Exploratory Data Analysis - Data Visualization for Exploration

About

This module introduces matplotlib and plotting different chart types within pandas.

A rundown of how to build different chart types with matplotlib, including heatmaps with seaborn, and an intro to matplotlib’s object-oriented interface.

Learning Objectives

You will be able to:

Apply appropriate chart types when exploring a data set.
Explore patterns and relationships in data with bar charts and scatter plots.
Create line charts to explore temporal trends in a data set.
Understand the object-oriented way of working with matplotlib to fully control your visualizations.
Generate heat maps in seaborn.

Content

Topic	About
Full Lesson Deck	Overview of EDA and `pandas`, including the notebooks below
02 Distributions	A guided walkthrough of summarizing and visualizing distributions
03 Descriptive statistics and data joins in `pandas`	A guided walkthrough of calculating descriptive statistics with `pandas`, summarising data using `groupby` and joining data
04 Exercise - descriptive statistics with `pandas`	A descriptive statistics exercise using `pandas`

Prerequisites

Write and run Python code in a Jupyter notebook.
Create basic Python functions.
Python programming skills including using modules and libraries.
Basic knowledge of the pandas library.
Data visualization basics (different chart types).

References

📖 Reference Materials

Important note

Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)