I've found a Python module which may be able to help converting Python to R code http://www.xavierdupre.fr/app/pyensae/helpsphinx/notebooks/r2python.html

And interfacing Python to R https://rstudio.github.io/reticulate/

I thought you might want to look at the difference between Python and R, https://shiring.github.io/r_vs_python/2017/01/22/R_vs_Py_post#:~:text=R%20and%20Python%20are%20both,while%20Python%20is%20more%20generalist.

I believe it's best your spend time learning R script https://r4ds.had.co.nz/introduction.html

from rpy2.robjects import r

ggplotRegression = r("""
    function (fit) {

        require(ggplot2)

        ggplot(fit$model,
               aes_string(x = names(fit$model)[2],
                          y = names(fit$model)[1])) + 
            geom_point() +
            stat_smooth(method = "lm", col = "red") +
            labs(title = paste("Adj R2 = ",
                               signif(summary(fit)$adj.r.squared, 5),
                               "Intercept =",
                               signif(fit$coef[[1]],5 ),
                               " Slope =",signif(fit$coef[[2]], 5),
                               " P =",signif(summary(fit)$coef[2,4],         
                               5)))
    }""")

With this you can call the function ggplotRegression in your Python code.

rpy(2) does not convert code. It only allows you an interface to communicate with R from python and issue R commands from within python.

Given that R is very dependent upon statistical libraries that aren't available in python the conversion would generally be a tremendous amount of work. To convert a single line like

lmer (y ~ x1 + x1 + (1|id), mydat)

to something in python would take a great deal of work. If you just want to run that code from python use rpy. Going the other way, which seems to be more what you are interested in, the conversion could often be a bit more straightforward. However, the code would tend to be very poorly optimized for R and run very badly. I recently optimized some R code converted from python by someone who wrote the R code as a more or less direct translation line by line. The optimization came out 1/3 the length and between 20 and 1000 times faster (depending on the function).

You can use assign to get it all in one chain:

(
test.groupby("InvoiceDocNumber", as_index=False)
 .itemprob.max()
 .rename(columns={"itemprob":"invoiceprob"})
 .assign(invoicerank = lambda x: x.invoiceprob.rank(ascending=False))
)

Output:

   InvoiceDocNumber  invoiceprob  invoicerank
0                 0     0.924193          5.0
1                 1     0.974173          4.0
2                 2     0.978962          3.0
3                 3     0.992663          2.0
4                 4     0.994243          1.0

Data:

import numpy as np
import pandas as pd
n = 100
test = pd.DataFrame({"InvoiceDocNumber": np.random.choice(np.arange(5), size=n),
                     "itemprob": np.random.uniform(size=n)})