[Python] how can Python's pd.qcut give the same result as R's statar::xtile?

I need to create bins based on one column in a dataframe. One problem is the values of that column are oddly distributed. Consequently, Python's pd.qcut may arbitrarily put observations into different bins, even though they have the same value.

In R (or in Stata), I use the xtile function of the statar package. R is able to group all observations with the same value into one bin.

library(tidyverse)

sample_df <- data.frame(customer_id = seq(1:10),
purch_frequency = c(1, 1, 1, 1, 1, 2, 3, 10, 11, 11))

sample_df <- sample_df %>%
mutate(freq_bins1=statar::xtile(purch_frequency, 2),
freq_bins2=statar::xtile(purch_frequency, 3))

print(sample_df)


A corresponding implementation in Python,

import pandas as pd

data = {'customer_id': range(1,11),
'purch_frequency': [1, 1, 1, 1, 1, 2, 3, 10, 11, 11]}
sample_df = pd.DataFrame(data)

sample_df['freq_bins1'] = \
(sample_df['purch_frequency'].rank(method = 'first')
.transform(lambda x: pd.qcut(x, 2, labels = False)))
sample_df['freq_bins2'] = \
(sample_df['purch_frequency'].rank(method = 'first')
.transform(lambda x: pd.qcut(x, 3, labels = False)))
print(sample_df)



As you can see, R and Python give different answers for the last column, freq_bins2. I wondered how I can modify the Python code so that it matches R's result. Thanks!


A quick follow-up. The R and Python outputs are now attached below. For R (python's index is "1" less than R's, which is fine):

customer_id purch_frequency freq_bins1 freq_bins2
1 1 1 1 1
2 2 1 1 1
3 3 1 1 1
4 4 1 1 1
5 5 1 1 1
6 6 2 2 2
7 7 3 2 2
8 8 10 2 3
9 9 11 2 3
10 10 11 2 3


For Python:

customer_id purch_frequency freq_bins1 freq_bins2
0 1 1 0 0
1 2 1 0 0
2 3 1 0 0
3 4 1 0 0
4 5 1 0 1
5 6 2 1 1
6 7 3 1 1
7 8 10 1 2
8 9 11 1 2
9 10 11 1 2

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