Better Visualization : Seaborn

After discussing basic visualization with Matplotlib, now let’s try another but more attractive visualization library called Seaborn. Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

There are 5 main plots that really useful for your visualization.

1. Relational Plot
2. Distribution Plot
3. Categorical Plot
4. Regression Plot
5. Matrix Plot

and 3 multi-plot grids which are

1. Facet grids
2. Pair grids
3. Joint grids

But first, let’s use Seaborn own dataset sample called tips

tips = sns.load_dataset('tips')

Relational Plot

This function provides access to several different axes-level functions that show the relationship between two variables or features with semantic mappings of subsets. The kind parameter selects the underlying axes-level function like scatter and line

Example

sns.relplot(data=tips, x='total_bill', y='tip', kind='scatter')

sns.relplot(data=tips, x='total_bill', y='tip', kind='line')

When thinking about how to assign variables to different facets, a general rule is that it makes sense to use hue for the most important comparison, followed by col and row. However, always think about your particular dataset and the goals of the visualization you are creating.

You can visualize comparison between 2 features with another categorical features with hue (by color), column, and row (by another plot).

Example

sns.relplot(data=tips, x="total_bill", y="tip", hue="day", col="time", row="sex")

Distribution Plot

This function provides access to several approaches for visualizing the univariate (single feature) or bivariate (double features) distribution of data, including subsets of data defined by semantic mapping and faceting across multiple subplots. The kind parameter selects the approach to use are hist (Histogram), kde (A kernel density estimate (KDE) plot is a method for visualizing the density of dataset), and ecdf (stair-like, univariate only).

Example

sns.displot(data=tips, x='total_bill', kind='hist')

sns.displot(data=tips, x='total_bill', kind='kde')

sns.displot(data=tips, x='total_bill', kind='ecdf')

You can also showing histogram or ecdf with kde.

sns.displot(data=tips, x='total_bill', kind='hist', kde=True)

Then, if you want to visualize between 2 features

sns.displot(data=tips, x='total_bill', y='tip', kind='kde')

Categorical Plot

This function provides access to several axes-level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations. The kind parameter selects the underlying axes-level function to use:

Categorical scatterplots:

• stripplot() (with kind="strip"; the default)

  sns.catplot(data=tips, x='sex', y='total_bill', kind='strip')
  

  

• swarmplot() (with kind="swarm")

sns.catplot(data=tips, x='sex', y='total_bill', kind='swarm')

Categorical distribution plots:

• boxplot() (with kind="box")

  sns.catplot(data=tips, x='sex', y='total_bill', kind='box')
  

  

• violinplot() (with kind="violin")

  sns.catplot(data=tips, x='sex', y='total_bill', kind='violin')
  

  

• boxenplot() (with kind="boxen")

  sns.catplot(data=tips, x='sex', y='total_bill', kind='boxen')
  

  

Categorical estimate plots:

• pointplot() (with kind="point")

  sns.catplot(data=tips, x='day', y='total_bill', kind='point')
  

  

• barplot() (with kind="bar")

  sns.catplot(data=tips, x='day', y='total_bill', kind='bar')  
  

  

• countplot() (with kind="count")

  sns.catplot(data=tips, x='sex', hue='day', kind='count')
  

  

Regression Plot

To plot data with regression model fits across a FacetGrid.

sns.lmplot(data=tips, x='tip', y='total_bill', hue='sex', col='smoker')

Matrix Plot

Plot rectangular data as a color-encoded matrix. This is an Axes-level function and will draw the heatmap into the currently-active Axes if none is provided to the ax argument. Part of this Axes space will be taken and used to plot a colormap, unless cbar is False or a separate Axes is provided to cbar_ax.

Example

sns.heatmap(tips.corr())