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])Bottleneck comes with a benchmark suite:
>>> bn.bench()
Bottleneck performance benchmark
Bottleneck 1.3.0; Numpy 1.16.0
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 83.7 1.5 1.9 2.3 2.4
nanmean 245.3 2.3 2.4 3.4 2.9
nanstd 316.8 2.0 2.1 2.6 2.5
nanvar 298.9 1.8 2.1 2.7 2.5
nanmin 63.8 0.7 1.9 1.0 3.3
nanmax 53.8 0.7 1.8 0.8 2.6
median 155.1 1.3 6.0 1.1 6.2
nanmedian 166.7 7.3 8.4 7.2 8.5
ss 35.4 1.2 1.2 1.6 1.6
nanargmin 105.2 12.2 3.0 2.3 5.8
nanargmax 123.5 3.1 6.6 2.3 5.7
anynan 24.0 0.3 41.5 0.5 28.5
allnan 51.6 147.5 129.4 119.3 57.8
rankdata 67.5 2.5 2.4 2.9 2.9
nanrankdata 71.1 2.7 2.6 3.2 3.1
partition 4.1 1.2 2.7 1.0 1.4
argpartition 3.7 1.1 1.4 1.1 1.6
replace 15.1 1.5 1.5 1.5 1.5
push 3199.6 3.1 9.9 21.2 16.2
move_sum 4901.4 75.1 116.5 201.3 201.3
move_mean 11550.7 109.8 182.6 243.5 252.4
move_std 11910.9 98.0 195.0 140.5 248.8
move_var 14669.2 129.3 237.2 211.7 316.8
move_min 2484.2 19.5 35.6 25.0 43.8
move_max 2401.6 20.0 36.0 25.6 44.1
move_argmin 4531.0 41.8 74.8 42.9 83.8
move_argmax 4339.2 42.7 74.8 43.5 82.2
move_median 2752.2 150.2 151.0 153.2 154.7
move_rank 1585.6 1.9 2.0 2.5 2.7You 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.
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https://pypi.python.org/pypi/Bottleneck |
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https://kwgoodman.github.io/bottleneck-doc |
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https://github.com/pydata/bottleneck |
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https://groups.google.com/group/bottle-neck |
Bottleneck is distributed under a Simplified BSD license. See the LICENSE file and LICENSES directory for details.
Requirements:
| Bottleneck | Python 2.7, 3.5, 3.6; NumPy 1.16.0 |
| Compile | gcc, clang, MinGW or MSVC |
| Unit tests | nose |
| Documentation | sphinx, numpydoc |
To install Bottleneck on GNU/Linux, Mac OS X, et al.:
$ sudo python setup.py installTo 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=mingw32Alternatively, you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck
After you have installed Bottleneck, run the suite of unit tests:
>>> import bottleneck as bn
>>> bn.test()
<snip>
Ran 169 tests in 57.205s
OK
<nose.result.TextTestResult run=169 errors=0 failures=0>