ihp
Pandas
ihp | Pandas | |
---|---|---|
124 | 399 | |
4,226 | 42,039 | |
0.3% | 0.7% | |
9.5 | 10.0 | |
6 days ago | 5 days ago | |
Haskell | Python | |
MIT 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.
ihp
- IHP – The Haskell Framework for Non-Haskellers
-
Ask HN: Why are all of the best back end web frameworks dynamically typed?
I found IHP straightforward:
https://ihp.digitallyinduced.com/
despite not remembering much haskell!
This assumes you can get past nix for the install.
I find IHP well-designed. I just wish the licensing scheme were more transparent.
- IHP v1.1.0 has been released 🎉
- IHP Haskell Framework v1.1.0 has been released
-
Servant or framework
You can find the docs at https://ihp.digitallyinduced.com/ and some getting started videos at https://www.youtube.com/watch?v=PLl9Sjq6Nzc&list=PLenFm8BWuKlS0IaE31DmKB_PbkMLmwWmG
-
Haskell Optimization Handbook
In case this got you interested in Haskell, and you want a good way to start your Haskell journey (and have something to apply the optimization handbook to), check out IHP. It's the Rails/Laravel of the Haskell world. You can start here https://ihp.digitallyinduced.com/Guide/index.html or check it out on GitHub here https://github.com/digitallyinduced/ihp
-
Show HN: Algora.io – Open-source development bounties
At IHP we've been using Algora for a while now and it works really great. Here's e.g. one PR that was merged last week with a bounty attached https://github.com/digitallyinduced/ihp/issues/1621 Everything was set up in less than 15 minutes and ioannis and zafer have been super helpful with any questions we had.
In general I think this is a good direction and an interesting take on the open question around sustainable open source. Congrats on the launch and keep up the great work! :)
- Por que Elm é uma linguagem tão deliciosa?
-
Any open source projects to contribute to for beginners
You could contribute to IHP! We have some great docs to get started here https://github.com/digitallyinduced/ihp/blob/master/CONTRIBUTING.md And we have some low hanging fruits in GitHub issues for you to get started with, e.g. https://github.com/digitallyinduced/ihp/issues/1601 (also there's always lots of activity in the IHP Slack, in case you have any questions/need help)
- IHP Haskell Framework v1.0.1 has been released
Pandas
- The Birth of Parquet
- PDEP-13: The Pandas Logical Type System
- PHP Doesn't Suck Anymore
-
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.
-
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.
-
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.
-
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
-
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.
-
Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
-
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.
What are some alternatives?
miso - :ramen: A tasty Haskell front-end framework
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Ruby on Rails - Ruby on Rails
tensorflow - An Open Source Machine Learning Framework for Everyone
haskell-ux - Let's make Haskells error messages helpful :)
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Phoenix - Peace of mind from prototype to production
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
ghc-proposals - Proposed compiler and language changes for GHC and GHC/Haskell
Keras - Deep Learning for humans
purescript-flame - Fast & simple framework for building web applications
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration