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1.前言
join操作是关系型数据库中最核心没有之一的操作，实际中最容易出问题，经常需要优化的点也是join操作。如果我们将dataframe类比为一张表，自然也会涉及到join操作，而且非常非常常见。下面我们就来仔细看看pandas中的join用法。
2.join方法原型
pandas源码中join方法的签名如下
def join(
self, other, on=None, how="left", lsuffix="", rsuffix="", sort=False
) -> "DataFrame":
"""
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.
Parameters
----------
other : DataFrame, Series, or list of DataFrame
Index should be similar to one of the columns in this one. If a
Series is passed, its name attribute must be set, and that will be
used as the column name in the resulting joined DataFrame.
on : str, list of str, or array-like, optional
Column or index level name(s) in the caller to join on the index
in `other`, otherwise joins index-on-index. If multiple
values given, the `other` DataFrame must have a MultiIndex. Can
pass an array as the join key if it is not already contained in
the calling DataFrame. Like an Excel VLOOKUP operation.
how : {'left', 'right', 'outer', 'inner'}, default 'left'
How to handle the operation of the two objects.
* left: use calling frame's index (or column if on is specified)
* right: use `other`'s index.
* outer: form union of calling frame's index (or column if on is
specified) with `other`'s index, and sort it.
lexicographically.
* inner: form intersection of calling frame's index (or column if
on is specified) with `other`'s index, preserving the order
of the calling's one.
lsuffix : str, default ''
Suffix to use from left frame's overlapping columns.
rsuffix : str, default ''
Suffix to use from right frame's overlapping columns.
sort : bool, default False
Order result DataFrame lexicographically by the join key. If False,
the order of the join key depends on the join type (how keyword).
Returns
-------
DataFrame
A dataframe containing columns from both the caller and `other`.
def join(self, other, on=None, how=“left”, lsuffix="“, rsuffix=”", sort=False)
其中
other：DataFrame, Series, or list of DataFrame，另外一个dataframe, series，或者dataframe list。
on: 参与join的列，与sql中的on参数类似。
how: {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘left’， 与sql中的join方式类似。
lsuffix: 左DataFrame中重复列的后缀
rsuffix: 右DataFrame中重复列的后缀
sort: 按字典序对结果在连接键上排序
3.按指定列进行join
实际中最常见的join方式为按某个相同列进行join。我们先来尝试一个简单的join实例
import pandas as pd
def joindemo():
age_df = pd.DataFrame({'name': ['lili', 'lucy', 'tracy', 'mike'],
'age': [18, 28, 24, 36]})
score_df = pd.DataFrame({'name': ['tony', 'mike', 'akuda', 'tracy'],
'score': ['A', 'B', 'C', 'B']})
result = age_df.join(score_df, on='name')
print(result)上面的代码会报如下错误：
ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat原因在于，join的时候会根据dataframe的索引进行。如果不理解，下面看一段测试代码就明白
def joindemo2():
age_df = pd.DataFrame({'name': ['lili', 'lucy', 'tracy', 'mike'],
'age': [18, 28, 24, 36]})
score_df = pd.DataFrame({'name': ['tony', 'mike', 'akuda', 'tracy'],
'score': ['A', 'B', 'C', 'B']})
print(age_df)
age_df.set_index('name', inplace=True)
print(age_df)上面这段代码运行的结果如下
name age
0 lili 18
1 lucy 28
2 tracy 24
3 mike 36
age
name
lili 18
lucy 28
tracy 24
mike 36dataframe默认的index是从0开始递增的整数，前面的数字0,1,2,3表示的就是index。如果我们指定index为name，输出的dataframe结构就发生了改变，前面递增的数字就没有了。
如果要实现最开始的join需求，可以按如下代码
def joindemo():
age_df = pd.DataFrame({'name': ['lili', 'lucy', 'tracy', 'mike'],
'age': [18, 28, 24, 36]})
score_df = pd.DataFrame({'name': ['tony', 'mike', 'akuda', 'tracy'],
'score': ['A', 'B', 'C', 'B']})
age_df.set_index('name', inplace=True)
score_df.set_index('name', inplace=True)
result = age_df.join(score_df, on='name')
print(result)代码的输出结果为
age score
name
lili 18 NaN
lucy 28 NaN
tracy 24 B
mike 36 B默认的为left join，这就实现了我们上面的需求。
4.按默认自增index进行join
如果想按默认的自增index进行join，我们接下来进行尝试。
def joindemo():
age_df = pd.DataFrame({'name': ['lili', 'lucy', 'tracy', 'mike'],
'age': [18, 28, 24, 36]})
score_df = pd.DataFrame({'name': ['tony', 'mike', 'akuda', 'tracy'],
'score': ['A', 'B', 'C', 'B']})
result = age_df.join(score_df)
print(result)上面的代码也会报错
ValueError: columns overlap but no suffix specified: Index(['name'], dtype='object')这个时候，就需要lsuffix,rsuffix参数了
def joindemo():
age_df = pd.DataFrame({'name': ['lili', 'lucy', 'tracy', 'mike'],
'age': [18, 28, 24, 36]})
score_df = pd.DataFrame({'name': ['tony', 'mike', 'akuda', 'tracy'],
'score': ['A', 'B', 'C', 'B']})
result = age_df.join(score_df, lsuffix='_left', rsuffix='_right')
print(result) name_left age name_right score
0 lili 18 tony A
1 lucy 28 mike B
2 tracy 24 akuda C
3 mike 36 tracy B5.与merge的区别
pandas中还有merge方法，也能实现join的功能。他们的具体区别，可以参考如下链接：
https://stackoverflow.com/questions/22676081/what-is-the-difference-between-join-and-merge-in-pandas