• 2.2.2 API Integration:

  • • o Identify a public API (e.g., OpenWeatherMap, CoinGecko).

    • o Use Python to fetch data from the API and store it in a

    • DataFrame.

    • o Display the first few rows of the DataFrame.

Step 1: Install Required Libraries 

pip install requests pandas

import requests

import pandas as pd

# Define the API endpoint for OpenWeatherMap

url = "http://api.openweathermap.org/data/2.5/weather"

# Replace 'your_api_key' with your actual OpenWeatherMap API key

api_key = 'your_api_key'

# Specify the city for which you want to fetch weather data

city = "London"

# Define parameters for the API request

params = {

    "q": city,

    "appid": api_key,

    "units": "metric"  # Use "metric" for Celsius

}

# Make a GET request to the OpenWeatherMap API

response = requests.get(url, params=params)

# Check if the request was successful (status code 200)

if response.status_code == 200:

    # Convert the JSON response to a dictionary

    data = response.json()

    # Extract relevant information from the data

    weather_data = {

        "City": [data["name"]],

        "Weather": [data["weather"][0]["description"]],

        "Temperature (C)": [data["main"]["temp"]],

        "Humidity (%)": [data["main"]["humidity"]],

        "Wind Speed (m/s)": [data["wind"]["speed"]]

    }

    # Create a pandas DataFrame from the dictionary

    df = pd.DataFrame(weather_data)

    # Display the first few rows of the DataFrame

    print(df.head())

else:

    print(f"Failed to fetch data. Status code: {response.status_code}")

Expected Output

      City           Weather  Temperature (C)  Humidity (%)  Wind Speed (m/s)

0  London  light rain        15.0             82            5.1

Explanation:

How This Script Works:

1. API Endpoint: Uses the OpenWeatherMap API endpoint for fetching weather data.

2. API Key: Replace 'your_api_key' with your actual API key.

3. City Parameter: Specifies the city for which you want to retrieve weather data (e.g., London).

4. Units Parameter: Uses "metric" to get temperature data in Celsius.

5. Request and Response: Makes a GET request with requests.get().

6. Data Extraction: Extracts data like city name, weather description, temperature, humidity, and wind speed.

7. DataFrame Creation: Stores this data in a pandas DataFrame and displays the first few rows using print(df.head()).