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目录1. concat2. append3. merge4. join5. combine总结1. concat concat是pandas中专门用于数据连接合并的函数，功能非常强大
concat是pandas中专门用于数据连接合并的函数，功能非常强大，支持纵向合并和横向合并，默认情况下是纵向合并，具体可以通过参数进行设置。
pd.concat(
objs: 'Iterable[NDFrame] | Mapping[Hashable, NDFrame]',
axis=0,
join='outer',
ignore_index: 'bool' = False,
keys=None,
levels=None,
names=None,
verify_integrity: 'bool' = False,
sort: 'bool' = False,
copy: 'bool' = True,
) -> 'FrameOrSeriesUNIOn'
在函数方法中，各参数含义如下：
接下来，我们就对该函数功能进行演示
基础连接
In [1]: import pandas as pd
In [2]: s1 = pd.Series(['a', 'b'])
In [3]: s2 = pd.Series(['c', 'd'])
In [4]: s1
Out[4]:
0 a
1 b
dtype: object
In [5]: s2
Out[5]:
0 c
1 d
dtype: object
In [6]: pd.concat([s1, s2])
Out[6]:
0 a
1 b
0 c
1 d
dtype: object
In [7]: df1 = pd.DataFrame([['a', 1], ['b', 2]],
...: columns=['letter', 'number'])
In [8]: df2 = pd.DataFrame([['c', 3], ['d', 4]],
...: columns=['letter', 'number'])
In [9]: pd.concat([df1, df2])
Out[9]:
letter number
0 a 1
1 b 2
0 c 3
1 d 4
横向连接
In [10]: pd.concat([df1, df2], axis=1)
Out[10]:
letter number letter number
0 a 1 c 3
1 b 2 d 4
默认情况下，concat是取并集，如果两个数据中有个数据没有对应行或列，则会填充为空值NaN。
合并交集
In [11]: df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
...: columns=['letter', 'number', 'animal'])
In [12]: df1
Out[12]:
letter number
0 a 1
1 b 2
In [13]: df3
Out[13]:
letter number animal
0 c 3 cat
1 d 4 dog
In [14]: pd.concat([df1, df3], join='inner')
Out[14]:
letter number
0 a 1
1 b 2
0 c 3
1 d 4
索引重置（不保留原有索引）
In [15]: pd.concat([df1, df3], join='inner', ignore_index=True)
Out[15]:
letter number
0 a 1
1 b 2
2 c 3
3 d 4
# 以下方式和上述的输出结果等价
In [16]: pd.concat([df1, df3], join='inner').reset_index(drop=True)
Out[16]:
letter number
0 a 1
1 b 2
2 c 3
3 d 4
指定索引
In [17]: pd.concat([df1, df3], keys=['df1','df3'])
Out[17]:
letter number animal
df1 0 a 1 NaN
1 b 2 NaN
df3 0 c 3 cat
1 d 4 dog
In [18]: pd.concat([df1, df3], keys=['df1','df3'], names=['df名称','行ID'])
Out[18]:
letter number animal
df名称 行ID
df1 0 a 1 NaN
1 b 2 NaN
df3 0 c 3 cat
1 d 4 dog
检测重复
如果索引出现重复，则无法通过检测，会报错
In [19]: pd.concat([df1, df3], verify_integrity=True)
Traceback (most recent call last):
...
ValueError: Indexes have overlapping values: Int64Index([0, 1], dtype='int64')
合并并集下columns排序
In [21]: pd.concat([df1, df3], sort=True)
Out[21]:
animal letter number
0 NaN a 1
1 NaN b 2
0 cat c 3
1 dog d 4
DataFrame与Series合并
In [22]: pd.concat([df1, s1])
Out[22]:
letter number 0
0 a 1.0 NaN
1 b 2.0 NaN
0 NaN NaN a
1 NaN NaN b
In [23]: pd.concat([df1, s1], axis=1)
Out[23]:
letter number 0
0 a 1 a
1 b 2 b
# 新增列一般可选以下两种方式
In [24]: df1.assign(新增列=s1)
Out[24]:
letter number 新增列
0 a 1 a
1 b 2 b
In [25]: df1['新增列'] = s1
In [26]: df1
Out[26]:
letter number 新增列
0 a 1 a
1 b 2 b
以上就concat函数方法的一些功能，相比之下，另外一个函数append也可以用于数据追加（纵向合并）
append主要用于追加数据，是比较简单直接的数据合并方式。
df.append(
other,
ignore_index: 'bool' = False,
verify_integrity: 'bool' = False,
sort: 'bool' = False,
) -> 'DataFrame'
在函数方法中，各参数含义如下：
接下来，我们就对该函数功能进行演示
基础追加
In [41]: df1.append(df2)
Out[41]:
letter number
0 a 1
1 b 2
0 c 3
1 d 4
In [42]: df1.append([df1,df2,df3])
Out[42]:
letter number animal
0 a 1 NaN
1 b 2 NaN
0 a 1 NaN
1 b 2 NaN
0 c 3 NaN
1 d 4 NaN
0 c 3 cat
1 d 4 dog
columns重置（不保留原有索引）
In [43]: df1.append([df1,df2,df3], ignore_index=True)
Out[43]:
letter number animal
0 a 1 NaN
1 b 2 NaN
2 a 1 NaN
3 b 2 NaN
4 c 3 NaN
5 d 4 NaN
6 c 3 cat
7 d 4 dog
检测重复
如果索引出现重复，则无法通过检测，会报错
In [44]: df1.append([df1,df2], verify_integrity=True)
Traceback (most recent call last):
...
ValueError: Indexes have overlapping values: Int64Index([0, 1], dtype='int64')
索引排序
In [46]: df1.append([df1,df2,df3], sort=True)
Out[46]:
animal letter number
0 NaN a 1
1 NaN b 2
0 NaN a 1
1 NaN b 2
0 NaN c 3
1 NaN d 4
0 cat c 3
1 dog d 4
追加Series
In [49]: s = pd.Series({'letter':'s1','number':9})
In [50]: s
Out[50]:
letter s1
number 9
dtype: object
In [51]: df1.append(s)
Traceback (most recent call last):
...
TypeError: Can only append a Series if ignore_index=True or if the Series has a name
In [53]: df1.append(s, ignore_index=True)
Out[53]:
letter number
0 a 1
1 b 2
2 s1 9
追加字典
这个在爬虫的时候比较好使，每爬取一条数据就合并到DataFrame类似数据中存储起来
In [54]: dic = {'letter':'s1','number':9}
In [55]: df1.append(dic, ignore_index=True)
Out[55]:
letter number
0 a 1
1 b 2
2 s1 9merge函数方法类似sql里的join，可以是pd.merge或者df.merge，区别就在于后者待合并的数据是
pd.merge(
left: 'DataFrame | Series',
right: 'DataFrame | Series',
how: 'str' = 'inner',
on: 'IndexLabel | None' = None,
left_on: 'IndexLabel | None' = None,
right_on: 'IndexLabel | None' = None,
left_index: 'bool' = False,
right_index: 'bool' = False,
sort: 'bool' = False,
suffixes: 'Suffixes' = ('_x', '_y'),
copy: 'bool' = True,
indicator: 'bool' = False,
validate: 'str | None' = None,
) -> 'DataFrame'
在函数方法中，关键参数含义如下：
接下来，我们就对该函数功能进行演示
基础合并
In [55]: df1 = pd.DataFrame({'key': ['foo', 'bar', 'bal'],
...: 'value2': [1, 2, 3]})
In [56]: df2 = pd.DataFrame({'key': ['foo', 'bar', 'baz'],
...: 'value1': [5, 6, 7]})
In [57]: df1.merge(df2)
Out[57]:
key value2 value1
0 foo 1 5
1 bar 2 6
其他连接方式
In [58]: df1.merge(df2, how='left')
Out[58]:
key value2 value1
0 foo 1 5.0
1 bar 2 6.0
2 bal 3 NaN
In [59]: df1.merge(df2, how='right')
Out[59]:
key value2 value1
0 foo 1.0 5
1 bar 2.0 6
2 baz NaN 7
In [60]: df1.merge(df2, how='outer')
Out[60]:
key value2 value1
0 foo 1.0 5.0
1 bar 2.0 6.0
2 bal 3.0 NaN
3 baz NaN 7.0
In [61]: df1.merge(df2, how='cross')
Out[61]:
key_x value2 key_y value1
0 foo 1 foo 5
1 foo 1 bar 6
2 foo 1 baz 7
3 bar 2 foo 5
4 bar 2 bar 6
5 bar 2 baz 7
6 bal 3 foo 5
7 bal 3 bar 6
8 bal 3 baz 7
指定连接键
可以指定单个连接键，也可以指定多个连接键
In [62]: df1 = pd.DataFrame({'lkey1': ['foo', 'bar', 'bal'],
...: 'lkey2': ['a', 'b', 'c'],
...: 'value2': [1, 2, 3]})
In [63]: df2 = pd.DataFrame({'rkey1': ['foo', 'bar', 'baz'],
...: 'rkey2': ['a', 'b', 'c'],
...: 'value2': [5, 6, 7]})
In [64]: df1
Out[64]:
lkey1 lkey2 value2
0 foo a 1
1 bar b 2
2 bal c 3
In [65]: df2
Out[65]:
rkey1 rkey2 value2
0 foo a 5
1 bar b 6
2 baz c 7
In [66]: df1.merge(df2, left_on='lkey1', right_on='rkey1')
Out[66]:
lkey1 lkey2 value2_x rkey1 rkey2 value2_y
0 foo a 1 foo a 5
1 bar b 2 bar b 6
In [67]: df1.merge(df2, left_on=['lkey1','lkey2'], right_on=['rkey1','rkey2'])
Out[67]:
lkey1 lkey2 value2_x rkey1 rkey2 value2_y
0 foo a 1 foo a 5
1 bar b 2 bar b 6
指定索引为键
Out[68]: df1.merge(df2, left_index=True, right_index=True)
Out[68]:
lkey1 lkey2 value2_x rkey1 rkey2 value2_y
0 foo a 1 foo a 5
1 bar b 2 bar b 6
2 bal c 3 baz c 7
设置重复列后缀
In [69]: df1.merge(df2, left_on='lkey1', right_on='rkey1', suffixes=['左','右'])
Out[69]:
lkey1 lkey2 value2左 rkey1 rkey2 value2右
0 foo a 1 foo a 5
1 bar b 2 bar b 6
连接指示
新增一列用于显示数据来源
In [70]: df1.merge(df2, left_on='lkey1', right_on='rkey1', suffixes=['左','右'], how='outer',
...: indicator=True
...: )
Out[70]:
lkey1 lkey2 value2左 rkey1 rkey2 value2右 _merge
0 foo a 1.0 foo a 5.0 both
1 bar b 2.0 bar b 6.0 both
2 bal c 3.0 NaN NaN NaN left_only
3 NaN NaN NaN baz c 7.0 right_only
join就有点想append之于concat，用于数据合并
df.join(
other: 'FrameOrSeriesUnion',
on: 'IndexLabel | None' = None,
how: 'str' = 'left',
lsuffix: 'str' = '',
rsuffix: 'str' = '',
sort: 'bool' = False,
) -> 'DataFrame'
在函数方法中，关键参数含义如下：
接下来，我们就对该函数功能进行演示
In [71]: df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
...: 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
In [72]: other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...: 'B': ['B0', 'B1', 'B2']})
In [73]: df
Out[73]:
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K3 A3
4 K4 A4
5 K5 A5
In [74]: other
Out[74]:
key B
0 K0 B0
1 K1 B1
2 K2 B2
In [75]: df.join(other, on='key')
Traceback (most recent call last):
...
ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat
如果想用key关键字， 则需要key是索引。。。
指定key
In [76]: df.set_index('key').join(other.set_index('key'))
Out[76]:
A B
key
K0 A0 B0
K1 A1 B1
K2 A2 B2
K3 A3 NaN
K4 A4 NaN
K5 A5 NaN
In [77]: df.join(other.set_index('key'), on='key')
Out[77]:
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 NaN
4 K4 A4 NaN
5 K5 A5 NaN
指定重复列后缀
In [78]: df.join(other, lsuffix='_左', rsuffix='右')
Out[78]:
key_左 A key右 B
0 K0 A0 K0 B0
1 K1 A1 K1 B1
2 K2 A2 K2 B2
3 K3 A3 NaN NaN
4 K4 A4 NaN NaN
5 K5 A5 NaN NaN
其他参数就不多做介绍了，和merge基本一样。
在数据合并的过程中，我们可能需要对对应位置的值进行一定的计算，pandas提供了combine和combine_first函数方法来进行这方面的合作操作。
df.combine(
other: 'DataFrame',
func,
fill_value=None,
overwrite: 'bool' = True,
) -> 'DataFrame'
比如，数据合并的时候取单元格最小的值
In [79]: df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
In [80]: df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
In [81]: df1
Out[81]:
A B
0 0 4
1 0 4
In [82]: df2
Out[82]:
A B
0 1 3
1 1 3
In [83]: take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
In [84]: df1.combine(df2, take_smaller)
Out[84]:
A B
0 0 3
1 0 3
# 也可以调用numpy的函数
In [85]: import numpy as np
In [86]: df1.combine(df2, np.minimum)
Out[86]:
A B
0 0 3
1 0 3
fill_value填充缺失值
In [87]: df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
In [87]: df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
In [88]: df1
Out[88]:
A B
0 0 NaN
1 0 4.0
In [89]: df2
Out[89]:
A B
0 1 3
1 1 3
In [90]: df1.combine(df2, take_smaller, fill_value=-88)
Out[90]:
A B
0 0 -88.0
1 0 4.0
overwrite=False保留
In [91]: df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
In [92]: df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
In [93]: df1
Out[93]:
A B
0 0 4
1 0 4
In [94]: df2
Out[94]:
B C
1 3 -10
2 3 1
In [95]: df1.combine(df2, take_smaller)
Out[95]:
A B C
0 NaN NaN NaN
1 NaN 3.0 -10.0
2 NaN 3.0 1.0
# 保留A列原有的值
In [96]: df1.combine(df2, take_smaller, overwrite=False)
Out[96]:
A B C
0 0.0 NaN NaN
1 0.0 3.0 -10.0
2 NaN 3.0 1.0
另外一个combine_first
df.combine_first(other: 'DataFrame') -> 'DataFrame'
当df中元素为空采用other里的进行替换，结果为并集合并
In [97]: df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
In [98]: df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
In [99]: df1
Out[99]:
A B
0 NaN NaN
1 0.0 4.0
In [100]: df2
Out[100]:
A B
0 1 3
1 1 3
In [101]: df1.combine_first(df2)
Out[101]:
A B
0 1.0 3.0
1 0.0 4.0
In [102]: df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
In [103]: df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
In [104]: df1
Out[104]:
A B
0 NaN 4.0
1 0.0 NaN
In [105]: df2
Out[105]:
B C
1 3 1
2 3 1
In [106]: df1.combine_first(df2)
Out[106]:
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
以上就本次介绍的关于Pandas数据合并的全部内容，相比之下我们可以发现：
到此这篇关于python必备技巧之Pandas数据合并函数的文章就介绍到这了,更多相关Pandas数据合并内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！
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