Pandas检查列是否含指定值赋值时报TypeError如何解决

需要遍历整个DataFrame逐行检查text列是否包含指定列表中的关键词，若当前行匹配到关键词则将新增的test列对应值设为True，否则设为False。运行代码时抛出错误：TypeError: 'bool' object does not support item assignment。

示例DataFrame数据 #

customerId                text
0           1  Something with Cat
1           3  That is a huge dog
2           3         Hello agian

原实现代码 #

import pandas as pd
import copy
import re
d = {
    "customerId": [1, 3, 3],
    "text": ["Something with Cat", "That is a huge dog", "Hello agian"],
}
df = pd.DataFrame(data=d)
my_list = ['cat', 'dog', 'mouse']
def f(x):
    match = False
    for element in my_list:
        x = bool(re.search(element, x['text'], re.IGNORECASE))
        if(x):
            match = True
            break
    x['test'] = str(match)
    return x
df['test'] = None
df = df.apply(lambda x: f(x), axis = 1)

期望输出结果 #

customerId                text   test
0           1  Something with Cat   True
1           3  That is a huge dog   True
2           3         Hello agian   False

完整错误栈 #

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
File <timed exec>:13, in <module>

File ~\Anaconda3\lib\site-packages\pandas\core\frame.py:8839, in DataFrame.apply(self, func, axis, raw, result_type, args, **kwargs)
   8828 from pandas.core.apply import frame_apply
   8830 op = frame_apply(
   8831     self,
   8832     func=func,
   (...)
   8837     kwargs=kwargs,
   8838 )
-> 8839 return op.apply().__finalize__(self, method="apply")

File ~\Anaconda3\lib\site-packages\pandas\core\apply.py:727, in FrameApply.apply(self)
    724 elif self.raw:
    725     return self.apply_raw()
--> 727 return self.apply_standard()

File ~\Anaconda3\lib\site-packages\pandas\core\apply.py:851, in FrameApply.apply_standard(self)
    850 def apply_standard(self):
--> 851     results, res_index = self.apply_series_generator()
    853     # wrap results
    854     return self.wrap_results(results, res_index)

File ~\Anaconda3\lib\site-packages\pandas\core\apply.py:867, in FrameApply.apply_series_generator(self)
    864 with option_context("mode.chained_assignment", None):
    865     for i, v in enumerate(series_gen):
    866         # ignore SettingWithCopy here in case the user mutates
--> 867         results[i] = self.f(v)
    868         if isinstance(results[i], ABCSeries):
    869             # If the return value is a view of v, make a copy to avoid errors from underlying data swap
    870             results[i] = results[i].copy(deep=False)

File <timed exec>:13, in <lambda>(x)

File <timed exec>:9, in f(x)

TypeError: 'bool' object does not support item assignment

函数f(x)中参数x代表传入的当前行Series对象，但循环判断关键词时，执行了x = bool(re.search(element, x['text'], re.IGNORECASE))，直接把x从行对象覆盖成了布尔值。后续执行x['test'] = str(match)时，相当于对布尔类型对象做键赋值操作，而布尔类型不支持该操作，因此抛出对应错误。另外原代码将匹配结果转为字符串存储，和期望的布尔类型输出也不匹配。

方案1：修正原apply逻辑 #

不要覆盖行对象变量x，将关键词匹配结果存入单独的临时变量，同时去掉多余的导入、提前赋值逻辑，直接存储布尔类型结果：

import pandas as pd
import re
d = {
    "customerId": [1, 3, 3],
    "text": ["Something with Cat", "That is a huge dog", "Hello agian"],
}
df = pd.DataFrame(data=d)
my_list = ['cat', 'dog', 'mouse']

def f(x):
    match = False
    for element in my_list:
        # 匹配结果存入临时变量，不要覆盖行对象x
        is_match = bool(re.search(element, x['text'], re.IGNORECASE))
        if is_match:
            match = True
            break
    x['test'] = match
    return x

df = df.apply(lambda x: f(x), axis = 1)

方案2：向量化实现（性能更优） #

逐行apply在数据量大时运行效率低，可直接用pandas字符串向量化方法实现，不需要手写循环：

# 将关键词拼接为正则或模式，忽略大小写一次性完成全列匹配
pattern = '|'.join(my_list)
df['test'] = df['text'].str.contains(pattern, flags=re.IGNORECASE, na=False)

两种方案运行后都能得到符合预期的输出结果。

内容的提问来源于stack exchange，提问作者Test