seaborn VS owid-grapher

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.

Matplotlib Seaborn Example data sets

Seaborn - Statistical data visualization using Matplotlib.

Github: https://github.com/mwaskom/seaborn

Seaborn Documentation

You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts

Let’s begin with a data visualization-friendly resource.

Here's a dashboard: https://ourworldindata.org/

Pick almost anything to see a positive trend.

I think the idea of Framework is really good, but static data limits the applications, excluding monitoring and other cases in which the data is constantly changing, but the dashboard can stay as it is. For example, I'd love to see a revamped Framework version of the LHC beam monitor and related pages (see https://op-webtools.web.cern.ch/vistar/, but check again in 2 months or so, when the accelerator will be running).

In high-energy physics, ROOT is /the/ toolkit for data analysis, and I guess jsROOT (https://root.cern.ch/js/) could also be used to load data to be shown in Framework dashboards. I thought the idea of Framework as a blogging engine with powerful data visualization built-in could be very interesting. Think, for example, about physicists pulling open data (https://opendata.cern.ch) and writing about their analysis or someone pulling data from https://ourworldindata.org/ in their own visualizations to support their case while writing about a particular subject, etc.

This is patently false. Visit ourworldindata.org and look at the data for the past few hundred years. 17th-century English philosopher Thomas Hobbes famously wrote the "the life of man [is] solitary, poor, nasty, brutish, and short," which was largely accurate in the 17th century. Today, the poorest people in developed nations enjoy a standard of living that royalty of Hobbes time would have envied. And while the percentage of humanity living in extreme poverty increased from 8.5% to just above 9% in 2022, overall it's down from 80% in the year 1800. We have made similar strides in the areas of education and healthcare.

If your statement were true, we wouldn't be living in a world where every measure of human well being only goes up.

I don't have any ideas, but I'm just sharing this in case you're not aware https://ourworldindata.org/

Not according to Worldometers.info, nor by ourworldindata.org or worldpopulationreview.com. Wikipedia gives India a slight edge.

The data comes from ourworldindata.org and from the OECD website. Pretty simple !