python - 通过设置值选择 Pandas Dataframe

Question

让下面的 Pandas Dataframe df，如何找到值 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

我可以通过基于集合的解决方案获得这些行:

>>> 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:在 Pandas 中是否有更好的方法在存在这些给定值的行中获得 True ？比如:

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

数据示例:

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

这个数据框只是我真实数据的一小部分。0 到 11 之间的整数在每一行中可以出现 0 到 2 次。例如，在这些行中，值 4、8 和 11 各出现两次。

        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 来测试成员资格，然后调用 dropna 并传递 thresh=2 以仅显示至少有 2 的行存在非 NaN 值:

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

我认为实际上最好测试每个值并应用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

对于 3 个值，您可以执行以下操作:

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