在Pandas中加入和合并有什么区别? [英] What is the difference between join and merge in Pandas?
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问题描述
假设我有两个这样的DataFrame:
Suppose I have two DataFrames like so:
left = pd.DataFrame({'key1': ['foo', 'bar'], 'lval': [1, 2]})
right = pd.DataFrame({'key2': ['foo', 'bar'], 'rval': [4, 5]})
我想合并它们,所以我尝试这样的事情:
I want to merge them, so I try something like this:
pd.merge(left, right, left_on='key1', right_on='key2')
我很高兴
key1 lval key2 rval
0 foo 1 foo 4
1 bar 2 bar 5
但是我正在尝试使用join方法,我被认为这是非常相似的.
But I'm trying to use the join method, which I've been lead to believe is pretty similar.
left.join(right, on=['key1', 'key2'])
我明白了:
//anaconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in _validate_specification(self)
406 if self.right_index:
407 if not ((len(self.left_on) == self.right.index.nlevels)):
--> 408 raise AssertionError()
409 self.right_on = [None] * n
410 elif self.right_on is not None:
AssertionError:
我想念什么?
推荐答案
我总是在索引上使用join
:
import pandas as pd
left = pd.DataFrame({'key': ['foo', 'bar'], 'val': [1, 2]}).set_index('key')
right = pd.DataFrame({'key': ['foo', 'bar'], 'val': [4, 5]}).set_index('key')
left.join(right, lsuffix='_l', rsuffix='_r')
val_l val_r
key
foo 1 4
bar 2 5
使用以下列中的merge
可以具有相同的功能:
The same functionality can be had by using merge
on the columns follows:
left = pd.DataFrame({'key': ['foo', 'bar'], 'val': [1, 2]})
right = pd.DataFrame({'key': ['foo', 'bar'], 'val': [4, 5]})
left.merge(right, on=('key'), suffixes=('_l', '_r'))
key val_l val_r
0 foo 1 4
1 bar 2 5
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