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 · 2012-08-09 18:55:51Z

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.)

answered Aug 9, 2012 at 18:13

BrenBarn

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3 Comments

it doesn't work, because the type of the columns is int: ValueError: cannot convert float NaN to integer

Well, then you'll have to convert the data to floats. You can't use NaN with integer data structures. See stackoverflow.com/questions/11548005/… .

so you should explain this in your answer, in the moment the answer doesn't fit to the question.