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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.
Install required libraries: pip install requests pandas matplotlib

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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)

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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
...

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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()

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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")

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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")

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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.

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Next steps

Ready to organize your code like a pro? Let’s learn proper project structure!

Practical Python

Build real programs with proper structure