seaborn.
barplot
(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, orient=None, color=None, palette=None, saturation=0.75, errcolor='.26', errwidth=None, capsize=None, ax=None, **kwargs)¶Show point estimates and confidence intervals as rectangular bars.
A bar plot represents an estimate of central tendency for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars. Bar plots include 0 in the quantitative axis range, and they are a good choice when 0 is a meaningful value for the quantitative variable, and you want to make comparisons against it.
For datasets where 0 is not a meaningful value, a point plot will allow you to focus on differences between levels of one or more categorical variables.
It is also important to keep in mind that a bar 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:
x
, y
, and/or hue
parameters.x
, y
, and hue
variables will determine how the data are plotted.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 : DataFrame, array, or list of arrays, optional
order, hue_order : lists of strings, optional
estimator : callable that maps vector > scalar, optional
ci : float or None, optional
n_boot : int, optional
units : name of variable in
orient : “v”  “h”, optional
color : matplotlib color, optional
palette : seaborn color palette or dict, optional
saturation : float, optional
errcolor : matplotlib color
ax : matplotlib Axes, optional
errwidth : float, optional
capsize : float, optional
kwargs : key, value mappings


Returns:  ax : matplotlib Axes

See also
countplot
pointplot
factorplot
Examples
Draw a set of vertical bar plots grouped by a categorical variable:
>>> import seaborn as sns
>>> sns.set_style("whitegrid")
>>> tips = sns.load_dataset("tips")
>>> ax = sns.barplot(x="day", y="total_bill", data=tips)
Draw a set of vertical bars with nested grouping by a two variables:
>>> ax = sns.barplot(x="day", y="total_bill", hue="sex", data=tips)
Draw a set of horizontal bars:
>>> ax = sns.barplot(x="tip", y="day", data=tips)
Control bar order by passing an explicit order:
>>> ax = sns.barplot(x="time", y="tip", data=tips,
... order=["Dinner", "Lunch"])
Use median as the estimate of central tendency:
>>> from numpy import median
>>> ax = sns.barplot(x="day", y="tip", data=tips, estimator=median)
Show the standard error of the mean with the error bars:
>>> ax = sns.barplot(x="day", y="tip", data=tips, ci=68)
Add “caps” to the error bars:
>>> ax = sns.barplot(x="day", y="tip", data=tips, capsize=.2)
Use a different color palette for the bars:
>>> ax = sns.barplot("size", y="total_bill", data=tips,
... palette="Blues_d")
Plot all bars in a single color:
>>> ax = sns.barplot("size", y="total_bill", data=tips,
... color="salmon", saturation=.5)
Use plt.bar
keyword arguments to further change the aesthetic:
>>> ax = sns.barplot("day", "total_bill", data=tips,
... linewidth=2.5, facecolor=(1, 1, 1, 0),
... errcolor=".2", edgecolor=".2")