Make a 2D histogram plot.
Input values
The bin specification:
[int, int]
, the number of bins in each dimension (nx, ny = bins).[array, array]
, the bin edges in each dimension (x_edges, y_edges = bins).The default value is 10.
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.
Normalize histogram. See the documentation for the density parameter of hist
for more details.
An array of values w_i weighing each sample (x_i, y_i).
All bins that has count less than cmin or more than cmax will not be displayed (set to NaN before passing to imshow) and these count values in the return value count histogram will also be set to nan upon return.
The bi-dimensional 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.
The bin edges along the x axis.
The bin edges along the y axis.
QuadMesh
A colors.Colormap
instance. If not set, use rc settings.
A colors.Normalize
instance is used to scale luminance data to [0, 1]
. If not set, defaults to colors.Normalize()
.
Arguments passed to the Normalize
instance.
0 <= scalar <= 1
or None
, optional
The alpha blending value.
If given, the following parameters also accept a string s
, which is interpreted as data[s]
(unless this raises an exception):
x, y, weights
Additional parameters are passed along to the pcolormesh
method and QuadMesh
constructor.
hist2d
calculates its own axis limits, and any limits previously set are ignored.colors.LogNorm
instance to the norm keyword argument. Likewise, power-law normalization (similar in effect to gamma correction) can be accomplished with colors.PowerNorm
.matplotlib.axes.Axes.hist2d
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https://matplotlib.org/3.5.1/api/_as_gen/matplotlib.axes.Axes.hist2d.html