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# NumPy Reference

Release: | 1.3
Date: | March 20, 2009

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
  - Fourier transforms (`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
  - 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`)

- Packaging (`numpy.distutils`)
  - Modules in `numpy.distutils`
  - Conversion of `.src` files

- Numpy C-API
  - Python Types and C-Structures
  - Configuration defines
  - Data Type API
  - Array API
  - UFunc 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.

The Documentation Marathon is still ongoing. Please help us write better documentation for Numpy by joining it! Instructions on how to join and what to do can be found on the scipy.org website

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