基于熊猫的列值重复并填充行？

Question

我收集了许多天的数据，可以选择说一天中的数据应该是另一天的重复。如何用重复标记列指定的数据填充NaN行？

此问题的变化：Repeat sections of dataframe based on a column value

#Example Dataframes 
example_data = [[1,np.NaN,"3a+b"],[2,np.NaN,"c"],[3,1,np.NaN],[4,np.NaN,"b+c"], [5,2,np.NaN], [6,0,0]]
to_solve = pd.DataFrame(example_data,columns=['Day','repeat_tag','calculation'])

desired= [[1,np.NaN,"3a+b"],[2,np.NaN,"c"],[3,1,"3a+b"],[4,np.NaN,"b+c"], [5,2,"c"],[6,0,0]]
desired_table=pd.DataFrame(desired,columns=['Day','repeat_tag','calculation'])

Answer 1

IIUC，您可以在map天使用一次带有重复计算的值的repeat_tag上的set_index，然后使用fillna将值分配回计算。

to_solve['calculation'] = to_solve['calculation']\
                            .fillna(to_solve['repeat_tag']\
                                      .map(to_solve.set_index('Day')['calculation']))
print(to_solve)
   Day  repeat_tag calculation
0    1         NaN        3a+b
1    2         NaN           c
2    3         1.0        3a+b
3    4         NaN         b+c
4    5         2.0           c
5    6         0.0           0