parquet RENAMED
File without changes
{003_Taxi โ†’ data/004_Taxi}/info.yml RENAMED
File without changes
data/004_Taxi/qa.csv ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Are there any trips with a total distance greater than 30 miles?,False,boolean,['trip_distance'],['number[double]'],False
3
+ Were there any trips that cost more than $100 in total?,False,boolean,['total_amount'],['number[double]'],False
4
+ Is there any trip with more than 6 passengers?,False,boolean,['passenger_count'],['number[uint8]'],False
5
+ Did all the trips use a payment type of either 1 or 2?,False,boolean,['payment_type'],['number[uint8]'],True
6
+ What is the maximum fare amount charged for a trip?,75.25,number,['fare_amount'],['number[double]'],85.0
7
+ How many unique pickup locations are in the dataset?,96,number,['PULocationID'],['number[uint16]'],193
8
+ What is the average tip amount given by passengers?,2.74,number,['tip_amount'],['number[double]'],1.5
9
+ How many trips took place in the airport area?,99807,number,['Airport_fee'],['number[UInt8]'],194
10
+ Which payment type is the most common in the dataset?,1,category,['payment_type'],['number[uint8]'],1
11
+ Which vendor has the most trips recorded?,2,category,['VendorID'],['number[uint8]'],2
12
+ What is the most common drop-off location?,236,category,['DOLocationID'],['number[uint16]'],161
13
+ 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
14
+ Which are the top 3 most frequent pickup locations?,"[161, 237, 236]",list[category],['PULocationID'],['number[uint16]'],"[237, 236, 161]"
15
+ Name the 4 most common rate codes used.,"[1, 2, 5, 4]",list[category],['RatecodeID'],['number[uint8]'],"[1, 2, 5, 3]"
16
+ list the 2 most frequent store and forward flags.,"['N', 'Y']",list[category],['store_and_fwd_flag'],['category'],"['N', 'Y']"
17
+ Identify the top 4 payment types used by frequency,"[1, 2, 4, 3]",list[category],['payment_type'],['number[uint8]'],"[1, 2, 3]"
18
+ Report the 4 highest toll amounts paid.,"[0, 0, 0, 0]",list[number],['tolls_amount'],['number[uint8]'],"[0, 0, 0, 0]"
19
+ list the top 3 longest trip distances,"[19.83, 19.74, 19.68]",list[number],['trip_distance'],['number[double]'],"[8.32,
20
+ 5.93,
21
+ 2.8]"
22
+ 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,
23
+ 39.9,
24
+ 33.2,
25
+ 25.2,
26
+ 24.87]"
27
+ 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
+ 28.9,
29
+ 21.2,
30
+ 17.0,
31
+ 14.9,
32
+ 13.5]"
{003_Taxi โ†’ data/004_Taxi}/sample.csv RENAMED
@@ -1,21 +1,21 @@
1
  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
2
- N,1,2023-02-01T20:33:05Z,10.7,2,90,0,4.71,246,0.0,1.59,1,20.41,2
3
- N,1,2023-02-01T21:17:13Z,6.5,1,50,0,1.75,143,0.0,0.8,1,13.25,2
4
- N,1,2023-02-01T10:17:39Z,17.0,2,170,0,4.2,43,0.0,2.44,1,25.2,2
5
  N,2,2023-02-01T20:33:36Z,40.8,2,238,0,0.0,13,0.0,8.32,1,45.8,2
 
 
 
 
6
  N,1,2023-02-02T01:48:49Z,12.8,2,163,0,3.56,68,0.0,2.29,1,21.36,1
7
- N,1,2023-02-01T20:20:43Z,9.3,1,170,0,2.15,113,0.0,1.1,1,16.45,1
8
  N,1,2023-02-01T22:29:07Z,8.6,2,90,0,2.72,230,0.0,1.37,1,16.32,2
9
- N,1,2023-02-01T21:33:10Z,14.9,2,90,0,4.97,231,0.0,2.64,1,24.87,1
10
  N,1,2023-02-01T18:47:54Z,21.2,1,137,0,5.5,142,0.0,2.8,1,33.2,1
11
- Y,1,2023-02-01T16:42:37Z,12.8,1,48,0,3.85,100,0.0,0.9,1,23.15,1
 
12
  N,3,2023-02-01T12:42:04Z,8.6,1,162,0,0.0,161,0.0,0.6,1,12.6,1
13
  N,1,2023-02-01T15:46:37Z,13.5,1,144,0,3.5,170,0.0,2.0,1,21.0,1
14
- N,1,2023-02-01T15:53:16Z,7.2,1,230,0,2.2,186,0.0,0.6,1,13.4,2
15
- N,1,2023-02-01T10:34:53Z,7.9,1,75,0,2.95,237,0.0,1.3,1,14.85,1
16
- N,1,2023-02-01T10:29:39Z,12.8,2,234,0,3.36,161,0.0,1.74,1,20.16,4
17
- N,1,2023-02-01T23:21:43Z,11.4,2,13,0,1.64,125,0.0,1.86,1,18.04,2
18
- N,1,2023-02-01T00:00:34Z,28.9,2,181,0,6.0,234,0.0,5.93,1,39.9,1
19
- N,1,2023-02-01T02:59:27Z,5.8,2,141,0,1.51,263,0.0,0.79,1,12.31,1
20
- N,2,2023-02-01T18:40:29Z,10.7,2,236,0,0.0,163,0.0,1.35,1,17.2,1
21
  N,1,2023-02-01T19:26:30Z,13.5,2,43,0,4.0,163,0.0,2.15,1,24.0,1
 
 
 
1
  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
2
+ Y,1,2023-02-01T16:42:37Z,12.8,1,48,0,3.85,100,0.0,0.9,1,23.15,1
3
+ N,1,2023-02-01T02:59:27Z,5.8,2,141,0,1.51,263,0.0,0.79,1,12.31,1
 
4
  N,2,2023-02-01T20:33:36Z,40.8,2,238,0,0.0,13,0.0,8.32,1,45.8,2
5
+ N,1,2023-02-01T20:33:05Z,10.7,2,90,0,4.71,246,0.0,1.59,1,20.41,2
6
+ N,1,2023-02-01T21:33:10Z,14.9,2,90,0,4.97,231,0.0,2.64,1,24.87,1
7
+ N,1,2023-02-01T10:34:53Z,7.9,1,75,0,2.95,237,0.0,1.3,1,14.85,1
8
+ N,1,2023-02-01T10:29:39Z,12.8,2,234,0,3.36,161,0.0,1.74,1,20.16,4
9
  N,1,2023-02-02T01:48:49Z,12.8,2,163,0,3.56,68,0.0,2.29,1,21.36,1
10
+ N,1,2023-02-01T10:17:39Z,17.0,2,170,0,4.2,43,0.0,2.44,1,25.2,2
11
  N,1,2023-02-01T22:29:07Z,8.6,2,90,0,2.72,230,0.0,1.37,1,16.32,2
12
+ N,2,2023-02-01T18:40:29Z,10.7,2,236,0,0.0,163,0.0,1.35,1,17.2,1
13
  N,1,2023-02-01T18:47:54Z,21.2,1,137,0,5.5,142,0.0,2.8,1,33.2,1
14
+ N,1,2023-02-01T00:00:34Z,28.9,2,181,0,6.0,234,0.0,5.93,1,39.9,1
15
+ N,1,2023-02-01T23:21:43Z,11.4,2,13,0,1.64,125,0.0,1.86,1,18.04,2
16
  N,3,2023-02-01T12:42:04Z,8.6,1,162,0,0.0,161,0.0,0.6,1,12.6,1
17
  N,1,2023-02-01T15:46:37Z,13.5,1,144,0,3.5,170,0.0,2.0,1,21.0,1
18
+ N,1,2023-02-01T21:17:13Z,6.5,1,50,0,1.75,143,0.0,0.8,1,13.25,2
 
 
 
 
 
 
19
  N,1,2023-02-01T19:26:30Z,13.5,2,43,0,4.0,163,0.0,2.15,1,24.0,1
20
+ N,1,2023-02-01T15:53:16Z,7.2,1,230,0,2.2,186,0.0,0.6,1,13.4,2
21
+ N,1,2023-02-01T20:20:43Z,9.3,1,170,0,2.15,113,0.0,1.1,1,16.45,1
{004_NYC_Calls โ†’ data/005_NYC}/all.parquet RENAMED
File without changes
{004_NYC_Calls โ†’ data/005_NYC}/info.yml RENAMED
File without changes
data/005_NYC/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Are there any complaints made in Brooklyn?,True,boolean,['borough'],['category'],True
3
+ Do any complaints have 'Dog' as a descriptor?,True,boolean,['descriptor'],['category'],False
4
+ Were there any complaints raised in April?,True,boolean,['month_name'],['category'],True
5
+ Is the Mayor's office of special enforcement one of the agencies handling complaints?,True,boolean,['agency'],['category'],False
6
+ How many complaints have been made in Queens?,23110,number,['borough'],['category'],0
7
+ What's the total number of unique agencies handling complaints?,22,number,['agency'],['category'],7
8
+ How many complaints were raised at midnight?,14811,number,['hour'],['number[uint8]'],2
9
+ How many unique descriptors are present in the dataset?,1131,number,['descriptor'],['category'],16
10
+ Which borough has the most complaints?,BROOKLYN,category,['borough'],['category'],QUEENS
11
+ Which month sees the highest number of complaints?,July,category,['month_name'],['category'],January
12
+ Which weekday has the least complaints?,Sunday,category,['weekday_name'],['category'],Thursday
13
+ Which agency is least frequently handling complaints?,ACS,category,['agency'],['category'],DOHMH
14
+ 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]"
15
+ Which 4 agencies handle the most complaints?,"['NYPD', 'HPD', 'DOT', 'DSNY']",list[category],['agency'],['category'],"[NYPD, HPD, DOB, DSNY]"
16
+ 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]"
17
+ Mention the 2 most common weekdays for complaints.,"['Tuesday', 'Monday']",list[category],['weekday_name'],['category'],"[Monday, Wednesday]"
18
+ What are the top 4 hours with the most complaints?,"[0, 12, 10, 11]",list[number],['hour'],['number[uint8]'],"[18, 21, 0, 16]"
19
+ State the 3 lowest unique complaint keys.,"[15628852, 15634748, 15634996]",list[number],['unique_key'],['number[uint32]'],"[18311800, 22322205, 25369019]"
20
+ Which 5 hours see the least complaints?,"[6, 2, 3, 5, 4]",list[number],['hour'],['number[uint8]'],"[22, 7, 14, 23, 8]"
21
+ 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]"
{004_NYC_Calls โ†’ data/005_NYC}/sample.csv RENAMED
File without changes
{005_London_Airbnbs โ†’ data/006_London}/all.parquet RENAMED
File without changes
{005_London_Airbnbs โ†’ data/006_London}/info.yml RENAMED
File without changes
data/006_London/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Are all properties in the dataset located in the same neighbourhood?,False,boolean,['neighbourhood_cleansed'],['category'],False
3
+ Do all hosts verify their identity?,False,boolean,['host_identity_verified'],['category'],False
4
+ Are all reviews_per_month values greater than 5?,False,boolean,['reviews_per_month'],['number[double]'],False
5
+ Are there any listings without a specified room type?,False,boolean,['room_type'],['category'],False
6
+ How many unique hosts are there in the dataset?,563,number,['host_neighbourhood'],['category'],20
7
+ How many listings have a valid price?,0,number,['price'],['category'],0
8
+ How many properties have received a perfect review score for communication?,0,number,['review_scores_communication'],['number[double]'],0
9
+ What is the maximum number of bedrooms a property has in this dataset?,22.0,number,['bedrooms'],['number[UInt8]'],3.0
10
+ Which neighbourhood has the most listings?,Westminster,category,['neighbourhood_cleansed'],['category'],Hammersmith and Fulham
11
+ What is the most common room type in the listings?,Entire home/apt,category,['room_type'],['category'],Private room
12
+ What property type has the least listings?,Hut,category,['property_type'],['category'],Entire condo
13
+ Which host verification method is the least used?,photographer],category,['host_verifications'],['list[category]'],[phone]
14
+ List the top 3 neighbourhoods with the most listings.,"['Westminster', 'Tower Hamlets', 'Hackney']",list[category],['neighbourhood_cleansed'],['category'],"['Hammersmith and Fulham', 'Hackney', 'Westminster']"
15
+ 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']"
16
+ List the 4 least common host verification methods.,"['[email]', '[]', '[None]', ' photographer']",list[category],['host_verifications'],['list[category]'],"['[phone]', ' phone', ' work_email', ' phone']"
17
+ Which are the 2 most preferred room types?,"['Entire home/apt', 'Private room']",list[category],['room_type'],['category'],"['Private room', 'Entire home/apt']"
18
+ 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]"
19
+ 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]"
20
+ 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]"
21
+ 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]"
{005_London_Airbnbs โ†’ data/006_London}/sample.csv RENAMED
File without changes
{006_Fifa โ†’ data/007_Fifa}/all.parquet RENAMED
File without changes
{006_Fifa โ†’ data/007_Fifa}/info.yml RENAMED
File without changes
data/007_Fifa/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ 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
3
+ 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
4
+ 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
5
+ 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
6
+ What is the average overall score of players from France?,67.861432,number,"['Nationality<gx:category>', 'Overall<gx:number>']","['category', 'number[uint8]']",
7
+ How many unique clubs are there in the dataset?,683,number,['Club<gx:category>'],['category'],19
8
+ What is the highest value (in โ‚ฌ) of a player in the dataset?,105500000,number,['Value_โ‚ฌ<gx:currency>'],['number[uint32]'],13500000
9
+ How many players have the position 'ST'?,414,number,['Position<gx:category>'],['category'],1
10
+ What is the most common nationality in the dataset?,England,category,['Nationality<gx:category>'],['category'],Ghana
11
+ What is the most common preferred foot amongst players?,Right,category,['Preferred Foot<gx:category>'],['category'],Right
12
+ Which club has the most players in the dataset?,Crystal Palace,category,['Club<gx:category>'],['category'],Lech Poznaล„
13
+ What is the most common position of players in the dataset?,SUB,category,['Position<gx:category>'],['category'],SUB
14
+ 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']"
15
+ Which are the top 3 clubs in terms of the total value (in โ‚ฌ) of their players?,"['Liverpool', 'Manchester City', 'Real Madrid']",list[category],"['Club<gx:category>', 'Value_โ‚ฌ<gx:currency>']","['category', 'number[uint32]']","['Sassuolo', 'Atalanta', 'DC United']"
16
+ 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']"
17
+ Which are the top 6 clubs in terms of the average potential score of their players?,"['FC Bayern Mรผnchen', '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']"
18
+ What are the top 3 overall scores in the dataset?,"[93, 92, 91]",list[number],['Overall<gx:number>'],['number[uint8]'],"[79, 77, 77]"
19
+ 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]"
20
+ What are the top 4 values (in โ‚ฌ) of players in the dataset?,"[105500000, 90000000, 87000000, 80000000]",list[number],['Value_โ‚ฌ<gx:currency>'],['number[uint32]'],"[13500000, 7500000, 5500000, 5500000]"
21
+ What are the top 2 wages (in โ‚ฌ) of players in the dataset?,"[560000, 370000]",list[number],['Wage_โ‚ฌ<gx:currency>'],['number[uint32]'],"[47000, 29000]"
{006_Fifa โ†’ data/007_Fifa}/sample.csv RENAMED
File without changes
{007_Tornados โ†’ data/008_Tornados}/all.parquet RENAMED
File without changes
{007_Tornados โ†’ data/008_Tornados}/info.yml RENAMED
File without changes
data/008_Tornados/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ There are no tornadoes that resulted in more than 500 injuries.,True,boolean,[inj],['number[uint16]'],True
3
+ All tornadoes in the dataset occurred in the 21st century.,False,boolean,[yr],['number[uint16]'],False
4
+ No tornado has a length greater than 100 miles.,True,boolean,[len],['number[double]'],True
5
+ There are no tornadoes that resulted in more than 100 fatalities.,True,boolean,[fat],['number[uint8]'],True
6
+ How many unique states are represented in the dataset?,53,number,[st],['category'],12
7
+ What is the highest magnitude of tornado recorded in the dataset?,5,number,[mag],['number[int8]'],2
8
+ What is the longest length of a tornado path in the dataset?,234.7,number,[len],['number[double]'],72.2
9
+ What is the maximum number of injuries caused by a single tornado?,1740,number,[inj],['number[uint16]'],3
10
+ Which state has experienced the most tornadoes?,TX,category,[st],['category'],IL
11
+ In which month do most tornadoes occur?,5,category,[mo],['number[uint8]'],6
12
+ 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
13
+ 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
14
+ 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']"
15
+ 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']"
16
+ 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']"
17
+ 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']"
18
+ 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]"
19
+ 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]"
20
+ 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]"
21
+ 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]"
{007_Tornados โ†’ data/008_Tornados}/sample.csv RENAMED
File without changes
{008_Central_Park โ†’ data/009_Central}/all.parquet RENAMED
File without changes
{008_Central_Park โ†’ data/009_Central}/info.yml RENAMED
File without changes
data/009_Central/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ There were no days when the precipitation was greater than 5 inches.,True,boolean,[PRCP],['number[double]'],True
3
+ All recorded temperatures are above freezing point.,False,boolean,"[TMIN, TMAX]","['number[Int8]', 'number[UInt8]']",False
4
+ There were no days when the snow depth was more than 10 inches.,True,boolean,[SNWD],['number[UInt8]'],True
5
+ There were no days when the maximum temperature was below freezing point.,False,boolean,[TMAX],['number[UInt8]'],False
6
+ What is the highest recorded precipitation in inches?,8.28,number,[PRCP],['number[double]'],0.66
7
+ What is the lowest minimum temperature recorded?,-15.0,number,[TMIN],['number[Int8]'],-13.0
8
+ What is the highest maximum temperature recorded?,106.0,number,[TMAX],['number[UInt8]'],81.0
9
+ What is the deepest recorded snow depth in inches?,26.0,number,[SNWD],['number[UInt8]'],4.0
10
+ 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
11
+ 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
12
+ 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
13
+ 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
14
+ 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']"
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']"
16
+ 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']"
17
+ 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']"
18
+ 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]"
19
+ 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]"
20
+ 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]"
21
+ What are the top 2 deepest snow depth recorded in inches?,"[26.0, 25.0]",list[number],[SNWD],['number[UInt8]'],"[4.0, 3.0]"
{008_Central_Park โ†’ data/009_Central}/sample.csv RENAMED
File without changes
{009_ECommerce_Reviews โ†’ data/010_ECommerce}/all.parquet RENAMED
File without changes
{009_ECommerce_Reviews โ†’ data/010_ECommerce}/info.yml RENAMED
File without changes
data/010_ECommerce/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Are there more than 20 unique clothing items in the dataset?,True,boolean,[Clothing ID],['number[uint16]'],False
3
+ Is the age of the reviewers above 50 years on average?,False,boolean,[Age],['number[uint8]'],False
4
+ Do all reviews come from the same department?,False,boolean,[Department Name],['category'],False
5
+ Are all products recommended?,False,boolean,[Recommended IND],['number[uint8]'],False
6
+ What is the average age of the reviewers?,43.198543813335604,number,[Age],['number[uint8]'],39.65
7
+ What's the highest number of positive feedback received for a review?,122,number,[Positive Feedback Count],['number[uint8]'],19
8
+ What is the most common rating given by reviewers?,5,number,[Rating],['number[uint8]'],5
9
+ How many unique clothing items are there in the dataset?,1206,number,[Clothing ID],['number[uint16]'],20
10
+ Which department has the most reviews?,Tops,category,['Department Name'],['category'],Dresses
11
+ Which class of clothing is most commonly reviewed?,Dresses,category,['Class Name'],['category'],Dresses
12
+ Which division is most commonly mentioned in the reviews?,General,category,['Division Name'],['category'],General
13
+ What is the most frequently reviewed clothing item?,1078,category,['Clothing ID'],['number[uint16]'],1095
14
+ 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]"
15
+ Which are the top 2 most reviewed categories in Class Name?,"['Dresses', 'Knits']",list[category],[Class Name],['category'],"[Dresses, Blouses]"
16
+ Which are the top 2 most reviewed categories in Division Name?,"['General', 'General Petite']",list[category],[Division Name],['category'],"[General, General Petite]"
17
+ What are the 4 most common ratings given by reviewers?,"[5, 4, 3, 2]",list[category],[Rating],['number[uint8]'],"[5, 4, 3, 2]"
18
+ What are the 5 most common Ages of reviewers?,"[39, 35, 36, 34, 38]",list[number],[Age],['number[uint8]'],"[36, 30, 56, 33, 34]"
19
+ 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]"
20
+ What are the 4 most common values for recommendation indicator?,"[1, 0]",list[number],[Recommended IND],['number[uint8]'],"[1, 0]"
21
+ What are the 2 most common clothing IDs in the reviews?,"[1078, 862]",list[number],[Clothing ID],['number[uint16]'],"[1095, 903]"
{009_ECommerce_Reviews โ†’ data/010_ECommerce}/sample.csv RENAMED
File without changes
{010_SF_Police โ†’ data/011_SF}/all.parquet RENAMED
File without changes
{010_SF_Police โ†’ data/011_SF}/info.yml RENAMED
File without changes
data/011_SF/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ 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
3
+ Are all incidents reported on Mondays resolved?,False,boolean,"[Incident Day of Week, Resolution]","['category', 'category']",False
4
+ Do any incidents reported in Police District 'Central' fall in Supervisor District 5?,False,boolean,"[Police District, Supervisor District]","['category', 'number[UInt8]']",False
5
+ Are there any incidents that occurred at the same latitude and longitude more than once?,True,boolean,"[Latitude, Longitude]","['number[double]', 'number[double]']",False
6
+ How many unique types of incident categories are there in the dataset?,49,number,[Incident Category],['category'],11
7
+ What's the total number of incidents reported online?,144099,number,[Filed Online],['boolean'],1
8
+ How many different police districts are there in the dataset?,11,number,[Police District],['category'],9
9
+ What is the average incident count per year?,118851.16666666667,number,[Incident Year],['number[uint16]'],3.3333333333333335
10
+ What is the most common incident category?,Larceny Theft,category,[Incident Category],['category'],Larceny Theft
11
+ Which day of the week has the highest number of incidents?,Friday,category,[Incident Day of Week],['category'],Saturday
12
+ What is the most common resolution for incidents reported online?,Open or Active,category,"[Filed Online, Resolution]","['boolean', 'category']",Open or Active
13
+ What is the Police District with the most incidents?,Central,category,[Police District],['category'],Northern
14
+ 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']"
15
+ Name the 4 most frequently occurring police districts.,"[Central, Northern, Mission, Southern]",list[category],[Police District],['category'],"['Northern', 'Central', 'Mission', 'Bayview']"
16
+ 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']"
17
+ 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']"
18
+ List the years with the top 4 highest incident counts.,"[2018, 2019, 2022, 2021]",list[number],[Incident Year],['number[uint16]'],"[2018, 2019, 2021, 2022]"
19
+ 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]"
20
+ 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)]"
21
+ 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]"
{010_SF_Police โ†’ data/011_SF}/sample.csv RENAMED
File without changes
{011_Heart_Failure โ†’ data/012_Heart}/all.parquet RENAMED
File without changes
{011_Heart_Failure โ†’ data/012_Heart}/info.yml RENAMED
File without changes
data/012_Heart/qa.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question,answer,type,columns_used,column_types,sample_answer
2
+ Do all patients experience exercise-induced angina?,False,boolean,['ExerciseAngina'],['category'],False
3
+ Does any patient have a resting blood pressure above 200?,False,boolean,['RestingBP'],['number[uint8]'],False
4
+ Are there patients without heart disease?,True,boolean,['HeartDisease'],['number[uint8]'],True
5
+ Does everyone have normal resting electrocardiographic results?,False,boolean,['RestingECG'],['category'],False
6
+ What is the maximum age of patients in the dataset?,77,number,['Age'],['number[uint8]'],69
7
+ What is the minimum resting blood pressure among the patients?,0,number,['RestingBP'],['number[uint8]'],95
8
+ What is the average cholesterol level in the dataset?,198.7995642701525,number,['Cholesterol'],['number[uint16]'],207.8
9
+ What is the standard deviation of maximum heart rate among the patients?,25.4603341382503,number,['MaxHR'],['number[uint8]'],27.360170821258063
10
+ What is the most common chest pain type among patients?,ASY,category,['ChestPainType'],['category'],ASY
11
+ What is the least common resting electrocardiographic result?,ST,category,['RestingECG'],['category'],ST
12
+ What is the most common ST slope among patients with heart disease?,Flat,category,"['ST_Slope', 'HeartDisease']","['category', 'number[uint8]']",Flat
13
+ What is the least common chest pain type among male patients?,TA,category,"['ChestPainType', 'Sex']","['category', 'category']",TA
14
+ What are the top 3 most common chest pain types?,"['ASY', 'NAP', 'ATA']",list[category],['ChestPainType'],['category'],"['ASY', 'NAP', 'ATA']"
15
+ Which 4 resting electrocardiographic results are least common?,"['ST', 'LVH', 'Normal']",list[category],['RestingECG'],['category'],"['ST', 'LVH', 'Normal']"
16
+ 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']"
17
+ 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']"
18
+ 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]"
19
+ 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]"
20
+ 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]"
21
+ What are the 3 least common maximum heart rates among the patients?,"[177, 187, 194]",list[number],['MaxHR'],['number[uint8]'],"[179, 86, 140]"