Bottleneck

Pre-release

This release is a pre-release and may not be stable for production use.

Bottleneck

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 1.6.0.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:

>>> 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.:

$ pip install .

To install bottleneck on Windows, first install MinGW and add it to your system path. Then install Bottleneck with the command:

$ python setup.py install --compiler=mingw32

Unit tests

After you have installed Bottleneck, run the suite of unit tests:

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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SHA256	`97d8eba46c24bac66164069a51430fea132d5744c2b204771b2949a1fc676972`
MD5	`52a91ef41df890a5d86122ba0d8a94a2`
BLAKE2b-256	`0b7ccb2e9c326868e6d93f42323b13e5b47d37bd286538b9b9d65c6954410e42`

Algorithm	Hash digest
SHA256	`d53be082362a1c8b0acf2fe0359fd7593d8c4a6ac845c5fc0e82dfd4a1566cb1`
MD5	`b6b2335135d595b5e9c39ebf69508db4`
BLAKE2b-256	`bef59779fd11a81e6af5adda65032d5be9cb7e0d23809fbf4d937d73b02ebb86`

Algorithm	Hash digest
SHA256	`4c35c7a923f8439e25d26b15c625fc1b40766dd2086ac4a5ca02b41ea03e1f18`
MD5	`dd8cafe95caddad534a337373983890b`
BLAKE2b-256	`d62871f9e3a13263e14469bd8c9766dcaefacf67f21020ee49d66e4ca4c58f0d`

Algorithm	Hash digest
SHA256	`5a700112625ac1722c65d95e2d4f8b1e9b8e020ddfadf4127fcbc40a052df0da`
MD5	`9f9ac95620088bc7d5ca511286760098`
BLAKE2b-256	`a04235c6408f5b6843e1b8ff664af3a191fdb113e6f65eaf4f5f7e122e6e9e2e`

Algorithm	Hash digest
SHA256	`109b73531b0071f375b3ff7440e28933403c80870e0133ba6b88f34b5ad6a2a7`
MD5	`99a234ef4afa9bc66f4259c4019e191c`
BLAKE2b-256	`4bd749540d29392e6a9b0f4d7162d385b33db74e5a7f4aa91841fd52fc9e3bd1`

Bottleneck 1.6.1rc2

Bottleneck

Benchmark

Where

License

Install

Installing from source

Unit tests

Project links

Key dates

5 maintainers

Credits

GitHub Statistics

License expression

Requires

Classifiers

Download files

Source Distribution

Built Distributions

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Algorithm	Hash digest
SHA256	`d6b64c08c21cf6eaede0a04b273de98cc877d8e597ba0e5d024985b179b5a2c6`
MD5	`2e2d024f64c9d708450fa60913b4d0a2`
BLAKE2b-256	`fab8866aa92f216c0af30f05087cff023ddc0f731fdf5972040ef5713c328d54`

Algorithm	Hash digest
SHA256	`2649262ca2f33cf311660cfe10f6998c1dbb99acc5a0843a654c9ab8051c1aef`
MD5	`587c98b1c56ed8405e64aa09f92f6062`
BLAKE2b-256	`ad3c0b1d88f17ef247d7a1446b5dc8f77166e688c2c72b35b82fe3cfc35b5728`

Algorithm	Hash digest
SHA256	`eb44896f66b0d3a68c4379814800b8e8294ef3cd57d4ed2dfefec71f62af0df1`
MD5	`70172a3931537494f532e92f9dc1fc4d`
BLAKE2b-256	`d3e50a1ff896cbfdbf67169d88416ed00a0976dae577412b8bdc6fcd69b3aa40`

Algorithm	Hash digest
SHA256	`48311a3872a59779a643f22f324a1e469b4eac8f09205ea7415ecb43da117700`
MD5	`529a90f52a9e4f205ae3e5e2491ac676`
BLAKE2b-256	`84593ed772dc831aee04dea7ad970ebe3871010c0f5637acf0874a784fa1b34f`

Algorithm	Hash digest
SHA256	`a6f6df70bdf4c0b76527157530cdb03c1172cc3d9979d854b2b066a13412956d`
MD5	`72e8af76d73de053e8d14ed88d35abe9`
BLAKE2b-256	`85164ec9c790215333e5c697a44f190d6685c5334f84f2c2306e625a2b47e2a0`

Algorithm	Hash digest
SHA256	`ff12f8a9629a29a9c6bc4e5e2d975230d44da7b60abd5112d48c3054246ba142`
MD5	`19ff926d483f2bd7b9fcf1551fe59f9e`
BLAKE2b-256	`f98791fad8c2dfc350fade17b03a8e11a046261c531ba193d61d2571096b2201`

Algorithm	Hash digest
SHA256	`528f4d2379626c337011a95ed97c7151dac719f4c69a0cf76d975e04af20fc35`
MD5	`321ce859f1480f327938b64540010539`
BLAKE2b-256	`8727ef0bcb48ddac7b4a34dccebc3af311024e9389ae358b57a0de7734b0b184`

Algorithm	Hash digest
SHA256	`f62b17d2dbf30036043cbf1dc241d2b02d917b5b3565131b72d107acf60f2159`
MD5	`f8e5e88946ea690d14deecf586eb931b`
BLAKE2b-256	`4e123c4e45dc4a7f387edcf41a61bda8dba9f679d498e7846fe729176bfc384e`

Algorithm	Hash digest
SHA256	`6246d90b27f3efeb414004afefc5bf30746cf4f1747b79117f062c92361fa8ce`
MD5	`5b3ce3bba7d2cc8996d7970e30db9688`
BLAKE2b-256	`da4166f08832d4da23ccacfc79bb5ce8070002b902cb4e2915b6aceb5e277440`

Algorithm	Hash digest
SHA256	`194cae52cb7eeefaedfb8eddffbcf8b0ca0370301767e9cbaee7cb58bf94e8f0`
MD5	`63d2c2e119e0854587718cc07c923eb5`
BLAKE2b-256	`bb902c7dbc2a94ac735e87d4b8d9f4dfb8b2eefe552ce10e77bf5310cf64e3e1`