seaborn.countplot(data=None, *, x=None, y=None, hue=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, ax=None, **kwargs)#

Show the counts of observations in each categorical bin using bars.

A count plot can be thought of as a histogram across a categorical, instead of quantitative, variable. The basic API and options are identical to those for barplot(), so you can compare counts across nested variables.

Note that the newer histplot() function offers more functionality, although its default behavior is somewhat different.


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.

dataDataFrame, 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.

x, y, huenames of variables in data or vector data, optional

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

order, hue_orderlists of strings, optional

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

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 and y 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 by color_palette(), or a dictionary mapping hue levels to matplotlib colors.

saturationfloat, optional

Proportion of the original saturation to draw colors at. Large patches often look better with slightly desaturated colors, but set this to 1 if you want the plot colors to perfectly match the input color.

dodgebool, optional

When hue nesting is used, whether elements should be shifted along the categorical axis.

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

axmatplotlib Axes

Returns the Axes object with the plot drawn onto it.

See also


Show point estimates and confidence intervals using bars.


Combine a categorical plot with a FacetGrid.


import seaborn as sns

Show the number of datapoints with each value of a categorical variable:

df = sns.load_dataset("titanic")

Group by a second variable:

sns.countplot(data=df, x="class", hue="alive")

Plot horizontally to make more space for category labels:

sns.countplot(data=df, y="deck", hue="alive")