np.select() 条件中的否定

Question

这是我的代码：

import pandas as pd

import numpy as np

df = pd.DataFrame({ 'var1': ['a', 'b', 'c',np.nan, np.nan],

                   'var2': [1, 2, np.nan , 4, np.nan]

                 })

conditions = [

    (not(pd.isna(df["var1"]))) & (not(pd.isna(df["var2"]))),

    (pd.isna(df["var1"])) & (pd.isna(df["var2"]))]

choices = ["No missing", "Both missing"]

df['Result'] = np.select(conditions, choices, default=np.nan)

输出：

  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1478, in __nonzero__

    f"The truth value of a {type(self).__name__} is ambiguous. "

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

问题出在 line 上(not(pd.isna(df["var1"]))) & (not(pd.isna(df["var2"])))。这条线应该给出TRUEwhen in bothvar1而var2不是一个NaN值。这里的问题在于否定，因为没有否定的条件就没有问题。

问题： 如何纠正(not(pd.isna(df["var1"]))) & (not(pd.isna(df["var2"])))线，以防万一在这两种情况下var1都不var2是NaN条件应该给出的值TRUE？

Answer 1

尝试：conditions = [(~pd.isna(df["var1"]) & ~pd.isna(df["var2"])),                (pd.isna(df["var1"]) &  pd.isna(df["var2"]))]