Data Visualization with Python

Welcome to Data Visualization#

You have mastered data manipulation with Pandas. Now you will learn Data Visualization—creating clear, informative charts and graphs to communicate insights from your data.

Data visualization transforms numbers into visual stories. A well-designed chart can reveal patterns, trends, and relationships that are invisible in raw data. This module teaches you to create professional visualizations using Matplotlib and Pandas plotting.

By the end of this module, you will be able to:

• Understand graph anatomy and coordinate systems
• Create line plots for time series data
• Build scatter plots to explore correlations
• Design bar charts and histograms for distributions
• Use Pandas plotting for quick visualizations
• Create multi-panel subplot grids
• Apply visualization best practices
• Complete a real-world traffic analysis project

This module contains 16 lessons organized into 5 modules covering all essential visualization techniques.

Prerequisites#

Before starting this module, you should:

• Python basics: Variables, functions, control flow
• NumPy fundamentals: Arrays and basic operations
• Pandas fundamentals: DataFrames, selection, and manipulation

If you need to review these topics, see the NumPy module and Pandas module first.

Module Structure#

This module contains 16 lessons divided into 5 modules:

Module 1: Line Plots and Time Series (Lessons 1-4)#

Learn graph fundamentals, create line plots, and visualize time series data.

Module 2: Scatter Plots and Correlation (Lessons 5-8)#

Explore relationships between variables using scatter plots and correlation analysis.

Module 3: Bar Plots and Histograms (Lessons 9-12)#

Create categorical visualizations and analyze data distributions.

Module 4: Pandas Plotting and Grids (Lessons 13-15)#

Master Pandas plotting methods and create multi-panel subplot grids.

Module 5: Final Project (Lesson 16)#

Apply all skills to analyze São Paulo traffic patterns in a comprehensive project.

Datasets Used#

This module uses three real-world datasets:

1. Capital Bikeshare Dataset

• 731 days of bike rental data from Washington DC (2011-2012)
• Weather conditions, seasonality, and user patterns
• Used in Lessons 2-4, 6-8, 11-15

2. São Paulo Traffic Dataset

• 135 hours of traffic slowness measurements
• Multiple incident types tracked
• Used in final project (Lesson 16)

3. COVID-19 Dataset

• Monthly new cases and deaths (January-July 2020)
• Simple arrays for learning visualization basics
• Used in Lessons 1-2

Complete data dictionaries and download links are provided in each lesson.

Learning Path#

Each lesson builds on previous concepts. Follow the lessons in order for best results:

1. Start with graph fundamentals (Lesson 1)
2. Progress through line plots and time series (Lessons 2-4)
3. Learn scatter plots and correlation (Lessons 5-8)
4. Master categorical plots (Lessons 9-12)
5. Explore Pandas plotting and grids (Lessons 13-15)
6. Complete the final project (Lesson 16)

All Lessons#

Get Started#

Ready to create stunning visualizations? Start with Lesson 1 to understand graph fundamentals, then progress through the lessons at your own pace.

Transform your data into visual insights with Python visualization!