piperider
ydata-profiling
piperider | ydata-profiling | |
---|---|---|
6 | 43 | |
469 | 12,070 | |
0.2% | 1.1% | |
9.5 | 8.5 | |
about 2 months ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | MIT 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.
piperider
- Show HN: PipeRider – open-source Data Impact Analysis for dbt changes
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Open source data observability tools with UI?
If you post a GitHub issue to request these connectors is might help persuade the product team to add these sooner than later.
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Data profiling as part of a data reliability strategy?
PS. I'm a bit biased -> I'm working for PipeRider; we're building an open-source data reliability toolkit with profiling at the core: https://github.com/InfuseAI/piperider
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Show HN: PipeRider, data reliability automated tool
I was rush to Show HN, and now I want to tell a bit more.
PipeRider, it’s our take on a data reliability and quality tool for data pipelines. It’s based on data profiling and assertions that test against the data profile.
It’s open-source and ready to use on Github here: https://github.com/infuseai/piperider
Here is a quick start to get you up and running easily:
ydata-profiling
- FLaNK 25 December 2023
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First 15 Open Source Advent projects
6. Ydata-synthetic and Ydata-profiling by YData | Github | tutorial
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Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code
Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
- Data exploration is not dead
- Explore your data in a single line of code
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Which preprocessing steps to improve the performance of a naive bayes classifier
My suggestion start with the EDA - there are a lot of packages that automate that for you already. My usual go-to: https://github.com/ydataai/ydata-profiling.
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Simulating sales data
If you're not sure about the behaviour of your data (i.e., if the original data has properties like seasonality), you can use ydata-profiling to profile your data first.
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I recorded a Data Science Project using Python and uploaded it on Youtube
Super cool! For EDA, you could give ydata-profiling a spin sometime and speed up the process!
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Ydata-Profiling and Dask
Hey guys,
We've been recently at the Dask Demo Day and we're hoping to launch a new feature on ydata-profiling, with the support for Dask dataframes!
We're looking for Dask Wizards to start collaborating on this feature, so if you're interested, please join us to define the roadmap of the project and start making it real
Current GitHub branch is here: https://github.com/ydataai/ydata-profiling/tree/feat/dask
Dedicated dask channel here: https://discord.gg/EHDBuSSDuy
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🧠 ydata-profiling + Dask!
We're looking for Dask Wizards 🧙🏻♂️ to start collaborating on this branch, so if you're interested, please join us to define the roadmap of the project and start making it real 🚀