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Topics

Python Basics

Introduction to PythonInstalling PythonPython SyntaxPython CommentsPython VariablesPython Data TypesPython Type ConversionPython OperatorsPython Input/OutputPython Indentation

Control Structures

Python If...ElsePython While LoopsPython For LoopsPython Break and ContinuePython Pass StatementPython Match-Case Statements

Python Data Structures

Python ListsPython TuplesPython SetsPython DictionariesPython ArraysPython List Comprehension

Python Functions

Defining Python FunctionsPython Function ArgumentsPython Function Return ValuesPython Lambda FunctionsPython Recursive FunctionsPython Global and Local Variables

Python OOP

Python Classes and ObjectsPython InheritancePython EncapsulationPython PolymorphismPython Method OverridingPython Abstract Classes

Python Modules and Packages

Creating Python ModulesImporting Python ModulesPython Built-in ModulesPython Package Management (pip)Creating Python Packages

Python File Handling

Python File Open/ClosePython File ReadPython File WritePython File ManagementPython Directory Handling

Python Exception Handling

Python Try...ExceptPython User-defined ExceptionsPython Raise KeywordPython Finally Clause

Python Advanced Topics

Python IteratorsPython GeneratorsPython DecoratorsPython Context ManagersPython Regular ExpressionsPython MultithreadingPython Multiprocessing

Python Standard Library

Python Math ModulePython Random ModulePython DateTime ModulePython JSON ModulePython OS ModulePython Sys Module

Python Data Manipulation

Python String ManipulationPython List OperationsPython Dictionary OperationsPython Set Operations

Python Testing

Python Unit TestingPython DoctestPython PytestPython Test-Driven Development

Python Performance

Python Code OptimizationPython ProfilingPython Memory ManagementPython Caching Techniques

Python and Databases

Python SQLitePython MySQL ConnectorPython PostgreSQLPython MongoDB

Python Web Scraping

Python Requests LibraryPython BeautifulSoupPython Scrapy Basics

Python and APIs

Python REST API BasicsPython API AuthenticationPython API Data Handling

Python API Data Handling

API data handling in Python is a crucial skill for developers working with web services and external data sources. It involves retrieving, parsing, and manipulating data from APIs efficiently.

Understanding API Responses

Most APIs return data in JSON format. Python's JSON Module is essential for parsing these responses. Here's a basic example:


import json
import requests

response = requests.get('https://api.example.com/data')
data = json.loads(response.text)
print(data['key'])

Working with the Requests Library

The Requests Library simplifies HTTP requests in Python. It's widely used for API interactions:


import requests

response = requests.get('https://api.example.com/users', params={'limit': 10})
if response.status_code == 200:
    users = response.json()
    for user in users:
        print(user['name'])

Handling Pagination

Many APIs use pagination to limit the amount of data returned in a single request. Here's how to handle it:


import requests

url = 'https://api.example.com/items'
all_items = []

while url:
    response = requests.get(url)
    data = response.json()
    all_items.extend(data['items'])
    url = data.get('next_page_url')

print(f"Total items collected: {len(all_items)}")

Error Handling

Robust error handling is crucial when working with APIs. Use try...except blocks to manage potential issues:


import requests

try:
    response = requests.get('https://api.example.com/data')
    response.raise_for_status()
    data = response.json()
except requests.exceptions.HTTPError as http_err:
    print(f"HTTP error occurred: {http_err}")
except requests.exceptions.RequestException as req_err:
    print(f"Request error occurred: {req_err}")
except json.JSONDecodeError as json_err:
    print(f"JSON decoding error: {json_err}")

Best Practices

  • Use API keys securely, never hardcode them in your scripts.
  • Implement rate limiting to avoid overwhelming the API server.
  • Cache responses when appropriate to reduce API calls.
  • Validate and sanitize data received from APIs before processing.
  • Use asynchronous requests for improved performance when dealing with multiple API calls.

Conclusion

Mastering API data handling in Python opens up a world of possibilities for integrating external services and data sources into your applications. By combining the power of the Requests Library with Python's built-in JSON Module, you can efficiently interact with APIs and process the data they provide.