Delve into data visualization and analysis with Python's Seaborn library. Learn about variable types, data cleaning, creating detailed visualizations, and applying themes for more appealing results.

Data Visualization and Analysis With Seaborn Library (1).png

jupyter.tar.gz

Adding Hue Parameter for color encoding

Data cleaning with Pandas

Getting Started with Seaborn

Intro to Pandas

Line Plots in Seaborn

Pair Plot

Scatter Plot

libraries

project

solution

This course aims to provide an introduction to data visualization and analysis using Python and the Seaborn library.

The course begins by introducing various variable types and statistical analysis methods. Then, you get to review the foundations of data cleaning and extraction using the pandas library. In the second half of the course, you will go over different plots in Seaborn for numerical, continuous, and categorical data, as well as distribution and regression plots to gain insightful information and identify patterns in the data. Lastly, you get to learn to create complex visualizations that are also aesthetically pleasing and go into great detail about the Seaborn themes, color palettes, styling, and multiplot grids.

By the end of this course, you’ll apply the knowledge you’ve gained with a hands-on project.

Data Visualization and Analysis With Seaborn Library

A **violin plot** is a categorical distribution plot similar to a box plot. However, in contrast to a  box plot (which plots the actual data points), the violin plot represents data distribution using the **kernel density estimation (KDE)** technique. The KDE estimates the probability density function in a nonparametric manner, which means that we make no assumptions about the underlying distribution of our data. 




To get started with our visualizations, we import the required libraries and the `mpg` dataset from seaborn and view the data using the `head()` method.  

import seaborn as sns
import pandas as pd 
import matplotlib.pyplot as plt
sns.set_theme()
mpg_df = sns.load_dataset('mpg')
print(mpg_df.head())

Let’s plot a violin plot of a single variable `weight` using the `sns.violinplot()` function. The plot is shown below: 

sns.violinplot(data = mpg_df , x ='weight')     
plt.savefig('output/graph.png')

The different parts of a violin plot are demonstrated below. The small dot we see in the plot is the median of the data, and the bolded line shows the IQR. 

Learn how to visualize data distribution and presentation through violin plots.

About the Course

Introduction to Seaborn and Statistical Analysis

Plotting Numerical Data

Assessment on Plotting Numerical Data

Plotting Categorical Data

Plotting the Categorical Data

Assessment on Categorical Plots

Visualizing Distribution of Data

Data Visualization with Seaborn for Walmart Sales Projection

Assessment on Distributions of Data

Visualizing Regression Models

Assessment on Visualization of Regression Models

Styling and Figure Aesthetics

Assessment on Styling and Figure Aesthetics

Multiplot Grids

Assessment on Multi-Plot Grids

Project

Closing Remarks

Visualization with Violin Plots

Overview

Plotting the violin plot