Build a Weather Data Analytics & Forecasting Tool: Day 2 - Weather Data Analysis & Visualization

Level: Intermediate

Projects in this week’s series:

This week, we progressively build a weather data analytics and forecasting tool with Python.

• Day 1: Weather Data Collector

• Day 2: Weather Data Analysis & Visualization (Today)

• Day 3: Advanced Analytics & Predictions

View All Projects This Week

---

Today’s Project

Yesterday we built a data collector that saves weather information to CSV files. Today we’re analyzing that data — creating charts, identifying patterns, comparing cities, and turning raw numbers into visual insights!

We’re transforming your CSV data into compelling visualizations that reveal weather trends, patterns, and comparisons at a glance!

---

Project Task

Create a weather data analyzer that:

• Loads weather data from CSV files

• Calculates statistical summaries (averages, highs, lows)

• Creates temperature trend line charts

• Generates comparison bar charts for multiple cities

• Visualizes precipitation probability patterns

• Shows humidity and wind speed distributions

• Creates multi-city comparison charts

• Saves all charts as image files

This project gives you hands-on practice with pandas data analysis, matplotlib visualization, statistical calculations, chart creation, and building data analytics pipelines — essential skills for any data science project!

Download these sample CSV data here if you don’t have them.

---

Expected Output

The app will produce several PNG charts in the local directory:

Here is how they look:

---

Coming Tomorrow

Tomorrow we’re adding advanced analytics — moving averages, correlation analysis, seasonal patterns, simple forecasting with linear regression, and comprehensive PDF reports with all charts embedded!

---

View Code Evolution

Compare today’s solution with earlier versions and see how we’re building a complete weather analytics system.