Weather Data Analysis Project with APIs and Python

Everything you’ve learned so far — APIs, pandas, matplotlib, project structure, virtual environments — comes together in this project. You’ll fetch a full week of historical weather data from the Open-Meteo API (free, no key required), load it into a DataFrame, compute statistics, produce a visualization, and save both the chart and the raw data to disk. This is exactly the kind of end-to-end data analysis workflow you’ll use in real-world Python and AI projects.

Project setup

Before writing any analysis code, you’ll create a clean, isolated project environment using uv.

Initialize the project

mkdir weather-analysis
cd weather-analysis
uv init

Create the folder structure

A clean layout keeps your source code, data files, and outputs organized:

weather-analysis/
├── data/               # Raw weather CSV files
├── src/                # Python source files
│   └── main.py         # Main analysis script
├── pyproject.toml      # Project config and dependencies
└── .gitignore          # Ignored files (data/, .venv/, etc.)

Create the folders and move the default script:

mkdir data src
mv main.py src/

Install dependencies

uv add requests pandas matplotlib

This creates .venv automatically and locks exact package versions in uv.lock for reproducibility.

Fetch 7 days of weather data

The Open-Meteo API provides historical weather data with no authentication required. The following code calculates the date range and builds the request URL dynamically:

import requests
from datetime import datetime, timedelta

# Calculate the date range
today    = datetime.now()
week_ago = today - timedelta(days=7)

# Format dates as required by the API (YYYY-MM-DD)
start_date = week_ago.strftime("%Y-%m-%d")
end_date   = today.strftime("%Y-%m-%d")

# Request daily max and min temperatures for Paris
url = (
    f"https://api.open-meteo.com/v1/forecast"
    f"?latitude=48.85&longitude=2.35"
    f"&start_date={start_date}&end_date={end_date}"
    f"&daily=temperature_2m_max,temperature_2m_min"
)

response = requests.get(url)
data = response.json()
print(data)

Load the data into pandas

Organize the API response into a structured DataFrame:

import pandas as pd

# Extract the daily section of the response
daily = data["daily"]

# Build the DataFrame
df = pd.DataFrame({
    "date":     daily["time"],
    "max_temp": daily["temperature_2m_max"],
    "min_temp": daily["temperature_2m_min"],
})

# Convert date strings to proper datetime objects
df["date"] = pd.to_datetime(df["date"])

print(df)

You’ll see output like:

        date  max_temp  min_temp
0 2024-01-08      12.3       5.1
1 2024-01-09      11.8       4.2
2 2024-01-10      13.5       6.0
...

Visualize the data

Create a line chart showing max, min, and average temperature over the week:

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(df["date"], df["max_temp"], marker="o", label="Max Temp")
plt.plot(df["date"], df["min_temp"], marker="o", label="Min Temp")

plt.xlabel("Date")
plt.ylabel("Temperature (°C)")
plt.title("Paris Weather — Past 7 Days")
plt.legend()
plt.xticks(rotation=45)
plt.tight_layout()

plt.savefig("data/weather_chart.png")
plt.show()

Save the data to CSV

import os

# Ensure the data folder exists
os.makedirs("data", exist_ok=True)

# Save the DataFrame
df.to_csv("data/paris_weather.csv", index=False)
print("Data saved to data/paris_weather.csv")

Complete example

Here’s the full script in one place, ready to run from the src/ directory:

# src/main.py
import requests
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import os

# ── 1. Fetch weather data ─────────────────────────────────────────────────────
today    = datetime.now()
week_ago = today - timedelta(days=7)

url = (
    f"https://api.open-meteo.com/v1/forecast"
    f"?latitude=48.85&longitude=2.35"
    f"&start_date={week_ago.strftime('%Y-%m-%d')}"
    f"&end_date={today.strftime('%Y-%m-%d')}"
    f"&daily=temperature_2m_max,temperature_2m_min"
)

data = requests.get(url).json()

# ── 2. Process with pandas ────────────────────────────────────────────────────
df = pd.DataFrame({
    "date":     pd.to_datetime(data["daily"]["time"]),
    "max_temp": data["daily"]["temperature_2m_max"],
    "min_temp": data["daily"]["temperature_2m_min"],
})

df["avg_temp"] = (df["max_temp"] + df["min_temp"]) / 2

# ── 3. Visualize ──────────────────────────────────────────────────────────────
plt.figure(figsize=(10, 6))
plt.plot(df["date"], df["max_temp"], "r-o", label="Max")
plt.plot(df["date"], df["min_temp"], "b-o", label="Min")
plt.plot(df["date"], df["avg_temp"], "g--",  label="Average")

plt.xlabel("Date")
plt.ylabel("Temperature (°C)")
plt.title("Paris Weather — Past Week")
plt.legend()
plt.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()

# ── 4. Save results ───────────────────────────────────────────────────────────
os.makedirs("data", exist_ok=True)

plt.savefig("data/weather_chart.png")
df.to_csv("data/paris_weather.csv", index=False)

print(f"Average temperature: {df['avg_temp'].mean():.1f}°C")
print("Files saved in 'data/' folder")

Run the script from the project root:

uv run python src/main.py

What you’ve accomplished

Look at what this project brings together:

Real API integration — fetching live data from a public HTTP endpoint
Date arithmetic — computing dynamic date ranges with datetime and timedelta
Data processing — reshaping and enriching a DataFrame with pandas
Visualization — producing a multi-series chart with matplotlib
File handling — creating directories and saving CSV and image files
Project structure — organized folders, a virtual environment, and locked dependencies

This is exactly how data analysis and AI projects work in the real world.

Try modifying the script to fetch weather for your own city. Look up your coordinates at latlong.net and update the latitude and longitude values in the URL.

What’s next?

Now that you’ve built and organized a complete Python project, the next step is creating interactive web interfaces and AI dashboards using Streamlit.

Building UIs with Streamlit

Create interactive web applications and AI dashboards

Developer Tools

Environment & Secrets

Project Structure

Weather Project

Weather Data Analysis Project with APIs and Python

Project setup

Fetch 7 days of weather data

Load the data into pandas

Visualize the data

Save the data to CSV

Complete example

What you’ve accomplished

What’s next?

Building UIs with Streamlit

​Project setup

​Fetch 7 days of weather data

​Load the data into pandas

​Visualize the data

​Save the data to CSV

​Complete example

​What you’ve accomplished

​What’s next?

Building UIs with Streamlit

Project setup

Fetch 7 days of weather data

Load the data into pandas

Visualize the data

Save the data to CSV

Complete example

What you’ve accomplished

What’s next?