matplotlib.pyplot.ecdf#

matplotlib.pyplot.ecdf(x, weights=None, *, complementary=False, orientation='vertical', compress=False, data=None, **kwargs)[source]#

Compute and plot the empirical cumulative distribution function of x.

Added in version 3.8.

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 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.

Returns:

Line2D

Other Parameters:

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, weights

**kwargs

Keyword arguments control the Line2D properties:

Property	Description
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	float or None
animated	bool
antialiased or aa	bool
blend_mode	blend mode
clip_box	BboxBase or None
clip_on	bool
clip_path	Patch or (Path, Transform) or None
color or c	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	Figure or SubFigure
fillstyle	{'full', 'left', 'right', 'bottom', 'top', 'none'}
gapcolor	color or None
gid	str
in_layout	bool
label	object
linestyle or ls	linestyle
linewidth or lw	float
marker	marker style string, Path or MarkerStyle
markeredgecolor or mec	color
markeredgewidth or mew	float
markerfacecolor or mfc	color
markerfacecoloralt or mfcalt	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

Notes

Note

This is the pyplot wrapper for axes.Axes.ecdf.

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).

Examples using matplotlib.pyplot.ecdf#

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Cumulative distributions

Cumulative distributions