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I have a dataframe in pandas called 'munged_data' with two columns 'entry_date' and 'dob' which i have converted to Timestamps using pd.to_timestamp.I am trying to figure out how to calculate ages of people based on the time difference between 'entry_date' and 'dob' and to do this i need to get the difference in days between the two columns ( so that i can then do somehting like round(days/365.25). I do not seem to be able to find a way to do this using a vectorized operation. When I do munged_data.entry_date-munged_data.dob i get the following :
internal_quote_id 2 15685977 days, 23:54:30.457856 3 11651985 days, 23:49:15.359744 4 9491988 days, 23:39:55.621376 7 11907004 days, 0:10:30.196224 9 15282164 days, 23:30:30.196224 15 15282227 days, 23:50:40.261632 However i do not seem to be able to extract the days as an integer so that i can continue with my calculation. Any help appreciated.
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Converting a timedelta to days is easier, and less confusing, than seconds. According to the docs, only days, seconds and microseconds are stored internally. To get the number of days in a time delta, just use the timedelta. days .
Timedelta. Represents a duration, the difference between two dates or times. Timedelta is the pandas equivalent of python's datetime. timedelta and is interchangeable with it in most cases.
The to_timedelta() function is used to convert argument to datetime. Timedeltas are absolute differences in times, expressed in difference units (e.g. days, hours, minutes, seconds). This method converts an argument from a recognized timedelta format / value into a Timedelta type. The data to be converted to timedelta.
Using the Pandas type Timedelta available since v0.15.0 you also can do:
In[1]: import pandas as pd In[2]: df = pd.DataFrame([ pd.Timestamp('20150111'), pd.Timestamp('20150301') ], columns=['date']) In[3]: df['today'] = pd.Timestamp('20150315') In[4]: df Out[4]: date today 0 2015-01-11 2015-03-15 1 2015-03-01 2015-03-15 In[5]: (df['today'] - df['date']).dt.days Out[5]: 0 63 1 14 dtype: int64 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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