Extracting Live Weather API Data into a Pandas DataFrame

To fetch live meteorological data from a public Representational State Transfer Application Programming Interface (REST API) without requiring an API key, use the Python requests library combined with Pandas. Run this snippet instantly inside Google Colab:

import requests
import pandas as pd

# Fetch live hourly weather forecast endpoint (Open-Meteo)
url = "https://api.open-meteo.com/v1/forecast?latitude=52.52&longitude=13.41&hourly=temperature_2m,relative_humidity_2m"
response = requests.get(url)

if response.status_code == 200:
    data = response.json()
    hourly_data = data['hourly']
    
    # Build DataFrame from time-series lists
    df = pd.DataFrame({
        'Time': pd.to_datetime(hourly_data['time']),
        'Temperature_C': hourly_data['temperature_2m'],
        'Humidity_Percent': hourly_data['relative_humidity_2m']
    })
    
    print(df.head())
else:
    print(f"Error fetching weather data: {response.status_code}")

Prerequisites & Required Libraries Top ↑

Ensure you have the required libraries installed in your Python environment or Google Colab notebook:

!pip install --upgrade requests pandas

Handling Time-Series & Datetime Indexing Top ↑

Weather APIs return timestamp arrays as string lists. Converting them using pd.to_datetime() and setting them as the DataFrame index allows powerful time-based slicing and filtering:

import requests
import pandas as pd

url = "https://api.open-meteo.com/v1/forecast?latitude=28.61&longitude=77.20&hourly=temperature_2m"
response = requests.get(url)
data = response.json()

df = pd.DataFrame({
    'Timestamp': pd.to_datetime(data['hourly']['time']),
    'Temp': data['hourly']['temperature_2m']
})

# Set timestamp as index for time-series operations
df.set_index('Timestamp', inplace=True)
print(df.head())

Resampling Meteorological Data Top ↑

You can aggregate hourly readings into daily summary statistics using Pandas resample():

# Calculate daily maximum and minimum temperatures
daily_summary = df.resample('D').agg({'Temp': ['max', 'min', 'mean']})
print(daily_summary.head())

Saving Weather Data (SQLite, CSV, Excel) Top ↑

Persist your processed weather forecasts across various formats:

• SQLite Database: Use pandas to_sql().
• CSV Files: Export using pandas to_csv().
• Excel Workbooks: Save using pandas to_excel().

import sqlite3

# Save to CSV
df.to_csv("weather_forecast.csv")

# Save to SQLite
conn = sqlite3.connect("weather.db")
df.to_sql("hourly_forecast", conn, if_exists="replace", index=True)
conn.close()
print("Weather data successfully persisted!")

Interactive Companion Jupyter Notebook

Explore live weather API extraction code cells, test datetime resampling, and run self-validating data assertions in Google Colab.

View on GitHub

Frequently Asked Questions Top ↑

Related Python & Pandas Tutorials

• Pandas Input and Output Guide
• Extracting Live API Data into a Pandas DataFrame
• Github Live API Data — Create and save from DataFrames.
• Pandas to_csv() Guide
• Getting Started with Google Colab Notebooks

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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