PandasGUI VS seaborn

This works with plotly but does a lot on its own if visualization isn’t the only thing you need, https://github.com/adamerose/PandasGUI

the errors aren't exactly the same, but possible solutions on these two suggest it might be an issue with the version of qt or pyqt that was installed: https://github.com/adamerose/pandasgui/issues/56

Similar to the several pandas low-code GUI tools such as [bamboolib](https://bamboolib.8080labs.com) or [PandasGUI](https://github.com/adamerose/PandasGUI), is there something available for PySpark?

I usually use PandasGui to view or plot DataFrames. If I do export a CSV I'm bringing it into Tabeau or JMP. Excel isn't very good for plotting.

I'll take this excuse to plug my open source project with a drag and drop UI for quickly making EDA graphs in Plotly https://github.com/adamerose/PandasGUI

seaborn

Additionally, Seaborn (https://seaborn.pydata.org/) is a great mention for people that want to use Matplotlib with better default aesthetics, amongst other conveniences:

"Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics."

Seaborn: built on top of matplotlib, adds a number of functions to make common statistical visualizations easier to generate.

pandas - The standard data analysis and manipulation tool numpy - scientific computing library seaborn - statistical data visualization sklearn - basic machine learning and predictive analysis CausalML - a suite of uplift modeling and causal inference methods PyTorch - professional deep learning framework PivotTablejs - Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook LazyPredict - build and work with and compare multiple models phidata - Build AI Assistants with memory, knowledge and tools. Lux - automates visualization and data analysis pycaret - low-code machine learning library. really nice Cleanlab - for when you are working with messy data drawdata - draw a dataset from inside Jupyter pyforest - lazy import popular data science libs streamlit - simple ui builder, useful for demonstrating ML results

How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative.

If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.

I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.

I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.

For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.

Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).

The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.

It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).

While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.

https://seaborn.pydata.org/

Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.