Pandas: Adding varying numbers of days to a date in a dataframe

Question

I have a dataframe with a date column and then a number of days that I want to add to that column. I want to create a new column, 'Recency_Date', with the resulting value.

df:

 fan Community Name Count Mean_Days Date_Min 0 855 AAA Games 6 353 2013-04-16 1 855 First Person Shooters 2 420 2012-10-16 2 855 Playstation 3 108 2014-06-12 3 3148 AAA Games 1 0 2015-04-17 4 3148 Mobile Gaming 1 0 2013-01-19

df info:

merged.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 4627415 entries, 0 to 4627414 Data columns (total 5 columns): fan int64 Community Name object Count int64 Mean_Days int32 Date_Min datetime64[ns] dtypes: datetime64[ns](1), int32(1), int64(2), object(1) memory usage: 194.2+ MB

Sample data as csv:

fan,Community Name,Count,Mean_Days,Date_Min 855,AAA Games,6,353,2013-04-16 00:00:00 855,First Person Shooters,2,420,2012-10-16 00:00:00 855,Playstation,3,108,2014-06-12 00:00:00 3148,AAA Games,1,0,2015-04-17 00:00:00 3148,Mobile Gaming,1,0,2013-01-19 00:00:00 3148,Power PCs,2,0,2014-06-17 00:00:00 3148,XBOX,1,0,2009-11-12 00:00:00 3860,AAA Games,1,0,2012-11-28 00:00:00 3860,Minecraft,3,393,2011-09-07 00:00:00 4044,AAA Games,5,338,2010-11-15 00:00:00 4044,Blizzard Games,1,0,2013-07-12 00:00:00 4044,Geek Culture,1,0,2011-06-03 00:00:00 4044,Indie Games,2,112,2013-01-09 00:00:00 4044,Minecraft,1,0,2014-01-02 00:00:00 4044,Professional Gaming,1,0,2014-01-02 00:00:00 4044,XBOX,2,785,2010-11-15 00:00:00 4827,AAA Games,1,0,2010-08-24 00:00:00 4827,Gaming Humour,1,0,2012-05-05 00:00:00 4827,Minecraft,2,10,2012-03-21 00:00:00 5260,AAA Games,4,27,2013-09-17 00:00:00 5260,Indie Games,8,844,2011-06-08 00:00:00 5260,MOBA,2,0,2012-10-27 00:00:00 5260,Minecraft,5,106,2012-02-17 00:00:00 5260,XBOX,1,0,2011-06-15 00:00:00 5484,AAA Games,21,1296,2009-08-01 00:00:00 5484,Free to Play,1,0,2014-12-08 00:00:00 5484,Indie Games,1,0,2014-05-28 00:00:00 5484,Music Games,1,0,2012-09-12 00:00:00 5484,Playstation,1,0,2012-02-22 00:00:00

I've tried:

merged['Recency_Date'] = merged['Date_Min'] + timedelta(days=merged['Mean_Days'])

and:

merged['Recency_Date'] = pd.DatetimeIndex(merged['Date_Min']) + pd.DateOffset(merged['Mean_Days'])

But am having trouble finding something that will work for a Series rather than an individual int value. Any and all help would be very much appreciated with this.

You'll need to post more info, preferable raw input data, code to reproduce your df and desired output, also can you post the output from df.info() if column 'Date_Min' is already datetime dtype then pd.TimedeltaIndex(merged['Mean_Days'], unit='D') will construct a time delta index that you can use to offset 'Date_Min' column — EdChum
– EdChum, Commented Sep 15, 2015 at 12:58

EdChum · Accepted Answer · 2015-09-15 13:04:26Z

If 'Date_Min' dtype is already datetime then you can construct a Timedeltaindex from your 'Mean_Days' column and add these:

In [174]: df = pd.DataFrame({'Date_Min':[dt.datetime.now(), dt.datetime(2015,3,4), dt.datetime(2011,6,9)], 'Mean_Days':[1,2,3]}) df Out[174]: Date_Min Mean_Days 0 2015-09-15 14:02:37.452369 1 1 2015-03-04 00:00:00.000000 2 2 2011-06-09 00:00:00.000000 3 In [175]: df['Date_Min'] + pd.TimedeltaIndex(df['Mean_Days'], unit='D') Out[175]: 0 2015-09-16 14:02:37.452369 1 2015-03-06 00:00:00.000000 2 2011-06-12 00:00:00.000000 Name: Date_Min, dtype: datetime64[ns]

Thanks, this worked perfectly, exactly what I was looking for.

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