├── 01_Resolving Python Framework ├── 01_Anchoring the Function │ ├── .ipynb_checkpoints │ │ ├── 01A_Function-checkpoint.ipynb │ │ └── 01B_Function-checkpoint.ipynb │ ├── 01A_Function.ipynb │ └── 01B_Function.ipynb ├── 02_Discover Method │ ├── .ipynb_checkpoints │ │ └── 02B_Discover-checkpoint.ipynb │ ├── 02A_Discover.ipynb │ ├── 02B_Discover.ipynb │ ├── best_tennis_players_stats.csv │ ├── src │ │ ├── doc-excel.png │ │ ├── excel-two-tables.png │ │ ├── excel_file.png │ │ ├── suggestion-csv.png │ │ ├── suggestion-excel.png │ │ ├── suggestion-pandas.png │ │ └── suggestion-string.png │ └── tsla_stock.xlsx └── 03_Libraries Topics │ ├── .ipynb_checkpoints │ └── 03B_Libraries-checkpoint.ipynb │ ├── 03A_Libraries.ipynb │ ├── 03B_Libraries.ipynb │ ├── best_tennis_players_stats.csv │ └── russia-world-cup.xlsx ├── 02_Data Visualization ├── 04_Practical Foundations │ ├── .ipynb_checkpoints │ │ ├── 04A_Foundations-checkpoint.ipynb │ │ ├── 04B_Foundations-checkpoint.ipynb │ │ └── 04C_Foundations-checkpoint.ipynb │ ├── 04A_Foundations.ipynb │ ├── 04B_Foundations.ipynb │ ├── 04C_Foundations.ipynb │ └── 04D_Foundations.ipynb ├── 05_Visualize Excel Tables │ ├── .ipynb_checkpoints │ │ ├── 05A_Excel-checkpoint.ipynb │ │ ├── 05B_Excel-checkpoint.ipynb │ │ └── 05C_Excel-checkpoint.ipynb │ ├── 05A_Excel.ipynb │ ├── 05B_Excel.ipynb │ ├── 05C_Excel.ipynb │ ├── 05D_Excel.ipynb │ ├── gapminder.xlsx │ └── premier_league.xlsx └── 06_Optimal DataViz │ ├── .ipynb_checkpoints │ ├── 06A_Optimal-checkpoint.ipynb │ └── 06C_Optimal-checkpoint.ipynb │ ├── 06A_Optimal.ipynb │ ├── 06B_Optimal.ipynb │ ├── 06C_Optimal.ipynb │ ├── 06D_Optimal.ipynb │ ├── cars.xlsx │ └── gapminder.xlsx ├── 03_Best Practices ├── 01_Enhance Methodology │ ├── 07A_Framework.ipynb │ ├── 07B_Framework.ipynb │ ├── 07C_Framework.ipynb │ ├── 07D_Framework.ipynb │ ├── src │ │ ├── doc-px-line.png │ │ └── suggestion-plotly.png │ └── tsla_stock.xlsx └── 02_Methodology DataViz │ ├── 08A_Libraries.ipynb │ ├── 08B_Libraries.ipynb │ ├── 08C_Libraries.ipynb │ ├── 08D_Libraries.ipynb │ ├── premier_league.xlsx │ └── russia-world-cup.csv ├── 04_DataFrame Operations DataViz ├── 09_Grouping Categories │ ├── .ipynb_checkpoints │ │ ├── 09A_Groupby-checkpoint.ipynb │ │ └── 09B_Groupby-checkpoint.ipynb │ ├── 09A_Groupby.ipynb │ ├── 09B_Groupby.ipynb │ ├── 09C_Groupby.ipynb │ ├── 09D_Groupby.ipynb │ └── src │ │ ├── chart.png │ │ ├── df-aggregated-bg.png │ │ ├── df-aggregated.png │ │ └── df_complete.png ├── 10_Filtering with Mask │ ├── 10A_Mask.ipynb │ ├── 10B_Mask.ipynb │ ├── 10C_Mask.ipynb │ ├── 10D_Mask.ipynb │ └── key_stats.csv └── 11_Hacking with Pandas │ ├── 11A_Hacks.ipynb │ ├── 11B_Hacks.ipynb │ ├── 11C_Hacks.ipynb │ └── all-technology_2019-2021.csv ├── 05_Data Analysis & Statistics ├── 12_Hypothesis Testing │ ├── 11A_Hypothesis.ipynb │ ├── 11B_Hypothesis.ipynb │ ├── 11C_Hypothesis.ipynb │ ├── 11D_Hypothesis.ipynb │ ├── premier_league.xlsx │ ├── salaries2.csv │ └── salaries3.csv ├── 13_Machine Learning System │ ├── 12A_Machine Learning.ipynb │ ├── 12B_Machine Learning.ipynb │ ├── 12C_Machine Learning.ipynb │ ├── 12D_Machine Learning.ipynb │ └── premier_league.xlsx └── 14_Machine Learning Interpretation │ ├── 13A_Machine Learning.ipynb │ ├── 13B_Machine Learning.ipynb │ ├── 13C_Machine Learning.ipynb │ ├── 13D_Machine Learning.ipynb │ └── premier_league.xlsx ├── 06_Advanced Practical Use Cases ├── 15_Energy Generation in Spain │ ├── .ipynb_checkpoints │ │ ├── 14B_Energy2021-checkpoint.ipynb │ │ └── Kahoot-checkpoint.ipynb │ ├── 14A_Energy2021.ipynb │ ├── 14B_Energy2021.ipynb │ ├── 14C_Energy2019.ipynb │ ├── 14D_Energy2019.ipynb │ ├── Kahoot.ipynb │ ├── all-technology_2019-2021.csv │ └── energy_2021.csv └── 16_The Olympic Games │ ├── 15A_OlympicsRio2016.ipynb │ ├── 15B_OlympicsRio2016.ipynb │ ├── 15C_OlympicsLondon2012.ipynb │ ├── 15D_OlympicsLondon2012.ipynb │ └── athlete_events.csv └── README.md /01_Resolving Python Framework/01_Anchoring the 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