pydata/bottleneck


Fast NumPy array functions written in C

License: BSD-2-Clause

Language: Python

Keywords: c, c-extension, fast, numpy, python


https://travis-ci.org/pydata/bottleneck.svg?branch=master

Bottleneck

Bottleneck is a collection of fast NumPy array functions written in C.

Let's give it a try. Create a NumPy array:

>>> import numpy as np
>>> a = np.array([1, 2, np.nan, 4, 5])

Find the nanmean:

>>> import bottleneck as bn
>>> bn.nanmean(a)
3.0

Moving window mean:

>>> bn.move_mean(a, window=2, min_count=1)
array([ 1. ,  1.5,  2. ,  4. ,  4.5])

Benchmark

Bottleneck comes with a benchmark suite:

>>> bn.bench()
Bottleneck performance benchmark
    Bottleneck 1.3.0.dev0+122.gb1615d7; Numpy 1.16.4
    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         29.7        1.4        1.6        2.0        2.1
nanmean        99.0        2.0        1.8        3.2        2.5
nanstd        145.6        1.8        1.8        2.7        2.5
nanvar        138.4        1.8        1.8        2.8        2.5
nanmin         27.6        0.5        1.7        0.7        2.4
nanmax         26.6        0.6        1.6        0.7        2.5
median        120.6        1.3        4.9        1.1        5.7
nanmedian     117.8        5.0        5.7        4.8        5.5
ss             13.2        1.2        1.3        1.5        1.5
nanargmin      66.8        5.5        4.8        3.5        7.1
nanargmax      57.6        2.9        5.1        2.5        5.3
anynan         10.2        0.3       52.3        0.8       41.6
allnan         15.1      196.0      156.3      135.8      111.2
rankdata       45.9        1.2        1.2        2.1        2.1
nanrankdata    50.5        1.4        1.3        2.4        2.3
partition       3.3        1.1        1.6        1.0        1.5
argpartition    3.4        1.2        1.5        1.1        1.6
replace         9.0        1.5        1.5        1.5        1.5
push         1565.6        5.9        7.0       13.0       10.9
move_sum     2159.3       31.1       83.6      186.9      182.5
move_mean    6264.3       66.2      111.9      361.1      246.5
move_std     8653.6       86.5      163.7      232.0      317.7
move_var     8856.0       96.3      171.6      267.9      332.9
move_min     1186.6       13.4       30.9       23.5       45.0
move_max     1188.0       14.6       29.9       23.5       46.0
move_argmin  2568.3       33.3       61.0       49.2       86.8
move_argmax  2475.8       30.9       58.6       45.0       82.8
move_median  2236.9      153.9      151.4      171.3      166.9
move_rank     847.1        1.2        1.4        2.3        2.6

You can also run a detailed benchmark for a single function using, for example, the command:

>>> 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

Requirements:

Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8; 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.:

$ pip install .

To install bottleneck on Windows, first install MinGW and add it to your system path. Then install Bottleneck with the command:

python setup.py install --compiler=mingw32

Alternatively, you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck

Unit tests

After you have installed Bottleneck, run the suite of unit tests:

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

Project Statistics

Sourcerank 13
Repository Size 12.6 MB
Stars 432
Forks 57
Watchers 18
Open issues 12
Dependencies 1
Contributors 18
Tags 26
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Top Contributors See all

kwgoodman Christopher Whelan Moritz E. Beber Pietro Battiston Stephan Hoyer Dougal J. Sutherland jennolsen84 Lev E. Givon Christoph Gohlke Jaime Jens Hedegaard Nielsen Ghislain Antony Vaillant Benjamin Root Aleš Erjavec Daniel Hakimi John Benediktsson Alessandro Amici Thomas Robitaille

Packages Referencing this Repo

bottleneck
Bottleneck is a collection of fast NumPy array functions written in Cython.
Latest release 1.3.1 - Updated - 432 stars
Bottleneck
Fast NumPy array functions written in C
Latest release 1.3.1 - Updated - 432 stars

Recent Tags See all

v1.3.1 November 19, 2019
v1.3.0 November 13, 2019
v1.3.0rc2 November 09, 2019
v1.3.0rc1 November 03, 2019
v1.3.0.dev0 August 24, 2019
v1.2.1 May 15, 2017
v1.2.0 October 20, 2016
v1.1.0 June 22, 2016
v1.0.0 February 06, 2015
v0.8.0 January 21, 2014
v0.7.0 September 10, 2013
v0.6.0 June 04, 2012
v0.6rc1 May 23, 2012
v0.5.0 June 13, 2011
v0.5.0rc3 June 10, 2011

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biolab/bottlechest
Fast NumPy array functions written in Cython
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