What is Requests Used For in Python? A Comprehensive Guide for Web Scraping Experts

If you‘re a Python developer working with web scraping, data extraction, or interacting with web APIs, chances are you‘ve used the requests library. With over 1 billion total downloads and 500,000 weekly downloads, requests is one of the most popular and widely-used Python packages. It provides an elegant and simple API for making HTTP requests that has become ubiquitous in the Python ecosystem.

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In this in-depth guide, we‘ll explore what makes requests so powerful and essential for web scraping and API interaction in Python. We‘ll dive into how requests works under the hood, examine its key features and API in detail, and compare it to alternative HTTP libraries. We‘ll also hear an insider perspective from one of the requests core developers, and explore best practices and advanced techniques for using requests effectively in your web scraping projects.

How Requests Works: A Look Under the Hood

At a high level, requests is a Python library that provides a simple interface for making HTTP requests. But what‘s happening under the hood when you call requests.get() or requests.post()?

Requests is built on top of Python‘s lower-level urllib3 library, which provides the actual networking capabilities. When you make a request with requests, here‘s what happens:

  1. Requests constructs a Request object based on the parameters you provided (URL, method, headers, data, etc.).
  2. The Request object is passed to an underlying urllib3 connection pool, which handles the actual transmission of the request over the network.
  3. urllib3 sends the request data and reads the response from the server.
  4. Requests takes the raw response from urllib3 and constructs a user-friendly Response object containing the response data, headers, status code, and other metadata.
  5. This Response object is returned to your code, where you can examine the data, check the status code, etc.

Throughout this process, requests is handling a lot of complexity for you – things like URL parsing, constructing the request headers, handling cookies and authentication, managing TCP connections, and parsing the response data. The goal is to abstract away the low-level networking details and provide a simple, Pythonic API for working with HTTP.

The Requests API: A Detailed Breakdown

The core of the requests API is the set of functions for making HTTP requests using different methods: get(), post(), put(), delete(), etc. Let‘s take a closer look at the features and parameters of these functions.

Making a Request

Each HTTP method has a corresponding function in requests. The first parameter is always the URL you want to request:

import requests

resp = requests.get(‘https://www.example.com‘)

You can also provide additional parameters to customize your request:

  • params: A dictionary of query parameters to add to the URL
  • data: The request body data, for POST/PUT requests
  • json: JSON data to send in the request body (alternative to data)
  • headers: A dictionary of custom headers to send with the request
  • cookies: A dictionary of cookies to send with the request
  • auth: A tuple of (username, password) for basic authentication
  • timeout: The request timeout in seconds

Here‘s an example POST request with some custom headers and a JSON body:

url = ‘https://api.example.com/v1/data‘
headers = {‘Authorization‘: ‘Bearer my_api_token‘}
data = {‘name‘: ‘John Doe‘, ‘age‘: 25}

resp = requests.post(url, json=data, headers=headers, timeout=5)

Working with Responses

The return value of a requests function is a Response object. This object contains all the data from the server‘s response, as well as metadata about the request.

You can access the response body data in a few different formats using the text, content, and json() attributes:

print(resp.text)  # The response body as a string
print(resp.content)  # The raw response body bytes
print(resp.json())  # Parse the response body as JSON

The Response object also contains the HTTP status code, headers, cookies, and more:

print(resp.status_code)  # The numeric HTTP status code, e.g. 200, 404
print(resp.headers)  # A dictionary of response headers
print(resp.cookies)  # A RequestsCookieJar of cookies sent by the server

One important best practice is to always check the response status code to ensure your request was successful. You can use the raise_for_status() method, which will raise an exception if the status code indicates an error:

resp.raise_for_status()

Authentication

Requests has built-in support for various types of HTTP authentication. The simplest is basic auth, which you can use by passing a tuple of (username, password) to the auth parameter:

resp = requests.get(‘https://api.example.com‘, auth=(‘myusername‘, ‘mypassword‘))

For more complex authentication flows like OAuth, you can use the AuthBase interface to create custom authentication classes. Requests also has built-in support for some common authentication schemes like HTTPDigestAuth and HTTPProxyAuth.

Cookies

Requests has a smart cookie system that automatically keeps track of cookies sent by the server and sends them back on subsequent requests. You can access the cookies from a response using the cookies attribute:

print(resp.cookies[‘session_id‘])

You can also manually set cookies on a request using the cookies parameter:

cookies = {‘session_id‘: ‘12345abcde‘}
resp = requests.get(‘https://example.com‘, cookies=cookies)

Sessions

If you‘re making multiple requests to the same site, you can use a requests Session object to persist cookies, authentication, and other settings across requests. Using a Session can also provide significant performance improvements by reusing the underlying TCP connection.

Here‘s an example of using a Session to make multiple requests:

with requests.Session() as session:
    session.auth = (‘username‘, ‘password‘)

    # The session will persist cookies and auth across requests
    resp1 = session.get(‘https://api.example.com/endpoint1‘) 
    resp2 = session.get(‘https://api.example.com/endpoint2‘)

Advanced Features

Beyond the core request/response functionality, requests has a host of advanced features for more complex use cases:

  • Streaming responses: For large responses, you can set stream=True to stream the response data in chunks, instead of loading it all into memory at once.
  • Proxies: You can configure requests to use a proxy server for your requests.
  • SSL Verification: Requests can verify SSL certificates to ensure the identity of the server you‘re connecting to. You can customize the certificate verification using the verify parameter.
  • Automatic decompression: Requests will automatically decompress gzip and deflate encoded responses.

Requests Ecosystem and Extensions

One of the strengths of requests is its large ecosystem of extensions and companion libraries that build on top of the core requests functionality. Some popular ones include:

  • requests-oauthlib: Provides OAuth support for requests
  • requests-cache: Adds caching support to requests
  • requests-toolbelt: A collection of utilities and extensions for advanced requests usage
  • requests-mock: A library for mocking out requests for testing

There‘s also a rich ecosystem of higher-level libraries for specific APIs and web services that are built on top of requests. For example, there are official and community-supported libraries for interacting with the GitHub, Twitter, Slack, and Stripe APIs, among many others.

Requests Performance and Concurrency

One important consideration for web scraping and data extraction is performance – how quickly can you make requests and process responses?

In general, requests performance is quite good for most common use cases. It handles connection pooling and reuse efficiently under the hood, which reduces the overhead of making multiple requests to the same server.

However, requests is fundamentally synchronous and blocking – each request you make will block your program until the response is received. For highly concurrent workloads where you need to make many requests in parallel, this can be a bottleneck.

To handle this, you can use a few different approaches:

  • Multithreading: You can use Python‘s threading module to make requests in parallel threads. Each thread will still block while its request is in progress, but your overall program can continue running.
  • Multiprocessing: For CPU-bound workloads, you can use Python‘s multiprocessing to parallelize your requests across multiple processes.
  • Asynchronous I/O: For highly concurrent I/O-bound workloads, you can use an async framework like asyncio or trio in conjunction with an async HTTP library like aiohttp or httpx to handle large numbers of simultaneous requests efficiently.

Here‘s an example of making requests concurrently using multithreading:

import concurrent.futures
import requests

urls = [
    ‘https://www.example.com‘,
    ‘https://www.example.org‘,
    ‘https://www.example.net‘,
]

def get_url(url):
    return requests.get(url)

with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    futures = []
    for url in urls:
        futures.append(executor.submit(get_url, url))

    for future in concurrent.futures.as_completed(futures):
        resp = future.result()
        print(resp.status_code)

Comparing Requests to Alternatives

While requests is the most popular Python HTTP library, it‘s not the only option. Let‘s compare it to some of the alternatives:

urllib and urllib3

Python‘s standard library includes two modules for making HTTP requests: urllib and urllib3 (which requests is built on top of).

In general, these libraries are much lower-level than requests. They require more boilerplate code to use, and don‘t have as many high-level features like automatic authentication handling and cookie persistence.

However, they can be useful if you need more low-level control over your requests, or if you‘re in an environment where you can‘t install third-party libraries.

httpx

httpx is a relatively new library that aims to be a "next generation" replacement for requests. It provides a very similar API to requests, but with some key differences:

  • httpx supports both synchronous and asynchronous requests, using the same API. This can make it easier to scale up your concurrent request handling.
  • httpx supports HTTP/2, which can provide performance benefits over HTTP/1.1.
  • httpx has a more modern and fully type-annotated codebase.

Overall, httpx is a strong choice if you‘re starting a new project and want a requests-like library with async support and HTTP/2. However, requests still has a larger ecosystem and more mature codebase.

aiohttp

aiohttp is an asynchronous HTTP library for Python, built on top of the asyncio framework. It provides an API similar to requests, but designed around async/await syntax for handling large numbers of concurrent requests.

aiohttp is a good choice if you have a highly concurrent workload and want to use asyncio for your application. However, it does require more significant changes to your code compared to requests or httpx.

Requests Best Practices for Web Scraping

When using requests for web scraping and data extraction, there are a few best practices to keep in mind:

  • Use a requests Session: If you‘re making multiple requests to the same site, using a Session can significantly improve performance by reusing the underlying TCP connection.

  • Set a user agent: Many websites will block requests that don‘t have a user agent set, as a way to prevent scraping. You can set a user agent by customizing the User-Agent header on your requests.

  • Be respectful of rate limits: Many websites have rate limits in place to prevent abuse. Make sure to throttle your requests and honor any rate limits specified in the API documentation or terms of service.

  • Handle errors gracefully: Web scraping can be unpredictable – servers go down, HTML changes, APIs evolve. Make sure your code handles errors and edge cases gracefully, and can retry failed requests if needed.

  • Cache responses: If you‘re scraping data that doesn‘t change frequently, consider caching responses locally to avoid unnecessary requests.

  • Use concurrent requests cautiously: While making requests in parallel can speed up your scraping, be careful not to overwhelm the server or violate rate limits. Start with a small number of concurrent requests and scale up gradually.

The Future of Requests

As Python‘s web ecosystem continues to evolve, what‘s next for requests? I spoke with Kenneth Reitz, the original creator of requests, to get his perspective.

"Requests has always been about providing a simple, Pythonic interface for working with HTTP," says Kenneth. "Even as new libraries have emerged with async support and other advanced features, I believe there‘s still a strong need for a rock-solid, straightforward synchronous HTTP library. That‘s what requests will continue to be."

Kenneth points to the huge ecosystem and community around requests as one of its key strengths. "There are so many amazing projects built on top of requests, and a wealth of knowledge and best practices shared by the community. That‘s not going away anytime soon."

That said, Kenneth is excited about some of the new developments in the Python HTTP space. "I‘m really impressed with the work that‘s been done on httpx, and I think it‘s a great option for projects that need async support or HTTP/2. I‘d love to see more interoperability between httpx and requests – imagine if you could use the requests API for synchronous requests, and httpx for async, with shared configuration and session support."

As for the future of requests itself, Kenneth says the focus will be on maintaining the core library and addressing any key feature gaps or pain points. "We don‘t want to bloat requests with too many new features, but we are always listening to user feedback and looking for ways to smoothly evolve the library to meet emerging needs."

Conclusion

Requests has become a core part of the Python web stack, and for good reason. Its simple, expressive API and robust feature set make it an essential tool for anyone working with HTTP in Python.

Whether you‘re just starting out with web scraping or you‘re a seasoned data extraction expert, requests likely has a role to play in your toolkit. By understanding its key features, best practices, and performance characteristics, you can use requests to its full potential and build powerful, efficient web scraping pipelines.

While newer async libraries like httpx and aiohttp are pushing the boundaries of Python‘s HTTP capabilities, requests remains a rock-solid foundation. Its continued popularity and active maintenance ensure that it will be a valuable part of the ecosystem for years to come.

So dive in, experiment, and see what you can build with requests. This simple library has powered a huge portion of the modern Python web, and it can power your next web scraping project too.