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# numpy.take

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

  Take elements from an array along an axis.

  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]

  Parameters:
    a array_like (Ni…, M, Nk…)

      The source array.

    indices array_like (Nj…)

      The indices of the values to extract. 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 out is always buffered if 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.

  See also

  `compress`

    Take elements using a boolean mask

  `ndarray.take`

    equivalent method

  `take_along_axis`

    Take elements by matching the array and the index arrays

  Notes

  By eliminating the inner loop in the description above, and using `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(Nk):
              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)

  Examples

      >>> import numpy as np
      >>> 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]])