Code24Lab
Menu
Courses Categories Guides Pricing Log in Sign up

dtypes and Memory: Downcast Integers Safely

Lesson 2 of 20 Lab 🔒 Locked

Lessons in this course

  1. Vectorization vs Loops: Measure What a Python Loop CostsLabFree
  2. dtypes and Memory: Downcast Integers SafelyLab Locked
  3. Categoricals: When Strings Get Cheap (and When They Do Not)Lab Locked
  4. Vectorized Conditions: np.where and np.selectLab Locked
  5. apply, iterrows and the Hidden Python LoopLab Locked
  6. Copy-on-Write and Safe Assignment with .locLab Locked
  7. MultiIndex: Select by Tuple, Cross-Section and LevelLab Locked
  8. stack, unstack and pivot: Reshape Without LoopsLab Locked
  9. groupby transform: Group Statistics on Every RowLab Locked
  10. Rolling Windows: window, min_periods and centerLab Locked
  11. expanding and ewm: Running Records and SmoothingLab Locked
  12. Time-Based Windows: rolling("3D") on Irregular DatesLab Locked
  13. shift, diff and pct_change per GroupLab Locked
  14. Ranking Within Groups: rank Methods and Top-N per GroupLab Locked
  15. merge with validate: Catch Row Explosions Before They Count TwiceLab Locked
  16. Data Quality Checks: Duplicates, Ranges and the NaN TrapLab Locked
  17. Method Chaining and .pipe: Pipelines You Can ReadLab Locked
  18. Chunked Processing: Combine Partial Results CorrectlyLab Locked
  19. Reproducible Results: Seeds, Key-Based Splits and Stable SortsLab Locked
  20. Capstone: The Larkspur Weekly Revenue PipelineLab Locked