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# NumPy Reference

Release: | 1.5
Date: | September 02, 2010

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 also NumPy User Guide.

- Array objects
  - The N-dimensional array (`ndarray`)
  - Scalars
  - Data type objects (`dtype`)
  - Indexing
  - Standard array subclasses
  - Masked arrays
  - The Array Interface
- Universal functions (`ufunc`)
  - Broadcasting
  - Output type determination
  - Use of internal buffers
  - Error handling
  - Casting Rules
  - `ufunc`
  - Available ufuncs
- Routines
  - Array creation routines
  - Array manipulation routines
  - Indexing routines
  - Data type routines
  - Input and output
  - Discrete Fourier Transform (`numpy.fft`)
  - Linear algebra (`numpy.linalg`)
  - Random sampling (`numpy.random`)
  - Sorting and searching
  - Logic functions
  - Binary operations
  - Statistics
  - Mathematical functions
  - Functional programming
  - Polynomials
  - Financial functions
  - Set routines
  - Window functions
  - Floating point error handling
  - Masked array operations
  - Numpy-specific help functions
  - Miscellaneous routines
  - Test Support (`numpy.testing`)
  - Asserts
  - Mathematical functions with automatic domain (`numpy.emath`)
  - Matrix library (`numpy.matlib`)
  - Optionally Scipy-accelerated routines (`numpy.dual`)
  - Numarray compatibility (`numpy.numarray`)
  - Old Numeric compatibility (`numpy.oldnumeric`)
  - C-Types Foreign Function Interface (`numpy.ctypeslib`)
  - String operations
- Packaging (`numpy.distutils`)
  - Modules in `numpy.distutils`
  - Building Installable C libraries
  - Conversion of `.src` files
- Numpy C-API
  - Python Types and C-Structures
  - System configuration
  - Data Type API
  - Array API
  - UFunc API
  - Generalized Universal Function API
  - Numpy core libraries
- Numpy internals
  - Numpy C Code Explanations
  - Internal organization of numpy arrays
  - Multidimensional Array Indexing Order Issues

## 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, both prior to and during the Numpy Documentation Marathon.

Please help to improve NumPy’s documentation! Instructions on how to join the ongoing documentation marathon can be found on the scipy.org website

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