COM6018 Data Science with Python

Week 5 - Introducing Matplotlib and Seaborn

Jon Barker

Overview

• Review of Lab Class 4
• Principles of Data Visualization
• Introducing Matplotlib and Seaborn
• Preview of Lab Class 5
• The 1st Assignment

Review of Lab Class 4

• We continued our analysis of the atmospheric gas concentration data.
• We used Pandas to repeat the work we had done in Lab Class 2.
• We introduced more datasets, i.e., SF₆ and N₂O.

The Problem

We want to combine the gas concentration datasets to produce a single plot like this,

Tasks

• Reading the data from the CSV files.
• Renaming fields to be consistent across the datasets.
• Dealing with missing data.
• Converting all gas concentrations to the same units.
• Merging the dataset
• Computing the carbon dioxide equivalent (CO₂e) concentration.
• Making the plot.

We did all of this in NumPy in lab 2 (lots of code!). We will now do it in Pandas (much less code!).

The Solution Notebook

Open the Solution Notebook

Principles of Data Visualization

• Data visualization is a key part of data science.
• We need to understand how to present data in a way that is meaningful and informative.

https://www.biostat.wisc.edu/~kbroman/presentations/graphs2017.pdf

Introducing Matplotlib and Seaborn

What is Matplotlib?

• Matplotlib is a low-level Python library for creating plots.
• It is the most widely used plotting library in Python.
• It is very powerful and flexible.

What is Seaborn?

• Seaborn is a high-level Python data visualization library built on Matplotlib.
• It is particularly good for statistical data visualization (i.e., examining the distribution of data).

Matplotlib and Seaborn Tutorial

Link to the Matplotlib tutorial

Lab Class 5 Preview

• You will be provided with several complex datasets.
• You will use Matplotlib to reconstruct a series of plots.

Lab Class 5

Link to lab class

Assignment 1

• This has now been released.
• Worth 40% of the module mark.
• Due on Friday 8th November.

Next Steps

• Review the Week 4 materials:

  • Review the Lab 4 solution notebook if you have not already done so.
  • Make sure you have read the Week 4 tutorial on Pandas

• Prepare for the Week 5 lab class:

  • Read through the Matplotlib tutorial.

• Read through the assignment instructions. Bring any questions to the lab class on Friday.