Converting data to missing in pandas

Question

I have a DataFrame with a mix of 0's and other numbers. I would like to convert the 0's to missing.

For example, I am looking for the command that would convert

In [618]: a=DataFrame(data=[[1,2],[0,1],[1,2],[0,0]])

In [619]: a
Out[619]: 
   0  1
0  1  2
1  0  1
2  1  2
3  0  0

to

In [619]: a
Out[619]: 
   0   1
0  1   2
1  NaN 1
2  1   2
3  NaN NaN

I tried pandas.replace(0, NaN), but I get an error that NaN is not defined. And I don't see anywhere to import NaN from.

BrenBarn · Accepted Answer · 2012-08-09T18:55:51.253

11

Just do from numpy import nan. (You will have to convert your DataTable to float type, because you can't use NaN in integer arrays.)

edited Aug 09 '12 at 18:55

answered Aug 09 '12 at 18:13

BrenBarn

2

it doesn't work, because the type of the columns is int: `ValueError: cannot convert float NaN to integer` – bmu Aug 09 '12 at 18:44
2

Well, then you'll have to convert the data to floats. You can't use `NaN` with integer data structures. See http://stackoverflow.com/questions/11548005/numpy-or-pandas-keeping-array-type-as-integer-while-having-a-nan-value . – BrenBarn Aug 09 '12 at 18:46
3

so you should explain this in your answer, in the moment the answer doesn't fit to the question. – bmu Aug 09 '12 at 18:50

1 Answers1