Pandas의 join과 merge의 차이점은 무엇입니까?

Pandas의 join과 merge의 차이점은 무엇입니까?

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두 개의 DataFrame이 있다고 가정합니다.

left = pd.DataFrame({'key1': ['foo', 'bar'], 'lval': [1, 2]})

right = pd.DataFrame({'key2': ['foo', 'bar'], 'rval': [4, 5]})

그것들을 병합하고 싶기 때문에 다음과 같이 시도하십시오.

pd.merge(left, right, left_on='key1', right_on='key2')

그리고 나는 행복하다

    key1    lval    key2    rval
0   foo     1       foo     4
1   bar     2       bar     5

그러나 나는 조인 방법을 사용하려고 노력하고 있습니다.

left.join(right, on=['key1', 'key2'])

그리고 나는 이것을 얻는다 :

//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: 

내가 무엇을 놓치고 있습니까?

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나는 항상 join인덱스에 사용 합니다.

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

merge다음 열 을 사용하여 동일한 기능을 수행 할 수 있습니다 .

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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pandas.merge() 모든 병합 / 결합 동작에 사용되는 기본 함수입니다.

DataFrame은의 기능에 액세스하는 편리한 방법으로 pandas.DataFrame.merge()및 pandas.DataFrame.join()메소드를 제공합니다 pandas.merge(). 예를 df1.merge(right=df2, ...)들어와 같습니다 pandas.merge(left=df1, right=df2, ...).

와의 주요 차이점은 다음 df.join()과 df.merge()같습니다.

1. 오른쪽 테이블에서 찾아보기 : df1.join(df2)항상의 인덱스를 통해 조인 df2하지만 df1.merge(df2)하나 이상의 열 df2(기본값) 또는 인덱스 df2(with right_index=True)에 조인 할 수 있습니다 .
2. 왼쪽 룩업 테이블에 기본적으로 df1.join(df2)의 사용 지수 df1와 df1.merge(df2)의 사용 칼럼 (들) df1. 즉 지정하여 대체 할 수 있습니다 df1.join(df2, on=key_or_keys)또는 df1.merge(df2, left_index=True).
3. 내부 대에 가입 왼쪽 : df1.join(df2)왼쪽은 기본적으로 가입 않습니다 (모든 행을 유지 df1)하지만, df.merge내부는 기본적으로 가입하지 (만 반환의 행을 일치 df1하고 df2).

So, the generic approach is to use pandas.merge(df1, df2) or df1.merge(df2). But for a number of common situations (keeping all rows of df1 and joining to an index in df2), you can save some typing by using df1.join(df2) instead.

Some notes on these issues from the documentation at http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging:

merge is a function in the pandas namespace, and it is also available as a DataFrame instance method, with the calling DataFrame being implicitly considered the left object in the join.

The related DataFrame.join method, uses merge internally for the index-on-index and index-on-column(s) joins, but joins on indexes by default rather than trying to join on common columns (the default behavior for merge). If you are joining on index, you may wish to use DataFrame.join to save yourself some typing.

...

These two function calls are completely equivalent:

left.join(right, on=key_or_keys)
pd.merge(left, right, left_on=key_or_keys, right_index=True, how='left', sort=False)

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I believe that join() is just a convenience method. Try df1.merge(df2) instead, which allows you to specify left_on and right_on:

In [30]: left.merge(right, left_on="key1", right_on="key2")
Out[30]: 
  key1  lval key2  rval
0  foo     1  foo     4
1  bar     2  bar     5

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http://pandas.pydata.org/pandas-docs/stable/merging.html#brief-primer-on-merge-methods-relational-algebra

pandas provides a single function, merge, as the entry point for all standard database join operations between DataFrame objects:

merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=True, suffixes=('_x', '_y'), copy=True, indicator=False)

And :

DataFrame.join is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single result DataFrame. Here is a very basic example: The data alignment here is on the indexes (row labels). This same behavior can be achieved using merge plus additional arguments instructing it to use the indexes: result = pd.merge(left, right, left_index=True, right_index=True, how='outer')

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One of the difference is that merge is creating a new index, and join is keeping the left side index. It can have a big consequence on your later transformations if you wrongly assume that your index isn't changed with merge.

For example:

import pandas as pd

df1 = pd.DataFrame({'org_index': [101, 102, 103, 104],
                    'date': [201801, 201801, 201802, 201802],
                    'val': [1, 2, 3, 4]}, index=[101, 102, 103, 104])
df1

       date  org_index  val
101  201801        101    1
102  201801        102    2
103  201802        103    3
104  201802        104    4

-

df2 = pd.DataFrame({'date': [201801, 201802], 'dateval': ['A', 'B']}).set_index('date')
df2

       dateval
date          
201801       A
201802       B

-

df1.merge(df2, on='date')

     date  org_index  val dateval
0  201801        101    1       A
1  201801        102    2       A
2  201802        103    3       B
3  201802        104    4       B

-

df1.join(df2, on='date')
       date  org_index  val dateval
101  201801        101    1       A
102  201801        102    2       A
103  201802        103    3       B
104  201802        104    4       B

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• Join: Default Index (If any same column name then it will throw an error in default mode because u have not defined lsuffix or rsuffix))

df_1.join(df_2)

• Merge: Default Same Column Names (If no same column name it will throw an error in default mode)

df_1.merge(df_2)

• on parameter has different meaning in both cases

df_1.merge(df_2, on='column_1')

df_1.join(df_2, on='column_1') // It will throw error
df_1.join(df_2.set_index('column_1'), on='column_1')

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To put it analogously to SQL "Pandas merge is to outer/inner join and Pandas join is to natural join". Hence when you use merge in pandas, you want to specify which kind of sqlish join you want to use whereas when you use pandas join, you really want to have a matching column label to ensure it joins

참고URL : https://stackoverflow.com/questions/22676081/what-is-the-difference-between-join-and-merge-in-pandas