Data Visualization with Seaborn

What is Seaborn??

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

How to install Seaborn??

If you have Python and PIP already installed on a system, install it using this command:

C:\Users\Your Name>pip install seaborn

Types of Plots in Seaborn:

1- DISTRIBUTION PLOTS:

Distribution plots are also known as Distplot, it takes as input an array and plots a curve corresponding to the distribution of points in the array.A distplot plots a univariate distribution of observations.

The distplot() function combines the matplotlib hist function with the seaborn kdeplot() and rugplot() functions.

Let’s have a look at the following Examples:

EXAMPLE- 1:

EXAMPLE- 2:

There are 3 types of distribution plots namely:

1. joinplot
2. pairplot
3. rugplot

Besides providing different kinds of visualization plots, seaborn also contains some built-in datasets. We will be using the tips dataset in this article. The “tips” dataset contains information about people who probably had food at a restaurant and whether or not they left a tip, their age, gender and so on. Lets have a look at it.

1- Joint Plot:

It is used to draw a plot of two variables with bivariate and univariate graphs. It basically combines two different plots.

Syntax:

jointplot(x, y[, data, kind, stat_func, ...])

EXAMPLE:

2- Pair plot:

It represents pairwise relation across the entire dataframe and supports an additional argument called hue for categorical separation.

Syntax:

pairplot(data[, hue, hue_order, palette, …])

Example:

3- RugPlot:

It plots datapoints in an array as sticks on an axis.Just like a distplot it takes a single column. Instead of drawing a histogram it creates dashes all across the plot.

Syntax:

rugplot(a[, height, axis, ax])

EXAMPLE:

2- MATRIX PLOTS:

Matrix plots can be understood by plotting the Heat Maps.

Let’s have a look on to the following Example to understand it better:

3- CATEGORICAL PLOTS:

Categorical plots provided by the seaborn library can be used to visualize the counts distribution of 2 ore more categorical variables in relation to each other.

Let’s have a look on following types of categorical plots:

1- STRIP PLOT:

2- Swarm Plot:

3- Bar Plot:

4- Count Plot:

5- Violin Plot:

Hence, with the help of seaborn, we can plot various graphs and can make our diagrams more interesting and visualizing.

THANK YOU!!

KEEP LEARNING!!👍