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?
I was still confused after reading the other answers and comments. But the answer now seems simpler, so here you go.
Since Pandas version 0.9 (from 2012), you can read your csv with empty cells interpreted as empty strings by simply setting keep_default_na=False:
pd.read_csv('test.csv', keep_default_na=False)
This issue is more clearly explained in
That was fixed on on Aug 19, 2012 for Pandas version 0.9 in
We have a simple argument in Pandas read_csv for this:
Use:
df = pd.read_csv('test.csv', na_filter= False)
Pandas documentation clearly explains how the above argument works.
Source: Stackoverflow.com