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# numpy.histogramdd

`numpy.``histogramdd`(sample, bins=10, range=None, normed=False, weights=None)

  Compute the multidimensional histogram of some data.

  Parameters: | sample : array_like Data to histogram passed as a sequence of D arrays of length N, or as an (N,D) array. bins : sequence or int, optional The bin specification: A sequence of arrays describing the 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 lower and upper bin edges to be used if the edges are not given explicitely in bins. Defaults to the minimum and maximum values along each dimension. normed : boolean, optional If False, returns the number of samples in each bin. If True, returns the bin density, ie, the bin count divided by the bin hypervolume. weights : array_like (N,), optional An array of values w_i weighing each sample (x_i, y_i, z_i, ...). Weights are normalized to 1 if normed is True. If normed 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 normed and weights for the different possible semantics. edges : list A list of D arrays describing the bin edges for each dimension.

  See also

  `histogram`
    1D histogram
  `histogram2d`
    2D 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)

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