Pandas Apply vs. NumPy Vectorize: Which is Faster for Creating New Columns?

Performance of Pandas Apply vs. NumPy Vectorize in Column Creation

Introduction

While Pandas' df.apply() is a versatile function for operating on dataframes, its performance can be a concern, especially for large datasets. NumPy's np.vectorize() offers a potential alternative for creating new columns as a function of existing ones. This article investigates the speed difference between the two methods, explaining why np.vectorize() is generally faster.

Performance Comparison

Extensive benchmarking revealed that np.vectorize() consistently outperformed df.apply() by a significant margin. For example, in a dataset with 1 million rows, np.vectorize() was 25 times faster on a 2016 MacBook Pro. This disparity becomes even more pronounced as the dataset size increases.

Underlying Mechanisms

df.apply() operates through a series of Python-level loops, which introduces significant overhead. Each iteration involves creating a new Pandas Series object, invoking the function, and appending the results to a new column. In contrast, np.vectorize() utilizes NumPy's broadcasting rules to evaluate the function on arrays. This approach bypasses the overhead of Python loops and capitalizes on optimized C code, resulting in much faster execution.

True Vectorization

For true vectorized calculations, neither df.apply() nor np.vectorize() is optimal. Instead, native NumPy operations offer superior performance. Vectorized divide(), for instance, shows a dramatic performance advantage over either df.apply() or np.vectorize().

JIT Compilation with Numba

For even greater efficiency, Numba's @njit decorator can be employed to compile the divide() function into efficient C-level code. This approach further reduces execution time, yielding results in microseconds rather than seconds.

Conclusion

While df.apply() provides a convenient interface for applying functions to dataframes, its performance limitations become apparent with large datasets. For performance-critical applications, NumPy's np.vectorize() and its JIT-compiled counterpart in Numba offer superior speed for creating new columns. It is also worth noting that true vectorized operations using native NumPy functions are the most efficient option for large-scale data manipulation.

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