Bottleneck¶

Fast NumPy array functions written in C.

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.

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; 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

Function reference¶

Bottleneck provides the following functions:

reduce	`nansum`, `nanmean`, `nanstd`, `nanvar`, `nanmin`, `nanmax`, `median`, `nanmedian`, `ss`, `nanargmin`, `nanargmax`, `anynan`, `allnan`
non-reduce	`replace`
non-reduce with axis	`rankdata`, `nanrankdata`, `partition`, `argpartition`, `push`
moving window	`move_sum`, `move_mean`, `move_std`, `move_var`, `move_min`, `move_max`, `move_argmin`, `move_argmax`, `move_median`, `move_rank`

Reduce¶

Functions the reduce the input array along the specified axis.

bottleneck.nansum(a, axis=None)¶

Sum of array elements along given axis treating NaNs as zero.

The data type (dtype) of the output is the same as the input. On 64-bit operating systems, 32-bit input is NOT upcast to 64-bit accumulator and return values.

Parameters

aarray_like: Array containing numbers whose sum is desired. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the sum is computed. The default (axis=None) is to compute the sum of the flattened array.

Returns

yndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned.

Notes

No error is raised on overflow.

If positive or negative infinity are present the result is positive or negative infinity. But if both positive and negative infinity are present, the result is Not A Number (NaN).

Examples

>>> bn.nansum(1)
1
>>> bn.nansum([1])
1
>>> bn.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> bn.nansum(a)
3.0
>>> bn.nansum(a, axis=0)
array([ 2.,  1.])

When positive infinity and negative infinity are present:

>>> bn.nansum([1, np.nan, np.inf])
inf
>>> bn.nansum([1, np.nan, np.NINF])
-inf
>>> bn.nansum([1, np.nan, np.inf, np.NINF])
nan

bottleneck.nanmean(a, axis=None)¶

Mean of array elements along given axis ignoring NaNs.

float64 intermediate and return values are used for integer inputs.

Parameters

aarray_like: Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the means are computed. The default (axis=None) is to compute the mean of the flattened array.

Returns

yndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. float64 intermediate and return values are used for integer inputs.

See also

bottleneck.nanmedian: Median along specified axis, ignoring NaNs.

Notes

No error is raised on overflow. (The sum is computed and then the result is divided by the number of non-NaN elements.)

If positive or negative infinity are present the result is positive or negative infinity. But if both positive and negative infinity are present, the result is Not A Number (NaN).

Examples

>>> bn.nanmean(1)
1.0
>>> bn.nanmean([1])
1.0
>>> bn.nanmean([1, np.nan])
1.0
>>> a = np.array([[1, 4], [1, np.nan]])
>>> bn.nanmean(a)
2.0
>>> bn.nanmean(a, axis=0)
array([ 1.,  4.])

When positive infinity and negative infinity are present:

>>> bn.nanmean([1, np.nan, np.inf])
inf
>>> bn.nanmean([1, np.nan, np.NINF])
-inf
>>> bn.nanmean([1, np.nan, np.inf, np.NINF])
nan

bottleneck.nanstd(a, axis=None, ddof=0)¶

Standard deviation along the specified axis, ignoring NaNs.

float64 intermediate and return values are used for integer inputs.

Instead of a faster one-pass algorithm, a more stable two-pass algorithm is used.

An example of a one-pass algorithm:

>>> np.sqrt((a*a).mean() - a.mean()**2)

An example of a two-pass algorithm:

>>> np.sqrt(((a - a.mean())**2).mean())

Note in the two-pass algorithm the mean must be found (first pass) before the squared deviation (second pass) can be found.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the standard deviation is computed. The default (axis=None) is to compute the standard deviation of the flattened array.
ddofint, optional: Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of non-NaN elements. By default ddof is zero.

Returns

yndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. float64 intermediate and return values are used for integer inputs. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.

See also

bottleneck.nanvar: Variance along specified axis ignoring NaNs

Notes

If positive or negative infinity are present the result is Not A Number (NaN).

Examples

>>> bn.nanstd(1)
0.0
>>> bn.nanstd([1])
0.0
>>> bn.nanstd([1, np.nan])
0.0
>>> a = np.array([[1, 4], [1, np.nan]])
>>> bn.nanstd(a)
1.4142135623730951
>>> bn.nanstd(a, axis=0)
array([ 0.,  0.])

When positive infinity or negative infinity are present NaN is returned:

>>> bn.nanstd([1, np.nan, np.inf])
nan

bottleneck.nanvar(a, axis=None, ddof=0)¶

Variance along the specified axis, ignoring NaNs.

float64 intermediate and return values are used for integer inputs.

Instead of a faster one-pass algorithm, a more stable two-pass algorithm is used.

An example of a one-pass algorithm:

>>> (a*a).mean() - a.mean()**2

An example of a two-pass algorithm:

>>> ((a - a.mean())**2).mean()

Note in the two-pass algorithm the mean must be found (first pass) before the squared deviation (second pass) can be found.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the variance is computed. The default (axis=None) is to compute the variance of the flattened array.
ddofint, optional: Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of non_NaN elements. By default ddof is zero.

Returns

yndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. float64 intermediate and return values are used for integer inputs. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.

See also

bottleneck.nanstd: Standard deviation along specified axis ignoring NaNs.

Notes

If positive or negative infinity are present the result is Not A Number (NaN).

Examples

>>> bn.nanvar(1)
0.0
>>> bn.nanvar([1])
0.0
>>> bn.nanvar([1, np.nan])
0.0
>>> a = np.array([[1, 4], [1, np.nan]])
>>> bn.nanvar(a)
2.0
>>> bn.nanvar(a, axis=0)
array([ 0.,  0.])

When positive infinity or negative infinity are present NaN is returned:

>>> bn.nanvar([1, np.nan, np.inf])
nan

bottleneck.nanmin(a, axis=None)¶

Minimum values along specified axis, ignoring NaNs.

When all-NaN slices are encountered, NaN is returned for that slice.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the minimum is computed. The default (axis=None) is to compute the minimum of the flattened array.

Returns

yndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. The same dtype as a is returned.

See also

bottleneck.nanmax: Maximum along specified axis, ignoring NaNs.
bottleneck.nanargmin: Indices of minimum values along axis, ignoring NaNs.

Examples

>>> bn.nanmin(1)
1
>>> bn.nanmin([1])
1
>>> bn.nanmin([1, np.nan])
1.0
>>> a = np.array([[1, 4], [1, np.nan]])
>>> bn.nanmin(a)
1.0
>>> bn.nanmin(a, axis=0)
array([ 1.,  4.])

bottleneck.nanmax(a, axis=None)¶

Maximum values along specified axis, ignoring NaNs.

When all-NaN slices are encountered, NaN is returned for that slice.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the maximum is computed. The default (axis=None) is to compute the maximum of the flattened array.

Returns

yndarray: An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. The same dtype as a is returned.

See also

bottleneck.nanmin: Minimum along specified axis, ignoring NaNs.
bottleneck.nanargmax: Indices of maximum values along axis, ignoring NaNs.

Examples

>>> bn.nanmax(1)
1
>>> bn.nanmax([1])
1
>>> bn.nanmax([1, np.nan])
1.0
>>> a = np.array([[1, 4], [1, np.nan]])
>>> bn.nanmax(a)
4.0
>>> bn.nanmax(a, axis=0)
array([ 1.,  4.])

bottleneck.median(a, axis=None)¶

Median of array elements along given axis.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the median is computed. The default (axis=None) is to compute the median of the flattened array.

Returns

yndarray: An array with the same shape as a, except that the specified axis has been removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 return values are used for integer inputs. NaN is returned for a slice that contains one or more NaNs.

See also

bottleneck.nanmedian: Median along specified axis ignoring NaNs.

Examples

>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> bn.median(a)
    3.5
>>> bn.median(a, axis=0)
    array([ 6.5,  4.5,  2.5])
>>> bn.median(a, axis=1)
    array([ 7.,  2.])

bottleneck.nanmedian(a, axis=None)¶

Median of array elements along given axis ignoring NaNs.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the median is computed. The default (axis=None) is to compute the median of the flattened array.

Returns

yndarray: An array with the same shape as a, except that the specified axis has been removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 return values are used for integer inputs.

See also

bottleneck.median: Median along specified axis.

Examples

>>> a = np.array([[np.nan, 7, 4], [3, 2, 1]])
>>> a
array([[ nan,   7.,   4.],
       [  3.,   2.,   1.]])
>>> bn.nanmedian(a)
3.0
>> bn.nanmedian(a, axis=0)
array([ 3. ,  4.5,  2.5])
>> bn.nanmedian(a, axis=1)
array([ 5.5,  2. ])

bottleneck.ss(a, axis=None)¶

Sum of the square of each element along the specified axis.

Parameters

aarray_like: Array whose sum of squares is desired. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the sum of squares is computed. The default (axis=None) is to sum the squares of the flattened array.

Returns

yndarray: The sum of a**2 along the given axis.

Examples

>>> a = np.array([1., 2., 5.])
>>> bn.ss(a)
30.0

And calculating along an axis:

>>> b = np.array([[1., 2., 5.], [2., 5., 6.]])
>>> bn.ss(b, axis=1)
array([ 30., 65.])

bottleneck.nanargmin(a, axis=None)¶

Indices of the minimum values along an axis, ignoring NaNs.

For all-NaN slices ValueError is raised. Unlike NumPy, the results can be trusted if a slice contains only NaNs and Infs.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which to operate. By default (axis=None) flattened input is used.

Returns

index_arrayndarray: An array of indices or a single index value.

See also

bottleneck.nanargmax: Indices of the maximum values along an axis.
bottleneck.nanmin: Minimum values along specified axis, ignoring NaNs.

Examples

>>> a = np.array([[np.nan, 4], [2, 3]])
>>> bn.nanargmin(a)
2
>>> a.flat[2]
2.0
>>> bn.nanargmin(a, axis=0)
array([1, 1])
>>> bn.nanargmin(a, axis=1)
array([1, 0])

bottleneck.nanargmax(a, axis=None)¶

Indices of the maximum values along an axis, ignoring NaNs.

For all-NaN slices ValueError is raised. Unlike NumPy, the results can be trusted if a slice contains only NaNs and Infs.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which to operate. By default (axis=None) flattened input is used.

Returns

index_arrayndarray: An array of indices or a single index value.

See also

bottleneck.nanargmin: Indices of the minimum values along an axis.
bottleneck.nanmax: Maximum values along specified axis, ignoring NaNs.

Examples

>>> a = np.array([[np.nan, 4], [2, 3]])
>>> bn.nanargmax(a)
1
>>> a.flat[1]
4.0
>>> bn.nanargmax(a, axis=0)
array([1, 0])
>>> bn.nanargmax(a, axis=1)
array([1, 1])

bottleneck.anynan(a, axis=None)¶

Test whether any array element along a given axis is NaN.

Returns the same output as np.isnan(a).any(axis)

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which NaNs are searched. The default (axis = None) is to search for NaNs over a flattened input array.

Returns

ybool or ndarray: A boolean or new ndarray is returned.

See also

bottleneck.allnan: Test if all array elements along given axis are NaN

Examples

>>> bn.anynan(1)
False
>>> bn.anynan(np.nan)
True
>>> bn.anynan([1, np.nan])
True
>>> a = np.array([[1, 4], [1, np.nan]])
>>> bn.anynan(a)
True
>>> bn.anynan(a, axis=0)
array([False,  True], dtype=bool)

bottleneck.allnan(a, axis=None)¶

Test whether all array elements along a given axis are NaN.

Returns the same output as np.isnan(a).all(axis)

Note that allnan([]) is True to match np.isnan([]).all() and all([])

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which NaNs are searched. The default (axis = None) is to search for NaNs over a flattened input array.

Returns

ybool or ndarray: A boolean or new ndarray is returned.

See also

bottleneck.anynan: Test if any array element along given axis is NaN

Examples

>>> bn.allnan(1)
False
>>> bn.allnan(np.nan)
True
>>> bn.allnan([1, np.nan])
False
>>> a = np.array([[1, np.nan], [1, np.nan]])
>>> bn.allnan(a)
False
>>> bn.allnan(a, axis=0)
array([False,  True], dtype=bool)

An empty array returns True:

>>> bn.allnan([])
True

which is similar to:

>>> all([])
True
>>> np.isnan([]).all()
True

Non-reduce¶

Functions that do not reduce the input array and do not take axis as input.

bottleneck.replace(a, old, new)¶

Replace (inplace) given scalar values of an array with new values.

The equivalent numpy function:

a[a==old] = new

Or in the case where old=np.nan:

a[np.isnan(old)] = new

Parameters

anumpy.ndarray: The input array, which is also the output array since this functions works inplace.
oldscalar: All elements in a with this value will be replaced by new.
newscalar: All elements in a with a value of old will be replaced by new.

Returns

Returns a view of the input array after performing the replacements,
if any.

Examples

Replace zero with 3 (note that the input array is modified):

>>> a = np.array([1, 2, 0])
>>> bn.replace(a, 0, 3)
>>> a
array([1, 2, 3])

Replace np.nan with 0:

>>> a = np.array([1, 2, np.nan])
>>> bn.replace(a, np.nan, 0)
>>> a
array([ 1.,  2.,  0.])

Non-reduce with axis¶

Functions that do not reduce the input array but operate along a specified axis.

bottleneck.rankdata(a, axis=None)¶

Ranks the data, dealing with ties appropriately.

Equal values are assigned a rank that is the average of the ranks that would have been otherwise assigned to all of the values within that set. Ranks begin at 1, not 0.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the elements of the array are ranked. The default (axis=None) is to rank the elements of the flattened array.

Returns

yndarray: An array with the same shape as a. The dtype is ‘float64’.

See also

bottleneck.nanrankdata: Ranks the data dealing with ties and NaNs.

Examples

>>> bn.rankdata([0, 2, 2, 3])
array([ 1. ,  2.5,  2.5,  4. ])
>>> bn.rankdata([[0, 2], [2, 3]])
array([ 1. ,  2.5,  2.5,  4. ])
>>> bn.rankdata([[0, 2], [2, 3]], axis=0)
array([[ 1.,  1.],
       [ 2.,  2.]])
>>> bn.rankdata([[0, 2], [2, 3]], axis=1)
array([[ 1.,  2.],
       [ 1.,  2.]])

bottleneck.nanrankdata(a, axis=None)¶

Ranks the data, dealing with ties and NaNs appropriately.

Equal values are assigned a rank that is the average of the ranks that would have been otherwise assigned to all of the values within that set. Ranks begin at 1, not 0.

NaNs in the input array are returned as NaNs.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
axis{int, None}, optional: Axis along which the elements of the array are ranked. The default (axis=None) is to rank the elements of the flattened array.

Returns

yndarray: An array with the same shape as a. The dtype is ‘float64’.

See also

bottleneck.rankdata: Ranks the data, dealing with ties and appropriately.

Examples

>>> bn.nanrankdata([np.nan, 2, 2, 3])
array([ nan,  1.5,  1.5,  3. ])
>>> bn.nanrankdata([[np.nan, 2], [2, 3]])
array([ nan,  1.5,  1.5,  3. ])
>>> bn.nanrankdata([[np.nan, 2], [2, 3]], axis=0)
array([[ nan,   1.],
       [  1.,   2.]])
>>> bn.nanrankdata([[np.nan, 2], [2, 3]], axis=1)
array([[ nan,   1.],
       [  1.,   2.]])

bottleneck.partition(a, kth, axis=-1)¶

Partition array elements along given axis.

A 1d array B is partitioned at array index kth if three conditions are met: (1) B[kth] is in its sorted position, (2) all elements to the left of kth are less than or equal to B[kth], and (3) all elements to the right of kth are greater than or equal to B[kth]. Note that the array elements in conditions (2) and (3) are in general unordered.

Shuffling the input array may change the output. The only guarantee is given by the three conditions above.

This functions is not protected against NaN. Therefore, you may get unexpected results if the input contains NaN.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
kthint: The value of the element at index kth will be in its sorted position. Smaller (larger) or equal values will be to the left (right) of index kth.
axis{int, None}, optional: Axis along which the partition is performed. The default (axis=-1) is to partition along the last axis.

Returns

yndarray: A partitioned copy of the input array with the same shape and type of a.

See also

bottleneck.argpartition: Indices that would partition an array

Notes

Unexpected results may occur if the input array contains NaN.

Examples

Create a numpy array:

>>> a = np.array([1, 0, 3, 4, 2])

Partition array so that the first 3 elements (indices 0, 1, 2) are the smallest 3 elements (note, as in this example, that the smallest 3 elements may not be sorted):

>>> bn.partition(a, kth=2)
array([1, 0, 2, 4, 3])

Now Partition array so that the last 2 elements are the largest 2 elements:

>>> bn.partition(a, kth=3)
array([1, 0, 2, 3, 4])

bottleneck.argpartition(a, kth, axis=-1)¶

Return indices that would partition array along the given axis.

A 1d array B is partitioned at array index kth if three conditions are met: (1) B[kth] is in its sorted position, (2) all elements to the left of kth are less than or equal to B[kth], and (3) all elements to the right of kth are greater than or equal to B[kth]. Note that the array elements in conditions (2) and (3) are in general unordered.

Shuffling the input array may change the output. The only guarantee is given by the three conditions above.

This functions is not protected against NaN. Therefore, you may get unexpected results if the input contains NaN.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
kthint: The value of the element at index kth will be in its sorted position. Smaller (larger) or equal values will be to the left (right) of index kth.
axis{int, None}, optional: Axis along which the partition is performed. The default (axis=-1) is to partition along the last axis.

Returns

yndarray: An array the same shape as the input array containing the indices that partition a. The dtype of the indices is numpy.intp.

See also

bottleneck.partition: Partition array elements along given axis.

Notes

Unexpected results may occur if the input array contains NaN.

Examples

Create a numpy array:

>>> a = np.array([10, 0, 30, 40, 20])

Find the indices that partition the array so that the first 3 elements are the smallest 3 elements:

>>> index = bn.argpartition(a, kth=2)
>>> index
array([0, 1, 4, 3, 2])

Let’s use the indices to partition the array (note, as in this example, that the smallest 3 elements may not be in order):

>>> a[index]
array([10, 0, 20, 40, 30])

bottleneck.push(a, n=None, axis=-1)¶

Fill missing values (NaNs) with most recent non-missing values.

Filling proceeds along the specified axis from small index values to large index values.

Parameters

aarray_like: Input array. If a is not an array, a conversion is attempted.
n{int, None}, optional: How far to push values. If the most recent non-NaN array element is more than n index positions away, than a NaN is returned. The default (n = None) is to push the entire length of the slice. If n is an integer it must be nonnegative.
axisint, optional: Axis along which the elements of the array are pushed. The default (axis=-1) is to push along the last axis of the input array.

Returns

yndarray: An array with the same shape and dtype as a.

See also

bottleneck.replace: Replace specified value of an array with new value.

Examples

>>> a = np.array([5, np.nan, np.nan, 6, np.nan])
>>> bn.push(a)
    array([ 5.,  5.,  5.,  6.,  6.])
>>> bn.push(a, n=1)
    array([  5.,   5.,  nan,   6.,   6.])
>>> bn.push(a, n=2)
    array([ 5.,  5.,  5.,  6.,  6.])

Moving window functions¶

Functions that operate along a (1d) moving window.

bottleneck.move_sum(a, window, min_count=None, axis=-1)¶

Moving window sum along the specified axis, optionally ignoring NaNs.

This function cannot handle input arrays that contain Inf. When the window contains Inf, the output will correctly be Inf. However, when Inf moves out of the window, the remaining output values in the slice will incorrectly be NaN.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving sum of the input array along the specified axis. The output has the same shape as the input.

Examples

>>> a = np.array([1.0, 2.0, 3.0, np.nan, 5.0])
>>> bn.move_sum(a, window=2)
array([ nan,   3.,   5.,  nan,  nan])
>>> bn.move_sum(a, window=2, min_count=1)
array([ 1.,  3.,  5.,  3.,  5.])

bottleneck.move_mean(a, window, min_count=None, axis=-1)¶

Moving window mean along the specified axis, optionally ignoring NaNs.

This function cannot handle input arrays that contain Inf. When the window contains Inf, the output will correctly be Inf. However, when Inf moves out of the window, the remaining output values in the slice will incorrectly be NaN.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving mean of the input array along the specified axis. The output has the same shape as the input.

Examples

>>> a = np.array([1.0, 2.0, 3.0, np.nan, 5.0])
>>> bn.move_mean(a, window=2)
array([ nan,  1.5,  2.5,  nan,  nan])
>>> bn.move_mean(a, window=2, min_count=1)
array([ 1. ,  1.5,  2.5,  3. ,  5. ])

bottleneck.move_std(a, window, min_count=None, axis=-1, ddof=0)¶

Moving window standard deviation along the specified axis, optionally ignoring NaNs.

This function cannot handle input arrays that contain Inf. When Inf enters the moving window, the outout becomes NaN and will continue to be NaN for the remainer of the slice.

Unlike bn.nanstd, which uses a two-pass algorithm, move_nanstd uses a one-pass algorithm called Welford’s method. The algorithm is slow but numerically stable for cases where the mean is large compared to the standard deviation.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.
ddofint, optional: Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.

Returns

yndarray: The moving standard deviation of the input array along the specified axis. The output has the same shape as the input.

Examples

>>> a = np.array([1.0, 2.0, 3.0, np.nan, 5.0])
>>> bn.move_std(a, window=2)
array([ nan,  0.5,  0.5,  nan,  nan])
>>> bn.move_std(a, window=2, min_count=1)
array([ 0. ,  0.5,  0.5,  0. ,  0. ])

bottleneck.move_var(a, window, min_count=None, axis=-1, ddof=0)¶

Moving window variance along the specified axis, optionally ignoring NaNs.

This function cannot handle input arrays that contain Inf. When Inf enters the moving window, the outout becomes NaN and will continue to be NaN for the remainer of the slice.

Unlike bn.nanvar, which uses a two-pass algorithm, move_nanvar uses a one-pass algorithm called Welford’s method. The algorithm is slow but numerically stable for cases where the mean is large compared to the standard deviation.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.
ddofint, optional: Means Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.

Returns

yndarray: The moving variance of the input array along the specified axis. The output has the same shape as the input.

Examples

>>> a = np.array([1.0, 2.0, 3.0, np.nan, 5.0])
>>> bn.move_var(a, window=2)
array([ nan,  0.25,  0.25,  nan,  nan])
>>> bn.move_var(a, window=2, min_count=1)
array([ 0. ,  0.25,  0.25,  0. ,  0. ])

bottleneck.move_min(a, window, min_count=None, axis=-1)¶

Moving window minimum along the specified axis, optionally ignoring NaNs.

float64 output is returned for all input data types.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving minimum of the input array along the specified axis. The output has the same shape as the input. The dtype of the output is always float64.

Examples

>>> a = np.array([1.0, 2.0, 3.0, np.nan, 5.0])
>>> bn.move_min(a, window=2)
array([ nan,   1.,   2.,  nan,  nan])
>>> bn.move_min(a, window=2, min_count=1)
array([ 1.,  1.,  2.,  3.,  5.])

bottleneck.move_max(a, window, min_count=None, axis=-1)¶

Moving window maximum along the specified axis, optionally ignoring NaNs.

float64 output is returned for all input data types.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving maximum of the input array along the specified axis. The output has the same shape as the input. The dtype of the output is always float64.

Examples

>>> a = np.array([1.0, 2.0, 3.0, np.nan, 5.0])
>>> bn.move_max(a, window=2)
array([ nan,   2.,   3.,  nan,  nan])
>>> bn.move_max(a, window=2, min_count=1)
array([ 1.,  2.,  3.,  3.,  5.])

bottleneck.move_argmin(a, window, min_count=None, axis=-1)¶

Moving window index of minimum along the specified axis, optionally ignoring NaNs.

Index 0 is at the rightmost edge of the window. For example, if the array is monotonically decreasing (increasing) along the specified axis then the output array will contain zeros (window-1).

If there is a tie in input values within a window, then the rightmost index is returned.

float64 output is returned for all input data types.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving index of minimum values of the input array along the specified axis. The output has the same shape as the input. The dtype of the output is always float64.

Examples

>>> a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> bn.move_argmin(a, window=2)
array([ nan,   1.,   1.,   1.,   1.])

>>> a = np.array([5.0, 4.0, 3.0, 2.0, 1.0])
>>> bn.move_argmin(a, window=2)
array([ nan,   0.,   0.,   0.,   0.])

>>> a = np.array([2.0, 3.0, 4.0, 1.0, 7.0, 5.0, 6.0])
>>> bn.move_argmin(a, window=3)
array([ nan,  nan,   2.,   0.,   1.,   2.,   1.])

bottleneck.move_argmax(a, window, min_count=None, axis=-1)¶

Moving window index of maximum along the specified axis, optionally ignoring NaNs.

Index 0 is at the rightmost edge of the window. For example, if the array is monotonically increasing (decreasing) along the specified axis then the output array will contain zeros (window-1).

If there is a tie in input values within a window, then the rightmost index is returned.

float64 output is returned for all input data types.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving index of maximum values of the input array along the specified axis. The output has the same shape as the input. The dtype of the output is always float64.

Examples

>>> a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> bn.move_argmax(a, window=2)
array([ nan,   0.,   0.,   0.,   0.])

>>> a = np.array([5.0, 4.0, 3.0, 2.0, 1.0])
>>> bn.move_argmax(a, window=2)
array([ nan,   1.,   1.,   1.,   1.])

>>> a = np.array([2.0, 3.0, 4.0, 1.0, 7.0, 5.0, 6.0])
>>> bn.move_argmax(a, window=3)
array([ nan,  nan,   0.,   1.,   0.,   1.,   2.])

bottleneck.move_median(a, window, min_count=None, axis=-1)¶

Moving window median along the specified axis, optionally ignoring NaNs.

float64 output is returned for all input data types.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving median of the input array along the specified axis. The output has the same shape as the input.

Examples

>>> a = np.array([1.0, 2.0, 3.0, 4.0])
>>> bn.move_median(a, window=2)
array([ nan,  1.5,  2.5,  3.5])
>>> bn.move_median(a, window=2, min_count=1)
array([ 1. ,  1.5,  2.5,  3.5])

bottleneck.move_rank(a, window, min_count=None, axis=-1)¶

Moving window ranking along the specified axis, optionally ignoring NaNs.

The output is normalized to be between -1 and 1. For example, with a window width of 3 (and with no ties), the possible output values are -1, 0, 1.

Ties are broken by averaging the rankings. See the examples below.

The runtime depends almost linearly on window. The more NaNs there are in the input array, the shorter the runtime.

Parameters

andarray: Input array. If a is not an array, a conversion is attempted.
windowint: The number of elements in the moving window.
min_count: {int, None}, optional: If the number of non-NaN values in a window is less than min_count, then a value of NaN is assigned to the window. By default min_count is None, which is equivalent to setting min_count equal to window.
axisint, optional: The axis over which the window is moved. By default the last axis (axis=-1) is used. An axis of None is not allowed.

Returns

yndarray: The moving ranking along the specified axis. The output has the same shape as the input. For integer input arrays, the dtype of the output is float64.

Examples

With window=3 and no ties, there are 3 possible output values, i.e. [-1., 0., 1.]:

>>> a = np.array([1, 2, 3, 9, 8, 7, 5, 6, 4])
>>> bn.move_rank(a, window=3)
    array([ nan,  nan,   1.,   1.,   0.,  -1.,  -1.,   0.,  -1.])

Ties are broken by averaging the rankings of the tied elements:

>>> a = np.array([1, 2, 3, 3, 3, 4])
>>> bn.move_rank(a, window=3)
    array([ nan,  nan,  1. ,  0.5,  0. ,  1. ])

In an increasing sequence, the moving window ranking is always equal to 1:

>>> a = np.array([1, 2, 3, 4, 5])
>>> bn.move_rank(a, window=2)
    array([ nan,   1.,   1.,   1.,   1.])

Release Notes¶

These are the major changes made in each release. For details of the changes see the commit log at https://github.com/pydata/bottleneck

Bottleneck 1.3.1¶

Release date: 2019-11-18

Bug Fixes¶

Fix memory leak in bottleneck.nanmedian() with the default argument of axis=None. Thanks to @jsmodic for reporting! (#276, #278)
Add regression test for memory leak case (#279)

Contributors¶

A total of 1 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Christopher Whelan

Older Releases¶

Bottleneck 1.3.0¶

Release date: 2019-11-12

Project Updates¶

Bottleneck has a new maintainer, Christopher Whelan (@qwhelan on GitHub).
Documentation now hosted at https://bottleneck.readthedocs.io
1.3.x will be the last release to support Python 2.7
Bottleneck now supports and is tested against Python 3.7 and 3.8. (#211, #268)
The LICENSE file has been restructured to only include the license for the Bottleneck project to aid license audit tools. There has been no change to the licensing of Bottleneck.
- Licenses for other projects incorporated by Bottleneck are now reproduced in full in separate files in the LICENSES/ directory (eg, LICENSES/NUMPY_LICENSE)
- All licenses have been updated. Notably, setuptools is now MIT licensed and no longer under the ambiguous dual PSF/Zope license.
Bottleneck now uses PEP 518 for specifying build dependencies, with per Python version specifications (#247)

Enhancements¶

Remove numpydoc package from Bottleneck source distribution
bottleneck.slow.reduce.nansum() and bottleneck.slow.reduce.ss() now longer coerce output to have the same dtype as input
Test (tox, travis, appveyor) against latest numpy (in conda)
Performance benchmarking also available via asv
versioneer now used for versioning (#213)
Test suite now uses pytest as nose is deprecated (#222)
python setup.py build_ext --inplace is now incremental (#224)
python setup.py clean now cleans all artifacts (#226)
Compiler feature support now identified by testing rather than hardcoding (#227)
The BN_OPT_3 macro allows selective use of -O3 at the function level (#223)
Contributors are now automatically cited in the release notes (#244)

Performance¶

Speed up bottleneck.reduce.anynan() and bottleneck.reduce.allnan() by 2x via BN_OPT_3 (#223)
All functions covered by asv benchmarks
bottleneck.nonreduce.replace() speedup of 4x via more explicit typing (#239)
bottleneck.reduce.median() up to 2x faster for Fortran-ordered arrays (#248)

Bug Fixes¶

Documentation fails to build on Python 3 (#170)
bottleneck.benchmark.bench() crashes on python 3.6.3, numpy 1.13.3 (#175)
bottleneck.nonreduce_axis.push() raises when n=None is explicitly passed (#178)
bottleneck.reduce.nansum() wrong output when a = np.ones((2, 2))[..., np.newaxis] same issue of other reduce functions (#183)
Silenced FutureWarning from NumPy in the slow version of move functions (#194)
Installing bottleneck onto a system that does not already have Numpy (#195)
Memory leaked when input was not a NumPy array (#201)
Tautological comparison in bottleneck.move.move_rank() removed (#207, #212)

Cleanup¶

The ez_setup.py module is no longer packaged (#211)
Building documentation is now self-contained in make doc (#214)
Codebase now flake8 compliant and run on every commit
Codebase now uses black for autoformatting (#253)

Contributors¶

A total of 9 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Ales Erjavec +
Christoph Gohlke
Christopher Whelan +
Daniel Hakimi +
Ghislain Antony Vaillant +
Keith Goodman
Stephan Hoyer
Thomas Robitaille +
kwgoodman

Bottleneck 1.2.1¶

Release date: 2017-05-15

This release adds support for NumPy’s relaxed strides checking and fixes a few bugs.

Bug Fixes

Installing bottleneck when two versions of NumPy are present (#156)
Compiling on Ubuntu 14.04 inside a Windows 7 WMware (#157)
Occasional segmentation fault in bn.nanargmin(), nanargmax(), median(), and nanmedian() when all of the following conditions are met: axis is None, input array is 2d or greater, and input array is not C contiguous. (#159)
Reducing np.array([2**31], dtype=np.int64) overflows on Windows (#163)

Contributors

A total of 1 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Keith Goodman

Bottleneck 1.2.0¶

Release date: 2016-10-20

This release is a complete rewrite of Bottleneck.

Port to C

Bottleneck is now written in C
Cython is no longer a dependency
Source tarball size reduced by 80%
Build time reduced by 66%
Install size reduced by 45%

Redesign

Besides porting to C, much of bottleneck has been redesigned to be simpler and faster. For example, bottleneck now uses its own N-dimensional array iterators, reducing function call overhead.

New features

The new function bench_detailed runs a detailed performance benchmark on a single bottleneck function.
Bottleneck can be installed on systems that do not yet have NumPy installed. Previously that only worked on some systems.

Beware

Functions partsort and argpartsort have been renamed to partition and argpartition to match NumPy. Additionally the meaning of the input arguments have changed: bn.partsort(a, n)() is now equivalent to bn.partition(a, kth=n-1)(). Similarly for bn.argpartition.
The keyword for array input has been changed from arr to a in all functions. It now matches NumPy.

Thanks

Moritz E. Beber: continuous integration with AppVeyor
Christoph Gohlke: Windows compatibility
Jennifer Olsen: comments and suggestions
A special thanks to the Cython developers. The quickest way to appreciate their work is to remove Cython from your project. It is not easy.

Contributors

A total of 3 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Keith Goodman
Moritz E. Beber +
kwgoodman

Bottleneck 1.1.0¶

Release date: 2016-06-22

This release makes Bottleneck more robust, releases GIL, adds new functions.

More Robust

bn.move_median() can now handle NaNs and min_count parameter
bn.move_std() is slower but numerically more stable
Bottleneck no longer crashes on byte-swapped input arrays

Faster

All Bottleneck functions release the GIL
median is faster if the input array contains NaN
move_median is faster for input arrays that contain lots of NaNs
No speed penalty for median, nanmedian, nanargmin, nanargmax for Fortran ordered input arrays when axis is None
Function call overhead cut in half for reduction along all axes (axis=None) if the input array satisfies at least one of the following properties: 1d, C contiguous, F contiguous
Reduction along all axes (axis=None) is more than twice as fast for long, narrow input arrays such as a (1000000, 2) C contiguous array and a (2, 1000000) F contiguous array

New Functions

move_var
move_argmin
move_argmax
move_rank
push

Beware

bn.median() now returns NaN for a slice that contains one or more NaNs
Instead of using the distutils default, the ‘-O2’ C compiler flag is forced
bn.move_std() output changed when mean is large compared to standard deviation
Fixed: Non-accelerated moving window functions used min_count incorrectly
bn.move_median() is a bit slower for float input arrays that do not contain NaN

Thanks

Alphabeticaly by last name

Alessandro Amici worked on setup.py
Pietro Battiston modernized bottleneck installation
Moritz E. Beber set up continuous integration with Travis CI
Jaime Frio improved the numerical stability of move_std
Christoph Gohlke revived Windows compatibility
Jennifer Olsen added NaN support to move_median

Contributors

A total of 10 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Alessandro Amici +
Christoph Gohlke
Jaime Fernandez +
Jenn Olsen +
Keith Goodman
Midnighter +
Pietro Battiston +
jaimefrio +
jennolsen84 +
kwgoodman

Bottleneck 1.0.0¶

Release date: 2015-02-06

This release is a complete rewrite of Bottleneck.

Faster

“python setup.py build” is 18.7 times faster
Function-call overhead cut in half—a big speed up for small input arrays
Arbitrary ndim input arrays accelerated; previously only 1d, 2d, and 3d
bn.nanrankdata is twice as fast for float input arrays
bn.move_max, bn.move_min are faster for int input arrays
No speed penalty for reducing along all axes when input is Fortran ordered

Smaller

Compiled binaries 14.1 times smaller
Source tarball 4.7 times smaller
9.8 times less C code
4.3 times less Cython code
3.7 times less Python code

Beware

Requires numpy 1.9.1
Single API, e.g.: bn.nansum instead of bn.nansum and nansum_2d_float64_axis0
On 64-bit systems bn.nansum(int32) returns int32 instead of int64
bn.nansum now returns 0 for all NaN slices (as does numpy 1.9.1)
Reducing over all axes returns, e.g., 6.0; previously np.float64(6.0)
bn.ss() now has default axis=None instead of axis=0
bn.nn() is no longer in bottleneck

min_count

Previous releases had moving window function pairs: move_sum, move_nansum
This release only has half of the pairs: move_sum
Instead a new input parameter, min_count, has been added
min_count=None same as old move_sum; min_count=1 same as old move_nansum
If # non-NaN values in window < min_count, then NaN assigned to the window
Exception: move_median does not take min_count as input

Bug Fixes

Can now install bottleneck with pip even if numpy is not already installed
bn.move_max, bn.move_min now return float32 for float32 input

Contributors

A total of 4 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Keith Goodman
Lev Givon +
Stephan Hoyer
kwgoodman

Bottleneck 0.8.0¶

Release date: 2014-01-21

This version of Bottleneck requires NumPy 1.8.

Breaks from 0.7.0

This version of Bottleneck requires NumPy 1.8
nanargmin and nanargmax behave like the corresponding functions in NumPy 1.8

Bug fixes

nanargmax/nanargmin wrong for redundant max/min values in 1d int arrays

Contributors

A total of 4 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Christoph Gohlke +
Keith Goodman
Stephan Hoyer +
kwgoodman

Bottleneck 0.7.0¶

Release date: 2013-09-10

Enhancements

bn.rankdata() is twice as fast (with input a = np.random.rand(1000000))
C files now included in github repo; cython not needed to try latest
C files are now generated with Cython 0.19.1 instead of 0.16
Test bottleneck across multiple python/numpy versions using tox
Source tarball size cut in half

Bug fixes

move_std, move_nanstd return inappropriate NaNs (sqrt of negative #) (#50)
make test fails on some computers (#52)
scipy optional yet some unit tests depend on scipy (#57)
now works on Mac OS X 10.8 using clang compiler (#49, #55)
nanstd([1.0], ddof=1) and nanvar([1.0], ddof=1) crash (#60)

Contributors

A total of 5 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Jens Hedegaard Nielsen +
John Benediktsson +
Keith Goodman
jmcloughlin +
kwgoodman

Bottleneck 0.6.0¶

Release date: 2012-06-04

Thanks to Dougal Sutherland, Bottleneck now runs on Python 3.2.

New functions

replace(arr, old, new), e.g, replace(arr, np.nan, 0)
nn(arr, arr0, axis) nearest neighbor and its index of 1d arr0 in 2d arr
anynan(arr, axis) faster alternative to np.isnan(arr).any(axis)
allnan(arr, axis) faster alternative to np.isnan(arr).all(axis)

Enhancements

Python 3.2 support (may work on earlier versions of Python 3)
C files are now generated with Cython 0.16 instead of 0.14.1
Upgrade numpydoc from 0.3.1 to 0.4 to support Sphinx 1.0.1

Breaks from 0.5.0

Support for Python 2.5 dropped
Default axis for benchmark suite is now axis=1 (was 0)

Bug fixes

Confusing error message in partsort and argpartsort (#31)
Update path in MANIFEST.in (#32)
Wrong output for very large (2**31) input arrays (#35)

Contributors

A total of 4 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

Ben Root +
Dougal Sutherland +
Keith Goodman
kwgoodman +

Bottleneck 0.5.0¶

Release date: 2011-06-13

The fifth release of bottleneck adds four new functions, comes in a single source distribution instead of separate 32 and 64 bit versions, and contains bug fixes.

J. David Lee wrote the C-code implementation of the double heap moving window median.

New functions

move_median(), moving window median
partsort(), partial sort
argpartsort()
ss(), sum of squares, faster version of scipy.stats.ss

Changes

Single source distribution instead of separate 32 and 64 bit versions
nanmax and nanmin now follow Numpy 1.6 (not 1.5.1) when input is all NaN

Bug fixes

Support python 2.5 by importing with statement (#14)
nanmedian wrong for particular ordering of NaN and non-NaN elements (#22)
argpartsort, nanargmin, nanargmax returned wrong dtype on 64-bit Windows (#26)
rankdata and nanrankdata crashed on 64-bit Windows (#29)

Bottleneck 0.4.3¶

Release date: 2011-03-17

This is a bug fix release.

Bug fixes

median and nanmedian modified (partial sort) input array (#11)
nanmedian wrong when odd number of elements with all but last a NaN (#12)

Enhancement

Lazy import of SciPy (rarely used) speeds Bottleneck import 3x

Bottleneck 0.4.2¶

Release date: 2011-03-08

This is a bug fix release.

Same bug fixed in Bottleneck 0.4.1 for nanstd() was fixed for nanvar() in this release. Thanks again to Christoph Gohlke for finding the bug.

Bottleneck 0.4.1¶

Release date: 2011-03-08

This is a bug fix release.

The low-level functions nanstd_3d_int32_axis1 and nanstd_3d_int64_axis1, called by bottleneck.nanstd(), wrote beyond the memory owned by the output array if arr.shape[1] == 0 and arr.shape[0] > arr.shape[2], where arr is the input array.

Thanks to Christoph Gohlke for finding an example to demonstrate the bug.

Bottleneck 0.4.0¶

Release date: 2011-03-08

The fourth release of Bottleneck contains new functions and bug fixes. Separate source code distributions are now made for 32 bit and 64 bit operating systems.

New functions

rankdata()
nanrankdata()

Enhancements

Optionally specify the shapes of the arrays used in benchmark
Can specify which input arrays to fill with one-third NaNs in benchmark

Breaks from 0.3.0

Removed group_nanmean() function
Bump dependency from NumPy 1.4.1 to NumPy 1.5.1
C files are now generated with Cython 0.14.1 instead of 0.13

Bug fixes

Some functions gave wrong output dtype for some input dtypes on 32 bit OS (#6)
Some functions choked on size zero input arrays (#7)
Segmentation fault with Cython 0.14.1 (but not 0.13) (#8)

Bottleneck 0.3.0¶

Release date: 2010-01-19

The third release of Bottleneck is twice as fast for small input arrays and contains 10 new functions.

Faster

All functions are faster (less overhead in selector functions)

New functions

nansum()
move_sum()
move_nansum()
move_mean()
move_std()
move_nanstd()
move_min()
move_nanmin()
move_max()
move_nanmax()

Enhancements

You can now specify the dtype and axis to use in the benchmark timings
Improved documentation and more unit tests

Breaks from 0.2.0

Moving window functions now default to axis=-1 instead of axis=0
Low-level moving window selector functions no longer take window as input

Bug fix

int input array resulted in call to slow, non-cython version of move_nanmean

Bottleneck 0.2.0¶

Release date: 2010-12-27

The second release of Bottleneck is faster, contains more functions, and supports more dtypes.

Faster

All functions faster (less overhead) when output is not a scalar
Faster nanmean() for 2d, 3d arrays containing NaNs when axis is not None

New functions

nanargmin()
nanargmax()
nanmedian()

Enhancements

Added support for float32
Fallback to slower, non-Cython functions for unaccelerated ndim/dtype
Scipy is no longer a dependency
Added support for older versions of NumPy (1.4.1)
All functions are now templated for dtype and axis
Added a sandbox for prototyping of new Bottleneck functions
Rewrote benchmarking code

Bottleneck 0.1.0¶

Release date: 2010-12-10

Initial release. The three categories of Bottleneck functions:

Faster replacement for NumPy and SciPy functions
Moving window functions
Group functions that bin calculations by like-labeled elements

Licenses¶

Bottleneck is distributed under a Simplified BSD license. Parts of NumPy and SciPy, which have BSD licenses, are included in Bottleneck. The setuptools project has a MIT license and is used for configuration and installation.

Bottleneck License¶

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Other licenses¶

NumPy License¶

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

SciPy License¶

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Setuptools License¶

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Bottleneck¶

Bottleneck¶

Benchmark¶

Where¶

License¶

Install¶

Unit tests¶

Function reference¶

Reduce¶

Non-reduce¶

Non-reduce with axis¶

Moving window functions¶

Release Notes¶

Bottleneck 1.3.1¶

Bug Fixes¶

Contributors¶

Older Releases¶

Bottleneck 1.3.0¶

Project Updates¶

Enhancements¶

Performance¶

Bug Fixes¶

Cleanup¶

Contributors¶

Bottleneck 1.2.1¶

Bottleneck 1.2.0¶

Bottleneck 1.1.0¶

Bottleneck 1.0.0¶

Bottleneck 0.8.0¶

Bottleneck 0.7.0¶

Bottleneck 0.6.0¶

Bottleneck 0.5.0¶

Bottleneck 0.4.3¶

Bottleneck 0.4.2¶

Bottleneck 0.4.1¶

Bottleneck 0.4.0¶

Bottleneck 0.3.0¶

Bottleneck 0.2.0¶

Bottleneck 0.1.0¶

Licenses¶

Bottleneck License¶

Other licenses¶

NumPy License¶

SciPy License¶

Setuptools License¶

Indices and tables¶