这是一个基于现有答案的可重复的例子:

import pandas as pd


def bool_to_int(s: pd.Series) -> pd.Series:
    """Convert the boolean to binary representation, maintain NaN values."""
    return s.replace({True: 1, False: 0})


# generate a random dataframe
df = pd.DataFrame({"a": range(10), "b": range(10, 0, -1)}).assign(
    a_bool=lambda df: df["a"] > 5,
    b_bool=lambda df: df["b"] % 2 == 0,
)

# select all bool columns (or specify which cols to use)
bool_cols = [c for c, d in df.dtypes.items() if d == "bool"]

# apply the new coding to a new dataframe (or can replace the existing one)
df_new = df.assign(**{c: lambda df: df[c].pipe(bool_to_int) for c in bool_cols})

这个问题特别提到了一个列，所以目前公认的答案是有效的。但是，它不能泛化到多个列。对于那些对通用解决方案感兴趣的人，请使用以下方法:

df.replace({False: 0, True: 1}, inplace=True)

这适用于包含许多不同类型列的DataFrame，而不管有多少是布尔类型。

只需将你的数据帧乘以1 (int)

[1]: data = pd.DataFrame([[True, False, True], [False, False, True]])
[2]: print data
          0      1     2
     0   True  False  True
     1   False False  True

[3]: print data*1
         0  1  2
     0   1  0  1
     1   0  0  1

这是一个基于现有答案的可重复的例子:

import pandas as pd


def bool_to_int(s: pd.Series) -> pd.Series:
    """Convert the boolean to binary representation, maintain NaN values."""
    return s.replace({True: 1, False: 0})


# generate a random dataframe
df = pd.DataFrame({"a": range(10), "b": range(10, 0, -1)}).assign(
    a_bool=lambda df: df["a"] > 5,
    b_bool=lambda df: df["b"] % 2 == 0,
)

# select all bool columns (or specify which cols to use)
bool_cols = [c for c, d in df.dtypes.items() if d == "bool"]

# apply the new coding to a new dataframe (or can replace the existing one)
df_new = df.assign(**{c: lambda df: df[c].pipe(bool_to_int) for c in bool_cols})

将布尔值的单列转换为整数1或0的列的简洁方法:

df["somecolumn"] = df["somecolumn"].astype(int)

你也可以直接在框架上这样做

In [104]: df = DataFrame(dict(A = True, B = False),index=range(3))

In [105]: df
Out[105]: 
      A      B
0  True  False
1  True  False
2  True  False

In [106]: df.dtypes
Out[106]: 
A    bool
B    bool
dtype: object

In [107]: df.astype(int)
Out[107]: 
   A  B
0  1  0
1  1  0
2  1  0

In [108]: df.astype(int).dtypes
Out[108]: 
A    int64
B    int64
dtype: object