python - Python Pandas：根据其他空白列的条件使用 ffill

Question

那时我尝试了 group ，但没有任何效果。:(

如何根据名为“Rec”的空白列使用 ffill。因此，如果此列为空白，则在“Rec”列中填充先前的值，然后将“Loc1”和 Loc 替换为先前的值？

这是我的df：

+--------+----------+----------------------------+---------+------------+
| Lot    | Rec      | Part                       | Loc1    | Loc        |
+--------+----------+----------------------------+---------+------------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM  |
+--------+----------+----------------------------+---------+------------+
| Y19105 |          | PHEMT6_BGATE_70NM_EBEAM.05 | B_PC    | B_PC_STOCK |
+--------+----------+----------------------------+---------+------------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM  |
+--------+----------+----------------------------+---------+------------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM  |
+--------+----------+----------------------------+---------+------------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM  |
+--------+----------+----------------------------+---------+------------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM  |
+--------+----------+----------------------------+---------+------------+
| Y19105 |          | PHEMT6_BGATE_70NM_EBEAM.05 | B_PC    | B_PC_STOCK |
+--------+----------+----------------------------+---------+------------+
| Y19105 |          | PHEMT6_BGATE_70NM_EBEAM.05 | B_PC    | B_PC_STOCK |
+--------+----------+----------------------------+---------+------------+
| Y19105 |          | PHEMT6_BGATE_70NM_EBEAM.05 | B_PC    | B_PC_STOCK |
+--------+----------+----------------------------+---------+------------+

我想要的输出：

+--------+----------+----------------------------+---------+-----------+
| Lot    | Rec      | Part                       | Loc1    | Loc       |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+
| Y19105 | BIPA6.02 | PHEMT6_BGATE_70NM_EBEAM.05 | B_EBEAM | B_F_EBEAM |
+--------+----------+----------------------------+---------+-----------+

非常感激你的帮助！

标签： pythonpython-3.xpandas

Answer 1

IIUC，您可以尝试将Loc1andLoc值更改为NaNif the Reccolumn is NaN，然后ffill在整个 DataFrame 上使用。

尝试这个：

df.loc[df['Rec'].isnull(), ['Loc1', 'Loc']] = np.nan
df.ffill()