monaco
ydata-profiling
monaco | ydata-profiling | |
---|---|---|
1 | 43 | |
81 | 12,070 | |
- | 1.1% | |
8.0 | 8.5 | |
5 days ago | 3 days ago | |
Python | Python | |
MIT License | 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.
monaco
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ACX Grants ++: The First Half
At the heart of all serious forecasting is a statistical tool known as Monte Carlo analysis. It allows you to quantify uncertainty by introducing randomness to the inputs of computational models and looking at the range of results. If you want a good example, you might recognize Monte Carlo techniques from Nate Silver’s election forecasts at 538. It's been a gold-standard throughout my career in the space industry, and I can attest to how powerful it is - I've used it to successfully send a rocket to Mars. However, there aren't any tools out there that make it easy for researchers to take their existing models and wrap a Monte Carlo around it. So, I wrote one. It's an open-source python library which I'm calling "monaco". I'm at a point in development where the basic feature set is complete and working well, and I'm looking to finish up the extended roadmap in the next few months. See the project github page for the code, examples, and a lot more info: https://github.com/scottshambaugh/monaco. I’m looking for $1000 to help me present version 1.0 of this tool to the scientific community at the 2022 SciPy Conference in Austin, TX this summer. That amount should cover conference fees, hotel, and airfare, and if you're feeling generous I could use additional funds for some external monitors and cloud compute time. My name is Scott Shambaugh, and if you’re interested in helping fund this please email me at wsshambaugh AT gmail.com. Thank you!
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?
rebop - Fast stochastic simulator for chemical reaction networks
dtale - Visualizer for pandas data structures
emukit - A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
ebisu - Public-domain Python library for flashcard quiz scheduling using Bayesian statistics. (JavaScript, Java, Dart, and other ports available!)
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
scikit-learn - scikit-learn: machine learning in Python
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.
volesti - Practical volume computation and sampling in high dimensions
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b