ydata-profiling VS dtale

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 🚀

[D-Tale](https://github.com/man-group/dtale) has recently released version 3.2.0 on pypi & conda-forge: ``` pip install -U dtale conda install dtale -c conda-forge ``` But if you want to take it one step further you can now integrate it with [ArcticDB](https://github.com/man-group/ArcticDB): ``` pip install -U dtale[arcticdb] ``` This allows you the ability to navigate your libraries of datasets saved to your ArcticDB database! But the best part is that all the reads are occuring directly against ArcticDB so some of the memory constraints you may have been hit with before are now a thing of the past. Here's a full write up how to use this functionality along with a quick demo: https://github.com/man-group/dtale/blob/master/docs/arcticdb/ARCTICDB\_INTEGRATION.md Hope this helps & please support open-source by throwing your star on the [repo](https://github.com/man-group/dtale). Thanks! 🙏

I've looked into external tooling, libs such as dtale, which feel overly complicated for my use case (but I'm open to alternatives). What I would like to have instead is something akin to Spyder's variable viewer, which allows sorting by column. VSCode goes a step further and also provides the ability to filter the dataframe.

D-Tale: A Python library that provides an interactive web-based interface for data exploration and analysis.

Try this: https://github.com/man-group/dtale

https://github.com/man-group/dtale

I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it.

If I really want to show everything, I just use:

'''

Just added "Time Series Analysis" in v1.60.1 of D-Tale on pypi & conda-forge: pip install -U dtale conda install dtale -c conda-forge This feature provides a quick and easy way to visualize the usage of the following time series analysis function in statsmodels:

Your data is stored in memory so the size of your dataframe is limited to the memory of your machine. That being said we’ve allowed users to swap out the machanism which stores the data so you can use something like Redis or Shelve to allieviate memory. Here’s some documentation: https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md