cumsum（）的一个月，但重复的值，如果在当月没有数据

Question

我有数据：df

    date    col1    col2
0   1/16/2016   apple   20
1   2/1/2016    apple   40
2   2/2/2016    pear    60
3   3/13/2016   apple   10
4   5/4/2016    apple   50
5   6/15/2016   pear    5

随着cumsum()我能得到的值的累积和。但是，如果在某个月没有价值，价值就不重复了。

df.set_index('date', inplace=True)
df = df.groupby([df.index.month, 'col1']).sum()
df['cumsum'] = df.groupby('col1')['cumsum'].cumsum()

date    col1    cumsum
Jan-16  apple   20
Feb-16  apple   60
Feb-16  pear    60
Mar-16  apple   70
May-16  apple   120
Jun-16  pear    65

但我想要得到以下结果：要重复col1值的cumsum即使在特定月份的数据。

date    col1    cumsum
Jan-16  apple   20
Feb-16  apple   60
Feb-16  pear    60
Mar-16  apple   70
Mar-16  pear    60
Apr-16  apple   70
Apr-16  pear    60
May-16  apple   120
May-16  pear    60
Jun-16  apple   120
Jun-16  pear    65

在此先感谢您的帮助。

Answer 1

采用：

#create month period column  for correct ordering
df['months'] = df['date'].dt.to_period('m')
#aggregate month
df1 = df.groupby(['months', 'col1'])['col2'].sum()

#MultiIndex with all possible combinations
mux = pd.MultiIndex.from_product([pd.period_range(df['months'].min(),
                                                  df['months'].max(), freq='M'),
                                  df['col1'].unique()], names=df1.index.names)

#add missing values with reindex reshape, cumulative sum
#forward fill missing values and reshape back
df2 = (df1.reindex(mux)
          .unstack()
          .cumsum()
          .ffill()
          .stack()
          .astype(int)
          .reset_index(name='cumsum')
         )
print (df2)
     months   col1  cumsum
0   2016-01  apple      20
1   2016-02  apple      60
2   2016-02   pear      60
3   2016-03  apple      70
4   2016-03   pear      60
5   2016-04  apple      70
6   2016-04   pear      60
7   2016-05  apple     120
8   2016-05   pear      60
9   2016-06  apple     120
10  2016-06   pear      65

最后，如果有必要转换日期时间为自定义字符串：

df2['months'] = df2['months'].dt.strftime('%b-%y')
print (df2)
    months   col1  cumsum
0   Jan-16  apple      20
1   Feb-16  apple      60
2   Feb-16   pear      60
3   Mar-16  apple      70
4   Mar-16   pear      60
5   Apr-16  apple      70
6   Apr-16   pear      60
7   May-16  apple     120
8   May-16   pear      60
9   Jun-16  apple     120
10  Jun-16   pear      65