更新时间:2023-01-11 21:44:46
由于您是按年龄分组的,因此,请返回每个组的所有排列,然后取乘积(使用itertools的乘积和排列函数) :
Since you're grouping by age, let's do that and return all the permutations for each group and then take the product (using itertools' product and permutation functions):
In [11]: age = df.groupby("age")
如果我们查看单个组的排列:
If we look at the permutations of a single group:
In [12]: age.get_group(21)
Out[12]:
age name
2 21 Chris
4 21 Evan
In [13]: list(permutations(age.get_group(21).index))
Out[13]: [(2, 4), (4, 2)]
In [14]: [df.loc[list(p)] for p in permutations(age.get_group(21).index)]
Out[14]:
[ age name
2 21 Chris
4 21 Evan, age name
4 21 Evan
2 21 Chris]
我们可以通过仅返回每个组的索引来在整个DataFrame上执行此操作(这假设索引是唯一的,如果在执行此操作之前不是reset_index
……您可以能够在较低级别上做一些事情):
We can do this on the entire DataFrame by returning just the index for each group (this assumes that the index is unique, if it's not reset_index
prior to doing this... you may be able to do something slightly more lower level):
In [21]: [list(permutations(grp.index)) for (name, grp) in age]
Out[21]: [[(1,)], [(2, 4), (4, 2)], [(3,)], [(0,)]]
In [22]: list(product(*[(permutations(grp.index)) for (name, grp) in age]))
Out[22]: [((1,), (2, 4), (3,), (0,)), ((1,), (4, 2), (3,), (0,))]
我们可以将它们加起来:
We can glue these up with sum:
In [23]: [sum(tups, ()) for tups in product(*[(permutations(grp.index)) for (name, grp) in age])]
Out[23]: [(1, 2, 4, 3, 0), (1, 4, 2, 3, 0)]
如果将这些列为列表,则可以应用loc(这将为您提供所需的结果):
If you make these a list you can apply loc (which gets you the desired result):
In [24]: [df.loc[list(sum(tups, ()))] for tups in product(*[list(permutations(grp.index)) for (name, grp) in age])]
Out[24]:
[ age name
1 20 Bob
2 21 Chris
4 21 Evan
3 22 David
0 28 Abe, age name
1 20 Bob
4 21 Evan
2 21 Chris
3 22 David
0 28 Abe]
以及名称"列(的列表):
And the (list of) the name column:
In [25]: [list(df.loc[list(sum(tups, ())), "name"]) for tups in product(*[(permutations(grp.index)) for (name, grp) in age])]
Out[25]:
[['Bob', 'Chris', 'Evan', 'David', 'Abe'],
['Bob', 'Evan', 'Chris', 'David', 'Abe']]
注意:使用 numpy置换矩阵和pd.tools.util.cartesian_product
.我怀疑这太多了,除非它变得非常慢(除非它可能会变慢,因为可能会有很多排列),否则它不会进行探索.
Note: It may be faster to use a numpy permutation matrix and pd.tools.util.cartesian_product
. I suspect it's much of a muchness and wouldn't explore this unless this was unusably slow (it's potentially going to be slow anyway because there could be many many permutations)...