├── .DS_Store ├── .gitignore ├── Cleaning_Data_in_Python ├── 01Exploring_your_data.ipynb ├── 02Tidying_data_for_analysis.ipynb ├── 03Combining_data_for_analysis.ipynb ├── 04Cleaning_data_for_analysis.ipynb ├── 05Case_study.ipynb ├── airquality.csv ├── concat.PNG ├── concat2.PNG ├── dob_job_application_filings_subset.csv ├── ebola.csv ├── gapminder.csv ├── literacy_birth_rate.csv ├── mp_data.csv ├── nyc_uber_2014.csv ├── re1.PNG ├── state_cod.csv ├── state_pop.csv ├── tb.csv ├── tiddy.csv ├── tiddy_done.csv ├── tips.csv ├── tips_nan.csv ├── treat.csv ├── treat_duplicate.csv ├── w1.csv ├── w2.csv └── weather_tidy.csv ├── Importing_Data_in_python ├── 01Introduction_and_flat_files.ipynb ├── 02Importing_data_from_other_file_types.ipynb ├── 03Working_with_relational_databases_in_Python.ipynb ├── 04Importing_data_from_the_Internet.ipynb ├── 05Interacting_with_APIs_to_import_data_from_the_web.ipynb ├── 06Diving_deep_into_the_Twitter_API.ipynb ├── Chinook.sqlite ├── L-L1_LOSC_4_V1-1126259446-32.hdf5 ├── Northwind.sqlite ├── battledeath.xlsx ├── disarea.dta ├── huck_finn.txt ├── ja_data2.mat ├── latitude.xls ├── mnist_kaggle_some_rows.csv ├── sales.sas7bdat ├── seaslug.txt ├── snakes.json ├── titanic_sub.csv ├── tweets3.txt ├── winequality-red.csv └── winequality-white.csv ├── Interactive_Data_Visualizaton_Bokeh ├── 01Basic_plotting_w_Bokeh.ipynb ├── 02Layouts_Interactions_ Annotations.ipynb ├── 03Building_interactive_apps_with_Bokeh.ipynb ├── 04Putting_It_All_Together_Case_Study.ipynb ├── aapl.csv ├── auto.csv ├── colormap.html ├── fert_column.html ├── fert_lit_separate.html ├── fert_lit_separate_colors.html ├── fert_row.html ├── gapminder_tidy.csv ├── glucose.csv ├── hover_glyph.html ├── line.html ├── literacy_birth_rate.csv ├── selection_glyph.html ├── sprint.csv └── sprint.html ├── Introduction_to_Data_Visualization_with_Python ├── 01CustomizingPlots.ipynb ├── 02Plotting2Darrays.ipynb ├── 03StatisticalPlotsWithSeaborn.ipynb ├── 04VizualizingTimeSeries.ipynb ├── Astronaut.jpg ├── Unequalized_Hawkes_Bay_NZ.jpg ├── auto-mpg.csv ├── axis_limits.png ├── gdp_csv.csv ├── google.png ├── img │ ├── 04-0.PNG │ ├── 04-1.PNG │ ├── 04-2.PNG │ ├── 04-3.PNG │ ├── hs-2004-32-b-small_web.jpg │ ├── image-high-contrast.jpg │ ├── moon1.png │ ├── moon2.jpg │ └── og-moon.PNG ├── iris.csv.txt ├── percent-bachelors-degrees-women-usa.csv ├── stocks.csv ├── weather_data_austin_2010.csv └── xlim_and_ylim.png ├── Introduction_to_Databases_in_Python ├── 01Basics_of_Relational_Databases.ipynb ├── 02Applying_Filtering_Ordering_ane_Grouping_to_Queries.ipynb ├── 03Advanced_SQLAlchemy_Queries.ipynb ├── 04Creating_and_Manipulating_yourown_Databases.ipynb ├── 05Putting _it_all_together.ipynb ├── census.csv ├── census.sqlite ├── chapter5.sqlite └── employees.sqlite ├── Merging_DataFrames_w_pandas ├── 01Preparing_data.ipynb ├── 02Concatenating_data.ipynb ├── 03Merging_data.ipynb ├── 04Case Study_Summer_Olympics.ipynb ├── Baby names │ ├── names1881.csv │ └── names1981.csv ├── GDP │ ├── gdp_china.csv │ └── gdp_usa.csv ├── Sales │ ├── feb-sales-Hardware.csv │ ├── feb-sales-Service.csv │ ├── feb-sales-Software.csv │ ├── sales-feb-2015.csv │ ├── sales-jan-2015.csv │ └── sales-mar-2015.csv ├── Summer Olympic medals │ ├── Bronze.csv │ ├── Gold.csv │ ├── Silver.csv │ ├── Summer Olympic medalists 1896 to 2008 - ALL MEDALISTS.tsv │ ├── Summer Olympic medalists 1896 to 2008 - EDITIONS.tsv │ ├── Summer Olympic medalists 1896 to 2008 - IOC COUNTRY CODES.csv │ ├── bronze_sorted.csv │ ├── bronze_top5.csv │ ├── gold_top5.csv │ └── silver_top5.csv ├── automobiles.csv ├── exchange.csv ├── gdp-2013.csv ├── managers.csv ├── managers_b.csv ├── oil_price.csv ├── pa_counties.csv ├── pa_zipcode_city.csv ├── pa_zipcode_population.csv ├── pittsburgh2013.csv ├── population_00.csv ├── population_01.csv ├── population_02.csv ├── q1_rainfall_2013.csv ├── q1_rainfall_2014.csv ├── revenue.csv ├── sp500.csv ├── stocks-2013.csv └── unemployment_00.csv ├── README.md ├── Statistical_Thinking_Python ├── 01Graphical exploratory data analysis.ipynb ├── 02Quantitative exploratory data analysis.ipynb ├── 03Thinking probabilistically_Discrete variables.ipynb ├── 04Thinking_probabilistically_Continous_variables.ipynb ├── 05Parameter estimation by optimization.ipynb ├── 06Bootstrap confidence intervals.ipynb ├── 07Introduction to hypothesis testing.ipynb ├── 08_AB_Testing.ipynb ├── 09Putting_it_all _together_case_study.ipynb ├── 2008_all_states.csv ├── 2008_swing_states.csv ├── anscombe.csv ├── bee_sperm.csv ├── belmont.csv ├── female_literacy_fertility.csv ├── finch_beaks_1975.csv ├── finch_beaks_2012.csv ├── fortis_beak_depth_heredity.csv ├── frog_tongue.csv ├── michelson_speed_of_light.csv ├── mlb_nohitters.csv ├── scandens_beak_depth_heredity.csv ├── sheffield_weather_station.csv ├── stat1.PNG └── stat_func.py ├── Untitled.ipynb ├── Visualizing_Time_Series_Data_in_Python ├── 01Introduction.ipynb ├── 02Clean_your_time_series_data.ipynb ├── ch1_discoveries.csv ├── ch2_co2_levels.csv ├── ch3_airline_passengers.csv ├── ch4_meat.csv ├── ch5_employment.csv ├── chapter1.pdf └── chapter2.pdf ├── datatypes_for_datascience ├── Answering Data Science Questions.ipynb ├── Dictionaries - the root of Python.ipynb ├── Fundamental data types.ipynb ├── Meet the collections module.ipynb ├── There and Back Again a DateTime Journey.ipynb └── Untitled.ipynb ├── environment.yml ├── manipulating_DF_WPD ├── 01Extracting_and_transforming_data.ipynb ├── 02Advanced_indexing.ipynb ├── 03Rearranging_and_reshaping_data.ipynb ├── 04Grouping_data.ipynb ├── 05Bringing_it_all_together.ipynb ├── all_medalists.csv ├── auto-mpg.csv ├── gapminder_tidy.csv ├── life_expectancy.csv ├── pennsylvania2012_turnout.csv ├── pittsburgh2013.csv ├── regions.csv ├── sales-feb-2015.csv ├── sales.csv ├── titanic.csv ├── trials_01.csv ├── trials_02.csv ├── trials_03.csv ├── users.csv └── visitors.csv ├── nlp ├── Classifying fake news using supervised learning with NLP.ipynb ├── Named Entity Recognition.ipynb ├── Simple topic identification.ipynb └── regex and word tokenization.ipynb ├── pandas_foundation ├── 01Data_ingestion_and_inspection.ipynb ├── 02Exploratory_data_analysis.ipynb ├── 03Time_series_pandas.ipynb ├── ISSN_D_tot.csv ├── aapl.csv ├── aapl.jpg ├── aapl.pdf ├── aapl.png ├── austin_airport_departure_data_2015_july.csv ├── auto-mpg.csv ├── iris_d.csv ├── life_expectancy_at_birth.csv ├── messy_stock_data.tsv ├── percent-bachelors-degrees-women-usa.csv ├── sales-feb-2015.csv ├── sunspots.csv ├── sunspots.tsv ├── sunspots.xlsx ├── tips.csv ├── titanic.csv ├── users.csv ├── weather_data_austin_2010 (1).csv ├── world_ind_pop_data.csv └── world_population.csv ├── supervised_learning_w_sklearn ├── Classifications.ipynb ├── auto.csv ├── boston.csv ├── diabetes.csv ├── gm_2008_region.csv ├── house-votes-84.csv ├── regression.ipynb ├── white-wine.csv └── winequality-red.csv └── working_with_data_in_databases └── sql_basics.md /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/.DS_Store -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | .static_storage/ 56 | .media/ 57 | local_settings.py 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | .DS_Store 93 | 94 | # Spyder project settings 95 | .spyderproject 96 | .spyproject 97 | 98 | # Rope project settings 99 | .ropeproject 100 | 101 | # mkdocs documentation 102 | /site 103 | 104 | # mypy 105 | .mypy_cache/ -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/airquality.csv: -------------------------------------------------------------------------------- 1 | "Ozone","Solar.R","Wind","Temp","Month","Day" 2 | 41,190,7.4,67,5,1 3 | 36,118,8,72,5,2 4 | 12,149,12.6,74,5,3 5 | 18,313,11.5,62,5,4 6 | NA,NA,14.3,56,5,5 7 | 28,NA,14.9,66,5,6 8 | 23,299,8.6,65,5,7 9 | 19,99,13.8,59,5,8 10 | 8,19,20.1,61,5,9 11 | NA,194,8.6,69,5,10 12 | 7,NA,6.9,74,5,11 13 | 16,256,9.7,69,5,12 14 | 11,290,9.2,66,5,13 15 | 14,274,10.9,68,5,14 16 | 18,65,13.2,58,5,15 17 | 14,334,11.5,64,5,16 18 | 34,307,12,66,5,17 19 | 6,78,18.4,57,5,18 20 | 30,322,11.5,68,5,19 21 | 11,44,9.7,62,5,20 22 | 1,8,9.7,59,5,21 23 | 11,320,16.6,73,5,22 24 | 4,25,9.7,61,5,23 25 | 32,92,12,61,5,24 26 | NA,66,16.6,57,5,25 27 | NA,266,14.9,58,5,26 28 | NA,NA,8,57,5,27 29 | 23,13,12,67,5,28 30 | 45,252,14.9,81,5,29 31 | 115,223,5.7,79,5,30 32 | 37,279,7.4,76,5,31 33 | NA,286,8.6,78,6,1 34 | NA,287,9.7,74,6,2 35 | NA,242,16.1,67,6,3 36 | NA,186,9.2,84,6,4 37 | NA,220,8.6,85,6,5 38 | NA,264,14.3,79,6,6 39 | 29,127,9.7,82,6,7 40 | NA,273,6.9,87,6,8 41 | 71,291,13.8,90,6,9 42 | 39,323,11.5,87,6,10 43 | NA,259,10.9,93,6,11 44 | NA,250,9.2,92,6,12 45 | 23,148,8,82,6,13 46 | NA,332,13.8,80,6,14 47 | NA,322,11.5,79,6,15 48 | 21,191,14.9,77,6,16 49 | 37,284,20.7,72,6,17 50 | 20,37,9.2,65,6,18 51 | 12,120,11.5,73,6,19 52 | 13,137,10.3,76,6,20 53 | NA,150,6.3,77,6,21 54 | NA,59,1.7,76,6,22 55 | NA,91,4.6,76,6,23 56 | NA,250,6.3,76,6,24 57 | NA,135,8,75,6,25 58 | NA,127,8,78,6,26 59 | NA,47,10.3,73,6,27 60 | NA,98,11.5,80,6,28 61 | NA,31,14.9,77,6,29 62 | NA,138,8,83,6,30 63 | 135,269,4.1,84,7,1 64 | 49,248,9.2,85,7,2 65 | 32,236,9.2,81,7,3 66 | NA,101,10.9,84,7,4 67 | 64,175,4.6,83,7,5 68 | 40,314,10.9,83,7,6 69 | 77,276,5.1,88,7,7 70 | 97,267,6.3,92,7,8 71 | 97,272,5.7,92,7,9 72 | 85,175,7.4,89,7,10 73 | NA,139,8.6,82,7,11 74 | 10,264,14.3,73,7,12 75 | 27,175,14.9,81,7,13 76 | NA,291,14.9,91,7,14 77 | 7,48,14.3,80,7,15 78 | 48,260,6.9,81,7,16 79 | 35,274,10.3,82,7,17 80 | 61,285,6.3,84,7,18 81 | 79,187,5.1,87,7,19 82 | 63,220,11.5,85,7,20 83 | 16,7,6.9,74,7,21 84 | NA,258,9.7,81,7,22 85 | NA,295,11.5,82,7,23 86 | 80,294,8.6,86,7,24 87 | 108,223,8,85,7,25 88 | 20,81,8.6,82,7,26 89 | 52,82,12,86,7,27 90 | 82,213,7.4,88,7,28 91 | 50,275,7.4,86,7,29 92 | 64,253,7.4,83,7,30 93 | 59,254,9.2,81,7,31 94 | 39,83,6.9,81,8,1 95 | 9,24,13.8,81,8,2 96 | 16,77,7.4,82,8,3 97 | 78,NA,6.9,86,8,4 98 | 35,NA,7.4,85,8,5 99 | 66,NA,4.6,87,8,6 100 | 122,255,4,89,8,7 101 | 89,229,10.3,90,8,8 102 | 110,207,8,90,8,9 103 | NA,222,8.6,92,8,10 104 | NA,137,11.5,86,8,11 105 | 44,192,11.5,86,8,12 106 | 28,273,11.5,82,8,13 107 | 65,157,9.7,80,8,14 108 | NA,64,11.5,79,8,15 109 | 22,71,10.3,77,8,16 110 | 59,51,6.3,79,8,17 111 | 23,115,7.4,76,8,18 112 | 31,244,10.9,78,8,19 113 | 44,190,10.3,78,8,20 114 | 21,259,15.5,77,8,21 115 | 9,36,14.3,72,8,22 116 | NA,255,12.6,75,8,23 117 | 45,212,9.7,79,8,24 118 | 168,238,3.4,81,8,25 119 | 73,215,8,86,8,26 120 | NA,153,5.7,88,8,27 121 | 76,203,9.7,97,8,28 122 | 118,225,2.3,94,8,29 123 | 84,237,6.3,96,8,30 124 | 85,188,6.3,94,8,31 125 | 96,167,6.9,91,9,1 126 | 78,197,5.1,92,9,2 127 | 73,183,2.8,93,9,3 128 | 91,189,4.6,93,9,4 129 | 47,95,7.4,87,9,5 130 | 32,92,15.5,84,9,6 131 | 20,252,10.9,80,9,7 132 | 23,220,10.3,78,9,8 133 | 21,230,10.9,75,9,9 134 | 24,259,9.7,73,9,10 135 | 44,236,14.9,81,9,11 136 | 21,259,15.5,76,9,12 137 | 28,238,6.3,77,9,13 138 | 9,24,10.9,71,9,14 139 | 13,112,11.5,71,9,15 140 | 46,237,6.9,78,9,16 141 | 18,224,13.8,67,9,17 142 | 13,27,10.3,76,9,18 143 | 24,238,10.3,68,9,19 144 | 16,201,8,82,9,20 145 | 13,238,12.6,64,9,21 146 | 23,14,9.2,71,9,22 147 | 36,139,10.3,81,9,23 148 | 7,49,10.3,69,9,24 149 | 14,20,16.6,63,9,25 150 | 30,193,6.9,70,9,26 151 | NA,145,13.2,77,9,27 152 | 14,191,14.3,75,9,28 153 | 18,131,8,76,9,29 154 | 20,223,11.5,68,9,30 155 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/concat.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Cleaning_Data_in_Python/concat.PNG -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/concat2.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Cleaning_Data_in_Python/concat2.PNG -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/ebola.csv: -------------------------------------------------------------------------------- 1 | Date,Day,Cases_Guinea,Cases_Liberia,Cases_SierraLeone,Cases_Nigeria,Cases_Senegal,Cases_UnitedStates,Cases_Spain,Cases_Mali,Deaths_Guinea,Deaths_Liberia,Deaths_SierraLeone,Deaths_Nigeria,Deaths_Senegal,Deaths_UnitedStates,Deaths_Spain,Deaths_Mali 2 | 1/5/2015,289,2776,,10030,,,,,,1786,,2977,,,,, 3 | 1/4/2015,288,2775,,9780,,,,,,1781,,2943,,,,, 4 | 1/3/2015,287,2769,8166,9722,,,,,,1767,3496,2915,,,,, 5 | 1/2/2015,286,,8157,,,,,,,,3496,,,,,, 6 | 12/31/2014,284,2730,8115,9633,,,,,,1739,3471,2827,,,,, 7 | 12/28/2014,281,2706,8018,9446,,,,,,1708,3423,2758,,,,, 8 | 12/27/2014,280,2695,,9409,,,,,,1697,,2732,,,,, 9 | 12/24/2014,277,2630,7977,9203,,,,,,,3413,2655,,,,, 10 | 12/21/2014,273,2597,,9004,,,,,,1607,,2582,,,,, 11 | 12/20/2014,272,2571,7862,8939,,,,,,1586,3384,2556,,,,, 12 | 12/18/2014,271,,7830,,,,,,,,3376,,,,,, 13 | 12/14/2014,267,2416,,8356,,,,,,1525,,2085,,,,, 14 | 12/9/2014,262,,7797,,,,,,,,3290,,,,,, 15 | 12/7/2014,260,2292,,7897,20,1,4,1,7,1428,,1768,8,0,1,0,6 16 | 12/3/2014,256,,7719,,,,,,,,3177,,,,,, 17 | 11/30/2014,253,2164,,7312,20,1,4,1,7,1327,,1583,8,0,1,0,6 18 | 11/28/2014,251,,7635,,,,,,,,3145,,,,,, 19 | 11/23/2014,246,2134,,6599,20,1,4,1,7,1260,,1398,8,0,1,0,6 20 | 11/22/2014,245,,7168,,,,,,,,3016,,,,,, 21 | 11/18/2014,241,2047,7082,6190,20,1,4,1,6,1214,2963,1267,8,0,1,0,6 22 | 11/16/2014,239,1971,,6073,20,1,4,1,5,1192,,1250,8,0,1,0,5 23 | 11/15/2014,238,,7069,,,,,,,,2964,,,,,, 24 | 11/11/2014,234,1919,,5586,20,1,4,1,4,1166,,1187,8,0,1,0,3 25 | 11/10/2014,233,,6878,,,,,,,,2812,,,,,, 26 | 11/9/2014,232,1878,,5368,20,1,4,1,1,1142,,1169,8,0,1,0,1 27 | 11/8/2014,231,,6822,,,,,,,,2836,,,,,, 28 | 11/4/2014,227,,6619,4862,20,1,4,1,1,,2766,1130,8,0,1,0,1 29 | 11/3/2014,226,1760,,,,,,,,1054,,,,,,, 30 | 11/2/2014,225,1731,,4759,20,1,4,1,1,1041,,1070,8,0,1,0,1 31 | 10/31/2014,222,,6525,,,,,,,,2697,,,,,, 32 | 10/29/2014,220,1667,,5338,20,1,4,1,1,1018,,1510,8,0,1,0,1 33 | 10/27/2014,218,1906,,5235,20,1,4,1,1,997,,1500,8,0,1,0,1 34 | 10/25/2014,216,,6535,,,,,,,,2413,,,,,, 35 | 10/22/2014,214,,,3896,,,4,1,1,,,1281,,,1,0,1 36 | 10/21/2014,213,1553,,,,,,,,926,,,,,,, 37 | 10/19/2014,211,1540,,3706,20,1,3,1,,904,,1259,8,0,1,0, 38 | 10/18/2014,210,,4665,,,,,,,,2705,,,,,, 39 | 10/14/2014,206,1519,,3410,20,1,3,1,,862,,1200,8,0,0,1, 40 | 10/13/2014,205,,4262,,,,,,,,2484,,,,,, 41 | 10/12/2014,204,1472,,3252,20,1,2,1,,843,,1183,8,0,1,1, 42 | 10/11/2014,203,,4249,,,,,,,,2458,,,,,, 43 | 10/8/2014,200,,,2950,20,1,1,1,,,,930,8,0,1,1, 44 | 10/7/2014,199,1350,4076,,,,,,,778,2316,,,,,, 45 | 10/5/2014,197,1298,,2789,20,1,1,,,768,,879,8,0,0,, 46 | 10/4/2014,196,,3924,,,,,,,,2210,,,,,, 47 | 10/1/2014,193,1199,3834,2437,20,1,1,,,739,2069,623,8,0,0,, 48 | 9/28/2014,190,1157,3696,2304,20,1,,,,710,1998,622,8,0,,, 49 | 9/23/2014,185,1074,3458,2021,20,1,,,,648,1830,605,8,0,,, 50 | 9/21/2014,183,1022,3280,1940,20,1,,,,635,1677,597,8,0,,, 51 | 9/20/2014,182,,,1813,,,,,,,,593,,,,, 52 | 9/19/2014,181,1008,,,,,,,,632,,,,,,, 53 | 9/17/2014,179,,3022,,,,,,,,1578,,,,,, 54 | 9/14/2014,176,942,2710,1673,,,,,,601,1459,562,,,,, 55 | 9/13/2014,175,936,,1620,21,1,,,,595,1296,562,8,0,,, 56 | 9/10/2014,172,899,,1478,21,1,,,,568,,536,8,,,, 57 | 9/9/2014,171,,2407,,,,,,,,,,,,,, 58 | 9/7/2014,169,861,2081,1424,21,3,,,,557,1137,524,8,0,,, 59 | 9/5/2014,167,812,1871,1261,22,1,,,,517,1089,491,8,,,, 60 | 8/31/2014,162,771,1698,1216,21,1,,,,494,871,476,7,,,, 61 | 8/26/2014,157,648,1378,1026,17,,,,,430,694,422,6,,,, 62 | 8/20/2014,151,607,1082,910,16,,,,,406,624,392,5,,,, 63 | 8/18/2014,149,579,972,907,15,,,,,396,576,374,4,,,, 64 | 8/16/2014,147,543,834,848,15,,,,,394,466,365,4,,,, 65 | 8/13/2014,144,519,786,810,12,,,,,380,413,348,4,,,, 66 | 8/11/2014,142,510,670,783,12,,,,,377,355,334,3,,,, 67 | 8/9/2014,140,506,599,730,13,,,,,373,323,315,2,,,, 68 | 8/6/2014,137,495,554,717,13,,,,,367,294,298,2,,,, 69 | 8/4/2014,135,495,516,691,9,,,,,363,282,286,1,,,, 70 | 8/1/2014,132,485,468,646,4,,,,,358,255,273,1,,,, 71 | 7/30/2014,129,472,391,574,3,,,,,346,227,252,1,,,, 72 | 7/27/2014,126,460,329,533,1,,,,,339,156,233,1,,,, 73 | 7/23/2014,123,427,249,525,0,,,,,319,129,224,0,,,, 74 | 7/20/2014,120,415,224,454,,,,,,314,127,219,,,,, 75 | 7/17/2014,117,410,196,442,,,,,,310,116,206,,,,, 76 | 7/14/2014,114,411,174,397,,,,,,310,106,197,,,,, 77 | 7/12/2014,112,406,172,386,,,,,,304,105,194,,,,, 78 | 7/8/2014,108,409,142,337,,,,,,309,88,142,,,,, 79 | 7/6/2014,106,408,131,305,,,,,,307,84,127,,,,, 80 | 7/2/2014,102,412,115,252,,,,,,305,75,101,,,,, 81 | 6/30/2014,100,413,107,239,,,,,,303,65,99,,,,, 82 | 6/22/2014,92,,51,,,,,,,,34,,,,,, 83 | 6/20/2014,90,390,,158,,,,,,270,,34,,,,, 84 | 6/19/2014,89,,41,,,,,,,,25,,,,,, 85 | 6/18/2014,88,390,,136,,,,,,267,,28,,,,, 86 | 6/17/2014,87,,,97,,,,,,,,49,,,,, 87 | 6/16/2014,86,398,33,,,,,,,264,24,,,,,, 88 | 6/10/2014,80,351,13,89,,,,,,226,24,7,,,,, 89 | 6/5/2014,75,,13,81,,,,,,,,6,,,,, 90 | 6/3/2014,73,344,13,,,,,,,215,12,6,,,,, 91 | 6/1/2014,71,328,13,79,,,,,,208,12,6,,,,, 92 | 5/28/2014,67,291,13,50,,,,,,193,12,6,,,,, 93 | 5/27/2014,66,281,12,16,,,,,,186,11,5,,,,, 94 | 5/23/2014,62,258,12,0,,,,,,174,11,0,,,,, 95 | 5/12/2014,51,248,12,0,,,,,,171,11,0,,,,, 96 | 5/10/2014,49,233,12,0,,,,,,157,11,0,,,,, 97 | 5/7/2014,46,236,13,0,,,,,,158,11,0,,,,, 98 | 5/5/2014,44,235,13,0,,,,,,157,11,0,,,,, 99 | 5/3/2014,42,231,13,0,,,,,,155,11,0,,,,, 100 | 5/1/2014,40,226,13,0,,,,,,149,11,0,,,,, 101 | 4/26/2014,35,224,,0,,,,,,143,,0,,,,, 102 | 4/24/2014,33,,35,0,,,,,,,,0,,,,, 103 | 4/23/2014,32,218,,0,,,,,,141,,0,,,,, 104 | 4/22/2014,31,,,0,,,,,,,,0,,,,, 105 | 4/21/2014,30,,34,,,,,,,,11,,,,,, 106 | 4/20/2014,29,208,,,,,,,,136,6,,,,,, 107 | 4/17/2014,26,203,27,,,,,,,129,,,,,,, 108 | 4/16/2014,25,197,27,,,,,,,122,13,,,,,, 109 | 4/15/2014,24,,,12,,,,,,,,,,,,, 110 | 4/14/2014,23,168,,,,,,,,108,,,,,,, 111 | 4/11/2014,20,159,26,2,,,,,,106,13,2,,,,, 112 | 4/9/2014,18,158,25,2,,,,,,101,12,2,,,,, 113 | 4/7/2014,16,151,21,2,,,,,,95,10,2,,,,, 114 | 4/4/2014,13,143,18,2,,,,,,86,7,2,,,,, 115 | 4/1/2014,10,127,8,2,,,,,,83,5,2,,,,, 116 | 3/31/2014,9,122,8,2,,,,,,80,4,2,,,,, 117 | 3/29/2014,7,112,7,,,,,,,70,2,,,,,, 118 | 3/28/2014,6,112,3,2,,,,,,70,3,2,,,,, 119 | 3/27/2014,5,103,8,6,,,,,,66,6,5,,,,, 120 | 3/26/2014,4,86,,,,,,,,62,,,,,,, 121 | 3/25/2014,3,86,,,,,,,,60,,,,,,, 122 | 3/24/2014,2,86,,,,,,,,59,,,,,,, 123 | 3/22/2014,0,49,,,,,,,,29,,,,,,, -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/literacy_birth_rate.csv: -------------------------------------------------------------------------------- 1 | continent,country,female literacy,fertility,population 2 | ASI,Chine,90.5,1.769,1324655000 3 | ASI,Inde,50.8,2.682,1139964932 4 | NAM,USA,99,2.077,304060000 5 | ASI,Indonésie,88.8,2.132,227345082 6 | LAT,Brésil,90.2,1.827, 7 | ASI,Pakistan,40,3.872,166111487 8 | ASI,Bangladesh,49.8,2.288,160000128 9 | AF,Nigéria,48.8,5.173,151212254 10 | EUR,Fédération de Russie,99.4,1.393,141950000 11 | ASI,Japan,99,1.262,127704000 12 | LAT,Mexique,91.5,2.156,106350434 13 | ASI,Philippines,93.9,3.026,90348437 14 | ASI,Viet Nam,90.2,2.033,86210781 15 | EUR,Germany,99,1.324,82110097 16 | AF,Egypte,57.8,2.816,81527172 17 | AF,Ethiopie,22.8,5.211,80713434 18 | ASI,Turquie,81.3,2.1,73914260 19 | ASI,"Iran, République islamique d'",77.2,1.781,71956322 20 | ASI,Thaïlande,91.5,1.822,67386383 21 | AF,Rép. Démocratique du Congo,56.1,5.908,64256635 22 | EUR,France,99,1.881,62277432 23 | EUR,UK,99,1.852,61414062 24 | EUR,Italie,98.5,1.39,59832179 25 | ASI,Myanmar,89.2,2.281,49563019 26 | AF,Afrique du Sud,88.1,2.505,48687000 27 | ASI,South Korea,96.6,1.224,48607000 28 | EUR,Ukraine,99.6,1.361,46258200 29 | EUR,Espagne,96.9,1.468,45555716 30 | LAT,Colombie,93.4,2.404,45012096 31 | AF,République-Unie de Tanzanie,66.3,5.52,42483923 32 | AF,Soudan,59.6,4.058,41347723 33 | LAT,Argentine,97.7,2.223,39882980 34 | AF,Kenya,82.8,4.859,38765312 35 | EUR,Pologne,99.3,1.267,38125759 36 | AF,Algérie,63.9,2.342,34373426 37 | NAM,Canada,99,1.579,33311400 38 | AF,Ouganda,66.8,6.254,31656865 39 | AF,Maroc,44.1,2.334,31605616 40 | ASI,Iraq,69.2,3.961,30711152 41 | ASI,Afghanistan,12.6,6.505,29021099 42 | LAT,Pérou,84.6,2.53,28836700 43 | ASI,Népal,45.4,2.823,28809526 44 | LAT,Venezuela,94.9,2.498,27935000 45 | ASI,Ouzbékistan,98.9,2.248,27313700 46 | ASI,Malaisie,89.8,2.508,27014337 47 | ASI,Arabie saoudite,80.2,3.04,24645686 48 | ASI,Rép. populaire démocratique de Corée,100,1.854,23818753 49 | AF,Ghana,59.3,4.22,23350927 50 | ASI,Yémen,42.8,5.1,22917485 51 | AF,Mozambique,40.1,4.967,22382533 52 | EUR,Roumanie,96.9,1.325,21513622 53 | AF,Côte d'Ivoire,44.3,4.514,20591302 54 | ASI,République arabe syrienne,77.2,3.173,20581290 55 | ASI,Sri Lanka,89.1,2.308,20156204 56 | AF,Madagascar,65.3,4.62,19110941 57 | AF,Cameroun,67.8,4.541,19088385 58 | AF,Angola,57,5.637,18020668 59 | LAT,Chili,98.7,1.926,16803952 60 | EUR,Netherlands,99,1.747,16445593 61 | ASI,Kazakhstan,99.5,2.294,15674833 62 | AF,Burkina Faso,21.6,5.841,15233884 63 | AF,Malawi,65.8,5.455,14846182 64 | AF,Niger,15.1,7.069,14704318 65 | ASI,Cambodge,70.9,2.859,14562008 66 | LAT,Guatemala,68.7,4.018,13686128 67 | LAT,Equateur,81.7,2.513,13481424 68 | AF,Mali,18.2,5.405,12705736 69 | AF,Zambie,61,5.737,12620219 70 | AF,Zimbabwe,88.8,3.363,12462879 71 | AF,Sénégal,33,4.89,12211181 72 | EUR,Grèce,95.9,1.385,11237094 73 | LAT,Cuba,99.8,1.505,11204735 74 | AF,Tchad,21.9,6.081,10913667 75 | EUR,Belgium,99,1.784,10708433 76 | EUR,Portugal,92.9,1.378,10622413 77 | EUR,Czech rep,99,1.45,10424336 78 | AF,Tunisie,71,1.841,10327800 79 | EUR,Hongrie,98.9,1.37,10038188 80 | LAT,République dominicaine,88.3,2.612,9952711 81 | AF,Guinée,26.4,5.329,9833055 82 | AF,Rwanda,66.1,5.33,9720694 83 | LAT,Bolivie,86,3.371,9694113 84 | EUR,Bélarus,99.7,1.281,9680850 85 | EUR,Sweden,99,1.871,9219637 86 | ASI,Azerbaïdjan,99.2,2.153,8680100 87 | AF,Bénin,28.1,5.378,8662086 88 | AF,Burundi,59.9,4.45,8074254 89 | EUR,Switzerland,99,1.46,7647675 90 | EUR,Bulgarie,97.9,1.436,7623395 91 | EUR,Serbie,96.2,1.612,7350221 92 | LAT,Honduras,83.5,3.19,7318789 93 | ASI,Israel,95.9,2.752,7308800 94 | ASI,Tadjikistan,99.5,3.35,6836083 95 | OCE,Papouasie-Nouvelle-Guinée,55.6,4.01,6576822 96 | AF,Togo,53.7,4.166,6458605 97 | AF,Jamahiriya arabe libyenne,81.3,2.642,6294181 98 | LAT,Paraguay,93.5,2.977,6237855 99 | ASI,Rép. démocratique populaire lao,63.2,3.415,6205341 100 | LAT,El Salvador,81.4,2.295,6133910 101 | ASI,Jordanie,88.9,3.019,5906043 102 | LAT,Nicaragua,77.9,2.683,5667325 103 | AF,Sierra Leone,28.9,5.165,5559853 104 | EUR,Denmark,99,1.849,5493621 105 | EUR,Finland,100,1.836,5313399 106 | ASI,Kirghizistan,99.1,2.518,5277900 107 | ASI,Turkménistan,99.3,2.43,5043618 108 | AF,Erythrée,54.5,4.528,4926877 109 | ASI,Singapour,91.6,1.263,4839400 110 | EUR,Norway,100,1.885,4768212 111 | LAT,Costa Rica,96.2,1.943,4519126 112 | ASI,Emirats arabes unis,91.5,1.899,4484935 113 | EUR,Croatie,98,1.442,4434000 114 | EUR,Ireland,99,1.953,4425675 115 | AF,République centrafricaine,41.1,4.697,4339263 116 | ASI,Géorgie,99.7,1.582,4307011 117 | OCE,New Zealand,99,2.025,4268900 118 | ASI,Liban,86,1.841,4193758 119 | AF,Libéria,53,5.011,3793400 120 | EUR,Bosnie-Herzégovine,95.9,1.212,3773100 121 | EUR,République de Moldova,97.8,1.502,3633369 122 | LAT,Panama,92.8,2.516,3398823 123 | EUR,Lituanie,99.7,1.367,3358115 124 | LAT,Uruguay,98.5,2.089,3334052 125 | AF,Mauritanie,49.5,4.388,3215043 126 | EUR,Albanie,98.7,1.854,3143291 127 | ASI,Arménie,99.4,1.748,3077087 128 | ASI,Oman,80.9,2.978,2785361 129 | ASI,Koweït,93.1,2.152,2728041 130 | LAT,Jamaïque,90.8,2.362,2687200 131 | ASI,Mongolie,97.8,1.988,2641216 132 | EUR,Lettonie,99.8,1.426,2266094 133 | AF,Namibie,87.7,3.29,2129854 134 | AF,Lesotho,95.1,3.264,2049429 135 | EUR,L'ex-Rép. yougoslave de Macédoine,95.4,1.436,2041342 136 | EUR,Slovénie,99.7,1.393,2021316 137 | AF,Botswana,83.5,2.822,1921122 138 | AF,Gambie,34.3,4.969,1660200 139 | AF,Guinée-Bissau,36.5,5.659,1575446 140 | AF,Gabon,83.2,3.24,1448159 141 | EUR,Estonie,99.8,1.693,1340675 142 | LAT,Trinité-et-Tobago,98.2,1.647,1333388 143 | ASI,Qatar,90.4,2.36,1280862 144 | AF,Maurice,84.8,1.792,1268854 145 | AF,Swaziland,85.6,3.45,1167834 146 | EUR,Chypre,96.7,1.516,862434 147 | ASI,Bahreïn,89.4,2.233,775585 148 | ASI,Bhoutan,38.7,2.563,686789 149 | AF,Guinée équatoriale,89.1,5.283,659197 150 | AF,Comores,67.8,3.885,643571 151 | ASI,"Macao, Chine",90.7,0.966,526178 152 | LAT,Suriname,88.4,2.373,515124 153 | AF,Cap-Vert,79.3,2.663,498672 154 | EUR,Malte,93.5,1.251,411950 155 | ASI,Brunéi Darussalam,93.3,2.052,392280 156 | LAT,Bahamas,96.5,3.371,337668 157 | EUR,Iceland,99,2.093,317414 158 | ASI,Maldives,98.4,2,305027 159 | OCE,Vanuatu,79.5,3.883,233866 160 | OCE,Samoa,98.5,3.852,178869 161 | AF,Sao Tomé-et-Principe,83.3,3.718,160174 162 | LAT,Aruba,98,1.732,105455 163 | ASI,Tonga,99.1,3.928,103566 164 | OCE,Australia,96,1.93,2313000000 165 | OCE,Sweden,90.2,1.827, 166 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/mp_data.csv: -------------------------------------------------------------------------------- 1 | country,year,m014,m1524 2 | AD,2000,0,0 3 | AE,2000,2,4 4 | AF,2000,52,228 5 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/re1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Cleaning_Data_in_Python/re1.PNG -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/state_cod.csv: -------------------------------------------------------------------------------- 1 | name,ANSI 2 | California,CA 3 | Florida,FL 4 | New York,NY 5 | Texas,TX 6 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/state_pop.csv: -------------------------------------------------------------------------------- 1 | state,population_2016 2 | California,39250017 3 | Texas,27862596 4 | Florida,20612439 5 | New York,19745289 6 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/tiddy.csv: -------------------------------------------------------------------------------- 1 | name,treatment a,treatment b 2 | Daniel,-,42 3 | Jhon,12,31 4 | Jane,24,27 5 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/tiddy_done.csv: -------------------------------------------------------------------------------- 1 | name,treatment a,treatment b 2 | Daniel,18,42 3 | Jhon,12,31 4 | Jane,24,27 5 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/tips.csv: -------------------------------------------------------------------------------- 1 | total_bill,tip,sex,smoker,day,time,size 2 | 16.99,1.01,Female,No,Sun,Dinner,2 3 | 10.34,1.66,Male,No,Sun,Dinner,3 4 | 21.01,3.5,Male,No,Sun,Dinner,3 5 | 23.68,3.31,Male,No,Sun,Dinner,2 6 | 24.59,3.61,Female,No,Sun,Dinner,4 7 | 25.29,4.71,Male,No,Sun,Dinner,4 8 | 8.77,2.0,Male,No,Sun,Dinner,2 9 | 26.88,3.12,Male,No,Sun,Dinner,4 10 | 15.04,1.96,Male,No,Sun,Dinner,2 11 | 14.78,3.23,Male,No,Sun,Dinner,2 12 | 10.27,1.71,Male,No,Sun,Dinner,2 13 | 35.26,5.0,Female,No,Sun,Dinner,4 14 | 15.42,1.57,Male,No,Sun,Dinner,2 15 | 18.43,3.0,Male,No,Sun,Dinner,4 16 | 14.83,3.02,Female,No,Sun,Dinner,2 17 | 21.58,3.92,Male,No,Sun,Dinner,2 18 | 10.33,1.67,Female,No,Sun,Dinner,3 19 | 16.29,3.71,Male,No,Sun,Dinner,3 20 | 16.97,3.5,Female,No,Sun,Dinner,3 21 | 20.65,3.35,Male,No,Sat,Dinner,3 22 | 17.92,4.08,Male,No,Sat,Dinner,2 23 | 20.29,2.75,Female,No,Sat,Dinner,2 24 | 15.77,2.23,Female,No,Sat,Dinner,2 25 | 39.42,7.58,Male,No,Sat,Dinner,4 26 | 19.82,3.18,Male,No,Sat,Dinner,2 27 | 17.81,2.34,Male,No,Sat,Dinner,4 28 | 13.37,2.0,Male,No,Sat,Dinner,2 29 | 12.69,2.0,Male,No,Sat,Dinner,2 30 | 21.7,4.3,Male,No,Sat,Dinner,2 31 | 19.65,3.0,Female,No,Sat,Dinner,2 32 | 9.55,1.45,Male,No,Sat,Dinner,2 33 | 18.35,2.5,Male,No,Sat,Dinner,4 34 | 15.06,3.0,Female,No,Sat,Dinner,2 35 | 20.69,2.45,Female,No,Sat,Dinner,4 36 | 17.78,3.27,Male,No,Sat,Dinner,2 37 | 24.06,3.6,Male,No,Sat,Dinner,3 38 | 16.31,2.0,Male,No,Sat,Dinner,3 39 | 16.93,3.07,Female,No,Sat,Dinner,3 40 | 18.69,2.31,Male,No,Sat,Dinner,3 41 | 31.27,5.0,Male,No,Sat,Dinner,3 42 | 16.04,2.24,Male,No,Sat,Dinner,3 43 | 17.46,2.54,Male,No,Sun,Dinner,2 44 | 13.94,3.06,Male,No,Sun,Dinner,2 45 | 9.68,1.32,Male,No,Sun,Dinner,2 46 | 30.4,5.6,Male,No,Sun,Dinner,4 47 | 18.29,3.0,Male,No,Sun,Dinner,2 48 | 22.23,5.0,Male,No,Sun,Dinner,2 49 | 32.4,6.0,Male,No,Sun,Dinner,4 50 | 28.55,2.05,Male,No,Sun,Dinner,3 51 | 18.04,3.0,Male,No,Sun,Dinner,2 52 | 12.54,2.5,Male,No,Sun,Dinner,2 53 | 10.29,2.6,Female,No,Sun,Dinner,2 54 | 34.81,5.2,Female,No,Sun,Dinner,4 55 | 9.94,1.56,Male,No,Sun,Dinner,2 56 | 25.56,4.34,Male,No,Sun,Dinner,4 57 | 19.49,3.51,Male,No,Sun,Dinner,2 58 | 38.01,3.0,Male,Yes,Sat,Dinner,4 59 | 26.41,1.5,Female,No,Sat,Dinner,2 60 | 11.24,1.76,Male,Yes,Sat,Dinner,2 61 | 48.27,6.73,Male,No,Sat,Dinner,4 62 | 20.29,3.21,Male,Yes,Sat,Dinner,2 63 | 13.81,2.0,Male,Yes,Sat,Dinner,2 64 | 11.02,1.98,Male,Yes,Sat,Dinner,2 65 | 18.29,3.76,Male,Yes,Sat,Dinner,4 66 | 17.59,2.64,Male,No,Sat,Dinner,3 67 | 20.08,3.15,Male,No,Sat,Dinner,3 68 | 16.45,2.47,Female,No,Sat,Dinner,2 69 | 3.07,1.0,Female,Yes,Sat,Dinner,1 70 | 20.23,2.01,Male,No,Sat,Dinner,2 71 | 15.01,2.09,Male,Yes,Sat,Dinner,2 72 | 12.02,1.97,Male,No,Sat,Dinner,2 73 | 17.07,3.0,Female,No,Sat,Dinner,3 74 | 26.86,3.14,Female,Yes,Sat,Dinner,2 75 | 25.28,5.0,Female,Yes,Sat,Dinner,2 76 | 14.73,2.2,Female,No,Sat,Dinner,2 77 | 10.51,1.25,Male,No,Sat,Dinner,2 78 | 17.92,3.08,Male,Yes,Sat,Dinner,2 79 | 27.2,4.0,Male,No,Thur,Lunch,4 80 | 22.76,3.0,Male,No,Thur,Lunch,2 81 | 17.29,2.71,Male,No,Thur,Lunch,2 82 | 19.44,3.0,Male,Yes,Thur,Lunch,2 83 | 16.66,3.4,Male,No,Thur,Lunch,2 84 | 10.07,1.83,Female,No,Thur,Lunch,1 85 | 32.68,5.0,Male,Yes,Thur,Lunch,2 86 | 15.98,2.03,Male,No,Thur,Lunch,2 87 | 34.83,5.17,Female,No,Thur,Lunch,4 88 | 13.03,2.0,Male,No,Thur,Lunch,2 89 | 18.28,4.0,Male,No,Thur,Lunch,2 90 | 24.71,5.85,Male,No,Thur,Lunch,2 91 | 21.16,3.0,Male,No,Thur,Lunch,2 92 | 28.97,3.0,Male,Yes,Fri,Dinner,2 93 | 22.49,3.5,Male,No,Fri,Dinner,2 94 | 5.75,1.0,Female,Yes,Fri,Dinner,2 95 | 16.32,4.3,Female,Yes,Fri,Dinner,2 96 | 22.75,3.25,Female,No,Fri,Dinner,2 97 | 40.17,4.73,Male,Yes,Fri,Dinner,4 98 | 27.28,4.0,Male,Yes,Fri,Dinner,2 99 | 12.03,1.5,Male,Yes,Fri,Dinner,2 100 | 21.01,3.0,Male,Yes,Fri,Dinner,2 101 | 12.46,1.5,Male,No,Fri,Dinner,2 102 | 11.35,2.5,Female,Yes,Fri,Dinner,2 103 | 15.38,3.0,Female,Yes,Fri,Dinner,2 104 | 44.3,2.5,Female,Yes,Sat,Dinner,3 105 | 22.42,3.48,Female,Yes,Sat,Dinner,2 106 | 20.92,4.08,Female,No,Sat,Dinner,2 107 | 15.36,1.64,Male,Yes,Sat,Dinner,2 108 | 20.49,4.06,Male,Yes,Sat,Dinner,2 109 | 25.21,4.29,Male,Yes,Sat,Dinner,2 110 | 18.24,3.76,Male,No,Sat,Dinner,2 111 | 14.31,4.0,Female,Yes,Sat,Dinner,2 112 | 14.0,3.0,Male,No,Sat,Dinner,2 113 | 7.25,1.0,Female,No,Sat,Dinner,1 114 | 38.07,4.0,Male,No,Sun,Dinner,3 115 | 23.95,2.55,Male,No,Sun,Dinner,2 116 | 25.71,4.0,Female,No,Sun,Dinner,3 117 | 17.31,3.5,Female,No,Sun,Dinner,2 118 | 29.93,5.07,Male,No,Sun,Dinner,4 119 | 10.65,1.5,Female,No,Thur,Lunch,2 120 | 12.43,1.8,Female,No,Thur,Lunch,2 121 | 24.08,2.92,Female,No,Thur,Lunch,4 122 | 11.69,2.31,Male,No,Thur,Lunch,2 123 | 13.42,1.68,Female,No,Thur,Lunch,2 124 | 14.26,2.5,Male,No,Thur,Lunch,2 125 | 15.95,2.0,Male,No,Thur,Lunch,2 126 | 12.48,2.52,Female,No,Thur,Lunch,2 127 | 29.8,4.2,Female,No,Thur,Lunch,6 128 | 8.52,1.48,Male,No,Thur,Lunch,2 129 | 14.52,2.0,Female,No,Thur,Lunch,2 130 | 11.38,2.0,Female,No,Thur,Lunch,2 131 | 22.82,2.18,Male,No,Thur,Lunch,3 132 | 19.08,1.5,Male,No,Thur,Lunch,2 133 | 20.27,2.83,Female,No,Thur,Lunch,2 134 | 11.17,1.5,Female,No,Thur,Lunch,2 135 | 12.26,2.0,Female,No,Thur,Lunch,2 136 | 18.26,3.25,Female,No,Thur,Lunch,2 137 | 8.51,1.25,Female,No,Thur,Lunch,2 138 | 10.33,2.0,Female,No,Thur,Lunch,2 139 | 14.15,2.0,Female,No,Thur,Lunch,2 140 | 16.0,2.0,Male,Yes,Thur,Lunch,2 141 | 13.16,2.75,Female,No,Thur,Lunch,2 142 | 17.47,3.5,Female,No,Thur,Lunch,2 143 | 34.3,6.7,Male,No,Thur,Lunch,6 144 | 41.19,5.0,Male,No,Thur,Lunch,5 145 | 27.05,5.0,Female,No,Thur,Lunch,6 146 | 16.43,2.3,Female,No,Thur,Lunch,2 147 | 8.35,1.5,Female,No,Thur,Lunch,2 148 | 18.64,1.36,Female,No,Thur,Lunch,3 149 | 11.87,1.63,Female,No,Thur,Lunch,2 150 | 9.78,1.73,Male,No,Thur,Lunch,2 151 | 7.51,2.0,Male,No,Thur,Lunch,2 152 | 14.07,2.5,Male,No,Sun,Dinner,2 153 | 13.13,2.0,Male,No,Sun,Dinner,2 154 | 17.26,2.74,Male,No,Sun,Dinner,3 155 | 24.55,2.0,Male,No,Sun,Dinner,4 156 | 19.77,2.0,Male,No,Sun,Dinner,4 157 | 29.85,5.14,Female,No,Sun,Dinner,5 158 | 48.17,5.0,Male,No,Sun,Dinner,6 159 | 25.0,3.75,Female,No,Sun,Dinner,4 160 | 13.39,2.61,Female,No,Sun,Dinner,2 161 | 16.49,2.0,Male,No,Sun,Dinner,4 162 | 21.5,3.5,Male,No,Sun,Dinner,4 163 | 12.66,2.5,Male,No,Sun,Dinner,2 164 | 16.21,2.0,Female,No,Sun,Dinner,3 165 | 13.81,2.0,Male,No,Sun,Dinner,2 166 | 17.51,3.0,Female,Yes,Sun,Dinner,2 167 | 24.52,3.48,Male,No,Sun,Dinner,3 168 | 20.76,2.24,Male,No,Sun,Dinner,2 169 | 31.71,4.5,Male,No,Sun,Dinner,4 170 | 10.59,1.61,Female,Yes,Sat,Dinner,2 171 | 10.63,2.0,Female,Yes,Sat,Dinner,2 172 | 50.81,10.0,Male,Yes,Sat,Dinner,3 173 | 15.81,3.16,Male,Yes,Sat,Dinner,2 174 | 7.25,5.15,Male,Yes,Sun,Dinner,2 175 | 31.85,3.18,Male,Yes,Sun,Dinner,2 176 | 16.82,4.0,Male,Yes,Sun,Dinner,2 177 | 32.9,3.11,Male,Yes,Sun,Dinner,2 178 | 17.89,2.0,Male,Yes,Sun,Dinner,2 179 | 14.48,2.0,Male,Yes,Sun,Dinner,2 180 | 9.6,4.0,Female,Yes,Sun,Dinner,2 181 | 34.63,3.55,Male,Yes,Sun,Dinner,2 182 | 34.65,3.68,Male,Yes,Sun,Dinner,4 183 | 23.33,5.65,Male,Yes,Sun,Dinner,2 184 | 45.35,3.5,Male,Yes,Sun,Dinner,3 185 | 23.17,6.5,Male,Yes,Sun,Dinner,4 186 | 40.55,3.0,Male,Yes,Sun,Dinner,2 187 | 20.69,5.0,Male,No,Sun,Dinner,5 188 | 20.9,3.5,Female,Yes,Sun,Dinner,3 189 | 30.46,2.0,Male,Yes,Sun,Dinner,5 190 | 18.15,3.5,Female,Yes,Sun,Dinner,3 191 | 23.1,4.0,Male,Yes,Sun,Dinner,3 192 | 15.69,1.5,Male,Yes,Sun,Dinner,2 193 | 19.81,4.19,Female,Yes,Thur,Lunch,2 194 | 28.44,2.56,Male,Yes,Thur,Lunch,2 195 | 15.48,2.02,Male,Yes,Thur,Lunch,2 196 | 16.58,4.0,Male,Yes,Thur,Lunch,2 197 | 7.56,1.44,Male,No,Thur,Lunch,2 198 | 10.34,2.0,Male,Yes,Thur,Lunch,2 199 | 43.11,5.0,Female,Yes,Thur,Lunch,4 200 | 13.0,2.0,Female,Yes,Thur,Lunch,2 201 | 13.51,2.0,Male,Yes,Thur,Lunch,2 202 | 18.71,4.0,Male,Yes,Thur,Lunch,3 203 | 12.74,2.01,Female,Yes,Thur,Lunch,2 204 | 13.0,2.0,Female,Yes,Thur,Lunch,2 205 | 16.4,2.5,Female,Yes,Thur,Lunch,2 206 | 20.53,4.0,Male,Yes,Thur,Lunch,4 207 | 16.47,3.23,Female,Yes,Thur,Lunch,3 208 | 26.59,3.41,Male,Yes,Sat,Dinner,3 209 | 38.73,3.0,Male,Yes,Sat,Dinner,4 210 | 24.27,2.03,Male,Yes,Sat,Dinner,2 211 | 12.76,2.23,Female,Yes,Sat,Dinner,2 212 | 30.06,2.0,Male,Yes,Sat,Dinner,3 213 | 25.89,5.16,Male,Yes,Sat,Dinner,4 214 | 48.33,9.0,Male,No,Sat,Dinner,4 215 | 13.27,2.5,Female,Yes,Sat,Dinner,2 216 | 28.17,6.5,Female,Yes,Sat,Dinner,3 217 | 12.9,1.1,Female,Yes,Sat,Dinner,2 218 | 28.15,3.0,Male,Yes,Sat,Dinner,5 219 | 11.59,1.5,Male,Yes,Sat,Dinner,2 220 | 7.74,1.44,Male,Yes,Sat,Dinner,2 221 | 30.14,3.09,Female,Yes,Sat,Dinner,4 222 | 12.16,2.2,Male,Yes,Fri,Lunch,2 223 | 13.42,3.48,Female,Yes,Fri,Lunch,2 224 | 8.58,1.92,Male,Yes,Fri,Lunch,1 225 | 15.98,3.0,Female,No,Fri,Lunch,3 226 | 13.42,1.58,Male,Yes,Fri,Lunch,2 227 | 16.27,2.5,Female,Yes,Fri,Lunch,2 228 | 10.09,2.0,Female,Yes,Fri,Lunch,2 229 | 20.45,3.0,Male,No,Sat,Dinner,4 230 | 13.28,2.72,Male,No,Sat,Dinner,2 231 | 22.12,2.88,Female,Yes,Sat,Dinner,2 232 | 24.01,2.0,Male,Yes,Sat,Dinner,4 233 | 15.69,3.0,Male,Yes,Sat,Dinner,3 234 | 11.61,3.39,Male,No,Sat,Dinner,2 235 | 10.77,1.47,Male,No,Sat,Dinner,2 236 | 15.53,3.0,Male,Yes,Sat,Dinner,2 237 | 10.07,1.25,Male,No,Sat,Dinner,2 238 | 12.6,1.0,Male,Yes,Sat,Dinner,2 239 | 32.83,1.17,Male,Yes,Sat,Dinner,2 240 | 35.83,4.67,Female,No,Sat,Dinner,3 241 | 29.03,5.92,Male,No,Sat,Dinner,3 242 | 27.18,2.0,Female,Yes,Sat,Dinner,2 243 | 22.67,2.0,Male,Yes,Sat,Dinner,2 244 | 17.82,1.75,Male,No,Sat,Dinner,2 245 | 18.78,3.0,Female,No,Thur,Dinner,2 246 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/tips_nan.csv: -------------------------------------------------------------------------------- 1 | total_bill,tip,sex,smoker,day,time,size 2 | 16.99,1.01,Female,No,Sun,Dinner,2 3 | ,1.66,Male,No,Sun,Dinner,3 4 | 21.01,3.5,Male,No,Sun,Dinner,3 5 | 23.68,,Male,No,Sun,Dinner,2 6 | 24.59,3.61,,,Sun,,4 7 | 25.29,4.71,Male,No,Sun,Dinner,4 8 | 8.77,2,Male,No,Sun,Dinner,2 9 | 26.88,3.12,Male,No,Sun,Dinner,4 10 | 15.04,1.96,Male,No,Sun,Dinner,2 11 | 14.78,3.23,Male,No,Sun,Dinner,2 12 | 10.27,1.71,Male,No,Sun,Dinner,2 13 | 35.26,5,Female,No,Sun,Dinner,4 14 | 15.42,1.57,Male,No,Sun,Dinner,2 15 | 18.43,3,Male,No,Sun,Dinner,4 16 | 14.83,3.02,Female,No,Sun,Dinner,2 17 | 21.58,3.92,Male,No,Sun,Dinner,2 18 | 10.33,1.67,Female,No,Sun,Dinner,3 19 | 16.29,3.71,Male,No,Sun,Dinner,3 20 | 16.97,3.5,Female,No,Sun,Dinner,3 21 | 20.65,3.35,Male,No,Sat,Dinner,3 22 | 17.92,4.08,Male,No,Sat,Dinner,2 23 | 20.29,2.75,Female,No,Sat,Dinner,2 24 | 15.77,2.23,Female,No,Sat,Dinner,2 25 | 39.42,7.58,Male,No,Sat,Dinner,4 26 | 19.82,3.18,Male,No,Sat,Dinner,2 27 | 17.81,2.34,Male,No,Sat,Dinner,4 28 | 13.37,2,Male,No,Sat,Dinner,2 29 | 12.69,2,Male,No,Sat,Dinner,2 30 | 21.7,4.3,Male,No,Sat,Dinner,2 31 | 19.65,3,Female,No,Sat,Dinner,2 32 | 9.55,1.45,Male,No,Sat,Dinner,2 33 | 18.35,2.5,Male,No,Sat,Dinner,4 34 | 15.06,3,Female,No,Sat,Dinner,2 35 | 20.69,2.45,Female,No,Sat,Dinner,4 36 | 17.78,3.27,Male,No,Sat,Dinner,2 37 | 24.06,3.6,Male,No,Sat,Dinner,3 38 | 16.31,2,Male,No,Sat,Dinner,3 39 | 16.93,3.07,Female,No,Sat,Dinner,3 40 | 18.69,2.31,Male,No,Sat,Dinner,3 41 | 31.27,5,Male,No,Sat,Dinner,3 42 | 16.04,2.24,Male,No,Sat,Dinner,3 43 | 17.46,2.54,Male,No,Sun,Dinner,2 44 | 13.94,3.06,Male,No,Sun,Dinner,2 45 | 9.68,1.32,Male,No,Sun,Dinner,2 46 | 30.4,5.6,Male,No,Sun,Dinner,4 47 | 18.29,3,Male,No,Sun,Dinner,2 48 | 22.23,5,Male,No,Sun,Dinner,2 49 | 32.4,6,Male,No,Sun,Dinner,4 50 | 28.55,2.05,Male,No,Sun,Dinner,3 51 | 18.04,3,Male,No,Sun,Dinner,2 52 | 12.54,2.5,Male,No,Sun,Dinner,2 53 | 10.29,2.6,Female,No,Sun,Dinner,2 54 | 34.81,5.2,Female,No,Sun,Dinner,4 55 | 9.94,1.56,Male,No,Sun,Dinner,2 56 | 25.56,4.34,Male,No,Sun,Dinner,4 57 | 19.49,3.51,Male,No,Sun,Dinner,2 58 | 38.01,3,Male,Yes,Sat,Dinner,4 59 | 26.41,1.5,Female,No,Sat,Dinner,2 60 | 11.24,1.76,Male,Yes,Sat,Dinner,2 61 | 48.27,6.73,Male,No,Sat,Dinner,4 62 | 20.29,3.21,Male,Yes,Sat,Dinner,2 63 | 13.81,2,Male,Yes,Sat,Dinner,2 64 | 11.02,1.98,Male,Yes,Sat,Dinner,2 65 | 18.29,3.76,Male,Yes,Sat,Dinner,4 66 | 17.59,2.64,Male,No,Sat,Dinner,3 67 | 20.08,3.15,Male,No,Sat,Dinner,3 68 | 16.45,2.47,Female,No,Sat,Dinner,2 69 | 3.07,1,Female,Yes,Sat,Dinner,1 70 | 20.23,2.01,Male,No,Sat,Dinner,2 71 | 15.01,2.09,Male,Yes,Sat,Dinner,2 72 | 12.02,1.97,Male,No,Sat,Dinner,2 73 | 17.07,3,Female,No,Sat,Dinner,3 74 | 26.86,3.14,Female,Yes,Sat,Dinner,2 75 | 25.28,5,Female,Yes,Sat,Dinner,2 76 | 14.73,2.2,Female,No,Sat,Dinner,2 77 | 10.51,1.25,Male,No,Sat,Dinner,2 78 | 17.92,3.08,Male,Yes,Sat,Dinner,2 79 | 27.2,4,Male,No,Thur,Lunch,4 80 | 22.76,3,Male,No,Thur,Lunch,2 81 | 17.29,2.71,Male,No,Thur,Lunch,2 82 | 19.44,3,Male,Yes,Thur,Lunch,2 83 | 16.66,3.4,Male,No,Thur,Lunch,2 84 | 10.07,1.83,Female,No,Thur,Lunch,1 85 | 32.68,5,Male,Yes,Thur,Lunch,2 86 | 15.98,2.03,Male,No,Thur,Lunch,2 87 | 34.83,5.17,Female,No,Thur,Lunch,4 88 | 13.03,2,Male,No,Thur,Lunch,2 89 | 18.28,4,Male,No,Thur,Lunch,2 90 | 24.71,5.85,Male,No,Thur,Lunch,2 91 | 21.16,3,Male,No,Thur,Lunch,2 92 | 28.97,3,Male,Yes,Fri,Dinner,2 93 | 22.49,3.5,Male,No,Fri,Dinner,2 94 | 5.75,1,Female,Yes,Fri,Dinner,2 95 | 16.32,4.3,Female,Yes,Fri,Dinner,2 96 | 22.75,3.25,Female,No,Fri,Dinner,2 97 | 40.17,4.73,Male,Yes,Fri,Dinner,4 98 | 27.28,4,Male,Yes,Fri,Dinner,2 99 | 12.03,1.5,Male,Yes,Fri,Dinner,2 100 | 21.01,3,Male,Yes,Fri,Dinner,2 101 | 12.46,1.5,Male,No,Fri,Dinner,2 102 | 11.35,2.5,Female,Yes,Fri,Dinner,2 103 | 15.38,3,Female,Yes,Fri,Dinner,2 104 | 44.3,2.5,Female,Yes,Sat,Dinner,3 105 | 22.42,3.48,Female,Yes,Sat,Dinner,2 106 | 20.92,4.08,Female,No,Sat,Dinner,2 107 | 15.36,1.64,Male,Yes,Sat,Dinner,2 108 | 20.49,4.06,Male,Yes,Sat,Dinner,2 109 | 25.21,4.29,Male,Yes,Sat,Dinner,2 110 | 18.24,3.76,Male,No,Sat,Dinner,2 111 | 14.31,4,Female,Yes,Sat,Dinner,2 112 | 14,3,Male,No,Sat,Dinner,2 113 | 7.25,1,Female,No,Sat,Dinner,1 114 | 38.07,4,Male,No,Sun,Dinner,3 115 | 23.95,2.55,Male,No,Sun,Dinner,2 116 | 25.71,4,Female,No,Sun,Dinner,3 117 | 17.31,3.5,Female,No,Sun,Dinner,2 118 | 29.93,5.07,Male,No,Sun,Dinner,4 119 | 10.65,1.5,Female,No,Thur,Lunch,2 120 | 12.43,1.8,Female,No,Thur,Lunch,2 121 | 24.08,2.92,Female,No,Thur,Lunch,4 122 | 11.69,2.31,Male,No,Thur,Lunch,2 123 | 13.42,1.68,Female,No,Thur,Lunch,2 124 | 14.26,2.5,Male,No,Thur,Lunch,2 125 | 15.95,2,Male,No,Thur,Lunch,2 126 | 12.48,2.52,Female,No,Thur,Lunch,2 127 | 29.8,4.2,Female,No,Thur,Lunch,6 128 | 8.52,1.48,Male,No,Thur,Lunch,2 129 | 14.52,2,Female,No,Thur,Lunch,2 130 | 11.38,2,Female,No,Thur,Lunch,2 131 | 22.82,2.18,Male,No,Thur,Lunch,3 132 | 19.08,1.5,Male,No,Thur,Lunch,2 133 | 20.27,2.83,Female,No,Thur,Lunch,2 134 | 11.17,1.5,Female,No,Thur,Lunch,2 135 | 12.26,2,Female,No,Thur,Lunch,2 136 | 18.26,3.25,Female,No,Thur,Lunch,2 137 | 8.51,1.25,Female,No,Thur,Lunch,2 138 | 10.33,2,Female,No,Thur,Lunch,2 139 | 14.15,2,Female,No,Thur,Lunch,2 140 | 16,2,Male,Yes,Thur,Lunch,2 141 | 13.16,2.75,Female,No,Thur,Lunch,2 142 | 17.47,3.5,Female,No,Thur,Lunch,2 143 | 34.3,6.7,Male,No,Thur,Lunch,6 144 | 41.19,5,Male,No,Thur,Lunch,5 145 | 27.05,5,Female,No,Thur,Lunch,6 146 | 16.43,2.3,Female,No,Thur,Lunch,2 147 | 8.35,1.5,Female,No,Thur,Lunch,2 148 | 18.64,1.36,Female,No,Thur,Lunch,3 149 | 11.87,1.63,Female,No,Thur,Lunch,2 150 | 9.78,1.73,Male,No,Thur,Lunch,2 151 | 7.51,2,Male,No,Thur,Lunch,2 152 | 14.07,2.5,Male,No,Sun,Dinner,2 153 | 13.13,2,Male,No,Sun,Dinner,2 154 | 17.26,2.74,Male,No,Sun,Dinner,3 155 | 24.55,2,Male,No,Sun,Dinner,4 156 | 19.77,2,Male,No,Sun,Dinner,4 157 | 29.85,5.14,Female,No,Sun,Dinner,5 158 | 48.17,5,Male,No,Sun,Dinner,6 159 | 25,3.75,Female,No,Sun,Dinner,4 160 | 13.39,2.61,Female,No,Sun,Dinner,2 161 | 16.49,2,Male,No,Sun,Dinner,4 162 | 21.5,3.5,Male,No,Sun,Dinner,4 163 | 12.66,2.5,Male,No,Sun,Dinner,2 164 | 16.21,2,Female,No,Sun,Dinner,3 165 | 13.81,2,Male,No,Sun,Dinner,2 166 | 17.51,3,Female,Yes,Sun,Dinner,2 167 | 24.52,3.48,Male,No,Sun,Dinner,3 168 | 20.76,2.24,Male,No,Sun,Dinner,2 169 | 31.71,4.5,Male,No,Sun,Dinner,4 170 | 10.59,1.61,Female,Yes,Sat,Dinner,2 171 | 10.63,2,Female,Yes,Sat,Dinner,2 172 | 50.81,10,Male,Yes,Sat,Dinner,3 173 | 15.81,3.16,Male,Yes,Sat,Dinner,2 174 | 7.25,5.15,Male,Yes,Sun,Dinner,2 175 | 31.85,3.18,Male,Yes,Sun,Dinner,2 176 | 16.82,4,Male,Yes,Sun,Dinner,2 177 | 32.9,3.11,Male,Yes,Sun,Dinner,2 178 | 17.89,2,Male,Yes,Sun,Dinner,2 179 | 14.48,2,Male,Yes,Sun,Dinner,2 180 | 9.6,4,Female,Yes,Sun,Dinner,2 181 | 34.63,3.55,Male,Yes,Sun,Dinner,2 182 | 34.65,3.68,Male,Yes,Sun,Dinner,4 183 | 23.33,5.65,Male,Yes,Sun,Dinner,2 184 | 45.35,3.5,Male,Yes,Sun,Dinner,3 185 | 23.17,6.5,Male,Yes,Sun,Dinner,4 186 | 40.55,3,Male,Yes,Sun,Dinner,2 187 | 20.69,5,Male,No,Sun,Dinner,5 188 | 20.9,3.5,Female,Yes,Sun,Dinner,3 189 | 30.46,2,Male,Yes,Sun,Dinner,5 190 | 18.15,3.5,Female,Yes,Sun,Dinner,3 191 | 23.1,4,Male,Yes,Sun,Dinner,3 192 | 15.69,1.5,Male,Yes,Sun,Dinner,2 193 | 19.81,4.19,Female,Yes,Thur,Lunch,2 194 | 28.44,2.56,Male,Yes,Thur,Lunch,2 195 | 15.48,2.02,Male,Yes,Thur,Lunch,2 196 | 16.58,4,Male,Yes,Thur,Lunch,2 197 | 7.56,1.44,Male,No,Thur,Lunch,2 198 | 10.34,2,Male,Yes,Thur,Lunch,2 199 | 43.11,5,Female,Yes,Thur,Lunch,4 200 | 13,2,Female,Yes,Thur,Lunch,2 201 | 13.51,2,Male,Yes,Thur,Lunch,2 202 | 18.71,4,Male,Yes,Thur,Lunch,3 203 | 12.74,2.01,Female,Yes,Thur,Lunch,2 204 | 13,2,Female,Yes,Thur,Lunch,2 205 | 16.4,2.5,Female,Yes,Thur,Lunch,2 206 | 20.53,4,Male,Yes,Thur,Lunch,4 207 | 16.47,3.23,Female,Yes,Thur,Lunch,3 208 | 26.59,3.41,Male,Yes,Sat,Dinner,3 209 | 38.73,3,Male,Yes,Sat,Dinner,4 210 | 24.27,2.03,Male,Yes,Sat,Dinner,2 211 | 12.76,2.23,Female,Yes,Sat,Dinner,2 212 | 30.06,2,Male,Yes,Sat,Dinner,3 213 | 25.89,5.16,Male,Yes,Sat,Dinner,4 214 | 48.33,9,Male,No,Sat,Dinner,4 215 | 13.27,2.5,Female,Yes,Sat,Dinner,2 216 | 28.17,6.5,Female,Yes,Sat,Dinner,3 217 | 12.9,1.1,Female,Yes,Sat,Dinner,2 218 | 28.15,3,Male,Yes,Sat,Dinner,5 219 | 11.59,1.5,Male,Yes,Sat,Dinner,2 220 | 7.74,1.44,Male,Yes,Sat,Dinner,2 221 | 30.14,3.09,Female,Yes,Sat,Dinner,4 222 | 12.16,2.2,Male,Yes,Fri,Lunch,2 223 | 13.42,3.48,Female,Yes,Fri,Lunch,2 224 | 8.58,1.92,Male,Yes,Fri,Lunch,1 225 | 15.98,3,Female,No,Fri,Lunch,3 226 | 13.42,1.58,Male,Yes,Fri,Lunch,2 227 | 16.27,2.5,Female,Yes,Fri,Lunch,2 228 | 10.09,2,Female,Yes,Fri,Lunch,2 229 | 20.45,3,Male,No,Sat,Dinner,4 230 | 13.28,2.72,Male,No,Sat,Dinner,2 231 | 22.12,2.88,Female,Yes,Sat,Dinner,2 232 | 24.01,2,Male,Yes,Sat,Dinner,4 233 | 15.69,3,Male,Yes,Sat,Dinner,3 234 | 11.61,3.39,Male,No,Sat,Dinner,2 235 | 10.77,1.47,Male,No,Sat,Dinner,2 236 | 15.53,3,Male,Yes,Sat,Dinner,2 237 | 10.07,1.25,Male,No,Sat,Dinner,2 238 | 12.6,1,Male,Yes,Sat,Dinner,2 239 | 32.83,1.17,Male,Yes,Sat,Dinner,2 240 | 35.83,4.67,Female,No,Sat,Dinner,3 241 | 29.03,5.92,Male,No,Sat,Dinner,3 242 | 27.18,2,Female,Yes,Sat,Dinner,2 243 | 22.67,2,Male,Yes,Sat,Dinner,2 244 | 17.82,1.75,Male,No,Sat,Dinner,2 245 | 18.78,3,Female,No,Thur,Dinner,2 246 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/treat.csv: -------------------------------------------------------------------------------- 1 | name,sex,treatment a,treatment b 2 | Daniel,male,-,42 3 | Jhon,male,12,31 4 | Jane,female,24,27 5 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/treat_duplicate.csv: -------------------------------------------------------------------------------- 1 | name,sex,treatment a,treatment b 2 | Daniel,male,-,42 3 | Jhon,male,12,31 4 | Jane,female,24,27 5 | Daniel,male,-,42 6 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/w1.csv: -------------------------------------------------------------------------------- 1 | date,element,value 2 | 1/30/2010,tmax,27.8 3 | 1/30/2010,tmin,14.5 4 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/w2.csv: -------------------------------------------------------------------------------- 1 | date,element,value 2 | 2/2/2010,tmax,27.3 3 | 2/2/2010,tmin,14.4 4 | -------------------------------------------------------------------------------- /Cleaning_Data_in_Python/weather_tidy.csv: -------------------------------------------------------------------------------- 1 | date,element,value 2 | 1/30/2010,tmax,27.8 3 | 1/30/2010,tmin,14.5 4 | 2/2/2010,tmax,27.3 5 | 2/2/2010,tmin,14.4 6 | -------------------------------------------------------------------------------- /Importing_Data_in_python/Chinook.sqlite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/Chinook.sqlite -------------------------------------------------------------------------------- /Importing_Data_in_python/L-L1_LOSC_4_V1-1126259446-32.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/L-L1_LOSC_4_V1-1126259446-32.hdf5 -------------------------------------------------------------------------------- /Importing_Data_in_python/Northwind.sqlite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/Northwind.sqlite -------------------------------------------------------------------------------- /Importing_Data_in_python/battledeath.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/battledeath.xlsx -------------------------------------------------------------------------------- /Importing_Data_in_python/disarea.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/disarea.dta -------------------------------------------------------------------------------- /Importing_Data_in_python/huck_finn.txt: -------------------------------------------------------------------------------- 1 | YOU don't know about me without you have read a book by the 2 | name of The Adventures of Tom Sawyer; but that ain't no 3 | matter. That book was made by Mr. Mark Twain, and he told 4 | the truth, mainly. There was things which he stretched, but 5 | mainly he told the truth. That is nothing. never seen 6 | anybody but lied one time or another, without it was Aunt 7 | Polly, or the widow, or maybe Mary. Aunt Polly--Tom's Aunt 8 | Polly, she is--and Mary, and the Widow Douglas is all told 9 | about in that book, which is mostly a true book, with some 10 | stretchers, as I said before. 11 | 12 | -------------------------------------------------------------------------------- /Importing_Data_in_python/ja_data2.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/ja_data2.mat -------------------------------------------------------------------------------- /Importing_Data_in_python/latitude.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/latitude.xls -------------------------------------------------------------------------------- /Importing_Data_in_python/sales.sas7bdat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Importing_Data_in_python/sales.sas7bdat -------------------------------------------------------------------------------- /Importing_Data_in_python/seaslug.txt: -------------------------------------------------------------------------------- 1 | Time Percent 2 | 99 0.067 3 | 99 0.133 4 | 99 0.067 5 | 99 0 6 | 99 0 7 | 0 0.5 8 | 0 0.467 9 | 0 0.857 10 | 0 0.5 11 | 0 0.357 12 | 0 0.533 13 | 5 0.467 14 | 5 0.467 15 | 5 0.125 16 | 5 0.4 17 | 5 0.214 18 | 5 0.4 19 | 10 0.067 20 | 10 0.067 21 | 10 0.333 22 | 10 0.333 23 | 10 0.133 24 | 10 0.133 25 | 15 0.267 26 | 15 0.286 27 | 15 0.333 28 | 15 0.214 29 | 15 0 30 | 15 0 31 | 20 0.267 32 | 20 0.2 33 | 20 0.267 34 | 20 0.437 35 | 20 0.077 36 | 20 0.067 37 | 25 0.133 38 | 25 0.267 39 | 25 0.412 40 | 25 0 41 | 25 0.067 42 | 25 0.133 43 | 30 0 44 | 30 0.071 45 | 30 0 46 | 30 0.067 47 | 30 0.067 48 | 30 0.133 -------------------------------------------------------------------------------- /Importing_Data_in_python/snakes.json: -------------------------------------------------------------------------------- 1 | {'Actors': 'Samuel L. Jackson, Julianna Margulies, Nathan 2 | Phillips, Rachel Blanchard', 3 | 'Awards': '3 wins & 7 nominations.', 4 | 'Country': 'Germany, USA, Canada', 5 | 'Director': 'David R. Ellis', 6 | 'Genre': 'Action, Adventure, Crime', 7 | 'Language': 'English', 8 | 'Rated': 'R', 9 | 'Released': '18 Aug 2006', 10 | 'Runtime': '105 min', 11 | 'Title': 'Snakes on a Plane', 12 | 'Type': 'movie', 13 | 'Writer': 'John Heffernan (screenplay), Sebastian Gutierrez 14 | (screenplay), David Dalessandro (story), John Heffernan (story)', 15 | 'Year': '2006', 16 | 'imdbID': 'tt0417148', 17 | 'imdbRating': '5.6', 18 | 'imdbVotes': '114,668'} 19 | -------------------------------------------------------------------------------- /Interactive_Data_Visualizaton_Bokeh/04Putting_It_All_Together_Case_Study.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "collapsed": true 7 | }, 8 | "source": [ 9 | "In this final chapter, you'll build a more sophisticated Bokeh data exploration application from the ground up, based on the famous Gapminder data set." 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "# A Case Study\n", 17 | "\n", 18 | "## The Gapminder Data Set" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [] 27 | } 28 | ], 29 | "metadata": { 30 | "kernelspec": { 31 | "display_name": "Python [conda env:datacamp]", 32 | "language": "python", 33 | "name": "conda-env-datacamp-py" 34 | }, 35 | "language_info": { 36 | "codemirror_mode": { 37 | "name": "ipython", 38 | "version": 3 39 | }, 40 | "file_extension": ".py", 41 | "mimetype": "text/x-python", 42 | "name": "python", 43 | "nbconvert_exporter": "python", 44 | "pygments_lexer": "ipython3", 45 | "version": "3.6.4" 46 | } 47 | }, 48 | "nbformat": 4, 49 | "nbformat_minor": 2 50 | } 51 | -------------------------------------------------------------------------------- /Interactive_Data_Visualizaton_Bokeh/literacy_birth_rate.csv: -------------------------------------------------------------------------------- 1 | Country ,Continent,female literacy,fertility,population 2 | Chine,ASI,90.5,1.769,1324655000.0 3 | Inde,ASI,50.8,2.682,1139964931.64188 4 | USA,NAM,99,2.077,304060000.0 5 | Indonésie,ASI,88.8,2.132,227345082.0 6 | Brésil,LAT,90.2,1.827,191971506.0 7 | Pakistan,ASI,40,3.872,166111487.118278 8 | Bangladesh,ASI,49.8,2.288,160000128.0 9 | Nigéria,AF,48.8,5.173,151212254.0 10 | Fédération de Russie,EUR,99.4,1.393,141950000.0 11 | Japan,ASI,99,1.262,127704000.0 12 | Mexique,LAT,91.5,2.156,106350433.683525 13 | Philippines,ASI,93.9,3.026,90348437.0 14 | Viet Nam,ASI,90.2,2.033,86210781.0 15 | Germany,EUR,99,1.324,82110097.0 16 | Egypte,AF,57.8,2.816,81527172.0 17 | Ethiopie,AF,22.8,5.211,80713434.0 18 | Turquie,ASI,81.3,2.1,73914260.0 19 | "Iran, République islamique d'",ASI,77.2,1.781,71956321.6875886 20 | Thaïlande,ASI,91.5,1.822,67386383.0 21 | Rép. Démocratique du Congo,AF,56.1,5.908,64256635.0 22 | France,EUR,99,1.881,62277432.0 23 | UK,EUR,99,1.852,61414062.0 24 | Italie,EUR,98.5,1.39,59832179.0 25 | Myanmar,ASI,89.2,2.281,49563019.0 26 | Afrique du Sud,AF,88.1,2.505,48687000.0 27 | South Korea,ASI,96.6,1.224,48607000.0 28 | Ukraine,EUR,99.6,1.361,46258200.0 29 | Espagne,EUR,96.9,1.468,45555716.0 30 | Colombie,LAT,93.4,2.404,45012096.0 31 | République-Unie de Tanzanie,AF,66.3,5.52,42483923.0 32 | Soudan,AF,59.6,4.058,41347723.0 33 | Argentine,LAT,97.7,2.223,39882980.0 34 | Kenya,AF,82.8,4.859,38765312.0 35 | Pologne,EUR,99.3,1.267,38125759.0 36 | Algérie,AF,63.9,2.342,34373426.0 37 | Canada,NAM,99,1.579,33311400.0 38 | Ouganda,AF,66.8,6.254,31656865.0 39 | Maroc,AF,44.1,2.334,31605616.0 40 | Iraq,ASI,69.2,3.961,30711152.2927428 41 | Afghanistan,ASI,12.6,6.505,29021098.9974162 42 | Pérou,LAT,84.6,2.53,28836700.0 43 | Népal,ASI,45.4,2.823,28809526.0 44 | Venezuela,LAT,94.9,2.498,27935000.0 45 | Ouzbékistan,ASI,98.9,2.248,27313700.0 46 | Malaisie,ASI,89.8,2.508,27014337.0 47 | Arabie saoudite,ASI,80.2,3.04,24645685.9216375 48 | Rép. populaire démocratique de Corée,ASI,100,1.854,23818753.0 49 | Ghana,AF,59.3,4.22,23350927.0 50 | Yémen,ASI,42.8,5.1,22917485.0 51 | Mozambique,AF,40.1,4.967,22382533.0 52 | Roumanie,EUR,96.9,1.325,21513622.0 53 | Côte d'Ivoire,AF,44.3,4.514,20591302.0 54 | République arabe syrienne,ASI,77.2,3.173,20581289.8697702 55 | Sri Lanka,ASI,89.1,2.308,20156204.3380748 56 | Madagascar,AF,65.3,4.62,19110941.0 57 | Cameroun,AF,67.8,4.541,19088385.0 58 | Angola,AF,57,5.637,18020668.0 59 | Chili,LAT,98.7,1.926,16803952.0 60 | Netherlands,EUR,99,1.747,16445593.0 61 | Kazakhstan,ASI,99.5,2.294,15674833.0 62 | Burkina Faso,AF,21.6,5.841,15233884.0 63 | Malawi,AF,65.8,5.455,14846182.0 64 | Niger,AF,15.1,7.069,14704318.0 65 | Cambodge,ASI,70.9,2.859,14562008.0 66 | Guatemala,LAT,68.7,4.018,13686128.0 67 | Equateur,LAT,81.7,2.513,13481424.0 68 | Mali,AF,18.2,5.405,12705736.0 69 | Zambie,AF,61,5.737,12620219.0 70 | Zimbabwe,AF,88.8,3.363,12462879.0 71 | Sénégal,AF,33,4.89,12211181.0 72 | Grèce,EUR,95.9,1.385,11237094.0 73 | Cuba,LAT,99.8,1.505,11204735.0 74 | Tchad,AF,21.9,6.081,10913667.0 75 | Belgium,EUR,99,1.784,10708433.0 76 | Portugal,EUR,92.9,1.378,10622413.0 77 | Czech rep,EUR,99,1.45,10424336.0 78 | Tunisie,AF,71,1.841,10327800.0 79 | Hongrie,EUR,98.9,1.37,10038188.0 80 | République dominicaine,LAT,88.3,2.612,9952711.0 81 | Guinée,AF,26.4,5.329,9833055.0 82 | Rwanda,AF,66.1,5.33,9720694.0 83 | Bolivie,LAT,86,3.371,9694113.0 84 | Bélarus,EUR,99.7,1.281,9680850.0 85 | Sweden,EUR,99,1.871,9219637.0 86 | Azerbaïdjan,ASI,99.2,2.153,8680100.0 87 | Bénin,AF,28.1,5.378,8662086.0 88 | Burundi,AF,59.9,4.45,8074254.0 89 | Switzerland,EUR,99,1.46,7647675.0 90 | Bulgarie,EUR,97.9,1.436,7623395.0 91 | Serbie,EUR,96.2,1.612,7350221.0 92 | Honduras,LAT,83.5,3.19,7318789.0 93 | Israel,ASI,95.9,2.752,7308800.0 94 | Tadjikistan,ASI,99.5,3.35,6836083.0 95 | Papouasie-Nouvelle-Guinée,OCE,55.6,4.01,6576822.0 96 | Togo,AF,53.7,4.166,6458605.0 97 | Jamahiriya arabe libyenne,AF,81.3,2.642,6294181.0 98 | Paraguay,LAT,93.5,2.977,6237855.0 99 | Rép. démocratique populaire lao,ASI,63.2,3.415,6205341.0 100 | El Salvador,LAT,81.4,2.295,6133910.0 101 | Jordanie,ASI,88.9,3.019,5906042.87150034 102 | Nicaragua,LAT,77.9,2.683,5667325.0 103 | Sierra Leone,AF,28.9,5.165,5559853.0 104 | Denmark,EUR,99,1.849,5493621.0 105 | Finland,EUR,100,1.836,5313399.0 106 | Kirghizistan,ASI,99.1,2.518,5277900.0 107 | Turkménistan,ASI,99.3,2.43,5043618.0 108 | Erythrée,AF,54.5,4.528,4926877.0 109 | Singapour,ASI,91.6,1.263,4839400.0 110 | Norway,EUR,100,1.885,4768212.0 111 | Costa Rica,LAT,96.2,1.943,4519126.0 112 | Emirats arabes unis,ASI,91.5,1.899,4484935.0 113 | Croatie,EUR,98,1.442,4434000.0 114 | Ireland,EUR,99,1.953,4425675.0 115 | République centrafricaine,AF,41.1,4.697,4339263.0 116 | Géorgie,ASI,99.7,1.582,4307011.0 117 | New Zealand,OCE,99,2.025,4268900.0 118 | Liban,ASI,86,1.841,4193758.0 119 | Libéria,AF,53,5.011,3793400.0 120 | Bosnie-Herzégovine,EUR,95.9,1.212,3773100.0 121 | République de Moldova,EUR,97.8,1.502,3633369.0 122 | Panama,LAT,92.8,2.516,3398823.0 123 | Lituanie,EUR,99.7,1.367,3358115.0 124 | Uruguay,LAT,98.5,2.089,3334052.0 125 | Mauritanie,AF,49.5,4.388,3215043.0 126 | Albanie,EUR,98.7,1.854,3143291.0 127 | Arménie,ASI,99.4,1.748,3077087.0 128 | Oman,ASI,80.9,2.978,2785361.0 129 | Koweït,ASI,93.1,2.152,2728040.83948046 130 | Jamaïque,LAT,90.8,2.362,2687200.0 131 | Mongolie,ASI,97.8,1.988,2641216.0 132 | Lettonie,EUR,99.8,1.426,2266094.0 133 | Namibie,AF,87.7,3.29,2129854.0 134 | Lesotho,AF,95.1,3.264,2049429.0 135 | L'ex-Rép. yougoslave de Macédoine,EUR,95.4,1.436,2041342.0 136 | Slovénie,EUR,99.7,1.393,2021316.0 137 | Botswana,AF,83.5,2.822,1921122.0 138 | Gambie,AF,34.3,4.969,1660200.0 139 | Guinée-Bissau,AF,36.5,5.659,1575446.0 140 | Gabon,AF,83.2,3.24,1448159.0 141 | Estonie,EUR,99.8,1.693,1340675.0 142 | Trinité-et-Tobago,LAT,98.2,1.647,1333388.0 143 | Qatar,ASI,90.4,2.36,1280862.0 144 | Maurice,AF,84.8,1.792,1268854.0 145 | Swaziland,AF,85.6,3.45,1167834.0 146 | Chypre,EUR,96.7,1.516,862434.0 147 | Bahreïn,ASI,89.4,2.233,775585.0 148 | Bhoutan,ASI,38.7,2.563,686789.0 149 | Guinée équatoriale,AF,89.1,5.283,659197.0 150 | Comores,AF,67.8,3.885,643571.312838574 151 | "Macao, Chine",ASI,90.7,0.966,526178.0 152 | Suriname,LAT,88.4,2.373,515124.0 153 | Cap-Vert,AF,79.3,2.663,498672.0 154 | Malte,EUR,93.5,1.251,411950.0 155 | Brunéi Darussalam,ASI,93.3,2.052,392280.0 156 | Bahamas,LAT,96.5,3.371,337668.0 157 | Iceland,EUR,99,2.093,317414.0 158 | Maldives,ASI,98.4,2,305027.0 159 | Vanuatu,OCE,79.5,3.883,233866.0 160 | Samoa,OCE,98.5,3.852,178869.0 161 | Sao Tomé-et-Principe,AF,83.3,3.718,160174.0 162 | Aruba,LAT,98,1.732,105455.0 163 | Tonga,ASI,99.1,3.928,103566.0 164 | ,,,, 165 | ,,,, 166 | ,,,, 167 | ,,,, 168 | ,,,, 169 | ,,,, 170 | ,,,, 171 | ,,,, 172 | ,,,, 173 | ,,,, 174 | ,,,, 175 | ,,,, 176 | ,,,, 177 | Country ,Continent,female literacy,fertility, 178 | Antigua-et-Barbuda,,99.4,, 179 | Antilles néerlandaises,,96.3,, 180 | Iles Caïmanes,,99,, 181 | Seychelles,,92.3,, 182 | Territoires autonomes palestiniens,,90.9,, 183 | WORLD,WORLD,77,, 184 | -------------------------------------------------------------------------------- /Interactive_Data_Visualizaton_Bokeh/sprint.csv: -------------------------------------------------------------------------------- 1 | Name,Country,Medal,Time,Year,color 2 | Usain Bolt,JAM,GOLD,9.63,2012,goldenrod 3 | Yohan Blake,JAM,SILVER,9.75,2012,silver 4 | Justin Gatlin,USA,BRONZE,9.79,2012,saddlebrown 5 | Usain Bolt,JAM,GOLD,9.69,2008,goldenrod 6 | Richard Thompson,TRI,SILVER,9.89,2008,silver 7 | Walter Dix,USA,BRONZE,9.91,2008,saddlebrown 8 | Justin Gatlin,USA,GOLD,9.85,2004,goldenrod 9 | Francis Obikwelu,POR,SILVER,9.86,2004,silver 10 | Maurice Greene,USA,BRONZE,9.87,2004,saddlebrown 11 | Maurice Greene,USA,GOLD,9.87,2000,goldenrod 12 | Ato Boldon,TRI,SILVER,9.99,2000,silver 13 | Obadele Thompson,BAR,BRONZE,10.04,2000,saddlebrown 14 | Donovan Bailey,CAN,GOLD,9.84,1996,goldenrod 15 | Frankie Fredericks,NAM,SILVER,9.89,1996,silver 16 | Ato Boldon,TRI,BRONZE,9.9,1996,saddlebrown 17 | Linford Christie,GBR,GOLD,9.96,1992,goldenrod 18 | Frankie Fredericks,NAM,SILVER,10.02,1992,silver 19 | Dennis Mitchell,USA,BRONZE,10.04,1992,saddlebrown 20 | Carl Lewis,USA,GOLD,9.92,1988,goldenrod 21 | Linford Christie,GBR,SILVER,9.97,1988,silver 22 | Calvin Smith,USA,BRONZE,9.99,1988,saddlebrown 23 | Carl Lewis,USA,GOLD,9.99,1984,goldenrod 24 | Sam Graddy,USA,SILVER,10.19,1984,silver 25 | Ben Johnson,CAN,BRONZE,10.22,1984,saddlebrown 26 | Allan Wells,GBR,GOLD,10.25,1980,goldenrod 27 | Silvio Leonard Tartabull,CUB,SILVER,10.25,1980,silver 28 | Petar Petrov,BUL,BRONZE,10.39,1980,saddlebrown 29 | Hasely Crawford,TRI,GOLD,10.06,1976,goldenrod 30 | Donald Quarrie,JAM,SILVER,10.08,1976,silver 31 | Valery Borzov,URS,BRONZE,10.14,1976,saddlebrown 32 | Valery Borzov,URS,GOLD,10.14,1972,goldenrod 33 | Robert Taylor,USA,SILVER,10.24,1972,silver 34 | Lennox Miller,JAM,BRONZE,10.33,1972,saddlebrown 35 | Jim Hines,USA,GOLD,9.95,1968,goldenrod 36 | Lennox Miller,JAM,SILVER,10.04,1968,silver 37 | Charles Greene,USA,BRONZE,10.07,1968,saddlebrown 38 | Bob Hayes,USA,GOLD,10.0,1964,goldenrod 39 | Enrique Figuerola Camue,CUB,SILVER,10.2,1964,silver 40 | Harry Jerome,CAN,BRONZE,10.2,1964,saddlebrown 41 | Armin Hary,EUA,GOLD,10.2,1960,goldenrod 42 | David Sime,USA,SILVER,10.2,1960,silver 43 | Peter Radford,GBR,BRONZE,10.3,1960,saddlebrown 44 | Bobby Joe Morrow,USA,GOLD,10.5,1956,goldenrod 45 | W. Thane Baker,USA,SILVER,10.5,1956,silver 46 | Hector Hogan,AUS,BRONZE,10.6,1956,saddlebrown 47 | Lindy Remigino,USA,GOLD,10.4,1952,goldenrod 48 | Herbert McKenley,JAM,SILVER,10.4,1952,silver 49 | E. McDonald Bailey,GBR,BRONZE,10.4,1952,saddlebrown 50 | W. Harrison Dillard,USA,GOLD,10.3,1948,goldenrod 51 | "H. Norwood ""Barney"" Ewell",USA,SILVER,10.4,1948,silver 52 | Lloyd LaBeach,PAN,BRONZE,10.4,1948,saddlebrown 53 | Jesse Owens,USA,GOLD,10.3,1936,goldenrod 54 | Ralph Metcalfe,USA,SILVER,10.4,1936,silver 55 | Martinus Osendarp,NED,BRONZE,10.5,1936,saddlebrown 56 | "T. ""Eddie"" Tolan",USA,GOLD,10.3,1932,goldenrod 57 | Ralph Metcalfe,USA,SILVER,10.3,1932,silver 58 | Arthur Jonath,GER,BRONZE,10.4,1932,saddlebrown 59 | Percy Williams,CAN,GOLD,10.8,1928,goldenrod 60 | "John ""Jack"" London",GBR,SILVER,10.9,1928,silver 61 | Georg Lammers,GER,BRONZE,10.9,1928,saddlebrown 62 | Harold Abrahams,GBR,GOLD,10.6,1924,goldenrod 63 | Jackson Scholz,USA,SILVER,10.8,1924,silver 64 | Arthur Porritt,NZL,BRONZE,10.9,1924,saddlebrown 65 | Charles Paddock,USA,GOLD,10.8,1920,goldenrod 66 | Morris Kirksey,USA,SILVER,10.9,1920,silver 67 | Harry Edward,GBR,BRONZE,10.9,1920,saddlebrown 68 | Ralph Craig,USA,GOLD,10.8,1912,goldenrod 69 | Alvah Meyer,USA,SILVER,10.9,1912,silver 70 | Donald Lippincott,USA,BRONZE,10.9,1912,saddlebrown 71 | Reginald Walker,RSA,GOLD,10.8,1908,goldenrod 72 | James Rector,USA,SILVER,11.0,1908,silver 73 | Robert Kerr,CAN,BRONZE,11.0,1908,saddlebrown 74 | "Charles ""Archie"" Hahn",USA,GOLD,11.2,1906,goldenrod 75 | Fay Moulton,USA,SILVER,11.3,1906,silver 76 | Nigel Barker,AUS,BRONZE,11.3,1906,saddlebrown 77 | "Charles ""Archie"" Hahn",USA,GOLD,11.0,1904,goldenrod 78 | Nathaniel Cartmell,USA,SILVER,11.2,1904,silver 79 | Bill Hogenson,USA,BRONZE,11.2,1904,saddlebrown 80 | Frank Jarvis,USA,GOLD,11.0,1900,goldenrod 81 | J. Walter Tewksbury,USA,SILVER,11.1,1900,silver 82 | Stanley Rowley,AUS,BRONZE,11.2,1900,saddlebrown 83 | Thomas Burke,USA,GOLD,12.0,1896,goldenrod 84 | Fritz Hofmann,GER,SILVER,12.2,1896,silver 85 | Alojz Sokol,HUN,BRONZE,12.6,1896,saddlebrown 86 | Francis Lane,USA,BRONZE,12.6,1896,saddlebrown 87 | -------------------------------------------------------------------------------- /Introduction_to_Data_Visualization_with_Python/Astronaut.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Introduction_to_Data_Visualization_with_Python/Astronaut.jpg -------------------------------------------------------------------------------- /Introduction_to_Data_Visualization_with_Python/Unequalized_Hawkes_Bay_NZ.jpg: -------------------------------------------------------------------------------- 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4.6,3.1,1.5,0.2,setosa 6 | 5,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3,1.4,0.1,setosa 15 | 4.3,3,1.1,0.1,setosa 16 | 5.8,4,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5,3,1.6,0.2,setosa 28 | 5,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.1,setosa 37 | 5,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.1,1.5,0.1,setosa 40 | 4.4,3,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5,3.3,1.4,0.2,setosa 52 | 7,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5,2,3.5,1,versicolor 63 | 5.9,3,4.2,1.5,versicolor 64 | 6,2.2,4,1,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3,5,1.7,versicolor 80 | 6,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6,2.7,5.1,1.6,versicolor 86 | 5.4,3,4.5,1.5,versicolor 87 | 6,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3,4.1,1.3,versicolor 91 | 5.5,2.5,4,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3,4.6,1.4,versicolor 94 | 5.8,2.6,4,1.2,versicolor 95 | 5,2.3,3.3,1,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3,5.8,2.2,virginica 107 | 7.6,3,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3,5.5,2.1,virginica 115 | 5.7,2.5,5,2,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6,2.2,5,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2,virginica 124 | 7.7,2.8,6.7,2,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6,3,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3,5.2,2.3,virginica 148 | 6.3,2.5,5,1.9,virginica 149 | 6.5,3,5.2,2,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3,5.1,1.8,virginica -------------------------------------------------------------------------------- /Introduction_to_Data_Visualization_with_Python/percent-bachelors-degrees-women-usa.csv: -------------------------------------------------------------------------------- 1 | Year,Agriculture,Architecture,Art and Performance,Biology,Business,Communications and Journalism,Computer Science,Education,Engineering,English,Foreign Languages,Health Professions,Math and Statistics,Physical Sciences,Psychology,Public Administration,Social Sciences and History 2 | 1970,4.22979798,11.92100539,59.7,29.08836297,9.064438975,35.3,13.6,74.53532758,0.8,65.57092343,73.8,77.1,38,13.8,44.4,68.4,36.8 3 | 1971,5.452796685,12.00310559,59.9,29.39440285,9.503186594,35.5,13.6,74.14920369,1,64.55648516,73.9,75.5,39,14.9,46.2,65.5,36.2 4 | 1972,7.42071022,13.21459351,60.4,29.81022105,10.5589621,36.6,14.9,73.55451996,1.2,63.6642632,74.6,76.9,40.2,14.8,47.6,62.6,36.1 5 | 1973,9.653602412,14.7916134,60.2,31.14791477,12.80460152,38.4,16.4,73.50181443,1.6,62.94150212,74.9,77.4,40.9,16.5,50.4,64.3,36.4 6 | 1974,14.07462346,17.44468758,61.9,32.99618284,16.20485038,40.5,18.9,73.33681143,2.2,62.41341209,75.3,77.9,41.8,18.2,52.6,66.1,37.3 7 | 1975,18.33316153,19.13404767,60.9,34.44990213,19.68624931,41.5,19.8,72.80185448,3.2,61.64720641,75,78.9,40.7,19.1,54.5,63,37.7 8 | 1976,22.25276005,21.39449143,61.3,36.07287146,23.4300375,44.3,23.9,72.16652471,4.5,62.14819377,74.4,79.2,41.5,20,56.9,65.6,39.2 9 | 1977,24.6401766,23.74054054,62,38.33138629,27.16342715,46.9,25.7,72.45639481,6.8,62.72306675,74.3,80.5,41.1,21.3,59,69.3,40.5 10 | 1978,27.14619175,25.84923973,62.5,40.11249564,30.52751868,49.9,28.1,73.19282134,8.4,63.61912216,74.3,81.9,41.6,22.5,61.3,71.5,41.8 11 | 1979,29.63336549,27.77047744,63.2,42.06555109,33.62163381,52.3,30.2,73.82114234,9.4,65.08838972,74.2,82.3,42.3,23.7,63.3,73.3,43.6 12 | 1980,30.75938956,28.08038075,63.4,43.99925716,36.76572529,54.7,32.5,74.98103152,10.3,65.28413007,74.1,83.5,42.8,24.6,65.1,74.6,44.2 13 | 1981,31.31865519,29.84169408,63.3,45.24951206,39.26622984,56.4,34.8,75.84512345,11.6,65.83832154,73.9,84.1,43.2,25.7,66.9,74.7,44.6 14 | 1982,32.63666364,34.81624758,63.1,45.96733794,41.94937335,58,36.3,75.84364914,12.4,65.84735212,72.7,84.4,44,27.3,67.5,76.8,44.6 15 | 1983,31.6353471,35.82625735,62.4,46.71313451,43.54206966,58.6,37.1,75.95060123,13.1,65.91837999,71.8,84.6,44.3,27.6,67.9,76.1,44.1 16 | 1984,31.09294748,35.45308311,62.1,47.66908276,45.12403027,59.1,36.8,75.86911601,13.5,65.74986233,72.1,85.1,46.2,28,68.2,75.9,44.1 17 | 1985,31.3796588,36.13334795,61.8,47.9098841,45.747782,59,35.7,75.92343971,13.5,65.79819852,70.8,85.3,46.5,27.5,69,75,43.8 18 | 1986,31.19871923,37.24022346,62.1,48.30067763,46.53291505,60,34.7,76.14301516,13.9,65.98256091,71.2,85.7,46.7,28.4,69,75.7,44 19 | 1987,31.48642948,38.73067535,61.7,50.20987789,46.69046648,60.2,32.4,76.96309168,14,66.70603055,72,85.5,46.5,30.4,70.1,76.4,43.9 20 | 1988,31.08508746,39.3989071,61.7,50.09981147,46.7648277,60.4,30.8,77.62766177,13.9,67.14449816,72.3,85.2,46.2,29.7,70.9,75.6,44.4 21 | 1989,31.6124031,39.09653994,62,50.77471585,46.7815648,60.5,29.9,78.11191872,14.1,67.01707156,72.4,84.6,46.2,31.3,71.6,76,44.2 22 | 1990,32.70344407,40.82404662,62.6,50.81809432,47.20085084,60.8,29.4,78.86685859,14.1,66.92190193,71.2,83.9,47.3,31.6,72.6,77.6,45.1 23 | 1991,34.71183749,33.67988118,62.1,51.46880537,47.22432481,60.8,28.7,78.99124597,14,66.24147465,71.1,83.5,47,32.6,73.2,78.2,45.5 24 | 1992,33.93165961,35.20235628,61,51.34974154,47.21939541,59.7,28.2,78.43518191,14.5,65.62245655,71,83,47.4,32.6,73.2,77.3,45.8 25 | 1993,34.94683208,35.77715877,60.2,51.12484404,47.63933161,58.7,28.5,77.26731199,14.9,65.73095014,70,82.4,46.4,33.6,73.1,78,46.1 26 | 1994,36.03267447,34.43353129,59.4,52.2462176,47.98392441,58.1,28.5,75.81493264,15.7,65.64197772,69.1,81.8,47,34.8,72.9,78.8,46.8 27 | 1995,36.84480747,36.06321839,59.2,52.59940342,48.57318101,58.8,27.5,75.12525621,16.2,65.93694921,69.6,81.5,46.1,35.9,73,78.8,47.9 28 | 1996,38.96977475,35.9264854,58.6,53.78988011,48.6473926,58.7,27.1,75.03519921,16.7,66.43777883,69.7,81.3,46.4,37.3,73.9,79.8,48.7 29 | 1997,40.68568483,35.10193413,58.7,54.99946903,48.56105033,60,26.8,75.1637013,17,66.78635548,70,81.9,47,38.3,74.4,81,49.2 30 | 1998,41.91240333,37.59854457,59.1,56.35124789,49.2585152,60,27,75.48616027,17.8,67.2554484,70.1,82.1,48.3,39.7,75.1,81.3,50.5 31 | 1999,42.88720191,38.63152919,59.2,58.22882288,49.81020815,61.2,28.1,75.83816206,18.6,67.82022113,70.9,83.5,47.8,40.2,76.5,81.1,51.2 32 | 2000,45.05776637,40.02358491,59.2,59.38985737,49.80361649,61.9,27.7,76.69214284,18.4,68.36599498,70.9,83.5,48.2,41,77.5,81.1,51.8 33 | 2001,45.86601517,40.69028156,59.4,60.71233149,50.27514494,63,27.6,77.37522931,19,68.57852029,71.2,85.1,47,42.2,77.5,80.9,51.7 34 | 2002,47.13465821,41.13295053,60.9,61.8951284,50.5523346,63.7,27,78.64424394,18.7,68.82995959,70.5,85.8,45.7,41.1,77.7,81.3,51.5 35 | 2003,47.93518721,42.75854266,61.1,62.1694558,50.34559774,64.6,25.1,78.54494815,18.8,68.89448726,70.6,86.5,46,41.7,77.8,81.5,50.9 36 | 2004,47.88714025,43.46649345,61.3,61.91458697,49.95089449,64.2,22.2,78.65074774,18.2,68.45473436,70.8,86.5,44.7,42.1,77.8,80.7,50.5 37 | 2005,47.67275409,43.10036784,61.4,61.50098432,49.79185139,63.4,20.6,79.06712173,17.9,68.57122114,69.9,86,45.1,41.6,77.5,81.2,50 38 | 2006,46.79029957,44.49933107,61.6,60.17284465,49.21091439,63,18.6,78.68630551,16.8,68.29759443,69.6,85.9,44.1,40.8,77.4,81.2,49.8 39 | 2007,47.60502633,43.10045895,61.4,59.41199314,49.00045935,62.5,17.6,78.72141311,16.8,67.87492278,70.2,85.4,44.1,40.7,77.1,82.1,49.3 40 | 2008,47.570834,42.71173041,60.7,59.30576517,48.88802678,62.4,17.8,79.19632674,16.5,67.59402834,70.2,85.2,43.3,40.7,77.2,81.7,49.4 41 | 2009,48.66722357,43.34892051,61,58.48958333,48.84047414,62.8,18.1,79.5329087,16.8,67.96979204,69.3,85.1,43.3,40.7,77.1,82,49.4 42 | 2010,48.73004227,42.06672091,61.3,59.01025521,48.75798769,62.5,17.6,79.61862451,17.2,67.92810557,69,85,43.1,40.2,77,81.7,49.3 43 | 2011,50.03718193,42.7734375,61.2,58.7423969,48.18041792,62.2,18.2,79.43281184,17.5,68.42673015,69.5,84.8,43.1,40.1,76.7,81.9,49.2 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12 | 1970,91.5062113063745 13 | 1971,98.56202384418131 14 | 1972,112.15981364037599 15 | 1973,136.769878359668 16 | 1974,142.254742077706 17 | 1975,161.162492226686 18 | 1976,151.627687364405 19 | 1977,172.349014326931 20 | 1978,148.38211152019198 21 | 1979,176.85652540572897 22 | 1980,189.649992463987 23 | 1981,194.369049090197 24 | 1982,203.549627211606 25 | 1983,228.950200773115 26 | 1984,258.082147252256 27 | 1985,307.47958585233897 28 | 1986,298.805792971544 29 | 1987,271.349773463863 30 | 1988,310.722213686031 31 | 1989,345.957485871286 32 | 1990,358.973230048399 33 | 1991,381.454703832753 34 | 1992,424.93406593406604 35 | 1993,442.87459638711897 36 | 1994,562.2611298687739 37 | 1995,732.032045217766 38 | 1996,860.8440980491209 39 | 1997,958.15942483534 40 | 1998,1025.27690207873 41 | 1999,1089.44710870589 42 | 2000,1205.26067839196 43 | 2001,1332.23471988982 44 | 2002,1461.90648785792 45 | 2003,1649.92871813459 46 | 2004,1941.74560216509 47 | 2005,2268.5989041162798 48 | 2006,2729.7840319060897 49 | 2007,3523.0943148209 50 | 2008,4558.4310734382 51 | 2009,5059.41973826741 52 | 2010,6039.65850848559 53 | 2011,7492.4320978101105 54 | 2012,8461.62316271407 55 | 2013,9490.60260014849 56 | 2014,10351.1117622164 57 | 2015,10866.4439983942 58 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/GDP/gdp_usa.csv: -------------------------------------------------------------------------------- 1 | DATE,VALUE 2 | 1947-01-01,243.1 3 | 1947-04-01,246.3 4 | 1947-07-01,250.1 5 | 1947-10-01,260.3 6 | 1948-01-01,266.2 7 | 1948-04-01,272.9 8 | 1948-07-01,279.5 9 | 1948-10-01,280.7 10 | 1949-01-01,275.4 11 | 1949-04-01,271.7 12 | 1949-07-01,273.3 13 | 1949-10-01,271.0 14 | 1950-01-01,281.2 15 | 1950-04-01,290.7 16 | 1950-07-01,308.5 17 | 1950-10-01,320.3 18 | 1951-01-01,336.4 19 | 1951-04-01,344.5 20 | 1951-07-01,351.8 21 | 1951-10-01,356.6 22 | 1952-01-01,360.2 23 | 1952-04-01,361.4 24 | 1952-07-01,368.1 25 | 1952-10-01,381.2 26 | 1953-01-01,388.5 27 | 1953-04-01,392.3 28 | 1953-07-01,391.7 29 | 1953-10-01,386.5 30 | 1954-01-01,385.9 31 | 1954-04-01,386.7 32 | 1954-07-01,391.6 33 | 1954-10-01,400.3 34 | 1955-01-01,413.8 35 | 1955-04-01,422.2 36 | 1955-07-01,430.9 37 | 1955-10-01,437.8 38 | 1956-01-01,440.5 39 | 1956-04-01,446.8 40 | 1956-07-01,452.0 41 | 1956-10-01,461.3 42 | 1957-01-01,470.6 43 | 1957-04-01,472.8 44 | 1957-07-01,480.3 45 | 1957-10-01,475.7 46 | 1958-01-01,468.4 47 | 1958-04-01,472.8 48 | 1958-07-01,486.7 49 | 1958-10-01,500.4 50 | 1959-01-01,511.1 51 | 1959-04-01,524.2 52 | 1959-07-01,525.2 53 | 1959-10-01,529.3 54 | 1960-01-01,543.3 55 | 1960-04-01,542.7 56 | 1960-07-01,546.0 57 | 1960-10-01,541.1 58 | 1961-01-01,545.9 59 | 1961-04-01,557.4 60 | 1961-07-01,568.2 61 | 1961-10-01,581.6 62 | 1962-01-01,595.2 63 | 1962-04-01,602.6 64 | 1962-07-01,609.6 65 | 1962-10-01,613.1 66 | 1963-01-01,622.7 67 | 1963-04-01,631.8 68 | 1963-07-01,645.0 69 | 1963-10-01,654.8 70 | 1964-01-01,671.1 71 | 1964-04-01,680.8 72 | 1964-07-01,692.8 73 | 1964-10-01,698.4 74 | 1965-01-01,719.2 75 | 1965-04-01,732.4 76 | 1965-07-01,750.2 77 | 1965-10-01,773.1 78 | 1966-01-01,797.3 79 | 1966-04-01,807.2 80 | 1966-07-01,820.8 81 | 1966-10-01,834.9 82 | 1967-01-01,846.0 83 | 1967-04-01,851.1 84 | 1967-07-01,866.6 85 | 1967-10-01,883.2 86 | 1968-01-01,911.1 87 | 1968-04-01,936.3 88 | 1968-07-01,952.3 89 | 1968-10-01,970.1 90 | 1969-01-01,995.4 91 | 1969-04-01,1011.4 92 | 1969-07-01,1032.0 93 | 1969-10-01,1040.7 94 | 1970-01-01,1053.5 95 | 1970-04-01,1070.1 96 | 1970-07-01,1088.5 97 | 1970-10-01,1091.5 98 | 1971-01-01,1137.8 99 | 1971-04-01,1159.4 100 | 1971-07-01,1180.3 101 | 1971-10-01,1193.6 102 | 1972-01-01,1233.8 103 | 1972-04-01,1270.1 104 | 1972-07-01,1293.8 105 | 1972-10-01,1332.0 106 | 1973-01-01,1380.7 107 | 1973-04-01,1417.6 108 | 1973-07-01,1436.8 109 | 1973-10-01,1479.1 110 | 1974-01-01,1494.7 111 | 1974-04-01,1534.2 112 | 1974-07-01,1563.4 113 | 1974-10-01,1603.0 114 | 1975-01-01,1619.6 115 | 1975-04-01,1656.4 116 | 1975-07-01,1713.8 117 | 1975-10-01,1765.9 118 | 1976-01-01,1824.5 119 | 1976-04-01,1856.9 120 | 1976-07-01,1890.5 121 | 1976-10-01,1938.4 122 | 1977-01-01,1992.5 123 | 1977-04-01,2060.2 124 | 1977-07-01,2122.4 125 | 1977-10-01,2168.7 126 | 1978-01-01,2208.7 127 | 1978-04-01,2336.6 128 | 1978-07-01,2398.9 129 | 1978-10-01,2482.2 130 | 1979-01-01,2531.6 131 | 1979-04-01,2595.9 132 | 1979-07-01,2670.4 133 | 1979-10-01,2730.7 134 | 1980-01-01,2796.5 135 | 1980-04-01,2799.9 136 | 1980-07-01,2860.0 137 | 1980-10-01,2993.5 138 | 1981-01-01,3131.8 139 | 1981-04-01,3167.3 140 | 1981-07-01,3261.2 141 | 1981-10-01,3283.5 142 | 1982-01-01,3273.8 143 | 1982-04-01,3331.3 144 | 1982-07-01,3367.1 145 | 1982-10-01,3407.8 146 | 1983-01-01,3480.3 147 | 1983-04-01,3583.8 148 | 1983-07-01,3692.3 149 | 1983-10-01,3796.1 150 | 1984-01-01,3912.8 151 | 1984-04-01,4015.0 152 | 1984-07-01,4087.4 153 | 1984-10-01,4147.6 154 | 1985-01-01,4237.0 155 | 1985-04-01,4302.3 156 | 1985-07-01,4394.6 157 | 1985-10-01,4453.1 158 | 1986-01-01,4516.3 159 | 1986-04-01,4555.2 160 | 1986-07-01,4619.6 161 | 1986-10-01,4669.4 162 | 1987-01-01,4736.2 163 | 1987-04-01,4821.5 164 | 1987-07-01,4900.5 165 | 1987-10-01,5022.7 166 | 1988-01-01,5090.6 167 | 1988-04-01,5207.7 168 | 1988-07-01,5299.5 169 | 1988-10-01,5412.7 170 | 1989-01-01,5527.4 171 | 1989-04-01,5628.4 172 | 1989-07-01,5711.6 173 | 1989-10-01,5763.4 174 | 1990-01-01,5890.8 175 | 1990-04-01,5974.7 176 | 1990-07-01,6029.5 177 | 1990-10-01,6023.3 178 | 1991-01-01,6054.9 179 | 1991-04-01,6143.6 180 | 1991-07-01,6218.4 181 | 1991-10-01,6279.3 182 | 1992-01-01,6380.8 183 | 1992-04-01,6492.3 184 | 1992-07-01,6586.5 185 | 1992-10-01,6697.6 186 | 1993-01-01,6748.2 187 | 1993-04-01,6829.6 188 | 1993-07-01,6904.2 189 | 1993-10-01,7032.8 190 | 1994-01-01,7136.3 191 | 1994-04-01,7269.8 192 | 1994-07-01,7352.3 193 | 1994-10-01,7476.7 194 | 1995-01-01,7545.3 195 | 1995-04-01,7604.9 196 | 1995-07-01,7706.5 197 | 1995-10-01,7799.5 198 | 1996-01-01,7893.1 199 | 1996-04-01,8061.5 200 | 1996-07-01,8159.0 201 | 1996-10-01,8287.1 202 | 1997-01-01,8402.1 203 | 1997-04-01,8551.9 204 | 1997-07-01,8691.8 205 | 1997-10-01,8788.3 206 | 1998-01-01,8889.7 207 | 1998-04-01,8994.7 208 | 1998-07-01,9146.5 209 | 1998-10-01,9325.7 210 | 1999-01-01,9447.1 211 | 1999-04-01,9557.0 212 | 1999-07-01,9712.3 213 | 1999-10-01,9926.1 214 | 2000-01-01,10031.0 215 | 2000-04-01,10278.3 216 | 2000-07-01,10357.4 217 | 2000-10-01,10472.3 218 | 2001-01-01,10508.1 219 | 2001-04-01,10638.4 220 | 2001-07-01,10639.5 221 | 2001-10-01,10701.3 222 | 2002-01-01,10834.4 223 | 2002-04-01,10934.8 224 | 2002-07-01,11037.1 225 | 2002-10-01,11103.8 226 | 2003-01-01,11230.1 227 | 2003-04-01,11370.7 228 | 2003-07-01,11625.1 229 | 2003-10-01,11816.8 230 | 2004-01-01,11988.4 231 | 2004-04-01,12181.4 232 | 2004-07-01,12367.7 233 | 2004-10-01,12562.2 234 | 2005-01-01,12813.7 235 | 2005-04-01,12974.1 236 | 2005-07-01,13205.4 237 | 2005-10-01,13381.6 238 | 2006-01-01,13648.9 239 | 2006-04-01,13799.8 240 | 2006-07-01,13908.5 241 | 2006-10-01,14066.4 242 | 2007-01-01,14233.2 243 | 2007-04-01,14422.3 244 | 2007-07-01,14569.7 245 | 2007-10-01,14685.3 246 | 2008-01-01,14668.4 247 | 2008-04-01,14813.0 248 | 2008-07-01,14843.0 249 | 2008-10-01,14549.9 250 | 2009-01-01,14383.9 251 | 2009-04-01,14340.4 252 | 2009-07-01,14384.1 253 | 2009-10-01,14566.5 254 | 2010-01-01,14681.1 255 | 2010-04-01,14888.6 256 | 2010-07-01,15057.7 257 | 2010-10-01,15230.2 258 | 2011-01-01,15238.4 259 | 2011-04-01,15460.9 260 | 2011-07-01,15587.1 261 | 2011-10-01,15785.3 262 | 2012-01-01,15973.9 263 | 2012-04-01,16121.9 264 | 2012-07-01,16227.9 265 | 2012-10-01,16297.3 266 | 2013-01-01,16475.4 267 | 2013-04-01,16541.4 268 | 2013-07-01,16749.3 269 | 2013-10-01,16999.9 270 | 2014-01-01,17025.2 271 | 2014-04-01,17285.6 272 | 2014-07-01,17569.4 273 | 2014-10-01,17692.2 274 | 2015-01-01,17783.6 275 | 2015-04-01,17998.3 276 | 2015-07-01,18141.9 277 | 2015-10-01,18222.8 278 | 2016-01-01,18281.6 279 | 2016-04-01,18436.5 280 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Sales/feb-sales-Hardware.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-02-04 21:52:45,Acme Coporation,Hardware,14 3 | 2015-02-07 22:58:10,Acme Coporation,Hardware,1 4 | 2015-02-19 10:59:33,Mediacore,Hardware,16 5 | 2015-02-02 20:54:49,Mediacore,Hardware,9 6 | 2015-02-21 20:41:47,Hooli,Hardware,3 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Sales/feb-sales-Service.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-02-26 08:57:45,Streeplex,Service,4 3 | 2015-02-25 00:29:00,Initech,Service,10 4 | 2015-02-09 08:57:30,Streeplex,Service,19 5 | 2015-02-26 08:58:51,Streeplex,Service,1 6 | 2015-02-05 22:05:03,Hooli,Service,10 7 | 2015-02-19 16:02:58,Mediacore,Service,10 8 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Sales/feb-sales-Software.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-02-16 12:09:19,Hooli,Software,10 3 | 2015-02-03 14:14:18,Initech,Software,13 4 | 2015-02-02 08:33:01,Hooli,Software,3 5 | 2015-02-05 01:53:06,Acme Coporation,Software,19 6 | 2015-02-11 20:03:08,Initech,Software,7 7 | 2015-02-09 13:09:55,Mediacore,Software,7 8 | 2015-02-11 22:50:44,Hooli,Software,4 9 | 2015-02-04 15:36:29,Streeplex,Software,13 10 | 2015-02-21 05:01:26,Mediacore,Software,3 11 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Sales/sales-feb-2015.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-02-26 08:57:45,Streeplex,Service,4 3 | 2015-02-16 12:09:19,Hooli,Software,10 4 | 2015-02-03 14:14:18,Initech,Software,13 5 | 2015-02-02 08:33:01,Hooli,Software,3 6 | 2015-02-25 00:29:00,Initech,Service,10 7 | 2015-02-05 01:53:06,Acme Coporation,Software,19 8 | 2015-02-09 08:57:30,Streeplex,Service,19 9 | 2015-02-11 20:03:08,Initech,Software,7 10 | 2015-02-04 21:52:45,Acme Coporation,Hardware,14 11 | 2015-02-09 13:09:55,Mediacore,Software,7 12 | 2015-02-07 22:58:10,Acme Coporation,Hardware,1 13 | 2015-02-11 22:50:44,Hooli,Software,4 14 | 2015-02-26 08:58:51,Streeplex,Service,1 15 | 2015-02-05 22:05:03,Hooli,Service,10 16 | 2015-02-04 15:36:29,Streeplex,Software,13 17 | 2015-02-19 16:02:58,Mediacore,Service,10 18 | 2015-02-19 10:59:33,Mediacore,Hardware,16 19 | 2015-02-02 20:54:49,Mediacore,Hardware,9 20 | 2015-02-21 05:01:26,Mediacore,Software,3 21 | 2015-02-21 20:41:47,Hooli,Hardware,3 22 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Sales/sales-jan-2015.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-01-21 19:13:21,Streeplex,Hardware,11 3 | 2015-01-09 05:23:51,Streeplex,Service,8 4 | 2015-01-06 17:19:34,Initech,Hardware,17 5 | 2015-01-02 09:51:06,Hooli,Hardware,16 6 | 2015-01-11 14:51:02,Hooli,Hardware,11 7 | 2015-01-01 07:31:20,Acme Coporation,Software,18 8 | 2015-01-24 08:01:16,Initech,Software,1 9 | 2015-01-25 15:40:07,Initech,Service,6 10 | 2015-01-13 05:36:12,Hooli,Service,7 11 | 2015-01-03 18:00:19,Hooli,Service,19 12 | 2015-01-16 00:33:47,Hooli,Hardware,17 13 | 2015-01-16 07:21:12,Initech,Service,13 14 | 2015-01-20 19:49:24,Acme Coporation,Hardware,12 15 | 2015-01-26 01:50:25,Acme Coporation,Software,14 16 | 2015-01-15 02:38:25,Acme Coporation,Service,16 17 | 2015-01-06 13:47:37,Acme Coporation,Software,16 18 | 2015-01-15 15:33:40,Mediacore,Hardware,7 19 | 2015-01-27 07:11:55,Streeplex,Service,18 20 | 2015-01-20 11:28:02,Streeplex,Software,13 21 | 2015-01-16 19:20:46,Mediacore,Service,8 22 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Sales/sales-mar-2015.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-03-22 14:42:25,Mediacore,Software,6 3 | 2015-03-12 18:33:06,Initech,Service,19 4 | 2015-03-22 03:58:28,Streeplex,Software,8 5 | 2015-03-15 00:53:12,Hooli,Hardware,19 6 | 2015-03-17 19:25:37,Hooli,Hardware,10 7 | 2015-03-16 05:54:06,Mediacore,Software,3 8 | 2015-03-25 10:18:10,Initech,Hardware,9 9 | 2015-03-25 16:42:42,Streeplex,Hardware,12 10 | 2015-03-26 05:20:04,Streeplex,Software,3 11 | 2015-03-06 10:11:45,Mediacore,Software,17 12 | 2015-03-22 21:14:39,Initech,Hardware,11 13 | 2015-03-17 19:38:12,Hooli,Hardware,8 14 | 2015-03-28 19:20:38,Acme Coporation,Service,5 15 | 2015-03-13 04:41:32,Streeplex,Hardware,8 16 | 2015-03-06 02:03:56,Mediacore,Software,17 17 | 2015-03-13 11:40:16,Initech,Software,11 18 | 2015-03-27 08:29:45,Mediacore,Software,6 19 | 2015-03-21 06:42:41,Mediacore,Hardware,19 20 | 2015-03-15 08:50:45,Initech,Hardware,18 21 | 2015-03-13 16:25:24,Streeplex,Software,9 22 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/Bronze.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,1052 3 | URS,Soviet Union,584 4 | GBR,United Kingdom,505 5 | FRA,France,475 6 | GER,Germany,454 7 | AUS,Australia,413 8 | ITA,Italy,374 9 | HUN,Hungary,345 10 | SWE,Sweden,325 11 | NED,Netherlands,320 12 | ROU,Romania,282 13 | JPN,Japan,270 14 | RUS,Russia,240 15 | CAN,Canada,227 16 | GDR,East Germany,225 17 | POL,Poland,223 18 | FIN,Finland,209 19 | CHN,China,193 20 | FRG,West Germany,180 21 | BRA,Brazil,174 22 | DEN,Denmark,152 23 | BEL,Belgium,150 24 | NOR,Norway,144 25 | SUI,Switzerland,138 26 | BUL,Bulgaria,136 27 | KOR,"Korea, South",135 28 | YUG,Yugoslavia,118 29 | CUB,Cuba,109 30 | TCH,Czechoslovakia,105 31 | ESP,Spain,92 32 | EUA,Unified team of Germany,92 33 | ARG,Argentina,88 34 | UKR,Ukraine,78 35 | EUN,Unified team,66 36 | NZL,New Zealand,61 37 | BLR,Belarus,53 38 | GRE,Greece,49 39 | MEX,Mexico,48 40 | AUT,Austria,44 41 | LTU,Lithuania,42 42 | JAM,Jamaica,39 43 | RSA,South Africa,36 44 | PAK,Pakistan,34 45 | IND,India,34 46 | PRK,"Korea, North",30 47 | URU,Uruguay,30 48 | NGR,Nigeria,27 49 | KEN,Kenya,24 50 | TUR,Turkey,22 51 | IRI,Iran,22 52 | CHI,Chile,21 53 | EST,Estonia,19 54 | CRO,Croatia,18 55 | POR,Portugal,18 56 | GHA,Ghana,15 57 | KAZ,Kazakhstan,14 58 | ETH,Ethiopia,14 59 | SRB,Serbia,14 60 | TPE,Taiwan,13 61 | CZE,Czech Republic,13 62 | INA,Indonesia,12 63 | TRI,Trinidad and Tobago,11 64 | SLO,Slovenia,11 65 | GEO,Georgia,11 66 | ZZX,Mixed teams,10 67 | EGY,Egypt,10 68 | MGL,Mongolia,10 69 | MAR,Morocco,10 70 | THA,Thailand,10 71 | RU1,0,9 72 | AZE,Azerbaijan,9 73 | SVK,Slovakia,8 74 | IRL,Ireland,8 75 | UZB,Uzbekistan,8 76 | ALG,Algeria,8 77 | VEN,Venezuela,8 78 | COL,Colombia,7 79 | PHI,Philippines,7 80 | ARM,Armenia,7 81 | HAI,Haiti,7 82 | BOH,Bohemia,6 83 | BAH,Bahamas,5 84 | ANZ,Australasia,5 85 | ISR,Israel,5 86 | PUR,Puerto Rico*,5 87 | BWI,British West Indies,5 88 | LAT,Latvia,4 89 | MAS,Malaysia,3 90 | MDA,Moldova,3 91 | TUN,Tunisia,3 92 | ISL,Iceland,2 93 | UGA,Uganda,2 94 | LIB,Lebanon,2 95 | CRC,Costa Rica,2 96 | IOP,Independent Olympic Participants (1992),2 97 | KGZ,Kyrgyzstan,2 98 | PAN,Panama,2 99 | QAT,Qatar,2 100 | ZIM,Zimbabwe,1 101 | CMR,Cameroon,1 102 | DOM,Dominican Republic,1 103 | SYR,Syria,1 104 | KSA,Saudi Arabia,1 105 | TJK,Tajikistan,1 106 | ZAM,Zambia,1 107 | MOZ,Mozambique,1 108 | SUR,Suriname,1 109 | AFG,Afghanistan,1 110 | BAR,Barbados,1 111 | BER,Bermuda*,1 112 | DJI,Djibouti,1 113 | ERI,Eritrea,1 114 | GUY,Guyana,1 115 | IRQ,Iraq,1 116 | KUW,Kuwait,1 117 | MKD,Macedonia,1 118 | MRI,Mauritius,1 119 | NIG,Niger,1 120 | TOG,Togo,1 121 | PAR,Paraguay, 122 | PER,Peru, 123 | SCG,Serbia, 124 | NAM,Namibia, 125 | SIN,Singapore, 126 | HKG,Hong Kong*, 127 | SRI,Sri Lanka, 128 | TAN,Tanzania, 129 | VIE,Vietnam, 130 | ECU,Ecuador, 131 | LUX,Luxembourg, 132 | AHO,Netherlands Antilles*, 133 | CIV,Cote d'Ivoire, 134 | ISV,Virgin Islands*, 135 | SEN,Senegal, 136 | SUD,Sudan, 137 | TGA,Tonga, 138 | BDI,Burundi, 139 | UAE,United Arab Emirates, 140 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/Gold.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,2088.0 3 | URS,Soviet Union,838.0 4 | GBR,United Kingdom,498.0 5 | FRA,France,378.0 6 | GER,Germany,407.0 7 | AUS,Australia,293.0 8 | ITA,Italy,460.0 9 | HUN,Hungary,400.0 10 | SWE,Sweden,347.0 11 | NED,Netherlands,212.0 12 | ROU,Romania,155.0 13 | JPN,Japan,206.0 14 | RUS,Russia,192.0 15 | CAN,Canada,154.0 16 | GDR,East Germany,329.0 17 | POL,Poland,103.0 18 | FIN,Finland,124.0 19 | CHN,China,234.0 20 | FRG,West Germany,143.0 21 | BRA,Brazil,59.0 22 | DEN,Denmark,147.0 23 | BEL,Belgium,91.0 24 | NOR,Norway,194.0 25 | SUI,Switzerland,73.0 26 | BUL,Bulgaria,53.0 27 | KOR,"Korea, South",140.0 28 | YUG,Yugoslavia,143.0 29 | CUB,Cuba,160.0 30 | TCH,Czechoslovakia,80.0 31 | ESP,Spain,92.0 32 | EUA,Unified team of Germany,68.0 33 | ARG,Argentina,68.0 34 | UKR,Ukraine,32.0 35 | EUN,Unified team,92.0 36 | NZL,New Zealand,76.0 37 | BLR,Belarus,14.0 38 | GRE,Greece,34.0 39 | MEX,Mexico,14.0 40 | AUT,Austria,21.0 41 | LTU,Lithuania,4.0 42 | JAM,Jamaica,23.0 43 | RSA,South Africa,24.0 44 | PAK,Pakistan,42.0 45 | IND,India,128.0 46 | PRK,"Korea, North",10.0 47 | URU,Uruguay,44.0 48 | NGR,Nigeria,19.0 49 | KEN,Kenya,26.0 50 | TUR,Turkey,37.0 51 | IRI,Iran,11.0 52 | CHI,Chile,3.0 53 | EST,Estonia,9.0 54 | CRO,Croatia,31.0 55 | POR,Portugal,4.0 56 | GHA,Ghana, 57 | KAZ,Kazakhstan,9.0 58 | ETH,Ethiopia,18.0 59 | SRB,Serbia, 60 | TPE,Taiwan,2.0 61 | CZE,Czech Republic,10.0 62 | INA,Indonesia,9.0 63 | TRI,Trinidad and Tobago,1.0 64 | SLO,Slovenia,4.0 65 | GEO,Georgia,5.0 66 | ZZX,Mixed teams,23.0 67 | EGY,Egypt,7.0 68 | MGL,Mongolia,2.0 69 | MAR,Morocco,6.0 70 | THA,Thailand,7.0 71 | RU1,0,1.0 72 | AZE,Azerbaijan,4.0 73 | SVK,Slovakia,10.0 74 | IRL,Ireland,8.0 75 | UZB,Uzbekistan,4.0 76 | ALG,Algeria,4.0 77 | VEN,Venezuela,1.0 78 | COL,Colombia,1.0 79 | PHI,Philippines, 80 | ARM,Armenia,1.0 81 | HAI,Haiti, 82 | BOH,Bohemia, 83 | BAH,Bahamas,9.0 84 | ANZ,Australasia,20.0 85 | ISR,Israel,1.0 86 | PUR,Puerto Rico*, 87 | BWI,British West Indies, 88 | LAT,Latvia,2.0 89 | MAS,Malaysia, 90 | MDA,Moldova, 91 | TUN,Tunisia,2.0 92 | ISL,Iceland, 93 | UGA,Uganda,1.0 94 | LIB,Lebanon, 95 | CRC,Costa Rica,1.0 96 | IOP,Independent Olympic Participants (1992), 97 | KGZ,Kyrgyzstan, 98 | PAN,Panama,1.0 99 | QAT,Qatar, 100 | ZIM,Zimbabwe,18.0 101 | CMR,Cameroon,20.0 102 | DOM,Dominican Republic,2.0 103 | SYR,Syria,1.0 104 | KSA,Saudi Arabia, 105 | TJK,Tajikistan, 106 | ZAM,Zambia, 107 | MOZ,Mozambique,1.0 108 | SUR,Suriname,1.0 109 | AFG,Afghanistan, 110 | BAR,Barbados, 111 | BER,Bermuda*, 112 | DJI,Djibouti, 113 | ERI,Eritrea, 114 | GUY,Guyana, 115 | IRQ,Iraq, 116 | KUW,Kuwait, 117 | MKD,Macedonia, 118 | MRI,Mauritius, 119 | NIG,Niger, 120 | TOG,Togo, 121 | PAR,Paraguay, 122 | PER,Peru,1.0 123 | SCG,Serbia, 124 | NAM,Namibia, 125 | SIN,Singapore, 126 | HKG,Hong Kong*,1.0 127 | SRI,Sri Lanka, 128 | TAN,Tanzania, 129 | VIE,Vietnam, 130 | ECU,Ecuador,1.0 131 | LUX,Luxembourg,1.0 132 | AHO,Netherlands Antilles*, 133 | CIV,Cote d'Ivoire, 134 | ISV,Virgin Islands*, 135 | SEN,Senegal, 136 | SUD,Sudan, 137 | TGA,Tonga, 138 | BDI,Burundi,1.0 139 | UAE,United Arab Emirates,1.0 140 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/Silver.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,1195.0 3 | URS,Soviet Union,627.0 4 | GBR,United Kingdom,591.0 5 | FRA,France,461.0 6 | GER,Germany,350.0 7 | AUS,Australia,369.0 8 | ITA,Italy,394.0 9 | HUN,Hungary,308.0 10 | SWE,Sweden,349.0 11 | NED,Netherlands,250.0 12 | ROU,Romania,187.0 13 | JPN,Japan,228.0 14 | RUS,Russia,206.0 15 | CAN,Canada,211.0 16 | GDR,East Germany,271.0 17 | POL,Poland,173.0 18 | FIN,Finland,118.0 19 | CHN,China,252.0 20 | FRG,West Germany,167.0 21 | BRA,Brazil,139.0 22 | DEN,Denmark,192.0 23 | BEL,Belgium,167.0 24 | NOR,Norway,199.0 25 | SUI,Switzerland,165.0 26 | BUL,Bulgaria,142.0 27 | KOR,"Korea, South",191.0 28 | YUG,Yugoslavia,174.0 29 | CUB,Cuba,126.0 30 | TCH,Czechoslovakia,144.0 31 | ESP,Spain,193.0 32 | EUA,Unified team of Germany,100.0 33 | ARG,Argentina,83.0 34 | UKR,Ukraine,38.0 35 | EUN,Unified team,65.0 36 | NZL,New Zealand,27.0 37 | BLR,Belarus,25.0 38 | GRE,Greece,62.0 39 | MEX,Mexico,18.0 40 | AUT,Austria,81.0 41 | LTU,Lithuania,4.0 42 | JAM,Jamaica,40.0 43 | RSA,South Africa,37.0 44 | PAK,Pakistan,45.0 45 | IND,India,16.0 46 | PRK,"Korea, North",12.0 47 | URU,Uruguay,2.0 48 | NGR,Nigeria,38.0 49 | KEN,Kenya,31.0 50 | TUR,Turkey,23.0 51 | IRI,Iran,15.0 52 | CHI,Chile,9.0 53 | EST,Estonia,9.0 54 | CRO,Croatia,30.0 55 | POR,Portugal,9.0 56 | GHA,Ghana,1.0 57 | KAZ,Kazakhstan,16.0 58 | ETH,Ethiopia,6.0 59 | SRB,Serbia,1.0 60 | TPE,Taiwan,27.0 61 | CZE,Czech Republic,18.0 62 | INA,Indonesia,14.0 63 | TRI,Trinidad and Tobago,8.0 64 | SLO,Slovenia,6.0 65 | GEO,Georgia,2.0 66 | ZZX,Mixed teams,15.0 67 | EGY,Egypt,7.0 68 | MGL,Mongolia,7.0 69 | MAR,Morocco,5.0 70 | THA,Thailand,4.0 71 | RU1,0,7.0 72 | AZE,Azerbaijan,3.0 73 | SVK,Slovakia,11.0 74 | IRL,Ireland,8.0 75 | UZB,Uzbekistan,5.0 76 | ALG,Algeria,2.0 77 | VEN,Venezuela,2.0 78 | COL,Colombia,3.0 79 | PHI,Philippines,2.0 80 | ARM,Armenia,1.0 81 | HAI,Haiti,1.0 82 | BOH,Bohemia,1.0 83 | BAH,Bahamas,9.0 84 | ANZ,Australasia,4.0 85 | ISR,Israel,1.0 86 | PUR,Puerto Rico*,1.0 87 | BWI,British West Indies, 88 | LAT,Latvia,11.0 89 | MAS,Malaysia,3.0 90 | MDA,Moldova,3.0 91 | TUN,Tunisia,2.0 92 | ISL,Iceland,15.0 93 | UGA,Uganda,3.0 94 | LIB,Lebanon,2.0 95 | CRC,Costa Rica,1.0 96 | IOP,Independent Olympic Participants (1992),1.0 97 | KGZ,Kyrgyzstan,1.0 98 | PAN,Panama, 99 | QAT,Qatar, 100 | ZIM,Zimbabwe,4.0 101 | CMR,Cameroon,1.0 102 | DOM,Dominican Republic,1.0 103 | SYR,Syria,1.0 104 | KSA,Saudi Arabia,1.0 105 | TJK,Tajikistan,1.0 106 | ZAM,Zambia,1.0 107 | MOZ,Mozambique, 108 | SUR,Suriname, 109 | AFG,Afghanistan, 110 | BAR,Barbados, 111 | BER,Bermuda*, 112 | DJI,Djibouti, 113 | ERI,Eritrea, 114 | GUY,Guyana, 115 | IRQ,Iraq, 116 | KUW,Kuwait, 117 | MKD,Macedonia, 118 | MRI,Mauritius, 119 | NIG,Niger, 120 | TOG,Togo, 121 | PAR,Paraguay,17.0 122 | PER,Peru,14.0 123 | SCG,Serbia,14.0 124 | NAM,Namibia,4.0 125 | SIN,Singapore,4.0 126 | HKG,Hong Kong*,2.0 127 | SRI,Sri Lanka,2.0 128 | TAN,Tanzania,2.0 129 | VIE,Vietnam,2.0 130 | ECU,Ecuador,1.0 131 | LUX,Luxembourg,1.0 132 | AHO,Netherlands Antilles*,1.0 133 | CIV,Cote d'Ivoire,1.0 134 | ISV,Virgin Islands*,1.0 135 | SEN,Senegal,1.0 136 | SUD,Sudan,1.0 137 | TGA,Tonga,1.0 138 | BDI,Burundi, 139 | UAE,United Arab Emirates, 140 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/Summer Olympic medalists 1896 to 2008 - EDITIONS.tsv: -------------------------------------------------------------------------------- 1 | Edition Bronze Gold Silver Grand Total City Country 2 | 1896 40 64 47 151 Athens Greece 3 | 1900 142 178 192 512 Paris France 4 | 1904 123 188 159 470 St. Louis United States 5 | 1908 211 311 282 804 London United Kingdom 6 | 1912 284 301 300 885 Stockholm Sweden 7 | 1920 355 497 446 1298 Antwerp Belgium 8 | 1924 285 301 298 884 Paris France 9 | 1928 242 229 239 710 Amsterdam Netherlands 10 | 1932 196 213 206 615 Los Angeles United States 11 | 1936 282 299 294 875 Berlin Germany 12 | 1948 268 276 270 814 London United Kingdom 13 | 1952 299 300 290 889 Helsinki Finland 14 | 1956 297 298 290 885 Melbourne Australia 15 | 1960 301 295 286 882 Rome Italy 16 | 1964 343 330 337 1010 Tokyo Japan 17 | 1968 351 341 339 1031 Mexico City Mexico 18 | 1972 412 387 386 1185 Munich West Germany (now Germany) 19 | 1976 447 429 429 1305 Montreal Canada 20 | 1980 472 460 455 1387 Moscow U.S.S.R. (now Russia) 21 | 1984 500 483 476 1459 Los Angeles United States 22 | 1988 535 506 505 1546 Seoul South Korea 23 | 1992 596 558 551 1705 Barcelona Spain 24 | 1996 634 615 610 1859 Atlanta United States 25 | 2000 685 663 667 2015 Sydney Australia 26 | 2004 679 659 660 1998 Athens Greece 27 | 2008 710 669 663 2042 Beijing China 28 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/Summer Olympic medalists 1896 to 2008 - IOC COUNTRY CODES.csv: -------------------------------------------------------------------------------- 1 | Country,NOC,ISO code 2 | Afghanistan,AFG,AF 3 | Albania,ALB,AL 4 | Algeria,ALG,DZ 5 | American Samoa*,ASA,AS 6 | Andorra,AND,AD 7 | Angola,ANG,AO 8 | Antigua and Barbuda,ANT,AG 9 | Argentina,ARG,AR 10 | Armenia,ARM,AM 11 | Aruba*,ARU,AW 12 | Australia,AUS,AU 13 | Austria,AUT,AT 14 | Azerbaijan,AZE,AZ 15 | Bahamas,BAH,BS 16 | Bahrain,BRN,BH 17 | Bangladesh,BAN,BD 18 | Barbados,BAR,BB 19 | Belarus,BLR,BY 20 | Belgium,BEL,BE 21 | Belize,BIZ,BZ 22 | Bermuda*,BER,BM 23 | Benin,BEN,BJ 24 | Bhutan,BHU,BT 25 | Bolivia,BOL,BO 26 | Bosnia and Herzegovina,BIH,BA 27 | Botswana,BOT,BW 28 | Brazil,BRA,BR 29 | British Virgin Islands,IVB,VG 30 | Brunei,BRU,BN 31 | Bulgaria,BUL,BG 32 | Burkina Faso,BUR,BF 33 | Burundi,BDI,BI 34 | Cambodia,CAM,KH 35 | Cameroon,CMR,CM 36 | Canada,CAN,CA 37 | Cape Verde,CPV,CV 38 | Cayman Islands*,CAY,KY 39 | Central African Republic,CAF,CF 40 | Chad,CHA,TD 41 | Chile,CHI,CL 42 | China,CHN,CN 43 | Colombia,COL,CO 44 | Comoros,COM,KM 45 | Congo,CGO,CG 46 | "Congo, Dem Rep",COD,CD 47 | Cook Islands,COK,CK 48 | Costa Rica,CRC,CR 49 | Cote d'Ivoire,CIV,CI 50 | Croatia,CRO,HR 51 | Cuba,CUB,CU 52 | Cyprus,CYP,CY 53 | Czech Republic,CZE,CZ 54 | Denmark,DEN,DK 55 | Djibouti,DJI,DJ 56 | Dominica,DMA,DM 57 | Dominican Republic,DOM,DO 58 | East Timor (Timor-Leste),TLS,TL 59 | Ecuador,ECU,EC 60 | Egypt,EGY,EG 61 | El Salvador,ESA,SV 62 | Equatorial Guinea,GEQ,GQ 63 | Eritrea,ERI,ER 64 | Estonia,EST,EE 65 | Ethiopia,ETH,ET 66 | Fiji,FIJ,FJ 67 | Finland,FIN,FI 68 | France,FRA,FR 69 | Gabon,GAB,GA 70 | Gambia,GAM,GM 71 | Georgia,GEO,GE 72 | Germany,GER,DE 73 | Ghana,GHA,GH 74 | Greece,GRE,GR 75 | Grenada,GRN,GD 76 | Guam,GUM,GU 77 | Guatemala,GUA,GT 78 | Guinea,GUI,GN 79 | Guinea-Bissau,GBS,GW 80 | Guyana,GUY,GY 81 | Haiti,HAI,HT 82 | Honduras,HON,HN 83 | Hong Kong*,HKG,HK 84 | Hungary,HUN,HU 85 | Iceland,ISL,IS 86 | India,IND,IN 87 | Indonesia,INA,ID 88 | Iran,IRI,IR 89 | Iraq,IRQ,IQ 90 | Ireland,IRL,IE 91 | Israel,ISR,IL 92 | Italy,ITA,IT 93 | Jamaica,JAM,JM 94 | Japan,JPN,JP 95 | Jordan,JOR,JO 96 | Kazakhstan,KAZ,KZ 97 | Kenya,KEN,KE 98 | "Korea, North",PRK,KP 99 | "Korea, South",KOR,KR 100 | Kuwait,KUW,KW 101 | Kyrgyzstan,KGZ,KG 102 | Laos,LAO,LA 103 | Latvia,LAT,LV 104 | Lebanon,LIB,LB 105 | Lesotho,LES,LS 106 | Liberia,LBR,LR 107 | Libya,LBA,LY 108 | Liechtenstein,LIE,LI 109 | Lithuania,LTU,LT 110 | Luxembourg,LUX,LU 111 | Macedonia,MKD,MK 112 | Madagascar,MAD,MG 113 | Malawi,MAW,MW 114 | Malaysia,MAS,MY 115 | Maldives,MDV,MV 116 | Mali,MLI,ML 117 | Malta,MLT,MT 118 | Mauritania,MTN,MR 119 | Mauritius,MRI,MU 120 | Mexico,MEX,MX 121 | Micronesia,FSM,FM 122 | Moldova,MDA,MD 123 | Monaco,MON,MC 124 | Mongolia,MGL,MN 125 | Morocco,MAR,MA 126 | Mozambique,MOZ,MZ 127 | Burma,MYA,MM 128 | Namibia,NAM, 129 | Nauru,NRU,NR 130 | Nepal,NEP,NP 131 | Netherlands,NED,NL 132 | Netherlands Antilles*,AHO,AN 133 | New Zealand,NZL,NZ 134 | Nicaragua,NCA,NI 135 | Niger,NIG,NE 136 | Nigeria,NGR,NG 137 | Norway,NOR,NO 138 | Oman,OMA,OM 139 | Pakistan,PAK,PK 140 | Palau,PLW,PW 141 | "Palestine, Occupied Territories",PLE,PS 142 | Panama,PAN,PA 143 | Papua New Guinea,PNG,PG 144 | Paraguay,PAR,PY 145 | Peru,PER,PE 146 | Philippines,PHI,PH 147 | Poland,POL,PL 148 | Portugal,POR,PT 149 | Puerto Rico*,PUR,PR 150 | Qatar,QAT,QA 151 | Romania,ROM,RO 152 | Russia,RUS,RU 153 | Rwanda,RWA,RW 154 | Saint Kitts and Nevis,SKN,KN 155 | Saint Lucia,LCA,LC 156 | Saint Vincent and the Grenadines,VIN,VC 157 | Samoa,SAM,WS 158 | San Marino,SMR,SM 159 | Sao Tome and Principe,STP,ST 160 | Saudi Arabia,KSA,SA 161 | Senegal,SEN,SN 162 | Serbia,SCG,RS 163 | Seychelles,SEY,SC 164 | Sierra Leone,SLE,SL 165 | Singapore,SIN,SG 166 | Slovakia,SVK,SK 167 | Slovenia,SLO,SI 168 | Solomon Islands,SOL,SB 169 | Somalia,SOM,SO 170 | South Africa,RSA,ZA 171 | Spain,ESP,ES 172 | Sri Lanka,SRI,LK 173 | Sudan,SUD,SD 174 | Suriname,SUR,SR 175 | Swaziland,SWZ,SZ 176 | Sweden,SWE,SE 177 | Switzerland,SUI,CH 178 | Syria,SYR,SY 179 | Taiwan,TPE,TW 180 | Tajikistan,TJK,TJ 181 | Tanzania,TAN,TZ 182 | Thailand,THA,TH 183 | Togo,TOG,TG 184 | Tonga,TGA,TO 185 | Trinidad and Tobago,TRI,TT 186 | Tunisia,TUN,TN 187 | Turkey,TUR,TR 188 | Turkmenistan,TKM,TM 189 | Uganda,UGA,UG 190 | Ukraine,UKR,UA 191 | United Arab Emirates,UAE,AE 192 | United Kingdom,GBR,GB 193 | United States,USA,US 194 | Uruguay,URU,UY 195 | Uzbekistan,UZB,UZ 196 | Vanuatu,VAN,VU 197 | Venezuela,VEN,VE 198 | Vietnam,VIE,VN 199 | Virgin Islands*,ISV,VI 200 | Yemen,YEM,YE 201 | Zambia,ZAM,ZM 202 | Zimbabwe,ZIM,ZW 203 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/bronze_sorted.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,1052 3 | URS,Soviet Union,584 4 | GBR,United Kingdom,505 5 | FRA,France,475 6 | GER,Germany,454 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/bronze_top5.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,1052 3 | URS,Soviet Union,584 4 | GBR,United Kingdom,505 5 | FRA,France,475 6 | GER,Germany,454 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/gold_top5.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,2088 3 | URS,Soviet Union,838 4 | GBR,United Kingdom,498 5 | ITA,Italy,460 6 | GER,Germany,407 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/Summer Olympic medals/silver_top5.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,1195 3 | USR,Soviet Union,627 4 | GBR,United Kingdom,591 5 | FRA,France,461 6 | ITA,Italy,394 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/exchange.csv: -------------------------------------------------------------------------------- 1 | "Date","GBP/USD" 2 | "2015/01/02",0.65101 3 | "2015/01/05",0.65644 4 | "2015/01/06",0.65896 5 | "2015/01/07",0.66344 6 | "2015/01/08",0.66151 7 | "2015/01/09",0.66004 8 | 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"2015/12/02",0.66998 233 | "2015/12/03",0.66281 234 | "2015/12/04",0.66229 235 | "2015/12/07",0.66339 236 | "2015/12/08",0.66672 237 | "2015/12/09",0.65937 238 | "2015/12/10",0.65903 239 | "2015/12/11",0.65739 240 | "2015/12/14",0.66097 241 | "2015/12/15",0.66473 242 | "2015/12/16",0.66614 243 | "2015/12/17",0.67228 244 | "2015/12/18",0.67066 245 | "2015/12/21",0.67089 246 | "2015/12/22",0.67518 247 | "2015/12/23",0.67285 248 | "2015/12/24",0.66926 249 | "2015/12/29",0.67597 250 | "2015/12/30",0.67427 251 | "2015/12/31",0.67820 252 | 253 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/gdp-2013.csv: -------------------------------------------------------------------------------- 1 | Date,GDP 2 | 3/31/2012,15973.9 3 | 6/30/2012,16121.9 4 | 9/30/2012,16227.9 5 | 12/31/2012,16297.3 6 | 3/31/2013,16475.4 7 | 6/30/2013,16541.4 8 | 9/30/2013,16749.3 9 | 12/31/2013,16999.9 10 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/managers.csv: -------------------------------------------------------------------------------- 1 | branch_id,city,manager 2 | 10,Austin,Charlers 3 | 20,Denver,Joel 4 | 47,Mendocino,Brett 5 | 31,Springfield,Sally 6 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/managers_b.csv: -------------------------------------------------------------------------------- 1 | branch_id,branch,manager 2 | 10,Austin,Charlers 3 | 20,Denver,Joel 4 | 47,Mendocino,Brett 5 | 31,Springfield,Sally 6 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/oil_price.csv: -------------------------------------------------------------------------------- 1 | Date,Price 2 | 1970-01-01,3.35 3 | 1970-02-01,3.35 4 | 1970-03-01,3.35 5 | 1970-04-01,3.35 6 | 1970-05-01,3.35 7 | 1970-06-01,3.35 8 | 1970-07-01,3.31 9 | 1970-08-01,3.31 10 | 1970-09-01,3.31 11 | 1970-10-01,3.31 12 | 1970-11-01,3.31 13 | 1970-12-01,3.56 14 | 1971-01-01,3.56 15 | 1971-02-01,3.56 16 | 1971-03-01,3.56 17 | 1971-04-01,3.56 18 | 1971-05-01,3.56 19 | 1971-06-01,3.56 20 | 1971-07-01,3.56 21 | 1971-08-01,3.56 22 | 1971-09-01,3.56 23 | 1971-10-01,3.56 24 | 1971-11-01,3.56 25 | 1971-12-01,3.56 26 | 1972-01-01,3.56 27 | 1972-02-01,3.56 28 | 1972-03-01,3.56 29 | 1972-04-01,3.56 30 | 1972-05-01,3.56 31 | 1972-06-01,3.56 32 | 1972-07-01,3.56 33 | 1972-08-01,3.56 34 | 1972-09-01,3.56 35 | 1972-10-01,3.56 36 | 1972-11-01,3.56 37 | 1972-12-01,3.56 38 | 1973-01-01,3.56 39 | 1973-02-01,3.56 40 | 1973-03-01,3.56 41 | 1973-04-01,3.56 42 | 1973-05-01,3.56 43 | 1973-06-01,3.56 44 | 1973-07-01,3.56 45 | 1973-08-01,4.31 46 | 1973-09-01,4.31 47 | 1973-10-01,4.31 48 | 1973-11-01,4.31 49 | 1973-12-01,4.31 50 | 1974-01-01,10.11 51 | 1974-02-01,10.11 52 | 1974-03-01,10.11 53 | 1974-04-01,10.11 54 | 1974-05-01,10.11 55 | 1974-06-01,10.11 56 | 1974-07-01,10.11 57 | 1974-08-01,10.11 58 | 1974-09-01,10.11 59 | 1974-10-01,11.16 60 | 1974-11-01,11.16 61 | 1974-12-01,11.16 62 | 1975-01-01,11.16 63 | 1975-02-01,11.16 64 | 1975-03-01,11.16 65 | 1975-04-01,11.16 66 | 1975-05-01,11.16 67 | 1975-06-01,11.16 68 | 1975-07-01,11.16 69 | 1975-08-01,11.16 70 | 1975-09-01,11.16 71 | 1975-10-01,11.16 72 | 1975-11-01,11.16 73 | 1975-12-01,11.16 74 | 1976-01-01,11.16 75 | 1976-02-01,12.03 76 | 1976-03-01,12.1 77 | 1976-04-01,12.17 78 | 1976-05-01,12.17 79 | 1976-06-01,12.17 80 | 1976-07-01,12.17 81 | 1976-08-01,12.17 82 | 1976-09-01,13.9 83 | 1976-10-01,13.9 84 | 1976-11-01,13.9 85 | 1976-12-01,13.9 86 | 1977-01-01,13.9 87 | 1977-02-01,13.9 88 | 1977-03-01,13.9 89 | 1977-04-01,13.9 90 | 1977-05-01,13.9 91 | 1977-06-01,13.9 92 | 1977-07-01,13.9 93 | 1977-08-01,14.85 94 | 1977-09-01,14.85 95 | 1977-10-01,14.85 96 | 1977-11-01,14.85 97 | 1977-12-01,14.85 98 | 1978-01-01,14.85 99 | 1978-02-01,14.85 100 | 1978-03-01,14.85 101 | 1978-04-01,14.85 102 | 1978-05-01,14.85 103 | 1978-06-01,14.85 104 | 1978-07-01,14.85 105 | 1978-08-01,14.85 106 | 1978-09-01,14.85 107 | 1978-10-01,14.85 108 | 1978-11-01,14.85 109 | 1978-12-01,14.85 110 | 1979-01-01,14.85 111 | 1979-02-01,15.85 112 | 1979-03-01,15.85 113 | 1979-04-01,15.85 114 | 1979-05-01,18.1 115 | 1979-06-01,19.1 116 | 1979-07-01,21.75 117 | 1979-08-01,26.5 118 | 1979-09-01,28.5 119 | 1979-10-01,29.0 120 | 1979-11-01,31.0 121 | 1979-12-01,32.5 122 | 1980-01-01,32.5 123 | 1980-02-01,37.0 124 | 1980-03-01,38.0 125 | 1980-04-01,39.5 126 | 1980-05-01,39.5 127 | 1980-06-01,39.5 128 | 1980-07-01,39.5 129 | 1980-08-01,38.0 130 | 1980-09-01,36.0 131 | 1980-10-01,36.0 132 | 1980-11-01,36.0 133 | 1980-12-01,37.0 134 | 1981-01-01,38.0 135 | 1981-02-01,38.0 136 | 1981-03-01,38.0 137 | 1981-04-01,38.0 138 | 1981-05-01,38.0 139 | 1981-06-01,36.0 140 | 1981-07-01,36.0 141 | 1981-08-01,36.0 142 | 1981-09-01,36.0 143 | 1981-10-01,35.0 144 | 1981-11-01,36.0 145 | 1981-12-01,35.0 146 | 1982-01-01,33.85 147 | 1982-02-01,31.56 148 | 1982-03-01,28.48 149 | 1982-04-01,33.45 150 | 1982-05-01,35.93 151 | 1982-06-01,35.07 152 | 1982-07-01,34.16 153 | 1982-08-01,33.95 154 | 1982-09-01,35.63 155 | 1982-10-01,35.68 156 | 1982-11-01,34.15 157 | 1982-12-01,31.72 158 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/pa_counties.csv: -------------------------------------------------------------------------------- 1 | CITY NAME,COUNTY NAME 2 | SALTSBURG,INDIANA 3 | MINERAL SPRINGS,CLEARFIELD 4 | BIGLERVILLE,ADAMS 5 | HANNASTOWN,WESTMORELAND 6 | TUNKHANNOCK,WYOMING 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/pa_zipcode_city.csv: -------------------------------------------------------------------------------- 1 | Zipcode,City,State 2 | 17545,MANHEIM,PA 3 | 18455,PRESTON PARK,PA 4 | 17307,BIGLERVILLE,PA 5 | 15705,INDIANA,PA 6 | 16833,CURWENSVILLE,PA 7 | 16220,CROWN,PA 8 | 18618,HARVEYS LAKE,PA 9 | 16855,MINERAL SPRINGS,PA 10 | 16623,CASSVILLE,PA 11 | 15635,HANNASTOWN,PA 12 | 15681,SALTSBURG,PA 13 | 18657,TUNKHANNOCK,PA 14 | 15279,PITTSBURGH,PA 15 | 17231,LEMASTERS,PA 16 | 18821,GREAT BEND,PA 17 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/pa_zipcode_population.csv: -------------------------------------------------------------------------------- 1 | Zipcode,2010 Census Popuation 2 | 16855,282 3 | 15681,5241 4 | 18657,11985 5 | 17307,5899 6 | 15635,220 7 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/population_00.csv: -------------------------------------------------------------------------------- 1 | Zip Code ZCTA,2010 Census Population 2 | 57538,322 3 | 59916,130 4 | 37660,40038 5 | 2860,45199 6 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/population_01.csv: -------------------------------------------------------------------------------- 1 | Zip Code ZCTA,2010 Census Population 2 | 66407,479 3 | 72732,4716 4 | 50579,2405 5 | 46241,30670 6 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/population_02.csv: -------------------------------------------------------------------------------- 1 | Zip Code ZCTA,2010 Census Population 2 | 12776,2180 3 | 76092,26669 4 | 98360,12221 5 | 49464,27481 6 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/q1_rainfall_2013.csv: -------------------------------------------------------------------------------- 1 | Month,Percipitation 2 | Jan,0.096129 3 | Feb,0.067143 4 | Mar,0.061613 5 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/q1_rainfall_2014.csv: -------------------------------------------------------------------------------- 1 | Month,Percipitation 2 | Jan,0.050323 3 | Feb,0.082143 4 | Mar,0.070968 5 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/revenue.csv: -------------------------------------------------------------------------------- 1 | branch_id,city,revenue 2 | 10,Austin,100 3 | 20,Denver,83 4 | 30,Springfield,4 5 | 47,Mendocino,200 6 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/stocks-2013.csv: -------------------------------------------------------------------------------- 1 | Date,AAPL,IBM,CSCO,MSFT 2 | 1/31/2013,497.822381,197.271905,20.699524,27.236667 3 | 2/28/2013,456.808953,200.735788,20.988947,27.704211 4 | 3/31/2013,441.840998,210.978001,21.335,28.141 5 | 4/30/2013,419.764998,204.733636,20.914545,29.870909 6 | 5/31/2013,446.45273,205.263639,22.386364,33.950909 7 | 6/30/2013,425.537999,200.85,24.3755,34.6325 8 | 7/31/2013,429.157272,194.354546,25.378636,33.650454 9 | 8/31/2013,484.843635,187.125,24.948636,32.485 10 | 9/30/2013,480.184499,188.767,24.08,32.5235 11 | 10/31/2013,504.744783,180.710002,22.847391,34.382174 12 | 11/30/2013,524.616499,181.333502,22.204,37.3625 13 | 12/31/2013,559.657613,179.114763,21.257619,37.455715 14 | -------------------------------------------------------------------------------- /Merging_DataFrames_w_pandas/unemployment_00.csv: -------------------------------------------------------------------------------- 1 | Zip,unemployment,participants 2 | 2860,0.11,34447 3 | 46167,0.02,4800 4 | 1097,0.33,42 5 | 80808,0.07,4310 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Datacamp 3 | 4 | [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/franksalas/datacamp/master) 5 | 6 | 7 | My notes on [Datacamp](https://www.datacamp.com) courses. 8 | They are markdown duplicates (on jupyter notebooks) of the lecutres along with *my* solution to each problem. 9 | 10 | 11 | ## Getting Started 12 | They can be viewed on github or download them. 13 | 14 | ## Prerequisites 15 | Only if are downloading them to your system. 16 | - Python enviroment 17 | - Jupyter notebook 18 | 19 | 1. change directory 20 | 21 | ```bash 22 | cd datacamp 23 | ``` 24 | 25 | 2. Create a conda enviroment with `environment.yml` 26 | 27 | ```bash 28 | conda env create -f environment.yml -n $ENV_NAME 29 | 30 | ``` 31 | 32 | 33 | ## Couses 34 | These are the courses Im currently working on. 35 | 36 | 37 | ### [Data Types for Data Science](https://www.datacamp.com/courses/data-types-for-data-science) 38 | 39 | Start/finish: 1/30/18 - 2/1/18 40 | - [x] Fundamental data types 41 | - [x] Dictionaries - the root of Python 42 | - [x] Meet the collections module 43 | - [x] Handling Dates and Times 44 | - [x] Answering Data Science Questions 45 | 46 | 47 | ### [Introduction to Data Visualization with Python](https://www.datacamp.com/courses/introduction-to-data-visualization-with-python) 48 | 49 | Finish 2/14/18 50 | - [x] 01 Customizing plots 51 | - [x] 02 Plotting 2D arrays 52 | - [x] 03 Statistical plots with Seaborn 53 | - [X] 04 Analyzing time series and images 54 | 55 | --- 56 | 57 | 58 | ### [Pandas Foundations](https://www.datacamp.com/courses/pandas-foundations) 59 | 60 | Finish 3/6/18 61 | - [x] Data ingestion & instpection 62 | - [x] Exploratory data analysis 63 | - [x] Time series in pandas 64 | - [x] Case Study - Sunlight in Austin 65 | 66 | 67 | ### [Manipulating DataFrames with pandas](https://www.datacamp.com/courses/manipulating-dataframes-with-pandas) 68 | Finish 03/08/18 69 | - [x] Extracting and transforming data 70 | - [x] Advanced indexing 71 | - [x] Rearranging and reshaping data 72 | - [x] Grouping data 73 | - [x] Bringing it all together 74 | 75 | 76 | ### [Merging DataFrames with pandas](https://www.datacamp.com/courses/merging-dataframes-with-pandas) 77 | Finish 03/13/18 78 | - [x] Preparing data 79 | - [x] Concatenating data 80 | - [x] Merging data 81 | - [x] Case Study - Summer Olympics 82 | 83 | 84 | ### [Cleaning Data in Python](https://www.datacamp.com/courses/cleaning-data-in-python) 85 | finish 03/16/18 86 | - [x] Exploring your data 87 | - [x] Tidying data for analysis 88 | - [x] Combining data for analysis 89 | - [x] Cleaning data for analysis 90 | - [x] Case study 91 | 92 | 93 | --- 94 | 95 | ### [Python Data Science Toolbox (Part 2)](https://www.datacamp.com/courses/python-data-science-toolbox-part-2) 96 | finish 03/17/18 97 | - [x] Using iterators in PythonLand 98 | - [x] List comprehensions and generators 99 | - [x] Bringing it all together! 100 | 101 | 102 | 103 | ### [Importing Data in Python (Part 1)](https://www.datacamp.com/courses/importing-data-in-python-part-1) 104 | finish 03/17/18 105 | - [x] Introduction and flat files 106 | - [x] Importing data from other file types 107 | - [x] Working with relational databases in Python 108 | 109 | 110 | 111 | ### [Importing Data in Python (Part 2)](https://www.datacamp.com/courses/importing-data-in-python-part-2) 112 | finish 03/18/18 113 | - [x] Importing data from the Internet 114 | - [x] Interacting with APIs to import data from the web 115 | - [x] Diving deep into the Twitter API 116 | 117 | 118 | 119 | --- 120 | 121 | ### [Interactive Data Visualization with Bokeh](https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh) 122 | 123 | - [x] Basic plotting with Bokeh 124 | - [x] Layouts, Interactions, and Annotations 125 | - [ ] Building interactive apps with Bokeh 126 | - [ ] Putting It All Together! A Case Study 127 | 128 | 129 | 130 | ### [Introduction to Databases in Python](https://www.datacamp.com/courses/introduction-to-relational-databases-in-python) 131 | 132 | finish 03/23/18 133 | - [x] Basics of Relational Databases 134 | - [x] Applying Filtering, Ordering and Grouping to Queries 135 | - [x] Advanced SQLAlchemy Queries 136 | - [x] Creating and Manipulating your own Databases 137 | - [x] Putting it all together! 138 | 139 | ### [Statistical Thinking in Python (Part 1) ](https://www.datacamp.com/courses/statistical-thinking-in-python-part-1) 140 | - [x] Graphical exploratory data analysis 141 | - [x] Quantitative exploratory data analysis 142 | - [x] Thinking probabilistically-- Discrete variables 143 | - [x] Work with Multiple Time Series 144 | - [x] Thinking probabilistically-- Continuous variables 145 | ### [Statistical Thinking in Python (Part 1) ](https://www.datacamp.com/courses/statistical-thinking-in-python-part-2) 146 | - [x] Parameter estimation by optimization 147 | - [x] Bootstrap confidence intervals 148 | - [x] Introduction to hypothesis testing 149 | - [x] Hypothesis test examples 150 | - [x] Putting it all together: a case study 151 | 152 | 153 | ### [Supervised Learning with scikit-learn ](https://www.datacamp.com/courses/supervised-learning-with-scikit-learn) 154 | - [x] Classification 155 | - [x] Regression 156 | - [ ] Fine-tuning your model 157 | - [ ] Preprocessing and pipelines 158 | 159 | 160 | 161 | 162 | ### [Visualizing Time Series Data in Python](https://www.datacamp.com/courses/visualizing-time-series-data-in-python) 163 | - [x] Introduction 164 | - [x] Summary Statistics and Diagnostics 165 | - [ ] Seasonality, Trend and Noise 166 | - [ ] Work with Multiple Time Series 167 | - [ ] Case Study 168 | -------------------------------------------------------------------------------- /Statistical_Thinking_Python/anscombe.csv: -------------------------------------------------------------------------------- 1 | 0,0,1,1,2,2,3,3 2 | x,y,x,y,x,y,x,y 3 | 10.0,8.04,10.0,9.14,10.0,7.46,8.0,6.58 4 | 8.0,6.95,8.0,8.14,8.0,6.77,8.0,5.76 5 | 13.0,7.58,13.0,8.74,13.0,12.74,8.0,7.71 6 | 9.0,8.81,9.0,8.77,9.0,7.11,8.0,8.84 7 | 11.0,8.33,11.0,9.26,11.0,7.81,8.0,8.47 8 | 14.0,9.96,14.0,8.10,14.0,8.84,8.0,7.04 9 | 6.0,7.24,6.0,6.13,6.0,6.08,8.0,5.25 10 | 4.0,4.26,4.0,3.10,4.0,5.39,19.0,12.50 11 | 12.0,10.84,12.0,9.13,12.0,8.15,8.0,5.56 12 | 7.0,4.82,7.0,7.26,7.0,6.42,8.0,7.91 13 | 5.0,5.68,5.0,4.74,5.0,5.73,8.0,6.89 14 | -------------------------------------------------------------------------------- /Statistical_Thinking_Python/belmont.csv: -------------------------------------------------------------------------------- 1 | Year,Winner,Jockey,Trainer,Owner,Time,Track,miles 2 | 2016,Creator,"Irad Ortiz, Jr",Steve Asmussen,WinStar Farm LLC,2:28.51,Belmont,1.5 3 | 2015,American Pharoah,Victor Espinoza,Bob Baffert,"Zayat Stables, LLC",2:26.65,Belmont,1.5 4 | 2014,Tonalist,Joel Rosario,Christophe Clement,Robert S. Evans,2:28.52,Belmont,1.5 5 | 2013,Palace Malice,Mike Smith,Todd Pletcher,Dogwood Stable,2:30.70,Belmont,1.5 6 | 2012,Union Rags,John Velazquez,Michael Matz,Phyllis M. Wyeth,2:30.42,Belmont,1.5 7 | 2011,Ruler on Ice,"Jose Valdivia, Jr.",Kelly Breen,George and Lori Hall,2:30.88,Belmont,1.5 8 | 2010,Drosselmeyer,Mike Smith,William Mott,WinStar Farm LLC,2:31.57,Belmont,1.5 9 | 2009,Summer Bird,Kent Desormeaux,Tim Ice,Kalarikkal & Vilasini Jayaraman,2:27.54,Belmont,1.5 10 | 2008,Da'Tara,Alan Garcia,Nick Zito,Robert V. LaPenta,2:29.65,Belmont,1.5 11 | 2007,Rags to Riches ‡,John Velazquez,Todd Pletcher,M. Tabor & D. Smith,2:28.74,Belmont,1.5 12 | 2006,Jazil,Fernando Jara,Kiaran McLaughlin,Shadwell Farm,2:27.86,Belmont,1.5 13 | 2005,Afleet Alex,Jeremy Rose,Timothy Ritchey,Cash is King LLC,2:28.75,Belmont,1.5 14 | 2004,Birdstone,Edgar Prado,Nick Zito,Marylou Whitney Stables,2:27.50,Belmont,1.5 15 | 2003,Empire Maker,Jerry Bailey,Robert Frankel,Juddmonte Farms,2:28.26,Belmont,1.5 16 | 2002,Sarava,Edgar Prado,Kenneth McPeek,New Phoenix Stable,2:29.71,Belmont,1.5 17 | 2001,Point Given,Gary Stevens,Bob Baffert,The Thoroughbred Corp.,2:26.56,Belmont,1.5 18 | 2000,Commendable,Pat Day,D. Wayne Lukas,Bob & Beverly Lewis,2:31.19,Belmont,1.5 19 | 1999,Lemon Drop Kid,Jose Santos,Scotty Schulhofer,Jeanne G. Vance,2:27.88,Belmont,1.5 20 | 1998,Victory Gallop,Gary Stevens,W. Elliott Walden,Prestonwood Farm,2:29.16,Belmont,1.5 21 | 1997,Touch Gold,Chris McCarron,David Hofmans,Frank Stronach,2:28.82,Belmont,1.5 22 | 1996,Editor's Note,Rene R. Douglas,D. Wayne Lukas,Overbrook Farm,2:28.96,Belmont,1.5 23 | 1995,Thunder Gulch,Gary Stevens,D. Wayne Lukas,Michael Tabor,2:32.02,Belmont,1.5 24 | 1994,Tabasco Cat,Pat Day,D. Wayne Lukas,Reynolds/Overbrook,2:26.82,Belmont,1.5 25 | 1993,Colonial Affair,Julie Krone,Scotty Schulhofer,Centennial Farms,2:29.97,Belmont,1.5 26 | 1992,A.P. Indy,Ed Delahoussaye,Neil Drysdale,Tomonori Tsurumaki,2:26.13,Belmont,1.5 27 | 1991,Hansel,Jerry Bailey,Frank L. Brothers,Lazy Lane Farm,2:28.10,Belmont,1.5 28 | 1990,Go And Go,Michael Kinane,Dermot K. Weld,Moyglare Stud Farm,2:27.20,Belmont,1.5 29 | 1989,Easy Goer,Pat Day,C. R. McGaughey III,Ogden Phipps,2:26.00,Belmont,1.5 30 | 1988,Risen Star,Ed Delahoussaye,Louie J. Roussel III,Louie J. Roussel III,2:26.40,Belmont,1.5 31 | 1987,Bet Twice,Craig Perret,Jimmy Croll,Blanche P. Levy,2:28.20,Belmont,1.5 32 | 1986,Danzig Connection,Chris McCarron,Woody Stephens,Henryk de Kwiatkowski,2:29.80,Belmont,1.5 33 | 1985,Creme Fraiche,Eddie Maple,Woody Stephens,Brushwood Stables,2:27.00,Belmont,1.5 34 | 1984,Swale,"Laffit Pincay, Jr.",Woody Stephens,Claiborne Farm,2:27.20,Belmont,1.5 35 | 1983,Caveat,"Laffit Pincay, Jr.",Woody Stephens,August Belmont IV,2:27.80,Belmont,1.5 36 | 1982,Conquistador Cielo,"Laffit Pincay, Jr.",Woody Stephens,Henryk de Kwiatkowski,2:28.20,Belmont,1.5 37 | 1981,Summing,George Martens,Luis Barrera,"Charles T. Wilson, Jr.",2:29.00,Belmont,1.5 38 | 1980,Temperence Hill,Eddie Maple,Joseph B. Cantey,Loblolly Stable,2:29.80,Belmont,1.5 39 | 1979,Coastal,Ruben Hernandez,David A. Whiteley,William Haggin Perry,2:28.60,Belmont,1.5 40 | 1978,Affirmed,Steve Cauthen,Laz Barrera,Harbor View Farm,2:26.80,Belmont,1.5 41 | 1977,Seattle Slew,Jean Cruguet,"William H. Turner, Jr.",Karen L. Taylor,2:29.60,Belmont,1.5 42 | 1976,Bold Forbes,"Angel Cordero, Jr.",Laz Barrera,E. Rodriguez Tizol,2:29.00,Belmont,1.5 43 | 1975,Avatar,Bill Shoemaker,Tommy Doyle,"Arthur A. Seeligson, Jr.",2:28.20,Belmont,1.5 44 | 1974,Little Current,Miguel A. Rivera,Lou Rondinello,Darby Dan Farm,2:29.20,Belmont,1.5 45 | 1973,Secretariat,Ron Turcotte,Lucien Laurin,Meadow Stable,2:24.00,Belmont,1.5 46 | 1972,Riva Ridge,Ron Turcotte,Lucien Laurin,Meadow Stud,2:28.00,Belmont,1.5 47 | 1971,Pass Catcher,Walter Blum,Eddie Yowell,October House Farm,2:30.40,Belmont,1.5 48 | 1970,High Echelon,John L. Rotz,John W. Jacobs,Ethel D. Jacobs,2:34.00,Belmont,1.5 49 | 1969,Arts and Letters,Braulio Baeza,J. Elliott Burch,Rokeby Stables,2:28.80,Belmont,1.5 50 | 1968,Stage Door Johnny,Heliodoro Gustines,"John M. Gaver, Sr.",Greentree Stable,2:27.20,Belmont,1.5 51 | 1967,Damascus,Bill Shoemaker,"Frank Y. Whiteley, Jr.",Edith W. Bancroft,2:28.80,Aqueduct,1.5 52 | 1966,Amberoid,William Boland,Lucien Laurin,Reginald N. Webster,2:29.60,Aqueduct,1.5 53 | 1965,Hail To All,Johnny Sellers,Eddie Yowell,Zelda Cohen,2:28.40,Aqueduct,1.5 54 | 1964,Quadrangle,Manuel Ycaza,J. Elliott Burch,Rokeby Stables,2:28.40,Aqueduct,1.5 55 | 1963,Chateaugay,Braulio Baeza,James P. Conway,Darby Dan Farm,2:30.20,Aqueduct,1.5 56 | 1962,Jaipur,Bill Shoemaker,Bert Mulholland,"George D. Widener, Jr.",2:28.80,Belmont,1.5 57 | 1961,Sherluck,Braulio Baeza,Harold Young,Jacob Sher,2:29.20,Belmont,1.5 58 | 1960,Celtic Ash,Bill Hartack,Thomas J. Barry,Joseph E. O'Connell,2:29.20,Belmont,1.5 59 | 1959,Sword Dancer,Bill Shoemaker,J. Elliott Burch,Brookmeade Stable,2:28.40,Belmont,1.5 60 | 1958,Cavan,Pete Anderson,Thomas J. Barry,Joseph E. O'Connell,2:30.20,Belmont,1.5 61 | 1957,Gallant Man,Bill Shoemaker,John A. Nerud,Ralph Lowe,2:26.60,Belmont,1.5 62 | 1956,Needles,David Erb,Hugh L. Fontaine,D & H Stable,2:29.80,Belmont,1.5 63 | 1955,Nashua,Eddie Arcaro,Jim Fitzsimmons,Belair Stud,2:29.00,Belmont,1.5 64 | 1954,High Gun,Eric Guerin,Max Hirsch,King Ranch,2:30.80,Belmont,1.5 65 | 1953,Native Dancer,Eric Guerin,Bill Winfrey,Alfred G. Vanderbilt II,2:28.60,Belmont,1.5 66 | 1952,One Count,Eddie Arcaro,Oscar White,Sarah F. Jeffords,2:30.20,Belmont,1.5 67 | 1951,Counterpoint,David Gorman,Sylvester Veitch,C. V. Whitney,2:29.00,Belmont,1.5 68 | 1950,Middleground,William Boland,Max Hirsch,King Ranch,2:28.60,Belmont,1.5 69 | 1949,Capot,Ted Atkinson,"John M. Gaver, Sr.",Greentree Stable,2:30.20,Belmont,1.5 70 | 1948,Citation,Eddie Arcaro,Horace A. Jones,Calumet Farm,2:28.20,Belmont,1.5 71 | 1947,Phalanx,Ruperto Donoso,Sylvester Veitch,C. V. Whitney,2:29.40,Belmont,1.5 72 | 1946,Assault,Warren Mehrtens,Max Hirsch,King Ranch,2:30.80,Belmont,1.5 73 | 1945,Pavot,Eddie Arcaro,Oscar White,"Walter M. Jeffords, Sr.",2:30.20,Belmont,1.5 74 | 1944,Bounding Home,Gayle Smith,Matt Brady,"William Ziegler, Jr.",2:32.20,Belmont,1.5 75 | 1943,Count Fleet,Johnny Longden,Don Cameron,Fannie Hertz,2:28.20,Belmont,1.5 76 | 1942,Shut Out,Eddie Arcaro,"John M. Gaver, Sr.",Greentree Stable,2:29.20,Belmont,1.5 77 | 1941,Whirlaway,Eddie Arcaro,Ben A. Jones,Calumet Farm,2:31.00,Belmont,1.5 78 | 1940,Bimelech,Fred A. Smith,William A. Hurley,Edward R. Bradley,2:29.60,Belmont,1.5 79 | 1939,Johnstown,James Stout,Jim Fitzsimmons,Belair Stud,2:29.60,Belmont,1.5 80 | 1938,Pasteurized,James Stout,George M. Odom,Carol Harriman Plunkett,2:29.40,Belmont,1.5 81 | 1937,War Admiral,Charley Kurtsinger,George Conway,Glen Riddle Farm,2:28.60,Belmont,1.5 82 | 1936,Granville,James Stout,Jim Fitzsimmons,Belair Stud,2:30.00,Belmont,1.5 83 | 1935,Omaha,Willie Saunders,Jim Fitzsimmons,Belair Stud,2:30.60,Belmont,1.5 84 | 1934,Peace Chance,Wayne D. Wright,Pete Coyne,Joseph E. Widener,2:29.20,Belmont,1.5 85 | 1933,Hurryoff,Mack Garner,Henry McDaniel,Joseph E. Widener,2:32.60,Belmont,1.5 86 | 1932,Faireno,Tommy Malley,Jim Fitzsimmons,Belair Stud,2:32.80,Belmont,1.5 87 | 1931,Twenty Grand,Charley Kurtsinger,"James G. Rowe, Jr.",Greentree Stable,2:29.60,Belmont,1.5 88 | 1930,Gallant Fox,Earl Sande,Jim Fitzsimmons,Belair Stud,2:31.60,Belmont,1.5 89 | 1929,Blue Larkspur,Mack Garner,Herbert J. Thompson,Edward R. Bradley,2:32.80,Belmont,1.5 90 | 1928,Vito,Clarence Kummer,Max Hirsch,Alfred H. Cosden,2:33.20,Belmont,1.5 91 | 1927,Chance Shot,Earl Sande,Pete Coyne,Joseph E. Widener,2:32.40,Belmont,1.5 92 | 1926,Crusader,Albert Johnson,George Conway,Glen Riddle Farm,2:32.20,Belmont,1.5 93 | -------------------------------------------------------------------------------- /Statistical_Thinking_Python/female_literacy_fertility.csv: -------------------------------------------------------------------------------- 1 | Country ,Continent,female literacy,fertility,population 2 | Chine,ASI,90.5,1.769,"1,324,655,000" 3 | Inde,ASI,50.8,2.682,"1,139,964,932" 4 | USA,NAM,99,2.077,"304,060,000" 5 | Indonésie,ASI,88.8,2.132,"227,345,082" 6 | Brésil,LAT,90.2,1.827,"191,971,506" 7 | Pakistan,ASI,40,3.872,"166,111,487" 8 | Bangladesh,ASI,49.8,2.288,"160,000,128" 9 | Nigéria,AF,48.8,5.173,"151,212,254" 10 | Fédération de Russie,EUR,99.4,1.393,"141,950,000" 11 | Japan,ASI,99,1.262,"127,704,000" 12 | Mexique,LAT,91.5,2.156,"106,350,434" 13 | Philippines,ASI,93.9,3.026,"90,348,437" 14 | Viet Nam,ASI,90.2,2.033,"86,210,781" 15 | Germany,EUR,99,1.324,"82,110,097" 16 | Egypte,AF,57.8,2.816,"81,527,172" 17 | Ethiopie,AF,22.8,5.211,"80,713,434" 18 | Turquie,ASI,81.3,2.1,"73,914,260" 19 | "Iran, République islamique d'",ASI,77.2,1.781,"71,956,322" 20 | Thaïlande,ASI,91.5,1.822,"67,386,383" 21 | Rép. Démocratique du Congo,AF,56.1,5.908,"64,256,635" 22 | France,EUR,99,1.881,"62,277,432" 23 | UK,EUR,99,1.852,"61,414,062" 24 | Italie,EUR,98.5,1.39,"59,832,179" 25 | Myanmar,ASI,89.2,2.281,"49,563,019" 26 | Afrique du Sud,AF,88.1,2.505,"48,687,000" 27 | South Korea,ASI,96.6,1.224,"48,607,000" 28 | Ukraine,EUR,99.6,1.361,"46,258,200" 29 | Espagne,EUR,96.9,1.468,"45,555,716" 30 | Colombie,LAT,93.4,2.404,"45,012,096" 31 | République-Unie de Tanzanie,AF,66.3,5.52,"42,483,923" 32 | Soudan,AF,59.6,4.058,"41,347,723" 33 | Argentine,LAT,97.7,2.223,"39,882,980" 34 | Kenya,AF,82.8,4.859,"38,765,312" 35 | Pologne,EUR,99.3,1.267,"38,125,759" 36 | Algérie,AF,63.9,2.342,"34,373,426" 37 | Canada,NAM,99,1.579,"33,311,400" 38 | Ouganda,AF,66.8,6.254,"31,656,865" 39 | Maroc,AF,44.1,2.334,"31,605,616" 40 | Iraq,ASI,69.2,3.961,"30,711,152" 41 | Afghanistan,ASI,12.6,6.505,"29,021,099" 42 | Pérou,LAT,84.6,2.53,"28,836,700" 43 | Népal,ASI,45.4,2.823,"28,809,526" 44 | Venezuela,LAT,94.9,2.498,"27,935,000" 45 | Ouzbékistan,ASI,98.9,2.248,"27,313,700" 46 | Malaisie,ASI,89.8,2.508,"27,014,337" 47 | Arabie saoudite,ASI,80.2,3.04,"24,645,686" 48 | Rép. populaire démocratique de Corée,ASI,100,1.854,"23,818,753" 49 | Ghana,AF,59.3,4.22,"23,350,927" 50 | Yémen,ASI,42.8,5.1,"22,917,485" 51 | Mozambique,AF,40.1,4.967,"22,382,533" 52 | Roumanie,EUR,96.9,1.325,"21,513,622" 53 | Côte d'Ivoire,AF,44.3,4.514,"20,591,302" 54 | République arabe syrienne,ASI,77.2,3.173,"20,581,290" 55 | Sri Lanka,ASI,89.1,2.308,"20,156,204" 56 | Madagascar,AF,65.3,4.62,"19,110,941" 57 | Cameroun,AF,67.8,4.541,"19,088,385" 58 | Angola,AF,57,5.637,"18,020,668" 59 | Chili,LAT,98.7,1.926,"16,803,952" 60 | Netherlands,EUR,99,1.747,"16,445,593" 61 | Kazakhstan,ASI,99.5,2.294,"15,674,833" 62 | Burkina Faso,AF,21.6,5.841,"15,233,884" 63 | Malawi,AF,65.8,5.455,"14,846,182" 64 | Niger,AF,15.1,7.069,"14,704,318" 65 | Cambodge,ASI,70.9,2.859,"14,562,008" 66 | Guatemala,LAT,68.7,4.018,"13,686,128" 67 | Equateur,LAT,81.7,2.513,"13,481,424" 68 | Mali,AF,18.2,5.405,"12,705,736" 69 | Zambie,AF,61,5.737,"12,620,219" 70 | Zimbabwe,AF,88.8,3.363,"12,462,879" 71 | Sénégal,AF,33,4.89,"12,211,181" 72 | Grèce,EUR,95.9,1.385,"11,237,094" 73 | Cuba,LAT,99.8,1.505,"11,204,735" 74 | Tchad,AF,21.9,6.081,"10,913,667" 75 | Belgium,EUR,99,1.784,"10,708,433" 76 | Portugal,EUR,92.9,1.378,"10,622,413" 77 | Czech rep,EUR,99,1.45,"10,424,336" 78 | Tunisie,AF,71,1.841,"10,327,800" 79 | Hongrie,EUR,98.9,1.37,"10,038,188" 80 | République dominicaine,LAT,88.3,2.612,"9,952,711" 81 | Guinée,AF,26.4,5.329,"9,833,055" 82 | Rwanda,AF,66.1,5.33,"9,720,694" 83 | Bolivie,LAT,86,3.371,"9,694,113" 84 | Bélarus,EUR,99.7,1.281,"9,680,850" 85 | Sweden,EUR,99,1.871,"9,219,637" 86 | Azerbaïdjan,ASI,99.2,2.153,"8,680,100" 87 | Bénin,AF,28.1,5.378,"8,662,086" 88 | Burundi,AF,59.9,4.45,"8,074,254" 89 | Switzerland,EUR,99,1.46,"7,647,675" 90 | Bulgarie,EUR,97.9,1.436,"7,623,395" 91 | Serbie,EUR,96.2,1.612,"7,350,221" 92 | Honduras,LAT,83.5,3.19,"7,318,789" 93 | Israel,ASI,95.9,2.752,"7,308,800" 94 | Tadjikistan,ASI,99.5,3.35,"6,836,083" 95 | Papouasie-Nouvelle-Guinée,OCE,55.6,4.01,"6,576,822" 96 | Togo,AF,53.7,4.166,"6,458,605" 97 | Jamahiriya arabe libyenne,AF,81.3,2.642,"6,294,181" 98 | Paraguay,LAT,93.5,2.977,"6,237,855" 99 | Rép. démocratique populaire lao,ASI,63.2,3.415,"6,205,341" 100 | El Salvador,LAT,81.4,2.295,"6,133,910" 101 | Jordanie,ASI,88.9,3.019,"5,906,043" 102 | Nicaragua,LAT,77.9,2.683,"5,667,325" 103 | Sierra Leone,AF,28.9,5.165,"5,559,853" 104 | Denmark,EUR,99,1.849,"5,493,621" 105 | Finland,EUR,100,1.836,"5,313,399" 106 | Kirghizistan,ASI,99.1,2.518,"5,277,900" 107 | Turkménistan,ASI,99.3,2.43,"5,043,618" 108 | Erythrée,AF,54.5,4.528,"4,926,877" 109 | Singapour,ASI,91.6,1.263,"4,839,400" 110 | Norway,EUR,100,1.885,"4,768,212" 111 | Costa Rica,LAT,96.2,1.943,"4,519,126" 112 | Emirats arabes unis,ASI,91.5,1.899,"4,484,935" 113 | Croatie,EUR,98,1.442,"4,434,000" 114 | Ireland,EUR,99,1.953,"4,425,675" 115 | République centrafricaine,AF,41.1,4.697,"4,339,263" 116 | Géorgie,ASI,99.7,1.582,"4,307,011" 117 | New Zealand,OCE,99,2.025,"4,268,900" 118 | Liban,ASI,86,1.841,"4,193,758" 119 | Libéria,AF,53,5.011,"3,793,400" 120 | Bosnie-Herzégovine,EUR,95.9,1.212,"3,773,100" 121 | République de Moldova,EUR,97.8,1.502,"3,633,369" 122 | Panama,LAT,92.8,2.516,"3,398,823" 123 | Lituanie,EUR,99.7,1.367,"3,358,115" 124 | Uruguay,LAT,98.5,2.089,"3,334,052" 125 | Mauritanie,AF,49.5,4.388,"3,215,043" 126 | Albanie,EUR,98.7,1.854,"3,143,291" 127 | Arménie,ASI,99.4,1.748,"3,077,087" 128 | Oman,ASI,80.9,2.978,"2,785,361" 129 | Koweït,ASI,93.1,2.152,"2,728,041" 130 | Jamaïque,LAT,90.8,2.362,"2,687,200" 131 | Mongolie,ASI,97.8,1.988,"2,641,216" 132 | Lettonie,EUR,99.8,1.426,"2,266,094" 133 | Namibie,AF,87.7,3.29,"2,129,854" 134 | Lesotho,AF,95.1,3.264,"2,049,429" 135 | L'ex-Rép. yougoslave de Macédoine,EUR,95.4,1.436,"2,041,342" 136 | Slovénie,EUR,99.7,1.393,"2,021,316" 137 | Botswana,AF,83.5,2.822,"1,921,122" 138 | Gambie,AF,34.3,4.969,"1,660,200" 139 | Guinée-Bissau,AF,36.5,5.659,"1,575,446" 140 | Gabon,AF,83.2,3.24,"1,448,159" 141 | Estonie,EUR,99.8,1.693,"1,340,675" 142 | Trinité-et-Tobago,LAT,98.2,1.647,"1,333,388" 143 | Qatar,ASI,90.4,2.36,"1,280,862" 144 | Maurice,AF,84.8,1.792,"1,268,854" 145 | Swaziland,AF,85.6,3.45,"1,167,834" 146 | Chypre,EUR,96.7,1.516,"862,434" 147 | Bahreïn,ASI,89.4,2.233,"775,585" 148 | Bhoutan,ASI,38.7,2.563,"686,789" 149 | Guinée équatoriale,AF,89.1,5.283,"659,197" 150 | Comores,AF,67.8,3.885,"643,571" 151 | "Macao, Chine",ASI,90.7,0.966,"526,178" 152 | Suriname,LAT,88.4,2.373,"515,124" 153 | Cap-Vert,AF,79.3,2.663,"498,672" 154 | Malte,EUR,93.5,1.251,"411,950" 155 | Brunéi Darussalam,ASI,93.3,2.052,"392,280" 156 | Bahamas,LAT,96.5,3.371,"337,668" 157 | Iceland,EUR,99,2.093,"317,414" 158 | Maldives,ASI,98.4,2,"305,027" 159 | Vanuatu,OCE,79.5,3.883,"233,866" 160 | Samoa,OCE,98.5,3.852,"178,869" 161 | Sao Tomé-et-Principe,AF,83.3,3.718,"160,174" 162 | Aruba,LAT,98,1.732,"105,455" 163 | Tonga,ASI,99.1,3.928,"103,566" -------------------------------------------------------------------------------- /Statistical_Thinking_Python/finch_beaks_1975.csv: -------------------------------------------------------------------------------- 1 | band,species,"Beak length, mm","Beak depth, mm" 2,fortis,9.4,8 9,fortis,9.2,8.3 12,fortis,9.5,7.5 15,fortis,9.5,8 305,fortis,11.5,9.9 307,fortis,11.1,8.6 308,fortis,9.9,8.4 309,fortis,11.5,9.8 311,fortis,10.8,9.2 312,fortis,11.3,9 313,fortis,11.5,9.5 314,fortis,11.5,8.9 315,fortis,9.7,8.8 316,fortis,10.9,9.7 316,fortis,10.9,9.85 320,fortis,10.1,8.9 321,fortis,10.6,8.5 323,fortis,9.6,8.2 324,fortis,10.3,9.3 326,fortis,10.5,9.1 328,fortis,10.5,8.8 329,fortis,10.9,9.4 330,fortis,11.2,9.6 340,fortis,9.4,8.5 341,fortis,10.1,8.1 342,fortis,9.6,8 343,fortis,10.5,9.7 344,fortis,9.9,8.2 345,fortis,9.9,8.4 346,fortis,9.6,7.9 347,fortis,10.7,9.3 348,fortis,11.8,10.5 349,fortis,10.2,9.7 350,fortis,10.9,9.6 352,fortis,9.3,7.7 354,fortis,11.6,9.8 355,fortis,11.2,9.8 356,fortis,10.1,8.5 360,fortis,10.7,9.2 366,fortis,11,10.1 368,fortis,10.8,9 369,fortis,10.2,9 401,fortis,10.8,9.4 402,fortis,10.4,8.8 403,fortis,11.2,9.9 405,fortis,11.4,9.9 410,fortis,10.9,8.9 411,fortis,10.4,9.4 413,fortis,9.7,8.2 414,fortis,10.2,8.2 415,fortis,9.2,8.1 416,fortis,10.1,8.4 417,fortis,11,10.1 418,fortis,10.3,8.6 419,fortis,10.2,8.5 420,fortis,11,9.7 422,fortis,11,10.3 423,fortis,10.1,8.6 424,fortis,10.7,9.3 425,fortis,10.7,8.9 426,fortis,11,9.7 427,fortis,12,10.2 428,fortis,11.6,10.2 450,fortis,9.9,8.5 451,fortis,10.8,8.8 452,fortis,10.5,8.9 453,fortis,10,8.7 454,fortis,9.6,9.3 455,fortis,11,9.7 456,fortis,10.2,9.6 457,fortis,9.7,7.85 458,fortis,11.1,9.6 459,fortis,10.4,9.3 461,fortis,11.1,9.8 462,fortis,10.2,8.8 463,fortis,10.3,9.1 465,fortis,9.9,9 466,fortis,11,10.4 468,fortis,10.8,9 473,fortis,10.5,9.5 474,fortis,10.5,8.7 475,fortis,11.2,9.6 476,fortis,11.2,9.4 477,fortis,11.7,9.5 479,fortis,10.5,8.6 501,fortis,10.5,9.9 502,fortis,11.2,9.4 503,fortis,10,9.1 504,fortis,10.4,8.6 505,fortis,10.8,9.3 506,fortis,11.1,9.2 507,fortis,10.3,8.8 509,fortis,11.1,9.2 510,fortis,9.8,8.3 511,fortis,10.5,8.8 512,fortis,11,9.4 513,fortis,11.2,10.4 514,fortis,9.8,8.5 515,fortis,9.8,7.9 516,fortis,9.8,7.9 517,fortis,10.3,10.1 518,fortis,11.3,9.8 519,fortis,10,8.3 520,fortis,11.1,9.4 521,fortis,10,9 522,fortis,10.3,8.4 524,fortis,10.2,8.7 526,fortis,10.4,9.2 527,fortis,11,9.5 528,fortis,11.5,10.1 529,fortis,11.8,9.8 560,fortis,10.2,8.5 561,fortis,11.7,10.2 563,fortis,10.1,8.6 564,fortis,10.2,9.3 565,fortis,9.8,8.7 566,fortis,10.6,8.9 567,fortis,10,8.4 572,fortis,12.1,10.3 574,fortis,11.1,9.9 576,fortis,10.5,8.9 578,fortis,10,9.1 579,fortis,10.2,8.6 601,fortis,9.7,7.8 602,fortis,10,8.6 603,fortis,10.7,9.5 604,fortis,11,9.5 605,fortis,10.9,10.2 606,fortis,10.5,9.1 607,fortis,10.9,9.7 608,fortis,10.7,9.4 609,fortis,11.9,10.5 610,fortis,10.2,9 611,fortis,10.5,9.8 615,fortis,11.2,10 616,fortis,10.7,9.6 617,fortis,10.3,9.6 618,fortis,11.6,9.9 619,fortis,10.5,9.3 620,fortis,10.9,9.6 621,fortis,9.8,7.6 622,fortis,10.9,9.2 623,fortis,10.3,8.8 624,fortis,11.7,10.1 625,fortis,11,8.9 626,fortis,9.8,9.5 627,fortis,10.4,8.2 628,fortis,10.8,9.7 629,fortis,11,9.8 670,fortis,10.6,9.3 671,fortis,11.3,9.8 672,fortis,10.1,8.8 673,fortis,11.4,10.1 674,fortis,11.8,10.5 675,fortis,9.9,8.6 676,fortis,11,9.7 677,fortis,10.8,10 678,fortis,11.3,9.7 679,fortis,11.1,10.5 680,fortis,11,10.4 681,fortis,10.4,9.2 682,fortis,9.1,8.1 683,fortis,10.8,9.4 684,fortis,10.6,8.9 685,fortis,8.7,8.2 686,fortis,10.7,9.3 687,fortis,10.3,8.6 688,fortis,10.5,9 689,fortis,9.1,7.6 701,fortis,9.5,8.7 702,fortis,10.8,9.8 703,fortis,10.6,9.4 704,fortis,9.9,9.9 705,fortis,11.5,10.1 706,fortis,11.2,9.8 707,fortis,9.7,7.9 708,fortis,12.2,10.8 709,fortis,9.2,7.9 710,fortis,10.5,10 711,fortis,10.5,8.4 712,fortis,10.8,9.5 713,fortis,10.7,8.8 714,fortis,11.8,10.4 715,fortis,9.1,8.1 716,fortis,10.1,9.8 717,fortis,10,8.2 719,fortis,11,9.5 720,fortis,12.2,10.5 721,fortis,11.4,9.7 723,fortis,10.9,9.6 725,fortis,10.6,9.6 726,fortis,9.3,7.9 780,fortis,10.9,10.1 781,fortis,10.6,9.3 785,fortis,11,10.3 786,fortis,9,7.9 787,fortis,11,9.1 788,fortis,10.4,8.9 789,fortis,10.4,8.3 790,fortis,9.6,9.4 801,fortis,10.6,9.5 802,fortis,10.1,8.5 803,fortis,9.7,8.6 804,fortis,9.6,8.5 805,fortis,10.1,8.2 807,fortis,9.9,7.9 808,fortis,11,10 809,fortis,10.9,9.4 810,fortis,9.7,8 811,fortis,10,8.6 812,fortis,10.4,9 813,fortis,11.6,9.9 814,fortis,9.6,8 815,fortis,10.8,9.6 817,fortis,10.9,9.2 818,fortis,10.2,9 818,fortis,10.2,9 819,fortis,10.4,9.2 820,fortis,11,9.5 821,fortis,10.7,9.2 822,fortis,11.1,9.7 823,fortis,10.8,9.1 824,fortis,10.9,9.4 825,fortis,9.9,8.6 826,fortis,11.8,9.8 827,fortis,9.7,8 828,fortis,11.9,10.4 829,fortis,9.6,8.1 830,fortis,10.9,9.9 831,fortis,10.2,8.6 891,fortis,9.9,8 892,fortis,11.3,9 893,fortis,10.9,9.7 894,fortis,9.8,8.1 895,fortis,10.3,8.8 896,fortis,8.8,7.5 897,fortis,11.6,10.2 898,fortis,9.9,8.2 899,fortis,9.9,8.6 900,fortis,9.9,8.7 902,fortis,10.4,8.6 903,fortis,11.2,9.9 904,fortis,10.8,9.5 905,fortis,9.9,8.4 906,fortis,9.9,8.7 907,fortis,10.2,8.2 908,fortis,10.4,9.2 909,fortis,9.4,8.2 910,fortis,10.2,9.2 911,fortis,11,9.3 912,fortis,10.6,8.9 913,fortis,10.5,9 914,fortis,10.4,9.1 915,fortis,10.2,8.8 917,fortis,10.2,8.8 919,fortis,11,9.6 921,fortis,11.1,10.2 922,fortis,9.7,8.2 923,fortis,10.5,8.9 924,fortis,10.9,9 925,fortis,10.7,8.8 926,fortis,11,9.9 927,fortis,10.9,9.3 928,fortis,11.2,9.9 929,fortis,11.5,9.5 930,fortis,10.8,10 931,fortis,10.2,8.9 932,fortis,11,9.8 933,fortis,10.9,9.9 934,fortis,10.5,8.7 936,fortis,10.8,9.2 941,fortis,9.8,8.2 942,fortis,9.7,7.9 943,fortis,10.3,9.1 944,fortis,10.3,8.3 944,fortis,10.3,8.3 945,fortis,11.6,10.8 945,fortis,11.6,10.8 951,fortis,10.9,9.9 952,fortis,10.6,9 954,fortis,11.5,9.3 991,fortis,10.8,9.6 1040,fortis,10.83,9.3 1368,fortis,11.73,10.2 1420,fortis,10.23,8.6 1433,fortis,11.83,10.98 1560,fortis,11.43,10.28 1581,fortis,10.73,9.6 1770,fortis,10.33,9.28 1857,fortis,10.23,9.5 1860,fortis,11.53,9.4 1884,fortis,11.03,9.15 2102,fortis,11.73,9.8 2105,fortis,10.53,9.35 2220,fortis,9.93,8.5 2381,fortis,11.23,10.5 2482,fortis,9.83,8.5 2939,fortis,9.63,8.31 2955,fortis,10.6,9.9 2974,fortis,11.88,10.36 3642,fortis,11.03,10.28 8016,fortis,10.73,8.9 8020,fortis,10.13,8.7 20139,fortis,10.15,9.15 20165,fortis,10.85,10.35 20166,fortis,10.95,8.85 20168,fortis,9.85,8.55 20204,fortis,10.25,8.95 20238,fortis,11.75,10.75 20267,fortis,10.95,10.05 20273,fortis,10.95,10.15 20283,fortis,10.55,8.45 20285,fortis,9.65,8.85 20286,fortis,11.45,10.25 20293,fortis,10.25,9.75 20298,fortis,10.05,8.95 302,scandens,13.9,8.4 304,scandens,14,8.8 306,scandens,12.9,8.4 310,scandens,13.5,8 317,scandens,12.9,7.9 318,scandens,14.6,8.9 319,scandens,13,8.6 322,scandens,14.2,8.5 325,scandens,14,8.9 327,scandens,14.2,9.1 351,scandens,13.1,8.6 353,scandens,15.1,9.8 357,scandens,13.5,8.2 358,scandens,14.4,9 359,scandens,14.9,9.7 361,scandens,12.9,8.6 362,scandens,13,8.2 363,scandens,14.9,9 364,scandens,14,8.4 365,scandens,13.8,8.6 367,scandens,13,8.9 404,scandens,14.75,9.1 406,scandens,13.7,8.3 407,scandens,13.8,8.7 408,scandens,14,9.6 409,scandens,14.6,8.5 412,scandens,15.2,9.1 421,scandens,13.5,9 429,scandens,15.1,9.2 460,scandens,15,9.9 464,scandens,12.8,8.6 467,scandens,14.9,9.2 469,scandens,15.3,8.4 470,scandens,13.4,8.9 471,scandens,14.2,8.5 472,scandens,15.1,10.4 478,scandens,15.1,9.6 508,scandens,14,9.1 523,scandens,13.6,9.3 525,scandens,14,9.3 562,scandens,14,8.8 568,scandens,13.9,8.3 569,scandens,14,8.8 570,scandens,14.9,9.1 571,scandens,15.6,10.1 573,scandens,13.8,8.9 575,scandens,14.4,9.2 577,scandens,12.8,8.5 612,scandens,14.2,10.2 613,scandens,13.4,10.1 614,scandens,14,9.2 718,scandens,14.8,9.7 722,scandens,14.2,9.1 724,scandens,13.5,8.5 727,scandens,13.4,8.2 728,scandens,14.6,9 729,scandens,13.5,9.3 782,scandens,13.7,8 783,scandens,13.9,9.1 784,scandens,13.1,8.1 806,scandens,13.4,8.3 816,scandens,13.8,8.7 890,scandens,13.6,8.8 901,scandens,14,8.6 916,scandens,13.5,8.7 918,scandens,12.8,8 920,scandens,14,8.8 935,scandens,13.4,9 953,scandens,14.9,9.1 1014,scandens,15.54,9.74 1642,scandens,14.63,9.1 1748,scandens,14.73,9.8 1841,scandens,15.73,10.4 1842,scandens,14.83,8.3 2084,scandens,15.94,9.44 2397,scandens,15.14,9.04 8110,scandens,14.23,9 20122,scandens,14.15,9.05 20151,scandens,14.35,9.65 20188,scandens,14.95,9.45 20210,scandens,13.95,8.65 20223,scandens,14.05,9.45 20225,scandens,14.55,9.45 20252,scandens,14.05,9.05 20255,scandens,14.45,8.75 20266,scandens,15.05,9.45 20279,scandens,13.25,8.35 -------------------------------------------------------------------------------- /Statistical_Thinking_Python/finch_beaks_2012.csv: -------------------------------------------------------------------------------- 1 | band,species,blength,bdepth 19022,fortis,10,8.5 19028,fortis,12.5,8.9 19032,fortis,9.3,7.5 19041,fortis,10.3,9.6 19044,fortis,11,9.2 19048,fortis,10.1,8.2 19072,fortis,9.6,7.8 19082,fortis,10.9,8.6 19104,fortis,10.3,8.4 19114,fortis,9.8,7.7 19121,fortis,10.1,8 19126,fortis,10.4,8.7 19146,fortis,9.6,8.1 19164,fortis,10.6,8.8 19174,fortis,10.6,9.4 19203,fortis,11.9,10 19210,fortis,11.3,9.6 19217,fortis,11.3,9.6 19224,fortis,9.7,8.1 19226,fortis,9.7,7.5 19252,fortis,10.1,8.4 19263,fortis,10,7.9 19274,fortis,10,8.3 19280,fortis,10,8.9 19288,fortis,11.5,9.1 19328,fortis,9.5,7.7 19349,fortis,11.2,8.3 19362,fortis,10.7,8.6 19372,fortis,10,8.4 19382,fortis,9.8,7.7 19384,fortis,10.9,9.1 19392,fortis,9.2,7.7 19394,fortis,10.2,9 19422,fortis,11.3,10.2 19439,fortis,10.3,8.1 19461,fortis,10.7,8.6 19482,fortis,10,8.4 19502,fortis,9.7,8.1 19511,fortis,9.9,8 19536,fortis,10.7,8.3 19563,fortis,11,10.3 19568,fortis,9.7,8 19602,fortis,10.5,8.8 19604,fortis,11.7,9 19614,fortis,10.8,9.3 19623,fortis,9.1,7.6 19627,fortis,10.9,8.2 19642,fortis,12.2,10 19649,fortis,10.9,8.2 19654,fortis,10.7,8.2 19674,fortis,10.4,8.3 19682,fortis,9.7,7.8 19712,fortis,10.3,8 19734,fortis,9.8,8.4 19746,fortis,10.6,9.3 19749,fortis,10.5,8.9 19774,fortis,12.5,9.7 19782,fortis,9.2,7.6 19815,fortis,10.1,9 19820,fortis,10.6,8.6 19821,fortis,11.5,8.9 19829,fortis,10.8,8.6 19832,fortis,10.5,8.8 19835,fortis,10.1,8.5 19840,fortis,10.7,9.2 19849,fortis,9.6,8 19874,fortis,10.7,9.4 19878,fortis,10.1,7.7 19889,fortis,10,8.4 19914,fortis,10,8.5 19921,fortis,10.4,9 19922,fortis,10.4,9 19928,fortis,11.7,9.3 19932,fortis,10.6,8.7 19942,fortis,11.5,8.1 19946,fortis,10.7,8.7 19947,fortis,10.4,7.9 19952,fortis,10.1,7.7 19974,fortis,10.8,8.4 19993,fortis,11.5,9.25 19994,fortis,11.1,8.1 21049,fortis,9.9,8.3 21052,fortis,10.5,8.5 21080,fortis,12.2,9.9 21082,fortis,10.9,8.4 21087,fortis,12.9,9.9 21088,fortis,9.7,7.2 21089,fortis,9.7,8.2 21090,fortis,10,8.2 21129,fortis,10.1,8.3 21160,fortis,10.2,8.4 21161,fortis,9.6,7.5 21162,fortis,9.9,8.2 21165,fortis,10.6,8.6 21169,fortis,9.8,8.2 21191,fortis,11.1,8.8 21244,fortis,10,8.5 21247,fortis,10.9,8.1 21249,fortis,10,8.3 21258,fortis,10.6,8.5 21259,fortis,10.7,8.1 21261,fortis,9.4,7.3 21262,fortis,10.1,8 21265,fortis,11.8,10.2 21266,fortis,12.2,11.1 21272,fortis,12.9,9.9 21273,fortis,10.1,8.7 21276,fortis,10.1,8.3 21277,fortis,9,7.8 21282,fortis,11.7,9.9 21283,fortis,10.9,10.3 21287,fortis,10.4,8.4 21293,fortis,12.7,8.7 21294,fortis,10.5,9.8 21296,fortis,9.6,8.7 21298,fortis,10.6,9 21299,fortis,10.4,7.8 21341,fortis,10.5,8.5 21343,fortis,10.1,8.2 21349,fortis,10.6,9.2 22000,fortis,10.6,9 19026,scandens,14.3,9.4 19028,scandens,12.5,8.9 19029,scandens,13.7,9.5 19094,scandens,13.8,11 19122,scandens,12,8.7 19125,scandens,13,8.4 19129,scandens,13,9.1 19172,scandens,13.6,8.7 19182,scandens,12.8,10.2 19212,scandens,13.6,9.6 19214,scandens,12.95,8.85 19244,scandens,13.1,8.8 19251,scandens,13.4,9.5 19260,scandens,13.9,9.2 19270,scandens,12.3,9 19278,scandens,14,9.8 19289,scandens,12.5,9.3 19299,scandens,12.3,9 19312,scandens,13.9,10.2 19326,scandens,13.1,7.7 19343,scandens,12.5,9 19374,scandens,13.9,9.5 19401,scandens,13.7,9.4 19406,scandens,12,8 19408,scandens,14.4,8.9 19426,scandens,13.5,9.4 19430,scandens,13.8,9.5 19433,scandens,13,8 19438,scandens,14.9,10 19452,scandens,12.5,8.95 19466,scandens,12.3,8.2 19469,scandens,12.8,8.8 19486,scandens,13.4,9.2 19492,scandens,13.8,9.4 19493,scandens,13.5,9.5 19494,scandens,13.5,8.1 19495,scandens,13.4,9.5 19496,scandens,12.3,8.4 19497,scandens,14.35,9.3 19510,scandens,13.2,9.3 19513,scandens,13.8,9.6 19518,scandens,14.6,9.2 19526,scandens,14.3,10 19527,scandens,13.8,8.9 19528,scandens,13.6,10.5 19543,scandens,12.9,8.9 19553,scandens,13,8.6 19554,scandens,13.5,8.8 19573,scandens,13.2,9.15 19592,scandens,13.7,9.5 19594,scandens,13.1,9.1 19597,scandens,13.2,10.2 19598,scandens,12.6,8.4 19599,scandens,13,10 19619,scandens,13.9,10.2 19622,scandens,13.2,9.3 19652,scandens,15,10.8 19653,scandens,13.37,8.3 19664,scandens,11.4,7.8 19692,scandens,13.8,9.8 19720,scandens,13,7.9 19740,scandens,13,8.9 19747,scandens,13.1,7.7 19766,scandens,12.8,8.9 19783,scandens,13.3,9.4 19844,scandens,13.5,9.4 19848,scandens,12.4,8.5 19852,scandens,13.1,8.5 19854,scandens,14,9.6 19855,scandens,13.5,10.2 19868,scandens,11.8,8.8 19882,scandens,13.7,9.5 19900,scandens,13.2,9.3 19910,scandens,12.2,9 19936,scandens,13,9.2 19940,scandens,13.1,8.7 19941,scandens,14.7,9 19951,scandens,13.7,9.1 19955,scandens,13.5,8.7 19956,scandens,13.3,9.4 21040,scandens,14.1,9.8 21041,scandens,12.5,8.6 21045,scandens,13.7,10.6 21047,scandens,14.6,9 21053,scandens,14.1,9.5 21057,scandens,12.9,8.1 21070,scandens,13.9,9.3 21081,scandens,13.4,9.6 21092,scandens,13,8.5 21093,scandens,12.7,8.2 21106,scandens,12.1,8 21109,scandens,14,9.5 21111,scandens,14.9,9.7 21113,scandens,13.9,9.9 21131,scandens,12.9,9.1 21135,scandens,14.6,9.5 21136,scandens,14,9.8 21159,scandens,13,8.4 21167,scandens,12.7,8.3 21176,scandens,14,9.6 21248,scandens,14.1,9.4 21253,scandens,14.1,10 21255,scandens,13,8.9 21256,scandens,13.5,9.1 21257,scandens,13.4,9.8 21260,scandens,13.9,9.3 21263,scandens,13.1,9.9 21267,scandens,12.9,8.9 21268,scandens,14,8.5 21270,scandens,14,10.6 21271,scandens,14.1,9.3 21278,scandens,14.7,8.9 21279,scandens,13.4,8.9 21280,scandens,13.8,9.7 21281,scandens,13.4,9.8 21285,scandens,13.8,10.5 21286,scandens,12.4,8.4 21288,scandens,14.1,10 21289,scandens,12.9,9 21290,scandens,13.9,8.7 21291,scandens,14.3,8.8 21292,scandens,13.2,8.4 21295,scandens,14.2,9.3 21297,scandens,13,9.8 21340,scandens,14.6,8.9 21342,scandens,13.1,9.8 21347,scandens,15.2,9.1 -------------------------------------------------------------------------------- /Statistical_Thinking_Python/fortis_beak_depth_heredity.csv: -------------------------------------------------------------------------------- 1 | Mid-offspr,Male BD,Female BD 2 | 10.7,10.9,9.3 3 | 9.78,10.7,8.4 4 | 9.48,10.7,8.1 5 | 9.6,10.7,9.8 6 | 10.27,9.85,10.4 7 | 9.5,9.8,9.6 8 | 9,9.9,8.2 9 | 7.46,9.3,5.5 10 | 7.65,9.4,8.6 11 | 8.63,9.4,7.9 12 | 9.81,9.4,9.85 13 | 9.4,10.1,9.7 14 | 9.48,10.1,9 15 | 8.75,8.3,9.8 16 | 7.6,8.7,8 17 | 10,10.1,10.1 18 | 10.09,10.1,10.1 19 | 9.74,9.7,10.1 20 | 9.64,9.7,10.75 21 | 8.49,11.8,8.2 22 | 10.15,10.4,10.7 23 | 10.28,10.5,10.4 24 | 9.2,10.3,8.1 25 | 10.01,10.3,10.1 26 | 9.03,8.6,9.3 27 | 9.94,9.5,10.6 28 | 10.5,10,10.4 29 | 9.7,10,9 30 | 10.02,9.1,10.75 31 | 10.04,11.2,8.7 32 | 9.43,10,10.1 33 | 8.1,9.6,7.9 34 | 9.5,10.3,8.1 35 | 9.9,10.5,9.8 36 | 9.48,11,8.6 37 | 10.18,11,10.4 38 | 10.16,11,10 39 | 9.08,9,10.1 40 | 10.39,10.6,10.5 41 | 9.9,10.6,10.35 42 | 8.4,8.1,9.2 43 | 10.6,10.2,11.2 44 | 8.75,8.6,9.6 45 | 9.46,9.4,9.4 46 | 9.6,10.3,10.3 47 | 9.6,10.3,9 48 | 9.95,10.3,8.7 49 | 10.05,9.9,9.5 50 | 10.16,10.3,10.75 51 | 10.1,10.6,9.3 52 | 9.83,10.6,9.6 53 | 9.46,9.5,10 54 | 9.7,10.4,9.7 55 | 9.82,10.4,9.4 56 | 10.34,10,10 57 | 8.02,10,8.2 58 | 9.65,9.1,9.8 59 | 9.87,9.1,9.4 60 | 9,9.9,9.1 61 | 11.14,9.9,10.1 62 | 9.25,10.1,10.95 63 | 8.14,10.1,9.7 64 | 10.23,10.7,10.1 65 | 8.7,9.3,8.6 66 | 9.8,8.3,10.5 67 | 10.54,11.4,10.5 68 | 11.19,11.4,10.1 69 | 9.85,11.4,8.8 70 | 8.1,8.1,8 71 | 9.3,8.1,10.1 72 | 9.34,9.3,9.8 73 | 9.19,9.3,8.8 74 | 9.52,11.2,9.2 75 | 9.36,10.8,9.2 76 | 8.8,10.8,10.3 77 | 8.6,11.1,10.4 78 | 8,8.6,7.75 79 | 8.5,9.4,10 80 | 8.3,9,8.6 81 | 10.38,11.3,10.2 82 | 8.54,8.9,9.7 83 | 8.94,9.8,9.6 84 | 10,9.8,9.4 85 | 9.76,9.9,9.6 86 | 9.45,9.9,9.3 87 | 9.89,11.6,9.3 88 | 10.9,11.6,10.4 89 | 9.91,11.6,10.1 90 | 9.39,11.6,8.7 91 | 9.86,10.9,9.8 92 | 9.74,10.8,10 93 | 9.9,10.8,9.1 94 | 9.09,10.1,8.1 95 | 9.69,10.1,10.1 96 | 10.24,9.2,10.5 97 | 8.9,8.95,10.3 98 | 9.67,8.95,10 99 | 8.93,9.3,8.7 100 | 9.3,9.3,9.2 101 | 8.67,9.3,8.9 102 | 9.15,10.3,8.2 103 | 9.23,9.7,8.7 104 | 9.59,9.7,10.2 105 | 9.03,10.1,7.2 106 | 9.58,10.1,9.5 107 | 8.97,9.3,9.5 108 | 8.57,9.3,8.7 109 | 8.47,9.2,7.9 110 | 8.71,9.2,8.3 111 | 9.21,9.3,10 112 | 9.13,9.3,8.6 113 | 8.5,9.9,8.4 114 | 9.58,10.85,8.85 115 | 9.21,10.85,9.6 116 | 9.6,10.85,8.8 117 | 9.32,8.8,11.2 118 | 8.7,10.45,8.4 119 | 10.46,10.8,10 120 | 9.29,9,10.75 121 | 9.24,9,8.9 122 | 9.45,9,8.8 123 | 9.35,9.8,8.9 124 | 10.19,9.55,11.3 125 | 9.91,10,10 126 | 9.18,9.6,10.75 127 | 9.89,9.6,10.15 128 | 9.6,9.6,10.15 129 | 10.3,8.9,9.4 130 | 9.45,9.5,9.4 131 | 8.79,9.5,8.55 132 | 9.2,9.5,9.9 133 | 8.8,9.9,9.5 134 | 9.69,10.3,9.8 135 | 10.61,10.3,10.3 136 | 9.6,9.6,9.6 137 | 9.9,10.6,9.4 138 | 9.26,10.6,9 139 | 10.2,9.6,10.5 140 | 8.79,8.6,8.9 141 | 9.28,11.8,9.3 142 | 8.83,10.8,8.6 143 | 9.76,10.8,9.2 144 | 10.2,9.7,10 145 | 9.43,9.7,9.9 146 | 9.4,9.7,8.65 147 | 9.9,9.7,9.6 148 | 9.5,9.8,9.3 149 | 8.95,9.8,10 150 | 9.98,12.7,10.4 151 | 9.72,12.7,9.9 152 | 9.86,11.4,9.4 153 | 11.1,11.4,10.2 154 | 9.14,11.4,8.2 155 | 10.49,10.3,10.6 156 | 9.75,10.4,9.6 157 | 10.35,11.1,10.4 158 | 9.73,8.8,9.9 159 | 9.83,11.5,10 160 | 8.69,9.75,8.6 161 | 9.58,8.9,10.4 162 | 8.42,8.9,8.7 163 | 9.25,10.9,10.2 164 | 10.12,10.9,10.45 165 | 9.31,10.6,9.3 166 | 9.99,8.6,10.5 167 | 8.59,8.6,9.05 168 | 8.74,10.4,9 169 | 8.79,9.8,9.9 170 | 9.6,9.6,10 171 | 9.52,9.4,9.7 172 | 8.93,9.85,8.7 173 | 10.23,10.2,10.45 174 | 9.35,9.9,8.4 175 | 9.35,9.2,9.5 176 | 9.09,8.7,9.6 177 | 9.04,9.8,9.5 178 | 9.75,10.55,10.6 179 | 10.5,10.55,9.4 180 | 9.09,9.7,9.4 181 | 9.05,9.7,8.7 182 | 9.54,9.7,10.15 183 | 9.3,9.7,8.7 184 | 9.06,9.7,8.9 185 | 8.7,9.05,8.5 186 | 9.32,9.05,9.6 187 | 8.4,9.05,9.3 188 | 8.67,9.05,9.6 189 | 8.6,9.05,8.9 190 | 9.53,10.3,9.1 191 | 9.77,10.3,8.7 192 | 9.65,10.3,10.15 193 | 9.43,10.05,10 194 | 8.35,7.5,8.9 195 | 8.26,6.4,10 196 | 9.5,9.8,9.3 197 | 8.6,9.8,8.3 198 | 9.57,9.8,9.4 199 | 9.14,9.3,9.9 200 | 10.79,10.3,10 201 | 8.91,10.3,9.45 202 | 9.93,12.37,8.6 203 | 10.7,12.37,10.6 204 | 9.3,12.37,9.6 205 | 9.93,9.6,9.8 206 | 9.51,9,10.3 207 | 9.44,9,9.7 208 | 10.05,9.6,10.5 209 | 10.13,10.9,9.3 210 | 9.24,9.8,10 211 | 8.21,9.8,8.1 212 | 8.9,9.8,8.8 213 | 9.34,9.2,10.7 214 | 8.77,9.9,9 215 | 9.4,8.4,10.6 216 | 8.82,8.4,8.5 217 | 8.83,8.4,9.2 218 | 8.6,8.4,8.65 219 | 9.5,9.6,9.15 220 | 10.2,10.6,9.8 221 | 8.09,7.65,7.6 222 | 9.07,7.65,9.1 223 | 9.29,9.7,8.8 224 | 9.1,9.9,8.9 225 | 10.19,10.6,10.5 226 | 9.25,8.9,8.9 227 | 8.98,8.9,8.7 228 | 9.02,8.9,9.1 229 | 8.6,8.9,8.25 230 | 8.25,9.15,8 231 | 8.7,10.3,8.9 232 | 9.9,10.3,8.45 233 | 9.65,10.3,8.9 234 | 9.45,9.9,10 235 | 9.38,10.1,9.1 236 | 10.4,10.4,10 237 | 9.96,10.3,9.4 238 | 9.46,10.05,9.2 239 | 8.26,10.05,8 240 | 10.05,10.05,10.7 241 | 8.92,10.6,9.9 242 | 9.5,9.9,8.7 243 | 9.43,9.9,9.1 244 | 8.97,9.9,9.2 245 | 8.44,9.2,7.9 246 | 8.92,9.2,8.9 247 | 10.3,11,8.8 248 | 8.4,10,9.6 249 | 9.37,10,9.5 250 | 9.91,11.4,9.1 251 | 10,9,9.2 252 | 9.21,10,9.3 253 | 9.95,10,10.6 254 | 8.84,10,7.8 255 | 9.82,10,9.9 256 | 9.5,8.9,10.1 257 | 10.29,9.7,9.85 258 | 8.4,10.65,8.2 259 | 8.31,6.7,8.8 260 | 9.29,6.7,8.4 261 | 8.86,8.6,9.6 262 | 9.4,10.6,8.6 263 | 9.62,9.95,9.2 264 | 8.62,9.5,8.4 265 | 8.3,10.2,9.1 266 | 9.8,10.3,9 267 | 8.48,11,8.3 268 | 9.61,10.5,8.55 269 | 9.5,10.5,9.2 270 | 9.37,9.3,8.8 271 | 8.74,9.2,9.4 272 | 9.31,9.2,8.6 273 | 9.5,9.2,9.7 274 | 9.49,9.7,10.3 275 | 9.74,9.7,10 276 | 9.2,9.7,8.8 277 | 9.24,10,10.2 278 | 9.7,9.9,8.35 279 | 9.64,9.9,9.4 280 | 9.2,9.9,8.3 281 | 7.5,6.4,9.7 282 | 7.5,6.4,8.4 283 | 8.7,7.7,10 284 | 8.31,7.7,10.45 285 | 9,9.7,8.3 286 | 9.74,9.1,10.3 287 | 9.31,8.9,8.5 288 | 10.5,8.9,10 289 | 9.3,9.4,10 290 | 8.12,9.4,7.3 291 | 9.34,8.6,9.1 292 | 9.72,9.8,9.6 293 | 9,9.8,9.1 294 | 9.65,10.6,10 295 | 9.9,9.7,10.3 296 | 10,10.1,10.8 297 | 10.1,10.1,8.8 298 | 8,8.5,8.5 299 | 9.07,7.7,8.9 300 | 9.75,9.4,10.6 301 | 9.33,10.1,8.35 302 | 8.11,8.9,10.6 303 | 9.36,9.1,9.2 304 | 9.74,10.2,8.9 305 | 9.9,9.5,8.5 306 | 9.23,9.1,9.45 307 | 9.7,9.5,9.2 308 | 8.2,9.5,8.4 309 | 9.35,9.85,9.9 310 | 9.49,7.9,9 311 | 9.34,7.9,9.3 312 | 8.87,8.6,10.8 313 | 9.03,8.6,8.5 314 | 9.07,8.1,10 315 | 9.43,10.3,8.9 316 | 8.2,8.5,8.8 317 | 9.19,9.2,9.2 318 | 9,9.2,8.7 319 | 9.2,10,9.2 320 | 9.06,9.3,9 321 | 9.81,9.1,9.7 322 | 8.89,9,8.9 323 | 9.4,11.5,8.4 324 | 10.45,11.4,9.7 325 | 9.64,10.3,9.1 326 | 9.03,8.9,8.8 327 | 8.71,8.9,8.7 328 | 9.91,9.6,10.4 329 | 8.33,9.6,8.5 330 | 8.2,7.2,9.2 331 | 7.83,7.2,9 332 | 7.14,7.2,7.3 333 | 8.91,7.2,9.4 334 | 9.18,8.9,9.4 335 | 8.8,8.9,8.3 336 | 9.9,11,8 337 | 7.73,7,9.1 338 | 9.25,9.85,9 339 | 8.7,9.8,8.8 340 | 9.5,9.8,9.8 341 | 9.3,10.2,8.4 342 | 9.05,10.2,9.5 343 | 10.18,9.6,9.4 344 | 8.85,8.3,9 345 | 9.24,9.5,10.15 346 | 9.15,9.5,8.5 347 | 9.98,10.9,10 348 | 8.77,9.5,8.7 349 | 9.8,10,9.1 350 | 8.65,8.5,9.6 351 | 10,8.8,11.2 352 | 8.81,9.8,8.9 353 | 8.01,8.8,7.95 354 | 7.9,8.8,7.8 355 | 9.41,8.9,8.7 356 | 10.18,9.8,10.4 357 | 9.55,9.6,9.4 358 | 9.08,10.4,9.1 359 | 8.4,10.4,9.8 360 | 9.75,9.45,9.7 361 | 8.9,9.45,9.4 362 | 9.07,10.4,8.9 363 | 9.35,8.45,9 364 | 8.9,9.5,8.55 365 | 8.19,9.7,7.3 366 | 8.65,9.6,8.3 367 | 9.19,8.7,9.9 368 | 8.9,10.4,7.3 369 | 9.28,9.6,8.3 370 | 10.58,9.1,10.5 371 | 9,9.2,9.8 372 | 9.4,8.4,8.9 373 | 8.91,9.9,8.3 374 | 9.93,9.1,9.7 375 | 10,7.95,9 376 | 9.37,9.6,9.1 377 | 7.4,8.5,7.4 378 | 9,8.8,9.9 379 | 8.8,7.8,9.35 380 | 9.18,9,9.1 381 | 8.3,7.9,8.45 382 | 10.08,9.9,9.8 383 | 7.9,8.2,7.5 384 | 9.96,9.3,10.4 385 | 10.4,10.1,10.1 386 | 9.65,9.4,9.3 387 | 8.8,9.4,8.3 388 | 8.65,9.1,8.4 389 | 9.7,9.9,9.35 390 | 9.23,9.5,9 391 | 9.43,9.3,9.8 392 | 9.93,10.35,10.3 393 | 8.47,9.8,7.3 394 | 9.55,9.8,9.55 395 | 9.28,9.5,8.8 396 | 8.85,9.6,8.4 397 | 8.9,10.1,9.2 398 | 8.75,8.5,8.7 399 | 8.63,9.25,8.35 400 | 9,8.7,9.3 401 | 9.43,10.3,9.6 402 | 8.28,8.9,7.9 403 | 9.23,9.5,9.2 404 | 10.4,10.1,10.5 405 | 9,8.3,9.8 406 | 9.8,9.45,10.5 407 | 9.77,10.8,9.15 408 | 8.97,8.5,8.8 409 | 8.37,8.8,8.65 410 | 7.7,7.4,9 411 | 7.9,7.4,8.3 412 | 9.5,8.85,8.7 413 | 8.2,9.3,7.7 414 | 8.8,9.4,9.4 -------------------------------------------------------------------------------- /Statistical_Thinking_Python/frog_tongue.csv: -------------------------------------------------------------------------------- 1 | date,ID,trial number,impact force (mN),impact time (ms),impact force / body weight,adhesive force (mN),time frog pulls on target (ms),adhesive force / body weight,adhesive impulse (N-s),total contact area (mm2),contact area without mucus (mm2),contact area with mucus / contact area without mucus,contact pressure (Pa),adhesive strength (Pa) 2 | 2013_02_26,I,3,1205,46,1.95,-785,884,1.27,-0.29,387,70,0.82,3117,-2030 3 | 2013_02_26,I,4,2527,44,4.08,-983,248,1.59,-0.181,101,94,0.07,24923,-9695 4 | 2013_03_01,I,1,1745,34,2.82,-850,211,1.37,-0.157,83,79,0.05,21020,-10239 5 | 2013_03_01,I,2,1556,41,2.51,-455,1025,0.74,-0.17,330,158,0.52,4718,-1381 6 | 2013_03_01,I,3,493,36,0.8,-974,499,1.57,-0.423,245,216,0.12,2012,-3975 7 | 2013_03_01,I,4,2276,31,3.68,-592,969,0.96,-0.176,341,106,0.69,6676,-1737 8 | 2013_03_05,I,1,556,43,0.9,-512,835,0.83,-0.285,359,110,0.69,1550,-1427 9 | 2013_03_05,I,2,1928,46,3.11,-804,508,1.3,-0.285,246,178,0.28,7832,-3266 10 | 2013_03_05,I,3,2641,50,4.27,-690,491,1.12,-0.239,269,224,0.17,9824,-2568 11 | 2013_03_05,I,4,1897,41,3.06,-462,839,0.75,-0.328,266,176,0.34,7122,-1733 12 | 2013_03_12,I,1,1891,40,3.06,-766,1069,1.24,-0.38,408,33,0.92,4638,-1879 13 | 2013_03_12,I,2,1545,48,2.5,-715,649,1.15,-0.298,141,112,0.21,10947,-5064 14 | 2013_03_12,I,3,1307,29,2.11,-613,1845,0.99,-0.768,455,92,0.8,2874,-1348 15 | 2013_03_12,I,4,1692,31,2.73,-677,917,1.09,-0.457,186,129,0.31,9089,-3636 16 | 2013_03_12,I,5,1543,38,2.49,-528,750,0.85,-0.353,153,148,0.03,10095,-3453 17 | 2013_03_15,I,1,1282,31,2.07,-452,785,0.73,-0.253,290,105,0.64,4419,-1557 18 | 2013_03_15,I,2,775,34,1.25,-430,837,0.7,-0.276,257,124,0.52,3019,-1677 19 | 2013_03_15,I,3,2032,60,3.28,-652,486,1.05,-0.257,147,134,0.09,13784,-4425 20 | 2013_03_15,I,4,1240,34,2,-692,906,1.12,-0.317,364,260,0.28,3406,-1901 21 | 2013_03_15,I,5,473,40,0.76,-536,1218,0.87,-0.382,259,168,0.35,1830,-2073 22 | 2013_03_19,II,1,1612,18,3.79,-655,3087,1.54,-0.385,348,15,0.96,4633,-1881 23 | 2013_03_19,II,2,605,55,1.42,-292,1261,0.69,-0.294,248,24,0.9,2441,-1177 24 | 2013_03_19,II,3,327,51,0.77,-246,1508,0.58,-0.282,130,34,0.74,2517,-1894 25 | 2013_03_19,II,4,946,59,2.23,-245,1841,0.58,-0.34,106,26,0.76,8893,-2301 26 | 2013_03_21,II,1,541,33,1.27,-553,3126,1.3,-0.432,276,16,0.94,1959,-2004 27 | 2013_03_21,II,2,1539,43,3.62,-664,741,1.56,-0.046,85,24,0.72,18073,-7802 28 | 2013_03_21,II,3,529,28,1.24,-261,2482,0.61,-0.414,325,33,0.9,1627,-803 29 | 2013_03_21,II,4,628,31,1.48,-691,998,1.63,-0.071,242,67,0.72,2600,-2860 30 | 2013_03_25,II,1,1453,72,3.42,-92,1652,0.22,-0.008,136,0,1,10645,-678 31 | 2013_03_25,II,2,297,42,0.7,-566,936,1.33,-0.084,126,4,0.97,2367,-4506 32 | 2013_03_25,II,3,703,33,1.65,-223,2152,0.52,-0.209,237,8,0.97,2972,-942 33 | 2013_03_25,II,4,269,57,0.63,-512,189,1.2,-0.055,29,28,0.03,9279,-17652 34 | 2013_03_28,II,1,751,39,1.77,-227,1195,0.53,-0.026,206,0,1,3647,-1101 35 | 2013_03_28,II,2,245,21,0.58,-573,1466,1.35,-0.215,190,46,0.76,1288,-3014 36 | 2013_04_03,II,1,1182,28,2.78,-522,1197,1.23,-0.118,281,0,1,4213,-1860 37 | 2013_04_03,II,2,515,29,1.21,-599,1486,1.41,-0.226,217,0,1,2369,-2757 38 | 2013_04_08,II,1,435,26,1.02,-364,1017,0.86,-0.211,189,89,0.53,2302,-1927 39 | 2013_04_08,II,2,383,31,0.9,-469,974,1.1,-0.26,221,72,0.67,1737,-2129 40 | 2013_04_08,II,3,457,15,1.08,-844,780,1.99,-0.328,171,106,0.38,2665,-4925 41 | 2013_04_12,II,1,730,42,1.72,-648,786,1.52,-0.121,142,43,0.7,5149,-4573 42 | 2013_05_27,III,1,614,88,4.94,-94,683,0.76,-0.001,97,15,0.83,6326,-967 43 | 2013_05_27,III,2,414,143,3.33,-163,245,1.31,-0.032,108,10,0.6,3824,-1507 44 | 2013_05_27,III,3,324,105,2.61,-172,619,1.38,-0.079,55,23,0.37,5946,-3149 45 | 2013_06_11,III,1,776,35,6.24,-225,1823,1.81,-0.132,124,17,0.77,6272,-1818 46 | 2013_06_11,III,2,611,29,4.91,-301,918,2.42,-0.155,128,43,0.02,4770,-2354 47 | 2013_06_11,III,3,544,16,4.38,-93,1351,0.75,-0.11,43,34,0.71,12699,-2181 48 | 2013_06_14,III,1,538,38,4.32,-131,1790,1.05,-0.036,130,74,1,4130,-1005 49 | 2013_06_14,III,2,579,31,4.66,-289,1006,2.33,-0.073,113,4,0.48,5110,-2555 50 | 2013_06_18,III,1,806,29,6.49,-104,883,0.84,-0.055,115,55,0.66,6993,-902 51 | 2013_06_18,III,2,459,32,3.7,-229,1218,1.85,-0.137,89,6,0.95,5165,-2580 52 | 2013_06_18,III,3,458,30,3.69,-259,910,2.08,-0.194,91,88,0.15,5048,-2855 53 | 2013_06_18,III,4,626,16,5.04,-231,550,1.86,-0.042,82,23,0.01,7633,-2819 54 | 2013_06_21,III,1,621,27,4.99,-267,2081,2.14,-0.183,120,58,0.9,5152,-2213 55 | 2013_06_21,III,2,544,30,4.38,-178,376,1.43,-0.034,19,17,0.05,28641,-9364 56 | 2013_06_21,III,3,535,35,4.3,-123,289,0.99,-0.029,21,29,0.05,25471,-5843 57 | 2013_06_21,III,4,385,39,3.09,-151,607,1.22,-0.082,31,126,0.03,12409,-4882 58 | 2013_06_26,III,1,401,36,3.23,-127,2932,1.02,-0.215,142,12,0.86,2835,-896 59 | 2013_06_26,III,2,614,34,4.94,-372,680,2.99,-0.14,72,1,0.42,8475,-5136 60 | 2013_06_26,III,3,665,40,5.35,-236,685,1.9,-0.118,129,0,0.16,5171,-1834 61 | 2013_06_26,III,4,488,34,3.93,-390,1308,3.14,-0.208,112,58,0.39,4376,-3492 62 | 2013_05_27,IV,2,172,26,1.28,-456,462,3.4,-0.05,133,0,0.88,1297,-3440 63 | 2013_05_27,IV,3,142,20,1.05,-193,250,1.44,-0.047,57,74,0.83,2498,-3400 64 | 2013_05_27,IV,4,37,55,0.28,-236,743,1.76,-0.119,51,44,0.54,735,-4647 65 | 2013_05_30,IV,1,453,38,3.37,-225,844,1.68,-0.11,142,108,0.48,3177,-1581 66 | 2013_05_30,IV,2,355,31,2.64,-217,728,1.61,-0.023,174,39,0.98,2037,-1245 67 | 2013_05_30,IV,3,22,33,0.17,-161,472,1.2,-0.052,56,4,0.02,397,-2866 68 | 2013_06_03,IV,1,502,74,3.74,-139,959,1.04,-0.089,65,77,0.91,7713,-2141 69 | 2013_06_11,IV,1,273,26,2.03,-264,844,1.97,-0.16,124,81,0.29,2205,-2136 70 | 2013_06_11,IV,2,720,27,5.36,-342,1515,2.55,-0.226,137,0,0.83,5259,-2497 71 | 2013_06_11,IV,3,582,33,4.34,-231,279,1.72,-0.033,60,4,0.03,9705,-3847 72 | 2013_06_11,IV,4,198,23,1.47,-209,1427,1.55,-0.151,110,69,0.84,1793,-1889 73 | 2013_06_14,IV,1,198,6,1.47,-292,2874,2.17,-0.232,145,50,0.99,1369,-2018 74 | 2013_06_18,IV,1,597,29,4.44,-339,4251,2.53,-0.281,191,12,1,3116,-1772 75 | 2013_06_18,IV,2,516,31,3.84,-371,626,2.76,-0.094,83,18,0.12,6184,-4447 76 | 2013_06_18,IV,3,815,34,6.07,-331,1254,2.47,-0.077,151,20,0.71,5386,-2190 77 | 2013_06_18,IV,4,402,38,3,-302,986,2.25,-0.122,117,30,0.07,3446,-2591 78 | 2013_06_21,IV,1,605,39,4.5,-216,1627,1.61,-0.139,123,20,1,4928,-1759 79 | 2013_06_21,IV,2,711,76,5.3,-163,2021,1.21,-0.217,129,42,0.97,5498,-1257 80 | 2013_06_21,IV,3,614,33,4.57,-367,1366,2.73,-0.198,128,108,0.46,4776,-2857 81 | 2013_06_21,IV,4,468,36,3.48,-218,1269,1.63,-0.122,129,68,0.61,3617,-1688 -------------------------------------------------------------------------------- /Statistical_Thinking_Python/scandens_beak_depth_heredity.csv: -------------------------------------------------------------------------------- 1 | mid_parent,mid_offspring 2 | 8.3318,8.4190 3 | 8.4035,9.2468 4 | 8.5317,8.1532 5 | 8.7202,8.0089 6 | 8.7089,8.2215 7 | 8.7541,8.3734 8 | 8.7730,8.5025 9 | 8.8107,8.6392 10 | 8.7919,8.7684 11 | 8.8069,8.8139 12 | 8.6523,8.7911 13 | 8.6146,8.9051 14 | 8.6938,8.9203 15 | 8.7127,8.8747 16 | 8.7466,8.9430 17 | 8.7504,9.0038 18 | 8.7805,8.9810 19 | 8.7428,9.0949 20 | 8.7164,9.2696 21 | 8.8032,9.1633 22 | 8.8258,9.1785 23 | 8.8560,9.1937 24 | 8.9012,9.2772 25 | 8.9125,9.0722 26 | 8.8635,8.9658 27 | 8.8258,8.9658 28 | 8.8522,8.5025 29 | 8.8974,8.4949 30 | 8.9427,8.4949 31 | 8.9879,8.5633 32 | 8.9615,8.6013 33 | 8.9238,8.6468 34 | 8.9351,8.1532 35 | 9.0143,8.3734 36 | 9.0558,8.6620 37 | 9.0596,8.6924 38 | 8.9917,8.7456 39 | 8.9050,8.8367 40 | 8.9314,8.8595 41 | 8.9465,8.9658 42 | 8.9879,8.9582 43 | 8.9804,8.8671 44 | 9.0219,8.8671 45 | 9.0520,8.9430 46 | 9.0407,9.0646 47 | 9.0407,9.1405 48 | 8.9955,9.2089 49 | 8.9992,9.2848 50 | 8.9992,9.3759 51 | 9.0747,9.4899 52 | 9.0747,9.4519 53 | 9.5385,8.1228 54 | 9.4781,8.2595 55 | 9.4517,8.3127 56 | 9.3537,8.4949 57 | 9.2707,8.6013 58 | 9.1199,8.4646 59 | 9.1689,8.5329 60 | 9.1425,8.7532 61 | 9.1350,8.8823 62 | 9.1011,9.0342 63 | 9.1727,8.6392 64 | 9.2217,8.6772 65 | 9.2255,8.6316 66 | 9.2821,8.7532 67 | 9.3235,8.8291 68 | 9.3198,8.8975 69 | 9.3198,8.9734 70 | 9.3198,9.0494 71 | 9.3273,9.1253 72 | 9.3725,9.1253 73 | 9.3989,9.1253 74 | 9.4253,9.1785 75 | 9.4593,9.2848 76 | 9.4442,9.4595 77 | 9.4291,9.3608 78 | 9.2632,9.2089 79 | 9.2293,9.2544 80 | 9.1878,9.3684 81 | 9.1425,9.3684 82 | 9.1275,9.2316 83 | 9.1802,9.1709 84 | 9.1765,9.2316 85 | 9.2481,9.0342 86 | 9.2481,8.8899 87 | 9.1991,8.8291 88 | 9.1689,8.9810 89 | 9.1765,8.8975 90 | 9.2406,10.4089 91 | 9.3198,10.1886 92 | 9.3235,9.7633 93 | 9.1991,9.7329 94 | 9.2971,9.6114 95 | 9.2443,9.5051 96 | 9.3160,9.5127 97 | 9.2934,9.3684 98 | 9.3914,9.6266 99 | 9.3989,9.5354 100 | 9.5121,10.0215 101 | 9.6176,10.0215 102 | 9.5535,9.6266 103 | 9.4668,9.6038 104 | 9.3725,9.4063 105 | 9.3348,9.2316 106 | 9.3763,9.3380 107 | 9.3839,9.2620 108 | 9.4216,9.2620 109 | 9.4065,9.4063 110 | 9.3348,9.4367 111 | 9.4442,9.0342 112 | 9.4367,8.9430 113 | 9.5083,8.9203 114 | 9.4480,8.7835 115 | 9.4781,8.7835 116 | 9.5950,9.0570 117 | 9.6101,8.9354 118 | 9.5686,8.8975 119 | 9.6365,8.8139 120 | 9.7119,8.8671 121 | 9.8213,9.0873 122 | 9.8250,9.2848 123 | 9.7609,9.2392 124 | 9.6516,9.2924 125 | 9.5988,9.4063 126 | 9.5460,9.3152 127 | 9.6516,9.4899 128 | 9.7572,9.5962 129 | 9.8854,9.6873 130 | 10.0023,9.5203 131 | 9.3914,9.6646 132 | -------------------------------------------------------------------------------- /Statistical_Thinking_Python/stat1.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/Statistical_Thinking_Python/stat1.PNG -------------------------------------------------------------------------------- /Statistical_Thinking_Python/stat_func.py: -------------------------------------------------------------------------------- 1 | # stat functions 2 | 3 | def ecdf(data): 4 | """Compute ECDF for a one-dimensional array of measurements.""" 5 | # Number of data points: n 6 | n = len(data) 7 | # x-data for the ECDF: x 8 | x = np.sort(data) 9 | # y-data for the ECDF: y 10 | y = np.arange(1, n+1) / n 11 | return x, y 12 | 13 | def pearson_r(x, y): 14 | """Compute Pearson correlation coefficient between two arrays.""" 15 | # Compute correlation matrix: corr_mat 16 | corr_mat = np.corrcoef(x,y) 17 | 18 | # Return entry [0,1] 19 | return corr_mat[0,1] 20 | 21 | def perform_bernoulli_trials(n, p): 22 | """Perform n Bernoulli trials with success probability p 23 | and return number of successes.""" 24 | # Initialize number of successes: n_success 25 | n_success = 0 26 | 27 | 28 | # Perform trials 29 | for i in range(n): 30 | # Choose random number between zero and one: random_number 31 | random_number = np.random.random() 32 | 33 | # If less than p, it's a success so add one to n_success 34 | if random_number < p: 35 | n_success+=1 36 | 37 | return n_success 38 | 39 | def successive_poisson(tau1, tau2, size=1): 40 | '''a function to sample out of the distribution''' 41 | # Draw samples out of first exponential distribution: t1 42 | t1 = np.random.exponential(tau1, size) 43 | 44 | # Draw samples out of second exponential distribution: t2 45 | t2 = np.random.exponential(tau2, size) 46 | 47 | return t1 + t2 48 | 49 | def bootstrap_replicate_1d(data, func): 50 | """Generate bootstrap replicate of 1D data.""" 51 | bs_sample = np.random.choice(data, len(data)) 52 | return func(bs_sample) 53 | 54 | def draw_bs_reps(data, func, size=1): 55 | """Draw bootstrap replicates.""" 56 | 57 | # Initialize array of replicates: bs_replicates 58 | bs_replicates = np.empty(size) 59 | 60 | # Generate replicates 61 | for i in range(size): 62 | bs_replicates[i] = bootstrap_replicate_1d(data,func) 63 | 64 | return bs_replicates 65 | 66 | def draw_bs_pairs_linreg(x, y, size=1): 67 | """Perform pairs bootstrap for linear regression.""" 68 | 69 | # Set up array of indices to sample from: inds 70 | inds = np.arange(len(x)) 71 | 72 | # Initialize replicates: bs_slope_reps, bs_intercept_reps 73 | bs_slope_reps = np.empty(size) 74 | bs_intercept_reps = np.empty(size) 75 | 76 | # Generate replicates 77 | for i in range(size): 78 | bs_inds = np.random.choice(inds, size=len(inds)) 79 | bs_x, bs_y = x[bs_inds], y[bs_inds] 80 | bs_slope_reps[i], bs_intercept_reps[i] = np.polyfit(bs_x,bs_y,1) 81 | 82 | return bs_slope_reps, bs_intercept_reps 83 | 84 | def diff_from_newcomb(data, newcomb_value=299860): 85 | return np.mean(data) - newcomb_value 86 | 87 | 88 | 89 | 90 | def diff_of_means(data_1, data_2): 91 | """Difference in means of two arrays.""" 92 | 93 | # The difference of means of data_1, data_2: diff 94 | diff = np.mean(data_1) - np.mean(data_2) 95 | 96 | return diff 97 | 98 | 99 | 100 | 101 | def permutation_sample(data1, data2): 102 | """Generate a permutation sample from two data sets.""" 103 | 104 | # Concatenate the data sets: data 105 | data = np.concatenate((data1,data2)) 106 | 107 | # Permute the concatenated array: permuted_data 108 | permuted_data = np.random.permutation(data) 109 | 110 | # Split the permuted array into two: perm_sample_1, perm_sample_2 111 | perm_sample_1 = permuted_data[:len(data1)] 112 | perm_sample_2 = permuted_data[len(data1):] 113 | 114 | return perm_sample_1, perm_sample_2 115 | 116 | 117 | 118 | 119 | 120 | def draw_perm_reps(data_1, data_2, func, size=1): 121 | """Generate multiple permutation replicates.""" 122 | 123 | # Initialize array of replicates: perm_replicates 124 | perm_replicates = np.empty(size) 125 | 126 | for i in range(size): 127 | # Generate permutation sample 128 | perm_sample_1, perm_sample_2 = permutation_sample(data_1, data_2) 129 | 130 | # Compute the test statistic 131 | perm_replicates[i] = func(perm_sample_1, perm_sample_2) 132 | 133 | return perm_replicates -------------------------------------------------------------------------------- /Untitled.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Visualizing_Time_Series_Data_in_Python/ch1_discoveries.csv: -------------------------------------------------------------------------------- 1 | date,Y 2 | 01-01-1860,5 3 | 01-01-1861,3 4 | 01-01-1862,0 5 | 01-01-1863,2 6 | 01-01-1864,0 7 | 01-01-1865,3 8 | 01-01-1866,2 9 | 01-01-1867,3 10 | 01-01-1868,6 11 | 01-01-1869,1 12 | 01-01-1870,2 13 | 01-01-1871,1 14 | 01-01-1872,2 15 | 01-01-1873,1 16 | 01-01-1874,3 17 | 01-01-1875,3 18 | 01-01-1876,3 19 | 01-01-1877,5 20 | 01-01-1878,2 21 | 01-01-1879,4 22 | 01-01-1880,4 23 | 01-01-1881,0 24 | 01-01-1882,2 25 | 01-01-1883,3 26 | 01-01-1884,7 27 | 01-01-1885,12 28 | 01-01-1886,3 29 | 01-01-1887,10 30 | 01-01-1888,9 31 | 01-01-1889,2 32 | 01-01-1890,3 33 | 01-01-1891,7 34 | 01-01-1892,7 35 | 01-01-1893,2 36 | 01-01-1894,3 37 | 01-01-1895,3 38 | 01-01-1896,6 39 | 01-01-1897,2 40 | 01-01-1898,4 41 | 01-01-1899,3 42 | 01-01-1900,5 43 | 01-01-1901,2 44 | 01-01-1902,2 45 | 01-01-1903,4 46 | 01-01-1904,0 47 | 01-01-1905,4 48 | 01-01-1906,2 49 | 01-01-1907,5 50 | 01-01-1908,2 51 | 01-01-1909,3 52 | 01-01-1910,3 53 | 01-01-1911,6 54 | 01-01-1912,5 55 | 01-01-1913,8 56 | 01-01-1914,3 57 | 01-01-1915,6 58 | 01-01-1916,6 59 | 01-01-1917,0 60 | 01-01-1918,5 61 | 01-01-1919,2 62 | 01-01-1920,2 63 | 01-01-1921,2 64 | 01-01-1922,6 65 | 01-01-1923,3 66 | 01-01-1924,4 67 | 01-01-1925,4 68 | 01-01-1926,2 69 | 01-01-1927,2 70 | 01-01-1928,4 71 | 01-01-1929,7 72 | 01-01-1930,5 73 | 01-01-1931,3 74 | 01-01-1932,3 75 | 01-01-1933,0 76 | 01-01-1934,2 77 | 01-01-1935,2 78 | 01-01-1936,2 79 | 01-01-1937,1 80 | 01-01-1938,3 81 | 01-01-1939,4 82 | 01-01-1940,2 83 | 01-01-1941,2 84 | 01-01-1942,1 85 | 01-01-1943,1 86 | 01-01-1944,1 87 | 01-01-1945,2 88 | 01-01-1946,1 89 | 01-01-1947,4 90 | 01-01-1948,4 91 | 01-01-1949,3 92 | 01-01-1950,2 93 | 01-01-1951,1 94 | 01-01-1952,4 95 | 01-01-1953,1 96 | 01-01-1954,1 97 | 01-01-1955,1 98 | 01-01-1956,0 99 | 01-01-1957,0 100 | 01-01-1958,2 101 | 01-01-1959,0 102 | -------------------------------------------------------------------------------- /Visualizing_Time_Series_Data_in_Python/ch3_airline_passengers.csv: -------------------------------------------------------------------------------- 1 | Month,AirPassengers 2 | 1949-01,112 3 | 1949-02,118 4 | 1949-03,132 5 | 1949-04,129 6 | 1949-05,121 7 | 1949-06,135 8 | 1949-07,148 9 | 1949-08,148 10 | 1949-09,136 11 | 1949-10,119 12 | 1949-11,104 13 | 1949-12,118 14 | 1950-01,115 15 | 1950-02,126 16 | 1950-03,141 17 | 1950-04,135 18 | 1950-05,125 19 | 1950-06,149 20 | 1950-07,170 21 | 1950-08,170 22 | 1950-09,158 23 | 1950-10,133 24 | 1950-11,114 25 | 1950-12,140 26 | 1951-01,145 27 | 1951-02,150 28 | 1951-03,178 29 | 1951-04,163 30 | 1951-05,172 31 | 1951-06,178 32 | 1951-07,199 33 | 1951-08,199 34 | 1951-09,184 35 | 1951-10,162 36 | 1951-11,146 37 | 1951-12,166 38 | 1952-01,171 39 | 1952-02,180 40 | 1952-03,193 41 | 1952-04,181 42 | 1952-05,183 43 | 1952-06,218 44 | 1952-07,230 45 | 1952-08,242 46 | 1952-09,209 47 | 1952-10,191 48 | 1952-11,172 49 | 1952-12,194 50 | 1953-01,196 51 | 1953-02,196 52 | 1953-03,236 53 | 1953-04,235 54 | 1953-05,229 55 | 1953-06,243 56 | 1953-07,264 57 | 1953-08,272 58 | 1953-09,237 59 | 1953-10,211 60 | 1953-11,180 61 | 1953-12,201 62 | 1954-01,204 63 | 1954-02,188 64 | 1954-03,235 65 | 1954-04,227 66 | 1954-05,234 67 | 1954-06,264 68 | 1954-07,302 69 | 1954-08,293 70 | 1954-09,259 71 | 1954-10,229 72 | 1954-11,203 73 | 1954-12,229 74 | 1955-01,242 75 | 1955-02,233 76 | 1955-03,267 77 | 1955-04,269 78 | 1955-05,270 79 | 1955-06,315 80 | 1955-07,364 81 | 1955-08,347 82 | 1955-09,312 83 | 1955-10,274 84 | 1955-11,237 85 | 1955-12,278 86 | 1956-01,284 87 | 1956-02,277 88 | 1956-03,317 89 | 1956-04,313 90 | 1956-05,318 91 | 1956-06,374 92 | 1956-07,413 93 | 1956-08,405 94 | 1956-09,355 95 | 1956-10,306 96 | 1956-11,271 97 | 1956-12,306 98 | 1957-01,315 99 | 1957-02,301 100 | 1957-03,356 101 | 1957-04,348 102 | 1957-05,355 103 | 1957-06,422 104 | 1957-07,465 105 | 1957-08,467 106 | 1957-09,404 107 | 1957-10,347 108 | 1957-11,305 109 | 1957-12,336 110 | 1958-01,340 111 | 1958-02,318 112 | 1958-03,362 113 | 1958-04,348 114 | 1958-05,363 115 | 1958-06,435 116 | 1958-07,491 117 | 1958-08,505 118 | 1958-09,404 119 | 1958-10,359 120 | 1958-11,310 121 | 1958-12,337 122 | 1959-01,360 123 | 1959-02,342 124 | 1959-03,406 125 | 1959-04,396 126 | 1959-05,420 127 | 1959-06,472 128 | 1959-07,548 129 | 1959-08,559 130 | 1959-09,463 131 | 1959-10,407 132 | 1959-11,362 133 | 1959-12,405 134 | 1960-01,417 135 | 1960-02,391 136 | 1960-03,419 137 | 1960-04,461 138 | 1960-05,472 139 | 1960-06,535 140 | 1960-07,622 141 | 1960-08,606 142 | 1960-09,508 143 | 1960-10,461 144 | 1960-11,390 145 | 1960-12,432 146 | -------------------------------------------------------------------------------- /Visualizing_Time_Series_Data_in_Python/chapter1.pdf: -------------------------------------------------------------------------------- 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pytz=2017.3=py36h63b9c63_0 103 | - pyyaml=3.12=py36hafb9ca4_1 104 | - pyzmq=16.0.3=py36he2533c7_0 105 | - qt=5.6.2=h974d657_12 106 | - qtconsole=4.3.1=py36h8f73b5b_0 107 | - readline=7.0=ha6073c6_4 108 | - regex=2018.06.21=py36h14c3975_0 109 | - requests=2.19.1=py36_0 110 | - s3transfer=0.1.13=py36_0 111 | - scipy=1.0.0=py36hbf646e7_0 112 | - seaborn=0.8.1=py36hfad7ec4_0 113 | - send2trash=1.4.2=py36_0 114 | - simplegeneric=0.8.1=py36h2cb9092_0 115 | - sip=4.18.1=py36h51ed4ed_2 116 | - six=1.11.0=py36h372c433_1 117 | - smart_open=1.6.0=py36_0 118 | - spacy=2.0.11=py36h04863e7_2 119 | - sqlite=3.22.0=h1bed415_0 120 | - statsmodels=0.8.0=py36h8533d0b_0 121 | - termcolor=1.1.0=py36_1 122 | - testpath=0.3.1=py36h8cadb63_0 123 | - thinc=6.10.1=py36hd61447b_0 124 | - tk=8.6.7=hc745277_3 125 | - toolz=0.9.0=py36_0 126 | - tqdm=4.24.0=py36_0 127 | - traitlets=4.3.2=py36h674d592_0 128 | - ujson=1.35=py36h14c3975_0 129 | - urllib3=1.23=py36_0 130 | - wcwidth=0.1.7=py36hdf4376a_0 131 | - webencodings=0.5.1=py36h800622e_1 132 | - wheel=0.30.0=py36hfd4bba0_1 133 | - wrapt=1.10.11=py36h14c3975_2 134 | - xz=5.2.3=h55aa19d_2 135 | - yaml=0.1.7=had09818_2 136 | - zeromq=4.2.2=hbedb6e5_2 137 | - zlib=1.2.11=ha838bed_2 138 | - pip: 139 | - en-core-web-sm==2.0.0 140 | - et-xmlfile==1.0.1 141 | - jdcal==1.3 142 | - lxml==4.1.1 143 | - morfessor==2.0.4 144 | - openpyxl==2.5.0 145 | - pandas-datareader==0.6.0 146 | - pillow==5.0.0 147 | - polyglot==16.7.4 148 | - psycopg2==2.7.4 149 | - pycld2==0.31 150 | - pyicu==2.0.3 151 | - pymysql==0.8.0 152 | - requests-file==1.4.3 153 | - requests-ftp==0.3.1 154 | - scikit-learn==0.19.1 155 | - sklearn==0.0 156 | - sqlalchemy==1.2.5 157 | - xlrd==1.1.0 158 | prefix: /home/frank/miniconda3/envs/datacamp 159 | 160 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/pennsylvania2012_turnout.csv: -------------------------------------------------------------------------------- 1 | county,state,total,Obama,Romney,winner,voters,turnout,margin 2 | Adams,PA,41973,35.48233388130465,63.11200057179616,Romney,61156,68.63267708810255,27.62966669049151 3 | Allegheny,PA,614671,56.64021891385799,42.1858197312058,Obama,924351,66.49757505536317,14.454399182652203 4 | Armstrong,PA,28322,30.69698467622344,67.90127815832216,Romney,42147,67.19813984387976,37.20429348209871 5 | Beaver,PA,80015,46.032618883959245,52.637630444291716,Romney,115157,69.48340092221923,6.605011560332475 6 | Bedford,PA,21444,22.057451967916432,76.9865696698377,Romney,32189,66.61903134611202,54.92911770192127 7 | Berks,PA,163253,48.939376305489034,49.52864572167129,Romney,250356,65.20834331911358,0.5892694161822537 8 | Blair,PA,47631,32.575423568684265,66.13340051647036,Romney,85328,55.82106694168385,33.5579769477861 9 | Bradford,PA,22501,36.84725123327852,61.450602195457975,Romney,40490,55.571746110150656,24.60335096217945 10 | Bucks,PA,319407,49.96697004135789,48.80168562367137,Obama,435606,73.32474759300837,1.1652844176865254 11 | Butler,PA,88924,31.92051639602357,66.81660744006118,Romney,122762,72.4360958602825,34.896091044037604 12 | Cambria,PA,57718,40.16251429363457,57.97844693163311,Romney,86988,66.35168069158965,17.81593263799854 13 | Cameron,PA,1967,34.4178952719878,64.26029486527707,Romney,3651,53.87565050671049,29.842399593289276 14 | Carbon,PA,24232,45.55959062396831,52.45130406074612,Romney,39017,62.10626137324756,6.891713436777806 15 | Centre,PA,68801,48.94841644743536,48.97748579235768,Romney,112949,60.91333256602537,0.029069344922322753 16 | Chester,PA,248295,49.228538633480326,49.650617209367894,Romney,337822,73.49876562213237,0.42207857588756487 17 | Clarion,PA,15227,31.06981020555592,67.17015827149143,Romney,24120,63.1301824212272,36.10034806593551 18 | Clearfield,PA,31894,34.78083652097573,63.65774126795011,Romney,51174,62.324617970062924,28.876904746974375 19 | Clinton,PA,12663,43.45731659164496,54.81323541025034,Romney,22969,55.13082850798903,11.355918818605373 20 | Columbia,PA,24305,42.888294589590615,55.3178358362477,Romney,39888,60.933112715603684,12.429541246657088 21 | Crawford,PA,33089,39.360512557043116,58.92290489286472,Romney,54711,60.479611047138604,19.562392335821606 22 | Cumberland,PA,109964,39.9976355898294,58.53279255028918,Romney,158194,69.51211803228946,18.53515696045978 23 | Dauphin,PA,122625,52.36207951070336,46.35107033639144,Obama,178924,68.5346851177036,6.011009174311926 24 | Delaware,PA,272853,60.40065529790767,38.58121406031819,Obama,397338,68.67025051719192,21.819441237589476 25 | Elk,PA,12425,41.400402414486926,57.13480885311871,Romney,20302,61.20086690966408,15.734406438631785 26 | Erie,PA,112732,57.77951247205762,40.89522052301032,Obama,176851,63.74405573053023,16.88429194904729 27 | Fayette,PA,48196,45.317038758403186,53.624782139596654,Romney,91681,52.569234628767134,8.307743381193468 28 | Forest,PA,2308,38.73483535528596,59.83535528596187,Romney,3232,71.4108910891089,21.100519930675905 29 | Franklin,PA,62802,30.110506034839656,68.5838030635967,Romney,87406,71.85090268402627,38.473297028757045 30 | Fulton,PA,6148,21.09629147690306,77.74886141834743,Romney,9344,65.79623287671232,56.652569941444376 31 | Greene,PA,13726,40.536208655107096,58.17426781290981,Romney,22663,60.565679742311254,17.63805915780271 32 | Huntingdon,PA,17587,30.60783533291637,67.96497412861773,Romney,28824,61.015126283652506,37.35713879570135 33 | Indiana,PA,36180,39.80928689883913,58.55997788833611,Romney,57378,63.055526508417856,18.75069098949697 34 | Jefferson,PA,17072,26.563964386129328,71.97750702905344,Romney,28549,59.798942169603144,45.413542642924114 35 | Juniata,PA,9480,26.719409282700425,71.95147679324894,Romney,13547,69.97859304643094,45.232067510548525 36 | Lackawanna,PA,97116,63.129659376415844,35.76135755179373,Obama,149474,64.97183456654669,27.368301824622126 37 | Lancaster,PA,219677,39.65276292010543,58.888276879236344,Romney,314567,69.83472519367892,19.235513959130913 38 | Lawrence,PA,38816,44.896434460016486,53.882419620774925,Romney,61709,62.90168370902137,8.985985160758432 39 | Lebanon,PA,53771,35.28853843149653,63.24970709118298,Romney,81476,65.9961215572684,27.961168659686443 40 | Lehigh,PA,144922,53.15204040794358,45.60453209312596,Obama,226453,63.99650258552547,7.547508314817626 41 | Luzerne,PA,123741,51.69992161046056,46.847043421339734,Obama,194137,63.73900905030984,4.852878189120819 42 | Lycoming,PA,46214,32.68273683299433,65.97567836586315,Romney,68064,67.89786083685942,33.29294153286882 43 | McKean,PA,15014,35.03396829625683,63.23431463966964,Romney,25861,58.056533003364144,28.200346343412814 44 | Mercer,PA,48065,48.022469572453964,50.56486008530116,Romney,75238,63.88394162524257,2.542390512847209 45 | Mifflin,PA,16311,26.111213291643683,72.91398442768684,Romney,24445,66.72530169768869,46.802771136043155 46 | Monroe,PA,59312,56.364647963312656,42.31858645805233,Obama,108879,54.47515131476226,14.046061505260319 47 | Montgomery,PA,401787,56.63722320533019,42.28683357102146,Obama,551105,72.90570762377405,14.350389634308724 48 | Montour,PA,7787,38.85963785796841,59.535122640297935,Romney,13518,57.604675247817724,20.675484782329523 49 | Northampton,PA,125883,51.64637004202314,47.061159966000176,Obama,209414,60.11202689409495,4.585210076022974 50 | Northumberland,PA,31512,39.32470170093932,58.75856816450876,Romney,54978,57.31747244352287,19.433866463569434 51 | Perry,PA,18240,29.76973684210527,68.59100877192985,Romney,27245,66.94806386492934,38.82127192982458 52 | Philadelphia,PA,653598,85.2242509922001,14.051450585834106,Obama,1099197,59.4614068269837,71.172800406366 53 | Pike,PA,23164,43.90433431186324,54.88257641167329,Romney,41840,55.36328871892925,10.97824209981005 54 | Potter,PA,7205,26.259541984732827,72.15822345593337,Romney,10913,66.02217538715294,45.89868147120054 55 | Schuylkill,PA,57505,42.52325884705678,55.91861577254152,Romney,86316,66.62148385003938,13.39535692548474 56 | Snyder,PA,14962,31.22577195562091,67.17016441652187,Romney,21573,69.35521253418625,35.94439246090096 57 | Somerset,PA,33875,27.80811808118081,70.65682656826569,Romney,51860,65.3200925568839,42.84870848708487 58 | Sullivan,PA,2934,35.03749147920927,63.36059986366735,Romney,4242,69.16548797736917,28.323108384458084 59 | Susquehanna,PA,17930,38.432794199665366,59.87172336865589,Romney,26163,68.53189618927493,21.438929168990526 60 | Tioga,PA,15943,31.69416044659098,66.48058709151353,Romney,26001,61.31687242798354,34.78642664492255 61 | Union,PA,16187,37.455983196392175,60.93161178723668,Romney,23950,67.5866388308977,23.47562859084451 62 | Venango,PA,20775,35.951865222623354,62.22864019253911,Romney,32773,63.39059591737101,26.276774969915756 63 | Warren,PA,16462,41.112865994411365,57.192321710606244,Romney,29111,56.54907079798015,16.079455716194886 64 | Washington,PA,90078,42.74406625369125,56.01256688647617,Romney,142331,63.28768855695527,13.268500632784919 65 | Wayne,PA,20966,38.815224649432416,59.768196127062865,Romney,32577,64.35828959081562,20.952971477630445 66 | Westmoreland,PA,168709,37.56764606511804,61.306154384176295,Romney,238006,70.88434745342555,23.73850831905825 67 | Wyoming,PA,11214,42.91064740502943,55.18994114499732,Romney,17255,64.98985801217039,12.279293739967898 68 | York,PA,186394,38.695451570329524,59.8602959322725,Romney,280280,66.50278293135436,21.164844361942972 69 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/regions.csv: -------------------------------------------------------------------------------- 1 | Country,region 2 | Afghanistan,South Asia 3 | Albania,Europe & Central Asia 4 | Algeria,Middle East & North Africa 5 | Angola,Sub-Saharan Africa 6 | Antigua and Barbuda,America 7 | Argentina,America 8 | Armenia,Europe & Central Asia 9 | Aruba,America 10 | Australia,East Asia & Pacific 11 | Austria,Europe & Central Asia 12 | Azerbaijan,Europe & Central Asia 13 | Bahamas,America 14 | Bahrain,Middle East & North Africa 15 | Bangladesh,South Asia 16 | Barbados,America 17 | Belarus,Europe & Central Asia 18 | Belgium,Europe & Central Asia 19 | Belize,America 20 | Benin,Sub-Saharan Africa 21 | Bhutan,South Asia 22 | Bolivia,America 23 | Bosnia and Herzegovina,Europe & Central Asia 24 | Botswana,Sub-Saharan Africa 25 | Brazil,America 26 | Brunei,East Asia & Pacific 27 | Bulgaria,Europe & Central Asia 28 | Burkina Faso,Sub-Saharan Africa 29 | Burundi,Sub-Saharan Africa 30 | Cambodia,East Asia & Pacific 31 | Cameroon,Sub-Saharan Africa 32 | Canada,America 33 | Cape Verde,Sub-Saharan Africa 34 | Central African Rep.,Sub-Saharan Africa 35 | Chad,Sub-Saharan Africa 36 | Channel Islands,Europe & Central Asia 37 | Chile,America 38 | China,East Asia & Pacific 39 | Colombia,America 40 | Comoros,Sub-Saharan Africa 41 | "Congo, Dem. Rep.",Sub-Saharan Africa 42 | "Congo, Rep.",Sub-Saharan Africa 43 | Costa Rica,America 44 | Cote d'Ivoire,Sub-Saharan Africa 45 | Croatia,Europe & Central Asia 46 | Cuba,America 47 | Cyprus,Europe & Central Asia 48 | Czech Rep.,Europe & Central Asia 49 | Denmark,Europe & Central Asia 50 | Djibouti,Middle East & North Africa 51 | Dominican Rep.,America 52 | Ecuador,America 53 | Egypt,Middle East & North Africa 54 | El Salvador,America 55 | Equatorial Guinea,Sub-Saharan Africa 56 | Eritrea,Sub-Saharan Africa 57 | Estonia,Europe & Central Asia 58 | Ethiopia,Sub-Saharan Africa 59 | Fiji,East Asia & Pacific 60 | Finland,Europe & Central Asia 61 | France,Europe & Central Asia 62 | French Guiana,America 63 | French Polynesia,East Asia & Pacific 64 | Gabon,Sub-Saharan Africa 65 | Gambia,Sub-Saharan Africa 66 | Georgia,Europe & Central Asia 67 | Germany,Europe & Central Asia 68 | Ghana,Sub-Saharan Africa 69 | Greece,Europe & Central Asia 70 | Greenland,Europe & Central Asia 71 | Grenada,America 72 | Guadeloupe,America 73 | Guam,East Asia & Pacific 74 | Guatemala,America 75 | Guinea,Sub-Saharan Africa 76 | Guinea-Bissau,Sub-Saharan Africa 77 | Guyana,America 78 | Haiti,America 79 | Honduras,America 80 | "Hong Kong, China",East Asia & Pacific 81 | Hungary,Europe & Central Asia 82 | Iceland,Europe & Central Asia 83 | India,South Asia 84 | Indonesia,East Asia & Pacific 85 | Iran,Middle East & North Africa 86 | Iraq,Middle East & North Africa 87 | Ireland,Europe & Central Asia 88 | Israel,Middle East & North Africa 89 | Italy,Europe & Central Asia 90 | Jamaica,America 91 | Japan,East Asia & Pacific 92 | Jordan,Middle East & North Africa 93 | Kazakhstan,Europe & Central Asia 94 | Kenya,Sub-Saharan Africa 95 | Kiribati,East Asia & Pacific 96 | "Korea, Dem. Rep.",East Asia & Pacific 97 | "Korea, Rep.",East Asia & Pacific 98 | Kuwait,Middle East & North Africa 99 | Kyrgyzstan,Europe & Central Asia 100 | Laos,East Asia & Pacific 101 | Latvia,Europe & Central Asia 102 | Lebanon,Middle East & North Africa 103 | Lesotho,Sub-Saharan Africa 104 | Liberia,Sub-Saharan Africa 105 | Libya,Middle East & North Africa 106 | Lithuania,Europe & Central Asia 107 | Luxembourg,Europe & Central Asia 108 | "Macao, China",East Asia & Pacific 109 | "Macedonia, FYR",Europe & Central Asia 110 | Madagascar,Sub-Saharan Africa 111 | Malawi,Sub-Saharan Africa 112 | Malaysia,East Asia & Pacific 113 | Maldives,South Asia 114 | Mali,Sub-Saharan Africa 115 | Malta,Europe & Central Asia 116 | Martinique,America 117 | Mauritania,Sub-Saharan Africa 118 | Mauritius,Sub-Saharan Africa 119 | Mayotte,Sub-Saharan Africa 120 | Mexico,America 121 | "Micronesia, Fed. Sts.",East Asia & Pacific 122 | Moldova,Europe & Central Asia 123 | Mongolia,East Asia & Pacific 124 | Montenegro,Europe & Central Asia 125 | Morocco,Middle East & North Africa 126 | Mozambique,Sub-Saharan Africa 127 | Myanmar,East Asia & Pacific 128 | Namibia,Sub-Saharan Africa 129 | Nepal,South Asia 130 | Netherlands,Europe & Central Asia 131 | Netherlands Antilles,America 132 | New Caledonia,East Asia & Pacific 133 | New Zealand,East Asia & Pacific 134 | Nicaragua,America 135 | Niger,Sub-Saharan Africa 136 | Nigeria,Sub-Saharan Africa 137 | Norway,Europe & Central Asia 138 | Oman,Middle East & North Africa 139 | Pakistan,South Asia 140 | Panama,America 141 | Papua New Guinea,East Asia & Pacific 142 | Paraguay,America 143 | Peru,America 144 | Philippines,East Asia & Pacific 145 | Poland,Europe & Central Asia 146 | Portugal,Europe & Central Asia 147 | Puerto Rico,America 148 | Qatar,Middle East & North Africa 149 | Reunion,Sub-Saharan Africa 150 | Romania,Europe & Central Asia 151 | Russia,Europe & Central Asia 152 | Rwanda,Sub-Saharan Africa 153 | Saint Lucia,America 154 | Saint Vincent and the Grenadines,America 155 | Samoa,East Asia & Pacific 156 | Sao Tome and Principe,Sub-Saharan Africa 157 | Saudi Arabia,Middle East & North Africa 158 | Senegal,Sub-Saharan Africa 159 | Serbia,Europe & Central Asia 160 | Seychelles,Sub-Saharan Africa 161 | Sierra Leone,Sub-Saharan Africa 162 | Singapore,East Asia & Pacific 163 | Slovak Republic,Europe & Central Asia 164 | Slovenia,Europe & Central Asia 165 | Solomon Islands,East Asia & Pacific 166 | Somalia,Sub-Saharan Africa 167 | South Africa,Sub-Saharan Africa 168 | South Sudan,Sub-Saharan Africa 169 | Spain,Europe & Central Asia 170 | Sri Lanka,South Asia 171 | Sudan,Sub-Saharan Africa 172 | Suriname,America 173 | Swaziland,Sub-Saharan Africa 174 | Sweden,Europe & Central Asia 175 | Switzerland,Europe & Central Asia 176 | Syria,Middle East & North Africa 177 | Taiwan,East Asia & Pacific 178 | Tajikistan,Europe & Central Asia 179 | Tanzania,Sub-Saharan Africa 180 | Thailand,East Asia & Pacific 181 | Timor-Leste,East Asia & Pacific 182 | Togo,Sub-Saharan Africa 183 | Tokelau,East Asia & Pacific 184 | Tonga,East Asia & Pacific 185 | Trinidad and Tobago,America 186 | Tunisia,Middle East & North Africa 187 | Turkey,Europe & Central Asia 188 | Turkmenistan,Europe & Central Asia 189 | Uganda,Sub-Saharan Africa 190 | Ukraine,Europe & Central Asia 191 | United Arab Emirates,Middle East & North Africa 192 | United Kingdom,Europe & Central Asia 193 | United States,America 194 | Uruguay,America 195 | Uzbekistan,Europe & Central Asia 196 | Vanuatu,East Asia & Pacific 197 | Venezuela,America 198 | Vietnam,East Asia & Pacific 199 | Virgin Islands (U.S.),America 200 | West Bank and Gaza,Middle East & North Africa 201 | Western Sahara,Middle East & North Africa 202 | "Yemen, Rep.",Middle East & North Africa 203 | Zambia,Sub-Saharan Africa 204 | Zimbabwe,Sub-Saharan Africa 205 | Åland,Europe & Central Asia 206 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/sales-feb-2015.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2015-02-02 08:30:00,Hooli,Software,3 3 | 2015-02-02 21:00:00,Mediacore,Hardware,9 4 | 2015-02-03 14:00:00,Initech,Software,13 5 | 2015-02-04 15:30:00,Streeplex,Software,13 6 | 2015-02-04 22:00:00,Acme Coporation,Hardware,14 7 | 2015-02-05 02:00:00,Acme Coporation,Software,19 8 | 2015-02-05 22:00:00,Hooli,Service,10 9 | 2015-02-07 23:00:00,Acme Coporation,Hardware,1 10 | 2015-02-09 09:00:00,Streeplex,Service,19 11 | 2015-02-09 13:00:00,Mediacore,Software,7 12 | 2015-02-11 20:00:00,Initech,Software,7 13 | 2015-02-11 23:00:00,Hooli,Software,4 14 | 2015-02-16 12:00:00,Hooli,Software,10 15 | 2015-02-19 11:00:00,Mediacore,Hardware,16 16 | 2015-02-19 16:00:00,Mediacore,Service,10 17 | 2015-02-21 05:00:00,Mediacore,Software,3 18 | 2015-02-21 20:30:00,Hooli,Hardware,3 19 | 2015-02-25 00:30:00,Initech,Service,10 20 | 2015-02-26 09:00:00,Streeplex,Service,4 21 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/sales.csv: -------------------------------------------------------------------------------- 1 | month,eggs,salt,spam 2 | Jan,47,12.0,17 3 | Feb,110,50.0,31 4 | Mar,221,89.0,72 5 | Apr,77,87.0,20 6 | May,132,,52 7 | Jun,205,60.0,55 8 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/trials_01.csv: -------------------------------------------------------------------------------- 1 | id,treatment,gender,response 2 | 1, A, F,5 3 | 2, A, M,3 4 | 3, B, F,8 5 | 4, B, M,9 6 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/trials_02.csv: -------------------------------------------------------------------------------- 1 | treatment,F,M 2 | A,5,3 3 | B,8,9 4 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/trials_03.csv: -------------------------------------------------------------------------------- 1 | id,treatment,gender,response 2 | 1,A,F,5 3 | 2,A,M,3 4 | 3,A,M,8 5 | 4,A,F,9 6 | 5,B,F,1 7 | 6,B,M,8 8 | 7,B,F,4 9 | 8,B,F,6 10 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/users.csv: -------------------------------------------------------------------------------- 1 | ,weekday,city,visitors,signups 2 | 0,Sun,Austin,139,7 3 | 1,Sun,Dallas,237,12 4 | 2,Mon,Austin,326,3 5 | 3,Mon,Dallas,456,5 6 | -------------------------------------------------------------------------------- /manipulating_DF_WPD/visitors.csv: -------------------------------------------------------------------------------- 1 | weekday,city,visitors,signups 2 | Sun,Austin,139,7 3 | Sun,Dallas,237,12 4 | Mon,Austin,326,3 5 | Mon,Dallas,456,5 6 | -------------------------------------------------------------------------------- /nlp/Classifying fake news using supervised learning with NLP.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## What is supervised learning?\n", 8 | "- Form of machine learning\n", 9 | "- Problem has predefined training data\n", 10 | "- This data has a label (or outcome) you want the model to learn\n", 11 | "- Classification problem\n", 12 | "Goal: Make good hypotheses about the species based on geometric features\n", 13 | "\n", 14 | "\n", 15 | "## Supervised learning with NLP\n", 16 | "\n", 17 | "- Need to use **language** instead of geometric features\n", 18 | "- scikit-learn: Powerful open-source library\n", 19 | "- How to create supervised learning data from text?\n", 20 | "- Use bag-of-words models or tf-idf as features\n", 21 | "\n", 22 | "\n", 23 | "## IMDB Movie Dataset\n", 24 | "\n", 25 | "\n", 26 | "![](https://i.imgur.com/tdlKkz1.png)\n", 27 | "\n", 28 | "- Goal: Predict movie genre based on plot summary\n", 29 | "- Categorical features generated using preprocessing\n", 30 | "\n", 31 | "\n", 32 | "## Supervised learning steps\n", 33 | "- Collect and preprocess our data\n", 34 | "- Determine a label (Example: Movie genre)\n", 35 | "- Split data into training and test sets\n", 36 | "- Extract features from the text to help predict the label\n", 37 | " - Bag-of-words vector built into scikit-learn\n", 38 | "- Evaluate trained model using the test set" 39 | ] 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "metadata": {}, 44 | "source": [ 45 | "## Let's Practice" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "metadata": {}, 66 | "outputs": [], 67 | "source": [] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": null, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "# Building word count vectors with scikitlearn\n", 81 | "\n", 82 | "\n", 83 | "## Predicting movie genre\n", 84 | "- Dataset consisting of movie plots and corresponding genre\n", 85 | "- Goal: Create bag-of-word vectors for the movie plots\n", 86 | " - Can we predict genre based on the words used in the plot summary?\n", 87 | " \n", 88 | " \n", 89 | "## Count Vectorize w Python\n", 90 | "\n", 91 | "```python\n", 92 | "# import req libraries\n", 93 | "In [1]: import pandas as pd\n", 94 | "In [2]: from sklearn.model_selection import train_test_split\n", 95 | "In [3]: from sklearn.feature_extraction.text import CountVectorizer\n", 96 | " \n", 97 | "# load dataset as df\n", 98 | "In [4]: df = ... # Load data into DataFrame\n", 99 | "\n", 100 | "# create y, labels or outcome we want the model to learn \n", 101 | "In [5]: y = df['Sci-Fi'] # 1 if scifi 0 if action\n", 102 | "\n", 103 | "# split df into training and testing\n", 104 | "# it splits the features and labels into train & test\n", 105 | "# test_size is the perc of split ex 33% of data as test data\n", 106 | "# random_state is seed to replicate split vals\n", 107 | "\n", 108 | "In [6]: X_train, X_test, y_train, y_test = train_test_split(\n", 109 | "df['plot'], y,\n", 110 | "test_size=0.33,\n", 111 | "random_state=53)\n", 112 | " \n", 113 | "# turns text into bag of words vectors similar to gensim corpus, also remove english stop words\n", 114 | "# from the movie plot summary as a preprocessing step \n", 115 | "# each token will act as a feature for ML classification problem like the flower mesurmens of iris dataset\n", 116 | "In [7]: count_vectorizer = CountVectorizer(stop_words='english')\n", 117 | " \n", 118 | "# call fit_transform\n", 119 | "In [8]: count_train = count_vectorizer.fit_transform(X_train.values)\n", 120 | "In [9]: count_test = count_vectorizer.transform(X_test.values)\n", 121 | " ```" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "## Let's Practice" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": null, 141 | "metadata": {}, 142 | "outputs": [], 143 | "source": [] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": null, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": null, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": null, 162 | "metadata": {}, 163 | "outputs": [], 164 | "source": [] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "metadata": {}, 170 | "outputs": [], 171 | "source": [] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": {}, 176 | "source": [ 177 | "# Training and testing a classification model with scikit-learn\n", 178 | "\n", 179 | "## Naive Bayes classifier\n", 180 | "- Naive Bayes Model\n", 181 | " - Commonly used for testing NLP classification problems\n", 182 | " - Basis in probability\n", 183 | "- Given a particular piece of data, how likely is a particular outcome?\n", 184 | "- Examples:\n", 185 | " - If the plot has a spaceship, how likely is it to be sci-fi?\n", 186 | " - Given a spaceship and an alien, how likely now is it sci-fi?\n", 187 | "- Each word from CountVectorizer acts as a feature\n", 188 | "- Naive Bayes: Simple and effective\n", 189 | "\n", 190 | "## Naive Bayes w scikit-learn\n", 191 | "\n", 192 | "```python\n", 193 | "In [10]: from sklearn.naive_bayes import MultinomialNB\n", 194 | "In [11]: from sklearn import metrics\n", 195 | "In [12]: nb_classifier = MultinomialNB()\n", 196 | "In [13]: nb_classifier.fit(count_train, y_train)\n", 197 | "In [14]: pred = nb_classifier.predict(count_test)\n", 198 | "In [15]: metrics.accuracy_score(\n", 199 | "```\n", 200 | "\n", 201 | "## Confustion Matrix\n", 202 | "\n", 203 | "```python\n", 204 | "In [16]: metrics.confusion_matrix(y_test, pred, labels=[0,1])\n", 205 | "Out [16]:\n", 206 | "array([[6410, 563],\n", 207 | "[ 864, 2242]])\n", 208 | "\n", 209 | "```\n", 210 | "\n", 211 | "\n", 212 | "![](https://i.imgur.com/yJowLkX.png)\n", 213 | "\n" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "## Let's Practice" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "metadata": {}, 234 | "outputs": [], 235 | "source": [] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": null, 254 | "metadata": {}, 255 | "outputs": [], 256 | "source": [] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "# Simple NLP, Complex Problems\n", 263 | "\n", 264 | "## Translation\n", 265 | "![](https://i.imgur.com/PfopGKE.png)\n", 266 | "\n", 267 | "(source: https://twitter.com/Lupintweets/status/865533182455685121)\n", 268 | "\n", 269 | "## Sentiment Analysis\n", 270 | "\n", 271 | "![](https://i.imgur.com/3NDgJQg.png)\n", 272 | "\n", 273 | "(source: https://nlp.stanford.edu/projects/socialsent/)\n", 274 | "\n", 275 | "## Language Biases\n", 276 | "\n", 277 | "![](https://i.imgur.com/sCT6oSV.png)\n", 278 | "\n", 279 | "(related talk: https://www.youtube.com/watch?v=j7FwpZB1hWc)\n" 280 | ] 281 | }, 282 | { 283 | "cell_type": "markdown", 284 | "metadata": {}, 285 | "source": [ 286 | "## Let's Practice" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": null, 292 | "metadata": {}, 293 | "outputs": [], 294 | "source": [] 295 | }, 296 | { 297 | "cell_type": "code", 298 | "execution_count": null, 299 | "metadata": {}, 300 | "outputs": [], 301 | "source": [] 302 | } 303 | ], 304 | "metadata": { 305 | "kernelspec": { 306 | "display_name": "Python [conda env:datacamp]", 307 | "language": "python", 308 | "name": "conda-env-datacamp-py" 309 | }, 310 | "language_info": { 311 | "codemirror_mode": { 312 | "name": "ipython", 313 | "version": 3 314 | }, 315 | "file_extension": ".py", 316 | "mimetype": "text/x-python", 317 | "name": "python", 318 | "nbconvert_exporter": "python", 319 | "pygments_lexer": "ipython3", 320 | "version": "3.6.4" 321 | } 322 | }, 323 | "nbformat": 4, 324 | "nbformat_minor": 2 325 | } 326 | -------------------------------------------------------------------------------- /pandas_foundation/aapl.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/pandas_foundation/aapl.jpg -------------------------------------------------------------------------------- /pandas_foundation/aapl.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/pandas_foundation/aapl.pdf -------------------------------------------------------------------------------- /pandas_foundation/aapl.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/pandas_foundation/aapl.png -------------------------------------------------------------------------------- /pandas_foundation/iris_d.csv: -------------------------------------------------------------------------------- 1 | SepalLength,SepalWidth,PetalLength,PetalWidth,Name 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3.0,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5.0,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5.0,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,3.7,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3.0,1.4,0.1,Iris-setosa 15 | 4.3,3.0,1.1,0.1,Iris-setosa 16 | 5.8,4.0,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1.0,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5.0,3.0,1.6,0.2,Iris-setosa 28 | 5.0,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.1,Iris-setosa 37 | 5.0,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.1,1.5,0.1,Iris-setosa 40 | 4.4,3.0,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5.0,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5.0,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3.0,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5.0,3.3,1.4,0.2,Iris-setosa 52 | 7.0,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4.0,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1.0,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5.0,2.0,3.5,1.0,Iris-versicolor 63 | 5.9,3.0,4.2,1.5,Iris-versicolor 64 | 6.0,2.2,4.0,1.0,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3.0,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1.0,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4.0,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3.0,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3.0,5.0,1.7,Iris-versicolor 80 | 6.0,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1.0,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1.0,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6.0,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3.0,4.5,1.5,Iris-versicolor 87 | 6.0,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3.0,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4.0,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3.0,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4.0,1.2,Iris-versicolor 95 | 5.0,2.3,3.3,1.0,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3.0,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3.0,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6.0,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3.0,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3.0,5.8,2.2,Iris-virginica 107 | 7.6,3.0,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2.0,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3.0,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5.0,2.0,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3.0,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6.0,2.2,5.0,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2.0,Iris-virginica 124 | 7.7,2.8,6.7,2.0,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6.0,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3.0,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3.0,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2.0,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3.0,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6.0,3.0,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3.0,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5.0,1.9,Iris-virginica 149 | 6.5,3.0,5.2,2.0,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3.0,5.1,1.8,Iris-virginica -------------------------------------------------------------------------------- /pandas_foundation/messy_stock_data.tsv: -------------------------------------------------------------------------------- 1 | The following stock data was collect on 2016-AUG-25 from an unknown source 2 | These kind of ocmments are not very useful, are they? 3 | probably should just throw this line away too, but not the next since those are column labels 4 | name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 5 | # So that line you just read has all the column headers labels 6 | IBM 156.08 160.01 159.81 165.22 172.25 167.15 164.75 152.77 145.36 146.11 137.21 137.96 7 | MSFT 45.51 43.08 42.13 43.47 47.53 45.96 45.61 45.51 43.56 48.70 53.88 55.40 8 | # That MSFT is MicroSoft 9 | GOOGLE 512.42 537.99 559.72 540.50 535.24 532.92 590.09 636.84 617.93 663.59 735.39 755.35 10 | APPLE 110.64 125.43 125.97 127.29 128.76 127.81 125.34 113.39 112.80 113.36 118.16 111.73 11 | # Maybe we should have bought some Apple stock in 2008? -------------------------------------------------------------------------------- /pandas_foundation/percent-bachelors-degrees-women-usa.csv: -------------------------------------------------------------------------------- 1 | Year,Agriculture,Architecture,Art and Performance,Biology,Business,Communications and Journalism,Computer Science,Education,Engineering,English,Foreign Languages,Health Professions,Math and Statistics,Physical Sciences,Psychology,Public Administration,Social Sciences and History 2 | 1970,4.22979798,11.92100539,59.7,29.08836297,9.064438975,35.3,13.6,74.53532758,0.8,65.57092343,73.8,77.1,38,13.8,44.4,68.4,36.8 3 | 1971,5.452796685,12.00310559,59.9,29.39440285,9.503186594,35.5,13.6,74.14920369,1,64.55648516,73.9,75.5,39,14.9,46.2,65.5,36.2 4 | 1972,7.42071022,13.21459351,60.4,29.81022105,10.5589621,36.6,14.9,73.55451996,1.2,63.6642632,74.6,76.9,40.2,14.8,47.6,62.6,36.1 5 | 1973,9.653602412,14.7916134,60.2,31.14791477,12.80460152,38.4,16.4,73.50181443,1.6,62.94150212,74.9,77.4,40.9,16.5,50.4,64.3,36.4 6 | 1974,14.07462346,17.44468758,61.9,32.99618284,16.20485038,40.5,18.9,73.33681143,2.2,62.41341209,75.3,77.9,41.8,18.2,52.6,66.1,37.3 7 | 1975,18.33316153,19.13404767,60.9,34.44990213,19.68624931,41.5,19.8,72.80185448,3.2,61.64720641,75,78.9,40.7,19.1,54.5,63,37.7 8 | 1976,22.25276005,21.39449143,61.3,36.07287146,23.4300375,44.3,23.9,72.16652471,4.5,62.14819377,74.4,79.2,41.5,20,56.9,65.6,39.2 9 | 1977,24.6401766,23.74054054,62,38.33138629,27.16342715,46.9,25.7,72.45639481,6.8,62.72306675,74.3,80.5,41.1,21.3,59,69.3,40.5 10 | 1978,27.14619175,25.84923973,62.5,40.11249564,30.52751868,49.9,28.1,73.19282134,8.4,63.61912216,74.3,81.9,41.6,22.5,61.3,71.5,41.8 11 | 1979,29.63336549,27.77047744,63.2,42.06555109,33.62163381,52.3,30.2,73.82114234,9.4,65.08838972,74.2,82.3,42.3,23.7,63.3,73.3,43.6 12 | 1980,30.75938956,28.08038075,63.4,43.99925716,36.76572529,54.7,32.5,74.98103152,10.3,65.28413007,74.1,83.5,42.8,24.6,65.1,74.6,44.2 13 | 1981,31.31865519,29.84169408,63.3,45.24951206,39.26622984,56.4,34.8,75.84512345,11.6,65.83832154,73.9,84.1,43.2,25.7,66.9,74.7,44.6 14 | 1982,32.63666364,34.81624758,63.1,45.96733794,41.94937335,58,36.3,75.84364914,12.4,65.84735212,72.7,84.4,44,27.3,67.5,76.8,44.6 15 | 1983,31.6353471,35.82625735,62.4,46.71313451,43.54206966,58.6,37.1,75.95060123,13.1,65.91837999,71.8,84.6,44.3,27.6,67.9,76.1,44.1 16 | 1984,31.09294748,35.45308311,62.1,47.66908276,45.12403027,59.1,36.8,75.86911601,13.5,65.74986233,72.1,85.1,46.2,28,68.2,75.9,44.1 17 | 1985,31.3796588,36.13334795,61.8,47.9098841,45.747782,59,35.7,75.92343971,13.5,65.79819852,70.8,85.3,46.5,27.5,69,75,43.8 18 | 1986,31.19871923,37.24022346,62.1,48.30067763,46.53291505,60,34.7,76.14301516,13.9,65.98256091,71.2,85.7,46.7,28.4,69,75.7,44 19 | 1987,31.48642948,38.73067535,61.7,50.20987789,46.69046648,60.2,32.4,76.96309168,14,66.70603055,72,85.5,46.5,30.4,70.1,76.4,43.9 20 | 1988,31.08508746,39.3989071,61.7,50.09981147,46.7648277,60.4,30.8,77.62766177,13.9,67.14449816,72.3,85.2,46.2,29.7,70.9,75.6,44.4 21 | 1989,31.6124031,39.09653994,62,50.77471585,46.7815648,60.5,29.9,78.11191872,14.1,67.01707156,72.4,84.6,46.2,31.3,71.6,76,44.2 22 | 1990,32.70344407,40.82404662,62.6,50.81809432,47.20085084,60.8,29.4,78.86685859,14.1,66.92190193,71.2,83.9,47.3,31.6,72.6,77.6,45.1 23 | 1991,34.71183749,33.67988118,62.1,51.46880537,47.22432481,60.8,28.7,78.99124597,14,66.24147465,71.1,83.5,47,32.6,73.2,78.2,45.5 24 | 1992,33.93165961,35.20235628,61,51.34974154,47.21939541,59.7,28.2,78.43518191,14.5,65.62245655,71,83,47.4,32.6,73.2,77.3,45.8 25 | 1993,34.94683208,35.77715877,60.2,51.12484404,47.63933161,58.7,28.5,77.26731199,14.9,65.73095014,70,82.4,46.4,33.6,73.1,78,46.1 26 | 1994,36.03267447,34.43353129,59.4,52.2462176,47.98392441,58.1,28.5,75.81493264,15.7,65.64197772,69.1,81.8,47,34.8,72.9,78.8,46.8 27 | 1995,36.84480747,36.06321839,59.2,52.59940342,48.57318101,58.8,27.5,75.12525621,16.2,65.93694921,69.6,81.5,46.1,35.9,73,78.8,47.9 28 | 1996,38.96977475,35.9264854,58.6,53.78988011,48.6473926,58.7,27.1,75.03519921,16.7,66.43777883,69.7,81.3,46.4,37.3,73.9,79.8,48.7 29 | 1997,40.68568483,35.10193413,58.7,54.99946903,48.56105033,60,26.8,75.1637013,17,66.78635548,70,81.9,47,38.3,74.4,81,49.2 30 | 1998,41.91240333,37.59854457,59.1,56.35124789,49.2585152,60,27,75.48616027,17.8,67.2554484,70.1,82.1,48.3,39.7,75.1,81.3,50.5 31 | 1999,42.88720191,38.63152919,59.2,58.22882288,49.81020815,61.2,28.1,75.83816206,18.6,67.82022113,70.9,83.5,47.8,40.2,76.5,81.1,51.2 32 | 2000,45.05776637,40.02358491,59.2,59.38985737,49.80361649,61.9,27.7,76.69214284,18.4,68.36599498,70.9,83.5,48.2,41,77.5,81.1,51.8 33 | 2001,45.86601517,40.69028156,59.4,60.71233149,50.27514494,63,27.6,77.37522931,19,68.57852029,71.2,85.1,47,42.2,77.5,80.9,51.7 34 | 2002,47.13465821,41.13295053,60.9,61.8951284,50.5523346,63.7,27,78.64424394,18.7,68.82995959,70.5,85.8,45.7,41.1,77.7,81.3,51.5 35 | 2003,47.93518721,42.75854266,61.1,62.1694558,50.34559774,64.6,25.1,78.54494815,18.8,68.89448726,70.6,86.5,46,41.7,77.8,81.5,50.9 36 | 2004,47.88714025,43.46649345,61.3,61.91458697,49.95089449,64.2,22.2,78.65074774,18.2,68.45473436,70.8,86.5,44.7,42.1,77.8,80.7,50.5 37 | 2005,47.67275409,43.10036784,61.4,61.50098432,49.79185139,63.4,20.6,79.06712173,17.9,68.57122114,69.9,86,45.1,41.6,77.5,81.2,50 38 | 2006,46.79029957,44.49933107,61.6,60.17284465,49.21091439,63,18.6,78.68630551,16.8,68.29759443,69.6,85.9,44.1,40.8,77.4,81.2,49.8 39 | 2007,47.60502633,43.10045895,61.4,59.41199314,49.00045935,62.5,17.6,78.72141311,16.8,67.87492278,70.2,85.4,44.1,40.7,77.1,82.1,49.3 40 | 2008,47.570834,42.71173041,60.7,59.30576517,48.88802678,62.4,17.8,79.19632674,16.5,67.59402834,70.2,85.2,43.3,40.7,77.2,81.7,49.4 41 | 2009,48.66722357,43.34892051,61,58.48958333,48.84047414,62.8,18.1,79.5329087,16.8,67.96979204,69.3,85.1,43.3,40.7,77.1,82,49.4 42 | 2010,48.73004227,42.06672091,61.3,59.01025521,48.75798769,62.5,17.6,79.61862451,17.2,67.92810557,69,85,43.1,40.2,77,81.7,49.3 43 | 2011,50.03718193,42.7734375,61.2,58.7423969,48.18041792,62.2,18.2,79.43281184,17.5,68.42673015,69.5,84.8,43.1,40.1,76.7,81.9,49.2 -------------------------------------------------------------------------------- /pandas_foundation/sales-feb-2015.csv: -------------------------------------------------------------------------------- 1 | Date,Company,Product,Units 2 | 2/26/15 8:57,Streeplex,Service,4 3 | 2/16/15 12:09,Hooli,Software,10 4 | 2/3/15 14:14,Initech,Software,13 5 | 2/2/15 8:33,Hooli,Software,3 6 | 2/25/15 0:29,Initech,Service,10 7 | 2/5/15 1:53,Acme Coporation,Software,19 8 | 2/9/15 8:57,Streeplex,Service,19 9 | 2/11/15 20:00,Initech,Software,7 10 | 2/4/15 21:52,Acme Coporation,Hardware,14 11 | 2/9/15 13:09,Mediacore,Software,7 12 | 2/7/15 22:58,Acme Coporation,Hardware,1 13 | 2/11/15 23:00,Hooli,Software,4 14 | 2/26/15 8:58,Streeplex,Service,1 15 | 2/5/15 22:05,Hooli,Service,10 16 | 2/4/15 15:36,Streeplex,Software,13 17 | 2/19/15 16:02,Mediacore,Service,10 18 | 2/19/15 11:00,Mediacore,Hardware,16 19 | 2/2/15 20:54,Mediacore,Hardware,9 20 | 2/21/15 5:01,Mediacore,Software,3 21 | 2/21/15 20:41,Hooli,Hardware,3 -------------------------------------------------------------------------------- /pandas_foundation/sunspots.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/just4jc/datacamp-2/208d9434d7a83e20abcc43a0060f81244dc8e2c4/pandas_foundation/sunspots.xlsx -------------------------------------------------------------------------------- /pandas_foundation/users.csv: -------------------------------------------------------------------------------- 1 | weekday,city,visitors,signups 2 | Sun,Austin,139,7 3 | Sun,Dallas,237,12 4 | Mon,Austin,326,3 5 | Mon,Dallas,456,5 6 | -------------------------------------------------------------------------------- /pandas_foundation/world_population.csv: -------------------------------------------------------------------------------- 1 | Year,Total Population 2 | 1960,3034970564.0 3 | 1970,3684822701.0 4 | 1980,4436590356.0 5 | 1990,5282715991.0 6 | 2000,6115974486.0 7 | 2010,6924282937.0 8 | -------------------------------------------------------------------------------- /working_with_data_in_databases/sql_basics.md: -------------------------------------------------------------------------------- 1 | # SQL Tutorial from Mode 2 | 3 | # BASIC SQL 4 | 5 | ## SQL SELECT 6 | ### Basic syntax: SELECT and FROM 7 | There are two required ingredients in any SQL query: `SELECT` and `FROM`—and they have to be in that order. SELECT indicates which columns you’d like to view, and FROM identifies the table that they live in. 8 | 9 | Let’s start by looking at a couple columns from the housing unit table: 10 | 11 | 12 | 13 | 14 | ### Practice problem: 15 | Write a query to select all of the columns in tutorial.us_housing_units and rename them so that their first letters are capitalized. 16 | 17 | ```sql 18 | SELECT year AS "Year", 19 | month AS "Month", 20 | month_name AS "Month name", 21 | south AS "South", 22 | west AS "West", 23 | midwest AS "Midwest", 24 | northeast AS "Northeast" 25 | FROM tutorial.us_housing_units 26 | ``` 27 | 28 | 29 | ## SQL LIMIT 30 | 31 | 32 | 33 | ### Why should you limit your results? 34 | Many analysts use limits as a simple way to keep their queries from taking too long to return. The aim of many of your queries will simply be to see what a particular table looks like—you’ll want to scan the first few rows of data to get an idea of which fields you care about and how you want to manipulate them. If you query a very large table (such as one with hundreds of thousands or millions of rows) and don’t use a limit, you could end up waiting a long time for all of your results to be displayed, which doesn’t make sense if you only care about the first few. 35 | 36 | 37 | ### Using the SQL LIMIT command 38 | The limiting functionality is built into Mode to prevent you from accidentally returning millions of rows without meaning to (we’ve all done it). However, if you’re ever using SQL outside of Mode, you can manually add a limit with a SQL command. The following syntax does the same thing as having the box checked with a value of 100: 39 | 40 | ```sql 41 | SELECT * 42 | FROM tutorial.us_housing_units 43 | LIMIT 100 44 | ``` 45 | 46 | #### Practice problem 47 | Write a query that uses the LIMIT command to restrict the result set to only 15 rows. 48 | 49 | ```sql 50 | SELECT * 51 | FROM tutorial.us_housing_units 52 | LIMIT 15 53 | 54 | ``` 55 | 56 | ## SQL WHERE 57 | 58 | ### The SQL WHERE clause 59 | Start by running a `SELECT` statement to re-familiarize yourself with the housing data used in this tutorial. Remember to switch over to Mode and run any of the code you see in the light blue boxes to get a sense of what the output will look like 60 | 61 | ```sql 62 | SELECT * FROM tutorial.us_housing_units 63 | ``` 64 | Once you know how to view some data using SELECT and FROM, the next step is filtering the data using the WHERE clause. Here’s what it looks like: 65 | 66 | ```sql 67 | SELECT * 68 | FROM tutorial.us_housing_units 69 | WHERE month = 1 70 | ``` 71 | 72 | > Note: the clauses always need to be in this order: SELECT, FROM, WHERE. 73 | 74 | ## How does WHERE work? 75 | The SQL WHERE clause works in a plain-English way: the above query does the same thing as `SELECT * FROM tutorial.us_housing_units`, except that the results will only include rows where the month column contains the value 1. 76 | 77 | In Excel, it’s possible to sort data in such a way that one column can be reordered without reordering any of the other columns—though that could badly scramble your data. When using SQL, entire rows of data are preserved together. If you write a WHERE clause that filters based on values in one column, you’ll limit the results in all columns to rows that satisfy the condition. The idea is that each row is one data point or observation, and all the information contained in that row belongs together. 78 | 79 | You can filter your results in a number of ways using comparison and logical operators, which you’ll learn about in the next lesson. 80 | 81 | 82 | 83 | --------------------------------------------------------------------------------