Python-Markdown
Pandas
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Python-Markdown | Pandas | |
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15 | 393 | |
3,578 | 41,923 | |
1.6% | 1.4% | |
8.0 | 10.0 | |
about 1 month ago | 5 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Python-Markdown
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Introducing AutoPyTabs: Automatically generate code examples for different Python versions in MkDocs or Sphinx based documentations
AutoPyTabs allows you to write code examples in your documentation targeting a single version of Python and then generates examples targeting higher Python versions on the fly, presenting them in tabs, using popular tabs extensions. This all comes packaged as a markdown extension, MkDocs plugin and a Sphinx, so it can easily be integrated with your documentation workflow.
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Creating a Python Wiki application
As a starting point, take a look at the Python-Markdown library. It's available from the Pypi repository, so is easy to install with pip / pipenv / ...
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Learning about SSG features with Docusarus
Issue Markdown Full Markdown Support Complete Markdown Support with the Help of Python-Markdown/markdown I wanted to finally Add full markdown support.
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Show HN: Weejur – micro-blog from your email account
I like the simplicity of your platform!
Thanks for the bug report. I've used python-markdown [0] for the markdown parsing–I'll have to double-check the implementation.
[0]: https://python-markdown.github.io/
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Help with understanding & breaking down a library
I believe a lot of the actual replacements (or at least mappings to replacements) are happening in inlinepatterns.py - you can see on lines 106-172 all of the regex patterns that are used for various matches. Line 442 you can see the Processor that was created to handle Asterisks, working with and .
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Breaking down a python package library
I see the https://github.com/Python-Markdown/markdown , but I am troubling identifying the supporting code that really is doing the leg work ie the core functions and logic supporting it to take markdown and turn it in to html.
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Is it a good practice to use /admin to create manage the blog in production?
Interesting, I also use markdown, but hadn't heard of Django-Markdownx before your today. What I do is create two fields: body_md and body_html, and on save use Python Markdown to turn my markdown in html.
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Spell checking Markdown documents using a Github action
Now we have to add a configuration file for the spelling checker. It uses PySpelling under the hood. When checking Markdown files, it first converts a Markdown text file's buffer using Python Markdown and returns a single SourceText object containing the text as HTML. Then it captures the HTML content, comments, and even attributes and performs the check. It has a lot of configuration options, but here we are going to see only an example with some basics. For further info you can read the docs of the rojopolis/spellcheck-github-actions Github action.
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What library/how to write nice documentation of experiments directly from python
Otherwise, I would use markdown with Python Markdown.
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How I Refactored my Code
To resolve the above issue, I thought the best approach was to avoid reinventing the wheel and save myself hours of debugging: use a third-party library. After implementing a Python implementation of John Gruber’s Markdown, 36 lines of code were cut down to a single function call. I've not benchmarked my SSG after the change, but in terms of code readability, it's certainly worth the overhead caused by the library.
Pandas
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
What are some alternatives?
markdown2 - markdown2: A fast and complete implementation of Markdown in Python
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Mistune - A fast yet powerful Python Markdown parser with renderers and plugins.
tensorflow - An Open Source Machine Learning Framework for Everyone
mistletoe - A fast, extensible and spec-compliant Markdown parser in pure Python.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Jinja2 - A very fast and expressive template engine.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
pymorphy2 - Morphological analyzer / inflection engine for Russian and Ukrainian languages.
Keras - Deep Learning for humans
MyST-Parser - An extended commonmark compliant parser, with bridges to docutils/sphinx
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration