如何仅将 dtype bool 列的 Pandas 数据框中的 True 和 False 映射到“是”和“否”？
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How to map True and False to 'Yes' and 'No' in a pandas data frame for columns of dtype bool only?
提问by Alex Tereshenkov
I have a pandasdata frame (v 0.20.3):
我有一个pandas数据框（v 0.20.3）：
df = pd.DataFrame({'coname1': ['Apple','Yahoo'], 'coname2':['Apple', 'Google']})
df['eq'] = df.apply(lambda row: row['coname1'] == row['coname2'], axis=1).astype(bool)
coname1 coname2 eq
0 Apple Apple True
1 Yahoo Google False
If I would like to replace True/Falseto 'Yes'/'No', I could run this:
如果我想替换True/False为'Yes'/'No'，我可以运行这个：
df.replace({
True: 'Yes',
False: 'No'
})
coname1 coname2 eq
0 Apple Apple Yes
1 Yahoo Google No
Which seems to get the job done. However, if a data frame is just one row with a value of 0/1in a column, it will be also replaced as it's being treated as Boolean.
这似乎完成了工作。但是，如果数据框只是一列中值为 的一行0/1，它也会被替换，因为它被视为布尔值。
df1 = pd.DataFrame({'coname1': [1], 'coname2':['Google'], 'coname3':[777]})
df1['eq'] = True
coname1 coname2 coname3 eq
0 1 Google 777 True
df1.replace({
True: 'Yes',
False: 'No'
})
coname1 coname2 coname3 eq
0 Yes Google 777 Yes
I would like to map True/Falseto Yes/Nofor all columns in the data frame that are of dtypebool.
我想映射True/False到Yes/No数据框中的所有列dtypebool。
How do I tell pandasto run map True/False to arbitrary strings only for the columns that are of dtypeboolwithout explicitly specifying the names of columns as I may not know them in advance?
我如何告诉pandas只为那些dtypebool没有明确指定列名的列运行映射 True/False 到任意字符串，因为我可能事先不知道它们？
回答by ayhan
Use the dtypes attribute to check if the column is boolean and filter based on that:
使用 dtypes 属性检查列是否为布尔值并基于此进行过滤：
df = pd.DataFrame({'A': [0, 1], 'B': ['x', 'y'],
'C': [True, False], 'D': [False, True]})
df
Out:
A B C D
0 0 x True False
1 1 y False True
bool_cols = df.columns[df.dtypes == 'bool']
df[bool_cols] = df[bool_cols].replace({True: 'Yes', False: 'No'})
df
Out:
A B C D
0 0 x Yes No
1 1 y No Yes
I think the fastest way would be to use map in a loop though:
我认为最快的方法是在循环中使用 map ：
for col in df.columns[df.dtypes == 'bool']:
df[col] = df[col].map({True: 'Yes', False: 'No'})
回答by Vinícius Aguiar
A nice workaround is to create a function that first checks if the element is of type bool or not, and then use applymap:
一个很好的解决方法是创建一个函数，首先检查元素是否为 bool 类型，然后使用applymap：
import pandas as pd
df1 = pd.DataFrame({'coname1': [1], 'coname2':['Google'], 'coname3':[777]})
df1['eq'] = True
def bool2yes(boolean):
if isinstance(boolean, bool):
if boolean == True:
return "Yes"
else:
return "No"
else:
return boolean
>>> df1.applymap(bool2yes)
coname1 coname2 coname3 eq
0 1 Google 777 Yes
回答by piRSquared
My Take
我的看法
cols = df.columns[df.dtypes.eq(bool)]
vals = np.column_stack([df[c].values for c in cols])
df[cols] = np.array(['No', 'Yes'])[vals.astype(int)]
df
A B C D
0 0 x Yes No
1 1 y No Yes
