# Plotting graph using Seaborn: Python

Seaborn is a Python **data visualization library** offers many advanced data visualization capabilities. It comes up with a high-level interface for drawing attractive and explanatory **statistical graphics** . Its plotting functions operate on **Pandas DataFrames** and numPy arrays containing whole datasets and internally carry out the necessary semantic mapping and statistical aggregation to produce explanatory plots. The **Seaborn library** is built on top of Matplotlib and integrates closely with pandas data structures.

## Install Seaborn

#### example

distplot - A Distplot or **distribution plot** shows the statistical distribution of data.

## Distplot Without the Histogram

## Joint Plot

The **jointplot** is used to output the mutual distribution of each column.

## Pair Plot

The **pairplot** is a type of distribution plot that basically plots a joint plot for all the possible combination of numeric and Boolean columns in your data.

## Rug Plot

The **rugplot** is used to draw small bars along x-axis for each point in the dataset.

## Bar Plot

A **barplot** is basically used to aggregate the categorical data according to some methods and by default it's the mean.

## Count Plot

The **count plot** is similar to the bar plot, however it displays the count of the categories in a specific column.

## Box Plot

A **box plot** helps to maintain the distribution of quantitative data in such a way that it facilitates the comparisons between variables or across levels of a categorical variable.

## Violin plot

Violin plots show the same summary statistics as **box plots** , but they also include **Kernel Density** Estimations that represent the shape/distribution of the data.

## Strip Plot

A **strip plot** is a scatter plot where one of the variables is categorical. They can be combined with other plots to provide additional information.

## Swarm Plot

The **swarm plot** is a combination of the strip and the violin plots.