numpy

package documentation
(source)

Provides
  1. An array object of arbitrary homogeneous items
  2. Fast mathematical operations over arrays
  3. Linear Algebra, Fourier Transforms, Random Number Generation

How to use the documentation

Documentation is available in two forms: docstrings provided with the code, and a loose standing reference guide, available from the NumPy homepage.

We recommend exploring the docstrings using IPython, an advanced Python shell with TAB-completion and introspection capabilities. See below for further instructions.

The docstring examples assume that numpy has been imported as np:

>>> import numpy as np

Code snippets are indicated by three greater-than signs:

>>> x = 42
>>> x = x + 1

Use the built-in help function to view a function's docstring:

>>> help(np.sort)
... # doctest: +SKIP

For some objects, np.info(obj) may provide additional help. This is particularly true if you see the line "Help on ufunc object:" at the top of the help() page. Ufuncs are implemented in C, not Python, for speed. The native Python help() does not know how to view their help, but our np.info() function does.

To search for documents containing a keyword, do:

>>> np.lookfor('keyword')
... # doctest: +SKIP

General-purpose documents like a glossary and help on the basic concepts of numpy are available under the doc sub-module:

>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP

Available subpackages

lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
distutils
Enhancements to distutils with support for Fortran compilers support and more.

Utilities

test
Run numpy unittests
show_config
Show numpy build configuration
dual
Overwrite certain functions with high-performance SciPy tools. Note: numpy.dual is deprecated. Use the functions from NumPy or Scipy directly instead of importing them from numpy.dual.
matlib
Make everything matrices.
__version__
NumPy version string

Viewing documentation using IPython

Start IPython and import numpy usually under the alias np: import numpy as np. Then, directly past or use the %cpaste magic to paste examples into the shell. To see which functions are available in numpy, type np.<TAB> (where <TAB> refers to the TAB key), or use np.*cos*?<ENTER> (where <ENTER> refers to the ENTER key) to narrow down the list. To view the docstring for a function, use np.cos?<ENTER> (to view the docstring) and np.cos??<ENTER> (to view the source code).

Copies vs. in-place operation

Most of the functions in numpy return a copy of the array argument (e.g., np.sort). In-place versions of these functions are often available as array methods, i.e. x = np.array([1,2,3]); x.sort(). Exceptions to this rule are documented.

Package array_api A NumPy sub-namespace that conforms to the Python array API standard.
Package compat Compatibility module.
Module conftest Pytest configuration and fixtures for the Numpy test suite.
Package core Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Module ctypeslib Load a C library.
Package distutils An enhanced distutils, providing support for Fortran compilers, for BLAS, LAPACK and other common libraries for numerical computing, and more.
Package doc No package docstring; 0/2 variable, 2/2 modules documented
Module dual Deprecated since version 1.20.
Package f2py Fortran to Python Interface Generator.
Package fft The SciPy module scipy.fft is a more comprehensive superset of numpy.fft, which includes only a basic set of routines.
Package lib Note: almost all functions in the numpy.lib namespace are also present in the main numpy namespace. Please use the functions as np.<funcname> where possible.
Package linalg The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient low level implementations of standard linear algebra algorithms. Those libraries may be provided by NumPy itself using C versions of a subset of their reference implementations but, when possible, highly optimized libraries that take advantage of specialized processor functionality are preferred...
Package ma Arrays sometimes contain invalid or missing data. When doing operations on such arrays, we wish to suppress invalid values, which is the purpose masked arrays fulfill (an example of typical use is given below).
Module matlib No module docstring; 8/8 functions documented
Package matrixlib Sub-package containing the matrix class and related functions.
Package polynomial A sub-package for efficiently dealing with polynomials.
Package random Use default_rng() to create a Generator and call its methods.
Module setup Undocumented
Package testing Common test support for all numpy test scripts.
Package tests No package docstring; 3/9 modules documented
Package typing New in version 1.20.
Module version Undocumented
Module _distributor_init Distributor init file
Module _globals Module defining global singleton classes.
Package _pyinstaller No package docstring; 2/3 modules documented
Module _pytesttester Pytest test running.
Package _typing Private counterpart of numpy.typing.
Module _version Undocumented