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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)
... # 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
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.`` (where ```` refers to the TAB key), or use
``np.*cos*?`` (where ```` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?`` (to view the docstring) and ``np.cos??`` (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.
N )_NoValue _CopyMode)ComplexWarningModuleDeprecationWarningVisibleDeprecationWarning TooHardError AxisErrorFz%Running from numpy source directory.
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polynomial)random) ctypeslib)ma) matrixliba module 'numpy' has no attribute '{n}'.
`np.{n}` was a deprecated alias for the builtin `{n}`. To avoid this error in existing code, use `{n}` by itself. Doing this will not modify any behavior and is safe. {extended_msg}
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationszCIf you specifically wanted the numpy scalar type, use `np.{}` here.zWhen replacing `np.{}`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.)object boolbool_floatfloat64complex
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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.
rT )dtype @gh㈵>zThe 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.N)onesfloat32r= dotAssertionErrorrX r* __file__)xr[ s r,
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Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
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If you compiled yourself, more information is available at:
https://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries
Otherwise report this to the vendor that provided NumPy.
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