Fast NumPy array functions written in C
Project description
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 1.2.0; Numpy 1.11.2 Speed is NumPy time divided by Bottleneck time NaN means approx one-fifth NaNs; float64 and axis=-1 are used no NaN NaN no NaN NaN (100,) (1000,) (1000,1000)(1000,1000) nansum 58.3 16.6 2.3 5.1 nanmean 258.7 46.1 3.5 5.1 nanstd 238.4 42.9 2.8 5.0 nanvar 229.9 41.4 2.7 5.0 nanmin 44.6 12.9 0.8 0.9 nanmax 41.8 12.9 0.8 1.8 median 99.6 51.4 1.1 5.7 nanmedian 102.1 26.5 5.0 31.2 ss 27.4 6.4 1.6 1.6 nanargmin 72.6 24.6 2.3 3.4 nanargmax 70.1 29.2 2.4 4.6 anynan 22.1 49.9 0.5 114.6 allnan 43.3 48.4 115.8 66.7 rankdata 50.3 8.0 2.6 6.5 nanrankdata 52.5 8.1 2.9 6.8 partition 4.1 3.6 1.0 2.0 argpartition 2.7 2.2 1.1 1.5 replace 13.7 4.9 1.5 1.5 push 3231.6 7437.4 20.1 19.6 move_sum 4173.5 8955.4 194.7 374.8 move_mean 10265.5 18540.0 222.8 372.2 move_std 8910.9 12158.5 128.7 234.5 move_var 11969.4 18323.8 202.7 378.7 move_min 2164.6 3676.3 23.9 57.2 move_max 1995.0 4206.0 23.8 108.8 move_argmin 3380.5 5559.1 40.5 180.5 move_argmax 3386.5 7278.1 43.0 227.2 move_median 1762.3 1134.9 157.9 118.5 move_rank 1203.6 223.2 2.7 7.8
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.
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License
Bottleneck is distributed under a Simplified BSD license. See the LICENSE file for details.
Install
Requirements:
Bottleneck |
Python 2.7, 3.4, 3.5; NumPy 1.11.2 |
Compile |
gcc, clang, MinGW or MSVC |
Unit tests |
nose |
To install Bottleneck on GNU/Linux, Mac OS X, et al.:
$ sudo python setup.py install
To 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=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:
>>> import bottleneck as bn >>> bn.test() <snip> Ran 169 tests in 57.205s OK <nose.result.TextTestResult run=169 errors=0 failures=0>
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