quantiles | Optimal Quantile Approximation in Streams | Machine Learning library

I need to group a dataframe by one variable and then summarising it by adding the number or rows (I can already do this) and number of columns relative to .25, .5, .75 quantiles of another variable.

In R I would do e.g.:

I used Pandas qcut function to sort a column of values into quantiles but I can't filter the DataFrame by those quantiles, here goes an example:

I would like to split a column into 4 distinct classes and label each class by 0,1,2,3 according to the 3 quantiles.

I have a little different data, but this here should make the point:

Here's my data

I have a list of variables I want to cap and floor but I want to save them with a different name to not overwrite the variables. Example with mtcars that yields an error:

I have a plot where I am comparing several (around 12) unrelated descriptors. To facilitate the display of all these plots, I make a list:

I want to create a graph where the variable on the x-axis is highly right skewed. When plotting this type of distribution, it is difficult to see anything in the plot along most of the distribution.

To circumvent this problem, I am now trying to plot the quantiles on the x-axis rather than the concrete values, so that the values on the x-axis are evenly distributed in my plot.

As an MWE, I demonstrate the problem using the Boston data. The variable "crim" has a mean: 3.61; min: 0.00; Q25: 0.08; median: 0.26; Q75: 3.68; max: 88.98.

The corresponding plot looks as follows:

Any ideas on how to achieve this?

Code:

I got a dataframe with one numerical value and one 5 level factor variable.