Boost Your Pandas Code Speed by 100x with This Simple Trick

Vectorization can significantly enhance the performance of Pandas operations. In a recent analysis, it was found that using vectorized operations instead of the apply() method can speed up Pandas code dramatically. Vectorization involves applying operations to entire data blocks at once, rather than processing each element individually. This method leverages NumPy’s optimized C-based functions, which are inherently faster. For instance, when dealing with complex if/then/else conditions, using NumPy’s where and select methods can replace slower Pandas operations. This approach not only reduces processing time but also simplifies code, making it more readable and maintainable. The study highlighted that vectorized operations can be up to 100 times faster than traditional methods on larger datasets.

Source: towardsdatascience.com