如何使用Python将特定选定行拆分为多行

我有一个示例数据帧

import pandas as pd
import numpy as np

data = {"Key" : ["First Row", "Sample sample first row: a Row to be splitted $ 369", "Sample second row : a Depreciation $ 458", "Last Row"],
        "Value1" : [365, 265.0, np.nan, 256],
        "value2" : [789, np.nan, np.nan, np.nan]
}


df = pd.DataFrame(data)

我知道使用

我找不到在所选部分拆分一行字符串

所需输出

              Key                  Value1   value2
0        First Row                  365.0    789.0
1   Sample sample first row:        265.0     NaN
2   a Row to be splitted $            369     NaN
3   Sample second row :               NaN     NaN
4   Depreciation $                    458     NaN
5    Last Row                       256.0     NaN

---

使用

.explode

.str.extract（）和

str.replace（）

---

拆分

，

分解

，

提取

和

更新

我们可以在一个或多个

空格

字符上

拆分
键
列，该字符前面有
：
，然后
分解
键
上的数据框，接下来
从
键
列中提取前面带有
$
符号的数字，并
更新
值1

df1 = df.assign(Key=df['Key'].str.split(r'(?<=:)\s+')).explode('Key')
df1['Value1'].update(df1['Key'].str.extract(r'\$\s*(\d+)', expand=False).astype(float))

很好地使用了regex，我的朋友
df1 = df.assign(Key=df['Key'].str.split(':')).explode('Key')

df1['Value1'] = df1['Value1'].fillna(
                      df1['Key'].str.extract('\$\s(\d+)').astype(float)[0]
                                  )
df1['Key'] = df1['Key'].str.replace('(\$\s)(\d+)',r'\1',regex=True)

                        Key  Value1  value2
0                 First Row   365.0   789.0
1   Sample sample first row   265.0     NaN
1   a Row to be splitted $    265.0     NaN
2        Sample second row      NaN     NaN
2         a Depreciation $    458.0     NaN
3                  Last Row   256.0     NaN

df1 = df.assign(Key=df['Key'].str.split(r'(?<=:)\s+')).explode('Key')
df1['Value1'].update(df1['Key'].str.extract(r'\$\s*(\d+)', expand=False).astype(float))

>>> df1
                          Key  Value1  value2
0                   First Row   365.0   789.0
1    Sample sample first row:   265.0     NaN
1  a Row to be splitted $ 369   369.0     NaN
2         Sample second row :     NaN     NaN
2        a Depreciation $ 458   458.0     NaN
3                    Last Row   256.0     NaN