要匹配完整的单词，请使用列表解析，并在set中搜索：

labels = {'high', 'important'}

df['new_col'] = ['High/Important' if
                   any(x.lower() in labels for x in l)
                 else '' for l in df['specification']]

字符串
输出量：

id             specification         new_col
0  123  [high, Important, pilot]  High/Important
1  234   [HIGH, Important, Baby]  High/Important
2  543               [important]  High/Important
3  542                    [week]                
4  857          [new, IMPORTANT]  High/Important
5  123          [super_hightest]

型
要匹配子字符串，请执行以下操作：

labels = ['high', 'important']
df['new_col'] = ['High/Important' if
                   any(w in x for x in map(str.lower, l) for w in labels)
                 else '' for l in df['specification']]

型
或者使用正则表达式匹配：

df['new_col'] = np.where(df['specification'].apply(' '.join)
                          .str.lower()
                          .str.contains('high|important'),
                         'High/Important', '')

型
输出量：

id             specification         new_col
0  123  [high, Important, pilot]  High/Important
1  234   [HIGH, Important, Baby]  High/Important
2  543               [important]  High/Important
3  542                    [week]                
4  857          [new, IMPORTANT]  High/Important
5  123          [super_hightest]  High/Important

型
使用的输入：

df = pd.DataFrame({'id': [123, 234, 543, 542, 857, 123],
                   'specification': [['high', 'Important', 'pilot'],
                                     ['HIGH', 'Important', 'Baby'],
                                     ['important'],
                                     ['week'],
                                     ['new', 'IMPORTANT'],
                                     ['super_hightest']]})

型

或者，考虑使用helper函数和apply创建一个新列。

import re

# helper function substring match
def high_important(specs):
    spec_string = "".join(specs)
    
    if re.findall(r"high|important", spec_string, re.IGNORECASE):
        return "High/Important"
    else:
        return ""

# new column based on existing df["specification"]
df["new_col"] = df.apply(
        lambda df: high_important(df["specification"]), 
        axis=1)
)

字符串
输出量：

id             specification         new_col
0  123  [high, Important, pilot]  High/Important
1  234   [HIGH, Important, Baby]  High/Important
2  543               [important]  High/Important
3  542                    [week]                
4  857          [new, IMPORTANT]  High/Important
5  123        [super_high, test]  High/Important

型