How can I replace all the NaN values with Zero's in a column of a pandas dataframe

Question

I have a 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 .isnan from the Math Module I have tried the pandas .replace attribute I tried the .sparse data attribute from pandas 0.9 I have also tried if NaN == NaN statement in a function. I have also looked at this article How do I replace NA values with zeros in an R dataframe? whilst looking at some other articles. All the methods I have tried have not worked or do not recognize NaN. Any Hints or solutions would be appreciated. Select the correct answer from above options

Answer 1

You can use DataFrame.fillna() method for this problem. This function fills the null value with the given value in the dataframe. For example: In [12]: df[1].fillna(0, inplace=True) Out[12]: 0 0.000000 1 0.570994 2 0.000000 3 -0.229738 4 0.000000 Name: 1 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