This week you'll learn how to visualize data and communicate insights using Plotly, a powerful Python library for interactive charts. You'll explore how to turn raw data into compelling visual stories that highlight trends, patterns, and relationships.
By the end of this week, you will be able to:
- Create basic and advanced plots with Plotly (scatter, bar, line, etc.)
- Customize visualizations (colors, labels, legends, layouts)
- Build interactive charts for data exploration
- Use Plotly to tell a story with data
- Apply visualization best practices for clarity and impact
- Lecture: Data Visualization I: Telling Stories with Plotly
- Tutorial: Data Visualization I: Plotly Basics
Assignment: Create your own analysis of the global temperature dataset using at least three different Plotly chart types. Your notebook should reveal patterns, trends, and insights about global temperatures over time.
Dataset: Average Monthly Surface Temperature (data/average-monthly-surface-temperature.csv)
Requirements:
- Explore the temperature dataset - Load and examine the data
- Create at least three different visualizations using different chart types (bar, line, scatter, box plots, etc.)
- Customize your charts with meaningful titles, labels, colors, and legends
- Add interactivity - Use hover information, zooming, or filtering features
- Interpret your findings - Write markdown cells explaining what patterns you discovered and what they reveal
Suggested Analyses:
- Compare average temperatures across different countries or regions
- Track how temperatures have changed over specific time periods
- Identify seasonal patterns in temperature data
- Compare temperature ranges (min/max variations) across locations
- Analyze temperature trends across decades
Next Week: Week 5: Organizing Projects with GitHub + AI Helpers Previous Week: Week 3: Working with DataFrames II – Cleaning & Transforming Data