stripplot(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor='gray', linewidth=0, ax=None, **kwargs)¶
Draw a scatterplot where one variable is categorical.
A strip plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.
Input data can be passed in a variety of formats, including:
Vectors of data represented as lists, numpy arrays, or pandas Series
objects passed directly to the
A “long-form” DataFrame, in which case the
variables will determine how the data are plotted.
A “wide-form” DataFrame, such that each numeric column will be plotted.
An array or list of vectors.
In most cases, it is possible to use numpy or Python objects, but pandas objects are preferable because the associated names will be used to annotate the axes. Additionally, you can use Categorical types for the grouping variables to control the order of plot elements.
This function always treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis, even when the data has a numeric or date type.
See the tutorial for more information.
dataor vector data, optional
Inputs for plotting long-form data. See examples for interpretation.
Dataset for plotting. If
y are absent, this is
interpreted as wide-form. Otherwise it is expected to be long-form.
Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.
1is special-cased, optional
Amount of jitter (only along the categorical axis) to apply. This
can be useful when you have many points and they overlap, so that
it is easier to see the distribution. You can specify the amount
of jitter (half the width of the uniform random variable support),
or just use
True for a good default.
hue nesting, setting this to
True will separate
the strips for different hue levels along the categorical axis.
Otherwise, the points for each level will be plotted on top of
Orientation of the plot (vertical or horizontal). This is usually
inferred based on the type of the input variables, but it can be used
to resolve ambiguity when both
y are numeric or when
plotting wide-form data.
Color for all of the elements, or seed for a gradient palette.
Colors to use for the different levels of the
hue variable. Should
be something that can be interpreted by
color_palette(), or a
dictionary mapping hue levels to matplotlib colors.
Radius of the markers, in points.
Color of the lines around each point. If you pass
brightness is determined by the color palette used for the body
of the points.
Width of the gray lines that frame the plot elements.
Axes object to draw the plot onto, otherwise uses the current Axes.
Other keyword arguments are passed through to
Returns the Axes object with the plot drawn onto it.
A categorical scatterplot where the points do not overlap. Can be used with other plots to show each observation.
A traditional box-and-whisker plot with a similar API.
A combination of boxplot and kernel density estimation.
Combine a categorical plot with a
Draw a single horizontal strip plot:
>>> import seaborn as sns >>> sns.set_theme(style="whitegrid") >>> tips = sns.load_dataset("tips") >>> ax = sns.stripplot(x=tips["total_bill"])
Group the strips by a categorical variable:
>>> ax = sns.stripplot(x="day", y="total_bill", data=tips)
Use a smaller amount of jitter:
>>> ax = sns.stripplot(x="day", y="total_bill", data=tips, jitter=0.05)
Draw horizontal strips:
>>> ax = sns.stripplot(x="total_bill", y="day", data=tips)
Draw outlines around the points:
>>> ax = sns.stripplot(x="total_bill", y="day", data=tips, ... linewidth=1)
Nest the strips within a second categorical variable:
>>> ax = sns.stripplot(x="sex", y="total_bill", hue="day", data=tips)
Draw each level of the
hue variable at different locations on the
major categorical axis:
>>> ax = sns.stripplot(x="day", y="total_bill", hue="smoker", ... data=tips, palette="Set2", dodge=True)
Control strip order by passing an explicit order:
>>> ax = sns.stripplot(x="time", y="tip", data=tips, ... order=["Dinner", "Lunch"])
Draw strips with large points and different aesthetics:
>>> ax = sns.stripplot(x="day", y="total_bill", hue="smoker", ... data=tips, palette="Set2", size=20, marker="D", ... edgecolor="gray", alpha=.25)
Draw strips of observations on top of a box plot:
>>> import numpy as np >>> ax = sns.boxplot(x="tip", y="day", data=tips, whis=np.inf) >>> ax = sns.stripplot(x="tip", y="day", data=tips, color=".3")
Draw strips of observations on top of a violin plot:
>>> ax = sns.violinplot(x="day", y="total_bill", data=tips, ... inner=None, color=".8") >>> ax = sns.stripplot(x="day", y="total_bill", data=tips)
catplot() to combine a
stripplot() and a
FacetGrid. This allows grouping within additional categorical
catplot() is safer than using
directly, as it ensures synchronization of variable order across facets:
>>> g = sns.catplot(x="sex", y="total_bill", ... hue="smoker", col="time", ... data=tips, kind="strip", ... height=4, aspect=.7);