ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.

错误解决：ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.

例：

data = {'Name': ['Microsoft', 'Google', 'huawei','Apple', 'Andriod'],'Shares': [63, 50, 71, 62, 65]}
data

我们的数据展示如下：

{'Name': ['Microsoft', 'Google', 'huawei', 'Apple', 'Andriod'],
 'Shares': [63, 50, 71, 62, 65]}

我最开始的目的是要找到'Shares'的取值范围在【60，75】之间的数据：

df = pd.DataFrame(data)
df[df['Shares']>60 & df['Shares']<75]

然后报错：ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我们看一下完整的错误说明：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [34], in <cell line: 1>()
----> 1 df[df['Shares']>60 & df['Shares']<75]

File F:\anaconda\envs\sklearn-env\lib\site-packages\pandas\core\generic.py:1442, in NDFrame.__nonzero__(self)
   1440 @final
   1441 def __nonzero__(self):
-> 1442     raise ValueError(
   1443         f"The truth value of a {type(self).__name__} is ambiguous. "
   1444         "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1445     )

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

问题思考

如果不使用圆括号（）对条件进行分组，Python将根据运算符优先级计算表达式，这可能会给出运算符&和~的意外结果。

df[df['Shares']>60 & df['Shares']<75]可能会理解成df['Shares']>（60 & df['Shares']）<75

问题解决

为条件关系添加括号即可！

df[(df['Shares']>60) & (df['Shares']<75)]