问题
This is related to this question, but now I need to find the difference between dates that are stored in 'YYYY-MM-DD'. Essentially the difference between values in the count column is what we need, but normalized by the number of days between each row.
My dataframe is:
date,site,country_code,kind,ID,rank,votes,sessions,avg_score,count
2017-03-20,website1,US,0,84,226,0.0,15.0,3.370812,53.0
2017-03-21,website1,US,0,84,214,0.0,15.0,3.370812,53.0
2017-03-22,website1,US,0,84,226,0.0,16.0,3.370812,53.0
2017-03-23,website1,US,0,84,234,0.0,16.0,3.369048,54.0
2017-03-24,website1,US,0,84,226,0.0,16.0,3.369048,54.0
2017-03-25,website1,US,0,84,212,0.0,16.0,3.369048,54.0
2017-03-27,website1,US,0,84,228,0.0,16.0,3.369048,58.0
2017-02-15,website2,AU,1,91,144,4.0,148.0,4.727272,521.0
2017-02-16,website2,AU,1,91,144,3.0,147.0,4.727272,524.0
2017-02-20,website2,AU,1,91,100,4.0,148.0,4.727272,531.0
2017-02-21,website2,AU,1,91,118,6.0,149.0,4.727272,533.0
2017-02-22,website2,AU,1,91,114,4.0,151.0,4.727272,534.0
And I'd like to find the difference between each date after grouping by date+site+country+kind+ID tuples.
[date,site,country_code,kind,ID,rank,votes,sessions,avg_score,count,day_diff
2017-03-20,website1,US,0,84,226,0.0,15.0,3.370812,0,0
2017-03-21,website1,US,0,84,214,0.0,15.0,3.370812,0,1
2017-03-22,website1,US,0,84,226,0.0,16.0,3.370812,0,1
2017-03-23,website1,US,0,84,234,0.0,16.0,3.369048,0,1
2017-03-24,website1,US,0,84,226,0.0,16.0,3.369048,0,1
2017-03-25,website1,US,0,84,212,0.0,16.0,3.369048,0,1
2017-03-27,website1,US,0,84,228,0.0,16.0,3.369048,4,2
2017-02-15,website2,AU,1,91,144,4.0,148.0,4.727272,0,0
2017-02-16,website2,AU,1,91,144,3.0,147.0,4.727272,3,1
2017-02-20,website2,AU,1,91,100,4.0,148.0,4.727272,7,4
2017-02-21,website2,AU,1,91,118,6.0,149.0,4.727272,3,1
2017-02-22,website2,AU,1,91,114,4.0,151.0,4.727272,1,1]
One option would be to convert the date column to a Pandas datetime one using pd.to_datetime() and use the diff function but that results in values of "x days", of type timetelda64. I'd like to use this difference to find the daily average count so if this can be accomplished in even a single/less painful step, that would work well.
回答1:
you can use .dt.days accessor:
In [72]: df['date'] = pd.to_datetime(df['date'])
In [73]: df['day_diff'] = df.groupby(['site','country_code','kind','ID'])['date'] \
.diff().dt.days.fillna(0)
In [74]: df
Out[74]:
date site country_code kind ID rank votes sessions avg_score count day_diff
0 2017-03-20 website1 US 0 84 226 0.0 15.0 3.370812 53.0 0.0
1 2017-03-21 website1 US 0 84 214 0.0 15.0 3.370812 53.0 1.0
2 2017-03-22 website1 US 0 84 226 0.0 16.0 3.370812 53.0 1.0
3 2017-03-23 website1 US 0 84 234 0.0 16.0 3.369048 54.0 1.0
4 2017-03-24 website1 US 0 84 226 0.0 16.0 3.369048 54.0 1.0
5 2017-03-25 website1 US 0 84 212 0.0 16.0 3.369048 54.0 1.0
6 2017-03-27 website1 US 0 84 228 0.0 16.0 3.369048 58.0 2.0
7 2017-02-15 website2 AU 1 91 144 4.0 148.0 4.727272 521.0 0.0
8 2017-02-16 website2 AU 1 91 144 3.0 147.0 4.727272 524.0 1.0
9 2017-02-20 website2 AU 1 91 100 4.0 148.0 4.727272 531.0 4.0
10 2017-02-21 website2 AU 1 91 118 6.0 149.0 4.727272 533.0 1.0
11 2017-02-22 website2 AU 1 91 114 4.0 151.0 4.727272 534.0 1.0
来源：https://stackoverflow.com/questions/46798252/difference-between-dates-in-pandas-dataframe