Python 如何选择处理潜在np.nan值的后续numpy数组

我有这样一个系列：

s = pd.Series({10: np.array([[0.72260683, 0.27739317, 0.        ],
                         [0.7187053 , 0.2812947 , 0.        ],
                         [0.71435467, 0.28564533, 1.        ],
                         [0.3268072 , 0.6731928 , 0.        ],
                         [0.31941951, 0.68058049, 1.        ],
                         [0.31260015, 0.68739985, 0.        ]]), 
           20: np.array([[0.7022099 , 0.2977901 , 0.        ],
                         [0.6983866 , 0.3016134 , 0.        ],
                         [0.69411673, 0.30588327, 1.        ],
                         [0.33857735, 0.66142265, 0.        ],
                         [0.33244109, 0.66755891, 1.        ],
                         [0.32675582, 0.67324418, 0.        ]]), 
           20: np.array([[0.68811957, 0.34188043, 0.        ],
                         [0.68425783, 0.31574217, 0.        ],
                         [0.67994496, 0.32005504, 1.        ],
                         [0.34872593, 0.66127407, 1.        ],
                         [0.34276171, 0.65723829, 1.        ],
                         [0.33722803, 0.66277197, 0.        ]]),
           38: np.array([[0.68811957, 0.31188043, 0.        ],
                         [0.68425783, 0.31574217, 0.        ],
                         [0.67994496, 0.32005504, 1.        ],
                         [0.34872593, 0.65127407, 0.        ],
                         [0.34276171, 0.65723829, 1.        ],
                         [0.33722803, 0.66277197, 0.        ]]),
           np.nan: np.nan}
)

我想用

np.array（[1,4,1,5]）

或

np.array（[1,4,1,np.nan]）

将其子集，返回

np.nan

，无论数组的最后一个元素上的值是什么。我怎样才能做到这一点

请注意，我不能简单地删除一个系列的最后一个元素。

您可以通过删除

系列的缺失值来修改上一个元素，并通过（仅需要
系列的唯一索引）最后添加它们：

编辑：如果索引中不需要唯一值，请使用以下内容创建唯一的多重索引：



在最后一步中，删除
多索引的帮助器级别：

c = c.reset_index(level=-1, drop=True)
print (c)
10.0    0.0
20.0    1.0
20.0    0.0
38.0    1.0
NaN     NaN
dtype: float64

这很有效，非常感谢！作为一个附带问题，我是否可以使用
np.stack（s[mask]）
而不是
np.array（s[mask].tolist（）
？两者似乎返回相同的内容，但可能有什么特殊性？
s = pd.Series({10: np.array([[0.72260683, 0.27739317, 0.        ],
                         [0.7187053 , 0.2812947 , 0.        ],
                         [0.71435467, 0.28564533, 1.        ],
                         [0.3268072 , 0.6731928 , 0.        ],
                         [0.31941951, 0.68058049, 1.        ],
                         [0.31260015, 0.68739985, 0.        ]]), 
           20: np.array([[0.7022099 , 0.2977901 , 0.        ],
                         [0.6983866 , 0.3016134 , 0.        ],
                         [0.69411673, 0.30588327, 1.        ],
                         [0.33857735, 0.66142265, 0.        ],
                         [0.33244109, 0.66755891, 1.        ],
                         [0.32675582, 0.67324418, 0.        ]]), 
           23: np.array([[0.68811957, 0.34188043, 0.        ],
                         [0.68425783, 0.31574217, 0.        ],
                         [0.67994496, 0.32005504, 1.        ],
                         [0.34872593, 0.66127407, 1.        ],
                         [0.34276171, 0.65723829, 1.        ],
                         [0.33722803, 0.66277197, 0.        ]]),
           38: np.array([[0.68811957, 0.31188043, 0.        ],
                         [0.68425783, 0.31574217, 0.        ],
                         [0.67994496, 0.32005504, 1.        ],
                         [0.34872593, 0.65127407, 0.        ],
                         [0.34276171, 0.65723829, 1.        ],
                         [0.33722803, 0.66277197, 0.        ]]),
           np.nan: np.nan}
).rename({23:20})

print (s)
10.0    [[0.72260683, 0.27739317, 0.0], [0.7187053, 0....
20.0    [[0.7022099, 0.2977901, 0.0], [0.6983866, 0.30...
20.0    [[0.68811957, 0.34188043, 0.0], [0.68425783, 0...
38.0    [[0.68811957, 0.31188043, 0.0], [0.68425783, 0...
NaN                                                   NaN
dtype: object

a = np.array([1, 4, 1, 2, np.nan])

s = s.to_frame('a').set_index(s.groupby(s.index).cumcount(), append=True)['a']
print (s)
10.0  0    [[0.72260683, 0.27739317, 0.0], [0.7187053, 0....
20.0  0    [[0.7022099, 0.2977901, 0.0], [0.6983866, 0.30...
      1    [[0.68811957, 0.34188043, 0.0], [0.68425783, 0...
38.0  0    [[0.68811957, 0.31188043, 0.0], [0.68425783, 0...
NaN   0                                                  NaN
Name: a, dtype: object

mask = s.notna()
b = np.array(s[mask].tolist())[np.arange(mask.sum()), a[mask].astype(int), 2]
print (b)
[0. 1. 0. 1.]

c = pd.Series(b, index=s[mask].index).reindex(s.index)
print (c)
10.0  0    0.0
20.0  0    1.0
      1    0.0
38.0  0    1.0
NaN   0    NaN
dtype: float64

c = c.reset_index(level=-1, drop=True)
print (c)
10.0    0.0
20.0    1.0
20.0    0.0
38.0    1.0
NaN     NaN
dtype: float64