Extracting Data from APIs
123456789101112131415161718import requests import pandas as pd # Define the API endpoint and parameters url = "https://jsonplaceholder.typicode.com/posts" params = {"_limit": 10} # Limit to 10 results for demonstration # Make the API request response = requests.get(url, params=params) # Check if the request was successful if response.status_code == 200: data = response.json() # Load the JSON data into a pandas DataFrame df = pd.DataFrame(data) print(df.head()) else: print(f"Request failed with status code {response.status_code}")
When extracting data from APIs, you will often encounter several important considerations. Many APIs require authentication, such as API keys or OAuth tokens, which you must include in your requests—commonly as headers or query parameters. Always check the API documentation to determine the required authentication method and how to securely manage credentials.
APIs that return large datasets may use pagination to split results across multiple pages. To retrieve all data, you need to detect and follow pagination links or increment page parameters in your requests. This often involves looping through requests until no more results are returned or until a specified limit is reached.
Error handling is essential when working with APIs. Always check the HTTP status code of each response and handle errors gracefully. For example, you should retry requests if you receive temporary errors, handle rate limits as specified by the API provider, and log failures for later review. Proper error handling ensures your extraction process is robust and reliable.
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123456789101112131415161718import requests import pandas as pd # Define the API endpoint and parameters url = "https://jsonplaceholder.typicode.com/posts" params = {"_limit": 10} # Limit to 10 results for demonstration # Make the API request response = requests.get(url, params=params) # Check if the request was successful if response.status_code == 200: data = response.json() # Load the JSON data into a pandas DataFrame df = pd.DataFrame(data) print(df.head()) else: print(f"Request failed with status code {response.status_code}")
When extracting data from APIs, you will often encounter several important considerations. Many APIs require authentication, such as API keys or OAuth tokens, which you must include in your requests—commonly as headers or query parameters. Always check the API documentation to determine the required authentication method and how to securely manage credentials.
APIs that return large datasets may use pagination to split results across multiple pages. To retrieve all data, you need to detect and follow pagination links or increment page parameters in your requests. This often involves looping through requests until no more results are returned or until a specified limit is reached.
Error handling is essential when working with APIs. Always check the HTTP status code of each response and handle errors gracefully. For example, you should retry requests if you receive temporary errors, handle rate limits as specified by the API provider, and log failures for later review. Proper error handling ensures your extraction process is robust and reliable.
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