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

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

Bython offers an alternative Python syntax using braces to define code blocks, appealing to C++ or Java developers. Eliminate indentation errors with Bython; it allows mixed tabs and spaces without ...

In 2005, Travis Oliphant was an information scientist working on medical and biological imaging at Brigham Young University in Provo, Utah, when he began work on NumPy, a library that has become a ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Data Parallel Extension for NumPy* or dpnp is a Python library that implements a subset of NumPy* that can be executed on any data parallel device. The subset is a drop-in replacement of core NumPy* ...

The numpy-financial package contains a collection of elementary financial functions. The financial functions in NumPy are deprecated and eventually will be removed from NumPy; see NEP-32 for more ...

In the dynamic scene of Python development, understanding the qualification between frameworks and libraries is pivotal for extended success. Python frameworks give structure and support for building ...

In today's data-driven world, organizations are inundated with vast amounts of data generated from various sources such as sensors, social media, and transactional systems. Effectively exploring and ...

But in many cases, it doesn’t have to be an either/or proposition. Properly optimized, Python applications can run with surprising speed—perhaps not as fast as Java or C, but fast enough for web ...