# numpy.histogramdd

numpy.histogramdd(sample, bins=10, range=None, density=None, weights=None)

  Compute the multidimensional histogram of some data.

  Parameters:
    sample(N, D) array, or (N, D) array_like

      The data to be histogrammed.

      Note the unusual interpretation of sample when an array_like:

      - When an array, each row is a coordinate in a D-dimensional space - such as `histogramdd(np.array([p1, p2, p3]))`.

      - When an array_like, each element is the list of values for single coordinate - such as `histogramdd((X, Y, Z))`.

      The first form should be preferred.

    bins sequence or int, optional

      The bin specification:

      - A sequence of arrays describing the monotonically increasing bin edges along each dimension.

      - The number of bins for each dimension (nx, ny, … =bins)

      - The number of bins for all dimensions (nx=ny=…=bins).

    range sequence, optional

      A sequence of length D, each an optional (lower, upper) tuple giving the outer bin edges to be used if the edges are not given explicitly in bins. An entry of None in the sequence results in the minimum and maximum values being used for the corresponding dimension. The default, None, is equivalent to passing a tuple of D None values.

    density bool, optional

      If False, the default, returns the number of samples in each bin. If True, returns the probability density function at the bin, `bin_count / sample_count / bin_volume`.

    weights(N,) array_like, optional

      An array of values w_i weighing each sample (x_i, y_i, z_i, …). Weights are normalized to 1 if density is True. If density is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin.

  Returns:
    H ndarray

      The multidimensional histogram of sample x. See density and weights for the different possible semantics.

    edges list

      A list of D arrays describing the bin edges for each dimension.

  See also

  `histogram`

    1-D histogram

  `histogram2d`

    2-D histogram

  Examples

      >>> r = np.random.randn(100,3)
      >>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
      >>> H.shape, edges[0].size, edges[1].size, edges[2].size
      ((5, 8, 4), 6, 9, 5)