如何在Python数据透视表中使用时间戳记日期作为索引？

Question

I have recently began working with the python.pivot_table and have encountered a challenge using timestamps properly with the pivot tables.

I have a large dataframe with data like the below

         Date          ID           Days  Quantity      Concern
0  2012-06-29         NaN            621       NaN            A
1  2012-06-29     1208985            874         1            A
2  2012-06-29         NaN            621         2            B
3  2012-06-29         NaN            874         1            C
4  2012-06-29         NaN            566       NaN            A
5  2012-06-29      251254            780       NaN            A
6  2012-06-29         NaN            566       NaN            C
7  2012-06-29      385379            566         1            B
8  2012-06-29      967911            780         1            B
9  2012-06-29         NaN            521       NaN            A
10 2012-06-29     1208985            834         1            C
11 2012-06-29      385379            374       NaN            A
12 2012-06-29      967909            780         1            B
13 2012-07-18         NaN            821       NaN            A
14 2012-07-18      251254            821       NaN            A
15 2012-08-04      756444            676         1            C
16 2012-08-04      756444            676         2            C
17 2012-08-04         NaN            676       NaN            A
18 2012-08-24         NaN            571       NaN            B
19 2012-08-24      251254            446         1            B

A line like the below works great:

pd.pivot_table(data,index=['Concern'],columns=['ID'],values=['Quantity'],aggfunc='sum')

Currently when I use the Date column for index=['Date'] it groups by the day. I would like to option of being able to group by month or year. Is there a way to implement this with pivot tables when the date column are TimeStamp objects?

Answer 1

You can access information like year and month through the .dt attribute that datetime series have, so you can easily make new columns like:

df['Month'] = df['Date'].dt.month

Then use those columns to create the pivot table:

pd.pivot_table(df, index=['Month'], columns=['ID'],
               values=['Quantity'],aggfunc='sum')

Output:

Out[16]: 
      Quantity                                        
ID     251254  385379  756444  967909  967911  1208985
Month                                                 
6          NaN       1     NaN       1       1       2
7          NaN     NaN     NaN     NaN     NaN     NaN
8            1     NaN       3     NaN     NaN     NaN