Python is in the midst of a resurgence. It never went away, but usage now grows like never before. With machine learning developers and data scientists relying on Python, much of the web development ecosystem around the language continues to grow. One aspect that affects all three of these specializations is the powerful benefits of APIs. Pulling in data, and connecting to external services, is an essential part of any language. In this article, we'll look at the primary libraries for making HTTP requests, along with some common use cases that allow you to connect to an API in Python. Before that, we should ask an important question. Is Python good for APIs? It seems like a strange question, but given the large web presence of Node.js and Ruby, you may think that Python isn't good for making API calls. This isn't true. In fact, Python has had a long and dedicated presence on the web, specifically with it's Flask and Django libraries. As Python is a powerful, accessible way to manipulate data, it makes sense to also use it for acquiring the data sources. This is where API calls come in. Let's start with the most popular Python HTTP library used for making API calls. . Requests Requests Requests is the accessible, leading library that developers use for making API requests in Python. It offers an interface to make HTTP requests . Let's get right into some common types of requests you can make with Requests. The following examples will all assume that your project includes Requests. You can follow their , but the gist is: synchronously installation instructions Install it via pip or pipenv: pip install requests Then, make sure to import requests into your project requests import Common types of API calls with Requests The most simple GET request is intuitive. response = requests.get( ) 'https://example.com' As we can see with the get method above, Requests offers shortcut methods for the HTTP verbs, including POST, PUT, DELETE, HEAD, and OPTIONS. The previous request is pretty simple. Let's look at more complex requests. Often, an APIs documentation will require that you pass query parameters to a specific endpoint. To pass query parameters, we can pass them into get as the second argument. response = requests.get( , params={ : }) 'https://example.com' 'name' 'Bearer' The response variable contains the data returned by the API in our examples. There are three primary ways to access the data. As text, with response.textAs bites with response.contentAs JSON with response.json()Or as the raw response with response.raw In addition to the body of the response, we can also access the status code with response.status_code, the headers with response.headers, and so on. You can find a full list of properties and methods available on Response . in the requests.Response documentation As we saw with the params argument, we can also pass headers to the request. response = requests.get( example-heade Beare 'https://example.com, headers={' r': ' r'}) Here, we pass the headers argument with a python dictionary of headers. The last common API call type we'll make is a full-featured POST, with authentication. This will combine the previous headers technique with the use of the data argument. url = headers = { : }
payload = { : , email: }

response = requests.post(url, headers=headers, data=payload) 'https://example.com' 'Authorization' 'Bearer example-auth-code' 'name' 'Mark' 'mark@bearer.sh' This sends the payload as form-encoded data. For most modern APIs, we often need to send data as JSON. In this next example, we use the built in json helper from . requests url = headers = { : }
payload = { : , email: }

response = requests.post(url, headers=headers, json=payload) 'https://example.com' 'Authorization' 'Bearer example-auth-code' 'name' 'Mark' 'mark@bearer.sh' This will encode the payload as JSON, as well as automatically change the Content-Type header to application/json. Requests is excellent for synchronous API calls, but sometimes your app may depend on asynchronous requests. For this, we can use an asynchronous HTTP library like . aiohttp aiohttp When making asynchronous HTTP requests, you'll need to take advantage of some newer features in Python 3. While the requests library does have variations and plugins to handle asynchronous programming, one of the more popular libraries for async is aiohttp. Used together with the , we can use to make requests in an async way. The code is a little more complex, but provides all the additional freedom that async calls provide. asyncio aiohttp To get started, we'll need to install aiohttp. Install aiohttp. pip install aiohttp Common types of API calls with aiohttp We will begin with the same GET request we saw earlier. To start, import both libraries, and define an async function. asyncio aiohttp aiohttp.ClientSession() session: session.get( ) resp: response = resp.read() print(response)

asyncio.run(main()) import #  [1] import async : def main () # [2] async with as # [3] async with 'http://example.com' as # [4] await # [5] # [6] In the code above, we perform the following: We import the required libraries. Define main as an async function. We set up a ClientSession from aiohttp. We use the session to perform an HTTP GET request. Next, we await the response and print it. Finally, we use the run method of Python's asyncio to call the asynchronous function. If you haven't worked with async in Python before, this may look strange and complicated compared to the earlier examples. The makers of aiohttp recommend setting a single session per application and opening/closing connections on the single session. To make our examples self-contained, I have left the examples in the less efficient format. Next, let's look at a full-featured POST with auth headers, like in the requests example. aiohttp.ClientSession() session: session.post( ,
			headers={ : , : },
			json={ : }) resp: response = resp.json() print(response)

asyncio.run(main()) # ... async : def main () async with as async with 'http://example.com' 'Authorization' 'Bearer 123456' 'Content-Type' 'application/json' 'title' 'Try Bearer' as # [1] await # [2] There are a few differences between this example and the previous: The session uses the post method, and passes in headers and json dictionaries in addition to the URL. We use the library's built-in json method from the response to parse the returned json. With these two snippets, we're able to perform the majority of common API-related tasks. For additional features like file uploading and form data, take a look at . aiohttp's developer documentation Additional libraries to try While is the most popular, you may find value in some of these additional libraries for more unique use cases. Requests : httpx offers both sync and async support. It also uses a requests-compatible API that will make moving between the two much easier. Currently the library is in a beta state with a 1.0 expected min-summer 2020, but it is worth keeping an eye on as it matures. httpx : Keeping with the pre-1.0 trend, httpcore is an interesting option if you are building a library. It is low-level, so you can build your own abstractions on top of it. They explicitly recommend to use it unless you need a low-level library. httpcore not : We shouldurllib3, if only because it is the the underlying library that Requests and many others (including pip) are built on top of. While less user-friendly than some of the high-level libraries, urllib3 is powerful and battle-tested. If for some reason you need something with fewer abstractions than requests, it is a good option. urllib3 Making the most of API calls With Python's power in data processing and recent resurgence, thanks in part of the ML and data science communities, it is a great option for interacting with APIs. It is important to remember that even the most battle-tested and popular third-party APIs and services still suffer problems and outages. At , we're building tools to help manage these problems and better monitor your third-party APIs. Bearer Also published here .

Python Tutorial: How to Make API Requests

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