Bioinformatics with Python Cookbook - Third Edition

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Getting to Know NumPy, pandas, Arrow, and Matplotlib

One of Python’s biggest strengths is its profusion of high-quality science and data processing libraries. At the core of all of them is NumPy, which provides efficient array and matrix support. On top of NumPy, we can find almost all of the scientific libraries. For example, in our field, there’s Biopython. But other generic data analysis libraries can also be used in our field. For example, pandas is the de facto standard for processing tabled data. More recently, Apache Arrow provides efficient implementations of some of pandas’ functionality, along with language interoperability. Finally, Matplotlib is the most common plotting library in the Python space and is appropriate for scientific ...