├── .vscode └── launch.json ├── 769-exam ├── syracuse-7-day-forecast.json ├── taco_city_1.csv ├── taco_city_2.csv ├── taco_city_3.csv └── taco_city_stores.txt ├── cars └── weight-vs-mpg.csv ├── chipolte └── orders.tsv ├── city-of-syracuse ├── wards.csv └── wards.geojson ├── classes ├── ischool-schedule-fall2015.csv ├── ischool-schedule-fall2017.csv └── ischool-schedule-spring2019.csv ├── clickstream ├── ip_lookup.csv ├── u_ex160211.log ├── u_ex160212.log └── u_ex160213.log ├── credit-cards └── credit_cards.txt ├── crime ├── nys-crime-1960-2012.csv └── syracuse-crime-data-1985-2012.csv ├── customers ├── customers.csv ├── fudgemart_customer_survey.csv ├── fudgemart_customer_survey.xls └── surveys.csv ├── dedupe ├── orders1.csv └── orders2.csv ├── delimited ├── apr-orders.csv ├── bbplayers.csv ├── bbteams.csv ├── campus-students.csv ├── class-schedule.csv ├── customers.csv ├── feb-orders.csv ├── fudge_companies.csv ├── jan-orders.csv ├── mar-orders.csv ├── online-students.csv ├── orders.csv ├── students-header-blanks.csv ├── students-header.csv ├── students-header.psv ├── students-header.tsv ├── students-no-header-blanks.ssv ├── students-no-header.csv └── webtraffic.log ├── dining └── check-data.csv ├── exam-scores ├── exam-scores.csv └── exam-scores.xlsx ├── excel-examples └── books_of_interest.xlsx ├── ffcu └── ffcu_mssql_script_1_0.sql ├── flights ├── AWS Academy Learner Lab - Educator Guide - English.pdf ├── AWS Academy Learner Lab - Services - English.pdf ├── aircraft.csv ├── airport-codes.csv ├── airports.json └── sample-flights.csv ├── fudgemart ├── mssql.sql └── mysql.sql ├── funny-names └── funny-names.tsv ├── golden-snowball └── golden-snowball.txt ├── grades ├── .allgrades.tsv.crc ├── fall2015.tsv ├── fall2016.tsv ├── spring2016.tsv └── spring2017.tsv ├── ist256 ├── 07-Files │ ├── beer-calories.txt │ ├── class-roster.txt │ ├── enron-allen-inbox.txt │ ├── enron-donohoe-inbox.txt │ ├── enron-lay-inbox.txt │ ├── enron-onemail-inbox.txt │ ├── enron-small-inbox.txt │ ├── enron-williams-inbox.txt │ ├── google-com.html │ ├── httpbin-org.html │ ├── ischool-syr-edu.html │ ├── mbox-short.txt │ ├── mbox-tiny.txt │ ├── poll-responses.txt │ └── wikipedia-President-of-the-United-States.html ├── 08-Lists │ ├── bad-passwords.txt │ ├── beer-calories.txt │ ├── fudgemart-products.txt │ └── test-fudgemart-products.txt ├── 09-Dictionaries │ ├── US-Senators-2019.json │ ├── europe.json │ ├── fudgemart-products.json │ ├── stocks.json │ ├── syr-weather-dec-2015.json │ └── usinfo.json ├── 12-pandas │ ├── titanic.csv │ └── top40.csv └── 13-visualization │ ├── moviegoers.csv │ ├── ny-cities.csv │ └── us-states.json ├── ist356 └── sample_cuse_vietnamese_restaurant_place_ids.csv ├── json-formats ├── students-columns.json ├── students-index.json ├── students-lines.json ├── students-records.json ├── students-split.json ├── students-table.json └── students-values.json ├── json-samples ├── US-Senators.json ├── employees-dict.json ├── employees.json ├── europe.json ├── fudgemart-products.json ├── google-places.json ├── movies.json ├── orders.json ├── people │ ├── 1.json │ ├── 2.json │ ├── 3.json │ ├── 4.json │ └── 5.json ├── reddit.json ├── stocks.json └── students.json ├── minimart ├── customers.csv ├── purchases-apr.csv ├── purchases-feb.csv ├── purchases-jan.csv └── purchases-mar.csv ├── mtrand.pyx ├── netflix-canceled-2021 ├── .ipynb_checkpoints │ └── blackaf-checkpoint.json ├── blackaf.json ├── bonding.json ├── country-comfort.json ├── cowboy-bebop.json ├── cursed.json ├── dad-stop-embarassing-me.json ├── grand-army.json ├── julie-and-the-phantoms.json ├── jupyters-legacy.json ├── kims-convenience.json ├── mr-iglesias.json ├── on-my-block.json ├── peaky-blinders.json ├── special.json ├── the-crew.json ├── the-dutchess.json ├── the-irregulars.json ├── the-last-kingdom.json └── zero-chill.json ├── nyc311 └── sr20160401.csv ├── orders ├── sample-orders.csv └── task.csv ├── pandas └── _libs │ └── parsers.pyx ├── readme.md ├── redditnews ├── crawlreddit.py ├── data.json ├── py2.py ├── sample.json ├── t3_4b92ci.json ├── t3_4b9ik9.json ├── t3_4bfd87.json ├── t3_4bfush.json ├── t3_4bg9ts.json ├── t3_4bhcqj.json ├── t3_4biicn.json ├── t3_4biwhq.json ├── t3_4bjk0q.json ├── t3_4bkl82.json ├── t3_4bky67.json ├── t3_4bm0qs.json ├── t3_4bn3xr.json ├── t3_4bogs2.json ├── t3_4boh86.json ├── t3_4bow28.json ├── t3_4boxh9.json ├── t3_4boxuw.json ├── t3_4boyf0.json ├── t3_4bqq9f.json ├── t3_4bqx12.json ├── t3_4bt7np.json ├── t3_4btn0g.json ├── t3_4bunjx.json ├── t3_4bur9m.json ├── t3_4bwops.json ├── t3_4bxp3h.json ├── t3_4bxsxf.json ├── t3_4bzpb0.json ├── t3_4c90n3.json └── top.json ├── spelling └── dict.txt ├── st-lucia ├── St-Lucia-1815-Plantation-Only.csv └── parishes.csv ├── stocks ├── AAPL.csv ├── AMZN.csv ├── DELL.csv ├── GM.csv ├── GOOG.csv ├── HD.csv ├── IBM.csv ├── LULU.csv ├── META.csv ├── MSFT.csv ├── NET.csv ├── NFLX.csv ├── Stocks.ipynb ├── TSLA.csv ├── TTD.csv ├── X.csv └── company-info.json ├── streaming ├── atm-datagen.py ├── kafka-atm-datagen.py ├── kafka-atm-stream.sh ├── kafka-weblogs-datagen.py └── kafka-weblogs-stream.sh ├── student_polls ├── class_roster.txt ├── poll-responses-2024-01-08.csv ├── poll-responses-2024-01-15.csv ├── poll-responses-2024-01-22.csv ├── poll-responses-2024-01-29.csv ├── poll-responses.txt └── roster.csv ├── superhero ├── superhero-movie-dataset-1978-2012-header.csv ├── superhero-movie-dataset-1978-2012-no-header.csv └── superhero-movies.mysql ├── text ├── 2016-state-of-the-union.txt ├── constitution.txt ├── english-words.txt ├── gnu-gpl3-license.txt ├── mbox-short.txt ├── preamble.txt └── zork1-walkthru.txt ├── tv-shows ├── breaking-bad.json ├── cobra-kai.json ├── daredevil.json ├── friends.json ├── game-of-thrones.json ├── greys-anatomy.json ├── https---api.tvmaze.com-singlesearch-shows-q=game+of+thrones&embed=episodes.url ├── seinfeld.json ├── the-flash.json ├── the-sopranos.json ├── the-wire.json └── westworld.json ├── tweets ├── fudgemart_tweets.txt ├── fudgemart_tweets.xls ├── logagent.conf ├── sample-tweet-stream.psv ├── simtweet-2.py ├── simtweet.py ├── test.py ├── tweet-stream.sh ├── tweet-stream2.sh ├── tweets.json └── tweets.psv ├── ufo-sightings ├── ufo-sightings-2016-01.csv ├── ufo-sightings-2016-02.csv ├── ufo-sightings-2016-03.csv ├── ufo-sightings-2016-04.csv ├── ufo-sightings-2016-05.csv └── ufo-sightings-tmp.csv ├── usa ├── geographic-centers.csv ├── us-pop-estimates-2010-2016.csv ├── us-states-geojson.json └── valid_us_addresses_with_ip_tiny.csv ├── weather └── syracuse-ny.csv ├── website-logs └── sample-website-logs.csv └── zipcodes ├── free-zipcode-database-Primary.csv └── free-zipcode-database-primary-no-header.csv /.vscode/launch.json: -------------------------------------------------------------------------------- 1 | { 2 | // Use IntelliSense to learn about possible attributes. 3 | // Hover to view descriptions of existing attributes. 4 | // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 5 | "version": "0.2.0", 6 | "configurations": [ 7 | { 8 | "name": "Python: Current File", 9 | "type": "python", 10 | "request": "launch", 11 | "program": "${file}", 12 | "console": "integratedTerminal" 13 | } 14 | ] 15 | } -------------------------------------------------------------------------------- /769-exam/syracuse-7-day-forecast.json: -------------------------------------------------------------------------------- 1 | [{"date": "2020-04-15", "date_epoch": 1586908800, "day": {"maxtemp_c": 5.9, "maxtemp_f": 42.6, "mintemp_c": -2.0, "mintemp_f": 28.4, "avgtemp_c": 2.2, "avgtemp_f": 36.0, "maxwind_mph": 12.5, "maxwind_kph": 20.2, "totalprecip_mm": 2.3, "totalprecip_in": 0.09, "avgvis_km": 7.8, "avgvis_miles": 4.0, "avghumidity": 61.0, "condition": {"text": "Moderate or heavy snow showers", "icon": "//cdn.weatherapi.com/weather/64x64/day/371.png", "code": 1258}, "uv": 3.9}, "astro": {"sunrise": "06:22 AM", "sunset": "07:48 PM", "moonrise": "03:11 AM", "moonset": "12:29 PM"}}, {"date": "2020-04-16", "date_epoch": 1586995200, "day": {"maxtemp_c": 3.5, "maxtemp_f": 38.3, "mintemp_c": -4.6, "mintemp_f": 23.7, "avgtemp_c": -0.0, "avgtemp_f": 32.0, "maxwind_mph": 15.2, "maxwind_kph": 24.5, "totalprecip_mm": 3.3, "totalprecip_in": 0.13, "avgvis_km": 7.5, "avgvis_miles": 4.0, "avghumidity": 69.0, "condition": {"text": "Moderate or heavy snow showers", "icon": "//cdn.weatherapi.com/weather/64x64/day/371.png", "code": 1258}, "uv": 4.0}, "astro": {"sunrise": "06:20 AM", "sunset": "07:49 PM", "moonrise": "03:50 AM", "moonset": "01:31 PM"}}, {"date": "2020-04-17", "date_epoch": 1587081600, "day": {"maxtemp_c": 4.1, "maxtemp_f": 39.4, "mintemp_c": -1.9, "mintemp_f": 28.6, "avgtemp_c": 1.3, "avgtemp_f": 34.4, "maxwind_mph": 9.2, "maxwind_kph": 14.8, "totalprecip_mm": 2.4, "totalprecip_in": 0.09, "avgvis_km": 7.0, "avgvis_miles": 4.0, "avghumidity": 79.0, "condition": {"text": "Moderate or heavy rain shower", "icon": "//cdn.weatherapi.com/weather/64x64/day/356.png", "code": 1243}, "uv": 3.7}, "astro": {"sunrise": "06:18 AM", "sunset": "07:50 PM", "moonrise": "04:22 AM", "moonset": "02:33 PM"}}, {"date": "2020-04-18", "date_epoch": 1587168000, "day": {"maxtemp_c": 6.7, "maxtemp_f": 44.1, "mintemp_c": -0.2, "mintemp_f": 31.6, "avgtemp_c": 2.7, "avgtemp_f": 36.9, "maxwind_mph": 12.3, "maxwind_kph": 19.8, "totalprecip_mm": 1.7, "totalprecip_in": 0.07, "avgvis_km": 8.0, "avgvis_miles": 4.0, "avghumidity": 73.0, "condition": {"text": "Patchy rain possible", "icon": "//cdn.weatherapi.com/weather/64x64/day/176.png", "code": 1063}, "uv": 5.1}, "astro": {"sunrise": "06:17 AM", "sunset": "07:52 PM", "moonrise": "04:50 AM", "moonset": "03:34 PM"}}, {"date": "2020-04-19", "date_epoch": 1587254400, "day": {"maxtemp_c": 10.7, "maxtemp_f": 51.3, "mintemp_c": 0.4, "mintemp_f": 32.7, "avgtemp_c": 5.9, "avgtemp_f": 42.6, "maxwind_mph": 17.2, "maxwind_kph": 27.7, "totalprecip_mm": 5.4, "totalprecip_in": 0.21, "avgvis_km": 8.5, "avgvis_miles": 5.0, "avghumidity": 78.0, "condition": {"text": "Moderate or heavy rain shower", "icon": "//cdn.weatherapi.com/weather/64x64/day/356.png", "code": 1243}, "uv": 0.6}, "astro": {"sunrise": "06:15 AM", "sunset": "07:53 PM", "moonrise": "05:15 AM", "moonset": "04:34 PM"}}, {"date": "2020-04-20", "date_epoch": 1587340800, "day": {"maxtemp_c": 12.1, "maxtemp_f": 53.8, "mintemp_c": -1.2, "mintemp_f": 29.8, "avgtemp_c": 5.9, "avgtemp_f": 42.6, "maxwind_mph": 9.2, "maxwind_kph": 14.8, "totalprecip_mm": 1.3, "totalprecip_in": 0.05, "avgvis_km": 9.5, "avgvis_miles": 5.0, "avghumidity": 64.0, "condition": {"text": "Patchy light snow", "icon": "//cdn.weatherapi.com/weather/64x64/day/323.png", "code": 1210}, "uv": 11.0}, "astro": {"sunrise": "06:14 AM", "sunset": "07:54 PM", "moonrise": "05:38 AM", "moonset": "05:34 PM"}}, {"date": "2020-04-21", "date_epoch": 1587427200, "day": {"maxtemp_c": 9.7, "maxtemp_f": 49.5, "mintemp_c": -0.2, "mintemp_f": 31.6, "avgtemp_c": 3.5, "avgtemp_f": 38.3, "maxwind_mph": 18.8, "maxwind_kph": 30.2, "totalprecip_mm": 0.5, "totalprecip_in": 0.02, "avgvis_km": 8.1, "avgvis_miles": 5.0, "avghumidity": 67.0, "condition": {"text": "Moderate rain at times", "icon": "//cdn.weatherapi.com/weather/64x64/day/299.png", "code": 1186}, "uv": 11.0}, "astro": {"sunrise": "06:12 AM", "sunset": "07:55 PM", "moonrise": "06:00 AM", "moonset": "06:33 PM"}}] -------------------------------------------------------------------------------- /769-exam/taco_city_stores.txt: -------------------------------------------------------------------------------- 1 | store_id city state 2 | 1 Syracuse NY 3 | 2 Syracuse NY 4 | 3 Syracuse NY 5 | 4 Rochester NY 6 | 5 Rochester NY 7 | 6 Buffalo NY 8 | 7 Buffalo NY 9 | 8 Syracuse NY 10 | 9 Rochester NY 11 | 10 Buffalo NY 12 | -------------------------------------------------------------------------------- /cars/weight-vs-mpg.csv: -------------------------------------------------------------------------------- 1 | Weight,MPG 2 | 4.36,16.9 3 | 4.054,15.5 4 | 3.605,19.2 5 | 3.94,18.5 6 | 2.155,30 7 | 2.56,27.5 8 | 2.3,27.2 9 | 2.23,30.9 10 | 2.83,20.3 11 | 3.14,19.5 12 | 2.795,21.6 13 | 3.41,16.2 14 | 3.38,20.6 15 | 3.07,20.8 16 | 3.62,18.6 17 | 3.41,18.1 18 | 3.84,17 19 | 3.725,17.6 20 | 3.955,16.5 21 | 3.83,18.2 22 | 2.585,26.5 23 | 2.91,21.9 24 | 1.975,34.1 25 | 1.915,35.1 26 | 2.67,27.4 27 | 1.99,31.5 28 | 2.135,29.5 29 | 2.67,28.4 30 | 2.595,28.8 31 | 2.7,26.8 32 | 2.556,33.5 33 | 2.2,34.2 34 | 2.02,31.8 35 | 2.13,37.3 36 | 2.19,30.5 37 | 2.815,22 38 | 2.6,21.5 39 | -------------------------------------------------------------------------------- /city-of-syracuse/wards.csv: -------------------------------------------------------------------------------- 1 | FID,CITY_WARD,Shape__Area,Shape__Length,amount 2 | 1,01,6201922.67578125,12101.7540030144,50 3 | 2,02,12930049.3476563,16225.7310510508,25 4 | 3,03,3510449.81640625,11067.9244879698,75 5 | 4,04,7018266.39453125,15600.4450755117,50 6 | 5,05,8780912.91796875,13809.3720017975,10 7 | 6,06,5378289.125,13219.5870883539,10 8 | 7,07,3870654.828125,9220.31315136463,15 9 | 8,08,5166991.47265625,17782.7173041993,20 10 | 9,09,3260355.8359375,14105.4353275556,60 11 | 10,10,2611572.9765625,10070.954005686,25 12 | 11,11,5694194.62109375,14960.4303674983,35 13 | 12,12,3546904.85546875,9453.05919314202,50 14 | 13,13,11391843.4648438,21234.3292296301,70 15 | 14,14,12832551.859375,16515.2988822535,60 16 | 15,15,2072686.3515625,8304.30346610421,15 17 | 16,16,4320155.921875,11059.6499180045,20 18 | 17,17,13325411.4765625,21786.765232795,25 19 | 18,18,2328897.58203125,6750.26185127914,40 20 | 19,19,10171780.9492188,17064.9767794157,30 21 | -------------------------------------------------------------------------------- /clickstream/ip_lookup.csv: -------------------------------------------------------------------------------- 1 | IP,Country,State,City,ApproxLat,ApproxLng 2 | 172.189.252.8,USA,VA,Dulles,38.955855,-77.447819 3 | 215.82.23.2,USA,OH,Columbus,39.961176,-82.998794 4 | 98.29.25.44,USA,OH,Cleveland,41.49932,-81.694361 5 | 68.199.40.156,USA,NY,Freeport,40.657602,-73.583184 6 | 155.100.169.152,USA,UT,Salt Lake City,40.760779,-111.891047 7 | 38.68.15.223,USA,TX,Dallas,32.776664,-96.796988 8 | 70.209.14.54,USA,FL,Tampa,27.950575,-82.457178 9 | 74.111.6.173,USA,VA,Arlington,38.87997,-77.10677 10 | 128.230.122.180,USA,NY,Syracuse,43.048122,-76.147424 11 | 128.122.140.238,USA,NY,New York,40.712784,-74.005941 12 | 56.216.127.219,USA,NC,Raleigh,35.77959,-78.638179 13 | 54.114.107.209,USA,NJ,Jersey City,40.728157,-74.077642 14 | 74.111.18.59,USA,NY,Syracuse,43.048122,-76.147424 15 | 8.37.70.170,USA,CA,Los Angeles,34.052234,-118.243685 16 | 8.37.70.77,USA,CA,Los Angeles,34.052234,-118.243685 17 | 8.37.70.112,USA,CA,Los Angeles,34.052234,-118.243685 18 | 8.37.70.226,USA,CA,Los Angeles,34.052234,-118.243685 19 | 8.37.70.99,USA,CA,Los Angeles,34.052234,-118.243685 20 | 8.37.71.43,USA,CA,Los Angeles,34.052234,-118.243685 21 | 8.37.71.25,USA,CA,Los Angeles,34.052234,-118.243685 22 | 8.37.71.69,USA,CA,Los Angeles,34.052234,-118.243685 23 | 8.37.71.9,USA,CA,Los Angeles,34.052234,-118.243685 24 | 8.37.71.57,USA,CA,Los Angeles,34.052234,-118.243685 25 | -------------------------------------------------------------------------------- /crime/nys-crime-1960-2012.csv: -------------------------------------------------------------------------------- 1 | Year,Population,Violent crime total,Murder and nonnegligent Manslaughter,Forcible rape,Robbery,Aggravated assault,Property crime total,Burglary,Larceny-theft,Motor vehicle theft, 2 | 1965, 18073000, 58802, 836, 2320, 28182, 27464, 495248, 183443, 253353, 58452 3 | 1966, 18258000, 62561, 882, 2439, 30098, 29142, 546904, 196127, 286409, 64368 4 | 1967, 18336000, 75124, 996, 2665, 40202, 31261, 617404, 219157, 314472, 83775 5 | 1968, 18113000, 98515, 1185, 2527, 59857, 34946, 730938, 250918, 375143, 104877 6 | 1969, 18321000, 105870, 1324, 2902, 64754, 36890, 731340, 248477, 367463, 115400 7 | 1970, 18190740, 124613, 1444, 2875, 81149, 39145, 779701, 267474, 386553, 125674 8 | 1971, 18391000, 145048, 1823, 3225, 97682, 42318, 789974, 273704, 388612, 127658 9 | 1972, 18366000, 138542, 2026, 4199, 86391, 45926, 666063, 239886, 321096, 105081 10 | 1973, 18265000, 135468, 2040, 4852, 80795, 47781, 678881, 246246, 320307, 112328 11 | 1974, 18111000, 145427, 1919, 5240, 86814, 51454, 766276, 271824, 390357, 104095 12 | 1975, 18120000, 155187, 1996, 5099, 93499, 54593, 866010, 301996, 447740, 116274 13 | 1976, 18084000, 156988, 1969, 4663, 95718, 54638, 968751, 318919, 516328, 133504 14 | 1977, 17924000, 149087, 1919, 5272, 84703, 57193, 942057, 309735, 498653, 133669 15 | 1978, 17748000, 149257, 1820, 5168, 83785, 58484, 878736, 292956, 466516, 119264 16 | 1979, 17649000, 161906, 2092, 5394, 93471, 60949, 933234, 308302, 500589, 124343 17 | 1980, 17506690, 180235, 2228, 5405, 112273, 60329, 1029749, 360925, 535783, 133041 18 | 1981, 17594000, 188178, 2166, 5479, 120344, 60189, 1026757, 350422, 539486, 136849 19 | 1982, 17659000, 174833, 2013, 5159, 107843, 59818, 967369, 295245, 534244, 137880 20 | 1983, 17667000, 161489, 1958, 5296, 94783, 59452, 881322, 249115, 504346, 127861 21 | 1984, 17735000, 162157, 1786, 5599, 89900, 64872, 826969, 222956, 488621, 115392 22 | 1985, 17783000, 165365, 1683, 5706, 89706, 68270, 828446, 219633, 502276, 106537 23 | 1986, 17772000, 175210, 1907, 5415, 91360, 76528, 849827, 217010, 519570, 113247 24 | 1987, 17825000, 179691, 2016, 5537, 89721, 82417, 881330, 216826, 539175, 125329 25 | 1988, 17898000, 196396, 2244, 5479, 97434, 91239, 932845, 218060, 560887, 153898 26 | 1989, 17950000, 203042, 2246, 5242, 103983, 91571, 926596, 211130, 544459, 171007 27 | 1990, 17990455, 212458, 2605, 5368, 112380, 92105, 932416, 208813, 536012, 187591 28 | 1991, 18058000, 210184, 2571, 5085, 112342, 90186, 917467, 204499, 531681, 181287 29 | 1992, 18119000, 203311, 2397, 5152, 108154, 87608, 858178, 193548, 495708, 168922 30 | 1993, 18197000, 195352, 2420, 5008, 102122, 85802, 814824, 181709, 481166, 151949 31 | 1994, 18169000, 175433, 2016, 4700, 86617, 82100, 745845, 164650, 452322, 128873 32 | 1995, 18136000, 152683, 1550, 4290, 72492, 74351, 674342, 146562, 425184, 102596 33 | 1996, 18185000, 132206, 1353, 4174, 61822, 64857, 619250, 129828, 399522, 89900 34 | 1997, 18137000, 124890, 1093, 4075, 56094, 63628, 584438, 118306, 386435, 79697 35 | 1998, 18175000, 115915, 924, 3843, 49125, 62023, 536287, 104821, 363295, 68171 36 | 1999, 18196601, 107147, 903, 3563, 43821, 58860, 489596, 93217, 338118, 58261 37 | 2000, 18976457, 105111, 952, 3530, 40539, 60090, 483078, 87946, 340901, 54231 38 | 2001, 19084350, 98022, 960, 3546, 36555, 56961, 458003, 80400, 329316, 48287 39 | 2002, 19134293, 95030, 909, 3885, 36653, 53583, 442091, 76700, 318025, 47366 40 | 2003, 19212425, 89486, 934, 3775, 35790, 48987, 432079, 75453, 311422, 45204 41 | 2004, 19280727, 84914, 889, 3608, 33506, 46911, 422734, 70696, 311036, 41002 42 | 2005, 19315721, 85839, 874, 3636, 35179, 46150, 405990, 68034, 302220, 35736 43 | 2006, 19306183, 84016, 922, 3168, 34459, 45467, 398577, 68617, 297827, 32133 44 | 2007, 19297729, 79962, 805, 2928, 31085, 45144, 383624, 64914, 290681, 28029 45 | 2008, 19490297, 77546, 836, 2798, 31787, 42125, 388599, 65544, 297962, 25093 46 | 2009, 19541453, 75110, 781, 2582, 28141, 43606, 377537, 62769, 292897, 21871 47 | 2010, 19395206, 76492, 868, 2797, 28630, 44197, 379710, 65839, 293232, 20639 48 | 2011, 19501616, 77463, 769, 2751, 28405, 45538, 371837, 65227, 287361, 19249 49 | 2012, 19570261, 79610, 684, 2848, 28655, 47423, 376140, 64553, 294239, 17348 -------------------------------------------------------------------------------- /crime/syracuse-crime-data-1985-2012.csv: -------------------------------------------------------------------------------- 1 | Year,Months,Population,Violent crime total,Murder and nonnegligent Manslaughter,Forcible rape,Robbery,Aggravated assault,Property crime total,Burglary,Larceny-theft,Motor vehicle theft,Violent Crime rate,Murder and nonnegligent manslaughter rate,Forcible rape rate,Robbery rate,Aggravated assault rate,Property crime rate,Burglary rate,Larceny-theft rate,Motor vehicle theft rate, 2 | 1985,12, 164659, 1013, 12, 80, 551, 370, 11037, 3741, 6870, 426, 615.2, 7.3, 48.6, 334.6, 224.7, 6702.9, 2272.0, 4172.3, 258.7 3 | 1986,12, 164560, 1108, 9, 61, 552, 486, 11097, 3646, 6954, 497, 673.3, 5.5, 37.1, 335.4, 295.3, 6743.4, 2215.6, 4225.8, 302.0 4 | 1987,12, 161228, 1158, 14, 87, 494, 563, 12120, 4721, 6836, 563, 718.2, 8.7, 54.0, 306.4, 349.2, 7517.3, 2928.2, 4240.0, 349.2 5 | 1988,12, 160353, 1366, 16, 103, 537, 710, 10894, 3649, 6707, 538, 851.9, 10.0, 64.2, 334.9, 442.8, 6793.8, 2275.6, 4182.6, 335.5 6 | 1989,12, 154960, 1360, 13, 113, 488, 746, 10290, 3502, 6224, 564, 877.6, 8.4, 72.9, 314.9, 481.4, 6640.4, 2259.9, 4016.5, 364.0 7 | 1990,12, 163860, 1400, 14, 118, 494, 774, 9914, 2999, 6326, 589, 854.4, 8.5, 72.0, 301.5, 472.4, 6050.3, 1830.2, 3860.6, 359.5 8 | 1991,12, 164474, 1559, 13, 86, 608, 852, 11148, 3328, 7108, 712, 947.9, 7.9, 52.3, 369.7, 518.0, 6778.0, 2023.4, 4321.7, 432.9 9 | 1992,12, 165029, 1512, 13, 85, 645, 769, 10326, 3023, 6629, 674, 916.2, 7.9, 51.5, 390.8, 466.0, 6257.1, 1831.8, 4016.9, 408.4 10 | 1993,12, 163626, 1361, 18, 79, 561, 703, 9754, 2824, 6358, 572, 831.8, 11.0, 48.3, 342.9, 429.6, 5961.2, 1725.9, 3885.7, 349.6 11 | 1994,12, 163374, 1234, 16, 58, 582, 578, 9409, 2945, 5680, 784, 755.3, 9.8, 35.5, 356.2, 353.8, 5759.2, 1802.6, 3476.7, 479.9 12 | 1995,12, 159603, 1467, 18, 84, 633, 732, 9873, 3048, 6032, 793, 919.2, 11.3, 52.6, 396.6, 458.6, 6186.0, 1909.7, 3779.4, 496.9 13 | 1996,12, 160033, 1398, 15, 62, 579, 742, 9601, 2821, 5940, 840, 873.6, 9.4, 38.7, 361.8, 463.7, 5999.4, 1762.8, 3711.7, 524.9 14 | 1997,12, 159610, 1430, 15, 55, 586, 774, 9075, 2388, 5956, 731, 895.9, 9.4, 34.5, 367.1, 484.9, 5685.7, 1496.1, 3731.6, 458.0 15 | 1998,12, 154911, 1423, 12, 49, 465, 897, 8526, 2194, 5678, 654, 918.6, 7.7, 31.6, 300.2, 579.0, 5503.8, 1416.3, 3665.3, 422.2 16 | 1999,12, 152393, 1439, 9, 47, 483, 900, 7429, 2016, 4663, 750, 944.3, 5.9, 30.8, 316.9, 590.6, 4874.9, 1322.9, 3059.9, 492.1 17 | 2000,12, 147306, 1565, 18, 47, 452, 1048, 7565, 1846, 4941, 778, 1062.4, 12.2, 31.9, 306.8, 711.4, 5135.6, 1253.2, 3354.2, 528.2 18 | 2001,12, 147577, 1562, 15, 42, 567, 938, 7905, 1810, 5194, 901, 1058.4, 10.2, 28.5, 384.2, 635.6, 5356.5, 1226.5, 3519.5, 610.5 19 | 2002,12, 148712, 1519, 23, 43, 551, 902, 8272, 1930, 5060, 1282, 1021.4, 15.5, 28.9, 370.5, 606.5, 5562.4, 1297.8, 3402.5, 862.1 20 | 2003,12, 145411, 1383, 17, 44, 477, 845, 7773, 1975, 4612, 1186, 951.1, 11.7, 30.3, 328.0, 581.1, 5345.5, 1358.2, 3171.7, 815.6 21 | 2004,12, 144278, 1312, 16, 68, 447, 781, 6609, 1678, 3792, 1139, 909.4, 11.1, 47.1, 309.8, 541.3, 4580.7, 1163.0, 2628.3, 789.4 22 | 2005,12, 143306, 1570, 19, 73, 554, 924, 6486, 1867, 3639, 980, 1095.6, 13.3, 50.9, 386.6, 644.8, 4526.0, 1302.8, 2539.3, 683.9 23 | 2006,12, 142062, 1515, 12, 66, 534, 903, 6677, 1904, 4037, 736, 1066.4, 8.4, 46.5, 375.9, 635.6, 4700.1, 1340.3, 2841.7, 518.1 24 | 2007,12, 139880, 1435, 19, 67, 446, 903, 5964, 1785, 3618, 561, 1025.9, 13.6, 47.9, 318.8, 645.6, 4263.7, 1276.1, 2586.5, 401.1 25 | 2008,12, 138211, 1366, 24, 71, 419, 852, 6165, 1938, 3725, 502, 988.3, 17.4, 51.4, 303.2, 616.4, 4460.6, 1402.2, 2695.2, 363.2 26 | 2009,12, 137208, 1343, 18, 70, 403, 852, 5779, 1946, 3495, 338, 978.8, 13.1, 51.0, 293.7, 621.0, 4211.9, 1418.3, 2547.2, 246.3 27 | 2010,12, 145170, 1291, 15, 68, 377, 831, 5708, 2174, 3167, 367, 889.3, 10.3, 46.8, 259.7, 572.4, 3931.9, 1497.6, 2181.6, 252.8 28 | 2011,12, 145822, 1302, 11, 63, 388, 840, 5275, 1705, 3261, 309, 892.9, 7.5, 43.2, 266.1, 576.0, 3617.4, 1169.2, 2236.3, 211.9 29 | 2012,12, 145934, 1372, 14, 75, 454, 829, 5976, 1896, 3698, 382, 940.2, 9.6, 51.4, 311.1, 568.1, 4095.0, 1299.2, 2534.0, 261.8 -------------------------------------------------------------------------------- /customers/customers.csv: -------------------------------------------------------------------------------- 1 | First,Last,Email,Gender,Last IP Address,City,State,Total Orders,Total Purchased,Months Customer 2 | Al,Fresco,afresco@dayrep.com,M,74.111.18.161,Syracuse,NY,1,45,1 3 | Abby,Kuss,akuss@rhyta.com,F,23.80.125.101,Phoenix,AZ,1,25,2 4 | Arial,Photo,aphoto@dayrep.com,F,24.0.14.56,Newark,NJ,1,680,1 5 | Bette,Alott,balott@rhyta.com,F,56.216.127.219,Raleigh,NC,6,560,18 6 | Barb ,Barion,bbarion@superrito.com,F,38.68.15.223,Dallas,TX,4,1590,1 7 | Barry,DeHatchett,bdehatchett@dayrep.com,M,23.192.215.78,Boston,MA,1,15,6 8 | Bill,Melator,bmelator@einrot.com,M,24.11.125.10,Orem,UT,9,6090,35 9 | Candi,Cayne,ccayne@rhyta.com,F,24.39.14.15,Portland,ME,1,620,2 10 | Carol,Ling,cling@superrito.com,F,23.180.242.66,Syracuse,NY,2,440,6 11 | Cam,Rha,crha@einrot.com,M,24.1.25.140,Chicago,IL,0,0,1 12 | Dan,Delyons,ddelyons@dayrep.com,M,24.38.224.161,Greenwich,CT,2,2570,10 13 | Erin,Detyers,edetyers@dayrep.com,F,70.209.14.54,Tampa,FL,5,1105,38 14 | Euron,Tasomthin,etasomthin@superrito.com,M,68.199.40.156,Hempstead,NY,13,4630,28 15 | Justin,Case,jcase@dayrep.com,M,23.192.215.44,Boston,MA,3,1050,1 16 | Jean,Poole,jpoole@dayrep.com,F,23.182.25.40,Kingston,NY,7,185,12 17 | Lee,Hvmeehom,lhvmeehom@einrot.com,F,215.82.23.2,Columbus,OH,9,207,18 18 | Lisa,Karfurless,lkarfurless@dayrep.com,F,172.189.252.8,Fairfax,VA,6,250,27 19 | Mary,Melator,mmelator@rhyta.com,F,23.88.15.5,Los Angeles,CA,8,4275,40 20 | Mike,Rofone,mrofone@dayrep.com,M,23.224.160.4,Cheyenne,WY,0,0,0 21 | Oren,Jouglad,ojouglad@einrot.com,M,128.122.140.238,New York,NY,12,4500,36 22 | Phil,Meaup,pmeaup@dayrep.com,M,23.83.132.200,Phoenix,AZ,4,930,24 23 | Rowan,Deboat,rdeboat@dayrep.com,M,23.84.32.22,Topeka,KS,1,3500,42 24 | Ray,Ovlight,rovlight@dayrep.com,M,74.111.18.59,Syracuse,NY,6,125,42 25 | Sara,Bellum,sbellum@superrito.com,F,74.111.6.173,Alexandria,VA,2,189,2 26 | Sal,Ladd,sladd@superrito.com,M,23.112.202.16,Rochester,NY,14,594,10 27 | Seymour,Ofewe,sofewe@dayrep.com,M,98.29.25.44,Cleveland,OH,9,1190,3 28 | Ty,Anott,tanott@rhyta.com,M,23.230.12.5,San Jose,CA,1,50,3 29 | Tally,Itupp,titupp@superrito.com,F,24.38.114.105,Sea Cliff,NY,11,380,42 30 | Tim,Pani,tpani@superrito.com,M,23.84.132.226,Buffalo,NY,0,0,1 31 | Victor,Rhee,vrhee@einrot.com,M,23.112.232.160,Green Bay,WI,0,0,2 -------------------------------------------------------------------------------- /customers/fudgemart_customer_survey.csv: -------------------------------------------------------------------------------- 1 | Email,Twitter Username,Marital Status,Household Income,Own Home,Education,Favorite Department 2 | ojouglad@gustr.com,ojouglad,Married,65000,No,4 Year Degree,Electronics 3 | lkarfurless@dayrep.com,lkarforless,Single,143000,Yes,Graduate Degree,Apparel 4 | lhvmeehom@einrot.com,lhvmeehom,Prefer not to Answer,75000,Yes,4 Year Degree,Prefer not to Answer 5 | sladd@superrito.com,sladd,Married,52000,Yes,2 Year Degree,None 6 | vrhee@einrot.com,vrhee,Prefer not to Answer,17500,No,High School,Books 7 | jpoole@dayrep.com,jpoole,Married,Prefer not to Answer,Yes,Some College,Books 8 | bdehatchett@dayrep.com,bdehatchett,Prefer not to Answer,67000,Yes,4 Year Degree,Electronics 9 | mrofone@dayrep.com,mrofone,Single,121000,Yes,Prefer not to Answer,Jewelry 10 | tanott@rhyta.com,tanott,Single,26000,No,2 Year Degree,Digital Downloads 11 | akuss@rhyta.com,akuss,Single,22500,No,High School,None 12 | tpani@superrito.com,tpani,Prefer not to Answer,89000,Yes,Graduate Degree,Jewelry 13 | rdeboat@dayrep.com,rdeboat,Single,69000,Yes,Graduate Degree,Electronics 14 | mmelator@rhyta.com,mmelator,Prefer not to Answer,42000,No,4 Year Degree,Books 15 | crha@einrot.com,crha,Married,34000,Prefer not to Answer,4 Year Degree,Books 16 | bmelator@einrot.com,bmelator,Single,13000,No,2 Year Degree,Digital Downloads 17 | ddelyons@dayrep.com,ddelyons,Single,105000,Yes,High School,Electronics 18 | ccayne@rhyta.com,ccayne,Married,62000,Yes,4 Year Degree,None 19 | bbarion@superrito.com,bbarion,Single,74000,No,4 Year Degree,Jewelry 20 | balott@rhyta.com,balott,Married,Prefer not to Answer,Prefer not to Answer,Graduate Degree,Apparel 21 | etasomthin@superrito.com,etasomthin,Married,39000,No,2 Year Degree,Prefer not to Answer 22 | edetyers@dayrep.com,edetyers,Single,Prefer not to Answer,No,Prefer not to Answer,Apparel 23 | afresco@dayrep.com,afresco,Married,45000,No,High School,Apparel 24 | rovlight@dayrep.com,rovlight,Prefer not to Answer,28000,No,2 Year Degree,Digital Downloads 25 | sbellum@superrito.com,sbellum,Married,100000,Yes,Graduate Degree,Jewelry 26 | sofewe@dayrep.com,sofewe,Single,50000,Yes,4 Year Degree,Books 27 | -------------------------------------------------------------------------------- /customers/fudgemart_customer_survey.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/customers/fudgemart_customer_survey.xls -------------------------------------------------------------------------------- /customers/surveys.csv: -------------------------------------------------------------------------------- 1 | Email,Twitter Username,Marital Status,Household Income,Own Home,Education,Favorite Department 2 | ojouglad@einrot.com,ojouglad,Married,65000,No,4 Year Degree,Electronics 3 | lkarfurless@dayrep.com,lkarfurless,Single,143000,Yes,Graduate Degree,Apparel 4 | lhvmeehom@einrot.com,lhvmeehom,Prefer not to Answer,75000,Yes,4 Year Degree,Prefer not to Answer 5 | sladd@superrito.com,sladd,Married,52000,Yes,2 Year Degree,None 6 | vrhee@einrot.com,vrhee,Prefer not to Answer,17500,No,High School,Books 7 | jpoole@dayrep.com,jpoole,Married,Prefer not to Answer,Yes,Some College,Books 8 | bdehatchett@dayrep.com,bdehatchett,Prefer not to Answer,67000,Yes,4 Year Degree,Electronics 9 | mrofone@dayrep.com,mrofone,Single,121000,Yes,Prefer not to Answer,Jewelry 10 | tanott@rhyta.com,tanott,Single,26000,No,2 Year Degree,Digital Downloads 11 | akuss@rhyta.com,akuss,Single,22500,No,High School,None 12 | tpani@superrito.com,tpani,Prefer not to Answer,89000,Yes,Graduate Degree,Jewelry 13 | rdeboat@dayrep.com,rdeboat,Single,69000,Yes,Graduate Degree,Electronics 14 | mmelator@rhyta.com,mmelator,Prefer not to Answer,42000,No,4 Year Degree,Books 15 | crha@einrot.com,crha,Married,34000,Prefer not to Answer,4 Year Degree,Books 16 | bmelator@einrot.com,bmelator,Single,13000,No,2 Year Degree,Digital Downloads 17 | ddelyons@dayrep.com,ddelyons,Single,105000,Yes,High School,Electronics 18 | ccayne@rhyta.com,ccayne,Married,62000,Yes,4 Year Degree,None 19 | bbarion@superrito.com,bbarion,Single,74000,No,4 Year Degree,Jewelry 20 | balott@rhyta.com,balott,Married,Prefer not to Answer,Prefer not to Answer,Graduate Degree,Apparel 21 | etasomthin@superrito.com,etasomthin,Married,39000,No,2 Year Degree,Prefer not to Answer 22 | edetyers@dayrep.com,edetyers,Single,Prefer not to Answer,No,Prefer not to Answer,Apparel 23 | afresco@dayrep.com,afresco,Married,45000,No,High School,Apparel 24 | rovlight@dayrep.com,rovlight,Prefer not to Answer,28000,No,2 Year Degree,Digital Downloads 25 | sbellum@superrito.com,sbellum,Married,100000,Yes,Graduate Degree,Jewelry 26 | sofewe@dayrep.com,sofewe,Single,50000,Yes,4 Year Degree,Books -------------------------------------------------------------------------------- /dedupe/orders1.csv: -------------------------------------------------------------------------------- 1 | orderid,orderdate,custname,custemail,custcountry,orderstatus,ordertotal,ordercreditcard,ordershipvia,shippingtotal 2 | 2,2023-03-24,Frayda Pepperd,fpepperd0@sciencedaily.com,Canada,delivered,228.39,Discover,RPS,12.05 3 | 4,2022-04-28,Carree Henworth,,Canada,pending,152.3,Discover,USPS,12.74 4 | 5,2019-11-22,Goldina Godsafe,ggodsafe3@dailymail.co.uk,United States,shipped,182.17,Amex,UPS,5.44 5 | 6,2022-05-03,Marris Chatten,mchatten4@csmonitor.com,Mexico,pending,208.28,Discover,RPS,2.16 6 | 7,2022-12-19,Logan Jacobsson,ljacobsson5@wufoo.com,United States,delivered,112.15,Amex,USPS,11.52 7 | 10,2023-04-19,Libbi Spadari,lspadari8@dot.gov,Mexico,pending,160.79,Discover,RPS,16.52 8 | 11,2020-01-20,Renato Hue,rhue9@un.org,Canada,delivered,120.52,Visa,USPS,5.57 9 | 12,2022-03-03,Lucky Helstrip,lhelstripa@tmall.com,Mexico,delivered,202.07,Amex,UPS,18.57 10 | 13,2021-09-04,Debi Myrie,dmyrieb@unc.edu,United States,delivered,131.62,Amex,UPS,2.37 11 | 15,2019-01-11,Crin Blanket,cblanketd@newsvine.com,United States,delivered,85.46,Visa,UPS,14.22 12 | -------------------------------------------------------------------------------- /dedupe/orders2.csv: -------------------------------------------------------------------------------- 1 | orderid,orderdate,custname,custemail,custcountry,orderstatus,ordertotal,ordercreditcard,ordershipvia,shippingtotal 2 | 2,2023-03-24,Frayda Pepperd,fpepperd0@sciencedaily.com,Canada,delivered,228.39,Discover,RPS,12.05 3 | 3,2020-02-23,Loy Siberry,lsiberry1@so-net.ne.jp,Canada,delivered,76.87,Discover,USPS,6.27 4 | 4,2022-04-28,Carree Henworth,,Canada,pending,152.3,Discover,USPS,12.74 5 | 6,2022-05-03,Marris Chatten,mchatten4@csmonitor.com,Mexico,pending,208.28,Discover,RPS,2.16 6 | 7,2022-12-19,Logan Jacobsson,ljacobsson5@wufoo.com,United States,delivered,112.15,Amex,USPS,11.52 7 | 8,2019-06-05,Lilli Feares,lfeares6@shop-pro.jp,Mexico,pending,237.9,Discover,FedEX,4.48 8 | 9,2019-02-17,Lowrance Sigsworth,lsigsworth7@youtube.com,United States,delivered,141.94,Discover,USPS,7.31 9 | 10,2023-04-19,Libbi Spadari,lspadari8@dot.gov,Mexico,pending,160.79,Discover,RPS,16.52 10 | 12,2022-03-03,Lucky Helstrip,lhelstripa@tmall.com,Mexico,delivered,202.07,Amex,UPS,18.57 11 | 13,2021-09-04,Debi Myrie,dmyrieb@unc.edu,United States,delivered,131.62,Amex,UPS,2.37 12 | 14,2022-02-27,Hyacinth Aveyard,haveyardc@ucoz.com,United States,pending,209.86,Amex,USPS,8.69 13 | -------------------------------------------------------------------------------- /delimited/apr-orders.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount" 2 | 1031,2024-04-01, 256.00 3 | 1032,2024-04-09, 42.30 4 | 1033,2024-04-17, 199.20 5 | 1034,2024-04-29, 26.88 -------------------------------------------------------------------------------- /delimited/bbplayers.csv: -------------------------------------------------------------------------------- 1 | "player_id","player_name","career_pts","player_team_id" 2 | 101,"Jordan",32292,1 3 | 102,"Pippen",18940,1 4 | 103,"Bryant",33643,2 5 | 104,"O'Neal",28596,2 6 | 105,"Fudge",0, -------------------------------------------------------------------------------- /delimited/bbteams.csv: -------------------------------------------------------------------------------- 1 | "team_id","team_name","team_location" 2 | 1,"Bulls","Chicago, IL" 3 | 2,"Lakers","Los Angeles, CA" 4 | 3,"Tropics","Flint, MI" -------------------------------------------------------------------------------- /delimited/campus-students.csv: -------------------------------------------------------------------------------- 1 | Name,Grade,Year 2 | Helen,,Sophomore 3 | Iris,10.0,Senior 4 | Jimmy,8.0,Freshman 5 | Karen,,Freshman 6 | Lynne,10.0,Sophomore 7 | Mike,10.0,Sophomore 8 | Nico,,Junior 9 | Pete,8.0,Freshman 10 | -------------------------------------------------------------------------------- /delimited/class-schedule.csv: -------------------------------------------------------------------------------- 1 | Course,Day,Time,Building,Room,Lat,Lon 2 | IST256,M,3:45pm,HBC,Gifford,43.03819,-76.13413 3 | MAT221,TuTh,12:45pm,Bowne,104,43.03674,-76.1332 4 | WRT206,MW,9:20am,Crouse,111,43.03852,-76.13676 5 | COM222,TuTh,9:20am,NH II,232,43.0402,-76.13521 6 | IST343,MW,2:15pm,Hinds,243,43.03834,-76.13364 7 | -------------------------------------------------------------------------------- /delimited/customers.csv: -------------------------------------------------------------------------------- 1 | customer_id, firstname, lastname 2 | 10,Abby,Kuss 3 | 20,Bette,Alott 4 | 30,Chris,Peanugget 5 | 40,Don,Atello -------------------------------------------------------------------------------- /delimited/feb-orders.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount" 2 | 1026,2024-02-10, 104.35 3 | 1027,2024-02-24, 33.70 -------------------------------------------------------------------------------- /delimited/fudge_companies.csv: -------------------------------------------------------------------------------- 1 | Month,Dept,Store,Sold,Returned,Ordered 2 | 1-Jan,Widgets,Fudgemart,100,0,80 3 | 1-Jan,Widgets,Mikeazon,80,0,60 4 | 1-Jan,Doodads,Fudgemart,200,0,50 5 | 1-Jan,Doodads,Mikeazon,50,0,50 6 | 1-Jan,Niknaks,Fudgemart,90,5,20 7 | 1-Jan,Niknaks,Mikeazon,110,10,20 8 | 2-Feb,Widgets,Fudgemart,120,0,100 9 | 2-Feb,Widgets,Mikeazon,90,10,70 10 | 2-Feb,Doodads,Fudgemart,180,30,50 11 | 2-Feb,Doodads,Mikeazon,80,0,30 12 | 2-Feb,Niknaks,Fudgemart,70,0,10 13 | 2-Feb,Niknaks,Mikeazon,80,0,20 14 | 3-Mar,Widgets,Fudgemart,150,30,100 15 | 3-Mar,Widgets,Mikeazon,100,10,90 16 | 3-Mar,Doodads,Fudgemart,100,50,30 17 | 3-Mar,Doodads,Mikeazon,90,10,10 18 | 3-Mar,Niknaks,Fudgemart,80,10,50 19 | 3-Mar,Niknaks,Mikeazon,120,20,15 20 | 4-Apr,Widgets,Fudgemart,170,15,160 21 | 4-Apr,Widgets,Mikeazon,90,20,90 22 | 4-Apr,Doodads,Fudgemart,30,80,30 23 | 4-Apr,Doodads,Mikeazon,100,10,10 24 | 4-Apr,Niknaks,Fudgemart,50,5,40 25 | 4-Apr,Niknaks,Mikeazon,130,10,10 26 | -------------------------------------------------------------------------------- /delimited/jan-orders.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount" 2 | 1023,2024-01-05, 56.90 3 | 1024,2024-01-17, 146.70 4 | 1025,2024-01-25, 36.40 -------------------------------------------------------------------------------- /delimited/mar-orders.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount" 2 | 1028,2024-03-06, 86.50 3 | 1029,2024-03-22, 209.00 4 | 1030,2024-03-30, 136.55 -------------------------------------------------------------------------------- /delimited/online-students.csv: -------------------------------------------------------------------------------- 1 | Name,Grade,Year,Location 2 | Abby,7.0,Freshman,NY 3 | Bob,9.0,Sophomore,CA 4 | Chris,10.0,Senior,CA 5 | Dave,8.0,Freshman,NY 6 | Ellen,7.0,Sophomore,TX 7 | Fran,10.0,Senior,FL 8 | Greg,8.0,Freshman,NY 9 | -------------------------------------------------------------------------------- /delimited/orders.csv: -------------------------------------------------------------------------------- 1 | order_id,order_customer_id,item,price,qty 2 | 1001,10,T-Shirt,10,3 3 | 1002,10,Jacket,20,1 4 | 1003,20,Shoes,25,1 5 | 1004,20,Jacket,20,1 6 | 1005,30,T-Shirt,10,1 7 | -------------------------------------------------------------------------------- /delimited/students-header-blanks.csv: -------------------------------------------------------------------------------- 1 | # This is 2 | # An Example of 3 | # Blanks / Comments 4 | # At the Top 5 | # of the file 6 | Name,Grade,Year 7 | Abby,7.0,Freshman 8 | Bob,9.0,Sophomore 9 | Chris,10.0,Senior 10 | Dave,8.0,Freshman 11 | Ellen,7.0,Sophomore 12 | Fran,10.0,Senior 13 | Greg,8.0,Freshman 14 | Helen,,Sophomore 15 | Iris,10.0,Senior 16 | Jimmy,8.0,Freshman 17 | Karen,7.5,Freshman 18 | Lynne,10.0,Sophomore 19 | Mike,10.0,Sophomore 20 | Nico,,Junior 21 | Pete,8.0,Freshman 22 | -------------------------------------------------------------------------------- /delimited/students-header.csv: -------------------------------------------------------------------------------- 1 | Name,Grade,Year 2 | Abby,7.0,Freshman 3 | Bob,9.0,Sophomore 4 | Chris,10.0,Senior 5 | Dave,8.0,Freshman 6 | Ellen,7.0,Sophomore 7 | Fran,10.0,Senior 8 | Greg,8.0,Freshman 9 | Helen,,Sophomore 10 | Iris,10.0,Senior 11 | Jimmy,8.0,Freshman 12 | Karen,7.5,Freshman 13 | Lynne,10.0,Sophomore 14 | Mike,10.0,Sophomore 15 | Nico,,Junior 16 | Pete,8.0,Freshman 17 | -------------------------------------------------------------------------------- /delimited/students-header.psv: -------------------------------------------------------------------------------- 1 | Name|Grade|Year 2 | Abby|7.0|Freshman 3 | Bob|9.0|Sophomore 4 | Chris|10.0|Senior 5 | Dave|8.0|Freshman 6 | Ellen|7.0|Sophomore 7 | Fran|10.0|Senior 8 | Greg|8.0|Freshman 9 | Helen||Sophomore 10 | Iris|10.0|Senior 11 | Jimmy|8.0|Freshman 12 | Karen|7.5|Freshman 13 | Lynne|10.0|Sophomore 14 | Mike|10.0|Sophomore 15 | Nico||Junior 16 | Pete|8.0|Freshman 17 | -------------------------------------------------------------------------------- /delimited/students-header.tsv: -------------------------------------------------------------------------------- 1 | Name Grade Year 2 | Abby 7.0 Freshman 3 | Bob 9.0 Sophomore 4 | Chris 10.0 Senior 5 | Dave 8.0 Freshman 6 | Ellen 7.0 Sophomore 7 | Fran 10.0 Senior 8 | Greg 8.0 Freshman 9 | Helen Sophomore 10 | Iris 10.0 Senior 11 | Jimmy 8.0 Freshman 12 | Karen 7.5 Freshman 13 | Lynne 10.0 Sophomore 14 | Mike 10.0 Sophomore 15 | Nico Junior 16 | Pete 8.0 Freshman 17 | -------------------------------------------------------------------------------- /delimited/students-no-header-blanks.ssv: -------------------------------------------------------------------------------- 1 | # This is 2 | # An Example of 3 | # Blanks / Comments 4 | # At the Top 5 | # of the file 6 | Abby;7.0;Freshman 7 | Bob;9.0;Sophomore 8 | Chris;10.0;Senior 9 | Dave;8.0;Freshman 10 | Ellen;7.0;Sophomore 11 | Fran;10.0;Senior 12 | Greg;8.0;Freshman 13 | Helen;;Sophomore 14 | Iris;10.0;Senior 15 | Jimmy;8.0;Freshman 16 | Karen;7.5;Freshman 17 | Lynne;10.0;Sophomore 18 | Mike;10.0;Sophomore 19 | Nico;;Junior 20 | Pete;8.0;Freshman 21 | -------------------------------------------------------------------------------- /delimited/students-no-header.csv: -------------------------------------------------------------------------------- 1 | Abby,7.0,Freshman 2 | Bob,9.0,Sophomore 3 | Chris,10.0,Senior 4 | Dave,8.0,Freshman 5 | Ellen,7.0,Sophomore 6 | Fran,10.0,Senior 7 | Greg,8.0,Freshman 8 | Helen,,Sophomore 9 | Iris,10.0,Senior 10 | Jimmy,8.0,Freshman 11 | Karen,7.5,Freshman 12 | Lynne,10.0,Sophomore 13 | Mike,10.0,Sophomore 14 | Nico,,Junior 15 | Pete,8.0,Freshman 16 | -------------------------------------------------------------------------------- /dining/check-data.csv: -------------------------------------------------------------------------------- 1 | check,date,party size,total items on check,total amount of check,gratuity 2 | 2827,2024-05-06,8,12,$415.08 ,$107.92 3 | 2443,2024-06-09,3,10,$286.40 ,$31.50 4 | 3685,2024-12-07,5,5,$252.95 ,$50.59 5 | 1957,2024-02-15,2,2,$42.44 ,$8.91 6 | 3010,2024-11-14,6,8,$758.16 ,$181.96 7 | 2191,2024-01-06,1,3,$17.85 ,$1.96 8 | 2527,2024-03-27,6,21,$921.48 ,$55.29 9 | 1564,2024-09-23,8,11,$928.40 ,$204.25 10 | 1066,2024-08-20,10,22,$485.76 ,$77.72 11 | 2968,2024-12-28,1,3,$122.97 ,$23.36 12 | 2809,2024-12-30,6,6,$104.46 ,$1.04 13 | 3693,2024-01-18,10,20,"$1,820.00 ",$309.40 14 | 4528,2024-01-02,4,6,$49.98 ,$7.50 15 | 3867,2024-05-02,4,14,$499.10 ,$119.78 16 | 3676,2024-02-25,1,1,$19.89 ,$1.99 17 | 2386,2024-03-31,5,12,"$1,147.80 ",$137.74 18 | 3694,2024-11-03,5,17,"$1,574.37 ",$173.18 19 | 3795,2024-02-21,3,7,$212.38 ,$46.72 20 | 2103,2024-02-24,8,9,$388.26 ,$116.48 21 | 3718,2024-10-30,2,5,$464.70 ,$120.82 22 | 3393,2024-08-26,5,6,$302.64 ,$24.21 23 | 4440,2024-06-11,1,3,$168.96 ,$10.14 24 | 1336,2024-08-30,8,28,"$1,199.80 ",$275.95 25 | 1194,2024-07-06,2,6,$453.06 ,$72.49 26 | 4310,2024-11-30,10,34,"$3,262.30 ",$913.44 27 | 4031,2024-08-12,6,14,$655.48 ,$65.55 28 | 4257,2024-01-22,6,8,$593.60 ,$47.49 29 | 3653,2024-10-29,1,4,$72.88 ,$16.76 30 | 2446,2024-12-15,4,12,$575.64 ,$28.78 31 | 4590,2024-05-08,3,5,$220.40 ,$22.04 32 | 2705,2024-07-08,10,19,$838.85 ,$671.08 33 | 1945,2024-02-05,3,7,$132.86 ,$21.26 34 | 1440,2024-11-30,3,8,$589.04 ,$141.37 35 | 3842,2024-03-31,6,6,$147.12 ,$5.88 36 | 1368,2024-12-21,10,25,"$2,193.00 ",$372.81 37 | 2486,2024-01-27,4,13,$569.01 ,$108.11 38 | 3193,2024-05-12,2,5,$383.30 ,$111.16 39 | 4829,2024-12-30,9,11,$816.20 ,$16.32 40 | 2341,2024-06-03,7,16,"$1,118.88 ",$313.29 41 | 1707,2024-03-24,7,8,$341.44 ,$47.80 42 | 2512,2024-03-30,3,12,$181.56 ,$39.94 43 | 4210,2024-03-15,7,18,"$1,698.30 ",$203.80 44 | 1361,2024-11-21,7,14,$65.80 ,$16.45 45 | 1186,2024-09-21,5,16,$298.72 ,$74.68 46 | 2053,2024-12-14,7,23,$588.11 ,$164.67 47 | 4031,2024-07-12,9,13,$129.74 ,$29.84 48 | 3621,2024-06-23,1,2,$138.76 ,$19.43 49 | 3588,2024-08-20,1,2,$123.46 ,$6.17 50 | 4161,2024-06-22,9,28,"$1,385.16 ",$235.48 51 | 3404,2024-07-19,9,26,"$2,382.90 ",$71.49 52 | -------------------------------------------------------------------------------- /exam-scores/exam-scores.csv: -------------------------------------------------------------------------------- 1 | Class_Section,Exam_Version,Completion_Time,Made_Own_Study_Guide,Did_Exam_Prep Assignment,Studied_In_Groups,Student_Score,Percentage,Letter_Grade 2 | M01,A,20,N,N,Y,24,80.00%,B 3 | M01,A,20,?,?,?,27,90.00%,A- 4 | M01,A,30,Y,Y,Y,30,100.00%,A 5 | M01,A,50,N,Y,Y,18,60.00%,C- 6 | M01,A,55,Y,Y,N,24,80.00%,B 7 | M01,A,60,N,Y,Y,25,83.30%,B 8 | M01,A,60,Y,Y,Y,30,100.00%,A 9 | M01,B,15,Y,Y,Y,26,86.70%,B+ 10 | M01,B,20,N,N,Y,13,43.30%,F 11 | M01,B,20,Y,Y,N,27,90.00%,A- 12 | M01,B,25,?,?,?,20,66.70%,C 13 | M01,B,25,N,N,N,24,80.00%,B 14 | M01,B,35,Y,N,Y,29,96.70%,A 15 | M01,B,60,Y,N,N,26,86.70%,B+ 16 | M01,C,20,Y,Y,Y,26,86.70%,B+ 17 | M01,C,40,N,Y,N,23,76.70%,B- 18 | M01,C,45,Y,N,N,22,73.30%,C+ 19 | M01,C,45,N,Y,Y,23,76.70%,B- 20 | M01,C,50,Y,Y,Y,27,90.00%,A- 21 | M01,C,55,Y,N,N,23,76.70%,B- 22 | M01,C,60,?,?,?,22,73.30%,C+ 23 | M01,D,25,N,N,Y,15,50.00%,D 24 | M01,D,25,?,?,?,24,80.00%,B 25 | M01,D,35,?,?,?,13,43.30%,F 26 | M01,D,40,?,?,?,20,66.70%,C 27 | M01,D,55,Y,N,N,20,66.70%,C 28 | M01,D,60,Y,Y,N,19,63.30%,C- 29 | M01,D,60,Y,N,Y,21,70.00%,C+ 30 | M01,D,60,?,?,?,26,86.70%,B+ 31 | M02,A,20,N,N,N,16,53.30%,D 32 | M02,A,25,?,?,?,17,56.70%,D 33 | M02,A,30,N,Y,Y,24,80.00%,B 34 | M02,A,35,?,?,?,22,73.30%,C+ 35 | M02,A,40,?,?,?,27,90.00%,A- 36 | M02,A,45,Y,Y,N,24,80.00%,B 37 | M02,A,50,?,?,?,27,90.00%,A- 38 | M02,A,55,Y,Y,N,25,83.30%,B 39 | M02,A,60,N,N,N,19,63.30%,C- 40 | M02,A,60,Y,N,N,23,76.70%,B- 41 | M02,B,15,N,N,Y,19,63.30%,C- 42 | M02,B,25,?,?,?,25,83.30%,B 43 | M02,B,35,N,N,Y,21,70.00%,C+ 44 | M02,B,40,Y,Y,Y,28,93.30%,A- 45 | M02,B,45,N,N,Y,24,80.00%,B 46 | M02,B,45,Y,Y,Y,25,83.30%,B 47 | M02,B,50,Y,Y,N,28,93.30%,A- 48 | M02,B,55,Y,N,N,17,56.70%,D 49 | M02,B,60,N,N,Y,22,73.30%,C+ 50 | M02,C,25,Y,Y,Y,28,93.30%,A- 51 | M02,C,30,N,Y,Y,24,80.00%,B 52 | M02,C,35,N,Y,N,23,76.70%,B- 53 | M02,C,35,N,Y,Y,23,76.70%,B- 54 | M02,C,40,Y,Y,N,16,53.30%,D 55 | M02,C,45,N,Y,N,20,66.70%,C 56 | M02,C,45,N,N,N,21,70.00%,C+ 57 | M02,C,45,N,Y,Y,25,83.30%,B 58 | M02,C,60,N,N,Y,16,53.30%,D 59 | M02,D,20,Y,Y,N,21,70.00%,C+ 60 | M02,D,25,N,N,Y,23,76.70%,B- 61 | M02,D,30,N,N,N,21,70.00%,C+ 62 | M02,D,40,?,?,?,23,76.70%,B- 63 | M02,D,45,Y,Y,N,22,73.30%,C+ 64 | M02,D,45,?,?,?,24,80.00%,B 65 | M02,D,55,Y,Y,N,24,80.00%,B 66 | M02,D,60,N,N,Y,24,80.00%,B 67 | -------------------------------------------------------------------------------- /exam-scores/exam-scores.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/exam-scores/exam-scores.xlsx -------------------------------------------------------------------------------- /excel-examples/books_of_interest.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/excel-examples/books_of_interest.xlsx -------------------------------------------------------------------------------- /ffcu/ffcu_mssql_script_1_0.sql: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/ffcu/ffcu_mssql_script_1_0.sql -------------------------------------------------------------------------------- /flights/AWS Academy Learner Lab - Educator Guide - English.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/flights/AWS Academy Learner Lab - Educator Guide - English.pdf -------------------------------------------------------------------------------- /flights/AWS Academy Learner Lab - Services - English.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/flights/AWS Academy Learner Lab - Services - English.pdf -------------------------------------------------------------------------------- /flights/aircraft.csv: -------------------------------------------------------------------------------- 1 | Aircraft,Capacity 2 | Embraer E190,85 3 | Boeing 777,380 4 | Boeing 747,315 5 | Airbus A350,340 6 | Boeing 737,185 7 | Airbus A320,180 8 | -------------------------------------------------------------------------------- /funny-names/funny-names.tsv: -------------------------------------------------------------------------------- 1 | Abby Kuss 2 | Adam Antium 3 | Addie Owse 4 | Aiden Somewun 5 | Aiden Knowone 6 | Al Frecso 7 | Al Kohol 8 | Allan Wrench 9 | Ally Gator 10 | Alma Frienzergon 11 | Amanda Hugginkiss 12 | Amber Wavesofgrain 13 | Anita Job 14 | Anita Favor 15 | Anita Shower 16 | Anita Sandwich 17 | Anne Dewey 18 | April First 19 | Arial Photo 20 | Arial Survellence 21 | Artie Choke 22 | Aurora Borealis 23 | Barb Dwyer 24 | Barb Barion 25 | Barry DeHatchett 26 | Barry Mii 27 | Basil Leif 28 | Bette Alott 29 | Bette Itall 30 | Bill Melator 31 | Bill Islate 32 | Blanche Dalmonds 33 | Bo Enarreau 34 | Bob Enweave 35 | Brayden Yourhair 36 | Brayden Hair 37 | Buck Naked 38 | Buck Annaquarter 39 | Cam Corder 40 | Cam Payne 41 | Cam Rha 42 | Carmen Dryve 43 | Candi Kane 44 | Candi Dish 45 | Carol Ling 46 | Carrie Dababbi 47 | Carrie Meehom 48 | Cesar Salad 49 | Chase Lounge 50 | Chris Peanugget 51 | Chuck Itupp 52 | Clay Potts 53 | Clifton Owhere 54 | Cole Deggs 55 | Cook Myefoud 56 | Craven Chocolate 57 | Crystal Cleer 58 | Crystal Ball 59 | Dean Ofstudents 60 | Dan Delyons 61 | Dom Inator 62 | Doris Closed 63 | Doris Nolongeropen 64 | Drew Apicture 65 | Dustin DeWinned 66 | Eileen Touda-Wright 67 | Eileen Touda-Left 68 | Elieen Onyewe 69 | Erin Detyers 70 | Erin Yortires 71 | Eura Quittin 72 | Eura Lyre 73 | Euron Tasomthin 74 | Euron Fyre 75 | Frank Furter 76 | Frank Klee 77 | Gail Forcewynds 78 | Gamble Moore 79 | Gamble Ling 80 | Ginger Snaps 81 | Ginger Beer 82 | Gus Toffwind 83 | Hank Erchief 84 | Harry Pits 85 | Harry Face 86 | Hazel Eyes 87 | Heath Barr 88 | Heywood Yamind 89 | Holden Strong 90 | Holden Yorenose 91 | Hugh Japple 92 | Ian Ewe 93 | Ida Knowe 94 | Isabelle Gunnering 95 | Isabelle Ringing 96 | Isadore Stuckopen 97 | Isadore Jammed 98 | Isreal Ornotte 99 | Isreal Difficult 100 | Ivana Sandwich 101 | Jack Itupp 102 | Jack O'Lantern 103 | Jean Poole 104 | Jin Netics 105 | Joanne Bill 106 | Joe King 107 | Joe Czonou 108 | Joy Touda-World 109 | Joy Fulle 110 | June Bugg 111 | June Nipper 112 | Justin Case 113 | Justin Thyme 114 | Karen Less 115 | Kenny Pas-D'course 116 | Kenny Doit 117 | Kent Belevit 118 | Kitty Kat 119 | Kurt Tain 120 | Lee Hvmeehom 121 | Les Ismoore 122 | Les Tocleanup 123 | Leiv Meehom 124 | Leiv Amess 125 | Lilly Padz 126 | Lisa Karfurless 127 | Lola Dabridgeda 128 | Loren Medowne 129 | Loren Dabucket 130 | Luther Enne 131 | Mark Itdowne 132 | Mark Smann 133 | Martin Eyezing 134 | Mary Melator 135 | Mary Mi 136 | Mattie Fur 137 | Mac Arronne 138 | Mac Donalds 139 | Mac Intosh 140 | May Day 141 | May Flies 142 | Max Imumm 143 | Mike Rophone 144 | Mike Dupp 145 | Minnie Ature-Doberman 146 | Misty Meadows 147 | Misty Shores 148 | Morris Less 149 | Myra Sisdone 150 | Nat Tural 151 | Nat Turallie 152 | Neil Downe 153 | Nick Ofthyme 154 | Oliver Stuffismission 155 | Oliver Thingz 156 | Oren Jouglad 157 | Otto Moni 158 | Otto Thyme 159 | Patty O'Furniture 160 | Patty O'Beef 161 | Penny Pincher 162 | Penny Loafer 163 | Pete Moss 164 | Pete Terpan 165 | Phil Meaup 166 | Phil Itall-Theweigh 167 | Phil McCup 168 | Philip D'Tanks 169 | Ray Ovlight 170 | Ray Tracing 171 | Rusty Carz 172 | Rusty Nail 173 | Rip Itupp 174 | Rip Cord 175 | Rip Yerpanz 176 | Robin Banks 177 | Robin Eue 178 | Roland Totowne 179 | Rose Abov-Duresst 180 | Ross Tefarian 181 | Rowan Deboat 182 | Roxanne Gravel 183 | Roxanne Styx 184 | Ruby Gems 185 | Ruby Slippers 186 | Russel Upsome-Gayme 187 | Sal Boat 188 | Sal Debote 189 | Sal Ladd 190 | Sal Ladd-Furdiner 191 | Sally Mander 192 | Sandy Beeches 193 | Sandy Shores 194 | Sara Bellum 195 | Sara Docktur-Indahaus 196 | Sara Isnomor 197 | Seymour Ofewe 198 | Sharon Yerthings 199 | Sherry Wyne 200 | Sheryl Mytoyz 201 | Shirley Youjest 202 | Sonny Shores 203 | Sonny Dayz 204 | Sonny Skye 205 | Sue Ewe 206 | Sue Mii 207 | Tally Itupp 208 | Terry Cloth 209 | Tera Cotta 210 | Tera Dactyl 211 | Tim Pani 212 | Tim Idd 213 | Theo Door 214 | Theodore Istrong 215 | Tuck Androll 216 | Ty Anott 217 | Ty Dibol 218 | Ty Itdowne 219 | Val Uation 220 | Val Idation 221 | Victor Edance 222 | Victor Rhee 223 | Wade Indawater 224 | Wade Needeep 225 | Willie Pas-D'course 226 | Willie Work 227 | Willie Survive 228 | Willow Tree 229 | Willow Wisp 230 | Wilma Trainbelate 231 | Windy Sees 232 | Windy Shores 233 | Woodie Forrest 234 | Woodrow Aboate 235 | Yolanda Smyland -------------------------------------------------------------------------------- /golden-snowball/golden-snowball.txt: -------------------------------------------------------------------------------- 1 | City Snowfall 2 | Syracuse 80.3 3 | Rochester 63.7 4 | Buffalo 55.1 5 | Binghamton 32.0 6 | Albany 16.9 7 | -------------------------------------------------------------------------------- /grades/.allgrades.tsv.crc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/grades/.allgrades.tsv.crc -------------------------------------------------------------------------------- /grades/fall2015.tsv: -------------------------------------------------------------------------------- 1 | 2015 Fall IST101 1 A 2 | 2015 Fall IST195 3 A 3 | 2015 Fall IST233 3 B+ 4 | 2015 Fall SOC101 3 A- 5 | 2015 Fall MAT221 3 C 6 | -------------------------------------------------------------------------------- /grades/fall2016.tsv: -------------------------------------------------------------------------------- 1 | 2016 Fall IST346 3 A 2 | 2016 Fall CHE111 4 A- 3 | 2016 Fall PSY120 3 B+ 4 | 2016 Fall IST256 3 A 5 | 2016 Fall ENG121 3 B+ 6 | -------------------------------------------------------------------------------- /grades/spring2016.tsv: -------------------------------------------------------------------------------- 1 | 2016 Spring GEO110 3 B+ 2 | 2016 Spring MAT222 3 A 3 | 2016 Spring SOC121 3 C+ 4 | 2016 Spring BIO240 3 B- 5 | -------------------------------------------------------------------------------- /grades/spring2017.tsv: -------------------------------------------------------------------------------- 1 | 2017 Spring IST462 3 A 2 | 2017 Spring MAT411 3 C 3 | 2017 Spring SOC422 3 B- 4 | 2017 Spring ENV201 3 A- 5 | -------------------------------------------------------------------------------- /ist256/07-Files/beer-calories.txt: -------------------------------------------------------------------------------- 1 | Abita Amber,128 2 | Abita Golden,125 3 | Abita Jockamo IPA,190 4 | Abita Light,118 5 | Abita Purple Haze,128 6 | Abita Restoration,167 7 | Abita Turbodog,168 8 | Amstel Light,99 9 | Anchor Porter,209 10 | Anchor Steam,153 11 | Aspen Edge,94 12 | Augustiner Amber Lager,135 13 | Beach Bum Blonde Ale,163 14 | Beck's,143 15 | Beck's Light,64 16 | Blatz Beer,153 17 | Blue Moon Belgian White,164 18 | Blue Moon Full Moon Winter Ale,180 19 | Blue Moon Harvest Moon Pumpkin Ale,180 20 | Blue Moon Honey Moon Summer Ale,157 21 | Blue Moon Rising Moon Spring Ale,161 22 | Blue Point Toasted Lager,175 23 | Boddington's Ale,148 24 | Brooklyn Black Chocolate Stout,320 25 | Brooklyn Brown Ale,190 26 | Brooklyn East India Pale Ale,200 27 | Brooklyn Lager,170 28 | Brooklyn Monster,305 29 | Brooklyn Pennant Pale Ale,160 30 | Brooklyn Pilsner,155 31 | Brooklyn Summer Ale,150 32 | Brooklyn Winter Ale,205 33 | Bud American Ale,182 34 | Bud Dry,130 35 | Bud Ice,123 36 | Bud Ice Light,115 37 | Bud Light,110 38 | Bud Light Chelada,151 39 | Bud Light Lime,116 40 | Bud Light Platinum,137 41 | Bud Light Wheat,118 42 | Budweiser,145 43 | Budweiser Chelada,186 44 | Budweiser Select,99 45 | Budweiser Select 55,55 46 | Busch,132 47 | Busch Ice,171 48 | Busch Light,95 49 | Carling Black Label,138 50 | Coors Banquet,149 51 | Coors Extra Gold,152 52 | Coors Light,102 53 | Corona Extra,148 54 | Corona Light,99 55 | Cristal (Peru),134 56 | Cusque,140 57 | Dogfish Head 120 Minute IPA,450 58 | Dogfish Head 60 Minute IPA,209 59 | Dogfish Head 90 Minute IPA,294 60 | Dogfish Head Midas Touch,307 61 | Dogfish Head Red & White,310 62 | Dogfish Head Shelter Pale Ale,168 63 | Efes Pils,170 64 | Firestone DBA,166 65 | Flying Dog Doggie Style Pale Ale,150 66 | Flying Dog Double Dog,313 67 | Flying Dog Gonzo,271 68 | Flying Dog Horn Dog,314 69 | Flying Dog In Heat Wheat,138 70 | Flying Dog Kerberos Tripel,238 71 | Flying Dog Old Scratch Amber Lager,154 72 | Flying Dog Raging Bitch,221 73 | Flying Dog Road Dog,163 74 | Flying Dog Snake Dog IPA,188 75 | Flying Dog Tire Bite Golden Ale,129 76 | Foster's,145 77 | Foster's Premium Ale,160 78 | Genesee Beer,148 79 | Genesee Cream Ale,162 80 | Genesee Ice,156 81 | Genesee Red,148 82 | George Killian's Irish Red,162 83 | Grolsch Amber Ale,160 84 | Grolsch Blonde Lager,120 85 | Grolsch Light Lager,97 86 | Grolsch Premium Lager,147 87 | Guinness Draught,125 88 | Guinness Extra Stout,153 89 | Hamm's Beer,144 90 | Hamm's Special Light,110 91 | Harbin,144 92 | Harp Lager,155 93 | Heineken,150 94 | Heineken Light,99 95 | Hiland Light,97 96 | Hoegaarden Belgian White,153 97 | Icehouse 5.0,132 98 | Icehouse 5.5,149 99 | Icehouse Light,123 100 | Irish Red Ale,196 101 | Iron City,140 102 | Iron City Light,95 103 | Keystone Ice,142 104 | Keystone Light,104 105 | Keystone Premium,111 106 | Kirin,147 107 | Kirin Light,95 108 | Lech,143 109 | Leinenkugel Amber Light,110 110 | Leinenkugel Creamy Dark,170 111 | Leinenkugel Honey Weiss,149 112 | Leinenkugel Light,105 113 | Leinenkugel Northwoods Lager,163 114 | Leinenkugel Original,152 115 | Leinenkugel Red,166 116 | Leinenkugel Sunset Wheat,165 117 | Lowenbrau Dark,160 118 | Lowenbrau Special Beer,160 119 | Magic Hat #9,153 120 | Michael Shea's,145 121 | Michelob AmberBock,155 122 | Michelob Beer,164 123 | Michelob Dunkelweisse,167 124 | Michelob Golden Draft,152 125 | Michelob Golden Draft Light,110 126 | Michelob Honey Lager,174 127 | Michelob Light,123 128 | Michelob Pale Ale,187 129 | Michelob Porter,187 130 | Michelob Ultra,95 131 | Michelob Ultra Amber,114 132 | Michelob Ultra Flavored,107 133 | Mickey's Ice,157 134 | Miller Chill,100 135 | Miller Genuine Draft,143 136 | Miller Genuine Draft 64,64 137 | Miller Genuine Draft Light,110 138 | Miller High Life,143 139 | Miller High Life Light,110 140 | Miller Lite,96 141 | Miller Lite B.C. Amber,110 142 | Miller Lite B.C. Blonde,110 143 | Miller Lite B.C. Wheat,110 144 | Milwaukee's Best,128 145 | Milwaukee's Best Ice,144 146 | Milwaukee's Best Light,98 147 | Modelo Especial,145 148 | Molson Canadian,136 149 | Molson Canadian 67,67 150 | Molson Canadian Light,113 151 | Molson Ice,160 152 | Natural Ice,157 153 | Natural Light,95 154 | Negra Modelo,170 155 | New Belgium 1554,205 156 | New Belgium 2 Below,200 157 | New Belgium Abbey,200 158 | New Belgium Blue Paddle,140 159 | New Belgium Fat Tire,160 160 | New Belgium Mothership Wit,155 161 | New Belgium Skinny Dip,110 162 | New Belgium Sunshine Wheat,145 163 | New Belgium Trippel,215 164 | New Planet Tread Lightly Ale,125 165 | Newcastle Brown Ale,150 166 | Old Milwaukee Beer,146 167 | Old Milwaukee Light,114 168 | Olympia Premium Lager,146 169 | Ommegang Three Philosophers,290 170 | Pabst Blue Ribbon,153 171 | Pabst Extra Light Low Alcohol,67 172 | Pacifico,145 173 | Peroni Nastro Azzurro,150 174 | Pete's Wicked Ale,174 175 | Pilsner Urquell,156 176 | Red Bridge,160 177 | Red Dog,147 178 | Red Stripe,153 179 | Redhook ESB,179 180 | Redhook IPA,188 181 | Redhook Slim Chance,125 182 | Rock Bottom Illuminator Doppelback,288 183 | Rolling Rock Extra Pale,142 184 | Rolling Rock Green Light,83 185 | Rolling Rock Premium Beer,132 186 | Sam Adams Black Lager,191 187 | Sam Adams Blackberry Witbier,176 188 | Sam Adams Boston Ale,180 189 | Sam Adams Boston Lager,170 190 | Sam Adams Brown Ale,159 191 | Sam Adams Cherry Wheat,180 192 | Sam Adams Coastal Wheat,167 193 | Sam Adams Cream Stout,190 194 | Sam Adams Hefeweizen,182 195 | Sam Adams Honey Porter,192 196 | Sam Adams IPA,175 197 | Sam Adams Imperial Double Bock,320 198 | Sam Adams Imperial Stout,316 199 | Sam Adams Imperial White,328 200 | Sam Adams Irish Red,180 201 | Sam Adams Light,119 202 | Sam Adams Octoberfest,180 203 | Sam Adams Pale Ale,160 204 | Sam Adams Scotch Ale,200 205 | Sam Adams Summer Ale,160 206 | Sam Adams White Ale,175 207 | Sam Adams Winter Lager,200 208 | Schaefer Beer,142 209 | Schlitz Beer,146 210 | Schlitz Light,110 211 | Shipyard Light,97 212 | Shock Top,168 213 | Sierra Nevada Anniversary Ale,190 214 | Sierra Nevada Bigfoot,330 215 | Sierra Nevada Celebration Ale,214 216 | Sierra Nevada Draft Ale,157 217 | Sierra Nevada Early Spring Beer,190 218 | Sierra Nevada Harvest Ale,215 219 | Sierra Nevada India Pale Ale,231 220 | Sierra Nevada Pale Ale,175 221 | Sierra Nevada Pale Bock,218 222 | Sierra Nevada Porter,194 223 | Sierra Nevada Stout,225 224 | Sierra Nevada Summerfest,158 225 | Sierra Nevada Wheat Beer,153 226 | Signature Stroh Beer,153 227 | Smithwick's,150 228 | Sol Cerveza,130 229 | Southpaw Light,123 230 | St. Pauli Girl,148 231 | St. Pauli Girl Special Dark,150 232 | Stella Artois,154 233 | Strauss Endless Summer Light,110 234 | Stroh's Beer,149 235 | Stroh's Light,113 236 | Tsingtao,157 237 | Tuborg Deluxe Dark Export,163 238 | Tuborg Export Quality,156 239 | Tyskie,153 240 | Victoria,135 241 | Weinhard's Amber Light,135 242 | Weinhard's Blonde Lager,161 243 | Weinhard's Hefeweizen,151 244 | Weinhard's Pale Ale,147 245 | Weinhard's Private Reserve,150 246 | Widmer Hefeweizen,159 247 | Winter's Bourbon Cask Ale,165 248 | Wyder's Apple Cider,150 249 | Wyder's Pear Cider,136 250 | Yuengling Ale,145 251 | Yuengling Lager,135 252 | Yuengling Light,98 253 | Yuengling Porter,150 254 | Yuengling Premium Beer,135 255 | -------------------------------------------------------------------------------- /ist256/07-Files/class-roster.txt: -------------------------------------------------------------------------------- 1 | lh Lee Hmveehom 2 | sb Sara Bellum 3 | ku Kent Undastajou 4 | af Alma Frienzergon 5 | cp Chris Peanugget -------------------------------------------------------------------------------- /ist256/07-Files/enron-onemail-inbox.txt: -------------------------------------------------------------------------------- 1 | Message-ID: <710327.1075862885420.JavaMail.evans@thyme> 2 | Date: Tue, 6 Nov 2001 11:24:58 -0800 (PST) 3 | From: wbd_5@hotmail.com 4 | To: kenneth.lay@enron.com 5 | Subject: Discussion 6 | Mime-Version: 1.0 7 | Content-Type: text/plain; charset=us-ascii 8 | Content-Transfer-Encoding: 7bit 9 | X-From: "william dowdy" @ENRON 10 | X-To: Lay, Kenneth 11 | X-cc: 12 | X-bcc: 13 | X-Folder: \KLAY (Non-Privileged)\Lay, Kenneth\Inbox 14 | X-Origin: Lay-K 15 | X-FileName: KLAY (Non-Privileged).pst 16 | 17 | Mr. Lay, 18 | I'am the President of Bilco Commercial Finance, LLC a direct lender to 19 | companies listed on NASDAQ and the NYSE. I would like to discuss with you 20 | an offer to lend a large amount of capital to your firm. Please e-mail a 21 | contact name and phone number with the best time to call in reference to 22 | this matter. 23 | 24 | Best regards, 25 | Bill Dowdy, 26 | Bilco Commercial Finance,LLC 27 | wbd_5@hotmail.com 28 | 29 | _________________________________________________________________ 30 | Get your FREE download of MSN Explorer at http://explorer.msn.com/intl.asp -------------------------------------------------------------------------------- /ist256/07-Files/httpbin-org.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | httpbin.org 7 | 9 | 10 | 11 | 29 | 30 | 31 | 32 | 33 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 51 | 52 | 53 | 54 | 56 | 57 | 58 | 59 | 61 | 62 | 63 | 64 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 |
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httpbin.org 89 | 90 | 
0.9.2
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[ Base URL: httpbin.org/ ]
94 |
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A simple HTTP Request & Response Service. 98 |
99 |
100 | Run locally: 101 | $ docker run -p 80:80 kennethreitz/httpbin 102 |

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112 | 113 |
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122 | 123 | 124 |
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127 | 128 | [Powered by 129 | Flasgger] 130 |
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192 | 193 |

Other Utilities

194 | 195 |
    196 |
  • 197 | HTML form that posts to /post /forms/post
    • 198 |
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206 | 207 | 208 | -------------------------------------------------------------------------------- /ist256/07-Files/poll-responses.txt: -------------------------------------------------------------------------------- 1 | 1 lh 2 | 1 sb 3 | 1 af 4 | 1 cp 5 | 2 sb 6 | 2 af 7 | 2 cp 8 | 3 cp 9 | 3 sb 10 | 3 lh 11 | 4 lh 12 | 4 sb -------------------------------------------------------------------------------- /ist256/08-Lists/beer-calories.txt: -------------------------------------------------------------------------------- 1 | Abita Amber,128 2 | Abita Golden,125 3 | Abita Jockamo IPA,190 4 | Abita Light,118 5 | Abita Purple Haze,128 6 | Abita Restoration,167 7 | Abita Turbodog,168 8 | Amstel Light,99 9 | Anchor Porter,209 10 | Anchor Steam,153 11 | Aspen Edge,94 12 | Augustiner Amber Lager,135 13 | Beach Bum Blonde Ale,163 14 | Beck's,143 15 | Beck's Light,64 16 | Blatz Beer,153 17 | Blue Moon Belgian White,164 18 | Blue Moon Full Moon Winter Ale,180 19 | Blue Moon Harvest Moon Pumpkin Ale,180 20 | Blue Moon Honey Moon Summer Ale,157 21 | Blue Moon Rising Moon Spring Ale,161 22 | Blue Point Toasted Lager,175 23 | Boddington's Ale,148 24 | Brooklyn Black Chocolate Stout,320 25 | Brooklyn Brown Ale,190 26 | Brooklyn East India Pale Ale,200 27 | Brooklyn Lager,170 28 | Brooklyn Monster,305 29 | Brooklyn Pennant Pale Ale,160 30 | Brooklyn Pilsner,155 31 | Brooklyn Summer Ale,150 32 | Brooklyn Winter Ale,205 33 | Bud American Ale,182 34 | Bud Dry,130 35 | Bud Ice,123 36 | Bud Ice Light,115 37 | Bud Light,110 38 | Bud Light Chelada,151 39 | Bud Light Lime,116 40 | Bud Light Platinum,137 41 | Bud Light Wheat,118 42 | Budweiser,145 43 | Budweiser Chelada,186 44 | Budweiser Select,99 45 | Budweiser Select 55,55 46 | Busch,132 47 | Busch Ice,171 48 | Busch Light,95 49 | Carling Black Label,138 50 | Coors Banquet,149 51 | Coors Extra Gold,152 52 | Coors Light,102 53 | Corona Extra,148 54 | Corona Light,99 55 | Cristal (Peru),134 56 | Cusque,140 57 | Dogfish Head 120 Minute IPA,450 58 | Dogfish Head 60 Minute IPA,209 59 | Dogfish Head 90 Minute IPA,294 60 | Dogfish Head Midas Touch,307 61 | Dogfish Head Red & White,310 62 | Dogfish Head Shelter Pale Ale,168 63 | Efes Pils,170 64 | Firestone DBA,166 65 | Flying Dog Doggie Style Pale Ale,150 66 | Flying Dog Double Dog,313 67 | Flying Dog Gonzo,271 68 | Flying Dog Horn Dog,314 69 | Flying Dog In Heat Wheat,138 70 | Flying Dog Kerberos Tripel,238 71 | Flying Dog Old Scratch Amber Lager,154 72 | Flying Dog Raging Bitch,221 73 | Flying Dog Road Dog,163 74 | Flying Dog Snake Dog IPA,188 75 | Flying Dog Tire Bite Golden Ale,129 76 | Foster's,145 77 | Foster's Premium Ale,160 78 | Genesee Beer,148 79 | Genesee Cream Ale,162 80 | Genesee Ice,156 81 | Genesee Red,148 82 | George Killian's Irish Red,162 83 | Grolsch Amber Ale,160 84 | Grolsch Blonde Lager,120 85 | Grolsch Light Lager,97 86 | Grolsch Premium Lager,147 87 | Guinness Draught,125 88 | Guinness Extra Stout,153 89 | Hamm's Beer,144 90 | Hamm's Special Light,110 91 | Harbin,144 92 | Harp Lager,155 93 | Heineken,150 94 | Heineken Light,99 95 | Hiland Light,97 96 | Hoegaarden Belgian White,153 97 | Icehouse 5.0,132 98 | Icehouse 5.5,149 99 | Icehouse Light,123 100 | Irish Red Ale,196 101 | Iron City,140 102 | Iron City Light,95 103 | Keystone Ice,142 104 | Keystone Light,104 105 | Keystone Premium,111 106 | Kirin,147 107 | Kirin Light,95 108 | Lech,143 109 | Leinenkugel Amber Light,110 110 | Leinenkugel Creamy Dark,170 111 | Leinenkugel Honey Weiss,149 112 | Leinenkugel Light,105 113 | Leinenkugel Northwoods Lager,163 114 | Leinenkugel Original,152 115 | Leinenkugel Red,166 116 | Leinenkugel Sunset Wheat,165 117 | Lowenbrau Dark,160 118 | Lowenbrau Special Beer,160 119 | Magic Hat #9,153 120 | Michael Shea's,145 121 | Michelob AmberBock,155 122 | Michelob Beer,164 123 | Michelob Dunkelweisse,167 124 | Michelob Golden Draft,152 125 | Michelob Golden Draft Light,110 126 | Michelob Honey Lager,174 127 | Michelob Light,123 128 | Michelob Pale Ale,187 129 | Michelob Porter,187 130 | Michelob Ultra,95 131 | Michelob Ultra Amber,114 132 | Michelob Ultra Flavored,107 133 | Mickey's Ice,157 134 | Miller Chill,100 135 | Miller Genuine Draft,143 136 | Miller Genuine Draft 64,64 137 | Miller Genuine Draft Light,110 138 | Miller High Life,143 139 | Miller High Life Light,110 140 | Miller Lite,96 141 | Miller Lite B.C. Amber,110 142 | Miller Lite B.C. Blonde,110 143 | Miller Lite B.C. Wheat,110 144 | Milwaukee's Best,128 145 | Milwaukee's Best Ice,144 146 | Milwaukee's Best Light,98 147 | Modelo Especial,145 148 | Molson Canadian,136 149 | Molson Canadian 67,67 150 | Molson Canadian Light,113 151 | Molson Ice,160 152 | Natural Ice,157 153 | Natural Light,95 154 | Negra Modelo,170 155 | New Belgium 1554,205 156 | New Belgium 2 Below,200 157 | New Belgium Abbey,200 158 | New Belgium Blue Paddle,140 159 | New Belgium Fat Tire,160 160 | New Belgium Mothership Wit,155 161 | New Belgium Skinny Dip,110 162 | New Belgium Sunshine Wheat,145 163 | New Belgium Trippel,215 164 | New Planet Tread Lightly Ale,125 165 | Newcastle Brown Ale,150 166 | Old Milwaukee Beer,146 167 | Old Milwaukee Light,114 168 | Olympia Premium Lager,146 169 | Ommegang Three Philosophers,290 170 | Pabst Blue Ribbon,153 171 | Pabst Extra Light Low Alcohol,67 172 | Pacifico,145 173 | Peroni Nastro Azzurro,150 174 | Pete's Wicked Ale,174 175 | Pilsner Urquell,156 176 | Red Bridge,160 177 | Red Dog,147 178 | Red Stripe,153 179 | Redhook ESB,179 180 | Redhook IPA,188 181 | Redhook Slim Chance,125 182 | Rock Bottom Illuminator Doppelback,288 183 | Rolling Rock Extra Pale,142 184 | Rolling Rock Green Light,83 185 | Rolling Rock Premium Beer,132 186 | Sam Adams Black Lager,191 187 | Sam Adams Blackberry Witbier,176 188 | Sam Adams Boston Ale,180 189 | Sam Adams Boston Lager,170 190 | Sam Adams Brown Ale,159 191 | Sam Adams Cherry Wheat,180 192 | Sam Adams Coastal Wheat,167 193 | Sam Adams Cream Stout,190 194 | Sam Adams Hefeweizen,182 195 | Sam Adams Honey Porter,192 196 | Sam Adams IPA,175 197 | Sam Adams Imperial Double Bock,320 198 | Sam Adams Imperial Stout,316 199 | Sam Adams Imperial White,328 200 | Sam Adams Irish Red,180 201 | Sam Adams Light,119 202 | Sam Adams Octoberfest,180 203 | Sam Adams Pale Ale,160 204 | Sam Adams Scotch Ale,200 205 | Sam Adams Summer Ale,160 206 | Sam Adams White Ale,175 207 | Sam Adams Winter Lager,200 208 | Schaefer Beer,142 209 | Schlitz Beer,146 210 | Schlitz Light,110 211 | Shipyard Light,97 212 | Shock Top,168 213 | Sierra Nevada Anniversary Ale,190 214 | Sierra Nevada Bigfoot,330 215 | Sierra Nevada Celebration Ale,214 216 | Sierra Nevada Draft Ale,157 217 | Sierra Nevada Early Spring Beer,190 218 | Sierra Nevada Harvest Ale,215 219 | Sierra Nevada India Pale Ale,231 220 | Sierra Nevada Pale Ale,175 221 | Sierra Nevada Pale Bock,218 222 | Sierra Nevada Porter,194 223 | Sierra Nevada Stout,225 224 | Sierra Nevada Summerfest,158 225 | Sierra Nevada Wheat Beer,153 226 | Signature Stroh Beer,153 227 | Smithwick's,150 228 | Sol Cerveza,130 229 | Southpaw Light,123 230 | St. Pauli Girl,148 231 | St. Pauli Girl Special Dark,150 232 | Stella Artois,154 233 | Strauss Endless Summer Light,110 234 | Stroh's Beer,149 235 | Stroh's Light,113 236 | Tsingtao,157 237 | Tuborg Deluxe Dark Export,163 238 | Tuborg Export Quality,156 239 | Tyskie,153 240 | Victoria,135 241 | Weinhard's Amber Light,135 242 | Weinhard's Blonde Lager,161 243 | Weinhard's Hefeweizen,151 244 | Weinhard's Pale Ale,147 245 | Weinhard's Private Reserve,150 246 | Widmer Hefeweizen,159 247 | Winter's Bourbon Cask Ale,165 248 | Wyder's Apple Cider,150 249 | Wyder's Pear Cider,136 250 | Yuengling Ale,145 251 | Yuengling Lager,135 252 | Yuengling Light,98 253 | Yuengling Porter,150 254 | Yuengling Premium Beer,135 255 | -------------------------------------------------------------------------------- /ist256/08-Lists/fudgemart-products.txt: -------------------------------------------------------------------------------- 1 | Hardware|Straight Claw Hammer|15.9500 2 | Hardware|Sledge Hammer|21.9500 3 | Hardware|Rip Claw Hammer|19.9500 4 | Clothing|Dri-Fit Tee|20.0000 5 | Clothing|Running Pants|35.0000 6 | Clothing|Wool Socks|8.0000 7 | Clothing|Squeaky Sneaks|65.0000 8 | Clothing|Cool Jeans|45.0000 9 | Clothing|Denim Jacket|60.0000 10 | Clothing|Leather Jacket|95.0000 11 | Clothing|Courdory Pants|24.0000 12 | Clothing|Work Pants|38.0000 13 | Clothing|Work Gloves|8.0000 14 | Clothing|Comfor-fit Tee|12.0000 15 | Clothing|Running Shorts|20.0000 16 | Clothing|X-Train Shoes|75.0000 17 | Clothing|Baseball Cap|10.0000 18 | Electronics|DVD Player|45.0000 19 | Electronics|HD-DVD Player|150.0000 20 | Electronics|Blu-Ray DVD Player|150.0000 21 | Electronics|"40"" LCD HD TV"|1000.0000 22 | Electronics|"50"" LCD HD TV"|1300.0000 23 | Electronics|"65"" LCD HD TV"|1900.0000 24 | Electronics|PC Webcam|20.0000 25 | Electronics|Computer Mouse|10.0000 26 | Electronics|Ergonomic Keyboard|22.0000 27 | Electronics|"20"" LCD Monitor"|300.0000 28 | Electronics|"17"" LCD Monitor"|150.0000 29 | Hardware|18v Drill Driver Set|90.0000 30 | Hardware|19.2v Drill Driver Set|90.0000 31 | Hardware|"10"" Miter Saw"|200.0000 32 | Hardware|Lazer Level|45.0000 33 | Hardware|Table Saw|290.0000 34 | Hardware|Power Washer|290.0000 35 | Hardware|Cold Chisel Set|10.0000 36 | Hardware|Screwdriver Set|10.0000 37 | Hardware|Drill Bit Set|25.0000 38 | Hardware|Belt Sander|250.0000 39 | Housewares|Crock Pot|25.0000 40 | Housewares|Monsignor Coffee|20.0000 41 | Housewares|Electric Griddle|20.0000 42 | Sporting Goods|Tennis Racket|50.0000 43 | Sporting Goods|Tennis Balls|8.0000 44 | Sporting Goods|Basketball|35.0000 45 | Sporting Goods|12 Pack Golf Balls|20.0000 46 | Sporting Goods|Pro. Football|65.0000 47 | Sporting Goods|Baseball Glove|75.0000 48 | Sporting Goods|Heart Monitor|20.0000 49 | Sporting Goods|Pedometer|10.0000 50 | Sporting Goods|Sport Cycle|255.0000 51 | Sporting Goods|Soccer Ball|45.0000 52 | Housewares|Steam Iron|15.0000 53 | Housewares|Blender|45.0000 54 | -------------------------------------------------------------------------------- /ist256/08-Lists/test-fudgemart-products.txt: -------------------------------------------------------------------------------- 1 | hardware|hammer|14.97 2 | hardware|saw|9.97 3 | clothing|boots|22.99 -------------------------------------------------------------------------------- /ist256/09-Dictionaries/stocks.json: -------------------------------------------------------------------------------- 1 | [ 2 | { "symbol" : "AAPL", "price" : 126.82 }, 3 | { "symbol" : "AMZN", "price" : 3098.12 }, 4 | { "symbol" : "FB", "price" : 251.11 }, 5 | { "symbol" : "GOOG", "price" : 1725.05 }, 6 | { "symbol" : "IBM", "price" : 128.39 }, 7 | { "symbol" : "MSFT", "price" : 212.55 }, 8 | { "symbol" : "NET", "price" : 78.00 }, 9 | { "symbol" : "NFLX", "price" : 497.00 }, 10 | { "symbol" : "TSLA", "price" : 823.80 }, 11 | { "symbol" : "TWTR", "price" : 45.11 } 12 | ] 13 | -------------------------------------------------------------------------------- /ist256/09-Dictionaries/usinfo.json: -------------------------------------------------------------------------------- 1 | [{"name":"United States","topLevelDomain":[".us"],"alpha2Code":"US","alpha3Code":"USA","callingCodes":["1"],"capital":"Washington, D.C.","altSpellings":["US","USA","United States of America"],"relevance":"3.5","region":"Americas","subregion":"Northern America","translations":{"de":"Vereinigte Staaten von Amerika","es":"Estados Unidos","fr":"États-Unis","ja":"アメリカ合衆国","it":"Stati Uniti D'America"},"population":321645000,"latlng":[38.0,-97.0],"demonym":"American","area":9629091.0,"gini":48.0,"timezones":["UTC-12:00","UTC-11:00","UTC-10:00","UTC-09:00","UTC-08:00","UTC-07:00","UTC-06:00","UTC-05:00","UTC-04:00","UTC+10:00","UTC+12:00"],"borders":["CAN","MEX"],"nativeName":"United States","numericCode":"840","currencies":["USD","USN","USS"],"languages":["en"]}] -------------------------------------------------------------------------------- /ist256/12-pandas/top40.csv: -------------------------------------------------------------------------------- 1 | ,Unnamed: 0,TITLE,ARTIST,PEAK,SENTIMENT 2 | 0,1,BLINDING LIGHTS,WEEKND,1,negative 3 | 1,2,DANCE MONKEY,TONES & I,1,negative 4 | 2,3,ROSES,SAINT JHN,1,negative 5 | 3,4,DON'T START NOW,DUA LIPA,2,mixed 6 | 4,5,BEFORE YOU GO,LEWIS CAPALDI,1,negative 7 | 5,6,ROCKSTAR,DABABY FT RODDY RICCH,1,unknown 8 | 6,7,SOMEONE YOU LOVED,LEWIS CAPALDI,1,negative 9 | 7,8,OWN IT,STORMZY/ED SHEERAN/BURNA BOY,1,positive 10 | 8,9,THE BOX,RODDY RICCH,2,mixed 11 | 9,10,SAY SO,DOJA CAT,2,mixed 12 | 10,11,LONELY,JOEL CORRY,4,negative 13 | 11,12,WATERMELON SUGAR,HARRY STYLES,4,positive 14 | 12,13,ADORE YOU,HARRY STYLES,7,mixed 15 | 13,14,PHYSICAL,DUA LIPA,3,mixed 16 | 14,15,HEAD & HEART,JOEL CORRY FT MNEK,1,unknown 17 | 15,16,ROVER,S1MBA FT DTG,3,unknown 18 | 16,17,SAVAGE LOVE (LAXED - SIREN BEAT),JAWSH 685 & JASON DERULO,1,positive 19 | 17,18,DEATH BED,POWFU FT BEABADOOBEE,4,unknown 20 | 18,19,BREAKING ME,TOPIC FT A7S,3,unknown 21 | 19,20,RAIN ON ME,LADY GAGA & ARIANA GRANDE,1,positive 22 | 20,21,LIFE IS GOOD,FUTURE FT DRAKE,3,unknown 23 | 21,22,TOOSIE SLIDE,DRAKE,1,mixed 24 | 22,23,BRUISES,LEWIS CAPALDI,6,positive 25 | 23,24,GODZILLA,EMINEM FT JUICE WRLD,1,unknown 26 | 24,25,EVERYTHING I WANTED,BILLIE EILISH,3,positive 27 | 25,26,ROXANNE,ARIZONA ZERVAS,4,unknown 28 | 26,27,BAD GUY,BILLIE EILISH,2,unknown 29 | 27,28,INTENTIONS,JUSTIN BIEBER FT QUAVO,8,unknown 30 | 28,29,MEMORIES,MAROON 5,5,positive 31 | 29,30,RAIN,AITCH/AJ TRACEY/TAY KEITH,3,negative 32 | 30,31,SAVAGE,MEGAN THEE STALLION,3,negative 33 | 31,32,FLOWERS,NATHAN DAWE FT JAYKAE,12,unknown 34 | 32,33,RIDE IT,REGARD,2,unknown 35 | 33,34,BLUEBERRY FAYGO,LIL MOSEY,9,negative 36 | 34,35,YOU SHOULD BE SAD,HALSEY,12,negative 37 | 35,36,FALLING,HARRY STYLES,15,negative 38 | 36,37,BREAK MY HEART,DUA LIPA,6,mixed 39 | 37,38,THIS CITY,SAM FISCHER,16,negative 40 | 38,39,DINNER GUEST,AJ TRACEY FT MOSTACK,5,unknown 41 | 39,40,BETTER OFF WITHOUT YOU,BECKY HILL FT SHIFT K3Y,14,unknown 42 | -------------------------------------------------------------------------------- /ist356/sample_cuse_vietnamese_restaurant_place_ids.csv: -------------------------------------------------------------------------------- 1 | Google Place ID 2 | ChIJB1EHMbTz2YkRda7GCxfLHg8 3 | ChIJq3w1Wn_x2YkR9HofEc9puio 4 | ChIJXzHY70ny2YkRMytGWVnP-I0 5 | ChIJFfNDji3z2YkROhmZ_HvA6Wk 6 | -------------------------------------------------------------------------------- /json-formats/students-columns.json: -------------------------------------------------------------------------------- 1 | { 2 | "Name":{ 3 | "0":"Abby", 4 | "1":"Bob", 5 | "2":"Chris", 6 | "3":"Dave", 7 | "4":"Ellen", 8 | "5":"Fran", 9 | "6":"Greg", 10 | "7":"Helen", 11 | "8":"Iris", 12 | "9":"Jimmy", 13 | "10":"Karen", 14 | "11":"Lynne", 15 | "12":"Mike", 16 | "13":"Nico", 17 | "14":"Pete" 18 | }, 19 | "Grade":{ 20 | "0":7.0, 21 | "1":9.0, 22 | "2":10.0, 23 | "3":8.0, 24 | "4":7.0, 25 | "5":10.0, 26 | "6":8.0, 27 | "7":null, 28 | "8":10.0, 29 | "9":8.0, 30 | "10":7.5, 31 | "11":10.0, 32 | "12":10.0, 33 | "13":null, 34 | "14":8.0 35 | }, 36 | "Year":{ 37 | "0":"Freshman", 38 | "1":"Sophomore", 39 | "2":"Senior", 40 | "3":"Freshman", 41 | "4":"Sophomore", 42 | "5":"Senior", 43 | "6":"Freshman", 44 | "7":"Sophomore", 45 | "8":"Senior", 46 | "9":"Freshman", 47 | "10":"Freshman", 48 | "11":"Sophomore", 49 | "12":"Sophomore", 50 | "13":"Junior", 51 | "14":"Freshman" 52 | } 53 | } -------------------------------------------------------------------------------- /json-formats/students-index.json: -------------------------------------------------------------------------------- 1 | { 2 | "0":{ 3 | "Name":"Abby", 4 | "Grade":7.0, 5 | "Year":"Freshman" 6 | }, 7 | "1":{ 8 | "Name":"Bob", 9 | "Grade":9.0, 10 | "Year":"Sophomore" 11 | }, 12 | "2":{ 13 | "Name":"Chris", 14 | "Grade":10.0, 15 | "Year":"Senior" 16 | }, 17 | "3":{ 18 | "Name":"Dave", 19 | "Grade":8.0, 20 | "Year":"Freshman" 21 | }, 22 | "4":{ 23 | "Name":"Ellen", 24 | "Grade":7.0, 25 | "Year":"Sophomore" 26 | }, 27 | "5":{ 28 | "Name":"Fran", 29 | "Grade":10.0, 30 | "Year":"Senior" 31 | }, 32 | "6":{ 33 | "Name":"Greg", 34 | "Grade":8.0, 35 | "Year":"Freshman" 36 | }, 37 | "7":{ 38 | "Name":"Helen", 39 | "Grade":null, 40 | "Year":"Sophomore" 41 | }, 42 | "8":{ 43 | "Name":"Iris", 44 | "Grade":10.0, 45 | "Year":"Senior" 46 | }, 47 | "9":{ 48 | "Name":"Jimmy", 49 | "Grade":8.0, 50 | "Year":"Freshman" 51 | }, 52 | "10":{ 53 | "Name":"Karen", 54 | "Grade":7.5, 55 | "Year":"Freshman" 56 | }, 57 | "11":{ 58 | "Name":"Lynne", 59 | "Grade":10.0, 60 | "Year":"Sophomore" 61 | }, 62 | "12":{ 63 | "Name":"Mike", 64 | "Grade":10.0, 65 | "Year":"Sophomore" 66 | }, 67 | "13":{ 68 | "Name":"Nico", 69 | "Grade":null, 70 | "Year":"Junior" 71 | }, 72 | "14":{ 73 | "Name":"Pete", 74 | "Grade":8.0, 75 | "Year":"Freshman" 76 | } 77 | } -------------------------------------------------------------------------------- /json-formats/students-lines.json: -------------------------------------------------------------------------------- 1 | {"Name":"Abby","Grade":7.0,"Year":"Freshman"} 2 | {"Name":"Bob","Grade":9.0,"Year":"Sophomore"} 3 | {"Name":"Chris","Grade":10.0,"Year":"Senior"} 4 | {"Name":"Dave","Grade":8.0,"Year":"Freshman"} 5 | {"Name":"Ellen","Grade":7.0,"Year":"Sophomore"} 6 | {"Name":"Fran","Grade":10.0,"Year":"Senior"} 7 | {"Name":"Greg","Grade":8.0,"Year":"Freshman"} 8 | {"Name":"Helen","Grade":null,"Year":"Sophomore"} 9 | {"Name":"Iris","Grade":10.0,"Year":"Senior"} 10 | {"Name":"Jimmy","Grade":8.0,"Year":"Freshman"} 11 | {"Name":"Karen","Grade":7.5,"Year":"Freshman"} 12 | {"Name":"Lynne","Grade":10.0,"Year":"Sophomore"} 13 | {"Name":"Mike","Grade":10.0,"Year":"Sophomore"} 14 | {"Name":"Nico","Grade":null,"Year":"Junior"} 15 | {"Name":"Pete","Grade":8.0,"Year":"Freshman"} 16 | -------------------------------------------------------------------------------- /json-formats/students-records.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "Name":"Abby", 4 | "Grade":7.0, 5 | "Year":"Freshman" 6 | }, 7 | { 8 | "Name":"Bob", 9 | "Grade":9.0, 10 | "Year":"Sophomore" 11 | }, 12 | { 13 | "Name":"Chris", 14 | "Grade":10.0, 15 | "Year":"Senior" 16 | }, 17 | { 18 | "Name":"Dave", 19 | "Grade":8.0, 20 | "Year":"Freshman" 21 | }, 22 | { 23 | "Name":"Ellen", 24 | "Grade":7.0, 25 | "Year":"Sophomore" 26 | }, 27 | { 28 | "Name":"Fran", 29 | "Grade":10.0, 30 | "Year":"Senior" 31 | }, 32 | { 33 | "Name":"Greg", 34 | "Grade":8.0, 35 | "Year":"Freshman" 36 | }, 37 | { 38 | "Name":"Helen", 39 | "Grade":null, 40 | "Year":"Sophomore" 41 | }, 42 | { 43 | "Name":"Iris", 44 | "Grade":10.0, 45 | "Year":"Senior" 46 | }, 47 | { 48 | "Name":"Jimmy", 49 | "Grade":8.0, 50 | "Year":"Freshman" 51 | }, 52 | { 53 | "Name":"Karen", 54 | "Grade":7.5, 55 | "Year":"Freshman" 56 | }, 57 | { 58 | "Name":"Lynne", 59 | "Grade":10.0, 60 | "Year":"Sophomore" 61 | }, 62 | { 63 | "Name":"Mike", 64 | "Grade":10.0, 65 | "Year":"Sophomore" 66 | }, 67 | { 68 | "Name":"Nico", 69 | "Grade":null, 70 | "Year":"Junior" 71 | }, 72 | { 73 | "Name":"Pete", 74 | "Grade":8.0, 75 | "Year":"Freshman" 76 | } 77 | ] -------------------------------------------------------------------------------- /json-formats/students-split.json: -------------------------------------------------------------------------------- 1 | { 2 | "columns":[ 3 | "Name", 4 | "Grade", 5 | "Year" 6 | ], 7 | "index":[ 8 | 0, 9 | 1, 10 | 2, 11 | 3, 12 | 4, 13 | 5, 14 | 6, 15 | 7, 16 | 8, 17 | 9, 18 | 10, 19 | 11, 20 | 12, 21 | 13, 22 | 14 23 | ], 24 | "data":[ 25 | [ 26 | "Abby", 27 | 7.0, 28 | "Freshman" 29 | ], 30 | [ 31 | "Bob", 32 | 9.0, 33 | "Sophomore" 34 | ], 35 | [ 36 | "Chris", 37 | 10.0, 38 | "Senior" 39 | ], 40 | [ 41 | "Dave", 42 | 8.0, 43 | "Freshman" 44 | ], 45 | [ 46 | "Ellen", 47 | 7.0, 48 | "Sophomore" 49 | ], 50 | [ 51 | "Fran", 52 | 10.0, 53 | "Senior" 54 | ], 55 | [ 56 | "Greg", 57 | 8.0, 58 | "Freshman" 59 | ], 60 | [ 61 | "Helen", 62 | null, 63 | "Sophomore" 64 | ], 65 | [ 66 | "Iris", 67 | 10.0, 68 | "Senior" 69 | ], 70 | [ 71 | "Jimmy", 72 | 8.0, 73 | "Freshman" 74 | ], 75 | [ 76 | "Karen", 77 | 7.5, 78 | "Freshman" 79 | ], 80 | [ 81 | "Lynne", 82 | 10.0, 83 | "Sophomore" 84 | ], 85 | [ 86 | "Mike", 87 | 10.0, 88 | "Sophomore" 89 | ], 90 | [ 91 | "Nico", 92 | null, 93 | "Junior" 94 | ], 95 | [ 96 | "Pete", 97 | 8.0, 98 | "Freshman" 99 | ] 100 | ] 101 | } -------------------------------------------------------------------------------- /json-formats/students-table.json: -------------------------------------------------------------------------------- 1 | { 2 | "schema":{ 3 | "fields":[ 4 | { 5 | "name":"index", 6 | "type":"integer" 7 | }, 8 | { 9 | "name":"Name", 10 | "type":"string" 11 | }, 12 | { 13 | "name":"Grade", 14 | "type":"number" 15 | }, 16 | { 17 | "name":"Year", 18 | "type":"string" 19 | } 20 | ], 21 | "primaryKey":[ 22 | "index" 23 | ], 24 | "pandas_version":"1.4.0" 25 | }, 26 | "data":[ 27 | { 28 | "index":0, 29 | "Name":"Abby", 30 | "Grade":7.0, 31 | "Year":"Freshman" 32 | }, 33 | { 34 | "index":1, 35 | "Name":"Bob", 36 | "Grade":9.0, 37 | "Year":"Sophomore" 38 | }, 39 | { 40 | "index":2, 41 | "Name":"Chris", 42 | "Grade":10.0, 43 | "Year":"Senior" 44 | }, 45 | { 46 | "index":3, 47 | "Name":"Dave", 48 | "Grade":8.0, 49 | "Year":"Freshman" 50 | }, 51 | { 52 | "index":4, 53 | "Name":"Ellen", 54 | "Grade":7.0, 55 | "Year":"Sophomore" 56 | }, 57 | { 58 | "index":5, 59 | "Name":"Fran", 60 | "Grade":10.0, 61 | "Year":"Senior" 62 | }, 63 | { 64 | "index":6, 65 | "Name":"Greg", 66 | "Grade":8.0, 67 | "Year":"Freshman" 68 | }, 69 | { 70 | "index":7, 71 | "Name":"Helen", 72 | "Grade":null, 73 | "Year":"Sophomore" 74 | }, 75 | { 76 | "index":8, 77 | "Name":"Iris", 78 | "Grade":10.0, 79 | "Year":"Senior" 80 | }, 81 | { 82 | "index":9, 83 | "Name":"Jimmy", 84 | "Grade":8.0, 85 | "Year":"Freshman" 86 | }, 87 | { 88 | "index":10, 89 | "Name":"Karen", 90 | "Grade":7.5, 91 | "Year":"Freshman" 92 | }, 93 | { 94 | "index":11, 95 | "Name":"Lynne", 96 | "Grade":10.0, 97 | "Year":"Sophomore" 98 | }, 99 | { 100 | "index":12, 101 | "Name":"Mike", 102 | "Grade":10.0, 103 | "Year":"Sophomore" 104 | }, 105 | { 106 | "index":13, 107 | "Name":"Nico", 108 | "Grade":null, 109 | "Year":"Junior" 110 | }, 111 | { 112 | "index":14, 113 | "Name":"Pete", 114 | "Grade":8.0, 115 | "Year":"Freshman" 116 | } 117 | ] 118 | } -------------------------------------------------------------------------------- /json-formats/students-values.json: -------------------------------------------------------------------------------- 1 | [["Abby",7.0,"Freshman"],["Bob",9.0,"Sophomore"],["Chris",10.0,"Senior"],["Dave",8.0,"Freshman"],["Ellen",7.0,"Sophomore"],["Fran",10.0,"Senior"],["Greg",8.0,"Freshman"],["Helen",null,"Sophomore"],["Iris",10.0,"Senior"],["Jimmy",8.0,"Freshman"],["Karen",7.5,"Freshman"],["Lynne",10.0,"Sophomore"],["Mike",10.0,"Sophomore"],["Nico",null,"Junior"],["Pete",8.0,"Freshman"]] -------------------------------------------------------------------------------- /json-samples/employees-dict.json: -------------------------------------------------------------------------------- 1 | { 2 | "accounting": [ 3 | { 4 | "firstName": "John", 5 | "lastName": "Doe", 6 | "age": 23 7 | }, 8 | { 9 | "firstName": "Mary", 10 | "lastName": "Smith", 11 | "age": 32 12 | } 13 | ], 14 | "sales": [ 15 | { 16 | "firstName": "Sally", 17 | "lastName": "Green", 18 | "age": 27 19 | }, 20 | { 21 | "firstName": "Jim", 22 | "lastName": "Galley", 23 | "age": 41 24 | } 25 | ], 26 | "marketing": [ 27 | { 28 | "firstName": "Tom", 29 | "lastName": "Brown", 30 | "age": 28 31 | } 32 | ] 33 | } 34 | -------------------------------------------------------------------------------- /json-samples/employees.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "dept": "accounting", 4 | "employees": [ 5 | { 6 | "firstName": "John", 7 | "lastName": "Doe", 8 | "age": 23 9 | }, 10 | { 11 | "firstName": "Mary", 12 | "lastName": "Smith", 13 | "age": 32 14 | } 15 | ] 16 | }, 17 | { 18 | "dept": "sales", 19 | "employees": [ 20 | { 21 | "firstName": "Sally", 22 | "lastName": "Green", 23 | "age": 27 24 | }, 25 | { 26 | "firstName": "Jim", 27 | "lastName": "Galley", 28 | "age": 41 29 | } 30 | ] 31 | } 32 | ] -------------------------------------------------------------------------------- /json-samples/movies.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "12 Strong": { 4 | "Genre": "Action", 5 | "Gross": "$453,173", 6 | "IMDB Metascore": "54", 7 | "Popcorn Score": 72, 8 | "Rating": "R", 9 | "Tomato Score": 54 10 | }, 11 | "A Fantastic Woman (Una Mujer Fant\u00e1stica)": { 12 | "popcornscore": 83, 13 | "rating": "R", 14 | "tomatoscore": 90 15 | }, 16 | "All The Money In The World": { 17 | "popcornscore": 71, 18 | "rating": "R", 19 | "tomatoscore": 77 20 | }, 21 | "Bilal: A New Breed Of Hero": { 22 | "popcornscore": 91, 23 | "rating": "PG13", 24 | "tomatoscore": 57 25 | }, 26 | "Call Me By Your Name": { 27 | "popcornscore": 87, 28 | "rating": "R", 29 | "tomatoscore": 96 30 | }, 31 | "Darkest Hour": { 32 | "popcornscore": 84, 33 | "rating": "PG13", 34 | "tomatoscore": 86 35 | }, 36 | "Den Of Thieves": { 37 | "Genre": "Action", 38 | "Gross": "$491,898", 39 | "IMDB Metascore": "49", 40 | "Popcorn Score": 69, 41 | "Rating": "R", 42 | "Tomato Score": 40 43 | }, 44 | "Ferdinand": { 45 | "popcornscore": 49, 46 | "rating": "PG", 47 | "tomatoscore": 71 48 | }, 49 | "Fifty Shades Freed": { 50 | "Genre": "Drama", 51 | "Gross": "unknown", 52 | "IMDB Metascore": "34", 53 | "Popcorn Score": "unknown", 54 | "Rating": "unrated", 55 | "Tomato Score": "unkown" 56 | }, 57 | "Film Stars Don'T Die In Liverpool": { 58 | "popcornscore": 69, 59 | "rating": "R", 60 | "tomatoscore": 78 61 | }, 62 | "Forever My Girl": { 63 | "popcornscore": 91, 64 | "rating": "PG", 65 | "tomatoscore": 21 66 | }, 67 | "Golden Exits": { 68 | "Genre": "Drama", 69 | "Gross": "unknown", 70 | "IMDB Metascore": "72", 71 | "Popcorn Score": "unknown", 72 | "Rating": "unrated", 73 | "Tomato Score": "unkown" 74 | }, 75 | "Hostiles": { 76 | "Genre": "Adventure", 77 | "Gross": "$548,886", 78 | "IMDB Metascore": "65", 79 | "Popcorn Score": 71, 80 | "Rating": "R", 81 | "Tomato Score": 72 82 | }, 83 | "I, Tonya": { 84 | "popcornscore": 89, 85 | "rating": "R", 86 | "tomatoscore": 90 87 | }, 88 | "Insidious: The Last Key": { 89 | "popcornscore": 51, 90 | "rating": "PG13", 91 | "tomatoscore": 32 92 | }, 93 | "Jumanji: Welcome To The Jungle": { 94 | "Genre": "Action", 95 | "Gross": "$760,867", 96 | "IMDB Metascore": "58", 97 | "Popcorn Score": 89, 98 | "Rating": "PG13", 99 | "Tomato Score": 76 100 | }, 101 | "Mary And The Witch'S Flower": { 102 | "popcornscore": 78, 103 | "rating": "PG", 104 | "tomatoscore": 84 105 | }, 106 | "Maze Runner: The Death Cure": { 107 | "Genre": "Action", 108 | "Gross": "$720,463", 109 | "IMDB Metascore": "51", 110 | "Popcorn Score": 71, 111 | "Rating": "PG13", 112 | "Tomato Score": 43 113 | }, 114 | "Molly'S Game": { 115 | "popcornscore": 85, 116 | "rating": "R", 117 | "tomatoscore": 82 118 | }, 119 | "Paddington 2": { 120 | "Genre": "Animation", 121 | "Gross": "$184,414", 122 | "IMDB Metascore": "88", 123 | "Popcorn Score": 89, 124 | "Rating": "PG", 125 | "Tomato Score": 100 126 | }, 127 | "Padmaavat": { 128 | "popcornscore": 62, 129 | "rating": "NR", 130 | "tomatoscore": 74 131 | }, 132 | "Permission": { 133 | "Genre": "Comedy", 134 | "Gross": "unknown", 135 | "IMDB Metascore": "53", 136 | "Popcorn Score": "unknown", 137 | "Rating": "unrated", 138 | "Tomato Score": "unkown" 139 | }, 140 | "Peter Rabbit": { 141 | "Genre": "Animation", 142 | "Gross": "unknown", 143 | "IMDB Metascore": "56", 144 | "Popcorn Score": "unknown", 145 | "Rating": "unrated", 146 | "Tomato Score": "unkown" 147 | }, 148 | "Phantom Thread": { 149 | "popcornscore": 68, 150 | "rating": "R", 151 | "tomatoscore": 91 152 | }, 153 | "Pitch Perfect 3": { 154 | "popcornscore": 52, 155 | "rating": "PG13", 156 | "tomatoscore": 31 157 | }, 158 | "Proud Mary": { 159 | "popcornscore": 56, 160 | "rating": "R", 161 | "tomatoscore": 26 162 | }, 163 | "Sanpo Suru Shinryakusha": { 164 | "Genre": "Drama", 165 | "Gross": "unknown", 166 | "IMDB Metascore": "65", 167 | "Popcorn Score": "unknown", 168 | "Rating": "unrated", 169 | "Tomato Score": "unkown" 170 | }, 171 | "Star Wars: The Last Jedi": { 172 | "popcornscore": 48, 173 | "rating": "PG13", 174 | "tomatoscore": 91 175 | }, 176 | "The 15:17 To Paris": { 177 | "Genre": "Drama", 178 | "Gross": "unknown", 179 | "IMDB Metascore": "52", 180 | "Popcorn Score": "unknown", 181 | "Rating": "unrated", 182 | "Tomato Score": "unkown" 183 | }, 184 | "The Commuter": { 185 | "popcornscore": 48, 186 | "rating": "PG13", 187 | "tomatoscore": 58 188 | }, 189 | "The Disaster Artist": { 190 | "popcornscore": 89, 191 | "rating": "R", 192 | "tomatoscore": 91 193 | }, 194 | "The Greatest Showman": { 195 | "Genre": "Biography", 196 | "Gross": "$627,248", 197 | "IMDB Metascore": "48", 198 | "Popcorn Score": 90, 199 | "Rating": "PG", 200 | "Tomato Score": 55 201 | }, 202 | "The Insult (L'Insulte)": { 203 | "popcornscore": 86, 204 | "rating": "R", 205 | "tomatoscore": 89 206 | }, 207 | "The Post": { 208 | "Genre": "Biography", 209 | "Gross": "$463,228", 210 | "IMDB Metascore": "83", 211 | "Popcorn Score": 73, 212 | "Rating": "PG13", 213 | "Tomato Score": 88 214 | }, 215 | "The Shape Of Water": { 216 | "Genre": "Adventure", 217 | "Gross": "$448,287", 218 | "IMDB Metascore": "86", 219 | "Popcorn Score": 78, 220 | "Rating": "R", 221 | "Tomato Score": 92 222 | }, 223 | "Three Billboards Outside Ebbing, Missouri": { 224 | "popcornscore": 87, 225 | "rating": "R", 226 | "tomatoscore": 93 227 | }, 228 | "Till The End Of The World": { 229 | "popcornscore": -1, 230 | "rating": "NR", 231 | "tomatoscore": null 232 | }, 233 | "Winchester": { 234 | "Genre": "Biography", 235 | "Gross": "$696,786", 236 | "IMDB Metascore": "28", 237 | "Popcorn Score": 40, 238 | "Rating": "PG13", 239 | "Tomato Score": 12 240 | } 241 | } 242 | ] -------------------------------------------------------------------------------- /json-samples/orders.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "Customer" : { "FirstName" : "Abby", "LastName" : "Kuss"}, 4 | "Items" : [ 5 | { "Name" : "T-Shirt", "Price" : 10.0, "Quantity" : 3}, 6 | { "Name" : "Jacket", "Price" : 20.0, "Quantity" : 1} 7 | ] 8 | }, 9 | { 10 | "Customer" : { "FirstName" : "Bette", "LastName" : "Alott"}, 11 | "Items" : [ 12 | { "Name" : "Shoes", "Price" : 25.0, "Quantity" : 1}, 13 | { "Name" : "Jacket", "Price" : 20.0, "Quantity" : 1} 14 | ] 15 | }, 16 | { 17 | "Customer" : { "FirstName" : "Chris", "LastName" : "Peanugget"}, 18 | "Items" : [ 19 | { "Name" : "T-Shirt", "Price" : 10.0, "Quantity" : 1} 20 | ] 21 | } 22 | ] -------------------------------------------------------------------------------- /json-samples/people/1.json: -------------------------------------------------------------------------------- 1 | { "id" : 1, "name": "John", "age": 30, "weight": 175 } -------------------------------------------------------------------------------- /json-samples/people/2.json: -------------------------------------------------------------------------------- 1 | { "id" : 2, "name": "Jacob", "age": 35, "weight": 166 } -------------------------------------------------------------------------------- /json-samples/people/3.json: -------------------------------------------------------------------------------- 1 | { "id" : 3, "name": "Jingle", "age": 22, "weight": 188 } -------------------------------------------------------------------------------- /json-samples/people/4.json: -------------------------------------------------------------------------------- 1 | { "id" : 4, "name": "Heimer", "age": 27, "weight": 201 } -------------------------------------------------------------------------------- /json-samples/people/5.json: -------------------------------------------------------------------------------- 1 | { "id" : 5, "name": "Smith", "age": 38} -------------------------------------------------------------------------------- /json-samples/stocks.json: -------------------------------------------------------------------------------- 1 | [ 2 | { "symbol" : "AAPL", "price" : 126.82 }, 3 | { "symbol" : "AMZN", "price" : 3098.12 }, 4 | { "symbol" : "FB", "price" : 251.11 }, 5 | { "symbol" : "GOOG", "price" : 1725.05 }, 6 | { "symbol" : "IBM", "price" : 128.39 }, 7 | { "symbol" : "MSFT", "price" : 212.55 }, 8 | { "symbol" : "NET", "price" : 78.00 }, 9 | { "symbol" : "NFLX", "price" : 497.00 }, 10 | { "symbol" : "TSLA", "price" : 823.80 }, 11 | { "symbol" : "TWTR", "price" : 45.11 } 12 | ] 13 | -------------------------------------------------------------------------------- /json-samples/students.json: -------------------------------------------------------------------------------- 1 | [ 2 | {"Name":"Abby", "GPA": 4.0, "Year": "So"}, 3 | {"Name":"Bette", "GPA": 3.7, "Year": "Jr"}, 4 | {"Name":"Chris", "Year": "Fr"}, 5 | {"Name":"Dee", "GPA": 3.4, "Year": "Fr"} 6 | ] -------------------------------------------------------------------------------- /minimart/customers.csv: -------------------------------------------------------------------------------- 1 | customer_id,firstname,lastname 2 | 10,Abby,Kuss 3 | 20,Bette,Alott 4 | 30,Chris,Peanugget 5 | 40,Don,Atello 6 | 50,Erin,Detyres -------------------------------------------------------------------------------- /minimart/purchases-apr.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount","customer_id" 2 | 1031,2024-04-01, 256.00,20 3 | 1032,2024-04-09, 42.30,20 4 | 1033,2024-04-17, 199.20,30 5 | 1034,2024-04-29, 26.88,40 -------------------------------------------------------------------------------- /minimart/purchases-feb.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount","customer_id" 2 | 1026,2024-02-10, 104.35,10 3 | 1027,2024-02-24, 33.70,20 -------------------------------------------------------------------------------- /minimart/purchases-jan.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount","customer_id" 2 | 1023,2024-01-05, 56.90,10 3 | 1024,2024-01-17, 146.70,20 4 | 1025,2024-01-25, 36.40,50 -------------------------------------------------------------------------------- /minimart/purchases-mar.csv: -------------------------------------------------------------------------------- 1 | "order_id","order_date","order_amount","customer_id" 2 | 1028,2024-03-06, 86.50,30 3 | 1029,2024-03-22, 209.00,50 4 | 1030,2024-03-30, 136.55,40 -------------------------------------------------------------------------------- /mtrand.pyx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/mtrand.pyx -------------------------------------------------------------------------------- /netflix-canceled-2021/bonding.json: -------------------------------------------------------------------------------- 1 | {"id":34493,"url":"https://www.tvmaze.com/shows/34493/bonding","name":"Bonding","type":"Scripted","language":"English","genres":["Drama","Comedy","Adult"],"status":"Ended","runtime":null,"averageRuntime":17,"premiered":"2019-04-24","ended":"2021-01-27","officialSite":"https://www.netflix.com/title/81004814","schedule":{"time":"","days":[]},"rating":{"average":6.2},"weight":94,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":344643,"imdb":"tt7718088"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/161/403020.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/161/403020.jpg"},"summary":"

A New York City grad student moonlighting as a dominatrix enlists her gay BFF from high school to be her assistant.

","updated":1625280398,"_links":{"self":{"href":"https://api.tvmaze.com/shows/34493"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/2016547"}},"_embedded":{"episodes":[{"id":1627875,"url":"https://www.tvmaze.com/episodes/1627875/bonding-1x01-old-friends-new-names","name":"Old Friends, New Names","season":1,"number":1,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":16,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482856.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482856.jpg"},"summary":"

Struggling to make ends meet as a waiter, Pete takes a job working for his friend Tiff -- aka Mistress May -- in her sex dungeon.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1627875"}}},{"id":1645145,"url":"https://www.tvmaze.com/episodes/1645145/bonding-1x02-pete-shy","name":"Pete Shy","season":1,"number":2,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":17,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482857.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482857.jpg"},"summary":"

Pete gets stage fright, a customer's phone number and a lesson in tying knots. Tiff consults with Daphne, a prospective client.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1645145"}}},{"id":1645146,"url":"https://www.tvmaze.com/episodes/1645146/bonding-1x03-the-past-is-not-always-behind","name":"The Past is Not Always Behind","season":1,"number":3,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":17,"rating":{"average":8.7},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482858.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482858.jpg"},"summary":"

Pete's roommate makes him an offer that's hard to refuse. Tiff and Pete run into an old -- and very drunk -- acquaintance.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1645146"}}},{"id":1645147,"url":"https://www.tvmaze.com/episodes/1645147/bonding-1x04-lets-get-physical","name":"Let's Get Physical","season":1,"number":4,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":15,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482859.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482859.jpg"},"summary":"

Tiff and Pete visit Daphne's immaculate home. Pete gears up for a coffee date with Josh, and Tiff interrupts a troubling encounter.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1645147"}}},{"id":1645149,"url":"https://www.tvmaze.com/episodes/1645149/bonding-1x05-double-date","name":"Double Date","season":1,"number":5,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":16,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482860.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482860.jpg"},"summary":"

Pete and Tiff argue as they get ready for their dates. While Josh and Pete flirt at a burlesque show, Doug tries to win over Tiff.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1645149"}}},{"id":1645150,"url":"https://www.tvmaze.com/episodes/1645150/bonding-1x06-penguins","name":"Penguins","season":1,"number":6,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":14,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482861.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482861.jpg"},"summary":"

When Tiff doesn't show up for work, Pete takes charge. Tiff reveals to Doug that she's a dominatrix, and Pete gives stand-up another shot.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1645150"}}},{"id":1645151,"url":"https://www.tvmaze.com/episodes/1645151/bonding-1x07-into-the-woods","name":"Into the Woods","season":1,"number":7,"type":"regular","airdate":"2019-04-24","airtime":"","airstamp":"2019-04-24T12:00:00+00:00","runtime":13,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/193/482862.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/193/482862.jpg"},"summary":"

After some nudging from Daphne and Andrew, Tiff and Pete make up. Back in business, they meet a wealthy new client at his place.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1645151"}}},{"id":1997771,"url":"https://www.tvmaze.com/episodes/1997771/bonding-2x01-the-kinks","name":"The Kinks","season":2,"number":1,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":18,"rating":{"average":7.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736174.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736174.jpg"},"summary":"

Ten months after their disastrous house call, Tiff and Pete's reputation is still trashed, so they seek help from Tiff's former mentor.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1997771"}}},{"id":2015532,"url":"https://www.tvmaze.com/episodes/2015532/bonding-2x02-dog-days","name":"Dog Days","season":2,"number":2,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":19,"rating":{"average":7.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736175.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736175.jpg"},"summary":"

A jealous Tiff accidentally calls Doug her boyfriend. Pete tries to be understanding with Josh — and embraces his submissive side.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2015532"}}},{"id":2015533,"url":"https://www.tvmaze.com/episodes/2015533/bonding-2x03-personal","name":"Personal","season":2,"number":3,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":19,"rating":{"average":7.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736176.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736176.jpg"},"summary":"

As a test, Mistress Mira sends Tiff and Pete to work with her personal submissive, who enjoys being financially dominated. Doug opens up to Tiff.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2015533"}}},{"id":2015534,"url":"https://www.tvmaze.com/episodes/2015534/bonding-2x04-threesomes","name":"Threesomes","season":2,"number":4,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":19,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736177.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736177.jpg"},"summary":"

Frank gets a job at a gay bar. Thrown together, Tiff and Gina bond. Pete meets Josh's ex — who looks exactly like Pete.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2015534"}}},{"id":2015535,"url":"https://www.tvmaze.com/episodes/2015535/bonding-2x05-nanci","name":"Nanci","season":2,"number":5,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":20,"rating":{"average":7.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736178.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736178.jpg"},"summary":"

With Pete's support, Josh says he's ready to come out to his dad and co-workers. Tiff helps an old classmate conquer her fears.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2015535"}}},{"id":2015536,"url":"https://www.tvmaze.com/episodes/2015536/bonding-2x06-the-lost-egg","name":"The Lost Egg","season":2,"number":6,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":16,"rating":{"average":7.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736179.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736179.jpg"},"summary":"

Tiff tells Pete they need to quit working together. Doug's frustrations with Tiff boil over, and Pete prepares to meet Josh's dad.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2015536"}}},{"id":2015537,"url":"https://www.tvmaze.com/episodes/2015537/bonding-2x07-stand-me-up-stand-me-down","name":"Stand Me Up, Stand Me Down","season":2,"number":7,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":21,"rating":{"average":7.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736180.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736180.jpg"},"summary":"

Blowups with Tiff and Josh leave Pete feeling crushed. And now he has to do a stand-up set with an agent in the audience.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2015537"}}},{"id":2016547,"url":"https://www.tvmaze.com/episodes/2016547/bonding-2x08-permission","name":"Permission","season":2,"number":8,"type":"regular","airdate":"2021-01-27","airtime":"","airstamp":"2021-01-27T12:00:00+00:00","runtime":22,"rating":{"average":7.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/294/736181.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/294/736181.jpg"},"summary":"

As their friendship falters and their paths diverge, Tiff and Pete navigate permission, consent, betrayal — and new opportunities.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2016547"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/country-comfort.json: -------------------------------------------------------------------------------- 1 | {"id":46077,"url":"https://www.tvmaze.com/shows/46077/country-comfort","name":"Country Comfort","type":"Scripted","language":"English","genres":["Comedy","Family","Music"],"status":"Ended","runtime":null,"averageRuntime":25,"premiered":"2021-03-19","ended":"2021-03-19","officialSite":"https://www.netflix.com/title/81064555","schedule":{"time":"","days":[]},"rating":{"average":7.6},"weight":76,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":382438,"imdb":"tt11717394"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/297/743327.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/297/743327.jpg"},"summary":"

When her career and personal life get derailed, an aspiring young country singer named Bailey takes a job as a nanny for a rugged cowboy named Beau and his five children. With a never-give-up attitude and loads of Southern charm, this newbie-nanny is able to navigate the family dynamics and be the mother figure they've been missing. To her surprise, Bailey also gets the band she's been missing in this musically talented family who help get her back on the road to stardom.

","updated":1625280382,"_links":{"self":{"href":"https://api.tvmaze.com/shows/46077"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/2029661"}},"_embedded":{"episodes":[{"id":2029652,"url":"https://www.tvmaze.com/episodes/2029652/country-comfort-1x01-crazy","name":"Crazy","season":1,"number":1,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":21,"rating":{"average":7.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750746.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750746.jpg"},"summary":"

Harmony leads to heartache as aspiring singer Bailey's big break falls to pieces. But a chance encounter on a stormy night leads to new possibilities.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029652"}}},{"id":2029653,"url":"https://www.tvmaze.com/episodes/2029653/country-comfort-1x02-teardrops-on-my-guitar","name":"Teardrops on My Guitar","season":1,"number":2,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":26,"rating":{"average":7.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750747.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750747.jpg"},"summary":"

As Bailey settles in over breakfast, Dylan kick-starts his career as her new manager. Will a last-minute audition lead to stardom... or serious drama?


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029653"}}},{"id":2029654,"url":"https://www.tvmaze.com/episodes/2029654/country-comfort-1x03-sign-sign-everywhere-a-sign","name":"Sign, Sign, Everywhere a Sign","season":1,"number":3,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":27,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750748.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750748.jpg"},"summary":"

Beau and the kids get cleaned up for a church wedding — and encounter an unexpected guest at the house. Meanwhile, Cassidy struggles with her faith.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029654"}}},{"id":2029655,"url":"https://www.tvmaze.com/episodes/2029655/country-comfort-1x04-my-girl","name":"My Girl","season":1,"number":4,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":23,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750749.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750749.jpg"},"summary":"

Bailey and the Haywoods make the best of bittersweet news as Beau prepares to replace his late wife's mare. Brody reconnects with an old friend.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029655"}}},{"id":2029656,"url":"https://www.tvmaze.com/episodes/2029656/country-comfort-1x05-blue","name":"Blue","season":1,"number":5,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":25,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750750.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750750.jpg"},"summary":"

While Tuck and Brody struggle with sibling rivalry, Bailey struggles to balance a big audition and a LeAnn Rimes concert with Cassidy.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029656"}}},{"id":2029657,"url":"https://www.tvmaze.com/episodes/2029657/country-comfort-1x06-summer-lovin","name":"Summer Lovin'","season":1,"number":6,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":27,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750751.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750751.jpg"},"summary":"

Beau and Summer's relationship takes center stage after she asks him to spend the night. Elsewhere, Brody and Tuck prepare for a double date night.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029657"}}},{"id":2029658,"url":"https://www.tvmaze.com/episodes/2029658/country-comfort-1x07-youre-nobody-till-somebody-loves-you","name":"You're Nobody Till Somebody Loves You","season":1,"number":7,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":24,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750752.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750752.jpg"},"summary":"

On the heels of exciting news, Bailey struggles to keep her past out of her present and future. Meanwhile, Beau navigates a bad case of heartache.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029658"}}},{"id":2029659,"url":"https://www.tvmaze.com/episodes/2029659/country-comfort-1x08-back-in-the-saddle-again","name":"Back in the Saddle Again","season":1,"number":8,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":25,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750753.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750753.jpg"},"summary":"

Boone's big ask leads to a big answer. The following morning, Beau tries to minimize his birthday, much to the dismay of Bailey and the kids.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029659"}}},{"id":2029660,"url":"https://www.tvmaze.com/episodes/2029660/country-comfort-1x09-you-matter-to-me","name":"You Matter to Me","season":1,"number":9,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":24,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750754.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750754.jpg"},"summary":"

It's lights, camera, action for Bailey as a Rocky Top Records film crew stops by the ranch… and encounters a less-than-cinematic day of disaster.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029660"}}},{"id":2029661,"url":"https://www.tvmaze.com/episodes/2029661/country-comfort-1x10-bless-the-broken-road","name":"Bless the Broken Road","season":1,"number":10,"type":"regular","airdate":"2021-03-19","airtime":"","airstamp":"2021-03-19T12:00:00+00:00","runtime":27,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/750755.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750755.jpg"},"summary":"

Bailey's time to shine finally arrives — but the Haywoods aren't ready to say goodbye. Brody revisits his feelings for Jo, and Boone drops a bombshell.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2029661"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/cowboy-bebop.json: -------------------------------------------------------------------------------- 1 | {"id":39892,"url":"https://www.tvmaze.com/shows/39892/cowboy-bebop","name":"Cowboy Bebop","type":"Scripted","language":null,"genres":["Action","Adventure","Science-Fiction"],"status":"Ended","runtime":null,"averageRuntime":46,"premiered":"2021-11-19","ended":"2021-11-19","officialSite":"https://www.netflix.com/title/80207033","schedule":{"time":"","days":[]},"rating":{"average":7.2},"weight":97,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null,"officialSite":"https://www.netflix.com/"},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":367234,"imdb":"tt1267295"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/394/985036.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/394/985036.jpg"},"summary":"

Cowboy Bebop is about three bounty hunters, aka \"cowboys,\" all trying to outrun the past. As different as they are deadly, Spike Spiegel, Jet Black, and Faye Valentine form a scrappy, snarky crew ready to hunt down the solar system's most dangerous criminals — for the right price. But they can only kick and quip their way out of so many scuffles before their pasts finally catch up with them.

 

","updated":1643644835,"_links":{"self":{"href":"https://api.tvmaze.com/shows/39892"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/2217516"}},"_embedded":{"episodes":[{"id":2154144,"url":"https://www.tvmaze.com/episodes/2154144/cowboy-bebop-1x01-cowboy-gospel","name":"Cowboy Gospel","season":1,"number":1,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":50,"rating":{"average":7.7},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940968.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940968.jpg"},"summary":"

After getting shortchanged on the fee for a bounty, Spike and Jet head to New Tijuana on the trail of another mark — but they're not the only ones.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2154144"}}},{"id":2217506,"url":"https://www.tvmaze.com/episodes/2217506/cowboy-bebop-1x02-venus-pop","name":"Venus Pop","season":1,"number":2,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":40,"rating":{"average":7.9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940969.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940969.jpg"},"summary":"

While chasing a bounty on a one-handed Venusian bomber, Jet and Spike grapple with rising interpersonal tensions, exacerbated by Spike's secret past.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217506"}}},{"id":2217507,"url":"https://www.tvmaze.com/episodes/2217507/cowboy-bebop-1x03-dog-star-swing","name":"Dog Star Swing","season":1,"number":3,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":47,"rating":{"average":7.9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940970.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940970.jpg"},"summary":"

Jet needs cash to buy his daughter a birthday present. To get it, he and Spike track a potential bounty to a brothel, but Spike has his own agenda.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217507"}}},{"id":2217508,"url":"https://www.tvmaze.com/episodes/2217508/cowboy-bebop-1x04-callisto-soul","name":"Callisto Soul","season":1,"number":4,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":42,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940971.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940971.jpg"},"summary":"

When a group of ecoterrorists screw up her attempted shakedown, Faye enlists Spike and Jet to help her bring them in for the bounty.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217508"}}},{"id":2217509,"url":"https://www.tvmaze.com/episodes/2217509/cowboy-bebop-1x05-dark-side-tango","name":"Dark Side Tango","season":1,"number":5,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":45,"rating":{"average":7.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940972.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940972.jpg"},"summary":"

Jet reunites with his ex-partner to track down a recently released criminal. While he's away, Spike and Faye try to pick up a quick job.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217509"}}},{"id":2217511,"url":"https://www.tvmaze.com/episodes/2217511/cowboy-bebop-1x06-binary-two-step","name":"Binary Two-Step","season":1,"number":6,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":42,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940973.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940973.jpg"},"summary":"

When the Bebop needs repairs, Jet sends Spike to chase down a tip on a phantom bounty to cover the cost, leaving Faye to supervise the mechanic.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217511"}}},{"id":2217512,"url":"https://www.tvmaze.com/episodes/2217512/cowboy-bebop-1x07-galileo-hustle","name":"Galileo Hustle","season":1,"number":7,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":49,"rating":{"average":7.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940974.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940974.jpg"},"summary":"

A con woman from Faye's past reappears, offering Faye's real identity in exchange for passage to Santo City in a hurry. Julia considers her options.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217512"}}},{"id":2217513,"url":"https://www.tvmaze.com/episodes/2217513/cowboy-bebop-1x08-sad-clown-a-go-go","name":"Sad Clown A-Go-Go","season":1,"number":8,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":45,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940975.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940975.jpg"},"summary":"

Vicious liberates a terrifying assassin and contracts him to kill Spike Spiegel. Later, he puts his plan for the Elders into motion.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217513"}}},{"id":2217515,"url":"https://www.tvmaze.com/episodes/2217515/cowboy-bebop-1x09-blue-crow-waltz","name":"Blue Crow Waltz","season":1,"number":9,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":52,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940976.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940976.jpg"},"summary":"

Years earlier, Spike and Vicious meet Julia at Ana's club. But romantic entanglements and Vicious' ambitions soon cause trouble for all of them.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217515"}}},{"id":2217516,"url":"https://www.tvmaze.com/episodes/2217516/cowboy-bebop-1x10-supernova-symphony","name":"Supernova Symphony","season":1,"number":10,"type":"regular","airdate":"2021-11-19","airtime":"","airstamp":"2021-11-19T12:00:00+00:00","runtime":49,"rating":{"average":8.2},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/376/940977.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/376/940977.jpg"},"summary":"

Faye and Jet track Spike to Ana's club, only to learn that Vicious has kidnapped Jet's daughter so he can trade Spike's life for hers.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2217516"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/grand-army.json: -------------------------------------------------------------------------------- 1 | {"id":44653,"url":"https://www.tvmaze.com/shows/44653/grand-army","name":"Grand Army","type":"Scripted","language":"English","genres":["Drama"],"status":"Ended","runtime":null,"averageRuntime":53,"premiered":"2020-10-16","ended":"2020-10-16","officialSite":"https://www.netflix.com/title/80211686","schedule":{"time":"","days":[]},"rating":{"average":7},"weight":47,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":null,"imdb":"tt10473150"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/275/687586.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/275/687586.jpg"},"summary":"

Five students at the largest public high school in Brooklyn take on a chaotic world as they fight to succeed, survive, break free and seize the future.

","updated":1624040842,"_links":{"self":{"href":"https://api.tvmaze.com/shows/44653"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/1924567"}},"_embedded":{"episodes":[{"id":1923724,"url":"https://www.tvmaze.com/episodes/1923724/grand-army-1x01-brooklyn-2020","name":"Brooklyn, 2020","season":1,"number":1,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":52,"rating":{"average":6},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696367.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696367.jpg"},"summary":"

A bombing blocks away sends Grand Army High School into lockdown, building pressure that spills over at the \"party of the century\" that night.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1923724"}}},{"id":1924550,"url":"https://www.tvmaze.com/episodes/1924550/grand-army-1x02-see-me","name":"See Me","season":1,"number":2,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":50,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696368.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696368.jpg"},"summary":"

Joey stages a protest at school. Owen and Jayson face a harsh penalty for their lockdown prank. A hookup leaves Leila feeling awkward.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924550"}}},{"id":1924560,"url":"https://www.tvmaze.com/episodes/1924560/grand-army-1x03-relationship-goals","name":"Relationship Goals","season":1,"number":3,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":53,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696369.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696369.jpg"},"summary":"

Sid struggles with his secret. Nudged by her girls, Dom finally acts on her crush on John. A night of partying takes a devastating turn.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924560"}}},{"id":1924561,"url":"https://www.tvmaze.com/episodes/1924561/grand-army-1x04-safety-on","name":"Safety On","season":1,"number":4,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":54,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696370.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696370.jpg"},"summary":"

Traumatized, Joey spirals. Sid opens up in his Harvard essay. A health crisis puts more pressure on Dom to financially support her family.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924561"}}},{"id":1924562,"url":"https://www.tvmaze.com/episodes/1924562/grand-army-1x05-valentines-day","name":"Valentine's Day","season":1,"number":5,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":47,"rating":{"average":5.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696371.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696371.jpg"},"summary":"

Joey's charges and Owen's discipline hearing send shockwaves at school. Dom's mother makes an uncomfortable proposal to bring in money.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924562"}}},{"id":1924563,"url":"https://www.tvmaze.com/episodes/1924563/grand-army-1x06-superman-this-st","name":"Superman This S**t","season":1,"number":6,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":51,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696372.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696372.jpg"},"summary":"

Sid and Victor's science experiments have revealing results. Dom's nonstop hustle takes a toll. Jayson gets into a jazz competition, with a catch.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924563"}}},{"id":1924564,"url":"https://www.tvmaze.com/episodes/1924564/grand-army-1x07-making-moves","name":"Making Moves","season":1,"number":7,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":50,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696373.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696373.jpg"},"summary":"

Exposed and suspicious after his essay leaks, Sid looks for his betrayer. Dom lets her vulnerability show — and shine — at her internship interview.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924564"}}},{"id":1924565,"url":"https://www.tvmaze.com/episodes/1924565/grand-army-1x08-spirit-day","name":"Spirit Day","season":1,"number":8,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":51,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696374.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696374.jpg"},"summary":"

Joey copes with a difficult setback in her case. Choking under pressure, Dom breaks trust with a teacher — and John. The real Sid stands up.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924565"}}},{"id":1924567,"url":"https://www.tvmaze.com/episodes/1924567/grand-army-1x09-freedom","name":"Freedom","season":1,"number":9,"type":"regular","airdate":"2020-10-16","airtime":"","airstamp":"2020-10-16T12:00:00+00:00","runtime":73,"rating":{"average":null},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/278/696375.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/278/696375.jpg"},"summary":"

Dom, Joey and Sid find some release. After helping to lead the Black Student Union sit-in, Jayson stuns the crowd at his Lincoln Center performance.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1924567"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/julie-and-the-phantoms.json: -------------------------------------------------------------------------------- 1 | {"id":49308,"url":"https://www.tvmaze.com/shows/49308/julie-and-the-phantoms","name":"Julie and the Phantoms","type":"Scripted","language":"English","genres":["Comedy","Music","Supernatural"],"status":"Ended","runtime":null,"averageRuntime":31,"premiered":"2020-09-10","ended":"2020-09-10","officialSite":"https://www.netflix.com/title/80230534","schedule":{"time":"","days":[]},"rating":{"average":8.3},"weight":96,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":385735,"imdb":"tt10183988"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/269/674832.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/269/674832.jpg"},"summary":"

Julie lost her passion for music when she lost her mom. But when three ghostly guys appear and lift her spirits, they decide to start a band together!

","updated":1640059667,"_links":{"self":{"href":"https://api.tvmaze.com/shows/49308"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/1927597"}},"_embedded":{"episodes":[{"id":1905771,"url":"https://www.tvmaze.com/episodes/1905771/julie-and-the-phantoms-1x01-wake-up","name":"Wake Up","season":1,"number":1,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":38,"rating":{"average":8.6},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681379.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681379.jpg"},"summary":"

A year after her mom passed away, Julie plays one of her old CDs. Suddenly, three ghosts appear — the guys from the '90s band Sunset Curve!


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1905771"}}},{"id":1927590,"url":"https://www.tvmaze.com/episodes/1927590/julie-and-the-phantoms-1x02-bright","name":"Bright","season":1,"number":2,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":32,"rating":{"average":8.2},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681380.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681380.jpg"},"summary":"

When Julie starts singing again, the boys and her best friend Flynn encourage her to fight for a spot in the school music program.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927590"}}},{"id":1927591,"url":"https://www.tvmaze.com/episodes/1927591/julie-and-the-phantoms-1x03-flying-solo","name":"Flying Solo","season":1,"number":3,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":28,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681381.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681381.jpg"},"summary":"

Julie's performance makes a big splash, and the guys try to convince her to join Sunset Curve — but she'll have to come clean to Flynn first.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927591"}}},{"id":1927592,"url":"https://www.tvmaze.com/episodes/1927592/julie-and-the-phantoms-1x04-i-got-the-music","name":"I Got the Music","season":1,"number":4,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":32,"rating":{"average":8.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681382.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681382.jpg"},"summary":"

Flynn books Julie and the Phantoms to play at the school dance, Alex hangs out with his new crush, and the boys decide to teach an old friend a lesson.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927592"}}},{"id":1927593,"url":"https://www.tvmaze.com/episodes/1927593/julie-and-the-phantoms-1x05-the-other-side-of-hollywood","name":"The Other Side of Hollywood","season":1,"number":5,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":31,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681383.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681383.jpg"},"summary":"

Willie brings the guys to an exclusive club, where a powerful ghost magician asks them to be in his band. But he's got something up his sleeve.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927593"}}},{"id":1927594,"url":"https://www.tvmaze.com/episodes/1927594/julie-and-the-phantoms-1x06-finally-free","name":"Finally Free","season":1,"number":6,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":30,"rating":{"average":8.2},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681384.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681384.jpg"},"summary":"

Julie pairs up with Nick for a school performance and learns he broke up with Carrie. The band gets a new gig, but Julie's grounded for missing class.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927594"}}},{"id":1927595,"url":"https://www.tvmaze.com/episodes/1927595/julie-and-the-phantoms-1x07-edge-of-great","name":"Edge of Great","season":1,"number":7,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":27,"rating":{"average":8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681385.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681385.jpg"},"summary":"

Julie rehearses with Nick — while daydreaming about Luke. Alex wonders why Willie's been acting so weird. The band plays a party at Julie's house.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927595"}}},{"id":1927596,"url":"https://www.tvmaze.com/episodes/1927596/julie-and-the-phantoms-1x08-unsaid-emily","name":"Unsaid Emily","season":1,"number":8,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":24,"rating":{"average":8.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681386.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681386.jpg"},"summary":"

Julie meets Luke's parents and shares a touching song he wrote. Luke confides in Julie about Caleb and the band's \"unfinished business.\"


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927596"}}},{"id":1927597,"url":"https://www.tvmaze.com/episodes/1927597/julie-and-the-phantoms-1x09-stand-tall","name":"Stand Tall","season":1,"number":9,"type":"regular","airdate":"2020-09-10","airtime":"","airstamp":"2020-09-10T12:00:00+00:00","runtime":35,"rating":{"average":8.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681387.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681387.jpg"},"summary":"

Julie and the Phantoms get their dream gig at the Orpheum! But when Caleb interferes, Julie searches for the courage to perform on her own.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1927597"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/the-dutchess.json: -------------------------------------------------------------------------------- 1 | {"id":39665,"url":"https://www.tvmaze.com/shows/39665/the-duchess","name":"The Duchess","type":"Scripted","language":"English","genres":["Comedy"],"status":"Ended","runtime":null,"averageRuntime":24,"premiered":"2020-09-11","ended":"2020-09-11","officialSite":"https://www.netflix.com/title/80223040","schedule":{"time":"","days":[]},"rating":{"average":5.8},"weight":53,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":386788,"imdb":"tt9310390"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/272/682085.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/682085.jpg"},"summary":"

The Duchess follows the powerful and problematic choices of a fashionably disruptive single mom living in London. Her daughter, Olive, is her greatest love so she debates a second child with her greatest enemy -- Olive's dad. Can two wrongs make another right?

","updated":1619764402,"_links":{"self":{"href":"https://api.tvmaze.com/shows/39665"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/1917872"}},"_embedded":{"episodes":[{"id":1913281,"url":"https://www.tvmaze.com/episodes/1913281/the-duchess-1x01-episode-1","name":"Episode 1","season":1,"number":1,"type":"regular","airdate":"2020-09-11","airtime":"","airstamp":"2020-09-11T12:00:00+00:00","runtime":28,"rating":{"average":5.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681814.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681814.jpg"},"summary":"

To celebrate her daughter Olive's birthday, Katherine visits a fertility clinic to discuss the odds of giving her a sibling with help from a sperm donor.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1913281"}}},{"id":1917868,"url":"https://www.tvmaze.com/episodes/1917868/the-duchess-1x02-episode-2","name":"Episode 2","season":1,"number":2,"type":"regular","airdate":"2020-09-11","airtime":"","airstamp":"2020-09-11T12:00:00+00:00","runtime":25,"rating":{"average":5.8},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681815.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681815.jpg"},"summary":"

Shep accepts Katherine's proposal — with oddly specific conditions. Jane and Katherine force the girls to hang out. Evan comes over to spend the night.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1917868"}}},{"id":1917869,"url":"https://www.tvmaze.com/episodes/1917869/the-duchess-1x03-episode-3","name":"Episode 3","season":1,"number":3,"type":"regular","airdate":"2020-09-11","airtime":"","airstamp":"2020-09-11T12:00:00+00:00","runtime":22,"rating":{"average":6},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681816.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681816.jpg"},"summary":"

Katherine meets Shep for a failed transaction, then meets Evan's \"nice and normal\" parents. Bev and Katherine speak at a feminist convention.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1917869"}}},{"id":1917870,"url":"https://www.tvmaze.com/episodes/1917870/the-duchess-1x04-episode-4","name":"Episode 4","season":1,"number":4,"type":"regular","airdate":"2020-09-11","airtime":"","airstamp":"2020-09-11T12:00:00+00:00","runtime":23,"rating":{"average":5.9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681817.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681817.jpg"},"summary":"

After Katherine strikes out with an adoption agent, Olive and Millie get suspended. Shep shocks everyone with big news. Bev spies Evan at a pub.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1917870"}}},{"id":1917871,"url":"https://www.tvmaze.com/episodes/1917871/the-duchess-1x05-episode-5","name":"Episode 5","season":1,"number":5,"type":"regular","airdate":"2020-09-11","airtime":"","airstamp":"2020-09-11T12:00:00+00:00","runtime":20,"rating":{"average":5.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681818.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681818.jpg"},"summary":"

Katherine renews her quest to get pregnant with help from a surprising source. Praise be! But is it too good to be true? Elsewhere, Olive's growing up.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1917871"}}},{"id":1917872,"url":"https://www.tvmaze.com/episodes/1917872/the-duchess-1x06-episode-6","name":"Episode 6","season":1,"number":6,"type":"regular","airdate":"2020-09-11","airtime":"","airstamp":"2020-09-11T12:00:00+00:00","runtime":25,"rating":{"average":5.5},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/272/681819.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/272/681819.jpg"},"summary":"

Secrets swirl around Shep's big day ... and savvy Jane has it all figured out. With so much at stake, can Katherine control the chaos?

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/1917872"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/the-irregulars.json: -------------------------------------------------------------------------------- 1 | {"id":40442,"url":"https://www.tvmaze.com/shows/40442/the-irregulars","name":"The Irregulars","type":"Scripted","language":"English","genres":["Drama","Crime","Supernatural"],"status":"Ended","runtime":null,"averageRuntime":54,"premiered":"2021-03-26","ended":"2021-03-26","officialSite":"https://www.netflix.com/title/80241581","schedule":{"time":"","days":[]},"rating":{"average":6.7},"weight":99,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":389340,"imdb":"tt10893694"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/300/750108.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/750108.jpg"},"summary":"

Set in Victorian London, the series follows a gang of troubled street teens who are manipulated into solving crimes for the sinister Doctor Watson and his mysterious business partner, the elusive Sherlock Holmes. As the crimes take on a horrifying supernatural edge and a dark power emerges, it'll be up to the Irregulars to come together to save not only London but the entire world.

","updated":1620162212,"_links":{"self":{"href":"https://api.tvmaze.com/shows/40442"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/2038309"}},"_embedded":{"episodes":[{"id":2036312,"url":"https://www.tvmaze.com/episodes/2036312/the-irregulars-1x01-chapter-one-an-unkindness-in-london","name":"Chapter One: An Unkindness in London","season":1,"number":1,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":58,"rating":{"average":7.7},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752432.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752432.jpg"},"summary":"

As Jessie's nightmares grow worse, Bea accepts Dr. Watson's job offer to investigate four kidnapped babies — and receives some unexpectedly posh help.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2036312"}}},{"id":2038303,"url":"https://www.tvmaze.com/episodes/2038303/the-irregulars-1x02-chapter-two-the-ghosts-of-221b","name":"Chapter Two: The Ghosts of 221B","season":1,"number":2,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":50,"rating":{"average":7.1},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752433.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752433.jpg"},"summary":"

Broke and in trouble, Bea reluctantly partners with Jessie to find out who's stealing children's teeth. Spike spies on Watson and Sherlock Holmes.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038303"}}},{"id":2038304,"url":"https://www.tvmaze.com/episodes/2038304/the-irregulars-1x03-chapter-three-ipsissimus","name":"Chapter Three: Ipsissimus","season":1,"number":3,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":52,"rating":{"average":7.2},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752434.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752434.jpg"},"summary":"

A gruesome murder sends Bea and her crew undercover at a country estate to determine whether the killer is part of a secret paranormal society.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038304"}}},{"id":2038305,"url":"https://www.tvmaze.com/episodes/2038305/the-irregulars-1x04-chapter-four-both-the-needle-and-the-knife","name":"Chapter Four: Both the Needle and the Knife","season":1,"number":4,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":52,"rating":{"average":7.1},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752435.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752435.jpg"},"summary":"

Jessie comes face to face with Inspector Lestrade at a crime scene, Bea plays cat and mouse with Watson, and Leo feels torn over his life at the palace.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038305"}}},{"id":2038306,"url":"https://www.tvmaze.com/episodes/2038306/the-irregulars-1x05-chapter-five-students-of-the-unhallowed-arts","name":"Chapter Five: Students of the Unhallowed Arts","season":1,"number":5,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":57,"rating":{"average":7.1},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752436.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752436.jpg"},"summary":"

Bea discovers more about her mother's long-ago connection to Sherlock and Watson, while Billy contemplates revenge after a brutal reminder of his past.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038306"}}},{"id":2038307,"url":"https://www.tvmaze.com/episodes/2038307/the-irregulars-1x06-chapter-six-hieracium-snowdoniense","name":"Chapter Six: Hieracium Snowdoniense","season":1,"number":6,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":54,"rating":{"average":6.9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752437.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752437.jpg"},"summary":"

Bea suspects a spate of stolen body parts links to an old case. Jessie approaches Sherlock about her nightmares. Leo makes a stand for his future.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038307"}}},{"id":2038308,"url":"https://www.tvmaze.com/episodes/2038308/the-irregulars-1x07-chapter-seven-the-ecstasy-of-death","name":"Chapter Seven: The Ecstasy of Death","season":1,"number":7,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":50,"rating":{"average":7},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752438.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752438.jpg"},"summary":"

After demanding the truth from Watson, Bea joins a plan to catch the Linen Man. Jessie fights her fears. Spike scrambles to keep his friends together.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038308"}}},{"id":2038309,"url":"https://www.tvmaze.com/episodes/2038309/the-irregulars-1x08-chapter-eight-the-ecstasy-of-life","name":"Chapter Eight: The Ecstasy of Life","season":1,"number":8,"type":"regular","airdate":"2021-03-26","airtime":"","airstamp":"2021-03-26T12:00:00+00:00","runtime":55,"rating":{"average":7.1},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/300/752439.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/300/752439.jpg"},"summary":"

As London descends into chaos, Bea and her crew head underground, where together they face untold terror in a dangerous quest to locate the rip.

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038309"}}}]}} -------------------------------------------------------------------------------- /netflix-canceled-2021/zero-chill.json: -------------------------------------------------------------------------------- 1 | {"id":46039,"url":"https://www.tvmaze.com/shows/46039/zero-chill","name":"Zero Chill","type":"Scripted","language":"English","genres":["Drama","Comedy","Family"],"status":"To Be Determined","runtime":null,"averageRuntime":30,"premiered":"2021-03-15","ended":null,"officialSite":"https://www.netflix.com/title/80996811","schedule":{"time":"","days":[]},"rating":{"average":6.6},"weight":82,"network":null,"webChannel":{"id":1,"name":"Netflix","country":null},"dvdCountry":null,"externals":{"tvrage":null,"thetvdb":387058,"imdb":"tt12083014"},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_portrait/298/746910.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/298/746910.jpg"},"summary":"

Zero Chill follows 15-year old figure skater Kayla, whose life is up-ended from Canada to the UK when her twin brother Mac gets a place at a legendary hockey academy. Distraught at her parents' decision to put her brother's ambitions before her own, Kayla must find her place on the ice again in the shadow of her superstar brother.

","updated":1618775709,"_links":{"self":{"href":"https://api.tvmaze.com/shows/46039"},"previousepisode":{"href":"https://api.tvmaze.com/episodes/2038349"}},"_embedded":{"episodes":[{"id":2038339,"url":"https://www.tvmaze.com/episodes/2038339/zero-chill-1x01-come-and-take-it-from-me","name":"Come and Take It from Me","season":1,"number":1,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":34,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749822.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749822.jpg"},"summary":"

Mac's off to a rocky start on his quest to impress his new team. Later on, a lonely Kayla makes a connection with a mysterious skater on the ice.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038339"}}},{"id":2038341,"url":"https://www.tvmaze.com/episodes/2038341/zero-chill-1x02-secret-skater","name":"Secret Skater","season":1,"number":2,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":29,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749823.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749823.jpg"},"summary":"

While working to unmask the mystery skater, Kayla learns things about Ava and Sky that she didn't expect. Sky opens up to Mac about her past.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038341"}}},{"id":2038342,"url":"https://www.tvmaze.com/episodes/2038342/zero-chill-1x03-sucker-punch","name":"Sucker Punch","season":1,"number":3,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":32,"rating":{"average":9.3},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749824.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749824.jpg"},"summary":"

Mac wants to bond with his teammates, but his arrogance keeps getting in the way. Kayla tracks down Jacob, and Ava's mom prepares a \"special surprise.\"


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038342"}}},{"id":2038343,"url":"https://www.tvmaze.com/episodes/2038343/zero-chill-1x04-ice-breaker","name":"Ice Breaker","season":1,"number":4,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":26,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749825.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749825.jpg"},"summary":"

Kayla's determined to prove that she and Jacob are the perfect pair. Mac faces off with a former enemy. Ava dons a disguise to be part of the team.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038343"}}},{"id":2038344,"url":"https://www.tvmaze.com/episodes/2038344/zero-chill-1x05-we-do-our-talking-on-the-ice","name":"We Do Our Talking on the Ice","season":1,"number":5,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":30,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749826.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749826.jpg"},"summary":"

Sky tries to warn Kayla that Jacob has doubts — but it's mistaken for jealousy. Ava makes a big decision. Mac uncovers Kayla's plan to ruin his game.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038344"}}},{"id":2038345,"url":"https://www.tvmaze.com/episodes/2038345/zero-chill-1x06-head-rush","name":"Head Rush","season":1,"number":6,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":27,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749827.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749827.jpg"},"summary":"

As Mac struggles to regain his confidence, Ava pushes her dad to put her on the ice. At the hospital, Holly blames Kayla for putting Sky in danger.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038345"}}},{"id":2038346,"url":"https://www.tvmaze.com/episodes/2038346/zero-chill-1x07-triple-threat","name":"Triple Threat","season":1,"number":7,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":26,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749828.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749828.jpg"},"summary":"

Elina makes Kayla an offer, and Bear and Sam get an unexpected visitor. Mac and Kayla both need Sky's support — but who will she choose?


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038346"}}},{"id":2038347,"url":"https://www.tvmaze.com/episodes/2038347/zero-chill-1x08-guilt-trip","name":"Guilt Trip","season":1,"number":8,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":29,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749829.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749829.jpg"},"summary":"

With team morale at an all-time low — and Mac's suffering at an all-time high — Ava makes a confession. Kayla begins her training with Elina.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038347"}}},{"id":2038348,"url":"https://www.tvmaze.com/episodes/2038348/zero-chill-1x09-this-is-happening","name":"This Is Happening","season":1,"number":9,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":28,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749830.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749830.jpg"},"summary":"

The pressure's on as Kayla works hard to prove that Mac's not the only one who can win. Bear burdens Mac with a secret before the big game.


 

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038348"}}},{"id":2038349,"url":"https://www.tvmaze.com/episodes/2038349/zero-chill-1x10-blade-star","name":"Blade Star","season":1,"number":10,"type":"regular","airdate":"2021-03-15","airtime":"","airstamp":"2021-03-15T12:00:00+00:00","runtime":35,"rating":{"average":9},"image":{"medium":"https://static.tvmaze.com/uploads/images/medium_landscape/299/749831.jpg","original":"https://static.tvmaze.com/uploads/images/original_untouched/299/749831.jpg"},"summary":"

Elina sees Kayla's friends and family as a distraction, but Kayla needs them now more than ever. Bear has second thoughts... but is it too late?

","_links":{"self":{"href":"https://api.tvmaze.com/episodes/2038349"}}}]}} -------------------------------------------------------------------------------- /orders/task.csv: -------------------------------------------------------------------------------- 1 | orderid,orderdate,custname,custemail,custcountry,orderstatus,ordertotal,ordercreditcard,ordershipvia,shippingtotal 2 | 2,2023-03-24,Frayda Pepperd,fpepperd0@sciencedaily.com,Canada,delivered,228.39,Discover,RPS,12.05 3 | 4,2022-04-28,Carree Henworth,,Canada,pending,152.3,Discover,USPS,12.74 4 | 5,2019-11-22,Goldina Godsafe,ggodsafe3@dailymail.co.uk,United States,shipped,182.17,Amex,UPS,5.44 5 | 6,2022-05-03,Marris Chatten,mchatten4@csmonitor.com,Mexico,pending,208.28,Discover,RPS,2.16 6 | 7,2022-12-19,Logan Jacobsson,ljacobsson5@wufoo.com,United States,delivered,112.15,Amex,USPS,11.52 7 | 10,2023-04-19,Libbi Spadari,lspadari8@dot.gov,Mexico,pending,160.79,Discover,RPS,16.52 8 | 11,2020-01-20,Renato Hue,rhue9@un.org,Canada,delivered,120.52,Visa,USPS,5.57 9 | 12,2022-03-03,Lucky Helstrip,lhelstripa@tmall.com,Mexico,pending,202.07,Amex,UPS,18.57 10 | 13,2021-09-04,Debi Myrie,dmyrieb@unc.edu,United States,delivered,131.62,Amex,UPS,2.37 11 | 15,2019-01-11,Crin Blanket,cblanketd@newsvine.com,United States,pending,85.46,Visa,UPS,14.22 12 | -------------------------------------------------------------------------------- /pandas/_libs/parsers.pyx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/pandas/_libs/parsers.pyx -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Data Sets 2 | 3 | This repository contains sample data sets used in the courses I teach. Each folder contains a separate data set. 4 | 5 | ## Clickstream 6 | 7 | This data set consists of 3 days of IIS web logs from a sample e-commerce website (nopCommerce). 8 | The data set contains activity of anonymous users browsing products on the website. 9 | With the exception of spiders and robots, the he original IP addresses were replaced with other valid IP's. 10 | Included in the data set is an IP to location CSV (comma separated values) file. 11 | 12 | NOTE: The IP addresses in the clickstream data cross reference to the customer data. 13 | 14 | ## Tweets 15 | This data set contains 6 months of simulated "tweets" about a company named Fudgemart. It can be used in sentiment analysis. There are two generated data sets of 200 tweets one in json format the other as pipe-delimited. 16 | 17 | There is also a python 3 script `simtweet.py` which can be used to generate additional tweets as desired. This can be useful if you would like to explore real-time scenairos. 18 | 19 | 20 | NOTE: You can cross-reference these tweets to customers via the survey.csv in customer data. 21 | 22 | 23 | ## Customers 24 | This data set contains base data about customers who have placed orders on an e-commerce website. It also includes a made up survey some of those customers might have filled out. 25 | 26 | - Customer features include Name, Email, Gender, Last IP Address used, City, State, Total Number of Orders, Total Dollar amount of goods purchased, and number of months they have been a customer. 27 | - Survey features include email, twitter username, marital status, household income, whether or not they own a home, Highest degree of education, and favorite department. 28 | 29 | 30 | ## Exam-Scores 31 | 32 | A table of sample examination scores. Includes the following features: 33 | 34 | - Class section (same exam issued to 2 different class periods) 35 | - Exam version, A thorough D (4 different version of the same exam) 36 | - Completion Time (time rounded up to nearest 5 minutes for student to complete the exam ) 37 | - Made Own Study Guide (whether or not the student made her own study guide to prepare for the exam) 38 | - Did Exam Prep Assignment (whether or not the student completed the exam prep assignment as part of their study) 39 | - Studied in groups (whether or not the student studied in a group) 40 | - Student Score (raw score on the exam out of 30) 41 | - Percentage (same score as a percentage) 42 | - Letter Grade (score translated to a letter grade) 43 | 44 | ## Funny-Names 45 | 46 | A list of fictitious, humorous people's names commonly used in my examples where I need the name of people to complete a dataset example. 47 | 48 | ## weather 49 | Scraped from weather underground, daily weather data for syracuse, NY. 1998 to present. 50 | 51 | # nyc311 52 | NYC 311 service requests on april fools day, 2016. 53 | -------------------------------------------------------------------------------- /redditnews/crawlreddit.py: -------------------------------------------------------------------------------- 1 | import json 2 | import requests 3 | from pprint import pprint as pp2 4 | 5 | #import os 6 | #print os.getcwd() 7 | 8 | #---------------------------------------------------------------------- 9 | def login(username, password): 10 | """logs into reddit, saves cookie""" 11 | 12 | print ('begin log in') 13 | #username and password 14 | UP = {'user': username, 'passwd': password, 'api_type': 'json',} 15 | headers = {'user-agent': '/u/mafudge\'s API python bot', } 16 | #POST with user/pwd 17 | client = requests.session() 18 | client.headers.update(headers) 19 | 20 | r = client.post('http://www.reddit.com/api/login', data=UP) 21 | 22 | #print r.text 23 | #print r.cookies 24 | 25 | #gets and saves the modhash 26 | j = json.loads(r.text) 27 | 28 | client.modhash = j['json']['data']['modhash'] 29 | print ('{USER}\'s modhash is: {mh}'.format(USER=username, mh=client.modhash)) 30 | client.user = username 31 | def name(): 32 | 33 | return '{}\'s client'.format(username) 34 | 35 | #pp2(j) 36 | 37 | return client 38 | 39 | #---------------------------------------------------------------------- 40 | def subredditInfo(client, limit=25, after='', sr='news', 41 | sorting='', return_json=False, **kwargs): 42 | """retrieves X (max 100) amount of stories in a subreddit\n 43 | 'sorting' is whether or not the sorting of the reddit should be customized or not, 44 | if it is: Allowed passing params/queries such as t=hour, week, month, year or all""" 45 | 46 | #query to send 47 | parameters = {'limit': limit, 'after' : after, 't' : 'all'} 48 | #parameters= defaults.copy() 49 | parameters.update(kwargs) 50 | 51 | url = r'http://www.reddit.com/r/{sr}/{top}.json'.format(sr=sr, top=sorting) 52 | r = client.get(url,params=parameters) 53 | print ('sent URL is', r.url) 54 | j = json.loads(r.text) 55 | ratelimitremaining = int(r.headers['x-ratelimit-remaining']) 56 | ratelimitreset = int(r.headers['x-ratelimit-reset']) 57 | 58 | #return raw json 59 | if return_json: 60 | return j 61 | 62 | #or list of stories 63 | else: 64 | stories = [] 65 | for story in j['data']['children']: 66 | #print story['data']['title'] 67 | stories.append(story) 68 | 69 | return ratelimitremaining,ratelimitreset,stories 70 | 71 | ########## main ########## 72 | client = login('mafudge', 'xrnT9sO04RaCu6MX7bxn') 73 | last = '' 74 | for i in range(1,8): 75 | filename = 'top.json' if last == '' else last + '.json' 76 | with open(filename,"w", encoding='utf-8') as appendfile: 77 | rlimit,reset, stories = subredditInfo(client, sr='news', limit=100, after=last) 78 | titles = [(s['data']['name'], s['data']['title']) for s in stories ] 79 | print(rlimit, reset, titles) 80 | appendfile.write(json.dumps(stories)) 81 | last = stories[-1]['data']['name'] 82 | 83 | #r2,s2 = subredditInfo(client, sr='news', limit=1, after=last) 84 | 85 | #t2 = [(s['data']['name'], s['data']['title']) for s in s2 ] 86 | #pp2(t1) 87 | #pp2(t2) 88 | 89 | 90 | 91 | -------------------------------------------------------------------------------- /redditnews/data.json: -------------------------------------------------------------------------------- 1 | [{"data": {"subreddit": "news", "subreddit_id": "t5_2qh3l", "selftext": "", "approved_by": null, "suggested_sort": null, "created_utc": 1458859520.0, "user_reports": [], "secure_media": null, "from_kind": null, "media_embed": {}, "archived": false, "num_comments": 18, "over_18": false, "from": null, "author_flair_css_class": null, "name": "t3_4btzcw", "visited": false, "likes": null, "selftext_html": null, "media": null, "permalink": "/r/news/comments/4btzcw/aclu_leaked_email_from_nj_police_chief_encourages/", "locked": false, "num_reports": null, "banned_by": null, "title": "ACLU: Leaked email from N.J. police chief encourages racial profiling", "hide_score": false, "created": 1458888320.0, "distinguished": null, "hidden": false, "mod_reports": [], "removal_reason": null, "author": "kurrock", "clicked": false, "edited": false, "report_reasons": null, "id": "4btzcw", "url": "http://www.phillyvoice.com/aclu-leaked-email-nj-police-chief-encourages-racial-profiling/", "is_self": false, "domain": "phillyvoice.com", "link_flair_css_class": null, "secure_media_embed": {}, "saved": false, "score": 72, "thumbnail": "", "from_id": null, "author_flair_text": null, "stickied": false, "ups": 72, "downs": 0, "quarantine": false, "link_flair_text": null, "gilded": 0}, "kind": "t3"}, {"data": {"subreddit": "news", "subreddit_id": "t5_2qh3l", "selftext": "", "approved_by": null, "suggested_sort": null, "created_utc": 1458849421.0, "user_reports": [], "secure_media": null, "from_kind": null, "media_embed": {}, "archived": false, "num_comments": 52, "over_18": false, "from": null, "author_flair_css_class": null, "name": "t3_4bt8z8", "visited": false, "likes": null, "selftext_html": null, "media": null, "permalink": "/r/news/comments/4bt8z8/a_japanese_fleet_killed_333_whales_for_research/", "locked": false, "num_reports": null, "banned_by": null, "title": "A Japanese fleet killed 333 whales for \u2018research\u2019", "hide_score": false, "created": 1458878221.0, "distinguished": null, "hidden": false, "mod_reports": [], "removal_reason": null, "author": "itsfoine", "clicked": false, "edited": false, "report_reasons": null, "id": "4bt8z8", "url": "https://www.washingtonpost.com/news/speaking-of-science/wp/2016/03/24/a-japanese-fleet-killed-333-whales-for-research/", "is_self": false, "domain": "washingtonpost.com", "link_flair_css_class": null, "secure_media_embed": {}, "saved": false, "score": 116, "thumbnail": "", "from_id": null, "author_flair_text": null, "stickied": false, "ups": 116, "downs": 0, "quarantine": false, "link_flair_text": null, "gilded": 0}, "kind": "t3"}] -------------------------------------------------------------------------------- /redditnews/py2.py: -------------------------------------------------------------------------------- 1 | import json 2 | import requests 3 | from pprint import pprint as pp2 4 | 5 | #import os 6 | #print os.getcwd() 7 | 8 | #---------------------------------------------------------------------- 9 | def login(username, password): 10 | """logs into reddit, saves cookie""" 11 | 12 | print 'begin log in' 13 | #username and password 14 | UP = {'user': username, 'passwd': password, 'api_type': 'json',} 15 | headers = {'user-agent': '/u/TankorSmash\'s API python bot', } 16 | #POST with user/pwd 17 | client = requests.session(headers=headers) 18 | 19 | r = client.post('http://www.reddit.com/api/login', data=UP) 20 | 21 | #print r.text 22 | #print r.cookies 23 | 24 | #gets and saves the modhash 25 | j = json.loads(r.text) 26 | 27 | client.modhash = j['json']['data']['modhash'] 28 | print '{USER}\'s modhash is: {mh}'.format(USER=username, mh=client.modhash) 29 | client.user = username 30 | def name(): 31 | 32 | return '{}\'s client'.format(username) 33 | 34 | #pp2(j) 35 | 36 | return client 37 | 38 | #---------------------------------------------------------------------- 39 | def subredditInfo(client, limit=25, sr='tankorsmash', 40 | sorting='', return_json=False, **kwargs): 41 | """retrieves X (max 100) amount of stories in a subreddit\n 42 | 'sorting' is whether or not the sorting of the reddit should be customized or not, 43 | if it is: Allowed passing params/queries such as t=hour, week, month, year or all""" 44 | 45 | #query to send 46 | parameters = {'limit': limit,} 47 | #parameters= defaults.copy() 48 | parameters.update(kwargs) 49 | 50 | url = r'http://www.reddit.com/r/{sr}/{top}.json'.format(sr=sr, top=sorting) 51 | r = client.get(url,params=parameters) 52 | print 'sent URL is', r.url 53 | j = json.loads(r.text) 54 | 55 | #return raw json 56 | if return_json: 57 | return j 58 | 59 | #or list of stories 60 | else: 61 | stories = [] 62 | for story in j['data']['children']: 63 | #print story['data']['title'] 64 | stories.append(story) 65 | 66 | return stories 67 | 68 | client = login('USERNAME', 'PASSWORD') 69 | 70 | j = subredditInfo(client, limit=1) 71 | 72 | pp2(j) -------------------------------------------------------------------------------- /redditnews/t3_4b92ci.json: -------------------------------------------------------------------------------- 1 | [] -------------------------------------------------------------------------------- /redditnews/t3_4bhcqj.json: -------------------------------------------------------------------------------- 1 | [] -------------------------------------------------------------------------------- /st-lucia/parishes.csv: -------------------------------------------------------------------------------- 1 | Parish,Lat,Lng 2 | Anse la Raye,13.91128,-61.007222 3 | Canaries,13.8921,-61.02864 4 | Castries,13.9635,-60.97701 5 | Choiseul,13.79454,-61.03688 6 | Dennery,13.92034,-60.92209 7 | Gros Islet,14.05071,-60.93319 8 | Laborie,13.77428,-61.00393 9 | Micoud,13.80478,-60.93395 10 | Soufriere,13.84306,-61.04127 11 | Vieux Fort,13.76681,-60.95944 12 | Dauphin,13.99363,-60.90975 13 | Praslin,13.85945,-60.91643 -------------------------------------------------------------------------------- /stocks/AAPL.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,AAPL,175.57000732421875,177.7100067138672,173.52000427246094,173.72000122070312,75604200 3 | 2024-03-19,AAPL,174.33999633789062,176.61000061035156,173.02999877929688,176.0800018310547,55215200 4 | 2024-03-20,AAPL,175.72000122070312,178.6699981689453,175.08999633789062,178.6699981689453,53423100 5 | 2024-03-21,AAPL,177.0500030517578,177.49000549316406,170.83999633789062,171.3699951171875,106181300 6 | 2024-03-22,AAPL,171.75999450683594,173.0500030517578,170.05999755859375,172.27999877929688,71106600 7 | -------------------------------------------------------------------------------- /stocks/AMZN.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,AMZN,175.8000030517578,176.69000244140625,174.27999877929688,174.47999572753906,31250700 3 | 2024-03-19,AMZN,174.22000122070312,176.08999633789062,173.52000427246094,175.89999389648438,26880900 4 | 2024-03-20,AMZN,176.13999938964844,178.52999877929688,174.63999938964844,178.14999389648438,29947200 5 | 2024-03-21,AMZN,179.99000549316406,181.4199981689453,178.14999389648438,178.14999389648438,32824300 6 | 2024-03-22,AMZN,177.75,179.25999450683594,176.75,178.8699951171875,27964100 7 | -------------------------------------------------------------------------------- /stocks/DELL.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,DELL,106.98999786376953,107.87000274658203,104.66000366210938,106.62999725341797,7403800 3 | 2024-03-19,DELL,107.0199966430664,108.87999725341797,105.05000305175781,107.5199966430664,7971700 4 | 2024-03-20,DELL,107.91000366210938,111.33000183105469,106.05999755859375,111.06999969482422,9600200 5 | 2024-03-21,DELL,113.18000030517578,115.7699966430664,112.25,114.04000091552734,11678000 6 | 2024-03-22,DELL,112.91000366210938,113.80000305175781,111.79000091552734,112.23999786376953,4707900 7 | -------------------------------------------------------------------------------- /stocks/GM.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,GM,40.88999938964844,40.91999816894531,40.33000183105469,40.81999969482422,16126600 3 | 2024-03-19,GM,40.91999816894531,41.68000030517578,40.720001220703125,41.5099983215332,15936400 4 | 2024-03-20,GM,41.41999816894531,42.88999938964844,41.36000061035156,42.849998474121094,16909300 5 | 2024-03-21,GM,42.91999816894531,43.59000015258789,42.84000015258789,43.41999816894531,15268300 6 | 2024-03-22,GM,43.29999923706055,43.65999984741211,43.0,43.060001373291016,9387400 7 | -------------------------------------------------------------------------------- /stocks/GOOG.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,GOOG,149.3699951171875,152.92999267578125,148.13999938964844,148.47999572753906,47676700 3 | 2024-03-19,GOOG,148.97999572753906,149.6199951171875,147.00999450683594,147.9199981689453,17748400 4 | 2024-03-20,GOOG,148.7899932861328,149.75999450683594,147.6649932861328,149.67999267578125,17730000 5 | 2024-03-21,GOOG,150.32000732421875,151.30499267578125,148.00999450683594,148.74000549316406,19843900 6 | 2024-03-22,GOOG,150.24000549316406,152.55999755859375,150.08999633789062,151.77000427246094,19226300 7 | -------------------------------------------------------------------------------- /stocks/HD.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,HD,376.489990234375,377.6000061035156,371.1400146484375,371.9100036621094,3455600 3 | 2024-03-19,HD,374.8900146484375,379.4599914550781,373.1300048828125,379.4100036621094,3493800 4 | 2024-03-20,HD,379.4200134277344,384.8800048828125,376.2300109863281,384.4100036621094,2750400 5 | 2024-03-21,HD,388.4100036621094,396.8699951171875,388.2900085449219,395.20001220703125,4212200 6 | 2024-03-22,HD,394.69000244140625,396.4200134277344,390.0899963378906,390.2799987792969,2910900 7 | -------------------------------------------------------------------------------- /stocks/IBM.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,IBM,191.6999969482422,193.22999572753906,190.32000732421875,191.69000244140625,5410600 3 | 2024-03-19,IBM,191.49000549316406,193.5800018310547,190.27999877929688,193.33999633789062,5317300 4 | 2024-03-20,IBM,192.8699951171875,193.97999572753906,191.30999755859375,193.9600067138672,3238600 5 | 2024-03-21,IBM,193.0,193.3699951171875,190.00999450683594,191.89999389648438,6013600 6 | 2024-03-22,IBM,192.0,192.99000549316406,190.50999450683594,190.83999633789062,3987700 7 | -------------------------------------------------------------------------------- /stocks/LULU.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,LULU,465.9200134277344,469.69000244140625,459.4800109863281,459.57000732421875,1261300 3 | 2024-03-19,LULU,457.69000244140625,467.8599853515625,454.2300109863281,467.2699890136719,1428900 4 | 2024-03-20,LULU,467.3399963378906,469.7900085449219,461.9200134277344,469.04998779296875,1627800 5 | 2024-03-21,LULU,472.0,480.94000244140625,469.0,478.8399963378906,4022000 6 | 2024-03-22,LULU,416.25,418.70001220703125,387.1099853515625,403.19000244140625,19659900 7 | -------------------------------------------------------------------------------- /stocks/META.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,META,491.9100036621094,497.4200134277344,486.80999755859375,496.9800109863281,11755300 3 | 2024-03-19,META,488.1700134277344,496.6300048828125,481.2799987792969,496.239990234375,10903100 4 | 2024-03-20,META,499.5,508.20001220703125,495.1700134277344,505.5199890136719,11711100 5 | 2024-03-21,META,514.7100219726562,515.0399780273438,506.010009765625,507.760009765625,9712500 6 | 2024-03-22,META,507.0,509.9700012207031,504.3399963378906,509.5799865722656,8117000 7 | -------------------------------------------------------------------------------- /stocks/MSFT.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,MSFT,414.25,420.7300109863281,413.7799987792969,417.32000732421875,20106000 3 | 2024-03-19,MSFT,417.8299865722656,421.6700134277344,415.54998779296875,421.4100036621094,19837900 4 | 2024-03-20,MSFT,422.0,425.9599914550781,420.6600036621094,425.2300109863281,17860100 5 | 2024-03-21,MSFT,429.8299865722656,430.82000732421875,427.1600036621094,429.3699951171875,21296200 6 | 2024-03-22,MSFT,429.70001220703125,429.8599853515625,426.07000732421875,428.739990234375,17636500 7 | -------------------------------------------------------------------------------- /stocks/NET.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,NET,92.95999908447266,95.74199676513672,92.08999633789062,94.63999938964844,3506100 3 | 2024-03-19,NET,93.18000030517578,95.54000091552734,91.8499984741211,95.16999816894531,2230200 4 | 2024-03-20,NET,95.30999755859375,98.41999816894531,94.66999816894531,97.9800033569336,2641400 5 | 2024-03-21,NET,100.0,100.9000015258789,96.31999969482422,96.41000366210938,3028000 6 | 2024-03-22,NET,96.0999984741211,96.80000305175781,94.7699966430664,96.56999969482422,1548300 7 | -------------------------------------------------------------------------------- /stocks/NFLX.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,NFLX,613.5599975585938,627.4099731445312,610.4500122070312,618.3900146484375,3344200 3 | 2024-03-19,NFLX,615.6199951171875,621.280029296875,608.0,620.739990234375,2142600 4 | 2024-03-20,NFLX,619.9500122070312,629.510009765625,618.3400268554688,627.6900024414062,2639500 5 | 2024-03-21,NFLX,630.6500244140625,634.3599853515625,622.3300170898438,622.7100219726562,2507700 6 | 2024-03-22,NFLX,624.1599731445312,629.0499877929688,621.0,628.010009765625,2134100 7 | -------------------------------------------------------------------------------- /stocks/TSLA.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,TSLA,170.02000427246094,174.72000122070312,165.89999389648438,173.8000030517578,108214400 3 | 2024-03-19,TSLA,172.36000061035156,172.82000732421875,167.4199981689453,171.32000732421875,77271400 4 | 2024-03-20,TSLA,173.0,176.25,170.82000732421875,175.66000366210938,83846700 5 | 2024-03-21,TSLA,176.38999938964844,178.17999267578125,171.8000030517578,172.82000732421875,73178000 6 | 2024-03-22,TSLA,166.69000244140625,171.1999969482422,166.3000030517578,170.8300018310547,75454700 7 | -------------------------------------------------------------------------------- /stocks/TTD.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,TTD,77.76000213623047,79.0199966430664,77.0199966430664,78.3499984741211,3047300 3 | 2024-03-19,TTD,78.37000274658203,80.08000183105469,77.94999694824219,79.76000213623047,3581300 4 | 2024-03-20,TTD,80.6500015258789,84.4800033569336,80.54399871826172,83.47000122070312,6542800 5 | 2024-03-21,TTD,84.7300033569336,85.41999816894531,84.29000091552734,85.05999755859375,4484600 6 | 2024-03-22,TTD,85.2699966430664,85.52999877929688,84.05999755859375,85.05999755859375,3025400 7 | -------------------------------------------------------------------------------- /stocks/X.csv: -------------------------------------------------------------------------------- 1 | Date,Symbol,Open,High,Low,Close,Volume 2 | 2024-03-18,X,38.650001525878906,39.33000183105469,38.599998474121094,38.869998931884766,4479300 3 | 2024-03-19,X,38.79999923706055,39.79999923706055,38.79999923706055,39.75,5023000 4 | 2024-03-20,X,39.75,40.16999816894531,39.599998474121094,39.689998626708984,3256700 5 | 2024-03-21,X,39.72999954223633,40.119998931884766,39.34000015258789,40.04999923706055,2877000 6 | 2024-03-22,X,40.11000061035156,40.16999816894531,39.630001068115234,39.65999984741211,2300600 7 | -------------------------------------------------------------------------------- /stocks/company-info.json: -------------------------------------------------------------------------------- 1 | [{"symbol": "X", "name": "United States Steel Corporation", "exchange": "NYQ", "industry": "Steel", "sector": "Basic Materials", "info": {"website": "https://www.ussteel.com", "city": "PA", "state": "Pittsburgh", "country": "United States"}}, {"symbol": "GM", "name": "General Motors Company", "exchange": "NYQ", "industry": "Auto Manufacturers", "sector": "Consumer Cyclical", "info": {"website": "https://www.gm.com", "city": "MI", "state": "Detroit", "country": "United States"}}, {"symbol": "AAPL", "name": "Apple Inc.", "exchange": "NMS", "industry": "Consumer Electronics", "sector": "Technology", "info": {"website": "https://www.apple.com", "city": "CA", "state": "Cupertino", "country": "United States"}}, {"symbol": "AMZN", "name": "Amazon.com, Inc.", "exchange": "NMS", "industry": "Internet Retail", "sector": "Consumer Cyclical", "info": {"website": "https://www.aboutamazon.com", "city": "WA", "state": "Seattle", "country": "United States"}}, {"symbol": "META", "name": "Meta Platforms, Inc.", "exchange": "NMS", "industry": "Internet Content & Information", "sector": "Communication Services", "info": {"website": "https://investor.fb.com", "city": "CA", "state": "Menlo Park", "country": "United States"}}, {"symbol": "GOOG", "name": "Alphabet Inc.", "exchange": "NMS", "industry": "Internet Content & Information", "sector": "Communication Services", "info": {"website": "https://abc.xyz", "city": "CA", "state": "Mountain View", "country": "United States"}}, {"symbol": "TTD", "name": "The Trade Desk, Inc.", "exchange": "NGM", "industry": "Software - Application", "sector": "Technology", "info": {"website": "https://www.thetradedesk.com", "city": "CA", "state": "Ventura", "country": "United States"}}, {"symbol": "DELL", "name": "Dell Technologies Inc.", "exchange": "NYQ", "industry": "Computer Hardware", "sector": "Technology", "info": {"website": "https://www.delltechnologies.com", "city": "TX", "state": "Round Rock", "country": "United States"}}, {"symbol": "IBM", "name": "International Business Machines", "exchange": "NYQ", "industry": "Information Technology Services", "sector": "Technology", "info": {"website": "https://www.ibm.com", "city": "NY", "state": "Armonk", "country": "United States"}}, {"symbol": "MSFT", "name": "Microsoft Corporation", "exchange": "NMS", "industry": "Software - Infrastructure", "sector": "Technology", "info": {"website": "https://www.microsoft.com", "city": "WA", "state": "Redmond", "country": "United States"}}, {"symbol": "NET", "name": "Cloudflare, Inc.", "exchange": "NYQ", "industry": "Software - Infrastructure", "sector": "Technology", "info": {"website": "https://www.cloudflare.com", "city": "CA", "state": "San Francisco", "country": "United States"}}, {"symbol": "NFLX", "name": "Netflix, Inc.", "exchange": "NMS", "industry": "Entertainment", "sector": "Communication Services", "info": {"website": "https://www.netflix.com", "city": "CA", "state": "Los Gatos", "country": "United States"}}, {"symbol": "TSLA", "name": "Tesla, Inc.", "exchange": "NMS", "industry": "Auto Manufacturers", "sector": "Consumer Cyclical", "info": {"website": "https://www.tesla.com", "city": "TX", "state": "Austin", "country": "United States"}}, {"symbol": "HD", "name": "Home Depot, Inc. (The)", "exchange": "NYQ", "industry": "Home Improvement Retail", "sector": "Consumer Cyclical", "info": {"website": "https://www.homedepot.com", "city": "GA", "state": "Atlanta", "country": "United States"}}, {"symbol": "LULU", "name": "lululemon athletica inc.", "exchange": "NMS", "industry": "Apparel Retail", "sector": "Consumer Cyclical", "info": {"website": "https://corporate.lululemon.com", "city": "BC", "state": "Vancouver", "country": "Canada"}}] -------------------------------------------------------------------------------- /streaming/atm-datagen.py: -------------------------------------------------------------------------------- 1 | import json 2 | import random 3 | import uuid 4 | import time 5 | 6 | users = [ 'abby', 'bob', 'chris', 'devin', 'elle', 'fred', 'gigi', 'hank', 'ida', 'karley', 'lisa', 'mike', 'nancy', 'otto', 'patty', 'quinn', 'rose', 'surah', 'tosh', 'vaibhav', 'walt', 'xavier', 'yolanda', 'zeke'] 7 | locations = ['syracuse', 'dewitt', 'tully', 'syracuse', 'syracuse', 'cicero', 'clay', 'dewitt', 'syracuse', 'clay', 'dewitt'] 8 | max_speed = 1 9 | min_speed = 10 10 | error_rate_pct = 2 11 | over_100_pct = 25 12 | 13 | try: 14 | user = random.choice(users) 15 | location = random.choice(locations) 16 | timestamp = int(time.time()) 17 | amount = random.randint(1,5)*20+100 if random.randint(1,100) <=25 else random.randint(1,5)*20 18 | id = int(uuid.uuid4()) 19 | status = "error" if random.randint(1,100) <=2 else "ok" 20 | 21 | data = { "Id" : id, "Location": location, "User" : user, "TimeStamp" : timestamp, "Amount" : amount, "Status" : status } 22 | 23 | #print("%d:%s" % (id,json.dumps(data))) 24 | print(json.dumps(data)) 25 | delay = random.randint(max_speed,min_speed) 26 | time.sleep(delay) 27 | 28 | except KeyboardInterrupt: 29 | pass -------------------------------------------------------------------------------- /streaming/kafka-atm-datagen.py: -------------------------------------------------------------------------------- 1 | import json 2 | import datetime 3 | import random 4 | import uuid 5 | import time 6 | from kafka import KafkaProducer 7 | user_list = [ 8 | { 'name': 'abby', 'gender' : 'female', 'level' : 'basic' }, 9 | { 'name': 'bob', 'gender' : 'male', 'level' : 'basic' }, 10 | { 'name': 'chris', 'gender' : 'female', 'level' : 'premium' }, 11 | { 'name': 'devin', 'gender' : 'male', 'level' : 'basic' }, 12 | { 'name': 'elle', 'gender' : 'female', 'level' : 'basic' }, 13 | { 'name': 'fred', 'gender' : 'male', 'level' : 'premium' }, 14 | { 'name': 'gigi', 'gender' : 'female', 'level' : 'basic' }, 15 | { 'name': 'hank', 'gender' : 'male', 'level' : 'premium' }, 16 | { 'name': 'ida', 'gender' : 'female', 'level' : 'basic' }, 17 | { 'name': 'karley', 'gender' : 'female', 'level' : 'basic' }, 18 | { 'name': 'lisa', 'gender' : 'female', 'level' : 'basic' }, 19 | { 'name': 'mike', 'gender' : 'male', 'level' : 'basic' }, 20 | { 'name': 'nancy', 'gender' : 'female', 'level' : 'basic' }, 21 | { 'name': 'otto', 'gender' : 'male', 'level' : 'basic' }, 22 | { 'name': 'patty', 'gender' : 'female', 'level' : 'basic' }, 23 | { 'name': 'quinn', 'gender' : 'female', 'level' : 'basic' }, 24 | { 'name': 'rose', 'gender' : 'female', 'level' : 'premium' }, 25 | { 'name': 'surah', 'gender' : 'male', 'level' : 'basic' }, 26 | { 'name': 'tosh', 'gender' : 'male', 'level' : 'basic' }, 27 | { 'name': 'vaibhav', 'gender' : 'male', 'level' : 'basic' }, 28 | { 'name': 'walt', 'gender' : 'male', 'level' : 'basic' }, 29 | { 'name': 'xavier', 'gender' : 'female', 'level' : 'basic' }, 30 | { 'name': 'yolanda', 'gender' : 'female', 'level' : 'basic' }, 31 | { 'name': 'zeke', 'gender' : 'male', 'level' : 'premium' } 32 | ] 33 | 34 | users = [ 'abby', 'bob', 'chris', 'devin', 'elle', 'fred', 'gigi', 'hank', 'ida', 'karley', 'lisa', 'mike', 'nancy', 'otto', 'patty', 'quinn', 'rose', 'surah', 'tosh', 'vaibhav', 'walt', 'xavier', 'yolanda', 'zeke'] 35 | locations = ['syracuse', 'dewitt', 'tully', 'syracuse', 'syracuse', 'cicero', 'clay', 'dewitt', 'syracuse', 'clay', 'dewitt'] 36 | max_speed = 1 37 | min_speed = 10 38 | error_rate_pct = 2 39 | over_100_pct = 25 40 | producer = KafkaProducer(bootstrap_servers= ['broker:9092', 'localhost:29092']) 41 | 42 | # main stream of writes. 43 | try: 44 | while True: 45 | 46 | user = random.choice(users) 47 | location = random.choice(locations) 48 | #timestamp = int(datetime.datetime.now().timestamp()*1000) #python 3 49 | timestamp = int(time.mktime(datetime.datetime.now().timetuple())*1000) #python 2 50 | amount = random.randint(1,5)*20+100 if random.randint(1,100) <=25 else random.randint(1,5)*20 51 | id = int(uuid.uuid4()) 52 | status = "error" if random.randint(1,100) <=2 else "ok" 53 | 54 | data = { "Id" : id, "Location": location, "User" : user, "TimeStamp" : timestamp, "Amount" : amount, "Status" : status } 55 | encoded = json.dumps(data).encode('utf-8') 56 | #print("%d:%s" % (id,json.dumps(data))) 57 | print(json.dumps(data)) 58 | producer.send('atm',encoded) 59 | delay = random.randint(max_speed,min_speed) 60 | time.sleep(delay) 61 | 62 | except KeyboardInterrupt: 63 | producer.close() 64 | -------------------------------------------------------------------------------- /streaming/kafka-atm-stream.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | pip install -q kafka && \ 3 | python kafka-atm-datagen.py 4 | -------------------------------------------------------------------------------- /streaming/kafka-weblogs-datagen.py: -------------------------------------------------------------------------------- 1 | import json 2 | import datetime 3 | import random 4 | import uuid 5 | import time 6 | from kafka import KafkaProducer 7 | user_list = [ 8 | { 'name': 'abby', 'browser' : 'chrome', 'os' : 'osx' }, 9 | { 'name': 'bob', 'browser' : 'firefox', 'os' : 'win' }, 10 | { 'name': 'chris', 'browser' : 'chrome', 'os' : 'win' }, 11 | { 'name': 'devin', 'browser' : 'edge', 'os' : 'win' }, 12 | { 'name': 'elle', 'browser' : 'chrome', 'os' : 'win' }, 13 | { 'name': 'fred', 'browser' : 'safari', 'os' : 'osx' }, 14 | { 'name': 'gigi', 'browser' : 'chrome', 'os' : 'win' }, 15 | { 'name': 'hank', 'browser' : 'edge', 'os' : 'win' }, 16 | { 'name': 'ida', 'browser' : 'chrome', 'os' : 'win' }, 17 | { 'name': 'karley', 'browser' : 'chrome', 'os' : 'win' }, 18 | { 'name': 'lisa', 'browser' : 'chrome', 'os' : 'osx' }, 19 | { 'name': 'mike', 'browser' : 'firefox', 'os' : 'osx' }, 20 | { 'name': 'nancy', 'browser' : 'chrome', 'os' : 'win' }, 21 | { 'name': 'otto', 'browser' : 'safari', 'os' : 'osx' }, 22 | { 'name': 'patty', 'browser' : 'firefox', 'os' : 'win' }, 23 | { 'name': 'quinn', 'browser' : 'chrome', 'os' : 'win' }, 24 | { 'name': 'rose', 'browser' : 'chrome', 'os' : 'osx' }, 25 | { 'name': 'surah', 'browser' : 'firefox', 'os' : 'win' }, 26 | { 'name': 'tosh', 'browser' : 'firefox', 'os' : 'win' }, 27 | { 'name': 'vaibhav', 'browser' : 'safari', 'os' : 'osx' }, 28 | { 'name': 'walt', 'browser' : 'edge', 'os' : 'win' }, 29 | { 'name': 'xavier', 'browser' : 'chrome', 'os' : 'win' }, 30 | { 'name': 'yolanda', 'browser' : 'chrome', 'os' : 'osx' }, 31 | { 'name': 'zeke', 'browser' : 'firefox', 'os' : 'win' } 32 | ] 33 | 34 | locations = ['/', '/about', '/products', '/services', '/contact', '/blog', '/', '/', '/blog', '/', '/'] 35 | max_speed = 1 36 | min_speed = 10 37 | producer = KafkaProducer(bootstrap_servers= ['broker:9092', 'localhost:29092']) 38 | 39 | # main stream of writes. 40 | try: 41 | while True: 42 | 43 | user = random.choice(user_list) 44 | location = random.choice(locations) 45 | #timestamp = int(datetime.datetime.now().timestamp()*1000) #python 3 46 | timestamp = int(time.mktime(datetime.datetime.now().timetuple())*1000) #python 2 47 | amount = random.randint(1,5)*20+100 if random.randint(1,100) <=25 else random.randint(1,5)*20 48 | id = int(uuid.uuid4()) 49 | data = { "Uri": location, "User" : user['name'], "TimeStamp" : timestamp, "Browser" : user['browser'], "OS" : user['os'] } 50 | encoded = json.dumps(data).encode('utf-8') 51 | #print("%d:%s" % (id,json.dumps(data))) 52 | print(json.dumps(data)) 53 | producer.send('weblogs',encoded) 54 | delay = random.randint(max_speed,min_speed) 55 | time.sleep(delay) 56 | 57 | except KeyboardInterrupt: 58 | producer.close() 59 | 60 | -------------------------------------------------------------------------------- /streaming/kafka-weblogs-stream.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | pip install -q kafka && \ 3 | python kafka-weblogs-datagen.py 4 | -------------------------------------------------------------------------------- /student_polls/class_roster.txt: -------------------------------------------------------------------------------- 1 | lh Lee Hmveehom 2 | sb Sara Bellum 3 | ku Kent Undastajou 4 | af Alma Frienzergon 5 | cp Chris Peanugget -------------------------------------------------------------------------------- /student_polls/poll-responses-2024-01-08.csv: -------------------------------------------------------------------------------- 1 | poll_num,student_id,answer 2 | 1,ac,A 3 | 1,ba,A 4 | 1,cp,A 5 | 1,do,B 6 | 1,ed,A 7 | 1,fr,C 8 | 1,gf,A 9 | 1,hb,A 10 | 1,jh,A 11 | 1,ku,B 12 | 1,lh,B 13 | 2,ac,B 14 | 2,ba,C 15 | 2,cp,C 16 | 2,do,C 17 | 2,ed,C 18 | 2,fr,C 19 | 2,gf,C 20 | 2,hb,C 21 | 2,jh,A 22 | 2,ku,B 23 | 2,lh,D 24 | 3,ac,D 25 | 3,ba,D 26 | 3,cp,D 27 | 3,do,C 28 | 3,ed,C 29 | 3,fr,D 30 | 3,gf,D 31 | 3,hb,D 32 | 3,jh,A 33 | 3,ku,D 34 | 3,lh,D 35 | -------------------------------------------------------------------------------- /student_polls/poll-responses-2024-01-15.csv: -------------------------------------------------------------------------------- 1 | poll_num,student_id,answer 2 | 1,ba,A 3 | 1,cp,A 4 | 1,ed,A 5 | 1,fr,C 6 | 1,gf,A 7 | 1,hb,A 8 | 1,jh,A 9 | 1,ku,B 10 | 1,lh,B 11 | 2,ac,B 12 | 2,ba,C 13 | 2,cp,C 14 | 2,ed,C 15 | 2,fr,C 16 | 2,gf,C 17 | 2,jh,A 18 | 2,ku,B 19 | 3,ac,D 20 | 3,ba,D 21 | 3,cp,D 22 | 3,ed,C 23 | 3,fr,D 24 | 3,gf,D 25 | 3,jh,A 26 | 3,ku,D 27 | 3,lh,D 28 | 4,ac,D 29 | 4,ba,D 30 | 4,cp,D 31 | 4,ed,C 32 | 4,fr,D 33 | 4,gf,D 34 | 4,jh,A 35 | 4,ku,D 36 | 4,lh,D -------------------------------------------------------------------------------- /student_polls/poll-responses-2024-01-22.csv: -------------------------------------------------------------------------------- 1 | poll_num,student_id,answer 2 | 1,ba,A 3 | 1,cp,A 4 | 1,ed,A 5 | 1,fr,C 6 | 1,gf,A 7 | 1,hb,A 8 | 1,jh,A 9 | 1,ku,B 10 | 2,ba,C 11 | 2,cp,C 12 | 2,ed,C 13 | 2,gf,C 14 | 2,hb,C 15 | 2,jh,A 16 | 2,ku,B 17 | 3,ac,D 18 | 3,ba,D 19 | 3,cp,D 20 | 3,ed,C 21 | 3,hb,D 22 | 3,jh,A 23 | 3,ku,D 24 | -------------------------------------------------------------------------------- /student_polls/poll-responses-2024-01-29.csv: -------------------------------------------------------------------------------- 1 | poll_num,student_id,answer 2 | 1,ac,A 3 | 1,do,B 4 | 1,fr,C 5 | 1,hb,A 6 | 1,ku,B 7 | 1,lh,B 8 | 2,ac,B 9 | 2,ba,C 10 | 2,do,C 11 | 2,fr,C 12 | 2,gf,C 13 | 2,hb,C 14 | 2,ku,B 15 | 3,ba,D 16 | 3,do,C 17 | 3,ed,C 18 | 3,fr,D 19 | 3,hb,D 20 | 3,ku,D 21 | 3,lh,D 22 | 4,ac,D 23 | 4,ba,D 24 | 4,do,C 25 | 4,fr,D 26 | 4,ku,D 27 | -------------------------------------------------------------------------------- /student_polls/poll-responses.txt: -------------------------------------------------------------------------------- 1 | 1 lh 2 | 1 sb 3 | 1 af 4 | 1 cp 5 | 2 sb 6 | 2 af 7 | 2 cp 8 | 3 cp 9 | 3 sb 10 | 3 lh 11 | 4 lh 12 | 4 sb -------------------------------------------------------------------------------- /student_polls/roster.csv: -------------------------------------------------------------------------------- 1 | netid,name 2 | ac,Artie Choke 3 | ba,Bette Alott 4 | cp,Chris Peanugget 5 | do,Deen Ofstudentz 6 | ed,Erin Detyres 7 | fr,Frank Furter 8 | gf,Gail Forse 9 | hb,Holly Bush 10 | jh,Joe King 11 | ku,Kent Undastajou 12 | lh,Lee Hmveehom 13 | -------------------------------------------------------------------------------- /superhero/superhero-movie-dataset-1978-2012-header.csv: -------------------------------------------------------------------------------- 1 | Year,Title,Comic,IMDB Score,RT Score,Composite Score,Opening Weekend Box Office,Avg Ticket Price,Opening Weekend Attendance,US Population That Year 2 | 1978,Superman,DC,7.3,95,84,7465343,2.34,3190317.521,222584545 3 | 1980,Superman II,DC,6.7,88,77.5,14100523,2.69,5241830.112,227224681 4 | 1982,Swamp Thing,DC,5.3,60,56.5,,2.94,,231664458 5 | 1983,Superman III,DC,4.9,24,36.5,13352357,3.15,4238843.492,233791994 6 | 1984,Supergirl,DC,4.2,8,25,5738249,3.36,1707812.202,235824902 7 | 1986,Howard the Duck,Marvel,4.3,16,29.5,5070136,3.71,1366613.477,240132887 8 | 1987,Superman IV: The Quest for Peace,DC,3.6,10,23,5683122,3.91,1453483.887,242288918 9 | 1989,Batman,DC,7.6,71,73.5,40489746,3.97,10198928.46,246819230 10 | 1989,The Return of Swamp Thing,DC,3.9,40,39.5,,3.97,,246819230 11 | 1989,The Punisher,Marvel,5.4,24,39,,3.97,,246819230 12 | 1992,Batman Returns,DC,7,78,74,45687711,4.15,11009086.99,255029699 13 | 1995,Batman Forever,DC,5.4,42,48,52784433,4.35,12134352.41,262803276 14 | 1997,Batman & Robin,DC,3.6,12,24,42872605,4.59,9340436.819,267783607 15 | 1997,Steel,DC,2.7,12,19.5,870068,4.59,189557.2985,267783607 16 | 1998,Blade,Marvel,7,55,62.5,17073856,4.69,3640481.023,270248003 17 | 2000,X-Men,Marvel,7.4,82,78,54471475,5.39,10106025.05,282171957 18 | 2002,Blade II,Marvel,6.6,59,62.5,32528016,5.81,5598625.818,287803914 19 | 2002,Spider-Man,Marvel,7.4,89,81.5,114844116,5.81,19766629.26,287803914 20 | 2003,Daredevil,Marvel,5.4,45,49.5,40310419,6.03,6684978.275,290326418 21 | 2003,Hulk,Marvel,5.7,62,59.5,62128420,6.03,10303220.56,290326418 22 | 2003,X2,Marvel,7.6,88,82,85558731,6.03,14188844.28,290326418 23 | 2004,Blade: Trinity,Marvel,5.8,26,42,16061271,6.21,2586356.039,293045739 24 | 2004,Catwoman,DC,3.2,10,21,16728411,6.21,2693785.99,293045739 25 | 2004,Spider-Man 2,Marvel,7.5,93,84,88156227,6.21,14195849.76,293045739 26 | 2004,The Punisher,Marvel,6.4,29,46.5,13834527,6.21,2227782.126,293045739 27 | 2005,Batman Begins,DC,8.3,85,84,48745440,6.41,7604592.824,295753151 28 | 2005,Elektra,Marvel,4.8,10,29,12804793,6.41,1997627.613,295753151 29 | 2005,Fantastic Four,Marvel,5.7,27,42,56061504,6.41,8745944.462,295753151 30 | 2006,Superman Returns,DC,6.3,76,69.5,52535096,6.55,8020625.344,298593212 31 | 2006,X-Men: The Last Stand,Marvel,6.8,57,62.5,102750665,6.55,15687124.43,298593212 32 | 2007,Fantastic Four: Rise of the Silver Surfer,Marvel,5.7,37,47,58051684,6.88,8437744.767,301579895 33 | 2007,Ghost Rider,Marvel,5.2,26,39,45388836,6.88,6597214.535,301579895 34 | 2007,Spider-Man 3,Marvel,6.3,63,63,151116516,6.88,21964609.88,301579895 35 | 2008,The Dark Knight,DC,8.9,94,91.5,158411483,7.18,22062880.64,304374846 36 | 2008,The Incredible Hulk,Marvel,7,67,68.5,55414050,7.18,7717834.262,304374846 37 | 2008,Iron Man,Marvel,7.9,94,86.5,98618668,7.18,13735190.53,304374846 38 | 2008,Punisher: War Zone,Marvel,6,26,43,4271451,7.18,594909.61,304374846 39 | 2009,Watchmen,DC,7.7,64,70.5,55214334,7.5,7361911.2,307006550 40 | 2009,X-Men Origins: Wolverine,Marvel,6.7,37,52,85058003,7.5,11341067.07,307006550 41 | 2010,Iron Man 2,Marvel,7.1,74,72.5,128122480,7.89,16238590.62,308745538 42 | 2010,Jonah Hex,DC,4.6,13,29.5,5379365,7.89,681795.3105,308745538 43 | 2011,Captain America: The First Avenger,Marvel,6.8,79,73.5,65058524,7.93,8204101.387,311591917 44 | 2011,Green Lantern,DC,5.9,27,43,53174303,7.93,6705460.656,311591917 45 | 2011,Thor,Marvel,7,77,73.5,65723338,7.93,8287936.696,311591917 46 | 2011,X-Men: First Class,Marvel,7.9,87,83,55101604,7.93,6948499.874,311591917 47 | 2012,Marvel's The Avengers,Marvel,8.7,92,89.5,207438708,7.92,26191756.06,314055984 48 | 2012,The Dark Knight Rises,DC,9.1,86,88.5,160887295,7.92,20314052.4,314055984 49 | 2012,Ghost Rider: Spirit of Vengeance,Marvel,4.5,17,31,22115334,7.92,2792340.152,314055984 50 | 2012,The Amazing Spider-Man,Marvel,7.6,74,75,62004688,7.92,7828874.747,314055984 51 | -------------------------------------------------------------------------------- /superhero/superhero-movie-dataset-1978-2012-no-header.csv: -------------------------------------------------------------------------------- 1 | 1978,Superman,DC,7.3,95,84,7465343,2.34,3190317.521,222584545 2 | 1980,Superman II,DC,6.7,88,77.5,14100523,2.69,5241830.112,227224681 3 | 1982,Swamp Thing,DC,5.3,60,56.5,,2.94,,231664458 4 | 1983,Superman III,DC,4.9,24,36.5,13352357,3.15,4238843.492,233791994 5 | 1984,Supergirl,DC,4.2,8,25,5738249,3.36,1707812.202,235824902 6 | 1986,Howard the Duck,Marvel,4.3,16,29.5,5070136,3.71,1366613.477,240132887 7 | 1987,Superman IV: The Quest for Peace,DC,3.6,10,23,5683122,3.91,1453483.887,242288918 8 | 1989,Batman,DC,7.6,71,73.5,40489746,3.97,10198928.46,246819230 9 | 1989,The Return of Swamp Thing,DC,3.9,40,39.5,,3.97,,246819230 10 | 1989,The Punisher,Marvel,5.4,24,39,,3.97,,246819230 11 | 1992,Batman Returns,DC,7,78,74,45687711,4.15,11009086.99,255029699 12 | 1995,Batman Forever,DC,5.4,42,48,52784433,4.35,12134352.41,262803276 13 | 1997,Batman & Robin,DC,3.6,12,24,42872605,4.59,9340436.819,267783607 14 | 1997,Steel,DC,2.7,12,19.5,870068,4.59,189557.2985,267783607 15 | 1998,Blade,Marvel,7,55,62.5,17073856,4.69,3640481.023,270248003 16 | 2000,X-Men,Marvel,7.4,82,78,54471475,5.39,10106025.05,282171957 17 | 2002,Blade II,Marvel,6.6,59,62.5,32528016,5.81,5598625.818,287803914 18 | 2002,Spider-Man,Marvel,7.4,89,81.5,114844116,5.81,19766629.26,287803914 19 | 2003,Daredevil,Marvel,5.4,45,49.5,40310419,6.03,6684978.275,290326418 20 | 2003,Hulk,Marvel,5.7,62,59.5,62128420,6.03,10303220.56,290326418 21 | 2003,X2,Marvel,7.6,88,82,85558731,6.03,14188844.28,290326418 22 | 2004,Blade: Trinity,Marvel,5.8,26,42,16061271,6.21,2586356.039,293045739 23 | 2004,Catwoman,DC,3.2,10,21,16728411,6.21,2693785.99,293045739 24 | 2004,Spider-Man 2,Marvel,7.5,93,84,88156227,6.21,14195849.76,293045739 25 | 2004,The Punisher,Marvel,6.4,29,46.5,13834527,6.21,2227782.126,293045739 26 | 2005,Batman Begins,DC,8.3,85,84,48745440,6.41,7604592.824,295753151 27 | 2005,Elektra,Marvel,4.8,10,29,12804793,6.41,1997627.613,295753151 28 | 2005,Fantastic Four,Marvel,5.7,27,42,56061504,6.41,8745944.462,295753151 29 | 2006,Superman Returns,DC,6.3,76,69.5,52535096,6.55,8020625.344,298593212 30 | 2006,X-Men: The Last Stand,Marvel,6.8,57,62.5,102750665,6.55,15687124.43,298593212 31 | 2007,Fantastic Four: Rise of the Silver Surfer,Marvel,5.7,37,47,58051684,6.88,8437744.767,301579895 32 | 2007,Ghost Rider,Marvel,5.2,26,39,45388836,6.88,6597214.535,301579895 33 | 2007,Spider-Man 3,Marvel,6.3,63,63,151116516,6.88,21964609.88,301579895 34 | 2008,The Dark Knight,DC,8.9,94,91.5,158411483,7.18,22062880.64,304374846 35 | 2008,The Incredible Hulk,Marvel,7,67,68.5,55414050,7.18,7717834.262,304374846 36 | 2008,Iron Man,Marvel,7.9,94,86.5,98618668,7.18,13735190.53,304374846 37 | 2008,Punisher: War Zone,Marvel,6,26,43,4271451,7.18,594909.61,304374846 38 | 2009,Watchmen,DC,7.7,64,70.5,55214334,7.5,7361911.2,307006550 39 | 2009,X-Men Origins: Wolverine,Marvel,6.7,37,52,85058003,7.5,11341067.07,307006550 40 | 2010,Iron Man 2,Marvel,7.1,74,72.5,128122480,7.89,16238590.62,308745538 41 | 2010,Jonah Hex,DC,4.6,13,29.5,5379365,7.89,681795.3105,308745538 42 | 2011,Captain America: The First Avenger,Marvel,6.8,79,73.5,65058524,7.93,8204101.387,311591917 43 | 2011,Green Lantern,DC,5.9,27,43,53174303,7.93,6705460.656,311591917 44 | 2011,Thor,Marvel,7,77,73.5,65723338,7.93,8287936.696,311591917 45 | 2011,X-Men: First Class,Marvel,7.9,87,83,55101604,7.93,6948499.874,311591917 46 | 2012,Marvel's The Avengers,Marvel,8.7,92,89.5,207438708,7.92,26191756.06,314055984 47 | 2012,The Dark Knight Rises,DC,9.1,86,88.5,160887295,7.92,20314052.4,314055984 48 | 2012,Ghost Rider: Spirit of Vengeance,Marvel,4.5,17,31,22115334,7.92,2792340.152,314055984 49 | 2012,The Amazing Spider-Man,Marvel,7.6,74,75,62004688,7.92,7828874.747,314055984 -------------------------------------------------------------------------------- /superhero/superhero-movies.mysql: -------------------------------------------------------------------------------- 1 | CREATE DATABASE IF NOT EXISTS `superhero` /*!40100 DEFAULT CHARACTER SET utf8 */; 2 | USE `superhero`; 3 | 4 | DROP TABLE IF EXISTS movies; 5 | 6 | CREATE TABLE movies ( 7 | year int, 8 | title varchar(255), 9 | comic varchar(10), 10 | imdb_rating decimal(18,4), 11 | rotten_tomatoes_rating decimal(18,4), 12 | composite_rating decimal(18,4), 13 | opening_weekend_box_office decimal(18,4), 14 | avg_ticket_price_for_year decimal(18,4), 15 | est_opening_weekend_attendance decimal(18,4), 16 | us_pop_year_of_opening decimal(18,4) 17 | ); 18 | 19 | LOAD DATA INFILE '/tmp/movies.csv' INTO TABLE movies FIELDS TERMINATED BY ','; 20 | 21 | ALTER TABLE movies ADD COLUMN id int UNSIGNED PRIMARY KEY AUTO_INCREMENT; 22 | -------------------------------------------------------------------------------- /text/preamble.txt: -------------------------------------------------------------------------------- 1 | We the people of the United States, in order to form a more perfect union, establish justice, insure domestic tranquility, provide for the common defense, promote the general welfare, and secure the blessings of liberty to ourselves and our posterity, do ordain and establish this Constitution for the United States of America. -------------------------------------------------------------------------------- /tv-shows/https---api.tvmaze.com-singlesearch-shows-q=game+of+thrones&embed=episodes.url: -------------------------------------------------------------------------------- 1 | [InternetShortcut] 2 | URL=https://api.tvmaze.com/singlesearch/shows?q=game+of+thrones&embed=episodes 3 | -------------------------------------------------------------------------------- /tweets/fudgemart_tweets.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mafudge/datasets/b67d9eb3d80a9385a41373e84d4b7b06d37c4ff1/tweets/fudgemart_tweets.xls -------------------------------------------------------------------------------- /tweets/logagent.conf: -------------------------------------------------------------------------------- 1 | # Licensed to the Apache Software Foundation (ASF) under one 2 | # or more contributor license agreements. See the NOTICE file 3 | # distributed with this work for additional information 4 | # regarding copyright ownership. The ASF licenses this file 5 | # to you under the Apache License, Version 2.0 (the 6 | # "License"); you may not use this file except in compliance 7 | # with the License. You may obtain a copy of the License at 8 | # 9 | # http://www.apache.org/licenses/LICENSE-2.0 10 | # 11 | # Unless required by applicable law or agreed to in writing, 12 | # software distributed under the License is distributed on an 13 | # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 14 | # KIND, either express or implied. See the License for the 15 | # specific language governing permissions and limitations 16 | # under the License. 17 | 18 | # The configuration file needs to define the sources, 19 | # the channels and the sinks. 20 | # Sources, channels and sinks are defined per agent, 21 | # in this case called 'logagent' 22 | 23 | agent.sources = weblog 24 | agent.channels = memoryChannel 25 | agent.sinks = mycluster 26 | 27 | ## Sources ######################################################### 28 | agent.sources.weblog.type = exec 29 | # TODO: Change this line to the command to recieve the data stream 30 | agent.sources.weblog.command = tail -F sample-tweet-stream.psv 31 | agent.sources.weblog.batchSize = 1 32 | agent.sources.weblog.channels = memoryChannel 33 | 34 | ## Channels ######################################################## 35 | agent.channels.memoryChannel.type = memory 36 | agent.channels.memoryChannel.capacity = 100 37 | agent.channels.memoryChannel.transactionCapacity = 100 38 | 39 | ## Sinks ########################################################### 40 | agent.sinks.mycluster.type = hdfs 41 | # TODO: change this line to the HDFS location for the data 42 | agent.sinks.mycluster.hdfs.path=/user/cloudera/flumetweets 43 | agent.sinks.mycluster.channel = memoryChannel 44 | -------------------------------------------------------------------------------- /tweets/test.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import getopt 3 | 4 | def printUsage(): 5 | print('python test.py -c -s -e -f ') 6 | print('python test.py --count= --startdate= --enddate= --format=') 7 | print('') 8 | print(' is the number of tweets to generate') 9 | print(' is the date and time of the earliest tweet') 10 | print(' is the date and time of the latest tweet.') 11 | print(' is either json or psv (pipe-separated values)') 12 | print('') 13 | print('This prints 10 random tweets in the year 2015 output as json.') 14 | print('python test.py -c 10 -s "1/1/2015 12:00 AM" -e "12/31/2015 11:59 PM" -f json') 15 | 16 | def getCommandArgs(argv): 17 | try: 18 | count = 0 19 | startDate = "" 20 | endDate = "" 21 | outFormat = "" 22 | opts, args = getopt.getopt(argv,"c:s:e:f:",["count=","startdate=","enddate=","format="]) 23 | 24 | except getopt.GetoptError as err: 25 | print(err) 26 | printUsage() 27 | sys.exit(2) 28 | 29 | for opt,arg in opts: 30 | if opt in ("-c", "--count"): 31 | count = arg 32 | elif opt in ("-s", "--startdate"): 33 | startDate = arg 34 | elif opt in ("-e", "--enddate"): 35 | endDate = arg 36 | elif opt in ("-f","--format"): 37 | outFormat = arg 38 | # check args 39 | if count == 0 or startDate== "" or endDate == "" or outFormat not in ('json','psv'): 40 | printUsage() 41 | sys.exit(2) 42 | return count, startDate, endDate, outFormat 43 | 44 | 45 | 46 | count, startDate, endDate, outFormat = getCommandArgs(sys.argv[1:]) 47 | print(count,startDate, endDate, outFormat) 48 | 49 | 50 | 51 | -------------------------------------------------------------------------------- /tweets/tweet-stream.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | max=100 3 | for i in `seq 1 $max`; 4 | do 5 | rnd=$(( ( RANDOM % 10 ) + 1 )) 6 | echo "Loop: $i of $max: Generating $rnd Tweets..." 7 | python simtweet.py -c $rnd -s "1/1/2015 12:00 AM" -e "12/31/2015 11:59 PM" -f psv >> sample-tweet-stream.psv 8 | sleep 10 9 | done 10 | 11 | -------------------------------------------------------------------------------- /tweets/tweet-stream2.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | max=100 3 | for i in `seq 1 $max`; 4 | do 5 | rnd=$(( ( RANDOM % 10 ) + 1 )) 6 | echo "Loop: $i of $max: Generating $rnd Tweets..." 7 | python simtweet-2.py -c $rnd -s "1/1/2018 12:00 AM" -e "12/31/2018 11:59 PM" -f psv >> sample-tweet-stream.psv 8 | sleep 10 9 | done 10 | 11 | -------------------------------------------------------------------------------- /usa/geographic-centers.csv: -------------------------------------------------------------------------------- 1 | Code,State,Latitude,Longitude 2 | AL,Alabama,32.806671,-86.79113 3 | AK,Alaska,61.370716,-152.404419 4 | AZ,Arizona,33.729759,-111.431221 5 | AR,Arkansas,34.969704,-92.373123 6 | CA,California,36.116203,-119.681564 7 | CO,Colorado,39.059811,-105.311104 8 | CT,Connecticut,41.597782,-72.755371 9 | DE,Delaware,39.318523,-75.507141 10 | FL,Florida,27.766279,-81.686783 11 | GA,Georgia,33.040619,-83.643074 12 | HI,Hawaii,21.094318,-157.498337 13 | ID,Idaho,44.240459,-114.478828 14 | IL,Illinois,40.349457,-88.986137 15 | IN,Indiana,39.849426,-86.258278 16 | IA,Iowa,42.011539,-93.210526 17 | KS,Kansas,38.5266,-96.726486 18 | KY,Kentucky,37.66814,-84.670067 19 | LA,Louisiana,31.169546,-91.867805 20 | ME,Maine,44.693947,-69.381927 21 | MD,Maryland,39.063946,-76.802101 22 | MA,Massachusetts,42.230171,-71.530106 23 | MI,Michigan,43.326618,-84.536095 24 | MN,Minnesota,45.694454,-93.900192 25 | MS,Mississippi,32.741646,-89.678696 26 | MO,Missouri,38.456085,-92.288368 27 | MT,Montana,46.921925,-110.454353 28 | NE,Nebraska,41.12537,-98.268082 29 | NV,Nevada,38.313515,-117.055374 30 | NH,New Hampshire,43.452492,-71.563896 31 | NJ,New Jersey,40.298904,-74.521011 32 | NM,New Mexico,34.840515,-106.248482 33 | NY,New York,42.165726,-74.948051 34 | NC,North Carolina,35.630066,-79.806419 35 | ND,North Dakota,47.528912,-99.784012 36 | OH,Ohio,40.388783,-82.764915 37 | OK,Oklahoma,35.565342,-96.928917 38 | OR,Oregon,44.572021,-122.070938 39 | PA,Pennsylvania,40.590752,-77.209755 40 | RI,Rhode Island,41.680893,-71.51178 41 | SC,South Carolina,33.856892,-80.945007 42 | SD,South Dakota,44.299782,-99.438828 43 | TN,Tennessee,35.747845,-86.692345 44 | TX,Texas,31.054487,-97.563461 45 | UT,Utah,40.150032,-111.862434 46 | VT,Vermont,44.045876,-72.710686 47 | VA,Virginia,37.769337,-78.169968 48 | WA,Washington,47.400902,-121.490494 49 | WV,West Virginia,38.491226,-80.954453 50 | WI,Wisconsin,44.268543,-89.616508 51 | WY,Wyoming,42.755966,-107.30249 52 | -------------------------------------------------------------------------------- /usa/us-pop-estimates-2010-2016.csv: -------------------------------------------------------------------------------- 1 | Code,StateName,2010,2011,2012,2013,2014,2015,2016 2 | AL,Alabama,4785492,4799918,4815960,4829479,4843214,4853875,4863300 3 | AK,Alaska,714031,722713,731089,736879,736705,737709,741894 4 | AZ,Arizona,6408312,6467163,6549634,6624617,6719993,6817565,6931071 5 | AR,Arkansas,2921995,2939493,2950685,2958663,2966912,2977853,2988248 6 | CA,California,37332685,37676861,38011074,38335203,38680810,38993940,39250017 7 | CO,Colorado,5048644,5118360,5189867,5267603,5349648,5448819,5540545 8 | CT,Connecticut,3579899,3589893,3593795,3596003,3591873,3584730,3576452 9 | DE,Delaware,899816,907924,916993,925395,934948,944076,952065 10 | FL,Florida,18849098,19096952,19344156,19582022,19888741,20244914,20612439 11 | GA,Georgia,9713521,9811610,9914668,9984938,10087231,10199398,10310371 12 | HI,Hawaii,1363945,1377864,1391820,1406481,1416349,1425157,1428557 13 | ID,Idaho,1571010,1584143,1595911,1612011,1633532,1652828,1683140 14 | IL,Illinois,12841578,12860012,12870798,12879505,12867544,12839047,12801539 15 | IN,Indiana,6490528,6516480,6537743,6569102,6595233,6612768,6633053 16 | IA,Iowa,3050738,3065223,3076310,3091930,3108030,3121997,3134693 17 | KS,Kansas,2858850,2869503,2885262,2892821,2899360,2906721,2907289 18 | KY,Kentucky,4348662,4369354,4384799,4400477,4413057,4424611,4436974 19 | LA,Louisiana,4544996,4575404,4603429,4626402,4647880,4668960,4681666 20 | ME,Maine,1327730,1328231,1328895,1329076,1330719,1329453,1331479 21 | MD,Maryland,5788584,5843603,5889651,5931129,5967295,5994983,6016447 22 | MA,Massachusetts,6565524,6611923,6658008,6706786,6749911,6784240,6811779 23 | MI,Michigan,9877495,9876213,9887238,9898982,9915767,9917715,9928300 24 | MN,Minnesota,5311147,5348562,5380285,5418521,5453109,5482435,5519952 25 | MS,Mississippi,2970322,2978162,2984945,2990482,2992400,2989390,2988726 26 | MO,Missouri,5996118,6010717,6025415,6042711,6060930,6076204,6093000 27 | MT,Montana,990641,997821,1005196,1014314,1022867,1032073,1042520 28 | NE,Nebraska,1830051,1842283,1855725,1868559,1881145,1893765,1907116 29 | NV,Nevada,2703284,2718379,2752565,2786464,2833013,2883758,2940058 30 | NH,New Hampshire,1316872,1318473,1321182,1322687,1328743,1330111,1334795 31 | NJ,New Jersey,8803729,8841243,8873211,8899162,8925001,8935421,8944469 32 | NM,New Mexico,2064756,2077756,2083784,2085193,2083024,2080328,2081015 33 | NY,New York,19402640,19519529,19602769,19673546,19718515,19747183,19745289 34 | NC,North Carolina,9558915,9650963,9746175,9841590,9934399,10035186,10146788 35 | ND,North Dakota,674526,685476,702087,724019,739904,756835,757952 36 | OH,Ohio,11540983,11544824,11550839,11570022,11594408,11605090,11614373 37 | OK,Oklahoma,3759603,3786274,3817054,3852415,3877499,3907414,3923561 38 | OR,Oregon,3838048,3868031,3899116,3925751,3968371,4024634,4093465 39 | PA,Pennsylvania,12712343,12744293,12771854,12781338,12790565,12791904,12784227 40 | RI,Rhode Island,1053337,1052451,1052901,1053033,1054480,1055607,1056426 41 | SC,South Carolina,4635943,4672637,4720760,4767894,4828430,4894834,4961119 42 | SD,South Dakota,816325,824398,834441,844922,852561,857919,865454 43 | TN,Tennessee,6356671,6397634,6454306,6494821,6544663,6595056,6651194 44 | TX,Texas,25244310,25646389,26071655,26473525,26944751,27429639,27862596 45 | UT,Utah,2775326,2816124,2855782,2902663,2941836,2990632,3051217 46 | VT,Vermont,625982,626730,626444,627140,626984,626088,624594 47 | VA,Virginia,8025773,8110035,8192048,8262692,8317372,8367587,8411808 48 | WA,Washington,6743226,6822520,6895226,6968006,7054196,7160290,7288000 49 | WV,West Virginia,1854230,1854972,1856560,1853231,1848514,1841053,1831102 50 | WI,Wisconsin,5690263,5709640,5726177,5742854,5758377,5767891,5778708 51 | WY,Wyoming,564513,567725,576765,582684,583642,586555,585501 52 | --------------------------------------------------------------------------------