Pandas 映射到 TRUE/FALSE 作为字符串，而不是布尔值
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Pandas mapping to TRUE/FALSE as String, not Boolean
提问by Dendrobates
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'.
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 会自动将 dtype 检测为布尔值。我想将 dtype 保留为字符串，字符串为“TRUE”和“FALSE”。
See code below:
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)
Unfortunately, python automatically converts dtype to boolean with the last operation. How can I prevent this?
不幸的是，python 会在最后一次操作中自动将 dtype 转换为 boolean。我怎样才能防止这种情况？
回答by jezrael
If need replace booleanvalues Trueand False:
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)
Sample:
样本：
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
EDIT:
编辑：
Simplier solution with replaceby dict:
使用replaceby 更简单的解决方案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
