matplotlib.axes.Axes.hist2d — Matplotlib 3.10.1 documentation (original) (raw)
Axes.hist2d(x, y, bins=10, *, range=None, density=False, weights=None, cmin=None, cmax=None, data=None, **kwargs)[source]#
Make a 2D histogram plot.
Parameters:
x, yarray-like, shape (n, )
Input values
binsNone or int or [int, int] or array-like or [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.
rangearray-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.
densitybool, default: False
Normalize histogram. See the documentation for the _density_parameter of hist for more details.
weightsarray-like, shape (n, ), optional
An array of values w_i weighing each sample (x_i, y_i).
cmin, cmaxfloat, default: None
All bins that has count less than cmin or more than cmax will not be displayed (set to NaN before passing to pcolormesh) and these count values in the return value count histogram will also be set to nan upon return.
Returns:
h2D array
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.
xedges1D array
The bin edges along the x-axis.
yedges1D array
The bin edges along the y-axis.
imageQuadMesh
Other Parameters:
cmapstr or Colormap, default: [rcParams["image.cmap"]](../../users/explain/customizing.html?highlight=image.cmap#matplotlibrc-sample) (default: 'viridis')
The Colormap instance or registered colormap name used to map scalar data to colors.
normstr or Normalize, optional
The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1.
If given, this can be one of the following:
- An instance of Normalize or one of its subclasses (see Colormap normalization).
- A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a list of available scales, call matplotlib.scale.get_scale_names(). In that case, a suitable Normalize subclass is dynamically generated and instantiated.
vmin, vmaxfloat, optional
When using scalar data and no explicit norm, vmin and vmax define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use_vmin_/vmax when a norm instance is given (but using a str _norm_name together with vmin/vmax is acceptable).
colorizerColorizer or None, default: None
The Colorizer object used to map color to data. If None, a Colorizer object is created from a norm and cmap.
alpha0 <= scalar <= 1 or None, optional
The alpha blending value.
dataindexable object, optional
If given, the following parameters also accept a string s, which is interpreted as data[s] if s is a key in data:
x, y, weights
**kwargs
Additional parameters are passed along to thepcolormesh method and QuadMeshconstructor.
See also
1D histogram plotting
2D histogram with hexagonal bins
Notes
- Currently
hist2dcalculates its own axis limits, and any limits previously set are ignored. - 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 withcolors.PowerNorm.