按设定值选择Pandas Dataframe

Question

让Pandas Dataframe df在下面，我如何找到值为6和10的行？

0 1 2 3 4 5 6 
0 11 1 3 4 6 8 10 
1 11 1 3 4 6 8 11 
2 11 1 3 4 6 8 0 
3 11 1 3 4 6 9 10 
4 11 1 3 4 6 9 11 
5 11 1 3 4 6 9 0 
6 11 1 3 4 6 10 10 
7 11 1 3 4 6 10 11 
8 11 1 3 4 6 10 0 
9 11 1 3 4 7 8 10 

我可以得到这些线与解决方案基于集：

>>> df.iloc[[i for i, s in enumerate(df.itertuples()) if {6, 10} <= set(s)]] 

    0 1 2 3 4 5 6 
0 11 1 3 4 6 8 10 
3 11 1 3 4 6 9 10 
6 11 1 3 4 6 10 10 
7 11 1 3 4 6 10 11 
8 11 1 3 4 6 10 0 

我的问题是：是否有大熊猫更好的方式在两种给定值存在的线来获得真实的吗？一些诸如：

df.where({6, 10} <= df) 

的数据例如：

pandas.DataFrame.from_dict({0: {0: 11, 1: 11, 2: 11, 3: 11, 4: 11, 5: 11, 6: 11, 7: 11, 8: 11, 9: 11}, 
1: {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1}, 
2: {0: 3, 1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3, 7: 3, 8: 3, 9: 3}, 
3: {0: 4, 1: 4, 2: 4, 3: 4, 4: 4, 5: 4, 6: 4, 7: 4, 8: 4, 9: 4}, 
4: {0: 6, 1: 6, 2: 6, 3: 6, 4: 6, 5: 6, 6: 6, 7: 6, 8: 6, 9: 7}, 
5: {0: 8, 1: 8, 2: 8, 3: 9, 4: 9, 5: 9, 6: 10, 7: 10, 8: 10, 9: 8}, 
6: {0: 10, 1: 11, 2: 0, 3: 10, 4: 11, 5: 0, 6: 10, 7: 11, 8: 0, 9: 10}}) 

编辑

该数据帧是只有一小段我的真实数据。 0到11之间的整数可以在每行中出现0到2次。例如，在这些行中，值4,8和11每个出现两次。

 0 1 2 3 4 5 6 
100  11 1 4 4 8 8 11 
343  11 2 4 4 8 8 11 
505  11 3 3 4 8 8 11 
586  11 3 4 4 8 8 11 
1558 1 1 4 4 8 8 11 

Answer 1

您可以使用isin来测试成员，然后调用dropna并通过thresh=2只显示其中至少2个非NaN值存在的行：

In [20]: 
df[df.isin([6,10])].dropna(thresh=2) 

Out[20]: 
    0 1 2 3 4 5 6 
0 NaN NaN NaN NaN 6 NaN 10 
3 NaN NaN NaN NaN 6 NaN 10 
6 NaN NaN NaN NaN 6 10 10 
7 NaN NaN NaN NaN 6 10 NaN 
8 NaN NaN NaN NaN 6 10 NaN 

我认为实际上这是更好地测试每个值和应用any：

In [41]: 
df.apply(lambda x: (x == 6).any() & (x == 10).any(), axis=1) 

Out[41]: 
0  True 
1 False 
2 False 
3  True 
4 False 
5 False 
6  True 
7  True 
8  True 
9 False 
dtype: bool 

为3倍的值，你可以这样做：

df.apply(lambda x: (x==5).any() & (x == 6).any() & (x == 10).any(), axis=1)