熊猫根据条件用值填充列

Question

我的 pandas 数据框中有一列，如下所示：

   Status
1  Past Due
2  Yet to Calc
3  Overdue
4  Past Due
5  Past Due
6  Yet to Calc
7  Past Due
8  Past Due
9  Past Due
10  Yet to Calc
11  Overdue
12  Yet to Calc
13  Past Due
14  Past Due
15  Past Due
16  Yet to Calc
17  Overdue
18  Past Due
19  Past Due
20  Past Due
21  Yet to Calc

我想用“过期”填充“过期”和“尚未计算”之间的所有值。所以我的预期输出是：

   Status
1  Past Due
2  Yet to Calc
3  Overdue
4  Overdue
5  Overdue
6  Yet to Calc
7  Past Due
8  Past Due
9  Past Due
10 Yet to Calc
11 Overdue
12 Yet to Calc
13 Past Due
14 Past Due
15 Past Due
16 Yet to Calc
17 Overdue
18 Overdue
19 Overdue
20 Overdue
21 Yet to Calc

我尝试按切片分组并在组内向前填充，如下所示：

df3['Inventory_1'] = df3.groupby(df3.loc['Overdue':'Yet to Calc','Inventory_1']).ffill()

但上面返回一个空系列并且不填充。

如果不使用 ffill，有什么方法可以解决这个问题？

Answer 1

这里的总体思路是屏蔽所有不是Yet to Calc 或Overdue 的值，并使用ffill。但是，这也将 ffill Yet to Calc 转发，这是永远不需要的。由于我们只需要保留 Yet to Calc 以停止填充 Overdue 值，因此我们可以将结果中不是 Overdue 的所有内容替换为原始 DataFrame 中包含的所有内容。

---

mask + ffill + isin

s = df.Status.mask(~df.Status.isin(['Overdue', 'Yet to Calc'])).ffill()
s[s.ne('Overdue')] = df.Status

print(s)

1        Past Due
2     Yet to Calc
3         Overdue
4         Overdue
5         Overdue
6     Yet to Calc
7        Past Due
8        Past Due
9        Past Due
10    Yet to Calc
11        Overdue
12    Yet to Calc
13       Past Due
14       Past Due
15       Past Due
16    Yet to Calc
17        Overdue
18        Overdue
19        Overdue
20        Overdue
21    Yet to Calc
Name: Status, dtype: object