你可以从你的单词列表中编程构建一个正则表达式，使用嵌套的可选部分来允许可能缺少的第二个，第三个等。匹配：

list_1 = ['chest', 'test', 'west', 'nest']
list_2 = ['mike', 'bike', 'like', 'pike']
list_3 = ['hay', 'day', 'may', 'say']
word_list = [list_1, list_2, list_3]
pattern = r'\b' + r'(?:\b\s+'.join(fr"(?P<match_{i+1}>{'|'.join(w)})" for i, w in enumerate(word_list)) + r'\b' + ''.join(')?' for _ in range(1, len(word_list)))

对于您的样本数据，这将提供：

\b(?P<match_1>chest|test|west|nest)(?:\b\s+(?P<match_2>mike|bike|like|pike)(?:\b\s+(?P<match_3>hay|day|may|say)\b)?)?

你可以在regex101上看到这一点。
然后，您可以将该正则表达式与extractall一起使用，以查找每个文本值中的所有匹配项，并将该结果连接回原始列。

out = df[['text']].join(
        df['text'].str.extractall(pattern)
        .droplevel(1)
      ).reset_index(drop=True)

对于您的示例数据，它给出以下结果：

text match_1 match_2 match_3
0   zzz zzz zz chest bike day zzzz z test mike zz...   chest    bike     day
1   zzz zzz zz chest bike day zzzz z test mike zz...    test    mike     NaN
2   zzz zzz zz chest bike day zzzz z test mike zz...    west     NaN     NaN
3   aaa aa aaa a nest aa aaaa aaa nest bike may a...    nest     NaN     NaN
4   aaa aa aaa a nest aa aaaa aaa nest bike may a...    nest    bike     may
5   ggg gg ggg ggg ggg test like hay ggg gg west ...    test    like     hay
6   ggg gg ggg ggg ggg test like hay ggg gg west ...    west     NaN     NaN
7   ggg gg ggg ggg ggg test like hay ggg gg west ...    west    like     NaN

请注意，使用变量list_1，list_2不是一个好的编程实践，你应该使用一个列表的列表（像上面的word_list）。

示例

import pandas as pd
data1 = {'text': [' zzz zzz zz chest bike day zzzz z test mike zzz zzzz west zzz zz ',
  ' aaa aa aaa a nest aa aaaa aaa nest bike may aaaa aaa            ',
  ' ggg gg ggg ggg ggg test like hay ggg gg west ggg gggg west like ']}
df1 = pd.DataFrame(data1)

df1

text
0   zzz zzz zz chest bike day zzzz z test mike zz...
1   aaa aa aaa a nest aa aaaa aaa nest bike may a...
2   ggg gg ggg ggg ggg test like hay ggg gg west ...

步骤1

首先制作图案

pat_list = ['(?P<match_{}>{})'.format(i, '|'.join(globals()["list_%i" % i])) for i in range(1, 4)]

pat_list

['(?P<match_1>chest|test|west|nest)',
 '(?P<match_2>mike|bike|like|pike)',
 '(?P<match_3>hay|day|may|say)']

我使用了for循环来提取要提取到list_1、list_2和list_3中的值。如果它与您的机制不同，您也可以手动创建。

第二步

接下来，从df1的“text”列中提取模式，并将生成的DataFrame定义为df2。

df2 = df1['text'].str.extractall('|'.join(pat_list)).droplevel(1)

DF2

match_1 match_2 match_3
0   chest   NaN     NaN
0   NaN     bike    NaN
0   NaN     NaN     day
0   test    NaN     NaN
0   NaN     mike    NaN
0   west    NaN     NaN
1   nest    NaN     NaN
1   nest    NaN     NaN
1   NaN     bike    NaN
1   NaN     NaN     may
2   test    NaN     NaN
2   NaN     like    NaN
2   NaN     NaN     hay
2   west    NaN     NaN
2   west    NaN     NaN
2   NaN     like    NaN

步骤3

按顺序压缩df2的值并与df1连接。

grp = df2['match_1'].notna().groupby(df2.index).cumsum()
df3 = df2.groupby([df2.index, grp]).first().droplevel(1)
out = df1[['text']].join(df3)

出来

text                                            match_1 match_2 match_3
0   zzz zzz zz chest bike day zzzz z test mike zz...    chest   bike    day
0   zzz zzz zz chest bike day zzzz z test mike zz...    test    mike    None
0   zzz zzz zz chest bike day zzzz z test mike zz...    west    None    None
1   aaa aa aaa a nest aa aaaa aaa nest bike may a...    nest    None    None
1   aaa aa aaa a nest aa aaaa aaa nest bike may a...    nest    bike    may
2   ggg gg ggg ggg ggg test like hay ggg gg west ...    test    like    hay
2   ggg gg ggg ggg ggg test like hay ggg gg west ...    west    None    None
2   ggg gg ggg ggg ggg test like hay ggg gg west ...    west    like    None

您可以通过编程方式创建一个正则表达式来与str.extractall一起使用：

lists = [list_1, list_2, list_3]
pats = [f"(?P<match_{i}>{'|'.join(l)})" for i, l in enumerate(lists, start=1)]
# ['(?P<match_1>chest|test|west|nest)',
#  '(?P<match_2>mike|bike|like|pike)',
#  '(?P<match_3>hay|day|may|say)']

pat = pats[-1]
for p in pats[-2::-1]:
    pat = f'{p}(?: +{pat})?'
# '(?P<match_1>chest|test|west|nest)(?: +(?P<match_2>mike|bike|like|pike)(?: +(?P<match_3>hay|day|may|say))?)?'

out = df['text'].str.extractall(pat).droplevel(1)

输出量：

match_1 match_2 match_3
0   chest    bike     day
0    test    mike     NaN
0    west     NaN     NaN
1    nest     NaN     NaN
1    nest    bike     may
2    test    like     hay
2    west     NaN     NaN
2    west    like     NaN

regex demo
要将结果连接到原始DataFrame，请执行以下操作：

out = df.join(df['text'].str.extractall(pat).droplevel(1))

输出量：

text match_1 match_2 match_3
0   zzz zzz zz chest bike day zzzz z test mike zz...   chest    bike     day
0   zzz zzz zz chest bike day zzzz z test mike zz...    test    mike     NaN
0   zzz zzz zz chest bike day zzzz z test mike zz...    west     NaN     NaN
1   aaa aa aaa a nest aa aaaa aaa nest bike may a...    nest     NaN     NaN
1   aaa aa aaa a nest aa aaaa aaa nest bike may a...    nest    bike     may
2   ggg gg ggg ggg ggg test like hay ggg gg west ...    test    like     hay
2   ggg gg ggg ggg ggg test like hay ggg gg west ...    west     NaN     NaN
2   ggg gg ggg ggg ggg test like hay ggg gg west ...    west    like     NaN