Which Method is Faster for Creating New Columns in a Pandas DataFrame: Pandas Apply or NumPy Vectorize?

Performance Considerations of Pandas apply vs NumPy vectorize for Column Creation

While Pandas apply is widely used, its performance lags behind NumPy vectorize when creating new columns from existing ones. This disparity is attributed to the fact that apply functions are Python-level loops, which incur significant overhead. In contrast, vectorize converts the input function to a Universal function, significantly improving efficiency.

Performance Benchmarks

Comparing Python-level loops and apply with raw=True, we observe:

• Map, list comprehension with zip, and vectorize exhibit similar performance.
• apply with raw=True is significantly faster than without raw=True.

True Vectorization

However, both apply and vectorize are eclipsed by true vectorization operations such as np.where, which perform calculations element-wise on NumPy arrays. This approach is remarkably faster, eliminating the need for looping.

Further Performance Considerations

For critical bottlenecks, consider numba, a tool that compiles Python functions to highly optimized C code. Using numba, calculations can be further accelerated.

Conclusion

When creating new columns from existing ones, NumPy vectorize offers superior performance compared to Pandas apply due to its native vectorization capabilities. For optimal efficiency, true vectorization should be employed where applicable.

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