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# matplotlib.pyplot.hist2d

`matplotlib.pyplot.``hist2d`(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, hold=None, data=None, **kwargs)

  Make a 2D histogram plot.

  Parameters: | x, y: array_like, shape (n, ) Input values bins: [None | int | [int, int] | array_like | [array, array]] The bin specification: If int, the number of bins for the two dimensions (nx=ny=bins). If [int, int], the number of bins in each dimension (nx, ny = bins). If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins). If [array, array], the bin edges in each dimension (x_edges, y_edges = bins). The default value is 10. range : array_like shape(2, 2), optional, default: None 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, default: False Normalize histogram. weights : array_like, shape (n, ), optional, default: None An array of values w_i weighing each sample (x_i, y_i). cmin : scalar, optional, default: None All bins that has count less than cmin will not be displayed and these count values in the return value count histogram will also be set to nan upon return cmax : scalar, optional, default: None All bins that has count more than cmax will not be displayed (set to none before passing to imshow) and these count values in the return value count histogram will also be set to nan upon return
  Returns: | The return value is `(counts, xedges, yedges, Image)`.
  Other Parameters:
  | cmap : {Colormap, string}, optional A `matplotlib.colors.Colormap` instance. If not set, use rc settings. norm : Normalize, optional A `matplotlib.colors.Normalize` instance is used to scale luminance data to `[0, 1]`. If not set, defaults to `Normalize()`. vmin/vmax : {None, scalar}, optional Arguments passed to the `Normalize` instance. alpha : `0 <= scalar <= 1` or `None`, optional The alpha blending value.

  See also

  `hist`
    1D histogram

  Notes

  Rendering the histogram with a logarithmic color scale is accomplished by passing a `colors.LogNorm` instance to the norm keyword argument. Likewise, power-law normalization (similar in effect to gamma correction) can be accomplished with `colors.PowerNorm`.

  Note

  In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:

  - All arguments with the following names: ‘weights’, ‘x’, ‘y’.