numpy

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)
... 

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')
... 

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)
... 

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
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.

Subpackages

Package Contents

exception ModuleDeprecationWarning

Bases:DeprecationWarning

Module deprecation warning.

The nose tester turns ordinary Deprecation warnings into test failures. That makes it hard to deprecate whole modules, because they get imported by default. So this is a special Deprecation warning that the nose tester will let pass without making tests fail.

exception VisibleDeprecationWarning

Bases:UserWarning

Visible deprecation warning.

By default, python will not show deprecation warnings, so this class can be used when a very visible warning is helpful, for example because the usage is most likely a user bug.