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# NumPy reference

Release:

  2.5

Date:

  June 28, 2026

This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation.

## Python API

- NumPy’s module structure

- Array objects
  - The N-dimensional array (`ndarray`)
  - Scalars
  - Data type objects (`dtype`)
  - Data type promotion in NumPy
  - Iterating over arrays
  - Standard array subclasses
  - Masked arrays
  - The array interface protocol
  - Datetimes and timedeltas

- Universal functions (`ufunc`)

- Routines and objects by topic
  - Constants
  - Array creation routines
  - Array manipulation routines
  - Bit-wise operations
  - String functionality
  - Datetime support functions
  - Data type routines
  - Mathematical functions with automatic domain
  - Floating point error handling
  - Exceptions and Warnings
  - Discrete Fourier Transform
  - Functional programming
  - Input and output
  - Indexing routines
  - Linear algebra
  - Logic functions
  - Masked array operations
  - Mathematical functions
  - Miscellaneous routines
  - Polynomials
  - Random sampling
  - Set routines
  - Sorting, searching, and counting
  - Statistics
  - Test support
  - Window functions

- Typing (`numpy.typing`)

## C API

- NumPy C-API
  - Python types and C-structures
  - System configuration
  - Data type API
  - Array API
  - Array iterator API
  - ufunc API
  - Generalized universal function API
  - NpyString API
  - NumPy core math library
  - Datetime API
  - C API deprecations
  - Memory management in NumPy

## Other topics

- Array API standard compatibility
- CPU/SIMD optimizations
- Thread Safety
- Global Configuration Options
- NumPy security
- Testing guidelines
- Status of `numpy.distutils` and migration advice
- NumPy and SWIG

## Acknowledgements

Large parts of this manual originate from Travis E. Oliphant’s book Guide to NumPy (which generously entered Public Domain in August 2008). The reference documentation for many of the functions are written by numerous contributors and developers of NumPy.