Abstract: In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort ...

Data analysis is an integral part of modern data-driven decision-making, encompassing a broad array of techniques and tools to process, visualize, and interpret data. Python, a versatile programming ...

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 ...

Generating random numbers is a common task in many applications, such as simulations, cryptography, games, and data analysis. NumPy is a popular library for scientific computing and data manipulation ...

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 ...

NumPy is very widely used in pretty much every field of science and engineering. Over 32,000 packages on GitHub depend on NumPy, and 6 million users visit our website every month. Its user base spans ...

Welcome, and thank you for taking an interest in NumPy! On this page, we will first provide some context about NumPy and the current state of its documentation, and then describe a couple of project ...