Question

我的数据集如下

Col1    Col2    Col3
A       10      x1
B       100     x2
C       1000    x3

这就是我得到的输出的样子，

Col1    Col2    Col3    Col4    Col5    Col6    Col7    Col8    Col9
A       10      x1      Empty   Empty   Empty   Empty   Empty   Empty
B       100     x2      Empty   Empty   Empty   Empty   Empty   Empty
C       1000    x3      Empty   Empty   Empty   Empty   Empty   Empty
A       10      x1      B       100     x2      Empty   Empty   Empty
B       100     x2      C       1000    x3      Empty   Empty   Empty
A       10      x1      B       100     x2      C       1000    x3

感谢本网站的帮助，可以使用-

arr = list(itertools.chain.from_iterable(
[[j for i in el for j in i] for el in itertools.combinations(df.values.tolist(), i)]
for i in range(1, len(df)+1)
)
)

pd.DataFrame(arr)

但是如果数据集如下，

        Col1 Col2   Col3   Structure
        A    10     x1     1
        B    100    x2     1
        C    1000   x3     2

输出必须是这个-

  Col1    Col2    Col3      Col4    Col5    Col6    Col7    Col8    Col9    Answer
    A       10      x1      Empty   Empty   Empty   Empty   Empty   Empty   No
    B       100     x2      Empty   Empty   Empty   Empty   Empty   Empty   No
    C       1000    x3      Empty   Empty   Empty   Empty   Empty   Empty   Yes
    A       10      x1      B       100     x2      Empty   Empty   Empty   Yes
    B       100     x2      C       1000    x3      Empty   Empty   Empty   No
    A       10      x1      B       100     x2      C       1000    x3      No

A和B基本上是说“是”，因为它们在同一结构中，而C本身是“是”，因为它本身在该结构中。其他所有行（例如A，B，ABC）均为“否”，因为它们的结构不同。如何获得上面想要的表？

代码

arr = list(itertools.chain.from_iterable(
[[j for i in el for j in i] for el in itertools.combinations(df.values.tolist(), i)]
for i in range(1, len(df)+1)
)
)

pd.DataFrame(arr)

将此输出提供给我

    Col1    Col2    Col3    Col4    Col5    Col6    Col7    Col8    Col9
    A       10      x1      Empty   Empty   Empty   Empty   Empty   Empty
    B       100     x2      Empty   Empty   Empty   Empty   Empty   Empty
    C       1000    x3      Empty   Empty   Empty   Empty   Empty   Empty
    A       10      x1      B       100     x2      Empty   Empty   Empty
    B       100     x2      C       1000    x3      Empty   Empty   Empty
    A       10      x1      B       100     x2      C       1000    x3

如何将“答案”列添加到此输出以获得最终表？

Answer 1

由于DataFrame的结构，我们知道，当我们应用itertools.combinations时，Structure列将首先显示在第三列中，然后每隔四列显示一次：

  0     1   2   3     4       5     6    7     8       9     10   11
0  A    10  x1   1  None     NaN  None  NaN  None     NaN  None  NaN
1  B   100  x2   1  None     NaN  None  NaN  None     NaN  None  NaN
2  C  1000  x3   2  None     NaN  None  NaN  None     NaN  None  NaN
3  A    10  x1   1     B   100.0    x2  1.0  None     NaN  None  NaN
4  A    10  x1   1     C  1000.0    x3  2.0  None     NaN  None  NaN
5  B   100  x2   1     C  1000.0    x3  2.0  None     NaN  None  NaN
6  A    10  x1   1     B   100.0    x2  1.0     C  1000.0    x3  2.0

我们可以使用它来仅索引Structure列，检查它们是否包含组中的所有成员，然后删除它们：

checker = df.groupby('Structure').size().to_dict()

def helper(row):                                               
    u = row[~row.isnull()].values                              
    return (len(np.unique(u)) == 1) & (checker[u[0]] == len(u))

s = out[out.columns[3::4]].apply(helper, 1).replace({False: 'No', True: 'Yes'})

0     No
1     No
2    Yes
3    Yes
4     No
5     No
6     No
dtype: object

要删除其他列并分配给DataFrame：

out.drop(out.columns[3::4], 1).assign(final=s)

   0     1   2     4       5     6     8       9    10 final
0  A    10  x1  None     NaN  None  None     NaN  None    No
1  B   100  x2  None     NaN  None  None     NaN  None    No
2  C  1000  x3  None     NaN  None  None     NaN  None   Yes
3  A    10  x1     B   100.0    x2  None     NaN  None   Yes
4  A    10  x1     C  1000.0    x3  None     NaN  None    No
5  B   100  x2     C  1000.0    x3  None     NaN  None    No
6  A    10  x1     B   100.0    x2     C  1000.0    x3    No

熊猫csv itertools组合

1 个答案: