This package provides a flake8 plugin to help you write better list/set/dict comprehensions.

This package lets you extract Swagger API documentation specs from your Flask-Restful projects.

This package provides a library to conncet to live Jupyter Kernels through HTTP and WebSocket.

This package provides a Jupyter Server extension with an implementation of the File ID service.

Python client library for Dynamic DNS using Domain Connect protocol.

pyxDamerauLevenshtein implements the Damerau-Levenshtein (DL) edit distance algorithm for Python in Cython for high performance.

This package provides a Python library for translating plain- and rich-text documents with Argos Translate.

sphinxcontrib-devhelp is a Sphinx extension which outputs https://wiki.gnome.org/Apps/Devhelp documents.

The Django REST framework is for building Web APIs with Django. It provides features like a Web-browsable API and authentication policies.

This package provides a Pytest plugin for customizing string representations of doctest results. It can change the display hook used by doctest to render the object representations.

IPython provides a rich architecture for interactive computing with: Powerful interactive shells, a browser-based notebook, support for interactive data visualization, embeddable interpreters and tools for parallel computing.

The conda-libmamba-solver package provides a fast solver backend for Conda based on the libmamba library. It significantly speeds up dependency resolution compared to the classic solver.

This package provides the fixtures ftpserver and ftpserver_TLS, which are threaded instances of a FTP server, with which you can upload files and test FTP functionality.

This module can convert Python docstrings to Markdown. It can recognise reStructuredText inside docstrings and convert multiple of its features to Markdown. It also includes initial support for Google-formatted docstrings.

This package provides a Python library to interact with Jupyter Server REST API for server management operations. It provides server-level functionality including kernel listing (read-only) while avoiding duplication with existing kernel management libraries.

yamlordereddictloader is a Python package that provides a loader and a dumper for PyYAML allowing to keep items order when loading a file (by putting them in OrderedDict objects) and to manage OrderedDict objects when dumping to a file.

django-configurations helps you organize the configuration of your Django project by providing glue code to bridge between Django'smodule based settings system and programming patterns like mixins, facades, factories and adapters that are useful for non-trivial configuration scenarios.

This piece of code sets up some reader macros that make it simpler to input string literals which contain backslashes and double quotes This is very useful for writing complicated docstrings and, as it turns out, writing code that contains string literals that contain code themselves.

This package aims to make the transition away from pytz easier. It is intended for temporary usage only, and should allow you to drop your dependency on pytz while also giving your users notice that eventually you will remove support for the pytz-specific interface.

This plugin package provides a way to include information about the system, Python installation, and select dependencies in the header of the output when running pytest. It can be used with packages that are not affiliated with the Astropy project, but is optimized for use with astropy-related projects.

gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute. This is a minimal version without the dashboard functionality. This is the fork of Marek Felšöci.

This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.

This package provides easy access to pretrained models for more than 100 languages, fine-tuned for various use-cases.

Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task.

Documentation at https://melpa.org/#/flymake-python-pyflakes

Documentation at https://melpa.org/#/python-insert-docstring