How do I round datetime column to nearest quarter hour

You can use round(freq). There is also a shortcut column.dt for datetime functions access (as @laurens-koppenol suggests).

Here's one-liner:

df['old column'].dt.round('15min')  

String aliases for valid frequencies can be found here. Full working example:

In [1]: import pandas as pd    In [2]: df = pd.DataFrame([pd.Timestamp('2015-07-18 13:53:33.280'),                           pd.Timestamp('2015-07-18 13:33:33.330')],                         columns=['old column'])In [3]: df['new column']=df['old column'].dt.round('15min')  In [4]: dfOut[4]:                old column          new column0 2015-07-18 13:53:33.280 2015-07-18 14:00:001 2015-07-18 13:33:33.330 2015-07-18 13:30:00

Assuming that your series is made up of datetime objects, You need to use Series.apply . Example -

import datetimedf['<column>'] = df['<column>'].apply(lambda dt: datetime.datetime(dt.year, dt.month, dt.day, dt.hour,15*(dt.minute // 15)))

The above example to always round to the previous quarter hour (behavior similar to floor function).

EDIT

To round to the correct quarter hour (as in , if its 7 mins 30 seconds past previous quarter, to show the next quarter) . We can use the below example -

import datetimedf['<column>'] = df['<column>'].apply(lambda dt: datetime.datetime(dt.year, dt.month, dt.day, dt.hour,15*round((float(dt.minute) + float(dt.second)/60) / 15)))

The above would only take the latest seconds into consideration , if you want the millisecond/microsecond into consideration , you can add that to the above equation as - (float(dt.minute) + float(dt.second)/60 + float(dt.microsecond)/60000000)

This looks a little nicer

column.dt. allows the datetime functions for datetime columns, like column.str. does for string-like columns

datetime-like properties API reference

import pandas as pd# test dfdf = pd.DataFrame([{'old_column':pd.Timestamp('2015-07-18 13:53:33.280')}])df['new_column'] = df['old_column'].dt.round('15min')df