========== Bottleneck ========== Bottleneck is a collection of fast NumPy array functions written in Cython. Let's give it a try. Create a NumPy array:: >>> import numpy as np >>> arr = np.array([1, 2, np.nan, 4, 5]) Find the nanmean:: >>> import bottleneck as bn >>> bn.nanmean(arr) 3.0 Moving window mean:: >>> bn.move_mean(arr, 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 1.0.0 Numpy (np) 1.9.1 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 36.5 4.0 36.6 9.1 nanmean 144.5 5.2 146.1 9.2 nanstd 253.2 4.3 253.1 8.4 nanvar 241.4 4.2 241.2 8.4 nanmin 30.6 1.1 30.5 1.7 nanmax 32.1 1.1 32.2 2.9 median 43.3 0.8 45.7 0.9 nanmedian 58.7 2.8 67.5 6.8 ss 14.3 3.5 14.4 3.4 nanargmin 60.8 4.1 61.1 7.3 nanargmax 61.4 4.1 61.3 9.0 anynan 12.9 1.0 13.5 89.2 allnan 13.6 98.5 13.5 97.8 rankdata 45.5 1.4 45.9 1.9 nanrankdata 60.7 26.3 54.1 37.9 partsort 6.4 0.9 6.5 1.1 argpartsort 3.3 0.7 3.3 0.5 replace 9.9 1.2 9.9 1.2 move_sum 276.1 121.1 283.2 330.9 move_mean 714.4 95.7 723.7 415.8 move_std 1102.5 56.2 1160.7 749.0 move_min 207.0 20.9 211.0 55.2 move_max 213.8 21.4 218.4 118.6 move_median 457.9 43.4 452.7 208.4 Only arrays with data type (dtype) int32, int64, float32, and float64 are accelerated. All other dtypes result in calls to slower, unaccelerated functions. Where ===== =================== ======================================================== download http://pypi.python.org/pypi/Bottleneck docs http://berkeleyanalytics.com/bottleneck code http://github.com/kwgoodman/bottleneck mailing list http://groups.google.com/group/bottle-neck =================== ======================================================== License ======= Bottleneck is distributed under a Simplified BSD license. See the LICENSE file for details. Install ======= Requirements: ======================== ==================================================== Bottleneck Python 2.7, 3.4; **NumPy 1.9.1** Compile gcc or clang or MinGW Unit tests nose ======================== ==================================================== Optional: ======================== ==================================================== tox, virtualenv Run unit tests across multiple python/numpy versions Cython Development of bottleneck ======================== ==================================================== To install Bottleneck on GNU/Linux, Mac OS X, et al.:: \$ python setup.py build \$ 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 build --compiler=mingw32 python setup.py install 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() Ran 79 tests in 70.712s OK