Data Wrangling with Python – Rome Weather & Air Quality

This repository contains the final project for the Data Wrangling with Python course.
The goal is to gather, assess, clean, and analyze two real-world datasets in order to answer a research question about the relationship between weather conditions and air quality in Rome.

📂 Project Structure

├── data/
│   ├── raw/                # Raw datasets before cleaning
│   │   ├── air_quality_rome_2022.csv
│   │   ├── air_quality_rome_2023.csv
│   │   └── weather_rome_2022_2024.csv
│   ├── cleaned/            # Cleaned datasets after wrangling
│   └── merged/             # Final dataset (merged and tidy)
├── modules/                # Custom Python modules for data gathering
│   ├── openaq_loader.py    # Fetch and save daily air quality data from OpenAQ API
│   └── openmeteo_weather.py # Fetch and process daily weather data from Open-Meteo API
├── data_wrangling_project_filled.ipynb # Main notebook with all steps
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation

📊 Datasets

Dataset 1 – Weather Data

• Source: Open-Meteo API
• Method: Programmatic API request using a custom Python module (openmeteo_weather.py).
• Variables:
  • date — date of observation
  • temp_c — daily average temperature (°C)
  • rhum_pct — average relative humidity (%)
  • wind_speed_ms — average wind speed (m/s)
  • precip_mm — daily precipitation (mm)

Dataset 2 – Air Quality Data

• Source: OpenAQ API
• Method: Programmatic API request, aggregated daily, and saved as CSV (openaq_loader.py).
• Variables:
  • date — date of observation
  • city — observation city (Rome)
  • pm25 — daily average PM2.5 concentration (µg/m³)
  • no2 — daily average NO₂ concentration (µg/m³)

⚙️ Project Steps

1. Gathering

   • Weather data via Open-Meteo API (JSON → DataFrame).
   • Air quality data via OpenAQ API, saved as CSV locally.

2. Assessing

   • Checked for missing values, duplicates, and outliers.
   • Identified data quality issues (e.g., NaN values, extreme outliers in precipitation).
   • Identified tidiness issues (pollutants stored as separate columns instead of a variable).

3. Cleaning

   • Converted date columns to proper datetime format.
   • Removed duplicates and handled missing values.
   • Reshaped the air quality dataset into tidy format with pd.melt().

4. Storing

   • Saved cleaned datasets to /data/cleaned/.
   • Produced a merged tidy dataset in /data/merged/.

5. Analysis & Visualization

   • Explored relationships between weather variables and pollutant concentrations.
   • Created visualizations (boxplots, scatterplots, line charts).
   • Justified outliers (e.g., heavy rainfall days are meteorologically plausible).

❓ Research Question

How do weather conditions (temperature, humidity, wind speed, precipitation) affect air quality (PM2.5 and NO₂) in Rome between 2022 and 2024?

📈 Results

• Higher wind speeds are generally associated with lower pollutant concentrations, as wind disperses particles.
• Heavy rainfall events correspond to outliers in precipitation but are meteorologically valid and often associated with cleaner air afterwards.
• NO₂ concentrations are higher in colder months, consistent with traffic and heating emissions.

🚀 Next Steps

If given more time:

• Extend analysis to multiple cities (e.g., Milan, Naples) to compare patterns.
• Integrate additional pollutants (O₃, CO).
• Apply regression or machine learning models to quantify relationships.

🛠 Requirements

Install dependencies with:

pip install -r requirements.txt

Main packages:

• pandas
• numpy
• matplotlib
• requests

👤 Author

Luca Scarpantonio
Data Wrangling with Python – Final Project