我建议编写一个正则表达式模式，以查找相应的列。
在你的情况下，你应该创建一个类似于以下的模式：
数字_组_数字_文本_日期_数字
所以最终的代码应该是：
```python
import re
import pandas as pd
data = """
101_group_1_Some text is here_23.06.2023_1
102_group_2_Some text is _ here_23.06.2023_1
103_group_3_Some text _ is _ here_23.06.2023_1
104_group_4_Some text is here_23.06.2023_1
"""
pattern = r"(\d+)_group_(\d+)_(.+)_(\d{2}.\d{2}.\d{4})_(\d)"
matches = re.findall(pattern, data)
df = pd.DataFrame(matches, columns=['ID', 'GROUP', 'TEXT', 'DATE', 'PART'])
print(df)

<details>
<summary>英文:</summary>
I would suggest writing a RegEx pattern in order to find the corresponding columns.
In your case you should create a pattern going like:
Number_group_n_text_date_Number
SO the final code should be:
```python
import re
import pandas as pd
data = &quot;&quot;&quot;
101_group_1_Some text is here_23.06.2023_1
102_group_2_Some text is _ here_23.06.2023_1
103_group_3_Some text _ is _ here_23.06.2023_1
104_group_4_Some text is here_23.06.2023_1
&quot;&quot;&quot;
pattern = r&quot;(\d+)_group_(\d+)_(.+)_(\d{2}.\d{2}.\d{4})_(\d)&quot;
matches = re.findall(pattern, data)
df = pd.DataFrame(matches, columns=[&#39;ID&#39;, &#39;GROUP&#39;, &#39;TEXT&#39;, &#39;DATE&#39;, &#39;PART&#39;])
print(df)