Kedro
PRAW
Kedro | PRAW | |
---|---|---|
29 | 528 | |
9,374 | 3,321 | |
0.7% | 0.8% | |
9.7 | 7.7 | |
2 days ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | BSD 2-clause "Simplified" 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.
Kedro
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Nextflow: Data-Driven Computational Pipelines
Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).
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A Polars exploration into Kedro
# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]
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What are some open-source ML pipeline managers that are easy to use?
So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.
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How do data scientists combine Kedro and Databricks?
We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.
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How do you organize yourself during projects?
you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data
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Futuristic documentation systems in Python, part 1: aiming for more
Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.
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Python projects with best practices on Github?
You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.
- Data Science/ Analyst Zertifikate für den Job Markt?
- What are examples of well-organized data science project that I can see on Github?
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Dabbling with Dagster vs. Airflow
An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.
PRAW
- PRAW documentation
- Testing
- `resubmit=False` started resubmitting duplicate URLs Jul 24 2023
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Just curious which person is the most popular user flair.
I'm... not sure I understand the question? PRAW still works just fine for "personal use" of the reddit API.
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How to use use Praw library with access and refresh tokens?
Thank you for pointing out. So there is no need then for the access token? Only with the refresh token is enough? To be honest I took a look at it but I did not expect that to be under authentication as strictly speaking, the user already made the authentication. Also I took a look at the code at https://github.com/praw-dev/praw/blob/master/praw/reddit.py and I did not get a hint whether was possible to pass it or not. I am just saying this to let you know I tried to search for the answer before asking. Again thank you for the help.
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PRAW VS redditwarp - a user suggested alternative
2 projects | 21 Jun 2023
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Migrating subreddits to Lemmy communities
To get the relevant IDs, you can use something like PRAW to query the subreddit for the top 1000 posts for example.
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Reddit Comment Nuke: A Python script to edit and save your Reddit comment history en masse
Huge thanks to the contributors to PRAW, which is the Python package that does all the heavy lifting relating to Reddit's API that I need for this script.
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Why does PRAW's stream_generator() use a BoundedSet limit of 301?
However, in practice duplicate items were yielded with these smaller numbers. So I increased the limit briefly to 250 in October 2016, and then increased it finally to 301 in December 2016 in order to resolve https://github.com/praw-dev/praw/issues/673. That issue provides an explanation for how 301 came to be.
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is there a list of http status code which reddit api returns?
Why? You gotta be ready for any status code. Even 777.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
asyncpraw - Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Pushshift API - Pushshift API
Dask - Parallel computing with task scheduling
pmaw - A multithread Pushshift.io API Wrapper for reddit.com comment and submission searches.
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
boto3 - AWS SDK for Python
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Telethon - Pure Python 3 MTProto API Telegram client library, for bots too!
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
django-wordpress - WordPress models and views for Django.