Python 将数值df与布尔df相乘=>；假乘法的结果=np.nan而不是零

当将数值df与布尔df相乘时，显然True为1，False为0。我想将所有值乘以False设置为np.nan，以便它们与初始数值df中的0值不同。初始数值df中的NaN值当然保持NaN值

所需输出：

   a    b    c
 NaN  0.0  3.0
 0.0  NaN  NaN
 9.0  NaN  NaN
 NaN  3.0  NaN

代码：

这应该行得通

import pandas as pd
import numpy as np

df = pd.DataFrame({"a": [4,0,9,3],
                   "b": [0,7,np.nan,3],
                   "c": [3,7,np.nan,3]})

boolean = pd.DataFrame({"a": [False,True,True,False],
                        "b": [True,False,True,True],
                        "c": [True,False,False,False]})

boolean[boolean==0]=np.nan

output = df.mul(boolean)
print(output)

import pandas as pd
import numpy as np

df = pd.DataFrame({"a": [4,0,9,3],
                   "b": [0,7,np.nan,3],
                   "c": [3,7,np.nan,3]})

boolean = pd.DataFrame({"a": [False,True,True,False],
                        "b": [True,False,True,True],
                        "c": [True,False,False,False]})

boolean[boolean==0]=np.nan

output = df.mul(boolean)
print(output)