Compute and plot the empirical cumulative distribution function of x.

Parameters¶

x1d 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.

weights1d 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.

complementarybool, 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.

compressbool, 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¶

dataindexable object, optional

If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception):

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 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).