从整个熊猫数据框中减去修正日期-python

Question

我有数据

    customer_id purchase_amount date_of_purchase
0   760       25.0  06-11-2009
1   860      50.0   09-28-2012
2   1200    100.0   10-25-2005
3   1420    50.0    09-07-2009
4   1940    70.0    01-25-2013
5   1960    40.0    10-29-2013
6   2620    30.0    09-03-2006
7   3050    50.0    12-04-2007
8   3120    150.0   08-11-2006
9   3260    45.0    10-20-2010
10  3510    35.0    04-05-2013
11  3970    30.0    07-06-2007
12  4000    20.0    11-25-2005
13  4180    20.0    09-22-2010
14  4390    30.0    04-15-2011
15  4750    60.0    02-12-2013
16  4840    30.0    10-14-2005
17  4910    15.0    12-13-2006
18  4950    50.0    05-19-2010
19  4970    30.0    01-12-2006
20  5250    50.0    12-20-2005

现在我想从01-01-2016的每一行中减去date_of_purchase

我尝试了以下操作，因此我应该在新的days_since栏中添加几天的时间。

NOW = pd.to_datetime('01/01/2016').strftime('%m-%d-%Y')
gb = customer_purchases_df.groupby('customer_id')
df2 = gb.agg({'date_of_purchase': lambda x: (NOW - x.max()).days})  

任何建议。我怎样才能做到这一点

预先感谢

Answer 1

pd.to_datetime(df['date_of_purchase']).sub(pd.to_datetime('2016-01-01')).dt.days.mul(-1)

0     2395
1     1190
2     3720
3     2307
4     1071
5      794
6     3407
7     2950
8     3430
9     1899
10    1001
11    3101
12    3689
13    1927
14    1722
15    1053
16    3731
17    3306
18    2053
19    3641
20    3664
Name: date_of_purchase, dtype: int64

Answer 2

我假设'date_of_purchase'列已经具有datetime dtype。

>>> df
   customer_id  purchase_amount date_of_purchase
0          760             25.0       2009-06-11
1          860             50.0       2012-09-28
2         1200            100.0       2005-10-25
>>> df['days_since'] = df['date_of_purchase'].sub(pd.to_datetime('01/01/2016')).dt.days.abs()
>>> df 
   customer_id  purchase_amount date_of_purchase  days_since
0          760             25.0       2009-06-11        2395
1          860             50.0       2012-09-28        1190
2         1200            100.0       2005-10-25        3720