Is np.vectorize() Always the Fastest Way to Create New Columns in Pandas?

Is np.vectorize() consistently faster than Pandas apply() for creating new columns?

Yes, np.vectorize() is generally faster than Pandas apply() for this task. Our tests show that np.vectorize() can be significantly faster, especially for larger datasets.

Why is np.vectorize() faster than apply()?

Pandas apply() relies on Python-level loops to iterate over rows or columns in a dataframe. This can introduce significant overhead compared to np.vectorize(), which uses optimized C-based code for vectorized operations.

np.vectorize() converts your input function into a universal function (ufunc) and evaluates it over successive tuples of input arrays using broadcasting. This avoids the overhead of creating and passing around Pandas objects, resulting in improved performance.

Should np.vectorize() be preferred over apply()?

For creating new columns as a function of existing columns, np.vectorize() is generally a better choice due to its superior performance. However, it's important to note that np.vectorize() has limited flexibility compared to apply(), especially when it comes to accessing other columns or performing complex operations.

Other faster options

For truly optimized vectorized calculations, NumPy operations like np.where() or element-wise operations are highly effective. If performance is critical, consider using these or exploring libraries like numba that allow for efficient JIT-compilation of custom functions.

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