Function reference¶
Bottleneck provides the following functions:
reduce  nansum , nanmean ,
nanstd , nanvar ,
nanmin , nanmax ,
median , nanmedian ,
ss , nanargmin ,
nanargmax , anynan ,
allnan 
nonreduce  replace 
nonreduce 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 64bit operating systems, 32bit input is NOT upcast to 64bit accumulator and return values.
Parameters: a : array_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: y : ndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d 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: a : array_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: y : ndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d 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 nonNaN 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 onepass algorithm, a more stable twopass algorithm is used.
An example of a onepass algorithm:
>>> np.sqrt((a*a).mean()  a.mean()**2)
An example of a twopass algorithm:
>>> np.sqrt(((a  a.mean())**2).mean())
Note in the twopass algorithm the mean must be found (first pass) before the squared deviation (second pass) can be found.
Parameters: a : array_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.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N  ddof
, whereN
represents the number of nonNaN elements. By default ddof is zero.Returns: y : ndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d 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 nonNaN 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 onepass algorithm, a more stable twopass algorithm is used.
An example of a onepass algorithm:
>>> (a*a).mean()  a.mean()**2
An example of a twopass algorithm:
>>> ((a  a.mean())**2).mean()
Note in the twopass algorithm the mean must be found (first pass) before the squared deviation (second pass) can be found.
Parameters: a : array_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.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N  ddof
, whereN
represents the number of non_NaN elements. By default ddof is zero.Returns: y : ndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d 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 nonNaN 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 allNaN slices are encountered, NaN is returned for that slice.
Parameters: a : array_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: y : ndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d 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 allNaN slices are encountered, NaN is returned for that slice.
Parameters: a : array_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: y : ndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d 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: a : array_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: y : ndarray
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: a : array_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: y : ndarray
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: a : array_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: y : ndarray
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 allNaN slices
ValueError
is raised. Unlike NumPy, the results can be trusted if a slice contains only NaNs and Infs.Parameters: a : array_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_array : ndarray
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 allNaN slices
ValueError
is raised. Unlike NumPy, the results can be trusted if a slice contains only NaNs and Infs.Parameters: a : array_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_array : ndarray
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: a : array_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: y : bool 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: a : array_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: y : bool 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
Nonreduce¶
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] = newOr in the case where old=np.nan:
a[np.isnan(old)] = newParameters: a : numpy.ndarray
The input array, which is also the output array since this functions works inplace.
old : scalar
All elements in a with this value will be replaced by new.
new : scalar
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.])
Nonreduce 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: a : array_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: y : ndarray
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: a : array_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: y : ndarray
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: a : array_like
Input array. If a is not an array, a conversion is attempted.
kth : int
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: y : ndarray
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: a : array_like
Input array. If a is not an array, a conversion is attempted.
kth : int
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: y : ndarray
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 nonmissing values.
Filling proceeds along the specified axis from small index values to large index values.
Parameters: a : array_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 nonNaN 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.
axis : int, 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: y : ndarray
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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 twopass algorithm, move_nanstd uses a onepass 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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N  ddof
, whereN
represents the number of elements. By default ddof is zero.Returns: y : ndarray
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 twopass algorithm, move_nanvar uses a onepass 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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N  ddof
, whereN
represents the number of elements. By default ddof is zero.Returns: y : ndarray
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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 (window1).
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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 (window1).
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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: a : ndarray
Input array. If a is not an array, a conversion is attempted.
window : int
The number of elements in the moving window.
min_count: {int, None}, optional :
If the number of nonNaN 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.
axis : int, 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: y : ndarray
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.])