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ðÿÿoœùÿÿo¸}6FVfv†–¦¶ÆÖæö&6FVfvpush(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
----------
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 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.
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.])

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.]])

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.]])

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])

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 non-reducing functions that operate along an axis.
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bottleneck/src/tmp/pip-build-env-bj9ib4j0/overlay/lib/python3.10/site-packages/numpy/core/include/numpy/opt/python/cp310-cp310/include/python3.10/opt/rh/devtoolset-10/root/usr/lib/gcc/x86_64-redhat-linux/10/include/usr/include/bits/usr/include/usr/include/sys/opt/python/cp310-cp310/include/python3.10/cpythonnonreduce_axis_template.c
ndarraytypes.hiterators.h
bottleneck.h
object.h__multiarray_api.hstddef.htypes.hstdio.hlibio.hunistd.hstdint.hpyport.htime.hobject.hmethodobject.hdescrobject.htupleobject.hmoduleobject.hpycapsule.hpyframe.hpystate.hpystate.hpyerrors.hnpy_common.hunicodeobject.hstdlib.habstract.himport.hdictobject.hceval.hmodsupport.hpythonrun.h	@ø

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