Tamale  Moses

Tamale Moses


OpenAPI Utilities: 8 Best OpenAPI Utilities Libraries In Python

In this Python article, let's learn about OpenAPI Utilities: 8 Best OpenAPI Utilities Libraries In Python for Developer

  1. pyramid_openapi3: Pyramid addon for OpenAPI3 validation of requests and responses.
  2. Django REST Swagger: Swagger Documentation Generator for Django REST Framework: deprecated
  3. apistar: The Web API toolkit. 🛠
  4. flask-swagger: A swagger 2.0 spec extractor for flask
  5. Spectacle: Beautiful static documentation generator for OpenAPI/Swagger 2.0
  6. pyramid_swagger: Convenient tools for using Swagger to define and validate your interfaces in a Pyramid webapp.

What is OpenAPI in Python?

OpenAPI helps developers specify the input/output schemas, the resources and the methods. With an OpenAPI specification, OpenAPI generator can prepare server-end and client-end artifacts (stubs) automatically in the programming language and framework of your choice

What is the use of OpenAPI?

An open API is a valuable type of technology for developers. It provides standards for how software programs communicate with each other. With an open API, developers have to write less code to make different software programs work together seamlessly.

OpenAPI Utilities: 8 Best OpenAPI Utilities Libraries In Python

  1. pyramid_openapi3

The reason this package exists is to give you peace of mind when providing a RESTful API. Instead of chasing down preventable bugs and saying sorry to consumers, you can focus on more important things in life.


Getting started

Declare pyramid_openapi3 as a dependency in your Pyramid project.

Include the following lines:

config.pyramid_openapi3_spec('openapi.yaml', route='/api/v1/openapi.yaml')
  1. Use the openapi view predicate to enable request/response validation:
@view_config(route_name="foobar", openapi=True, renderer='json')
def myview(request):
    return request.openapi_validated.parameters

For requests, request.openapi_validated is available with two fields: parameters and body. For responses, if the payload does not match the API document, an exception is raised.

View on GitHub

2.    Django REST Swagger

This project is no longer being maintained. Please consider drf-yasg as an alternative/successor. I haven't personally used it, but it looks feature-complete and is actively maintained.

An API documentation generator for Swagger UI and Django REST Framework

Full documentation: http://marcgibbons.github.io/django-rest-swagger/


pip install django-rest-swagger

Add rest_framework_swagger to your INSTALLED_APPS setting:


Rendering Swagger Specification and Documentation

This package ships with two renderer classes:

  1. OpenAPIRenderer generates the OpenAPI (fka Swagger) JSON schema specification. This renderer will be presented if:
  • Content-Type: application/openapi+json is specified in the headers.
  • ?format=openapi is passed as query param
  1. SwaggerUIRenderer generates the Swagger UI and requires the OpenAPIRenderer

Quick Start Example:

from django.conf.urls import url
from rest_framework_swagger.views import get_swagger_view

schema_view = get_swagger_view(title='Pastebin API')

urlpatterns = [
    url(r'^$', schema_view)

View on GitHub

3.    apistar

API Star is a toolkit for working with OpenAPI or Swagger schemas. It allows you to:

  • Build API documentation, with a selection of available themes.
  • Validate API schema documents, and provide contextual errors.
  • Make API requests using the dynamic client library.

You can use it to build static documentation, integrate it within a Web framework, or use it as the client library for interacting with other APIs.


Install API Star:

$ pip3 install apistar

Let's take a look at some of the functionality the toolkit provides...

We'll start by creating an OpenAPI schema, schema.yaml:

openapi: 3.0.0
  title: Widget API
  version: '1.0'
  description: An example API for widgets
  - url: https://www.example.org/
      summary: List all the widgets.
      operationId: listWidgets
      - in: query
        name: search
        description: Filter widgets by this search term.
          type: string

Let's also create a configuration file apistar.yml:

  path: schema.yaml
  format: openapi

We're now ready to start using the apistar command line tool.

View on GitHub

4.    flask-swagger

A Swagger 2.0 spec extractor for Flask

You can now specify base path for yml files:

app = Flask(__name__)

def spec():
    base_path = os.path.join(app.root_path, 'docs')
    return jsonify(swagger(app), from_file_keyword="swagger_from_file", base_path=base_path)

and use relative paths:

@app.route('/test', methods=['POST'])
def login():
    swagger_from_file: test.yml


pip install flask-swagger

Flask-swagger provides a method (swagger) that inspects the Flask app for endpoints that contain YAML docstrings with Swagger 2.0 Operation objects.

class UserAPI(MethodView):

    def post(self):
        Create a new user
          - users
          - schema:
              id: Group
                 type: string
                 description: the group's name
          - in: body
            name: body
              id: User
                - email
                - name
                  type: string
                  description: email for user
                  type: string
                  description: name for user
                  description: address for user
                    id: Address
                        type: string
                        type: string
                        type: string
                        type: string
                  type: array
                  description: list of groups
                    $ref: "#/definitions/Group"
            description: User created
        return {}

Flask-swagger supports docstrings in methods of MethodView classes (à la Flask-RESTful) and regular Flask view functions.

View on GitHub

5.    Spectacle

Spectacle generates beautiful static HTML5 documentation from OpenAPI/Swagger 2.0 API specifications.

The goal of Spectacle is help you "save time and look good" by providing an extensible platform for auto generating your REST API docs. The default layout is a three column single page, similar to those employed by Stripe and Intercom.


Simply install Spectacle from npm like so:

npm install -g spectacle-docs

Next pass your swagger.json document use the CLI to generate your documentation.

spectacle -d your_swagger_api.json

# Or use the cheese.json example to test it out
# spectacle -d -l test/fixtures/cheese.png test/fixtures/cheese.yml

Your generated documentation will be located in the public directory by default. You can either copy the generated HTML to your web server, or view your docs by pointing your browser to http://localhost:4400/.


Docker images are included that allow Spectacle to be run from the inside. It's useful, for instance, in a Gitlab CI pipeline. Thanks @alexeiaguiar.

How to use it: docker run -it sourcey/spectacle /bin/sh

View on GitHub

6.    pyramid_swagger

This project offers convenient tools for using Swagger to define and validate your interfaces in a Pyramid webapp.

Full documentation is available at http://pyramid-swagger.readthedocs.org/.

How to contribute

Fork this repository on Github: https://help.github.com/articles/fork-a-repo/

Clone your forked repository: https://help.github.com/articles/cloning-a-repository/

Make a feature branch for your changes:

git remote add upstream https://github.com/striglia/pyramid_swagger.git
git fetch upstream
git checkout upstream/master -b my-feature-branch

Create and activate the virtual environment, this will provide you with all the libraries and tools necessary for pyramid_swagger development:

source .activate.sh

Make sure the test suite works before you start:

tox -e py27    # Note: use py35 for Python 3.5, see tox.ini for possible values

Commit patches: http://gitref.org/basic/

Push to github: git pull && git push origin

Send a pull request: https://help.github.com/articles/creating-a-pull-request/

View on GitHub

Related videos:

Introducing OpenAPI Generator

Related posts:

#python #openapi 

What is GEEK

Buddha Community

OpenAPI Utilities: 8 Best OpenAPI Utilities Libraries In Python
bindu singh

bindu singh


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Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Ray  Patel

Ray Patel


Top 20 Most Useful Python Modules or Packages

 March 25, 2021  Deepak@321  0 Comments

Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.

Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:

  1. Web Development
  2. Data Science
  3. Machine Learning
  4. AI and graphical user interfaces.

Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.

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Top 7 Python Libraries Used For Hacking

python is one of the most go-for languages among the developers due to the availability of open-source libraries and frameworks. According to a survey reportPython is the top language preferred for Statistical Modelling, and an overwhelming majority of practitioners prefer Python as the language for statistical works.

Python has become a favourite language for hackers these days. The reason is the presence of pre-built tools and libraries, which makes hacking easy. In fact, the language is adequate for ethical hacking as ethical hackers need to develop smaller scripts, and Python fulfils this criterion.

Below here, we listed down the top 7 Python libraries used in hacking.

1| Requests

Stars: 43.3k

**About: **Requests is a simple HTTP library for Python that allows a user to send HTTP/1.1 requests extremely easily. This library helps in building robust HTTP applications and includes intuitive features such as automatic content decompression and decoding, connection timeouts, basic & digits authentication, among others.

Know more here.

2| Scapy

Stars: 5.5k

About: Scapy is a powerful Python-based interactive packet manipulation program and library. This library is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, store or read them using pcap files, match requests, and more. It allows the construction of tools that can easily scan or attack networks. It is designed to allow fast packet prototyping by using default values that work. It can also perform tasks such as sending invalid frames, injecting your own 802.11 frames, combining techniques, such as VLAN hopping with ARP cache poisoning, VOIP decoding on WEP encrypted channel, etc., which most other tools cannot.

Know more here.

3| IMpacket

**Stars: **5.3k

**About: **IMpacket is a library that includes a collection of Python classes for working with network protocols. It is focused on providing low-level programmatic access to network packets. It allows Python developers to craft and decode network packets in a simple and consistent manner. The library provides a set of tools as examples of what can be done within the context of this library.

Know more here.

4| Cryptography

**Stars: **3.5k

**About: **Cryptography is a package which provides cryptographic recipes and primitives to Python developers. It includes both high-level recipes and low-level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests and key derivation functions. This library is broadly divided into two levels. One is with safe cryptographic recipes that require little to no configuration choices. The other level is low-level cryptographic primitives, which are often dangerous and can be used incorrectly.

Know more here.

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August  Larson

August Larson


4 Cool Python Libraries That You Should Know About

Discover useful Python libraries that you should try out in your next project

Some of my most popular blogs are about Python libraries. I believe that they are so popular because Python libraries have the power to save us a lot of time and headaches. The problem is that most people focus on those most popular libraries but forget that multiple less-known Python libraries are just as good as their most famous cousins.

Finding new Python libraries can also be problematic. Sometimes we read about these great libraries, and when we try them, they don’t work as we expected. If this has ever happened to you, fear no more. I got your back!

In this blog, I will show you four Python libraries and why you should try them. Let’s get started.

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