In Python, how do I read and write the actual word "None" (not the Keyword) between a .csv file and DataFrame?

I have a .csv that has the actual word "None" as a value in a field. When I read it into a DataFrame, the df reads "None" as the keyword and inserts <NA>. Later, when I rewrite the df to the .csv, all of the places where "None" was are replaced with blanks (,,) in the .csv.

testdata.csv:

Membership number,Last name,Date of birth,Status,Color
240200,,Wilson,None,Red

import pandas as pd

filename = "testdata.csv"
data_file = "testoutput.csv"

with open(filename, 'r', newline='', ):
    # Read data into a DataFrame
    user_df = pd.read_csv(filename)
    
user_df.to_csv(data_file, index=False)

testoutput.csv:

Membership number,Last name,Date of birth,Status,Color
240200,,Wilson,,Red

I want testoutput.csv to be the same as the testdata.csv.

I've removed the extraneous file open, and as I expected, I cannot duplicate the issue. Here is a session demonstrating that the output equals the input.

Source:

import pandas as pd

user_df = pd.read_csv('x.csv')
print(user_df)    
user_df.to_csv('x1.csv', index=False)

Output:

timr@Tims-NUC:~/src$ cat x.csv
Membership number,Last name,Date of birth,Status,Color
240200,,Wilson,None,Red
timr@Tims-NUC:~/src$ python x.py
   Membership number  Last name Date of birth Status Color
0             240200        NaN        Wilson   None   Red
timr@Tims-NUC:~/src$ cat x1.csv
Membership number,Last name,Date of birth,Status,Color
240200,,Wilson,None,Red

Followup

This appears to be version-related. The read_csv function does include a na_values parameter that identifies the list of strings that should be interpreted as NaN, and (at least in 2.0) "None" is on that list.

So, the two solutions are: specify a shorter list to na_values, or set keep_default_na=False to stop all NaN interpretation.