https://github.com/pydata/bottleneck
Fast NumPy array functions written in C
https://github.com/pydata/bottleneck
c c-extension fast numpy python
Last synced: 4 days ago
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Fast NumPy array functions written in C
- Host: GitHub
- URL: https://github.com/pydata/bottleneck
- Owner: pydata
- License: bsd-2-clause
- Created: 2010-11-27T23:21:22.000Z (over 15 years ago)
- Default Branch: master
- Last Pushed: 2026-03-26T15:31:43.000Z (11 days ago)
- Last Synced: 2026-03-27T06:18:32.863Z (10 days ago)
- Topics: c, c-extension, fast, numpy, python
- Language: Python
- Homepage:
- Size: 13.1 MB
- Stars: 1,171
- Watchers: 32
- Forks: 114
- Open Issues: 48
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
.. image:: https://github.com/pydata/bottleneck/actions/workflows/ci.yml/badge.svg?branch=master
:target: https://github.com/pydata/bottleneck/actions/workflows/ci.yml
==========
Bottleneck
==========
Bottleneck is a collection of fast NumPy array functions written in C.
Let's give it a try. Create a NumPy array:
.. code-block:: pycon
>>> import numpy as np
>>> a = np.array([1, 2, np.nan, 4, 5])
Find the nanmean:
.. code-block:: pycon
>>> import bottleneck as bn
>>> bn.nanmean(a)
3.0
Moving window mean:
.. code-block:: pycon
>>> bn.move_mean(a, window=2, min_count=1)
array([ 1. , 1.5, 2. , 4. , 4.5])
Benchmark
=========
Bottleneck comes with a benchmark suite:
.. code-block:: pycon
>>> bn.bench()
Bottleneck performance benchmark
Bottleneck 1.6.0.post0.dev32; Numpy 2.4.2
Speed is NumPy time divided by Bottleneck time
NaN means approx one-fifth NaNs; float64 used
no NaN no NaN NaN no NaN NaN
(100,) (1000,1000)(1000,1000)(1000,1000)(1000,1000)
axis=0 axis=0 axis=0 axis=1 axis=1
nansum 12.2 0.4 2.0 0.4 2.0
nanmean 29.8 0.8 2.3 0.5 2.2
nanstd 34.2 0.8 2.2 0.7 2.1
nanvar 32.9 0.8 2.2 0.7 2.1
nanmin 12.7 0.1 0.1 0.1 0.1
nanmax 12.8 0.1 0.1 0.1 0.1
median 38.7 1.1 6.7 1.0 6.5
nanmedian 38.4 2.1 2.2 1.9 2.1
ss 5.2 0.3 0.3 0.3 0.3
nanargmin 25.9 1.2 3.2 0.9 2.8
nanargmax 26.0 1.2 3.2 0.9 2.8
anynan 8.1 0.3 42.1 0.3 35.7
allnan 11.6 58.4 58.6 47.1 47.5
rankdata 14.9 1.4 1.4 1.5 1.5
nanrankdata 16.4 1.5 1.4 1.6 1.5
partition 2.0 1.1 1.6 1.0 1.5
argpartition 2.4 1.3 1.8 1.2 1.8
replace 7.4 2.9 2.9 2.9 2.9
push 1453.8 16.2 8.8 24.1 10.3
move_sum 1159.7 89.4 143.3 168.6 192.1
move_mean 2575.8 182.0 171.7 214.2 202.4
move_std 2863.9 137.4 274.5 145.1 310.7
move_var 2792.3 137.9 279.7 154.1 325.8
move_min 690.7 4.1 4.2 5.2 5.2
move_max 659.9 4.2 4.2 5.2 5.2
move_argmin 1369.1 33.7 77.5 35.7 83.5
move_argmax 1344.7 32.8 78.2 35.9 83.3
move_median 686.6 153.5 156.9 156.0 159.8
move_rank 502.0 1.9 2.0 1.8 2.1
You can also run a detailed benchmark for a single function using, for
example, the command:
.. code-block:: pycon
>>> bn.bench_detailed("move_median", fraction_nan=0.3)
Only arrays with data type (dtype) int32, int64, float32, and float64 are
accelerated. All other dtypes result in calls to slower, unaccelerated
functions. In the rare case of a byte-swapped input array (e.g. a big-endian
array on a little-endian operating system) the function will not be
accelerated regardless of dtype.
Where
=====
=================== ========================================================
download https://pypi.python.org/pypi/Bottleneck
docs https://bottleneck.readthedocs.io
code https://github.com/pydata/bottleneck
mailing list https://groups.google.com/group/bottle-neck
=================== ========================================================
License
=======
Bottleneck is distributed under a Simplified BSD license. See the LICENSE file
and LICENSES directory for details.
Install
=======
Bottleneck provides binary wheels on PyPI for all the most common platforms.
Binary packages are also available in conda-forge. We recommend installing binaries
with ``pip``, ``uv``, ``conda`` or similar - it's faster and easier than building
from source.
Installing from source
----------------------
Requirements:
======================== ============================================================================
Bottleneck Python >3.9; NumPy 1.16.0+
Compile gcc, clang, MinGW or MSVC
Unit tests pytest
Documentation sphinx, numpydoc
======================== ============================================================================
To install Bottleneck on Linux, Mac OS X, et al.:
.. code-block:: console
$ pip install .
To install bottleneck on Windows, first install MinGW and add it to your
system path. Then install Bottleneck with the command:
.. code-block:: console
$ python setup.py install --compiler=mingw32
Unit tests
==========
After you have installed Bottleneck, run the suite of unit tests:
.. code-block:: pycon
In [1]: import bottleneck as bn
In [2]: bn.test()
============================= test session starts =============================
platform linux -- Python 3.7.4, pytest-4.3.1, py-1.8.0, pluggy-0.12.0
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/home/chris/code/bottleneck/.hypothesis/examples')
rootdir: /home/chris/code/bottleneck, inifile: setup.cfg
plugins: openfiles-0.3.2, remotedata-0.3.2, doctestplus-0.3.0, mock-1.10.4, forked-1.0.2, cov-2.7.1, hypothesis-4.32.2, xdist-1.26.1, arraydiff-0.3
collected 190 items
bottleneck/tests/input_modification_test.py ........................... [ 14%]
.. [ 15%]
bottleneck/tests/list_input_test.py ............................. [ 30%]
bottleneck/tests/move_test.py ................................. [ 47%]
bottleneck/tests/nonreduce_axis_test.py .................... [ 58%]
bottleneck/tests/nonreduce_test.py .......... [ 63%]
bottleneck/tests/reduce_test.py ....................................... [ 84%]
............ [ 90%]
bottleneck/tests/scalar_input_test.py .................. [100%]
========================= 190 passed in 46.42 seconds =========================
Out[2]: True
If developing in the git repo, simply run ``py.test``