Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python ...

Arrays in Python give you a huge amount of flexibility for storing, organizing, and accessing data. This is crucial, not least because of Python’s popularity for use in data science. But what ...

NumPy is known for being fast, but could it go even faster? Here’s how to use Cython to accelerate array iterations in NumPy. NumPy gives Python users a wickedly fast library for working with data in ...

The array interface (sometimes called array protocol) was created in 2005 as a means for array-like Python objects to reuse each other's data buffers intelligently whenever possible. The homogeneous N ...

"Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Part 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array.\n", "This ...

Every Python developer knows some or all of these libraries, because they’re stable, reliable, and excellent at what they do.

These are my go-to libraries for Python data crunching.