seaborn.pointplot

seaborn.pointplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean>, ci=95, n_boot=1000, units=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.
  • Anything accepted by plt.boxplot (e.g. a 2d 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.

Parameters:

x, y, hue : names of variables in data or vector data, optional

Inputs for plotting long-form data. See examples for interpretation.

data : DataFrame, array, or list of arrays, optional

Dataset for plotting. If x and y are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form.

order, hue_order : lists of strings, optional

Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.

estimator : callable that maps vector -> scalar, optional

Statistical function to estimate within each categorical bin.

ci : float or “sd” or None, optional

Size of confidence intervals to draw around estimated values. If “sd”, skip bootstrapping and draw the standard deviation of the observerations. If None, no bootstrapping will be performed, and error bars will not be drawn.

n_boot : int, optional

Number of bootstrap iterations to use when computing confidence intervals.

units : name of variable in data or vector data, optional

Identifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design.

markers : string or list of strings, optional

Markers to use for each of the hue levels.

linestyles : string or list of strings, optional

Line styles to use for each of the hue levels.

dodge : bool or float, optional

Amount to separate the points for each level of the hue variable along the categorical axis.

join : bool, optional

If True, lines will be drawn between point estimates at the same hue level.

scale : float, optional

Scale factor for the plot elements.

orient : “v” | “h”, optional

Orientation of the plot (vertical or horizontal). This is usually inferred from the dtype of the input variables, but can be used to specify when the “categorical” variable is a numeric or when plotting wide-form data.

color : matplotlib color, optional

Color for all of the elements, or seed for light_palette() when using hue nesting.

palette : seaborn color palette or dict, optional

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.

errwidth : float, optional

Thickness of error bar lines (and caps).

capsize : float, optional

Width of the “caps” on error bars.

ax : matplotlib Axes, optional

Axes object to draw the plot onto, otherwise uses the current Axes.

Returns:

ax : matplotlib Axes

Returns the Axes object with the boxplot drawn onto it.

See also

barplot
Show point estimates and confidence intervals using bars.
factorplot
Combine categorical plots and a class:FacetGrid.

Examples

Draw a set of vertical point plots grouped by a categorical variable:

>>> import seaborn as sns
>>> sns.set_style("darkgrid")
>>> tips = sns.load_dataset("tips")
>>> ax = sns.pointplot(x="time", y="total_bill", data=tips)
../_images/seaborn-pointplot-1.png

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)
../_images/seaborn-pointplot-2.png

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)
../_images/seaborn-pointplot-3.png

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=["-", "--"])
../_images/seaborn-pointplot-4.png

Draw a set of horizontal points:

>>> ax = sns.pointplot(x="tip", y="day", data=tips)
../_images/seaborn-pointplot-5.png

Don’t draw a line connecting each point:

>>> ax = sns.pointplot(x="tip", y="day", data=tips, join=False)
../_images/seaborn-pointplot-6.png

Use a different color for a single-layer plot:

>>> ax = sns.pointplot("time", y="total_bill", data=tips,
...                    color="#bb3f3f")
../_images/seaborn-pointplot-7.png

Use a different color palette for the points:

>>> ax = sns.pointplot(x="time", y="total_bill", hue="smoker",
...                    data=tips, palette="Set2")
../_images/seaborn-pointplot-8.png

Control point order by passing an explicit order:

>>> ax = sns.pointplot(x="time", y="tip", data=tips,
...                    order=["Dinner", "Lunch"])
../_images/seaborn-pointplot-9.png

Use median as the estimate of central tendency:

>>> from numpy import median
>>> ax = sns.pointplot(x="day", y="tip", data=tips, estimator=median)
../_images/seaborn-pointplot-10.png

Show the standard error of the mean with the error bars:

>>> ax = sns.pointplot(x="day", y="tip", data=tips, ci=68)
../_images/seaborn-pointplot-11.png

Show standard deviation of observations instead of a confidence interval:

>>> ax = sns.pointplot(x="day", y="tip", data=tips, ci="sd")
../_images/seaborn-pointplot-12.png

Add “caps” to the error bars:

>>> ax = sns.pointplot(x="day", y="tip", data=tips, capsize=.2)
../_images/seaborn-pointplot-13.png

Use factorplot() to combine a barplot() and a FacetGrid. This allows grouping within additional categorical variables. Using factorplot() is safer than using FacetGrid directly, as it ensures synchronization of variable order across facets:

>>> g = sns.factorplot(x="sex", y="total_bill",
...                    hue="smoker", col="time",
...                    data=tips, kind="point",
...                    dodge=True,
...                    size=4, aspect=.7);
../_images/seaborn-pointplot-14.png