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 1.3.0.dev0+122.gb1615d7; Numpy 1.16.4
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 29.7 1.4 1.6 2.0 2.1
nanmean 99.0 2.0 1.8 3.2 2.5
nanstd 145.6 1.8 1.8 2.7 2.5
nanvar 138.4 1.8 1.8 2.8 2.5
nanmin 27.6 0.5 1.7 0.7 2.4
nanmax 26.6 0.6 1.6 0.7 2.5
median 120.6 1.3 4.9 1.1 5.7
nanmedian 117.8 5.0 5.7 4.8 5.5
ss 13.2 1.2 1.3 1.5 1.5
nanargmin 66.8 5.5 4.8 3.5 7.1
nanargmax 57.6 2.9 5.1 2.5 5.3
anynan 10.2 0.3 52.3 0.8 41.6
allnan 15.1 196.0 156.3 135.8 111.2
rankdata 45.9 1.2 1.2 2.1 2.1
nanrankdata 50.5 1.4 1.3 2.4 2.3
partition 3.3 1.1 1.6 1.0 1.5
argpartition 3.4 1.2 1.5 1.1 1.6
replace 9.0 1.5 1.5 1.5 1.5
push 1565.6 5.9 7.0 13.0 10.9
move_sum 2159.3 31.1 83.6 186.9 182.5
move_mean 6264.3 66.2 111.9 361.1 246.5
move_std 8653.6 86.5 163.7 232.0 317.7
move_var 8856.0 96.3 171.6 267.9 332.9
move_min 1186.6 13.4 30.9 23.5 45.0
move_max 1188.0 14.6 29.9 23.5 46.0
move_argmin 2568.3 33.3 61.0 49.2 86.8
move_argmax 2475.8 30.9 58.6 45.0 82.8
move_median 2236.9 153.9 151.4 171.3 166.9
move_rank 847.1 1.2 1.4 2.3 2.6
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
=======
Requirements:
======================== ============================================================================
Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11; 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
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:
.. 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``
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"description": "Bottleneck is a collection of fast NumPy array functions written in C.\n\nLet's give it a try. Create a NumPy array:\n\n.. code-block:: pycon\n\n >>> import numpy as np\n >>> a = np.array([1, 2, np.nan, 4, 5])\n\nFind the nanmean:\n\n.. code-block:: pycon\n\n >>> import bottleneck as bn\n >>> bn.nanmean(a)\n 3.0\n\nMoving window mean:\n\n.. code-block:: pycon\n\n >>> bn.move_mean(a, window=2, min_count=1)\n array([ 1. , 1.5, 2. , 4. , 4.5])\n\nBenchmark\n=========\n\nBottleneck comes with a benchmark suite:\n\n.. code-block:: pycon\n\n >>> bn.bench()\n Bottleneck performance benchmark\n Bottleneck 1.3.0.dev0+122.gb1615d7; Numpy 1.16.4\n Speed is NumPy time divided by Bottleneck time\n NaN means approx one-fifth NaNs; float64 used\n\n no NaN no NaN NaN no NaN NaN\n (100,) (1000,1000)(1000,1000)(1000,1000)(1000,1000)\n axis=0 axis=0 axis=0 axis=1 axis=1\n nansum 29.7 1.4 1.6 2.0 2.1\n nanmean 99.0 2.0 1.8 3.2 2.5\n nanstd 145.6 1.8 1.8 2.7 2.5\n nanvar 138.4 1.8 1.8 2.8 2.5\n nanmin 27.6 0.5 1.7 0.7 2.4\n nanmax 26.6 0.6 1.6 0.7 2.5\n median 120.6 1.3 4.9 1.1 5.7\n nanmedian 117.8 5.0 5.7 4.8 5.5\n ss 13.2 1.2 1.3 1.5 1.5\n nanargmin 66.8 5.5 4.8 3.5 7.1\n nanargmax 57.6 2.9 5.1 2.5 5.3\n anynan 10.2 0.3 52.3 0.8 41.6\n allnan 15.1 196.0 156.3 135.8 111.2\n rankdata 45.9 1.2 1.2 2.1 2.1\n nanrankdata 50.5 1.4 1.3 2.4 2.3\n partition 3.3 1.1 1.6 1.0 1.5\n argpartition 3.4 1.2 1.5 1.1 1.6\n replace 9.0 1.5 1.5 1.5 1.5\n push 1565.6 5.9 7.0 13.0 10.9\n move_sum 2159.3 31.1 83.6 186.9 182.5\n move_mean 6264.3 66.2 111.9 361.1 246.5\n move_std 8653.6 86.5 163.7 232.0 317.7\n move_var 8856.0 96.3 171.6 267.9 332.9\n move_min 1186.6 13.4 30.9 23.5 45.0\n move_max 1188.0 14.6 29.9 23.5 46.0\n move_argmin 2568.3 33.3 61.0 49.2 86.8\n move_argmax 2475.8 30.9 58.6 45.0 82.8\n move_median 2236.9 153.9 151.4 171.3 166.9\n move_rank 847.1 1.2 1.4 2.3 2.6\n\nYou can also run a detailed benchmark for a single function using, for\nexample, the command:\n\n.. code-block:: pycon\n\n >>> bn.bench_detailed(\"move_median\", fraction_nan=0.3)\n\nOnly arrays with data type (dtype) int32, int64, float32, and float64 are\naccelerated. All other dtypes result in calls to slower, unaccelerated\nfunctions. In the rare case of a byte-swapped input array (e.g. a big-endian\narray on a little-endian operating system) the function will not be\naccelerated regardless of dtype.\n\nWhere\n=====\n\n=================== ========================================================\n download https://pypi.python.org/pypi/Bottleneck\n docs https://bottleneck.readthedocs.io\n code https://github.com/pydata/bottleneck\n mailing list https://groups.google.com/group/bottle-neck\n=================== ========================================================\n\nLicense\n=======\n\nBottleneck is distributed under a Simplified BSD license. See the LICENSE file\nand LICENSES directory for details.\n\nInstall\n=======\n\nRequirements:\n\n======================== ============================================================================\nBottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11; NumPy 1.16.0+\nCompile gcc, clang, MinGW or MSVC\nUnit tests pytest\nDocumentation sphinx, numpydoc\n======================== ============================================================================\n\nTo install Bottleneck on Linux, Mac OS X, et al.:\n\n.. code-block:: console\n\n $ pip install .\n\nTo install bottleneck on Windows, first install MinGW and add it to your\nsystem path. Then install Bottleneck with the command:\n\n.. code-block:: console\n\n $ python setup.py install --compiler=mingw32\n\nAlternatively, you can use the Windows binaries created by Christoph Gohlke:\nhttp://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck\n\nUnit tests\n==========\n\nAfter you have installed Bottleneck, run the suite of unit tests:\n\n.. code-block:: pycon\n\n In [1]: import bottleneck as bn\n\n In [2]: bn.test()\n ============================= test session starts =============================\n platform linux -- Python 3.7.4, pytest-4.3.1, py-1.8.0, pluggy-0.12.0\n hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/home/chris/code/bottleneck/.hypothesis/examples')\n rootdir: /home/chris/code/bottleneck, inifile: setup.cfg\n 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\n collected 190 items\n \n bottleneck/tests/input_modification_test.py ........................... 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