RasgoQL VS ydata-profiling

I built an open source package to bridge the gap between python and dbt, would love your feedback if you have a chance to check it out: https://github.com/rasgointelligence/RasgoQL

I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.

I created RasgoQL to give anyone a pandas-like syntax that you can use to quickly generate hundreds of lines of SQL that will run directly in your Snowflake or BigQuery data warehouse (with more data warehouse support coming soon). The best part? In one line of code, you can export this SQL to your dbt project so that it can run in production alongside other data pipelines.

You can check it out here: https://github.com/rasgointelligence/RasgoQL

6. Ydata-synthetic and Ydata-profiling by YData | Github | tutorial

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!

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.

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.

Super cool! For EDA, you could give ydata-profiling a spin sometime and speed up the process!

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

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 🚀