# numpy.histogram2d¶

numpy.histogram2d(x, y, bins=10, range=None, normed=False, weights=None)

Compute the bidimensional histogram of two data samples.

Parameters: x : array_like, shape(N,) A sequence of values to be histogrammed along the first dimension. y : array_like, shape(M,) A sequence of values to be histogrammed along the second dimension. bins : int or [int, int] or array-like or [array, array], optional The bin specification: the number of bins for the two dimensions (nx=ny=bins), the number of bins in each dimension (nx, ny = bins), the bin edges for the two dimensions (x_edges=y_edges=bins), the bin edges in each dimension (x_edges, y_edges = bins). range : array_like, shape(2,2), optional The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters): [[xmin, xmax], [ymin, ymax]]. All values outside of this range will be considered outliers and not tallied in the histogram. 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 area. weights : array-like, shape(N,), optional An array of values w_i weighing each sample (x_i, y_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. H : ndarray, shape(nx, ny) The bidimensional histogram of samples x and y. Values in x are histogrammed along the first dimension and values in y are histogrammed along the second dimension. xedges : ndarray, shape(nx,) The bin edges along the first dimension. yedges : ndarray, shape(ny,) The bin edges along the second dimension.

histogram
1D histogram
histogramdd
Multidimensional histogram

Notes

When normed is True, then the returned histogram is the sample density, defined such that:

where is the histogram array and the area of bin .

Please note that the histogram does not follow the cartesian convention where x values are on the abcissa and y values on the ordinate axis. Rather, x is histogrammed along the first dimension of the array (vertical), and y along the second dimension of the array (horizontal). This ensures compatibility with histogrammdd.

Examples

>>> x,y = np.random.randn(2,100)
>>> H, xedges, yedges = np.histogram2d(x, y, bins = (5, 8))
>>> H.shape, xedges.shape, yedges.shape
((5,8), (6,), (9,))


numpy.histogram

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numpy.histogramdd