Speed up your Numpy and Pandas with NumExpr Package - KDnuggets

Numpy and Pandas are probably the two most widely used core Python libraries for data science (DS) and machine learning (ML)tasks. Needless to say, the speed of evaluating numerical expressions is critically important for these DS/ML tasks and these two libraries do not disappoint in that regard. Under the hood, they use fast and optimized vectorized operations (as much as possible) to speed up the mathematical operations. Plenty of articles have been written about how Numpy is much superior (especially when you can vectorize your calculations) over plain-vanilla Python loops or list-based operations. How Fast Numpy Really is and Why? Data science with Python: Turn your conditional loops to Numpy vectors It pays to even vectorize conditional loops for speeding up the overall data transformation.