栏目分类:
子分类:
返回
名师互学网用户登录
快速导航关闭
当前搜索
当前分类
子分类
实用工具
热门搜索
名师互学网 > IT > 软件开发 > 后端开发 > Python

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

Python 更新时间: 发布时间: IT归档 最新发布 模块sitemap 名妆网 法律咨询 聚返吧 英语巴士网 伯小乐 网商动力

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

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

 

目录

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

问题:

解决:

完整错误:


问题:

出现此错误是因为Python的逻辑运算符(and、or、not)是用来与布尔值(boolean)一起使用的,所以当试图将它们与序列或数组一起使用时,系统程序不清楚如何确定它是真的还是假的,因此会导致ValueError。

import pandas as pd

data = {
    'Name': ['Microsoft Corporation', 'Google, LLC', 'Tesla, Inc.',
             'Apple Inc.', 'Netflix, Inc.'],
    'Symbol': ['MSFT', 'GOOG', 'TSLA', 'AAPL', 'NFLX'],
    'Industry': ['Tech', 'Tech', 'Automotive', 'Tech', 'Entertainment'],
    'Shares': [100, 50, 150, 200, 80]
}

df = pd.DataFrame(data)
# print(df)
df


df_filtered = df[(df['Shares']>=100) and (df['Shares']<=150)]
df_filtered
# print(df_filtered)

解决:

将and改为&

df_filtered = df[(df['Shares']>=100) & (df['Shares']<=150)]
df_filtered
# print(df_filtered)

 

完整错误:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
 in 
----> 1 df_filtered = df[(df['Shares']>=100) and (df['Shares']<=150)]
      2 df_filtered
      3 # print(df_filtered)

D:anacondalibsite-packagespandascoregeneric.py in __nonzero__(self)
   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().

参考:pandas

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

转载请注明:文章转载自 www.mshxw.com
本文地址:https://www.mshxw.com/it/834849.html
我们一直用心在做
关于我们 文章归档 网站地图 联系我们

版权所有 (c)2021-2022 MSHXW.COM

ICP备案号:晋ICP备2021003244-6号