["python","pandas","dataframe","nan","fillna"]
How to replace NaN values in a dataframe column

I have a Pandas Dataframe as below: itm Date Amount 67 420 2012-09-30 00:00:00 65211 68 421 2012-09-09 00:00:00 29424 69 421 2012-09-16 00:00:00 29877 70 421 2012-09-23 00:00:00 30990 71 421 2012-09-30 00:00:00 61303 72 485 2012-09-09 00:00:00 71781 73 485 2012-09-16 00:00:00 NaN 74 485 2012-09-23 00:00:00 11072 75 485 2012-09-30 00:00:00 113702 76 489 2012-09-09 00:00:00 64731 77 489 2012-09-16 00:00:00 NaN When I try to apply a function to the Amount column, I get the following error: ValueError: cannot convert float NaN to integer I have tried applying a function using math.isnan, pandas' .replace method, .sparse data attribute from pandas 0.9, if NaN == NaN statement in a function; I have also looked at this Q/A; none of them works. How do I do it?
DataFrame.fillna() or Series.fillna() will do this for you. Example: In [7]: df Out[7]: 0 1 0 NaN NaN 1 -0.494375 0.570994 2 NaN NaN 3 1.876360 -0.229738 4 NaN NaN In [8]: df.fillna(0) Out[8]: 0 1 0 0.000000 0.000000 1 -0.494375 0.570994 2 0.000000 0.000000 3 1.876360 -0.229738 4 0.000000 0.000000 To fill the NaNs in only one column, select just that column. In [12]: df[1] = df[1].fillna(0) In [13]: df Out[13]: 0 1 0 NaN 0.000000 1 -0.494375 0.570994 2 NaN 0.000000 3 1.876360 -0.229738 4 NaN 0.000000 Or you can use the built in column-specific functionality: df = df.fillna({1: 0})