[英]pandas Series elementwise boolean checks are ambigious
不知道如何使用.bool(),any,all或empty来使两个不同的示例正常工作。 每个都给我抛出歧义值错误
import pandas as pd
first = pd.Series([1,0,0])
second = pd.Series([1,2,1])
number_df = pd.DataFrame( {'first': first, 'second': second} )
bool_df = pd.DataFrame( {'testA': pd.Series([True, False, True]), 'testB': pd.Series([True, False, False])})
#ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
""" both the next two lines fail with the ambiguous Series issue"""
#each row should be true or false
bool_df['double_zero_check'] = (number_df['first'] != 0) and (number_df['second'] != 0 )
bool_df['parity'] = bool_df['testA'] and bool_df['testB']
您需要使用按位和( &
)来逐个比较Series元素- 文档中的更多内容
In [3]: bool_df['double_zero_check'] = (number_df['first'] != 0) & (number_df['second'] != 0 )
In [4]: bool_df['parity'] = bool_df['testA'] & bool_df['testB']
In [5]: bool_df
Out[5]:
testA testB double_zero_check parity
0 True True True True
1 False False False False
2 True False False False
您必须使用按位和(&)运算符。 and
适用于boolean而不适用于Pandas Series。
bool_df['double_zero_check'] = (number_df['first'] != 0) & (number_df['second'] != 0 )
bool_df['parity'] = bool_df['testA'] & bool_df['testB']
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