The examples out there will not do for your data and even if you wonder 'am I using the wrong chart for this?' you can find that you are not.
Recently I needed to do a stacked bar chart with different things in each stack. Imagine column 1 - fruit - with that broken down by apples/bananas/oranges and column 2 - vegetables - with that broken down by tomatoes/potatoes/carrots.
The other problem area was colouring the chart. I generated colours in the hsl colour space so that where something was on the chart determined colour and how much of it there was determined saturation/lightness. Some people get offended by eXcel style hard colours so I could not just use random clashing colours and call it a day.
Beautiful visualisations are hard, even with the best graph tools out there I expect some inevitable situation of being an edge case and having to find solutions the hard way. The goal being so that nobody even notices the hard work put in. Only if the graph is ultra intuitive and simple is it going to work with some audiences. Simplicity is hard.
It is just that visualizations are not a solved thing yet.
* What kind of plot do I want? Bar chart? Scatter plot? Boxplot? Pick the geom based on this.
* What column goes on each axis? Do I need to transform the axis? For e.g., is log scale better?
* Do I split data by some other column? Use different colors/fill, shapes, or facets.
* Do I need any annotations to point out interesting parts of the plot?
GGPlot and Plotnine are joy to work with once you grok the ideas of tidy data and grammar of graphics. Since the API is consistent, experimentation can be quick:
* That scatter plot looks to busy? Change the geom to a boxplot.
* Want to see if an attribute has any predictive power? Assign it to any unused aesthetic.
Seaborn can provide some higher-level functions compared to plotnine/ggplot like pair-plot. But as you said, you need to lookup seaborn's documentation all the time to work with it.
Even when focused carefully on the coding and choosing the right plot for the data, an article about plotting is still so much about the data. And so, it's ultimately disappointing if you can't relate the data to your most common problems.
For instance in the context of Text Mining or geo-spacial stuff.
Thanks for the submission and happy easter :-)