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This section brings together everything you’ve learned — project structure, APIs, data processing, and visualization — into a single end-to-end project. You’ll build a weather data analysis application that fetches real temperature data from a live API, processes it with pandas, and generates charts with matplotlib. Along the way, you’ll also learn to use uv, a modern Python tool that replaces pip and venv with a single, dramatically faster command-line tool. Once you start using uv, the traditional approach will feel slow and cumbersome by comparison.

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What you’ll build

By the end of this section, you’ll have a working Python project that:
  • Fetches 7 days of historical weather data from the Open-Meteo API (no API key required)
  • Loads the data into a pandas DataFrame for analysis
  • Generates a temperature chart with matplotlib
  • Saves both the chart and the raw data to your project’s data/ folder
  • Is set up using modern uv tooling with a proper pyproject.toml

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What you’ll learn

Why uv?

See why uv is 10–100x faster than pip and how it simplifies your entire workflow

Virtual Environments with uv

Create isolated project environments and manage dependencies the modern way

Complete Project Setup

Follow a step-by-step checklist to set up any new Python project professionally

Weather Data Analysis

Build the complete weather analysis project with API calls, pandas, and matplotlib
The Open-Meteo API used in this project is completely free and requires no API key — you can run the project immediately without signing up for anything.