NumPy Reference

• Release: 1.18.dev0
• Date: August 14, 2019

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
  • Iterating Over Arrays
  • Standard array subclasses
  • Masked arrays
  • The Array Interface
  • Datetimes and Timedeltas
• Constants
• Universal functions (ufunc)
  • Broadcasting
  • Output type determination
  • Use of internal buffers
  • Error handling
  • Casting Rules
  • Overriding Ufunc behavior
  • ufunc
  • Available ufuncs
• Routines
  • Array creation routines
  • Array manipulation routines
  • Binary operations
  • String operations
  • C-Types Foreign Function Interface (numpy.ctypeslib)
  • Datetime Support Functions
  • Data type routines
  • Optionally Scipy-accelerated routines (numpy.dual)
  • Mathematical functions with automatic domain (numpy.emath)
  • Floating point error handling
  • Discrete Fourier Transform (numpy.fft)
  • Financial functions
  • Functional programming
  • NumPy-specific help functions
  • Indexing routines
  • Input and output
  • Linear algebra (numpy.linalg)
  • Logic functions
  • Masked array operations
  • Mathematical functions
  • Matrix library (numpy.matlib)
  • Miscellaneous routines
  • Padding Arrays
  • Polynomials
  • Random sampling (numpy.random)
  • Set routines
  • Sorting, searching, and counting
  • Statistics
  • Test Support (numpy.testing)
  • Window functions
• Packaging (numpy.distutils)
  • Modules in numpy.distutils
  • Building Installable C libraries
  • Conversion of .src files
• NumPy Distutils - Users Guide
  • SciPy structure
  • Requirements for SciPy packages
  • The setup.py file
  • The init.py file
  • Extra features in NumPy Distutils
• NumPy C-API
  • Python Types and C-Structures
  • System configuration
  • Data Type API
  • Array API
  • Array Iterator API
  • UFunc API
  • Generalized Universal Function API
  • NumPy core libraries
  • C API Deprecations
• NumPy internals
  • NumPy C Code Explanations
  • Memory Alignment
  • Internal organization of numpy arrays
  • Multidimensional Array Indexing Order Issues
• NumPy and SWIG
  • numpy.i: a SWIG Interface File for NumPy
  • Testing the numpy.i Typemaps

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.