?? Speed up your Pandas code! ??

Working with large datasets in Pandas can be slow if you’re not careful.

Pandas performance optimization

Vectorization: Avoid Loops!

Vectorization means performing operations on entire arrays or Series simultaneously, rather than iterating through individual elements using loops. Pandas and NumPy are built to handle these vectorized operations incredibly efficiently using optimized C code under the hood.

• Why are loops slow? Python loops are interpreted, meaning each iteration requires overhead. This overhead becomes significant with large datasets.
• How does vectorization help? Vectorized operations are executed by highly optimized compiled code (often C or Fortran), bypassing the Python interpreter’s overhead for each element.

Examples:

You’ll see a dramatic difference in execution time. The vectorized version is often orders of magnitude faster.

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