Get pandas.read_csv to read empty values as empty string instead of nan

Question

I'm using the pandas library to read in some CSV data. In my data, certain columns contain strings. The string "nan" is a possible value, as is an empty string. I managed to get pandas to read "nan" as a string, but I can't figure out how to get it not to read an empty value as NaN. Here's sample data and output

One,Two,Three

a,1,one

b,2,two

,3,three

d,4,nan

e,5,five

nan,6,

g,7,seven

>>> pandas.read_csv('test.csv', na_values={'One': [], "Three": []})

    One  Two  Three

0    a    1    one

1    b    2    two

2  NaN    3  three

3    d    4    nan

4    e    5   five

5  nan    6    NaN

6    g    7  seven

It correctly reads "nan" as the string "nan', but still reads the empty cells as NaN. I tried passing in str in the converters argument to read_csv (with converters={'One': str})), but it still reads the empty cells as NaN.

I realize I can fill the values after reading, with fillna, but is there really no way to tell pandas that an empty cell in a particular CSV column should be read as an empty string instead of NaN?

Answer 1

Using read_csv:

df = pd.read_csv('test.csv', na_filter= False)