# Mastering Data Visualization with Python Visualize data using pandas, matplotlib and seaborn libraries for data analysis and data science

## What you’ll learn

• Understand what plots are suitable for a type of data you have
• Visualize data by creating various graphs using pandas, matplotlib and seaborn libraries

## Description

This course will help you draw meaningful knowledge from the data you have.

Three systems of data visualization in R are covered in this course:

A. Pandas    B. Matplotlib  C. Seaborn

A. Types of graphs covered in the course using the pandas package:

Time-series: Line Plot

Single Discrete Variable: Bar Plot, Pie Plot

Single Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot

Two Continuous Variable: Scatter Plot

Two Variable: One Continuous, One Discrete: Box-Whisker Plot

B. Types of graphs using Matplotlib library:

Time-series: Line Plot

Single Discrete Variable: Bar Plot, Pie Plot

Single Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot

Two Continuous Variable: Scatter Plot

In addition, we will cover subplots as well, where multiple axes can be plotted on a single figure.

C. Types of graphs using Seaborn library:

In this we will cover three broad categories of plots:

relplot (Relational Plots): Scatter Plot and Line Plot

displot (Distribution Plots): Histogram, KDE, ECDF and Rug Plots

catplot (Categorical Plots): Strip Plot, Swarm Plot, Box Plot, Violin Plot, Point Plot and Bar plot

In addition to these three categories, we will cover these three special kinds of plots: Joint Plot, Pair Plot and Linear Model Plot

In the end, we will discuss the customization of plots by creating themes based on the style, context, colour palette and font.

## Who this course is for:

• Data Science, Six Sigma and other professionals interested in data visualization
• Professionals interested in creating publication quality plots
• Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring

## Course content

5 sections • 73 lectures • 9h 26m total length
• Introduction
• Getting Data and Using the Pandas Package to Plot
• Matplotlib Library for Plots
• Seaborn Library for Plots
• Python for Absolute Beginners
Last updated 3/2021
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