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# numpy.cumulative_prod

numpy.cumulative_prod(x, /, *, axis=None, dtype=None, out=None, include_initial=False)

  Return the cumulative product of elements along a given axis.

  This function is an Array API compatible alternative to `numpy.cumprod`.

  Parameters:
    x array_like

      Input array.

    axis int, optional

      Axis along which the cumulative product is computed. The default (None) is only allowed for one-dimensional arrays. For arrays with more than one dimension `axis` is required.

    dtype dtype, optional

      Type of the returned array, as well as of the accumulator in which the elements are multiplied. If `dtype` is not specified, it defaults to the dtype of `x`, unless `x` has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.

    out ndarray, optional

      Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary. See Output type determination for more details.

    include_initial bool, optional

      Boolean indicating whether to include the initial value (ones) as the first value in the output. With `include_initial=True` the shape of the output is different than the shape of the input. Default: `False`.

  Returns:
    cumulative_prod_along_axis ndarray

      A new array holding the result is returned unless `out` is specified, in which case a reference to `out` is returned. The result has the same shape as `x` if `include_initial=False`.

  Notes

  Arithmetic is modular when using integer types, and no error is raised on overflow.

  Examples

      >>> a = np.array([1, 2, 3])
      >>> np.cumulative_prod(a)  # intermediate results 1, 1*2
      ...                        # total product 1*2*3 = 6
      array([1, 2, 6])
      >>> a = np.array([1, 2, 3, 4, 5, 6])
      >>> np.cumulative_prod(a, dtype=np.float64)  # specify type of output
      array([   1.,    2.,    6.,   24.,  120.,  720.])

  The cumulative product for each column (i.e., over the rows) of `b`:

      >>> b = np.array([[1, 2, 3], [4, 5, 6]])
      >>> np.cumulative_prod(b, axis=0)
      array([[ 1,  2,  3],
             [ 4, 10, 18]])

  The cumulative product for each row (i.e. over the columns) of `b`:

      >>> np.cumulative_prod(b, axis=1)
      array([[  1,   2,   6],
             [  4,  20, 120]])