# Mastering Data Visualization with Python

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

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.