This article shows how to install Python on Red Hat Enterprise Linux (RHEL), including both versions 3 and 2 of Python along with the pip, vent, virtualenv, and pipenv utilities. The article starts with RHEL 9 and continues with RHEL 8 and RHEL 7, including plenty of tips useful in all versions. The article also covers virtual environments, which help you ensure that your applications have the correct versions of Perl and related modules.

The pytest framework makes it easy to write small, readable tests, and can scale to support complex functional testing for applications and libraries.

pytest requires: Python 3.7+ or PyPy3.

his article shows how to install Python 3, pip, venv, virtualenv, and pipenv on Red Hat Enterprise Linux 7. After following the steps in this article, you should be in a good position to follow many Python guides and tutorials using RHEL. Note: For RHEL 8 installs, See Python on RHEL 8.

Using Python virtual environments is a best practice to isolate project-specific dependencies and create reproducible environments. Other tips and FAQs for working with Python and software collections on RHEL 7 are also covered.

There are a number of different ways to get Python 3 installed on RHEL. This article uses Red Hat Software Collections because these give you a current Python installation that is built and supported by Red Hat. During development, support might not seem that important to you. However, support is important to those who have to deploy and operate the applications you write. To understand why this is important, consider what happens when your application is in production and a critical security vulnerability in a core library (for example SSL/TLS) is discovered. This type of scenario is why many enterprises use Red Hat.

Python 3.6 is used in this article. It was the most recent, stable release when this was written. However, you should be able to use these instructions for any of the versions of Python in Red Hat Software Collections including 2.7, 3.4, 3.5, and future collections such as 3.7.

Mypy is a static type checker for Python 3 and Python 2.7. If you sprinkle your code with type annotations, mypy can type check your code and find common bugs. As mypy is a static analyzer, or a lint-like tool, the type annotations are just hints for mypy and don’t interfere when running your program. You run your program with a standard Python interpreter, and the annotations are treated effectively as comments.

Using the Python 3 annotation syntax (using PEP 484 and PEP 526 notation) or a comment-based annotation syntax for Python 2 code, you will be able to efficiently annotate your code and use mypy to check the code for common errors. Mypy has a powerful and easy-to-use type system with modern features such as type inference, generics, callable types, tuple types, union types, and structural subtyping.

As a developer, you decide how to use mypy in your workflow. You can always escape to dynamic typing as mypy’s approach to static typing doesn’t restrict what you can do in your programs. Using mypy will make your programs easier to understand, debug, and maintain.

This documentation provides a short introduction to mypy. It will help you get started writing statically typed code. Knowledge of Python and a statically typed object-oriented language, such as Java, are assumed.

Mypy is a static type checker for Python.

Type checkers help ensure that you're using variables and functions in your code correctly. With mypy, add type hints (PEP 484) to your Python programs, and mypy will warn you when you use those types incorrectly.

Python is a dynamic language, so usually you'll only see errors in your code when you attempt to run it. Mypy is a static checker, so it finds bugs in your programs without even running them!

Mypy is designed with gradual typing in mind. This means you can add type hints to your code base slowly and that you can always fall back to dynamic typing when static typing is not convenient.

Requests is an elegant and simple HTTP library for Python, built for human beings.

Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your POST data. Keep-alive and HTTP connection pooling are 100% automatic, thanks to urllib3.

Beloved Features:

• Requests is ready for today’s web.
• Keep-Alive & Connection Pooling
• International Domains and URLs
• Sessions with Cookie Persistence
• Browser-style SSL Verification
• Automatic Content Decoding
• Basic/Digest Authentication
• Elegant Key/Value Cookies
• Automatic Decompression
• Unicode Response Bodies
• HTTP(S) Proxy Support
• Multipart File Uploads
• Streaming Downloads
• Connection Timeouts
• Chunked Requests
• .netrc Support

Requests officially supports Python 2.7 & 3.6+, and runs great on PyPy.

Dnspython is a DNS toolkit for Python. It can be used for queries, zone transfers, dynamic updates, nameserver testing, and many other things.

Dnspython provides both high and low level access to the DNS. The high level classes perform queries for data of a given name, type, and class, and return an answer set. The low level classes allow direct manipulation of DNS zones, messages, names, and records. Almost all RR types are supported.

dnspython originated at Nominum where it was developed for testing DNS nameservers.

Noise is a framework for building crypto protocols. Noise protocols support mutual and optional authentication, identity hiding, forward secrecy, zero round-trip encryption, and other advanced features.

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

The key features are:

• Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available.

• Fast to code: Increase the speed to develop features by about 200% to 300% *.

• Fewer bugs: Reduce about 40% of human (developer) induced errors. *

• Intuitive: Great editor support. Completion everywhere. Less time debugging.

• Easy: Designed to be easy to use and learn. Less time reading docs.

• Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.

• Robust: Get production-ready code. With automatic interactive documentation.

• Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

live-wrapper is a tool initially produced by the Debian Live Team that can be used to create Debian-based live images for use with CDs, DVDs or USB sticks.

In this tutorial on decorators, we’ll look at what they are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions.

By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

This sounds confusing, but it’s really not, especially after you’ve seen a few examples of how decorators work. You can find all the examples from this article here.

PyInstaller freezes (packages) Python applications into stand-alone executables, under Windows, GNU/Linux, Mac OS X, FreeBSD, Solaris and AIX.

PyInstaller’s main advantages over similar tools are that PyInstaller works with Python 2.7 and 3.4—3.7, it builds smaller executables thanks to transparent compression, it is fully multi-platform, and use the OS support to load the dynamic libraries, thus ensuring full compatibility

SSLyze is a Python tool that can analyze the SSL configuration of a server by connecting to it. It is designed to be fast and comprehensive, and should help organizations and testers identify misconfigurations affecting their SSL servers.

Key features include:

• Multi-processed and multi-threaded scanning (it's fast)
• SSL 2.0/3.0 and TLS 1.0/1.1/1.2 compatibility
• Performance testing: session resumption and TLS tickets support
• Security testing: weak cipher suites, insecure renegotiation, CRIME, Heartbleed and more
• Server certificate validation and revocation checking through OCSP stapling
• Support for StartTLS handshakes on SMTP, XMPP, LDAP, POP, IMAP, RDP and FTP
• Support for client certificates when scanning servers that perform mutual authentication
• XML output to further process the scan results
• And much more !

As you know, Python leverages polymorphism at its maximum by dealing only with generic references to objects. This makes OOP not an addition to the language but part of its structure from the ground up. Moreover, Python pushes the EAFP appoach, which tries to avoid direct inspection of objects as much as possible.

In this guide, we will be setting up a simple Python application using the Flask micro-framework on Ubuntu 16.04. The bulk of this article will be about how to set up the uWSGI application server to launch the application and Nginx to act as a front end reverse proxy.

apt-get install -t strech-backports python3-nacl

PyNaCl is a Python binding to libsodium, which is a fork of the Networking and Cryptography library. These libraries have a stated goal of improving usability, security and speed. It supports Python 2.7 and 3.4+ as well as PyPy 2.6+.
Features

Digital signatures
Secret-key encryption
Public-key encryption
Hashing and message authentication
Password based key derivation and password hashing