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 <https://www.scipy.org>`_. We recommend exploring the docstrings using `IPython <https://ipython.org>`_, 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 --------------------- Topical documentation on broadcasting, indexing, etc. Basic functions used by several sub-packages. random Core Random Tools linalg Core Linear Algebra Tools Core FFT routines polynomial Polynomial tools testing NumPy testing tools Fortran to Python Interface Generator. distutils Enhancements to distutils with support for Fortran compilers support and more. Utilities --------- Run numpy unittests show_config Show numpy build configuration 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. from __future__ import division , absolute_import , print_function import sys import warnings from ._globals import ModuleDeprecationWarning , VisibleDeprecationWarning from ._globals import _NoValue # We first need to detect if we're being called as part of the numpy setup # procedure itself in a reliable manner. try : __NUMPY_SETUP__ except NameError : __NUMPY_SETUP__ = False if __NUMPY_SETUP__ : sys . stderr . write ( 'Running from numpy source directory. \n ' ) else : try : 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 interpreter from there.""" raise ImportError ( msg ) from .version import git_revision as __git_revision__ from .version import version as __version__ __all__ = [ 'ModuleDeprecationWarning' , 'VisibleDeprecationWarning' ] # Allow distributors to run custom init code from . import _distributor_init from . import core from .core import * from . import compat from . import lib # FIXME: why have numpy.lib if everything is imported here?? from .lib import * from . import linalg from . import fft from . import polynomial from . import random from . import ctypeslib from . import ma from . import matrixlib as _mat from .matrixlib import * from .compat import long # Make these accessible from numpy name-space # but not imported in from numpy import * # TODO[gh-6103]: Deprecate these if sys . version_info [ 0 ] >= 3 : from builtins import bool , int , float , complex , object , str unicode = str else : from __builtin__ import bool , int , float , complex , object , unicode , str from .core import round , abs , max , min # now that numpy modules are imported, can initialize limits core . getlimits . _register_known_types () __all__ . extend ([ '__version__' , 'show_config' ]) __all__ . extend ( core . __all__ ) __all__ . extend ( _mat . __all__ ) __all__ . extend ( lib . __all__ ) __all__ . extend ([ 'linalg' , 'fft' , 'random' , 'ctypeslib' , 'ma' ]) # These are added by `from .core import *` and `core.__all__`, but we # overwrite them above with builtins we do _not_ want to export. __all__ . remove ( 'long' ) __all__ . remove ( 'unicode' ) # Remove things that are in the numpy.lib but not in the numpy namespace # Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace) # that prevents adding more things to the main namespace by accident. # The list below will grow until the `from .lib import *` fixme above is # taken care of __all__ . remove ( 'Arrayterator' ) del Arrayterator # Filter out Cython harmless warnings warnings . filterwarnings ( "ignore" , message = "numpy.dtype size changed" ) warnings . filterwarnings ( "ignore" , message = "numpy.ufunc size changed" ) warnings . filterwarnings ( "ignore" , message = "numpy.ndarray size changed" ) # oldnumeric and numarray were removed in 1.9. In case some packages import # but do not use them, we define them here for backward compatibility. oldnumeric = 'removed' numarray = 'removed' if sys . version_info [: 2 ] >= ( 3 , 7 ): # Importing Tester requires importing all of UnitTest which is not a # cheap import Since it is mainly used in test suits, we lazy import it # here to save on the order of 10 ms of import time for most users # The previous way Tester was imported also had a side effect of adding # the full `numpy.testing` namespace # module level getattr is only supported in 3.7 onwards # https://www.python.org/dev/peps/pep-0562/ def __getattr__ ( attr ): if attr == 'testing' : import numpy.testing as testing return testing elif attr == 'Tester' : from .testing import Tester return Tester else : raise AttributeError ( "module {!r} has no attribute " " {!r} " . format ( __name__ , attr )) def __dir__ (): return list ( globals () . keys ()) + [ 'Tester' , 'testing' ] else : # We don't actually use this ourselves anymore, but I'm not 100% sure that # no-one else in the world is using it (though I hope not) from .testing import Tester # Pytest testing from numpy._pytesttester import PytestTester test = PytestTester ( __name__ ) del PytestTester def _sanity_check (): Quick sanity checks for common bugs caused by environment. There are some cases e.g. with wrong BLAS ABI that cause wrong results under specific runtime conditions that are not necessarily achieved during test suite runs, and it is useful to catch those early. See https://github.com/numpy/numpy/issues/8577 and other similar bug reports. try : x = ones ( 2 , dtype = float32 ) if not abs ( x . dot ( x ) - 2.0 ) < 1e-5 : raise AssertionError () except AssertionError : msg = ( "The current Numpy installation ( {!r} ) fails to " "pass simple sanity checks. This can be caused for example " "by incorrect BLAS library being linked in, or by mixing " "package managers (pip, conda, apt, ...). Search closed " "numpy issues for similar problems." ) raise RuntimeError ( msg . format ( __file__ )) _sanity_check () del _sanity_check