# numpy.put_along_axis

numpy.put_along_axis(arr, indices, values, axis)

  Put values into the destination array by matching 1d index and data slices.

  This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to place values into the latter. These slices can be different lengths.

  Functions returning an index along an axis, like `argsort` and `argpartition`, produce suitable indices for this function.

  New in version 1.15.0.

  Parameters
    arr ndarray (Ni…, M, Nk…)

      Destination array.

    indices ndarray (Ni…, J, Nk…)

      Indices to change along each 1d slice of arr. This must match the dimension of arr, but dimensions in Ni and Nj may be 1 to broadcast against arr.

    values array_like (Ni…, J, Nk…)

      values to insert at those indices. Its shape and dimension are broadcast to match that of `indices`.

    axis int

      The axis to take 1d slices along. If axis is None, the destination array is treated as if a flattened 1d view had been created of it.

  See also

  `take_along_axis`

    Take values from the input array by matching 1d index and data slices

  Notes

  This is equivalent to (but faster than) the following use of `ndindex` and `s_`, which sets each of `ii` and `kk` to a tuple of indices:

      Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
      J = indices.shape[axis]  # Need not equal M

      for ii in ndindex(Ni):
          for kk in ndindex(Nk):
              a_1d       = a      [ii + s_[:,] + kk]
              indices_1d = indices[ii + s_[:,] + kk]
              values_1d  = values [ii + s_[:,] + kk]
              for j in range(J):
                  a_1d[indices_1d[j]] = values_1d[j]

  Equivalently, eliminating the inner loop, the last two lines would be:

      a_1d[indices_1d] = values_1d

  Examples

  For this sample array

      >>> a = np.array([[10, 30, 20], [60, 40, 50]])

  We can replace the maximum values with:

      >>> ai = np.expand_dims(np.argmax(a, axis=1), axis=1)
      >>> ai
      array([[1],
             [0]])
      >>> np.put_along_axis(a, ai, 99, axis=1)
      >>> a
      array([[10, 99, 20],
             [99, 40, 50]])

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