Mastering Data Visualization with Python

Mastering Data Visualization with Python

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.

Visualize data using pandas, matplotlib and seaborn libraries for data analysis and data science

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What you will 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

Rating: 4.68919

Level: All Levels

Duration: 9.5 hours

Instructor: Sandeep Kumar, ­ Quality Gurus Inc.