Class is a combination of live demonstrations and hands-on labs. Another way to plan for your API’s future is to add a version number to the path.
Don’t worry if you don’t understand each individual line of code yet—explanations will be forthcoming once you have this initial version of the application working. Like other mainstream programming languages, Python also has the concept of modules to enable developers to organize source code according to subjects/functionalities.
It has the essential middleware features required for handling HTTP requests, templating and routing. Flask also allows APIs to be organized in modules for more complex APIs that comprise multiple endpoints. With Flask, you can customize every aspect of your application by adding custom fields and inputs based on your data type. This last example of our Distant Reading Archive API pulls in data from a database, implements error handling, and can filter books by publication date. The database used is SQLite, a lightweight database engine that is supported in Python by default.
- Bottle is a lightweight and simple WSGI micro web framework for API development.
- Being WSGI compliant, Falcon is compatible with multiple servers and platforms, and it comes with a built-in server and debugger.
- The only knowledge explicitly assumed for this lesson is the ability to use a text editor, such as BBEdit on macOS or Notepad++ on Windows.
- Remember that, when creating an API, you are assuming some responsibility for the applications that others may build with it.
- This post includes the same examples in two frameworks, so you can compare their approaches and decide which to use.
- However, as it is a newer framework, many more resources and libraries are compatible with frameworks like Django and Flask but not with FastAPI.
- Python has a number of web frameworks that can be used to create web apps and APIs.
Depending on your resources, this approach can be viable up to a download size of a few gigabytes. The API built in this course is for a social-media-type application where users can create/read/delete/update posts as well as like other users posts.
Dockerizing Flask Applications
JSON is generally the most common format for returning data through an API, XML being the second most common. Your users will need to access your data in real time, such as for display on another website or as part of an application. If the output for these commands includes a version number, Python is installed and available from the command line and you can proceed to the next step. To confirm that Python installed successfully, first open the command line. In macOS, click the spotlight icon on the top right corner of your desktop and type terminal. On Windows, click the Start menu icon and type cmd in the search box, then press Enter.
When should you not build an API?
You Should Probably Not Build an API If:
You just need a landing page or blog as a website. Your application is temporary and not intended to grow or change much. You never intend on expanding to other platforms (eg: mobile, desktop) You don't understand the technical implications of building one.
However, for more complex applications that deal with different entities and have multiple business rules and validations, we might need to encapsulate our data into Python classes. Pip installs packages globally, making it hard to manage multiple versions of the same package on the same machine. No matter how you create your REST API, whether through code or generated from an OpenAPI document, collaboration is important. Keep in mind, even as you stub out the endpoints in this tutorial, we’ll be using mock data. It’s up to you whether you iterate upon it for your eventual production API. This design-first API process is covered in the API Design Guide, which says design-second APIs are an oxymoron. Unfortunately, in code-first APIs, the design is a guess that gets harder to change.
Anaconda comes with Flask, so if you go this route you will not need to install Flask using the pip package manager. This will install Flask using the pip package manager for Python. You should see some output ending in a notification that Flask has been installed successfully. Learn some principles of good API design, applying them to an API that draws book metadata from a database. After creating the API, you will learn how to deploy the API using two different methods.
- Unfortunately, in code-first APIs, the design is a guess that gets harder to change.
- The general concept is that you can use the same API code for Django, Flask, Bottle, Pyramid or any other WSGI framework with minimal porting effort.
- Now that our application is structured, we can start coding some relevant endpoints.
- We looked at how to use pipenv to manage the dependencies of our API.
- They also require minimal setup and are easy to test and maintain.
- Using dictionaries in a simple use case like the one above is enough.
For this reason, I do not describe the example APIs here as REST APIs, but instead as web or HTTP APIs. HTTP implements a number of “methods,” which tell which direction data is moving and what should happen to it. The two most common are GET, which pulls data from a server, and POST, which pushes new data to a server. You can use the Windows, macOS, or Linux operating systems to complete this tutorial, and those few instructions that are not the same across platforms will be explicitly noted. Python 3, the Flask web framework, and a web browser are required for this tutorial, and installation instructions for all platforms are outlined below. Figure out other use cases and expand based on what you learned with the initial API use case.
Creating APIs with Python – Free 19-Hour Course
Flask developers call it a microframework, where micro means that the goal is to keep the core simple but extensible. Flask won’t make many decisions for us, such as what database to use or what template engine to choose. Lastly, Flask has extensive documentation that addresses everything developers need to start. FastAPI follows a similar “micro” approach to Flask, though it provides more tools like automatic Swagger UI and is an excellent choice for APIs. However, as it is a newer framework, many more resources and libraries are compatible with frameworks like Django and Flask but not with FastAPI. Thus far, we’ve created a working API with test data that we’ve provided right in our application. Our next version of our API will pull in data from a database before providing it to a user.
The first is to deploy on an Ubuntu machine and the second is to deploy to Heroku. First you will learn learn the fundamentals of API design including routes, serialization/deserialization, schema validation, and models. If you’re looking for a working example of a Django REST framework project, check out thePokeAPI, open sourced under the BSD license. Microsoft’sREST API Guidelinesare a detailed set of considerations for when you are building your own APIs that you want to be easily-consumable by other developers. Serialization is common for transforming objects into web API JSON results. One company found the serialization performance of Django REST framework was lacking so they createdSerpy andwrote a blog post with the results of its performance. Creating Web APIs with Python and Flaskis a free book on building APIs with Flask as the core web framework.