| 1. Project Genesis | |||
| 1. Project Overview.mp4 | 145.82 MB | ||
| 1. Project Overview.vtt | 10.14 KB | ||
| 1.1 GitHub Home Page.html | 99 B | ||
| 2. Pandas Cub Examples.mp4 | 253.8 MB | ||
| 2. Pandas Cub Examples.vtt | 14.38 KB | ||
| 3. Downloading the Material from GitHub.mp4 | 51.34 MB | ||
| 3. Downloading the Material from GitHub.vtt | 2.83 KB | ||
| 10. Pivot Tables | |||
| 1. Pivot Tables Part 1.mp4 | 147.01 MB | ||
| 1. Pivot Tables Part 1.vtt | 13.89 KB | ||
| 2. Pivot Tables Part 2.mp4 | 79.89 MB | ||
| 2. Pivot Tables Part 2.vtt | 8.38 KB | ||
| 3. Pivot Tables Part 3.mp4 | 44.37 MB | ||
| 3. Pivot Tables Part 3.vtt | 3.97 KB | ||
| 4. Pivot Tables Part 4.mp4 | 104.5 MB | ||
| 4. Pivot Tables Part 4.vtt | 8.21 KB | ||
| 5. Pivot Tables Part 5.mp4 | 91.92 MB | ||
| 5. Pivot Tables Part 5.vtt | 8.58 KB | ||
| 11. Documentation, Strings, and Reading CSVs | |||
| 1. Automatically Add Documentation.mp4 | 88.3 MB | ||
| 1. Automatically Add Documentation.vtt | 8.33 KB | ||
| 2. String-only Methods.mp4 | 164.86 MB | ||
| 2. String-only Methods.vtt | 13.91 KB | ||
| 3. The read_csv Function part 1.mp4 | 111.06 MB | ||
| 3. The read_csv Function part 1.vtt | 10.63 KB | ||
| 4. The read_csv Function Part 2.mp4 | 83.77 MB | ||
| 4. The read_csv Function Part 2.vtt | 8.13 KB | ||
| 5. Conclusion.mp4 | 18.37 MB | ||
| 5. Conclusion.vtt | 5.17 KB | ||
| 2. Environment Setup | |||
| 1. Opening the Project in VS Code.mp4 | 58.42 MB | ||
| 1. Opening the Project in VS Code.vtt | 3.36 KB | ||
| 2. Setting up the Development Environment.mp4 | 157.58 MB | ||
| 2. Setting up the Development Environment.vtt | 8.73 KB | ||
| 3. Test-Driven Development.mp4 | 139.86 MB | ||
| 3. Test-Driven Development.vtt | 7.49 KB | ||
| 4. Installing an IPython Kernel for Jupyter.mp4 | 243.71 MB | ||
| 4. Installing an IPython Kernel for Jupyter.vtt | 13.12 KB | ||
| 3. Getting Ready to Code | |||
| 1. Inspecting the __init__.py File.mp4 | 111.05 MB | ||
| 1. Inspecting the __init__.py File.vtt | 6.07 KB | ||
| 2. Importing Pandas Cub.mp4 | 113.35 MB | ||
| 2. Importing Pandas Cub.vtt | 6.43 KB | ||
| 3. Manually Test in a Jupyter Notebook.mp4 | 149.63 MB | ||
| 3. Manually Test in a Jupyter Notebook.vtt | 8.57 KB | ||
| 4. Getting Ready to Start.mp4 | 37.58 MB | ||
| 4. Getting Ready to Start.vtt | 2.01 KB | ||
| 4. DataFrame Construction | |||
| 1. Check DataFrame Constructor Input Types.mp4 | 379.25 MB | ||
| 1. Check DataFrame Constructor Input Types.vtt | 19.4 KB | ||
| 2. Check Array Lengths.mp4 | 76.09 MB | ||
| 2. Check Array Lengths.vtt | 5.46 KB | ||
| 3. Convert Unicode Arrays to Object.mp4 | 142.41 MB | ||
| 3. Convert Unicode Arrays to Object.vtt | 10.29 KB | ||
| 5. Basic Properties and Visual Representation | |||
| 1. Implementing the __len__ Special Method.mp4 | 114.63 MB | ||
| 1. Implementing the __len__ Special Method.vtt | 10.3 KB | ||
| 2. Return Columns as a List.mp4 | 69.04 MB | ||
| 2. Return Columns as a List.vtt | 6.6 KB | ||
| 3. Set New Column Names.mp4 | 127.36 MB | ||
| 3. Set New Column Names.vtt | 10.45 KB | ||
| 4. The shape Property.mp4 | 43.63 MB | ||
| 4. The shape Property.vtt | 4 KB | ||
| 5. Visual Notebook Representation.mp4 | 130.9 MB | ||
| 5. Visual Notebook Representation.vtt | 10.88 KB | ||
| 6. The values Property.mp4 | 36.35 MB | ||
| 6. The values Property.vtt | 3.31 KB | ||
| 7. The dtypes Property.mp4 | 114.68 MB | ||
| 7. The dtypes Property.vtt | 10.41 KB | ||
| 6. Subset Selection | |||
| 1. Select a Single Column.mp4 | 74.4 MB | ||
| 1. Select a Single Column.vtt | 5.9 KB | ||
| 10. Create a New Column.mp4 | 186.65 MB | ||
| 10. Create a New Column.vtt | 14.47 KB | ||
| 2. Select Multiple Columns.mp4 | 47.7 MB | ||
| 2. Select Multiple Columns.vtt | 4.07 KB | ||
| 3. Boolean Selection.mp4 | 109.98 MB | ||
| 3. Boolean Selection.vtt | 9.69 KB | ||
| 4. Check for Simultaneous Selection.mp4 | 106.01 MB | ||
| 4. Check for Simultaneous Selection.vtt | 7.57 KB | ||
| 5. Select a Single Cell.mp4 | 114.65 MB | ||
| 5. Select a Single Cell.vtt | 9.04 KB | ||
| 6. Select Rows as Booleans, Lists, or Slices.mp4 | 127.76 MB | ||
| 6. Select Rows as Booleans, Lists, or Slices.vtt | 10.46 KB | ||
| 7. Multiple Column Simultaneous Selection.mp4 | 76.33 MB | ||
| 7. Multiple Column Simultaneous Selection.vtt | 6.21 KB | ||
| 8. Column Slices.mp4 | 99.57 MB | ||
| 8. Column Slices.vtt | 7.98 KB | ||
| 9. Tab Completion for Columns.mp4 | 29.18 MB | ||
| 9. Tab Completion for Columns.vtt | 3.94 KB | ||
| 7. Basic Methods | |||
| 1. head and tail Methods.mp4 | 34.24 MB | ||
| 1. head and tail Methods.vtt | 3.51 KB | ||
| 2. Generic Aggregation Methods.mp4 | 95.09 MB | ||
| 2. Generic Aggregation Methods.vtt | 9.39 KB | ||
| 3. The isna Method.mp4 | 57.65 MB | ||
| 3. The isna Method.vtt | 5.07 KB | ||
| 4. The count Method.mp4 | 58.16 MB | ||
| 4. The count Method.vtt | 4.86 KB | ||
| 5. The unique Method.mp4 | 62.76 MB | ||
| 5. The unique Method.vtt | 5.88 KB | ||
| 6. The nunique Method.mp4 | 36.62 MB | ||
| 6. The nunique Method.vtt | 3.54 KB | ||
| 8. Value Counts | |||
| 1. The value_counts Method.mp4 | 103.93 MB | ||
| 1. The value_counts Method.vtt | 8.83 KB | ||
| 2. Normalize value_counts.mp4 | 36.9 MB | ||
| 2. Normalize value_counts.vtt | 3.56 KB | ||
| 9. Other Methods and Operators | |||
| 1. The rename Method.mp4 | 49.44 MB | ||
| 1. The rename Method.vtt | 4.4 KB | ||
| 2. The drop Method.mp4 | 40.64 MB | ||
| 2. The drop Method.vtt | 3.76 KB | ||
| 3. Non-Aggregation Methods.mp4 | 140.87 MB | ||
| 3. Non-Aggregation Methods.vtt | 12.02 KB | ||
| 4. The diff Method.mp4 | 96.14 MB | ||
| 4. The diff Method.vtt | 9.29 KB | ||
| 5. The pct_change Method.mp4 | 27.27 MB | ||
| 5. The pct_change Method.vtt | 3.07 KB | ||
| 6. Arithmetic and Comparison Operators.mp4 | 206.21 MB | ||
| 6. Arithmetic and Comparison Operators.vtt | 16.62 KB | ||
| 7. The sort_values Method.mp4 | 123.01 MB | ||
| 7. The sort_values Method.vtt | 10.6 KB | ||
| 8. The sample Method.mp4 | 131.48 MB | ||
| 8. The sample Method.vtt | 10.14 KB | ||
| [Tutorialsplanet.NET].url | 128 B |
Udemy - Build A Data Analysis Library From Scratch In Python [TP]
Immerse yourself in a long, comprehensive project that teaches advanced Python concepts to build an entire library
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