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Classe « Axes »

Méthode matplotlib.pyplot.Axes.ecdf

Signature de la méthode ecdf

def ecdf(self, x, weights=None, *, complementary=False, orientation='vertical', compress=False, data=None, **kwargs) 

Description

help(Axes.ecdf)

Compute and plot the empirical cumulative distribution function of *x*.

.. versionadded:: 3.8

Parameters
----------
x : 1d array-like
    The input data.  Infinite entries are kept (and move the relevant
    end of the ecdf from 0/1), but NaNs and masked values are errors.

weights : 1d array-like or None, default: None
    The weights of the entries; must have the same shape as *x*.
    Weights corresponding to NaN data points are dropped, and then the
    remaining weights are normalized to sum to 1.  If unset, all
    entries have the same weight.

complementary : bool, default: False
    Whether to plot a cumulative distribution function, which increases
    from 0 to 1 (the default), or a complementary cumulative
    distribution function, which decreases from 1 to 0.

orientation : {"vertical", "horizontal"}, default: "vertical"
    Whether the entries are plotted along the x-axis ("vertical", the
    default) or the y-axis ("horizontal").  This parameter takes the
    same values as in `~.Axes.hist`.

compress : bool, default: False
    Whether multiple entries with the same values are grouped together
    (with a summed weight) before plotting.  This is mainly useful if
    *x* contains many identical data points, to decrease the rendering
    complexity of the plot. If *x* contains no duplicate points, this
    has no effect and just uses some time and memory.

Other Parameters
----------------
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*, *weights*

**kwargs
    Keyword arguments control the `.Line2D` properties:

    Properties:
    agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
    alpha: scalar or None
    animated: bool
    antialiased or aa: bool
    clip_box: `~matplotlib.transforms.BboxBase` or None
    clip_on: bool
    clip_path: Patch or (Path, Transform) or None
    color or c: :mpltype:`color`
    dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}
    dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}
    dashes: sequence of floats (on/off ink in points) or (None, None)
    data: (2, N) array or two 1D arrays
    drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'
    figure: `~matplotlib.figure.Figure` or `~matplotlib.figure.SubFigure`
    fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}
    gapcolor: :mpltype:`color` or None
    gid: str
    in_layout: bool
    label: object
    linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
    linewidth or lw: float
    marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`
    markeredgecolor or mec: :mpltype:`color`
    markeredgewidth or mew: float
    markerfacecolor or mfc: :mpltype:`color`
    markerfacecoloralt or mfcalt: :mpltype:`color`
    markersize or ms: float
    markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]
    mouseover: bool
    path_effects: list of `.AbstractPathEffect`
    picker: float or callable[[Artist, Event], tuple[bool, dict]]
    pickradius: float
    rasterized: bool
    sketch_params: (scale: float, length: float, randomness: float)
    snap: bool or None
    solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}
    solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}
    transform: unknown
    url: str
    visible: bool
    xdata: 1D array
    ydata: 1D array
    zorder: float

Returns
-------
`.Line2D`

Notes
-----
The ecdf plot can be thought of as a cumulative histogram with one bin
per data entry; i.e. it reports on the entire dataset without any
arbitrary binning.

If *x* contains NaNs or masked entries, either remove them first from
the array (if they should not taken into account), or replace them by
-inf or +inf (if they should be sorted at the beginning or the end of
the array).

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