Pandas 映射到 TRUE/FALSE 作为字符串，而不是布尔值

Question

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'。

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 使用最后一个操作自动将 dtype 转换为 boolean。我怎样才能防止这种情况发生？

Answer 1

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

编辑：

replace by dict 的更简单解决方案：

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