Classe « Axes »

Méthode matplotlib.pyplot.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

hist2d.__doc__

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 imshow) 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 : Colormap or str, optional
    A `.colors.Colormap` instance.  If not set, use rc settings.

norm : Normalize, optional
    A `.colors.Normalize` instance is used to
    scale luminance data to ``[0, 1]``. If not set, defaults to
    `.colors.Normalize()`.

vmin/vmax : None or scalar, optional
    Arguments passed to the `~.colors.Normalize` instance.

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

**kwargs
    Additional parameters are passed along to the
    `~.Axes.pcolormesh` method and `~matplotlib.collections.QuadMesh`
    constructor.

See Also
--------
hist : 1D histogram plotting

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`.

.. 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 can also be string ``s``, which is
    interpreted as ``data[s]`` (unless this raises an exception):
    *x*, *y*, *weights*.

    Objects passed as **data** must support item access (``data[s]``) and
    membership test (``s in data``).