Data Visualization using Python, Matplotlib and Seaborn

Part 8 of "Data Analysis with Python: Zero to Pandas"

This tutorial is the eighth in a series on introduction to programming and data analysis using the Python language. These tutorials take a practical coding-based approach, and the best way to learn the material is to execute the code and experiment with the examples. Check out the full series here:

1. First Steps with Python and Jupyter
2. A Quick Tour of Variables and Data Types
3. Branching using Conditional Statements and Loops
4. Writing Reusable Code Using Functions
5. Reading from and Writing to Files
6. Numerical Computing with Python and Numpy
7. Analyzing Tabular Data using Pandas
8. Data Visulation using Matplotlib & Seaborn

Introduction

Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers. Visualizing data is an essential part of data analysis and machine learning. In this tutorial, we'll use Python libraries Matplotlib and Seaborn to learn and apply some popular data visualization techniques.

To begin let's import the libraries. We'll use the matplotlib.pyplot for basic plots like line & bar charts. It is often imported with the alias plt. The seaborn module will be used for more advanced plots, and it is imported with the alias sns.

Notice this we also include the special command %matplotlib inline to ensure that plots are shown and embedded within the Jupyter notebook itself. Without this command, sometimes plots may show up in pop-up windows.