seaborn.stripplot#
- seaborn.stripplot(data=None, *, x=None, y=None, hue=None, order=None, hue_order=None, jitter=True, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor='gray', linewidth=0, hue_norm=None, native_scale=False, formatter=None, legend='auto', ax=None, **kwargs)#
Draw a categorical scatterplot using jitter to reduce overplotting.
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.
Note
By default, this function treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis. This can be disabled with the
native_scale
parameter.See the tutorial for more information.
- Parameters:
- x, y, huenames of variables in
data
or vector data, optional Inputs for plotting long-form data. See examples for interpretation.
- dataDataFrame, array, or list of arrays, optional
Dataset for plotting. If
x
andy
are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form.- order, hue_orderlists of strings, optional
Order to plot the categorical levels in; otherwise the levels are inferred from the data objects.
- jitterfloat,
True
/1
is 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.- dodgebool, optional
When using
hue
nesting, setting this toTrue
will separate the strips for different hue levels along the categorical axis. Otherwise, the points for each level will be plotted on top of each other.- orient“v” | “h”, optional
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
x
andy
are numeric or when plotting wide-form data.- colormatplotlib color, optional
Single color for the elements in the plot.
- palettepalette name, list, or dict
Colors to use for the different levels of the
hue
variable. Should be something that can be interpreted bycolor_palette()
, or a dictionary mapping hue levels to matplotlib colors.- sizefloat, optional
Radius of the markers, in points.
- edgecolormatplotlib color, “gray” is special-cased, optional
Color of the lines around each point. If you pass
"gray"
, the brightness is determined by the color palette used for the body of the points. Note thatstripplot
haslinewidth=0
by default, so edge colors are only visible with nonzero line width.- linewidthfloat, optional
Width of the gray lines that frame the plot elements.
- native_scalebool, optional
When True, numeric or datetime values on the categorical axis will maintain their original scaling rather than being converted to fixed indices.
- formattercallable, optional
Function for converting categorical data into strings. Affects both grouping and tick labels.
- legend“auto”, “brief”, “full”, or False
How to draw the legend. If “brief”, numeric
hue
andsize
variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. IfFalse
, no legend data is added and no legend is drawn.- axmatplotlib Axes, optional
Axes object to draw the plot onto, otherwise uses the current Axes.
- kwargskey, value mappings
Other keyword arguments are passed through to
matplotlib.axes.Axes.scatter()
.
- x, y, huenames of variables in
- Returns:
- axmatplotlib Axes
Returns the Axes object with the plot drawn onto it.
See also
swarmplot
A categorical scatterplot where the points do not overlap. Can be used with other plots to show each observation.
boxplot
A traditional box-and-whisker plot with a similar API.
violinplot
A combination of boxplot and kernel density estimation.
catplot
Combine a categorical plot with a
FacetGrid
.
Examples
Assigning a single numeric variable shows its univariate distribution with points randomly “jittered” on the other axis:
tips = sns.load_dataset("tips") sns.stripplot(data=tips, x="total_bill")
Assigning a second variable splits the strips of points to compare categorical levels of that variable:
sns.stripplot(data=tips, x="total_bill", y="day")
Show vertically-oriented strips by swapping the assignment of the categorical and numerical variables:
sns.stripplot(data=tips, x="day", y="total_bill")
Prior to version 0.12, the levels of the categorical variable had different colors by default. To get the same effect, assign the
hue
variable explicitly:sns.stripplot(data=tips, x="total_bill", y="day", hue="day", legend=False)
Or you can assign a distinct variable to
hue
to show a multidimensional relationship:sns.stripplot(data=tips, x="total_bill", y="day", hue="sex")
If the
hue
variable is numeric, it will be mapped with a quantitative palette by default (note that this was not the case prior to version 0.12):sns.stripplot(data=tips, x="total_bill", y="day", hue="size")
Use
palette
to control the color mapping, including forcing a categorical mapping by passing the name of a qualitative palette:sns.stripplot(data=tips, x="total_bill", y="day", hue="size", palette="deep")
By default, the different levels of the
hue
variable are intermingled in each strip, but settingdodge=True
will split them:sns.stripplot(data=tips, x="total_bill", y="day", hue="sex", dodge=True)
The random jitter can be disabled by setting
jitter=False
:sns.stripplot(data=tips, x="total_bill", y="day", hue="sex", dodge=True, jitter=False)
If plotting in wide-form mode, each numeric column of the dataframe will be mapped to both
x
andhue
:sns.stripplot(data=tips)
To change the orientation while in wide-form mode, pass
orient
explicitly:sns.stripplot(data=tips, orient="h")
The
orient
parameter is also useful when both axis variables are numeric, as it will resolve ambiguity about which dimension to group (and jitter) along:sns.stripplot(data=tips, x="total_bill", y="size", orient="h")
By default, the categorical variable will be mapped to discrete indices with a fixed scale (0, 1, …), even when it is numeric:
sns.stripplot( data=tips.query("size in [2, 3, 5]"), x="total_bill", y="size", orient="h", )
To disable this behavior and use the original scale of the variable, set
native_scale=True
:sns.stripplot( data=tips.query("size in [2, 3, 5]"), x="total_bill", y="size", orient="h", native_scale=True, )
Further visual customization can be achieved by passing keyword arguments for
matplotlib.axes.Axes.scatter()
:sns.stripplot( data=tips, x="total_bill", y="day", hue="time", jitter=False, s=20, marker="D", linewidth=1, alpha=.1, )
To make a plot with multiple facets, it is safer to use
catplot()
than to work withFacetGrid
directly, becausecatplot()
will ensure that the categorical and hue variables are properly synchronized in each facet:sns.catplot(data=tips, x="time", y="total_bill", hue="sex", col="day", aspect=.5)