pydata/bottleneck

Fast NumPy array functions written in C

Language: Python

Keywords: c, c-extension, fast, numpy, python

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 1.3.0.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:

```>>> 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.

Install

Requirements:

 Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8; 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
```

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:

```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`

Project Statistics

 Sourcerank 13 Repository Size 12.6 MB Stars 432 Forks 57 Watchers 18 Open issues 12 Dependencies 1 Contributors 18 Tags 26 Created Nov 27, 2010 Last updated 12 days ago Last pushed 20 days ago

Packages Referencing this Repo

bottleneck
Bottleneck is a collection of fast NumPy array functions written in Cython.
Latest release 1.3.1 - Updated - 432 stars
Bottleneck
Fast NumPy array functions written in C
Latest release 1.3.1 - Updated - 432 stars

Recent Tags See all

 v1.3.1 November 19, 2019 v1.3.0 November 13, 2019 v1.3.0rc2 November 09, 2019 v1.3.0rc1 November 03, 2019 v1.3.0.dev0 August 24, 2019 v1.2.1 May 15, 2017 v1.2.0 October 20, 2016 v1.1.0 June 22, 2016 v1.0.0 February 06, 2015 v0.8.0 January 21, 2014 v0.7.0 September 10, 2013 v0.6.0 June 04, 2012 v0.6rc1 May 23, 2012 v0.5.0 June 13, 2011 v0.5.0rc3 June 10, 2011

Interesting Forks See all

biolab/bottlechest
Fast NumPy array functions written in Cython
C - Other - Last pushed - 1 stars - 4 forks