cumsum()按月,但如果该月没有数据,则重复该值

时间:2019-02-10 12:19:34

标签: python-3.x pandas pandas-groupby cumsum

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

使用cumsum(),我可以得到这些值的累加和。 但是,如果某个月中没有任何价值,则不会重复该价值。

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

但是我想得到以下结果:即使该月没有数据,也要重复col1值的总和。

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

预先感谢您的帮助。

1 个答案:

答案 0 :(得分:2)

使用:

#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

最后一次将日期时间转换为自定义字符串:

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