I have a pandas DataFrame, and set index to be the DateTime column:
data['DateTime'] = pandas.to_datetime (data['DateTime'])
data = data.set_index('DateTime')
which I need to interpolate the data. However, this indexing later prevents me from doing
data = data[pandas.to_datetime (data['DateTime']) <= cutoff]
where cutoff is some datetime. How can I go about this?
It seems you need .index for compare DatetimeIndex:
data['DateTime'] = pandas.to_datetime (data['DateTime'])
data = data.set_index('DateTime')
data = data[data.index <= cutoff]
Also is sorted DatetimeIndex use loc:
data1 = data1.loc[:cutoff]
Sample:
rng = pd.date_range('2017-04-03', periods=10)
data = pd.DataFrame({'a': range(10)}, index=rng)
print (data)
a
2017-04-03 0
2017-04-04 1
2017-04-05 2
2017-04-06 3
2017-04-07 4
2017-04-08 5
2017-04-09 6
2017-04-10 7
2017-04-11 8
2017-04-12 9
cutoff = '2017-04-08'
data1 = data[data.index <= cutoff]
print (data1)
a
2017-04-03 0
2017-04-04 1
2017-04-05 2
2017-04-06 3
2017-04-07 4
2017-04-08 5
data1 = data1.loc[:cutoff]
print (data1)
a
2017-04-03 0
2017-04-04 1
2017-04-05 2
2017-04-06 3
2017-04-07 4
2017-04-08 5
Thanks piRSquared:
data1 = data1[:cutoff]
print (data1)
a
2017-04-03 0
2017-04-04 1
2017-04-05 2
2017-04-06 3
2017-04-07 4
2017-04-08 5
