.. image:: https://github.com/pydata/bottleneck/actions/workflows/ci.yml/badge.svg?branch=master :target: https://github.com/pydata/bottleneck/actions/workflows/ci.yml ========== Bottleneck ========== Bottleneck is a collection of fast NumPy array functions written in C. Let's give it a try. Create a NumPy array: .. code-block:: pycon >>> import numpy as np >>> a = np.array([1, 2, np.nan, 4, 5]) Find the nanmean: .. code-block:: pycon >>> import bottleneck as bn >>> bn.nanmean(a) 3.0 Moving window mean: .. code-block:: pycon >>> bn.move_mean(a, window=2, min_count=1) array([ 1. , 1.5, 2. , 4. , 4.5]) Benchmark ========= Bottleneck comes with a benchmark suite: .. code-block:: pycon >>> 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 2.1 You can also run a detailed benchmark for a single function using, for example, the command: .. code-block:: pycon >>> 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 ===== =================== ======================================================== download https://pypi.python.org/pypi/Bottleneck docs https://bottleneck.readthedocs.io code https://github.com/pydata/bottleneck mailing list https://groups.google.com/group/bottle-neck =================== ======================================================== 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.: .. code-block:: console $ pip install . To install bottleneck on Windows, first install MinGW and add it to your system path. Then install Bottleneck with the command: .. code-block:: console $ python setup.py install --compiler=mingw32 Unit tests ========== After you have installed Bottleneck, run the suite of unit tests: .. code-block:: pycon 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]: True If developing in the git repo, simply run ``py.test``