• 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 CoinGecko

url = "https://api.coingecko.com/api/v3/coins/markets"

params = {

    "vs_currency": "usd",  # Currency to compare against

    "order": "market_cap_desc",  # Sort by market cap descending

    "per_page": 10,  # Number of coins to fetch

    "page": 1,  # Page number for pagination

    "sparkline": "false"  # Exclude sparkline data for simplicity

}

# Make a GET request to the CoinGecko 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 pandas DataFrame

    data = response.json()

    df = pd.DataFrame(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

Some bold text

Paragraph

Explanation:

How This Program Works:

1. API Endpoint: The program accesses the CoinGecko API endpoint that provides market data for cryptocurrencies.

2. Parameters: The params dictionary specifies:

   • vs_currency: Base currency (e.g., "usd").

   • order: Order by market cap (descending).

   • per_page: Number of items per page.

   • page: Page number.

   • sparkline: Set to "false" to exclude additional data.

3. Request and Response: It makes a GET request using requests.get() with the specified URL and parameters.

4. DataFrame Creation: The response is converted into a pandas DataFrame.

5. Display: The first few rows of the DataFrame are displayed using print(df.head()).