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take(a, indices, axis=None, out=None, mode='raise')

When axis is not None, this function does the same thing as "fancy" indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. A call such as np.take(arr, indices, axis=3) is equivalent to arr[:,:,:,indices,...] .

Explained without fancy indexing, this is equivalent to the following use of ndindex , which sets each of ii , jj , and kk to a tuple of indices:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
Nj = indices.shape
for ii in ndindex(Ni):
    for jj in ndindex(Nj):
        for kk in ndindex(Nk):
            out[ii + jj + kk] = a[ii + (indices[jj],) + kk]

Notes

By eliminating the inner loop in the description above, and using :None:None:`s_` to build simple slice objects, take can be expressed in terms of applying fancy indexing to each 1-d slice:

Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
    for kk in ndindex(Nj):
        out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]

For this reason, it is equivalent to (but faster than) the following use of apply_along_axis :

out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)

Parameters

a : array_like (Ni..., M, Nk...)

The source array.

indices : array_like (Nj...)

The indices of the values to extract.

versionadded

Also allow scalars for indices.

axis : int, optional

The axis over which to select values. By default, the flattened input array is used.

out : ndarray, optional (Ni..., Nj..., Nk...)

If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. Note that :None:None:`out` is always buffered if :None:None:`mode='raise'`; use other modes for better performance.

mode : {'raise', 'wrap', 'clip'}, optional

Specifies how out-of-bounds indices will behave.

'raise' -- raise an error (default)
'wrap' -- wrap around
'clip' -- clip to the range

'clip' mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers.

Returns

out : ndarray (Ni..., Nj..., Nk...): The returned array has the same type as a.

Take elements from an array along an axis.

Examples

>>> a = [4, 3, 5, 7, 6, 8]
... indices = [0, 1, 4]
... np.take(a, indices)
array([4, 3, 6])

In this example if a is an ndarray, "fancy" indexing can be used.

>>> a = np.array(a)
... a[indices]
array([4, 3, 6])

If indices is not one dimensional, the output also has these dimensions.

>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
       [5, 7]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.take_along_axis numpy.take numpy.extract pandas.core.generic.NDFrame.take scipy.signal._peak_finding.argrelmax numpy.compress numpy.place dask.array.routines.take numpy.select pandas.core.series.Series.take pandas.core.arrays.string_arrow.ArrowStringArray.take numpy.core._multiarray_umath.putmask pandas.core.algorithms.take numpy.putmask scipy.signal._peak_finding.argrelextrema

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