numpy.histogramdd

numpy.histogramdd(sample, bins=10, range=None, normed=False, weights=None) [source]

Compute the multidimensional histogram of some data.

Parameters:

sample : array_like The data to be histogrammed. It must be an (N,D) array or data that can be converted to such. The rows of the resulting array are the coordinates of points in a D dimensional polytope. 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 explicitly in bins. Defaults to the minimum and maximum values along each dimension. normed : bool, optional If False, returns the number of samples in each bin. If True, returns the bin density 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 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

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)