seaborn VS plotnine

Below are the key insights. If you want to see the Python code I used to do this analysis and generate the charts using Seaborn, you can find my full analysis Jupyter notebook on my Github repo here: Tip Analysis.ipynb

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/

To your last point, have you tried plotnine? It's meant to be ggplot2 for python.

https://github.com/has2k1/plotnine

plotnine - A grammar of graphics for Python based on ggplot2.

Github: https://github.com/has2k1/plotnine

This seems quite similar to plotnine [0], which also provides a grammar of graphics interface for Python. That said, I love ggplot and I can't wait to use this in my research! I hope we can port/re-implement ggthemes, scientificplots [1], and other ggplot libraries for lets-plot.

0: https://plotnine.readthedocs.io/en/stable/

1: https://github.com/garrettj403/SciencePlots

There is [plotnine](https://plotnine.readthedocs.io/en/stable/) which tries to implement ggplot in Python.

You could use plotnine if you like the grammar of graphics concept: https://plotnine.readthedocs.io/en/stable/

> Python doesn’t yet have anything remotely close to ggplot for rapidly making exploratory graphics, for example.

Plug for plotnine (https://plotnine.readthedocs.io/en/stable/). I don't know R but use ggplot indirectly through this library for exploratory data analysis, and comparing the experience to any other python plotting library, I understand why R folks are usually so sad to be using Python.

They have, https://plotnine.readthedocs.io/en/stable/