NumPy reference#
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 .
- Array objects
- The N-dimensional array ( ndarray )
- Scalars
- Data type objects ( dtype )
- Indexing routines
- Iterating over arrays
- Standard array subclasses
- Masked arrays
- The array interface protocol
- Datetimes and Timedeltas
- Table of Differences between numpy.array_api and numpy
- Inf
- Infinity
- NAN
- NINF
- NZERO
- NaN
- PINF
- PZERO
- e
- euler_gamma
- inf
- infty
- nan
- newaxis
- pi
- ufunc
- Available ufuncs
- Array creation routines
- Array manipulation routines
- Binary operations
- String operations
- C-Types foreign function interface ( numpy.ctypeslib )
- Datetime support functions
- Data type routines
- Mathematical functions with automatic domain
- Floating point error handling
- Discrete Fourier Transform ( numpy.fft )
- Functional programming
- NumPy-specific help functions
- 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 )
- Support for testing overrides ( numpy.testing.overrides )
- Window functions
- Mypy plugin
- Differences from the runtime NumPy API
- API
- Performance-related options
- Debugging-related options
- Testing planned future behavior
- Modules in numpy.distutils
- Configuration class
- Building Installable C libraries
- Conversion of .src files
- SciPy structure
- Requirements for SciPy packages
- The setup.py file
- The __init__.py file
- Extra features in NumPy Distutils
- Migration advice
- Interaction of numpy.distutils with setuptools
- 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
- Memory management in NumPy
- CPU build options
- How does the CPU dispatcher work?
- Advice for using NumPy on untrusted data
- 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.
NumPy reference#
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 .
- Array objects
- The N-dimensional array ( ndarray )
- Scalars
- Data type objects ( dtype )
- Indexing routines
- Iterating over arrays
- Standard array subclasses
- Masked arrays
- The array interface protocol
- Datetimes and Timedeltas
- Table of Differences between numpy.array_api and numpy
- Inf
- Infinity
- NAN
- NINF
- NZERO
- NaN
- PINF
- PZERO
- e
- euler_gamma
- inf
- infty
- nan
- newaxis
- pi
- ufunc
- Available ufuncs
- Array creation routines
- Array manipulation routines
- Binary operations
- String operations
- C-Types foreign function interface ( numpy.ctypeslib )
- Datetime support functions
- Data type routines
- Mathematical functions with automatic domain
- Floating point error handling
- Discrete Fourier Transform ( numpy.fft )
- Functional programming
- NumPy-specific help functions
- 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 )
- Support for testing overrides ( numpy.testing.overrides )
- Window functions
- Mypy plugin
- Differences from the runtime NumPy API
- API
- Performance-related options
- Debugging-related options
- Testing planned future behavior
- Modules in numpy.distutils
- Configuration class
- Building Installable C libraries
- Conversion of .src files
- SciPy structure
- Requirements for SciPy packages
- The setup.py file
- The __init__.py file
- Extra features in NumPy Distutils
- Migration advice
- Interaction of numpy.distutils with setuptools
- 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
- Memory management in NumPy
- CPU build options
- How does the CPU dispatcher work?
- Advice for using NumPy on untrusted data
- 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.