基于pandas中的多个键合并两个DataFrame [英] Merge two DataFrames based on multiple keys in pandas

问题描述

pandas(或其他模块)是否有支持基于多个键合并(或连接)两个表的功能?

例如，我有两个表(DataFrames)a 和 b:

<预><代码>>>>一个A B 值11 1 231 2 342 1 23422 2 333>>>乙A B值21 1 0.101 2 0.202 1 0.132 2 0.33

想要的结果是:

A B value1 value21 1 23 0.101 2 34 0.202 1 2342 0.132 2 333 0.33

解决方案

要通过多个键进行合并，只需将列表中的键传递给 pd.merge:

<预><代码>>>>pd.merge(a, b, on=['A', 'B'])A B 值1 值20 1 1 23 0.101 1 2 34 0.202 2 1 2342 0.133 2 2 333 0.33

事实上，pd.merge 的默认值是使用两个 DataFrames 的列标签的交集，所以 pd.merge(a, b) 会起作用在这种情况下同样好.

Does pandas (or another module) have any functions to support merge (or join) two tables based on multiple keys?

For example, I have two tables (DataFrames) a and b:

>>> a
A  B  value1
1  1      23
1  2      34
2  1    2342
2  2     333

>>> b
A  B  value2
1  1    0.10
1  2    0.20
2  1    0.13
2  2    0.33

The desired result is:

A  B  value1  value2
1  1      23    0.10
1  2      34    0.20
2  1    2342    0.13
2  2     333    0.33

解决方案

To merge by multiple keys, you just need to pass the keys in a list to pd.merge:

>>> pd.merge(a, b, on=['A', 'B'])
   A  B  value1  value2
0  1  1      23    0.10
1  1  2      34    0.20
2  2  1    2342    0.13
3  2  2     333    0.33

In fact, the default for pd.merge is to use the intersection of the two DataFrames' column labels, so pd.merge(a, b) would work equally well in this case.

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