Top 10 Best Python REST API Frameworks

In this lesson we want to talk about Top 10 Best Python REST API Frameworks, Python is popular and nice programming language that is widely used for web development. there are many frameworks available in Python to support web development, including Flask, Django, Pyramid, Tornado, CherryPy, Bottle, Flask-RESTfu and etc. These frameworks provide developers with different tools and resources to create robust and scalable web applications quickly and easily. also large and active Python community offers many libraries, plugins and tools to help developers with their web development projects. Python also supports different types of databases and it makes it easier to store and retrieve data. with its simple and easy syntax, Python is great choice for both novice and experienced web developers.

Contents hide

1 What is REST API ?

2 Capabilities of REST API Frameworks

3 Choosing the Right REST API Framework

4 Top 10 Best Python REST API Frameworks

5 What is Flask ?

6 What is Django REST Framework ?

11 What is CherryPy ?

12 What is Flask-RESTful ?

13 What is Connexion ?

14 What is Hug ?

15 Which Framework is Good for Python REST API ?

What is REST API ?

REST (Representational State Transfer) API is software architectural style that defines set of constraints to be used for creating web services. It is web based API and is based on HTTP protocol. RESTful APIs use HTTP requests to POST (create), PUT (update), GET (read) and DELETE (delete) data. RESTful API can be consumed by a number of clients like mobile devices, desktop applications and websites. It is a lightweight and flexible alternative to traditional Web Services and is often used to build modern and scalable applications.

Capabilities of REST API Frameworks

REST API frameworks generally provide the following capabilities:

Routing: Ability to define endpoints for REST APIs.
Request/Response Handling: Ability to handle incoming HTTP requests process them and send back appropriate HTTP responses.
Data Validation: Ability to validate incoming data such as request parameters, payload, etc. to ensure it meets the API requirements.
Content Negotiation: Ability to support multiple content types such as JSON, XML, etc. and negotiate the format of the response.
Error Handling: Ability to handle exceptions and return appropriate error responses.
API Documentation: Ability to automatically generate API documentation based on the API definitions.
Security: Ability to implement security measures such as authentication, authorization, and encryption to secure the API.
Testing: Ability to easily test the API and validate its behavior.
Scalability: Ability to scale the API to meet increased demand.
Performance: Ability to optimize the API for performance and reduce response times.

Choosing the Right REST API Framework

When choosing the right REST API framework you should consider the following factors:

Performance: Framework should have fast performance, handling high traffic and concurrency.
Scalability: Framework should be scalable, allowing you to easily grow your API as you scale your business.
Ease of use: Framework should be easy to use and understand, minimizing the learning curve for your developers.
Security: Framework should provide robust security features, such as user authentication, access control and encryption.
Community and Documentation: Large and active community, with excellent documentation and support, can help you quickly find solutions to problems you may encounter.
Customizability: Framework should be customizable, allowing you to tailor it to your specific needs.
Integration: The framework should be easily integrated with other tools and technologies, such as databases and front end frameworks.
Licensing: Make sure to consider the licensing model, whether it’s open source or proprietary and if it aligns with your company’s policies.

In result we can say that the choice of the right REST API framework will depend on your specific requirements and priorities. now let’s talk about Top 10 Best Python REST API Frameworks

Top 10 Best Python REST API Frameworks

These are top 10 best Python REST API frameworks:

What is Flask ?

Flask is lightweight Python web framework that provides useful tools and features for creating web applications. it is easy to get started with and provides simple and easy way to create dynamic web pages using Python. Flask is based on Werkzeug WSGI library and the Jinja2 template engine, and it offers flexible way to handle request and response objects, route URLs to views and handle database connection. it is good for small to medium sized web applications, and it can also be easily extended with different plugins and extensions. you can install Flask using pip package manager by running following command in your terminal:

pip install Flask

1	pip install Flask

This is simple REST API example using Flask:

from flask import Flask, jsonify, request

app = Flask(__name__)

# create a list of dictionaries to represent books
books = [
    {
        'id': 1,
        'title': 'Book 1',
        'author': 'Author 1'
    },
    {
        'id': 2,
        'title': 'Book 2',
        'author': 'Author 2'
    },
    {
        'id': 3,
        'title': 'Book 3',
        'author': 'Author 3'
    }
]

# route to handle GET requests to retrieve all books
@app.route('/books', methods=['GET'])
def get_books():
    return jsonify({'books': books})

# route to handle GET requests to retrieve a single book
@app.route('/books/<int:book_id>', methods=['GET'])
def get_book(book_id):
    book = [book for book in books if book['id'] == book_id]
    if len(book) == 0:
        return jsonify({'message': 'Book not found'}), 404
    return jsonify({'book': book[0]})

# route to handle POST requests to add a new book
@app.route('/books', methods=['POST'])
def add_book():
    book = {
        'id': books[-1]['id'] + 1,
        'title': request.json['title'],
        'author': request.json['author']
    }
    books.append(book)
    return jsonify({'book': book}), 201

if __name__ == '__main__':
    app.run(debug=True)

from flask import Flask, jsonify, request

app = Flask(__name__)

# create a list of dictionaries to represent books

books = [

{

'id': 1,

'title': 'Book 1',

'author': 'Author 1'

{

'id': 2,

'title': 'Book 2',

'author': 'Author 2'

{

'id': 3,

'title': 'Book 3',

'author': 'Author 3'

}

]

# route to handle GET requests to retrieve all books

@app.route('/books', methods=['GET'])

def get_books():

return jsonify({'books': books})

# route to handle GET requests to retrieve a single book

@app.route('/books/<int:book_id>', methods=['GET'])

def get_book(book_id):

book = [book for book in books if book['id'] == book_id]

if len(book) == 0:

return jsonify({'message': 'Book not found'}), 404

return jsonify({'book': book[0]})

# route to handle POST requests to add a new book

@app.route('/books', methods=['POST'])

def add_book():

book = {

'id': books[-1]['id'] + 1,

'title': request.json['title'],

'author': request.json['author']

}

books.append(book)

return jsonify({'book': book}), 201

if __name__ == '__main__':

app.run(debug=True)

This code defines simple REST API with three routes to retrieve all books, retrieve single book, and add new book. The books are stored in list of dictionaries and the Flask jsonify function is used to convert the books to JSON format and return them in the response.

What is Django REST Framework ?

Django REST framework (DRF) is third party library for Django that makes it easy to build, test and debug RESTful APIs written using the Django framework. it provides completed well documented tools to help developers build and test APIs, including request parsing, request and response handling and serialization. DRF also provides support for authentication and permissions and it makes it easy to build secure and robust APIs. also DRF supports number of popular third party packages and it makes it easy to add features such as pagination, filtering and documentation. with its comprehensive feature set and strong community, Django REST framework is popular choice for building REST APIs with Django.

This is basic example of how to use Django REST framework:

First of all install Django REST framework by running the following command in your terminal:

pip install djangorestframework

1	pip install djangorestframework

Next, add ‘rest_framework’ to your INSTALLED_APPS list in your Django settings file.
Then, include the following in your urls.py file:

from django.urls import path, include
from rest_framework import routers
from .views import BookViewSet

router = routers.DefaultRouter()
router.register(r'books', BookViewSet)

urlpatterns = [
    path('', include(router.urls)),
]

from django.urls import path, include

from rest_framework import routers

from .views import BookViewSet

router = routers.DefaultRouter()

router.register(r'books', BookViewSet)

urlpatterns = [

path('', include(router.urls)),

]

In your views.py file, create a ViewSet class to handle the CRUD operations for books:

from rest_framework import viewsets
from .models import Book
from .serializers import BookSerializer

class BookViewSet(viewsets.ModelViewSet):
    queryset = Book.objects.all()
    serializer_class = BookSerializer

from rest_framework import viewsets

from .models import Book

from .serializers import BookSerializer

class BookViewSet(viewsets.ModelViewSet):

queryset = Book.objects.all()

serializer_class = BookSerializer

Create models.py file to define the Book model:

from django.db import models

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.CharField(max_length=100)

    def __str__(self):
        return self.title

from django.db import models

class Book(models.Model):

title = models.CharField(max_length=100)

author = models.CharField(max_length=100)

def __str__(self):

return self.title

Finally create serializers.py file to define the serializers for the Book model:

from rest_framework import serializers
from .models import Book

class BookSerializer(serializers.ModelSerializer):
    class Meta:
        model = Book
        fields = ('id', 'title', 'author')

from rest_framework import serializers

from .models import Book

class BookSerializer(serializers.ModelSerializer):

class Meta:

model = Book

fields = ('id', 'title', 'author')

Now you can run your Django server and access the REST API endpoints at http://localhost:8000/books/. you can use provided CRUD operations to interact with the books in the database.

What is FastAPI ?

FastAPI is modern and high performance web framework for building APIs with Python 3.7+ based on standard Python type hints. it is built on top of Starlette for the web parts and Pydantic for the data parts.

FastAPI has number of advantages compared to other Python web frameworks, including

Fast to code: decrease the amount of redundant code, increase productivity. -Fewer bugs: reduce about 40% of human (developer) induced errors.

Easy to maintain: FastAPI makes it easier to keep the code up to date.

Fast to run: FastAPI is very high performance on par with NodeJS and Go (thanks to Pydantic and async support). one of the fastest Python frameworks available.

FastAPI also integrates with other popular libraries such as Django ORM, Tortoise ORM and asyncio. you can install FastAPI by using pip with the following command in your terminal or command prompt:

pip install fastapi

1	pip install fastapi

This is basic example of a REST API using FastAPI:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}

from fastapi import FastAPI

app = FastAPI()

@app.get("/")

def read_root():

return {"Hello": "World"}

@app.get("/items/{item_id}")

def read_item(item_id: int, q: str = None):

return {"item_id": item_id, "q": q}

In this example we have defined two endpoints: one at the root URL (“/”) and one at “/items/{item_id}”. read_root function returns simple JSON response {“Hello”: “World”}. read_item function accepts an item_id parameter, which is converted to an integer and query string parameter q which is optional and defaults to None. The function returns JSON response containing the values for item_id and q. To run the code, you can open a terminal window in the same directory as the code file and run the following command:

uvicorn main:app --reload

1	uvicorn main:app --reload

Here, main is the name of the file containing the code and app is the instance of the FastAPI application. –reload flag ensures that the server automatically reloads when changes are made to the code.

What is Tornado ?

Tornado is an open source Python based web framework and asynchronous network library. it is designed to handle large amounts of traffic and long lived connections and it makes it a good choice for real time web applications such as chat applications, online games and online real time data services. Tornado includes web server and supports WebSockets and other asynchronous features which allow for efficient and high performance handling of many simultaneous connections. for installing Tornado you can use Python pip. Run the following command in your terminal:

pip install tornado

1	pip install tornado

This will install the latest version of Tornado and its dependencies. you can then import and use Tornado in your Python code.

This is simple example of Tornado REST API:

import tornado.ioloop
import tornado.web
import json
import datetime

class CurrentDatetimeHandler(tornado.web.RequestHandler):
    def get(self):
        now = datetime.datetime.now()
        response_dict = {
            'date': now.strftime("%Y-%m-%d"),
            'time': now.strftime("%H:%M:%S")
        }
        self.write(response_dict)

def make_app():
    return tornado.web.Application([
        (r"/", CurrentDatetimeHandler),
    ])

if __name__ == "__main__":
    app = make_app()
    app.listen(8080)
    tornado.ioloop.IOLoop.current().start()

import tornado.ioloop

import tornado.web

import json

import datetime

class CurrentDatetimeHandler(tornado.web.RequestHandler):

def get(self):

now = datetime.datetime.now()

response_dict = {

'date': now.strftime("%Y-%m-%d"),

'time': now.strftime("%H:%M:%S")

}

self.write(response_dict)

def make_app():

return tornado.web.Application([

(r"/", CurrentDatetimeHandler),

])

if __name__ == "__main__":

app = make_app()

app.listen(8080)

tornado.ioloop.IOLoop.current().start()

In this example Tornado Application is created with single handler, CurrentDatetimeHandler which returns the current date and time in JSON format in response to GET request. The application listens on port 8080 and starts the Tornado IOLoop to handle incoming requests.

What is Pyramid ?

Pyramid is an open source web framework. and it is designed to be highly configurable and flexible and it is good choice for both small and large web applications. Pyramid follows the Model View Template (MVT) pattern and provides features such as URL routing, template engine integration and database abstraction layer. It is also easy to extend with plugins and packages and it allows developers to add new functionality as needed. Pyramid is known for its simplicity and ease of use and it is popular choice for beginners and experienced Python developers. for installing Pyramid you can use pip. Run the following command in your terminal:

pip install pyramid

1	pip install pyramid

This will install the latest version of Pyramid and its dependencies. you can then import and use Pyramid in your Python code.

This is basic example of a REST API built with Pyramid that returns the current date and time:

from pyramid.config import Configurator
from pyramid.response import Response
import json
import datetime

def get_current_datetime(request):
    now = datetime.datetime.now()
    response_dict = {
        'date': now.strftime("%Y-%m-%d"),
        'time': now.strftime("%H:%M:%S")
    }
    return Response(
        body=json.dumps(response_dict),
        content_type='application/json; charset=UTF-8'
    )

def main(global_config, **settings):
    config = Configurator(settings=settings)
    config.add_route('datetime', '/')
    config.add_view(get_current_datetime, route_name='datetime')
    return config.make_wsgi_app()

if __name__ == '__main__':
    from wsgiref.simple_server import make_server
    server = make_server('0.0.0.0', 8080, main({}))
    server.serve_forever()

from pyramid.config import Configurator

from pyramid.response import Response

import json

import datetime

def get_current_datetime(request):

now = datetime.datetime.now()

response_dict = {

'date': now.strftime("%Y-%m-%d"),

'time': now.strftime("%H:%M:%S")

}

return Response(

body=json.dumps(response_dict),

content_type='application/json; charset=UTF-8'

)

def main(global_config, **settings):

config = Configurator(settings=settings)

config.add_route('datetime', '/')

config.add_view(get_current_datetime, route_name='datetime')

return config.make_wsgi_app()

if __name__ == '__main__':

from wsgiref.simple_server import make_server

server = make_server('0.0.0.0', 8080, main({}))

server.serve_forever()

You can run this code by saving it to a file (e.g. app.py) and running python app.py in your terminal or command prompt. You can then access the date and time information by making a GET request to http://localhost:8080 in your web browser or using a tool like curl. The response will be in JSON format, containing the current date and time.

What is Bottle ?

Bottle is minimalist, single-file, fast and lightweight Python web framework for building small to medium sized web applications. it is designed to be easy to use and it provides simple and expressive API for creating HTTP based web services. Bottle does not require any external dependencies or separate server and can be run as standalone web application. Bottle supports different features such as URL routing, request handling and template engine integration and it is choice for building small to medium sized web applications. despite its simplicity, Bottle is also highly extensible and developers can add custom functionality by using plugins and middleware. for installing bottle you can use pip. Run the following command in your terminal :

pip install bottle

1	pip install bottle

This will install the latest version of Bottle and its dependencies. you can then import and use Bottle in your Python code.

This is basic example of a REST API built with Bottle that returns the current date and time:

from bottle import Bottle, response, run
import json
import datetime

app = Bottle()

@app.route('/')
def get_current_datetime():
    now = datetime.datetime.now()
    response_dict = {
        'date': now.strftime("%Y-%m-%d"),
        'time': now.strftime("%H:%M:%S")
    }
    response.content_type = 'application/json'
    return json.dumps(response_dict)

run(app, host='0.0.0.0', port=8080)

from bottle import Bottle, response, run

import json

import datetime

app = Bottle()

@app.route('/')

def get_current_datetime():

now = datetime.datetime.now()

response_dict = {

'date': now.strftime("%Y-%m-%d"),

'time': now.strftime("%H:%M:%S")

}

response.content_type = 'application/json'

return json.dumps(response_dict)

run(app, host='0.0.0.0', port=8080)

What is CherryPy ?

CherryPy is an open source Python web framework for building web applications. It is designed to be simple to use, fast and scalable and also provides clean and elegant API for building HTTP based services. CherryPy allows developers to build web applications using Python and focuses on ease of use and performance. it provides full featured and stand alone HTTP server, and can also be integrated with popular web servers such as Apache and Nginx. CherryPy supports many features such as request handling, URL routing and template engine integration and provides plug in system that allows developers to extend its functionality. it is good choice for building scalable and performant web applications in Python. for installing CherryPy, you can use Python pip. Run the following command in your terminal:

pip install cherrypy

1	pip install cherrypy

This will install the latest version of CherryPy and its dependencies. after that you can import the library and use CherryPy in your Python code.

This is simple example of REST API built with CherryPy that returns the current date and time:

import cherrypy
import json
import datetime

class DateTimeAPI:
    @cherrypy.expose
    def index(self):
        now = datetime.datetime.now()
        response = {
            'date': now.strftime("%Y-%m-%d"),
            'time': now.strftime("%H:%M:%S")
        }
        return json.dumps(response)

cherrypy.config.update({'server.socket_host': '0.0.0.0'})
cherrypy.config.update({'server.socket_port': 8080})
cherrypy.quickstart(DateTimeAPI())

import cherrypy

import json

import datetime

class DateTimeAPI:

@cherrypy.expose

def index(self):

now = datetime.datetime.now()

response = {

'date': now.strftime("%Y-%m-%d"),

'time': now.strftime("%H:%M:%S")

}

return json.dumps(response)

cherrypy.config.update({'server.socket_host': '0.0.0.0'})

cherrypy.config.update({'server.socket_port': 8080})

cherrypy.quickstart(DateTimeAPI())

What is Flask-RESTful ?

Flask-RESTful is an extension for Flask, a micro web framework in Python, that makes it easier to build RESTful APIs. it provides simple and easy way to define resources and routes, as well as handles request parsing, response generation and error handling.

with Flask-RESTful, you can define resource as Python class with methods that correspond to HTTP methods (e.g. GET, POST, etc.). resource class can be registered with the API using the api.add_resource method. this allows you to easily build and define the behavior of your REST API, with added benefits of using the Flask framework for your application. to install Flask-RESTful you can use pip:

pip install flask-restful

1	pip install flask-restful

This will download and install Flask-RESTful and its dependencies. after that you can import Api class from the flask_restful module in your Python code to use it in your Flask application.

This is basic example of using Flask-RESTful to create REST API:

from flask import Flask
from flask_restful import Api, Resource, reqparse
import datetime

app = Flask(__name__)
api = Api(app)

class CurrentDatetime(Resource):
    def get(self):
        now = datetime.datetime.now()
        response_dict = {
            'date': now.strftime("%Y-%m-%d"),
            'time': now.strftime("%H:%M:%S")
        }
        return response_dict

api.add_resource(CurrentDatetime, '/')

if __name__ == '__main__':
    app.run(debug=True)

from flask import Flask

from flask_restful import Api, Resource, reqparse

import datetime

app = Flask(__name__)

api = Api(app)

class CurrentDatetime(Resource):

def get(self):

now = datetime.datetime.now()

response_dict = {

'date': now.strftime("%Y-%m-%d"),

'time': now.strftime("%H:%M:%S")

}

return response_dict

api.add_resource(CurrentDatetime, '/')

if __name__ == '__main__':

app.run(debug=True)

In this example Flask application is created with the Flask class. The Api class is instantiated with the Flask application, creating an instance of a RESTful API. resource class, CurrentDatetime is defined that returns the current date and time in a dictionary in response to GET request. resource class is then added to the API using the api.add_resource method. Finally, the Flask application is run with debug mode enabled.

What is Connexion ?

Connexion is Python library for building and documenting RESTful APIs, built on top of the Flask web framework. it allows you to define your API using OpenAPI widely used specification for REST APIs and provides automatic validation of incoming requests and responses based on the defined OpenAPI specification.

with Connexion you can define your API by writing an OpenAPI specification in YAML or JSON file and then use specification to generate required Python code to handle incoming requests and generate responses. the generated code takes care of validating incoming requests and responses based on the defined API specification, freeing you from having to write this validation logic yourself.

Connexion also provides built in Swagger UI for easily testing and documenting your API, as well as support for implementing custom request handling logic and custom error handling. these features make it popular choice for building and documenting RESTful APIs in Python. for installing Connexion you can use pip package manager:

pip install connexion

1	pip install connexion

This installs Connexion and its dependencies.

This is basic example of a REST API built using Connexion:

from connexion import FlaskApp, App
from connexion.resolver import RestyResolver

app = FlaskApp(__name__, specification_dir='.')
app.add_api('api.yml', resolver=RestyResolver('api'))

@app.route('/')
def hello():
    return {"message": "Hello, World!"}

if __name__ == '__main__':
    app.run(port=8080)

from connexion import FlaskApp, App

from connexion.resolver import RestyResolver

app = FlaskApp(__name__, specification_dir='.')

app.add_api('api.yml', resolver=RestyResolver('api'))

@app.route('/')

def hello():

return {"message": "Hello, World!"}

if __name__ == '__main__':

app.run(port=8080)

In this example first of all we have imported the required classes from the connexion module. after that we have created FlaskApp instance and add our API definition from a YAML file named api.yml to it.

Next we define simple endpoint for the root path (/) that returns a JSON response with message.

finally we run the application on port 8080.

Note that in this example OpenAPI specification for the API is stored in the file api.yml, which Connexion will use to automatically generate the required code to handle incoming requests and generate responses.

What is Hug ?

Hug is Python library for building RESTful APIs. It is a fast and minimalistic framework for creating APIs with focus on speed and simplicity. Hug allows developers to quickly create RESTful APIs by simply declaring the API functions and decorating them with Hug specific annotations, without the need for complex routing definitions.

Hug has simple and lightweight design, making it easy to learn and use, yet still provides comprehensive set of features for building robust and scalable APIs. It is often compared to other popular Python web frameworks such as Flask and Django and is good for small to medium projects. you can install Hug using pip. for installing the latest version of Hug simply run the following command in terminal :

pip install hug

1	pip install hug

This is simple example of how to create a RESTful API using Hug:

import hug

@hug.get("/greet")
def greet(name: hug.types.text):
    return {"message": "Hello {0}".format(name)}

if __name__ == '__main__':
    hug.API(__name__).http.serve(port=8000)

import hug

@hug.get("/greet")

def greet(name: hug.types.text):

return {"message": "Hello {0}".format(name)}

if __name__ == '__main__':

hug.API(__name__).http.serve(port=8000)

This will start the API server on port 8000, which you can access using a web browser or a tool like curl by sending a GET request to http://localhost:8000/greet?name=John.

Which Framework is Good for Python REST API ?

It depends on your specific requirements and preferences. However here are some of the most popular REST API frameworks in Python each with its own strengths:

Flask: lightweight and flexible framework that is easy to learn and use.
Django Rest Framework: comprehensive and fully featured framework that is integrated with Django.
FastAPI: relatively new framework that is optimized for performance and ease of use.
Pyramid: flexible and scalable framework that provides many features for building large and complex applications.
Tornado: high performance and asynchronous framework that is good for building real time applications.
Connexion: framework that is built on top of Flask and allows you to define your API using the OpenAPI specification.
Flask-RESTful: An extension for Flask that makes it easy to build RESTful APIs.
Hug: minimalistic and very fast framework that is designed for building microservices.

In result we can say that the best framework for your REST API will depend on your particular requirements, such as the complexity of the API, the performance requirements, the team’s experience with a particular framework, etc.

What is REST API ?

Capabilities of REST API Frameworks

Choosing the Right REST API Framework

Top 10 Best Python REST API Frameworks

What is Flask ?

What is Django REST Framework ?

What is FastAPI ?

What is Tornado ?

What is Pyramid ?

What is Bottle ?

What is CherryPy ?

What is Flask-RESTful ?

What is Connexion ?

What is Hug ?

Which Framework is Good for Python REST API ?

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