Python熊猫：如何将数据框、特定单元格重新设置为新列

我需要先重塑数据帧，然后才能进入下一阶段

我有以下数据帧

+-------------------+-----+--------+
|       name        | age | gender |
+-------------------+-----+--------+
| country India     |     |        |
| Ali               |  13 | male   |
| Abu               |  12 | male   |
| Acik              |  13 | male   |
| country indonesia |     |        |
| natasha           |  15 | female |
| jenny             |  43 | female |
| eric              |  23 | male   |
| country singapore |     |        |
| max               |  23 | male   |
| jason             |  32 | male   |
| jack              |  45 | male   |
+-------------------+-----+--------+

我想这样

+---------+-----+--------+-----------+
|  name   | age | gender |  country  |
+---------+-----+--------+-----------+
| Ali     |  13 | male   | india     |
| Abu     |  12 | male   | india     |
| Acik    |  13 | male   | india     |
| natasha |  15 | female | indonesia |
| jenny   |  43 | female | indonesia |
| eric    |  23 | male   | indonesia |
| max     |  23 | male   | singapore |
| jason   |  32 | male   | singapore |
| jack    |  45 | male   | singapore |
+---------+-----+--------+-----------+

---

我不认为pivot/transpose对我有帮助，如果还有，我需要做什么？

您可以使用

str.extract

提取国家名称，然后还可以使用它来屏蔽有效行：

countries = df['name'].str.extract('^country (.+)')[0]
df['country'] = countries.ffill()

df = df[countries.isna()]

输出

       name   age  gender    country
1       Ali  13.0    male      India
2       Abu  12.0    male      India
3      Acik  13.0    male      India
5   natasha  15.0  female  indonesia
6     jenny  43.0  female  indonesia
7      eric  23.0    male  indonesia
9       max  23.0    male  singapore
10    jason  32.0    male  singapore
11     jack  45.0    male  singapore

替代解决方案：

(df.assign(country=df["name"].str.extract("^country (.+)", expand=False).ffill())
   .dropna()
 )

---

您可以使用

str.extract

提取国家名称，然后还可以使用该名称屏蔽有效行：

countries = df['name'].str.extract('^country (.+)')[0]
df['country'] = countries.ffill()

df = df[countries.isna()]

输出

       name   age  gender    country
1       Ali  13.0    male      India
2       Abu  12.0    male      India
3      Acik  13.0    male      India
5   natasha  15.0  female  indonesia
6     jenny  43.0  female  indonesia
7      eric  23.0    male  indonesia
9       max  23.0    male  singapore
10    jason  32.0    male  singapore
11     jack  45.0    male  singapore

替代解决方案：

(df.assign(country=df["name"].str.extract("^country (.+)", expand=False).ffill())
   .dropna()
 )