bottleneck.nonreduce_axis module¶
Module contents¶
Bottleneck nonreducing functions that operate along an axis.

bottleneck.nonreduce_axis.
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.nonreduce_axis.
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.nonreduce_axis.
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.nonreduce_axis.
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
 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 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.
 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.])

bottleneck.nonreduce_axis.
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.]])