自加入 Pandas
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self-join with Pandas
提问by Nucular
I would like to perform a self-join on a Pandas dataframe so that some rows get appended to the original rows. Each row has a marker 'i' indicating which row should get appended to it on the right.
我想对 Pandas 数据帧执行自联接，以便将某些行附加到原始行。每行都有一个标记“i”，指示应在右侧附加哪一行。
d = pd.DataFrame(['A','B','C'], columns = ['some_col'])
d['i'] = [2,1,1]
In [17]: d
Out[17]:
some_col i
0 A 2
1 B 1
2 C 1
Desired output:
期望的输出：
some_col i some_col_y
0 A 2 C
1 B 1 B
2 C 1 B
That is, row 2 gets appended to row 0, row 1 to row 1, row 1 to row 2 (as indicated by i).
也就是说，第 2 行被附加到第 0 行，第 1 行到第 1 行，第 1 行到第 2 行（如 i 所示）。
My idea of how to go about it was
我对如何去做的想法是
pd.merge(d, d, left_index = True, right_on = 'i', how = 'left')
But it produces something else altogether. How to do it correctly?
但它完全产生了别的东西。如何正确地做到这一点？
采纳答案by piRSquared
joinwith on='i'
join和 on='i'
d.join(d.drop('i', 1), on='i', rsuffix='_y')
some_col i some_col_y
0 A 2 C
1 B 1 B
2 C 1 B
回答by MSeifert
Instead of using mergeyou can also use indexing and assignment:
除了使用，merge您还可以使用索引和赋值：
>>> d['new_col'] = d['some_col'][d['i']].values
>>> d
some_col i new_col
0 A 2 C
1 B 1 B
2 C 1 B
回答by MaxU
Try this:
尝试这个：
In [69]: d.join(d.set_index('i'), rsuffix='_y')
Out[69]:
some_col i some_col_y
0 A 2 NaN
1 B 1 B
1 B 1 C
2 C 1 A
or:
或者：
In [64]: pd.merge(d[['some_col']], d, left_index=True, right_on='i', suffixes=['_y','']).sort_index()
Out[64]:
some_col_y some_col i
0 C A 2
1 B B 1
2 B C 1
