How is it possible that rpy2 is altering the values within my dataframe?

Question

I am trying to utilize some R based packages within a Python script using the rpy2 package. In order to implement the code, I first need to convert a Pandas dataframe into an R based data matrix. However, something incredibly strange is happening to the values within the code. Here is a minimally reproducible example of the code

import pandas as pd
import numpy as np
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri

pandas2ri.activate()

utils = importr('utils')

# Function to generate random column names
def generate_column_names(n, suffixes):
    columns = []
    for _ in range(n):
        name = ''.join(random.choices(string.ascii_uppercase, k=3))  # Random 3-character string
        suffix = random.choice(suffixes)  # Randomly choose between "_Healthy" and "_Sick"
        columns.append(name + suffix)
    return columns
    
# Number of rows and columns
n_rows = 1000
n_cols = 15

# Generate random float values between 0 and 10
data = np.random.uniform(0, 10, size=(n_rows, n_cols))

# Introduce NaN values sporadically
nan_indices = np.random.choice([True, False], size=data.shape, p=[0.1, 0.9])
data[nan_indices] = np.nan

# Generate random column names
column_names = generate_column_names(n_cols, ["_Healthy", "_Sick"])


# Create the DataFrame
df = pd.DataFrame(data, columns=column_names)

df = df.replace(np.nan, "NA")


with localconverter(ro.default_converter + pandas2ri.converter):
     R_df = ro.conversion.py2rpy(df)

r_matrix = r('data.matrix')(R_df)

Now, the input Pandas dataframe looks like this:

However, after turning it into a R based dataframe using ro.conversion.py2rpy(), and then recasting that as a data matrix using r('data.matrix'), I get a r_matrix dataframe that look like this:

How could this happen? I have checked the intermediate R_df and have found that it has the same values as the input Pandas df, so it seems that the line r('data.matrix') is drastically altering my contents.

I have run the analogous commands in R (after importing the exact same dataframe into R using readr), and data.matrix does not affect my dataframe's contents at all, so I am incredibly confused as to what the problem is. Has anyone else experienced this at all?

Answer 1

Your column is being coerced to a factor and then numeric

When in Python you do df = df.replace(np.nan, "NA"), you are replacing with the literal string "NA". That means that the "NA" values are then stored as an object rather than float64.

Unlike pandas, R does not have an object type. Columns (or vectors in R) need to all be the same type. If a vector contains numeric and string values, R ultimately treats the whole thing as character.

The behaviour that you get with a character vector using data.matrix() is:

Character columns are first converted to factors and then to integers.

For example:

set.seed(1)
(df <- data.frame(
    x = 1:5,
    y = (as.character(rnorm(5)))
))

#   x                  y
# 1 1 -0.626453810742332
# 2 2  0.183643324222082
# 3 3 -0.835628612410047
# 4 4   1.59528080213779
# 5 5   0.32950777181536

data.matrix(df)

#      x y
# [1,] 1 1
# [2,] 2 3
# [3,] 3 2
# [4,] 4 5
# [5,] 5 4

Use NA_real_

There is a class rpy2.rinterface_lib.sexp.NARealType. You need to instantiate this and then replace np.nan with this object. This means the entire column can remain a float64 in Python, and numeric in R, so there is no coercion to factor.

na = rpy2.rinterface_lib.sexp.NARealType()

df2 = df.replace(np.nan, na)

with localconverter(ro.default_converter + pandas2ri.converter):
     R_df = ro.conversion.py2rpy(df2)


r_matrix = ro.r('data.matrix')(R_df)
r_matrix

Output:

array([[6.71551482, 3.37235768, 1.73878498, ..., 9.26968137, 4.44605036,
        0.57638575],
       [2.14651571, 5.14706755, 7.43517449, ..., 7.56905516, 3.1960465 ,
        9.13240441],
       [0.67569123, 8.55601696, 3.34151056, ...,        nan, 4.12252086,
        5.79825217],
       ...,
       [2.93515376, 2.29766304, 2.70761156, ..., 7.80345898, 0.34809462,
        4.5128469 ],
       [5.66194126, 1.32135235, 2.57649142, ..., 3.49908635, 3.77794316,
        8.96322655],
       [8.43950172, 1.65306388, 7.37031975, ..., 8.01045219, 8.68857319,
        7.51309124]])