> ## Documentation Index
> Fetch the complete documentation index at: https://python4ai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Weather Data Analysis Project
> Analyze and visualize data from APIs
## 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`:
```bash theme={null}
# 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:
```text theme={null}
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:
```bash theme={null}
mkdir data src
mv main.py src/
```
### 3. Install Dependencies
Install the required packages for API requests, data analysis, and plotting using `uv add`:
```bash theme={null}
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:
```python theme={null}
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:
```python theme={null}
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:
```python theme={null}
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:
```python theme={null}
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:
```python theme={null}
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](https://www.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!
Create interactive web applications and AI dashboards