ecdfplot(data=None, *, x=None, y=None, hue=None, weights=None, stat='proportion', complementary=False, palette=None, hue_order=None, hue_norm=None, log_scale=None, legend=True, ax=None, **kwargs)¶
Plot empirical cumulative distribution functions.
An ECDF represents the proportion or count of observations falling below each unique value in a dataset. Compared to a histogram or density plot, it has the advantage that each observation is visualized directly, meaning that there are no binning or smoothing parameters that need to be adjusted. It also aids direct comparisons between multiple distributions. A downside is that the relationship between the appearance of the plot and the basic properties of the distribution (such as its central tendency, variance, and the presence of any bimodality) may not be as intuitive.
More information is provided in the user guide.
numpy.ndarray, mapping, or sequence
Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped.
Variables that specify positions on the x and y axes.
Semantic variable that is mapped to determine the color of plot elements.
If provided, weight the contribution of the corresponding data points towards the cumulative distribution using these values.
Distribution statistic to compute.
If True, use the complementary CDF (1 - CDF)
Method for choosing the colors to use when mapping the
String values are passed to
color_palette(). List or dict values
imply categorical mapping, while a colormap object implies numeric mapping.
Specify the order of processing and plotting for categorical levels of the
Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping.
Set a log scale on the data axis (or axes, with bivariate data) with the given base (default 10), and evaluate the KDE in log space.
If False, suppress the legend for semantic variables.
Pre-existing axes for the plot. Otherwise, call
Other keyword arguments are passed to
The matplotlib axes containing the plot.
Figure-level interface to distribution plot functions.
Plot a histogram of binned counts with optional normalization or smoothing.
Plot univariate or bivariate distributions using kernel density estimation.
Plot a tick at each observation value along the x and/or y axes.
Plot a univariate distribution along the x axis:
penguins = sns.load_dataset("penguins") sns.ecdfplot(data=penguins, x="flipper_length_mm")
Flip the plot by assigning the data variable to the y axis:
y is assigned, the dataset is treated as
wide-form, and a histogram is drawn for each numeric column:
You can also draw multiple histograms from a long-form dataset with hue mapping:
sns.ecdfplot(data=penguins, x="bill_length_mm", hue="species")
The default distribution statistic is normalized to show a proportion, but you can show absolute counts instead:
sns.ecdfplot(data=penguins, x="bill_length_mm", hue="species", stat="count")
It’s also possible to plot the empirical complementary CDF (1 - CDF):
sns.ecdfplot(data=penguins, x="bill_length_mm", hue="species", complementary=True)