# HIDDEN
# Clear previously defined variables
%reset -f
# Set directory for data loading to work properly
import os
os.chdir(os.path.expanduser('~/notebooks/20'))
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 |