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NumPy provides an easily readable, expressive, high-level API for array programming. It takes care of the underlying mechanics that make operations fast.
The array programming foundation provided by NumPy, combined with a rich surrounding ecosystem of tools — inside of IPython or Jupyter — forms an interactive environment ideally suited to exploratory data analysis.
NumPy includes protocols to facilitate interoperability with external libraries like PyTorch, Dask, and JAX.
Through such features, NumPy provides a standard API for tensor computation and is a central coordinating mechanism between array technologies in Python.
Through such features, NumPy provides a standard API for tensor computation and is a central coordinating mechanism between array technologies in Python.
For more history on NumPy & SciPy, see the background section of the "SciPy 1.0" paper: https://www.nature.com/articles/s41592-019-0686-2
NumPy plays a leading role in scientific computing, and continues to evolve in response to the changing landscape of data science.
To build a NumPy that meets the needs of the next decade of data science, we welcome your help!
https://numpy.org/contribute/
To build a NumPy that meets the needs of the next decade of data science, we welcome your help!
https://numpy.org/contribute/
