This release is a pre-release and may not be stable for production use.
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)
3.0Moving 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 1.6.0.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 2.1You 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
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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.:
$ 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=mingw32Unit 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]: TrueIf developing in the git repo, simply run py.test
Release files for Bottleneck 1.6.1rc2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distributions (wheels)
Total release size: 12.6 MB
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