Weather Data Analysis Project

Building on APIs

Let’s take the weather API from the previous page and create something useful. We’ll get weather data for the past 7 days, analyze it, visualize it, and save it. This brings together everything you’ve learned: APIs, data processing, file handling, and visualization.

Project Setup

Before writing any code, let’s set up a clean, isolated project environment using uv and organize our files.

1. Initialize the Project

Create a new directory for your project and initialize it with uv:

# Create and enter the project directory
mkdir weather-analysis
cd weather-analysis

# Initialize a new project with uv
uv init

2. Project Structure

A clean project structure helps keep your code, data, and output organized. Organize your folder structure like this:

weather-analysis/
├── data/               # For saving raw weather CSV files
├── src/                # For Python source files
│   └── main.py         # Main analysis and plotting script
├── pyproject.toml      # Project configuration and dependencies
└── .gitignore          # Ignored files (e.g. data/, .venv/)

Create the folders and move the default script using your terminal:

mkdir data src
mv main.py src/

3. Install Dependencies

Install the required packages for API requests, data analysis, and plotting using uv add:

uv add requests pandas matplotlib

This will automatically create a virtual environment (.venv) and lock exact versions in uv.lock for reproducibility.

Get 7 days of weather

The Open-Meteo API can give us historical data:

import requests
from datetime import datetime, timedelta

# Calculate dates
today = datetime.now()
week_ago = today - timedelta(days=7)

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

# Get Paris weather for past week
url = f"https://api.open-meteo.com/v1/forecast?latitude=48.85&longitude=2.35&start_date={start_date}&end_date={end_date}&daily=temperature_2m_max,temperature_2m_min"

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

Load into pandas

Now let’s organize this data:

import pandas as pd

# Extract the daily data
daily_data = data['daily']

# Create a DataFrame
df = pd.DataFrame({
    'date': daily_data['time'],
    'max_temp': daily_data['temperature_2m_max'],
    'min_temp': daily_data['temperature_2m_min']
})

# Convert date strings to datetime
df['date'] = pd.to_datetime(df['date'])

print(df)

Output:

        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 simple line chart:

import matplotlib.pyplot as plt

# Create the plot
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')

# Add labels and title
plt.xlabel('Date')
plt.ylabel('Temperature (°C)')
plt.title('Paris Weather - Past 7 Days')
plt.legend()

# Rotate x-axis labels for readability
plt.xticks(rotation=45)
plt.tight_layout()

# Save the plot
plt.savefig('weather_chart.png')
plt.show()

Save to CSV

Let’s save our data for later use:

import os

# Create data folder if it doesn't exist
if not os.path.exists('data'):
    os.makedirs('data')

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

Complete example

Here’s everything together:

import requests
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import os

# 1. Get weather data
today = datetime.now()
week_ago = today - timedelta(days=7)
start_date = week_ago.strftime("%Y-%m-%d")
end_date = today.strftime("%Y-%m-%d")

url = f"https://api.open-meteo.com/v1/forecast?latitude=48.85&longitude=2.35&start_date={start_date}&end_date={end_date}&daily=temperature_2m_max,temperature_2m_min"
response = requests.get(url)
data = response.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']
})

# 3. Calculate average
df['avg_temp'] = (df['max_temp'] + df['min_temp']) / 2

# 4. Create visualization
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()

# 5. Save everything
if not os.path.exists('data'):
    os.makedirs('data')

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")

What you’ve accomplished

Look at what you just did:

Connected to a real API
Worked with dates and time
Processed data with pandas
Created a visualization
Handled files and folders
Saved your results

This is exactly how data analysis works in the real world!

Try modifying the code to get weather for your city. Find your coordinates at latlong.net.

What’s next?

Now that you’ve built and organized your Python projects, let’s learn how to build interactive web interfaces and chatbots in pure Python using Streamlit!

Building UIs with Streamlit

Create interactive web applications and AI dashboards

Essential Tools

Environment & secrets

Practical Python

Project Development

Building on APIs

Project Setup

1. Initialize the Project

2. Project Structure

3. Install Dependencies

Get 7 days of weather

Load into pandas

Visualize the data

Save to CSV

Complete example

What you’ve accomplished

What’s next?

Building UIs with Streamlit

​Building on APIs

​Project Setup

​1. Initialize the Project

​2. Project Structure

​3. Install Dependencies

​Get 7 days of weather

​Load into pandas

​Visualize the data

​Save to CSV

​Complete example

​What you’ve accomplished

​What’s next?

Building UIs with Streamlit

Building on APIs

Project Setup

1. Initialize the Project

2. Project Structure

3. Install Dependencies

Get 7 days of weather

Load into pandas

Visualize the data

Save to CSV

Complete example

What you’ve accomplished

What’s next?