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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
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, ```` 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 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
Topical documentation on broadcasting, indexing, etc.
Basic functions used by several sub-packages.
Core Random Tools
Core Linear Algebra Tools
Core FFT routines
Polynomial tools
Numpy testing tools
Fortran to Python Interface Generator.
Enhancements to distutils with support for
Fortran compilers support and more.
Run numpy unittests
Show numpy build configuration
Overwrite certain functions with high-performance Scipy tools
Make everything matrices.
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.
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
except NameError:
__NUMPY_SETUP__ = False
import sys as _sys
_sys.stderr.write('Running from numpy source directory.\n')
del _sys
from numpy.__config__ import show as show_config
except ImportError:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python intepreter from there."""
raise ImportError(msg)
from version import git_revision as __git_revision__
from version import version as __version__
from _import_tools import PackageLoader
def pkgload(*packages, **options):
loader = PackageLoader(infunc=True)
return loader(*packages, **options)
import add_newdocs
__all__ = ['add_newdocs']
pkgload.__doc__ = PackageLoader.__call__.__doc__
from testing import Tester
test = Tester().test
bench = Tester().bench
import core
from core import *
import compat
import lib
from lib import *
import linalg
import fft
import polynomial
import random
import ctypeslib
import ma
import matrixlib as _mat
from matrixlib import *
# Make these accessible from numpy name-space
# but not imported in from numpy import *
from __builtin__ import bool, int, long, float, complex, \
object, unicode, str
from core import round, abs, max, min
__all__.extend(['__version__', 'pkgload', 'PackageLoader',
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# Filter annoying Cython warnings that serve no good purpose.
import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
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