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.

Cerberus provides powerful yet simple and lightweight data validation functionality out of the box and is designed to be easily extensible, allowing for custom validation. It has no dependencies and is thoroughly tested from Python 2.7 up to 3.6, PyPy and PyPy3.

Ansible Runner is a tool and python library that helps when interfacing with Ansible directly or as part of another system whether that be through a container image interface, as a standalone tool, or as a Python module that can be imported. The goal is to provide a stable and consistent interface abstraction to Ansible. This allows Ansible to be embedded into other systems that don’t want to manage the complexities of the interface on their own (such as CI/CD platforms, Jenkins, or other automated tooling).

A curated list of awesome Python frameworks, libraries, software and resources

NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support) is a Python library that implements a set of functions to interact with different network device Operating Systems using a unified API.

NAPALM supports several methods to connect to the devices, to manipulate configurations or to retrieve data.

Faust is a stream processing library, porting the ideas from Kafka Streams to Python.

It is used at Robinhood to build high performance distributed systems and real-time data pipelines that process billions of events every day.

Faust provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink,

It does not use a DSL, it’s just Python! This means you can use all your favorite Python libraries when stream processing: NumPy, PyTorch, Pandas, NLTK, Django, Flask, SQLAlchemy, ++

Faust requires Python 3.6 or later for the new async/await syntax, and variable type annotations.

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

NaCl (pronounced "salt") is a new easy-to-use high-speed software library for network communication, encryption, decryption, signatures, etc. NaCl's goal is to provide all of the core operations needed to build higher-level cryptographic tools.

Of course, other libraries already exist for these core operations. NaCl advances the state of the art by improving security, by improving usability, and by improving speed.

The following report contrasts NaCl with other libraries from a security perspective: (PDF) Daniel J. Bernstein, Tanja Lange, Peter Schwabe, "The security impact of a new cryptographic library". Pages 159–176 in Proceedings of LatinCrypt 2012, edited by Alejandro Hevia and Gregory Neven, Lecture Notes in Computer Science 7533, Springer, 2012. ISBN 978-3-642-33480-1.

The following report was created for Research Plaza and gives an introduction to NaCl for a wider audience: (PDF)

cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests, and key derivation functions.

docopt creates beautiful command-line interfaces

Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

• Python Fire is a simple way to create a CLI in Python. [1]
• Python Fire is a helpful tool for developing and debugging Python code. [2]
• Python Fire helps with exploring existing code or turning other people's code into a CLI. [3]
• Python Fire makes transitioning between Bash and Python easier. [4]
• Python Fire makes using a Python REPL easier by setting up the REPL with the modules and variables you'll need already imported and created. [5]

Snimpy is aimed at being the more Pythonic possible. You should forget that you are doing SNMP requests. Snimpy will rely on MIB to hide SNMP details. Here are some "features":

MIB parser based on libsmi (through CFFI)
SNMP requests are handled by PySNMP (SNMPv1, SNMPv2 and SNMPv3 support)
scalars are just attributes of your session object
columns are like a Python dictionary and made available as an attribute
getting an attribute is like issuing a GET method
setting an attribute is like issuing a SET method
iterating over a table is like using GETNEXT
when something goes wrong, you get an exception

Leaflet is the leading open-source JavaScript library for mobile-friendly interactive maps. Weighing just about 38 KB of JS, it has all the mapping features most developers ever need.

A cross-platform C library to retrieve CPU features (such as available instructions) at runtime

Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Features

• Data structures for graphs, digraphs, and multigraphs
• Many standard graph algorithms
• Network structure and analysis measures
• Generators for classic graphs, random graphs, and synthetic networks
• Nodes can be "anything" (e.g., text, images, XML records)
• Edges can hold arbitrary data (e.g., weights, time-series)
• Open source 3-clause BSD license
• Well tested with over 90% code coverage
• Additional benefits from Python include fast prototyping, easy to teach, and multi-platform

This wiki page is a resource for some brainstorming around the possibility of a Python Graph API in the form of an informational PEP, similar to PEP 249, the Python DB API. The goal would be, in other words, to define how a graph (or various kinds of graphs) would be expected to behave (possibly from different perspectives) in order to increase interoperability among graph algorithms. The numeric array interface, recently developed by the Numeric Python community to increase interoperability between array-handling software, illustrates the general idea.

A single distribution of libraries that automatically collects traces and metrics from your app, displays them locally, and sends them to any analysis tool.

The key features of OpenCensus include:

• Standard wire protocols and consistent APIs for handling trace and metric data.
• A single set of libraries for many languages, including Java, C++, Go, .Net, Python, PHP, Node.js, Erlang, and Ruby.
• Included integrations with web and RPC frameworks, making traces and metrics available out of the box.
• Included exporters for storage and analysis tools. Right now the list includes Zipkin, Prometheus, Datadog, Stackdriver, and Azure App Insights.
• Full open source availability for additional integrations and export options.
• No additional server or daemon is required to support OpenCensus.
• In process debugging: an optional agent for displaying request and metrics data on instrumented hosts.

...is a unit testing framework for Shell scripts - namely Bash.

This is a very light weight (as in one file) unit testing library for Bash scripts. It functions similarly to Java's JUnit.

You do not need to touch any of your shell scripts to run unit tests against it. That is to say, that your original source code does not need to know about the BSTL in order for the unit tests to work.