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I'm trying to add the row-wise result from a function into my dataframe using df.set_Value .

df in the format :

    Count   DTW
DateTime        
2015-01-16  10  0
2015-01-17  28  0
Using df.setValue
dw.set_Value(idx, 'col', dtw) # idx and dtw are int values
TypeError: cannot insert DatetimeIndex with incompatible label
How do I solve this error or what alternative method with comparable efficiency is there?
I think you have Series, not DataFrame, so use Series.set_value with index converted to datetime
dw = pd.Series([-2374], index = [pd.to_datetime('2015-01-18')])
dw.index.name = 'DateTime'
print (dw)
DateTime
2015-01-18   -2374
dtype: int64
print (dw.set_value(pd.to_datetime('2015-01-19'), 1))
DateTime
2015-01-18   -2374
2015-01-19       1
dtype: int64
                I'm getting the idx value from a range so I wanted to allocate it directly using an int value, I think this created a problem with the index as it is not in numerical order but is instead a DateTime index. I hope this clarified my question
– SerialDev
                Aug 19, 2016 at 14:02
                So do you have first index with int and datetime values? Or need overwrite some value, e.g. 28 in sample? Or add new row?
– jezrael
                Aug 19, 2016 at 14:04
                I want to set a value on an existing row that has a datetime index, is there an iterable that I could use to get datetime index as opposed to int?
– SerialDev
                Aug 19, 2016 at 14:10
        
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