Code24Lab
Python Advanced

Python Data Mastery: Performance, Pipelines & Advanced Pandas

Make pandas code fast, lean and trustworthy: vectorization costs, dtypes and categoricals, MultiIndex, window functions, safe merges, data-quality checks and reproducible pipelines, with every answer checked as you go.

20 lessons 20 labs Certificate

  • One-time payment
  • Instant access right after payment
  • Secure checkout with Stripe

About this course

This advanced course is for people who already use pandas and want code that is fast, lean and trustworthy. In 20 labs you read real Python code and compute exactly what it returns: you measure what a Python loop costs against a vectorized expression, shrink memory with downcast dtypes and categoricals, select from a MultiIndex, reshape with unstack, and work with transform, rolling, time-based and ewm windows. You catch row explosions in merges with validate, write data-quality checks that see the NaN trap, combine chunked results correctly and build reproducible splits. A capstone pipeline and a 15-question exam lead to the certificate, and the first lesson is free.

Lessons

  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