visions
ydata-profiling
Our great sponsors
| visions | ydata-profiling | |
|---|---|---|
| 6 | 43 | |
| 194 | 12,022 | |
| 0.0% | 1.5% | |
| 0.0 | 8.5 | |
| over 1 year ago | 7 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | 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.
visions
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Complete Beginner tasked with ML at work - where do I start
This one works pretty well: https://github.com/dylan-profiler/visions
- Visions – User defined data type systems
- Show HN: Visions – User defined data type systems
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Show HN: DataProfiler – What's in your data? Extract schema, stats and entities
This is really cool! I'm glad there's more work going into the area - visions[1] is a similar tool which embeds the type system (user defined schema) into a traversable graph rather than encoding all type information in the trained network. We originally wrote it as the backend for pandas-profiling[2].
I'm not sure if any of the authors are on here but y'all might look into Sherlock[3] as well. They've got pre-trained models for many other semantic types than you've currently implemented.
1. https://github.com/dylan-profiler/visions
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 🚀
What are some alternatives?
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
dtale - Visualizer for pandas data structures
superset - Apache Superset is a Data Visualization and Data Exploration Platform
scikit-learn - scikit-learn: machine learning in Python
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
calculadora-do-cidadao - 💵 Tool for Brazilian Reais monetary adjustment/correction
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
datacompy - Pandas and Spark DataFrame comparison for humans and more!
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b