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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?
650
DatetimeIndex [source] Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.
You can use pandas. merge() to merge DataFrames by matching their index. When merging two DataFrames on the index, the value of left_index and right_index parameters of merge() function should be True . and by default, the pd.
Example #1: Use set_value() function to set the value in the dataframe at a particular index.
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
print (dw.set_value(pd.datetime(2015, 1, 19), 1))
DateTime
2015-01-18 -2374
2015-01-19 1
dtype: int64
More standard way is use ix or iloc:
print (dw)
Count DTW
DateTime
2015-01-16 10 0
2015-01-17 28 0
dw.ix[1, 'DTW'] = 10
#dw.DTW.iloc[1] = 10
print (dw)
Count DTW
DateTime
2015-01-16 10 0
2015-01-17 28 10
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