seaborn.
pointplot
(*, x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean at 0x7fecadf1cee0>, ci=95, n_boot=1000, units=None, seed=None, markers='o', linestyles='-', dodge=False, join=True, scale=1, orient=None, color=None, palette=None, errwidth=None, capsize=None, ax=None, **kwargs)¶Show point estimates and confidence intervals using scatter plot glyphs.
A point plot represents an estimate of central tendency for a numeric variable by the position of scatter plot points and provides some indication of the uncertainty around that estimate using error bars.
Point plots can be more useful than bar plots for focusing comparisons
between different levels of one or more categorical variables. They are
particularly adept at showing interactions: how the relationship between
levels of one categorical variable changes across levels of a second
categorical variable. The lines that join each point from the same hue
level allow interactions to be judged by differences in slope, which is
easier for the eyes than comparing the heights of several groups of points
or bars.
It is important to keep in mind that a point plot shows only the mean (or other estimator) value, but in many cases it may be more informative to show the distribution of values at each level of the categorical variables. In that case, other approaches such as a box or violin plot may be more appropriate.
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 x
, y
, and/or hue
parameters.
A “long-form” DataFrame, in which case the x
, y
, and hue
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.
data
or vector data, optionalInputs for plotting long-form data. See examples for interpretation.
Dataset for plotting. If x
and 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.
Statistical function to estimate within each categorical bin.
Size of confidence intervals to draw around estimated values. If
“sd”, skip bootstrapping and draw the standard deviation of the
observations. If None
, no bootstrapping will be performed, and
error bars will not be drawn.
Number of bootstrap iterations to use when computing confidence intervals.
data
or vector data, optionalIdentifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design.
Seed or random number generator for reproducible bootstrapping.
Markers to use for each of the hue
levels.
Line styles to use for each of the hue
levels.
Amount to separate the points for each level of the hue
variable
along the categorical axis.
If True
, lines will be drawn between point estimates at the same
hue
level.
Scale factor for the plot elements.
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 ambiguitiy when both x
and 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.
Thickness of error bar lines (and caps).
Width of the “caps” on error bars.
Axes object to draw the plot onto, otherwise uses the current Axes.
Returns the Axes object with the plot drawn onto it.
See also
Examples
Draw a set of vertical point plots grouped by a categorical variable:
>>> import seaborn as sns
>>> sns.set_theme(style="darkgrid")
>>> tips = sns.load_dataset("tips")
>>> ax = sns.pointplot(x="time", y="total_bill", data=tips)
Draw a set of vertical points with nested grouping by a two variables:
>>> ax = sns.pointplot(x="time", y="total_bill", hue="smoker",
... data=tips)
Separate the points for different hue levels along the categorical axis:
>>> ax = sns.pointplot(x="time", y="total_bill", hue="smoker",
... data=tips, dodge=True)
Use a different marker and line style for the hue levels:
>>> ax = sns.pointplot(x="time", y="total_bill", hue="smoker",
... data=tips,
... markers=["o", "x"],
... linestyles=["-", "--"])
Draw a set of horizontal points:
>>> ax = sns.pointplot(x="tip", y="day", data=tips)
Don’t draw a line connecting each point:
>>> ax = sns.pointplot(x="tip", y="day", data=tips, join=False)
Use a different color for a single-layer plot:
>>> ax = sns.pointplot(x="time", y="total_bill", data=tips,
... color="#bb3f3f")
Use a different color palette for the points:
>>> ax = sns.pointplot(x="time", y="total_bill", hue="smoker",
... data=tips, palette="Set2")
Control point order by passing an explicit order:
>>> ax = sns.pointplot(x="time", y="tip", data=tips,
... order=["Dinner", "Lunch"])
Use median as the estimate of central tendency:
>>> from numpy import median
>>> ax = sns.pointplot(x="day", y="tip", data=tips, estimator=median)
Show the standard error of the mean with the error bars:
>>> ax = sns.pointplot(x="day", y="tip", data=tips, ci=68)
Show standard deviation of observations instead of a confidence interval:
>>> ax = sns.pointplot(x="day", y="tip", data=tips, ci="sd")
Add “caps” to the error bars:
>>> ax = sns.pointplot(x="day", y="tip", data=tips, capsize=.2)
Use catplot()
to combine a pointplot()
and a
FacetGrid
. This allows grouping within additional categorical
variables. Using catplot()
is safer than using FacetGrid
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="point",
... dodge=True,
... height=4, aspect=.7);