如何在python中拆分年和月列？

问题描述

我们如何拆分数据，如下所述？

样本数据：

  EmployeeId  City     join_month    Year
0   001        Mumbai        1       2018
1   001        Bangalore     3       2018
2   002        Pune          2       2019
3   002        Mumbai        6       2017
4   003        Delhi         9       2018
5   003        Mumbai        12      2019
6   004        Bangalore     11      2017
7   004        Pune          10      2018
8   005        Mumbai         5      2017

必需的输出应该是 -

   EmployeeId  City     join_month       Year    2018_jan_count   2018_feb_count 2018_march_count 
    0   001        Mumbai        1       2018
    1   001        Bangalore     3       2018
    2   002        Pune          2       2019
    3   002        Mumbai        6       2017
    4   003        Delhi         9       2018
    5   003        Mumbai        12      2019
    6   004        Bangalore     11      2017
    7   004        Pune          10      2018
    8   005        Mumbai         5      2017

解决方法

您可以使用df.apply

df.apply(pd.value_counts)

这将应用基于列的聚合函数（在本例中为日期）

,

我构建了 yearmonth 值，然后将其用作数据透视表上的一列。我统计了按城市和年月聚合的员工id

 months=[(1,'Jan'),(2,'Feb'),(3,'Mar'),(4,'Apr'),(5,'May'),(6,'Jun'),(7,'Jul'),(8,'Aug'),(9,'Sept'),(10,'Oct'),(11,'Nov'),(12,'Dec')]
 employeeId=['001','001','002','003','004','005']
 city=['Mumbai','Bangalore','Pune','Mumbai','Delhi','Mumbai']
 join_month=[1,3,2,6,9,12,11,10,1]
 char_month=[b for item in join_month for a,b in months if item==a ]
 year=[2018,2018,2019,2017,2018]
 char_yearmonth=[]
 [char_yearmonth.append(str(year[i])+"_"+char_month[i]) for i in range(len(year))]

 df=pd.DataFrame({'EmployeeId': employeeId,'City':city,'YearMonth':char_yearmonth})

 fp=df.pivot_table(index=['City'],columns=['YearMonth'],aggfunc='count').fillna(0)
 print(fp)

 EmployeeId                                                         \
 YearMonth   2017_Dec 2017_Jun 2017_Nov 2018_Jan 2018_Mar 2018_Oct 2018_Sept   
 City                                                                          
 Bangalore        0.0      0.0      1.0      0.0      1.0      0.0       0.0   
 Delhi            0.0      0.0      0.0      0.0      0.0      0.0       1.0   
 Mumbai           1.0      1.0      0.0      2.0      0.0      0.0       0.0   
 Pune             0.0      0.0      0.0      0.0      0.0      1.0       0.0   

                
 YearMonth 2019_Feb  
 City                
 Bangalore      0.0  
 Delhi          0.0  
 Mumbai         0.0  
 Pune           1.0