Function histogramdd

numpy/lib/histograms.py:902–1072

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

(sample, bins=10, range=None, density=None, weights=None)

@array_function_dispatch(_histogramdd_dispatcher)
def 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
    --------