concat dataframe pandas

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showing results for - "concat dataframe pandas"
Erika
16 Apr 2017
1In [1]: s1 = pd.Series([1, 2], index=['A', 'B'], name='s1')
2
3In [2]: s2 = pd.Series([3, 4], index=['A', 'B'], name='s2')
4
5In [3]: pd.concat([s1, s2], axis=1)
6Out[3]:
7   s1  s2
8A   1   3
9B   2   4
10
11In [4]: pd.concat([s1, s2], axis=1).reset_index()
12Out[4]:
13  index  s1  s2
140     A   1   3
151     B   2   4
16
source
Natalia
21 Apr 2018
1import pandas as pd
2df = pd.concat(list_of_dataframes)
source
Brynn
21 Mar 2018
1# Concating Means putting frames on bottom of one another
2#              ---   ---
3#              |  df1  |
4#              |  df2  |
5# Concating => |   .   |
6#              |   .   |
7#              |  dfn  |
8#              ---   ---
9# Command : pd.concat([df1,df2,...,dfn])   ; df = a dataframe
10
11				 	''':::Eaxmple;::'''
12df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
13                    'B': ['B0', 'B1', 'B2', 'B3'],
14                    'C': ['C0', 'C1', 'C2', 'C3'],
15                    'D': ['D0', 'D1', 'D2', 'D3']},
16                     index=[0, 1, 2, 3])
17
18df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
19                    'B': ['B4', 'B5', 'B6', 'B7'],
20                    'C': ['C4', 'C5', 'C6', 'C7'],
21                    'D': ['D4', 'D5', 'D6', 'D7']},
22                     index=[4, 5, 6, 7])
23
24df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
25                    'B': ['B8', 'B9', 'B10', 'B11'],
26                    'C': ['C8', 'C9', 'C10', 'C11'],
27                    'D': ['D8', 'D9', 'D10', 'D11']},
28                     index=[8, 9, 10, 11])
29
30frames = [df1, df2, df3]
31
32result = pd.concat(frames)
33
34# Note : use ignore_index=True if you need it in pd.concat
source
Matthew
27 Jan 2016
1# provide list of dataframes 
2res = pd.concat([df1, df2])
source
Laila
24 Jan 2021
1>>> s1 = pd.Series(['a', 'b'])
2>>> s2 = pd.Series(['c', 'd'])
3>>> pd.concat([s1, s2])
40    a
51    b
60    c
71    d
8dtype: object
9
source
Carla
09 Apr 2016
1In [1]: df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
2   ...:                     'B': ['B0', 'B1', 'B2', 'B3'],
3   ...:                     'C': ['C0', 'C1', 'C2', 'C3'],
4   ...:                     'D': ['D0', 'D1', 'D2', 'D3']},
5   ...:                    index=[0, 1, 2, 3])
6   ...: 
7
8In [2]: df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
9   ...:                     'B': ['B4', 'B5', 'B6', 'B7'],
10   ...:                     'C': ['C4', 'C5', 'C6', 'C7'],
11   ...:                     'D': ['D4', 'D5', 'D6', 'D7']},
12   ...:                    index=[4, 5, 6, 7])
13   ...: 
14
15In [3]: df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
16   ...:                     'B': ['B8', 'B9', 'B10', 'B11'],
17   ...:                     'C': ['C8', 'C9', 'C10', 'C11'],
18   ...:                     'D': ['D8', 'D9', 'D10', 'D11']},
19   ...:                    index=[8, 9, 10, 11])
20   ...: 
21
22In [4]: frames = [df1, df2, df3]
23
24In [5]: result = pd.concat(frames)
25
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