python - 如何在多索引 Pandas 中按次日值填充缺失值？

Question

我有一个数据框:

                    stock   price
symbol  DATE        
ABC     2014-01-02  000001  6
        2014-01-03  000001  7
        2014-01-06  000001  8
XYZ     2015-07-02  000002  9
        2015-07-04  000002  10
        2015-07-06  000002  11

我想获得一个新的数据框

                    stock   price
symbol  DATE        
ABC     2014-01-02  000001  6
        2014-01-03  000001  7
        2014-01-04  000001  8
        2014-01-05  000001  8
        2014-01-06  000001  8
XYZ     2015-07-02  000002  9
        2015-07-03  000002  10
        2015-07-04  000002  10
        2015-07-05  000002  11
        2015-07-06  000002  11

怎么做？

Answer 1

在 GroupBy.apply 中使用自定义函数与 Series.reindex :

f = lambda x: x.reindex(pd.date_range(x.index.min(), x.index.max(), name='DATE')).bfill()
df = df.reset_index(level=0).groupby(['symbol','stock'])['price'].apply(f).reset_index()
print (df)
  symbol   stock       DATE price
0    ABC  000001 2014-01-02     6
1    ABC  000001 2014-01-03     7
2    ABC  000001 2014-01-04     8
3    ABC  000001 2014-01-05     8
4    ABC  000001 2014-01-06     8
5    XYZ  000002 2015-07-02     9
6    XYZ  000002 2015-07-03    10
7    XYZ  000002 2015-07-04    10
8    XYZ  000002 2015-07-05    11
9    XYZ  000002 2015-07-06    11