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Classe « Axes »

Méthode matplotlib.axes.Axes.hist2d

Signature de la méthode hist2d

def hist2d(self, x, y, bins=10, *, range=None, density=False, weights=None, cmin=None, cmax=None, data=None, **kwargs) 

Description

help(Axes.hist2d)

Make a 2D histogram plot.

Parameters
----------
x, y : array-like, shape (n, )
    Input values

bins : None 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.

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.

density : bool, default: False
    Normalize histogram.  See the documentation for the *density*
    parameter of `~.Axes.hist` for more details.

weights : array-like, shape (n, ), optional
    An array of values w_i weighing each sample (x_i, y_i).

cmin, cmax : float, default: None
    All bins that has count less than *cmin* or more than *cmax* will not be
    displayed (set to NaN before passing to `~.Axes.pcolormesh`) and these count
    values in the return value count histogram will also be set to nan upon
    return.

Returns
-------
h : 2D 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.
xedges : 1D array
    The bin edges along the x-axis.
yedges : 1D array
    The bin edges along the y-axis.
image : `~.matplotlib.collections.QuadMesh`

Other Parameters
----------------
cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
    The Colormap instance or registered colormap name used to map scalar data
    to colors.

norm : str or `~matplotlib.colors.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 :ref:`colormapnorms`).
    - 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, vmax : float, 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).

colorizer : `~matplotlib.colorizer.Colorizer` 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*.

alpha : ``0 <= scalar <= 1`` or ``None``, optional
    The alpha blending value.

data : indexable 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 the
    `~.Axes.pcolormesh` method and `~matplotlib.collections.QuadMesh`
    constructor.

See Also
--------
hist : 1D histogram plotting
hexbin : 2D histogram with hexagonal bins

Notes
-----
- Currently ``hist2d`` calculates 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 with
  `.colors.PowerNorm`.

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