marked
Pandas
marked | Pandas | |
---|---|---|
60 | 395 | |
31,926 | 41,983 | |
0.7% | 0.6% | |
9.5 | 10.0 | |
5 days ago | 5 days ago | |
JavaScript | Python | |
GNU General Public License v3.0 or later | 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.
marked
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Eleventy vs. Next.js for static site generation
Next, install gray-matter to extract metadata from the front matter of markdown files, and marked to convert the markdown files to HTML:
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To learn svelte, I clone Github's issues page including useful features that you might consider reusing.
đź“‘ Marked Markdown parser. Use it to create your own markdown editor.
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🤖 AI Search and Q&A for Your Dev.to Content with Vrite
Vrite SDK provides a few built-in input and output transformers. These are functions, with standardized signatures to process the content from and into Vrite. In this case, gfmInputTransformer is essentially a GitHub Flavored Markdown parser, using Marked.js under the hood.
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Better code highlighting on the web: rehype-tree-sitter
Another contestant in this realm is Bright[1]. It runs entirely on the server and doesn't increase bundle size as seen here[2]. Regarding parsing speed tree-sitter is without a doubt performant since it is written in Rust, but I don't have any problems "parsing on every keystroke" with a setup containing Marked[3], highlight.js[4] and a sanitizer. I did however experience performance issues with other Markdown parser libraries than Marked.
[1]: https://bright.codehike.org/
[2]: https://aihelperbot.com/test-suite
[3]: https://github.com/markedjs/marked
[4]: https://highlightjs.org/
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[Project Share] List dialog that supports complex HTML and Markdown format.
The project uses markedJS to convert markdown into HTML, this is their GitHub page.
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Vrite Editor: Open-Source WYSIWYG Markdown Editor
To handle pasting block Markdown content like this, I had to tap into ProseMirror and implement a custom mechanism (though somewhat based on TipTap’s paste rules), detecting starting and ending points of the blocks and parsing them with Marked.js.
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Help needed!
I am using marked for markdown parsing together with marked-highlighting to handle syntax highlighting and everything is working as it should.
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Need help - sanitizeHtml with marked doesn't render special characters correctly (& is & and then &amp)
I'm trying to render user input using SvelteMarkdown (that uses marked).
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Looking for a Comprehensive Guide for Building Complex Chatbots with GPT-4 API
GPT API returns data in markdown format. You can parse it using a Markdown library and string manipulation. On Electron app I developed https://jhappsproducts.gumroad.com/l/gpteverywhere, I used https://github.com/markedjs/marked and a code syntax highlighting package to display code blocks. And used JavaScript string manipulation to detect when code blocks start and end so I could add COPY/SAVE buttons to the blocks. I hope this helps, and happy coding! :)
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How I put ChatGPT into a WYSIWYG editor
Again, with streaming enabled, you’ll now receive new tokens as soon as they’re available. Given that OpenAI’s API uses Markdown in its response format, a full message will need to be put together from the incoming tokens and parsed to HTML, as accepted by the replaceContent function. For this purpose, I’ve used the Marked.js parser.
Pandas
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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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.
What are some alternatives?
remark - markdown processor powered by plugins part of the @unifiedjs collective
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
markdown-it - Markdown parser, done right. 100% CommonMark support, extensions, syntax plugins & high speed
tensorflow - An Open Source Machine Learning Framework for Everyone
snarkdown - :smirk_cat: A snarky 1kb Markdown parser written in JavaScript
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
DOMPurify - DOMPurify - a DOM-only, super-fast, uber-tolerant XSS sanitizer for HTML, MathML and SVG. DOMPurify works with a secure default, but offers a lot of configurability and hooks. Demo:
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
MDsveX - A markdown preprocessor for Svelte.
Keras - Deep Learning for humans
js-yaml - JavaScript YAML parser and dumper. Very fast.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration