pandas 以字符串而不是布尔值映射到TRUE/FALSE [英] Pandas mapping to TRUE/FALSE as String, not Boolean

问题描述

当我尝试将pandas数据框中的某些列从"0"和"1"转换为"TRUE"和"FALSE"时，pandas自动将dtype检测为布尔值.我想将dtype保留为字符串，字符串为"TRUE"和"FALSE".

When I try to convert some columns in a pandas dataframe from '0' and '1' to 'TRUE' and 'FALSE', pandas automatically detects dtype as boolean. I want to keep dtype as string, with the strings 'TRUE' and 'FALSE'.

请参见下面的代码:

booleanColumns = pandasDF.select_dtypes(include=[bool]).columns.values.tolist()
booleanDictionary = {'1': 'TRUE', '0': 'FALSE'}

pandasDF.to_string(columns = booleanColumns)

for column in booleanColumns:
    pandasDF[column].map(booleanDictionary)

不幸的是，python在最后一次操作时自动将dtype转换为boolean.我该如何预防?

Unfortunately, python automatically converts dtype to boolean with the last operation. How can I prevent this?

推荐答案

如果需要替换boolean值True和False:

booleandf = pandasDF.select_dtypes(include=[bool])
booleanDictionary = {True: 'TRUE', False: 'FALSE'}

for column in booleandf:
    pandasDF[column] = pandasDF[column].map(booleanDictionary)

示例:

pandasDF = pd.DataFrame({'A':[True,False,True],
                   'B':[4,5,6],
                   'C':[False,True,False]})

print (pandasDF)
       A  B      C
0   True  4  False
1  False  5   True
2   True  6  False

booleandf = pandasDF.select_dtypes(include=[bool])
booleanDictionary = {True: 'TRUE', False: 'FALSE'}

#loop by df is loop by columns, same as for column in booleandf.columns:
for column in booleandf:
    pandasDF[column] = pandasDF[column].map(booleanDictionary)

print (pandasDF)
       A  B      C
0   TRUE  4  FALSE
1  FALSE  5   TRUE
2   TRUE  6  FALSE

使用 replace 的简单解决方案通过dict:

booleanDictionary = {True: 'TRUE', False: 'FALSE'}
pandasDF = pandasDF.replace(booleanDictionary)
print (pandasDF)
       A  B      C
0   TRUE  4  FALSE
1  FALSE  5   TRUE
2   TRUE  6  FALSE

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