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Pandas conditionally for index column

I have a pandas DataFrame and set the index as a DateTime column:

data['DateTime'] = pandas.to_datetime (data['DateTime'])
data = data.set_index('DateTime')

which I need for data interpolation. However, this indexing later prevents me from doing

data = data[pandas.to_datetime (data['DateTime']) <= cutoff]

where cutoff

is some datetime. How can i do this?

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1 answer


It seems you need .index

for comparison DatetimeIndex

:

data['DateTime'] = pandas.to_datetime (data['DateTime'])
data = data.set_index('DateTime')
data = data[data.index <= cutoff]

Also sorted to DatetimeIndex

use loc

:

data1 = data1.loc[:cutoff]

Example:



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
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