Seaborn

Function

Chapter

Description

sns.lmplot(x, y, data, fit_reg=True)

Data Visualization

Create a scatterplot of x versus y from DataFrame data, and by default overlay a least-squares regression line

sns.distplot(a, kde=True)

Data Visualization

Create a histogram of a, and by default overlay a kernel density estimator

sns.barplot(x, y, hue=None, data, ci=95)

Data Visualization

Create a barplot of x versus y from DataFrame data, optionally factoring data based on hue, and by default drawing a 95% confidence interval (which can be turned off with ci=None)

sns.countplot(x, hue=None, data)

Data Visualization

Create a barplot of value counts of variable x chosen from DataFrame data, optionally factored by categorical variable hue

sns.boxplot(x=None, y, data)

Data Visualization

Create a boxplot of y, optionally factoring by categorical variables x, from the DataFrame data

sns.kdeplot(x, y=None)

Data Visualization

If y=None, create a univariate density plot of x; if y is specified, create a bivariate density plot

sns.jointplot(x, y, data)

Data Visualization

Combine a bivariate scatterplot of x versus y from DataFrame data, with univariate density plots of each variable overlaid on the axes

sns.violinplot(x=None, y, data)

Data Visualization

Draws a combined boxplot and kernel density estimator of variable y, optionally factored by categorical variable x, chosen from DataFrame data