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 1.2.0dev; Numpy 1.11.0
Speed is NumPy time divided by Bottleneck time
NaN means approx one-third NaNs; float64 and axis=-1 are used
no NaN no NaN NaN NaN
(10,) (1000,1000) (10,) (1000,1000)
nansum 117.4 2.3 114.6 7.3
nanmean 433.2 3.5 429.7 8.2
nanstd 665.9 2.7 680.8 6.9
nanvar 710.8 2.6 664.4 6.9
nanmin 96.1 1.0 94.5 1.3
nanmax 95.0 1.0 98.4 2.1
median 251.5 1.1 372.5 5.2
nanmedian 268.9 5.3 335.5 41.8
ss 58.9 1.6 58.4 1.6
nanargmin 193.4 2.2 197.8 6.0
nanargmax 192.9 2.2 195.9 6.4
anynan 44.3 0.5 49.1 58.2
allnan 49.3 59.2 49.4 59.7
rankdata 137.4 2.7 142.2 5.2
nanrankdata 164.6 3.0 164.2 5.1
partition 9.0 1.0 9.8 1.1
argpartition 7.8 1.1 8.7 1.0
replace 21.3 1.4 21.3 1.4
push 564.4 22.7 564.1 29.4
move_sum 932.3 196.2 927.7 445.6
move_mean 2417.4 226.2 2431.8 373.0
move_std 3587.7 127.5 3547.3 200.5
move_var 3676.2 199.0 3690.7 349.5
move_min 755.3 27.3 769.2 65.8
move_max 731.9 27.2 753.2 111.9
move_argmin 1112.9 41.5 1173.6 242.5
move_argmax 812.3 44.2 780.7 306.5
move_median 1043.2 42.9 1043.1 142.1
move_rank 1680.8 2.4 1919.6 9.0You 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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http://berkeleyanalytics.com/bottleneck |
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https://github.com/kwgoodman/bottleneck |
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https://groups.google.com/group/bottle-neck |
Bottleneck is distributed under a Simplified BSD license. See the LICENSE file for details.
Requirements:
| Bottleneck | Python 2.7, 3.4, 3.5; NumPy 1.11.0 |
| Compile | gcc, clang, MinGW or MSVC |
| Unit tests | nose |
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 commands:
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 198 tests in 17.205s
OK
<nose.result.TextTestResult run=198 errors=0 failures=0>