heatmap | PowerBI Heatmap Visual enables users to draw a heatmap | Data Visualization library

There is no need to update matplotlib version if you are using version of matplotlib 3.4.3.

Here is the link for matplotlib 3.4.3 documentation and avoid using labels keyword in ax.set_xticks() function as said by JohnC

As the heatmap gets a default "equal" aspect ratio, and gets shrunk due to the colorbar, an idea is to manually resize the histograms once everything is created.

geom_tile() has a width argument you can use to programmatically set the width. But you have to be careful because the labels will end up in funny places if you're not explicit about where to put them. Therefore I pre-calculate the position of each label based on the specified widths. Here you also have to know the order you want these columns in so they appear correctly.

First of all, if you want only 1 item selected at a time, then use instead of .

You are using the general sibling combinator ~ :

The general sibling combinator (~) separates two selectors and matches all iterations of the second element, that are following the first element (though not necessarily immediately), and are children of the same parent element.

If you want to change the color of the label associated with a radio, you need to place the radio before its label.

It can be done with a single rule :

Use Python 3.9 (or 3.8, 3.7, 3.6) as it seems like both pandas and plt are not quite ready to be used with Python 3.10:

Here are a couple ways to adapt that link for your use case (1 label per 6 months):

1. Either: Show an empty string except on Jan 1 and Jul 1 (i.e., when %m%d evals to 0101 or 0701)

Currently, there does not seem to be an attribute for axes thickness in Plots.jl.

As a workaround, you may use the attribute thickness_scaling, which will scale the thickness of everything: lines, grid lines, axes lines, etc. Since you only want to change the thickness of axes, you need to scale down the others. Here is your example code doing that using pyplot backend.

Change group_df to a factor and specifying annotation_colors will work.

• using your sample data for points
• these points are in Saudi Arabia, so assumed that polygons are regional boundaries in Saudi Arabia. Downloaded this from http://www.naturalearthdata.com/downloads/10m-cultural-vectors/
• polygon data is a shape file
  1. loaded into geopandas to allow interface to GEOJSON __geo__interface
  2. dynamically filtered this to Saudi using pandas .loc
• confidence data is just a straight https://plotly.com/python/mapbox-density-heatmaps/
• boundaries are https://plotly.com/python/mapbox-layers/