numpy

package documentation

Provides

An array object of arbitrary homogeneous items
Fast mathematical operations over arrays
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

doc: Topical documentation on broadcasting, indexing, etc.
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
f2py: Fortran to Python Interface Generator.
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 with the NumPy profile (ipython -p numpy), which will import numpy under the alias np. Then, use the cpaste command 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.

Module	`ctypeslib`	============================ `ctypes` Utility Functions ============================
Package	`distutils`	An enhanced distutils, providing support for Fortran compilers, for BLAS, LAPACK and other common libraries for numerical computing, and more.
Module	`dual`	Deprecated since version 1.20.
Package	`f2py`	Fortran to Python Interface Generator.
Package	`fft`	Discrete Fourier Transform (`numpy.fft`) =============================================
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`	`numpy.linalg` ================
Package	`ma`	============= Masked Arrays =============
Module	`matlib`	No module docstring; 8/8 functions documented
Package	`polynomial`	A sub-package for efficiently dealing with polynomials.
Package	`random`	======================== Random Number Generation ========================
Package	`testing`	Common test support for all numpy test scripts.
Package	`typing`	============================ Typing (`numpy.typing`) ============================
Module	`_distributor_init`	Distributor init file
Module	`_globals`	Module defining global singleton classes.
Module	`_pytesttester`	Pytest test running.
Module	`_version`	Undocumented
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.
Package	`doc`	No package docstring; 0/2 variable, 2/2 modules documented
Package	`matrixlib`	Sub-package containing the matrix class and related functions.
Module	`setup`	Undocumented
Package	`tests`	No package docstring; 3/8 modules documented
Module	`version`	Undocumented

From __init__.py:

Constant __NUMPY_SETUP__ Undocumented

__NUMPY_SETUP__: bool =

Undocumented

Value

False