合并具有重复值的数据帧上的项目 [英] Merge items on dataframes with duplicate values
问题描述
所以我有一个数据框(或系列),其中每一列A"总是出现 4 次,如下所示:
So I have a dataframe (or series) where there are always 4 occurrences of each of column 'A', like this:
df = pd.DataFrame([['foo'],
['foo'],
['foo'],
['foo'],
['bar'],
['bar'],
['bar'],
['bar']],
columns=['A'])
A
0 foo
1 foo
2 foo
3 foo
4 bar
5 bar
6 bar
7 bar
我还有另一个数据框,其值类似于 A 列中的值,但它们并不总是有 4 个值.它们还有更多的列,如下所示:
I also have another dataframe, with values like the ones found in column A, but they don't always have 4 values. They also have more columns, like this:
df_key = pd.DataFrame([['foo', 1, 2],
['foo', 3, 4],
['bar', 5, 9],
['bar', 2, 4],
['bar', 1, 9]],
columns=['A', 'B', 'C'])
A B C
0 foo 1 2
1 foo 3 4
2 bar 5 9
3 bar 2 4
4 bar 1 9
我想合并它们,使它们最终像这样使用以下内容:
I wanted to merge them such they end up like this using something like:
df.merge(df_key, how='left', on='A', copy=False)
A B C
0 foo 1 2
1 foo 3 4
2 foo NaN NaN
3 foo NaN NaN
4 bar 5 9
5 bar 2 4
6 bar 1 9
7 bar NaN NaN
但我最终得到了这样的结果.有什么建议吗?
But instead I end up with something like this. Any advice?
A B C
0 foo 1 2
1 foo 3 4
2 foo 1 2
3 foo 3 4
4 foo 1 2
5 foo 3 4
6 foo 1 2
7 foo 3 4
8 bar 5 9
9 bar 2 4
10 bar 1 9
11 bar 5 9
12 bar 2 4
13 bar 1 9
14 bar 5 9
15 bar 2 4
16 bar 1 9
17 bar 5 9
18 bar 2 4
19 bar 1 9
推荐答案
您需要使用 groupby
+ cumcount
创建代理列以删除重复的行,然后包括调用 merge
时的那些列:
You'll need to create surrogate columns with groupby
+ cumcount
to deduplicate your rows, then include those columns when calling merge
:
a = df.assign(D=df.groupby('A').cumcount())
b = df_key.assign(D=df_key.groupby('A').cumcount())
a.merge(b, on=['A', 'D'], how='left').drop('D', 1)
A B C
0 foo 1.0 2.0
1 foo 3.0 4.0
2 foo NaN NaN
3 foo NaN NaN
4 bar 5.0 9.0
5 bar 2.0 4.0
6 bar 1.0 9.0
7 bar NaN NaN
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