Day 03 — Part 2: Data Visualization¶
A table of numbers is not insight. Visualization is the step that turns a DataFrame into something a human can reason about — and something a stakeholder will actually act on. This session covers the two libraries every Python data practitioner uses daily: Matplotlib for control, Seaborn for speed.
Session Overview
Difficulty: Beginner–Intermediate Reading time: ~1.5 hours | Exercises: ~1.5 hours Prerequisites: Python basics · Pandas DataFrames (Day 02)
What You Will Learn¶
By the end of this session you will be able to:
- Create line, bar, histogram, scatter, and box plots
- Add titles, labels, legends, and figure size to any chart
- Read a correlation heatmap and draw conclusions from it
- Choose the right chart type for a given analytical question
- Apply best-practice formatting to a publication-ready figure
Session Roadmap¶
| # | Topic | File | Time |
|---|---|---|---|
| 1 | Matplotlib Basics — figures, axes, plot(), show() | 01-matplotlib-basics.md |
20 min |
| 2 | Line, Bar & Histogram — when to use each, customisation | 02-line-bar-histogram.md |
20 min |
| 3 | Seaborn Basics — statistical plots with less code | 03-seaborn-basics.md |
20 min |
| 4 | Heatmaps — correlation matrices, sns.heatmap | 04-heatmaps.md |
15 min |
| 5 | Visualization Best Practices — chart selection, colour, labels | 05-visualization-best-practices.md |
15 min |
| 6 | Mini Project — EDA visualisation on a real dataset | 06-mini-project.md |
45 min |
Total active learning time: ~2.5 hours
How to Use This Session¶
Work through files 01–05 in sequence — Matplotlib concepts from file 01 are used throughout all later files. For each chart type, reproduce the example in a Jupyter notebook and then change one thing: the colour, the dataset, the axis labels. This "break and fix" approach builds intuition faster than passive reading.
The Mini Project in file 06 ties everything together: you will produce a complete EDA visualisation suite on a real dataset, which you can drop straight into a portfolio.
Before You Start
Run import matplotlib.pyplot as plt; import seaborn as sns; print(sns.__version__) to confirm both libraries are installed. If seaborn is missing, run pip install seaborn before opening file 01.