将链式布尔掩码转换为整数：

df['col_A_0_col_B_1'] = ((df['col_A']==0)&(df['col_B']==1)).astype(int)

为了获得更好的性能：

df['col_A_0_col_B_1'] = ((df['col_A'].values==0)&(df['col_B'].values==1)).astype(int)

性能：取决于行数和0、1值：

一个二个一个一个

您可以使用np.where

df['col_A_0_col_B_1'] = np.where((df['col_A']==0)&(df['col_B']==1), 1, 0)

首先创建列，然后分配，例如0表示False
df['col_A_0_col_B_1'] = 0
然后使用loc，您可以按where col_A == 0和col_B ==1进行过滤，然后将1赋给新列df.loc[(df.col_A == 0) & (df.col_B==1),'col_A_0_col_B_1'] = 1

如果我没理解错的话，你可以这样做：

import pandas as pd
data = [[0, 1],
        [0, 0],
        [0, 1],
        [0, 1],
        [1, 0],
        [1, 0],
        [1, 1]]

df = pd.DataFrame(data=data, columns=['col_A', 'col_B'])
df['col_A_0_col_B_1'] = pd.Series([a == 0 and b == 1 for a, b in zip(df.col_A, df.col_B)], dtype='uint')
print(df)

产出

col_A  col_B  col_A_0_col_B_1
0      0      1                1
1      0      0                0
2      0      1                1
3      0      1                1
4      1      0                0
5      1      0                0
6      1      1                0

或者作为替代：

df = pd.DataFrame(data=data, columns=['col_A', 'col_B'])
df['col_A_0_col_B_1'] = pd.Series((df.col_A == 0) & (df.col_B == 1), dtype='uint')
print(df)

您可以使用panda ~表示布尔非，再加上1和0表示真和假。

df['col_A_0_col_B_1'] = ~df['col_A'] & df['col_B']

我在Pandas中寻找类似于谷歌ML入门课程中使用的tensorflow “crossed_column”的东西，但没有找到。这将用于向 Dataframe 添加one-hot编码的特征crosses。所选列必须已经被有序编码/因子分解。

def cross_category_features(
    df: pd.DataFrame,
    cross: list[str],
    remove_originals: bool = True
) -> pd.DataFrame:
    """
    Add feature crosses to the  based on the columns in cross_cols.  The columns must have already been factorized / ordinal encoded.

    :param data: The data to add feature crosses to
    :param cross_cols: The columns to cross. Columns must be int categorical 0 to n-1
    :param remove_originals: If True, remove the original columns from the data

    :return: The data with the feature crosses added
    """
    def set_hot_index(row):
        hot_index = (row[cross] * offsets).sum()
        row[hot_index + org_col_len] = 1
        return row

    org_col_len = df.shape[1]
    str_values = [[col + str(val) for val in sorted(df[col].unique())]
                  for col in cross]
    cross_names = ["_".join(x) for x in product(*str_values)]

    cross_features = pd.DataFrame(
        data=np.zeros((df.shape[0], len(cross_names))),
        columns=cross_names,
        dtype="int64")
    df = pd.concat([df, cross_features], axis=1)
    
    max_vals = df[cross].max(axis=0) + 1
    offsets = [np.prod(max_vals[i+1:]) for i in range(len(max_vals))]
    df.apply(set_hot_index, axis=1)

    if remove_originals:
        df = df.drop(columns=cross)

    return df