Pandas Concat() | Mastering in Python Pandas Library
Pandas concate() Function in Python
import pandas as pd
sr1 = pd.Series([0,1,2]) sr1
Output >>>
0 0
1 1
2 2
dtype: int64
sr2 = pd.Series([3,4,5,6,7]) sr2
Output >>>
0 3
1 4
2 5
3 6
4 7
dtype: int64
pd.concat([sr1, sr2])
Output >>>
0 0
1 1
2 2
0 3
1 4
2 5
3 6
4 7
dtype: int64
df1 = pd.DataFrame({'ID': [1,2,3,4],
'Name': ['A', 'B', 'C','D'],
'Class': [5,6,7,8]})
df1
Output >>>
ID Name Class
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
df2 = pd.DataFrame({'ID': [5,6,7,8],
'Name': ['E', 'F', 'G', 'H'],
'Class': [9,10,11,12]})
df2
Output >>>
ID Name Class
0 5 E 9
1 6 F 10
2 7 G 11
3 8 H 12
pd.concat([df2, df1])
Output >>>
ID Name Class
0 5 E 9
1 6 F 10
2 7 G 11
3 8 H 12
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
pd.concat([df2, df1], axis = 1)
Output >>>
ID Name Class ID Name Class
0 5 E 9 1 A 5
1 6 F 10 2 B 6
2 7 G 11 3 C 7
3 8 H 12 4 D 8
pd.concat([df1, df2], axis = 0, ignore_index = True)
Output >>>
ID Name Class
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
4 5 E 9
5 6 F 10
6 7 G 11
7 8 H 12
df1 = pd.DataFrame({'ID': [1,2,3,4],
'Name': ['A', 'B', 'C','D'],
'Class': [5,6,7,8]})
df1
Output >>>
ID Name Class
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
df2 = pd.DataFrame({'ID': [3,4],
'Name': ['C','D'],
'Class': [7,8]})
df2
Output >>>
ID Name Class
0 3 C 7
1 4 D 8
pd.concat([df1, df2])
Output >>>
ID Name Class
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
0 3 C 7
1 4 D 8
pd.concat([df1, df2], axis = 1)
Output >>>
ID Name Class ID Name Class
0 1 A 5 3.0 C 7.0
1 2 B 6 4.0 D 8.0
2 3 C 7 NaN NaN NaN
3 4 D 8 NaN NaN NaN
pd.concat([df1, df2], axis = 1, join = 'inner')
Output >>>
ID Name Class ID Name Class
0 1 A 5 3 C 7
1 2 B 6 4 D 8
pd.concat([df1, df2], axis = 1, join_axes = [df1.index])
Output >>>
ID Name Class ID Name Class
0 1 A 5 3.0 C 7.0
1 2 B 6 4.0 D 8.0
2 3 C 7 NaN NaN NaN
3 4 D 8 NaN NaN NaN
pd.concat([df1, df2], axis = 1, join_axes = [df2.index])
Output >>>
ID Name Class ID Name Class
0 1 A 5 3 C 7
1 2 B 6 4 D 8
df1 = pd.DataFrame({'ID': [1,2,3,4],
'Name': ['A', 'B', 'C','D'],
'Class': [5,6,7,8]})
df1
Output >>>
ID Name Class
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
df2 = pd.DataFrame({'ID': [5,6,7,8],
'Name': ['E', 'F', 'G', 'H'],
'Class': [9,10,11,12]})
df2
Output >>>
ID Name Class
0 5 E 9
1 6 F 10
2 7 G 11
3 8 H 12
pd.concat([df1, df2], keys = ['df1','df2'])
Output >>>
ID Name Class
0 1 A 5
df1 1 2 B 6
2 3 C 7
3 4 D 8
0 5 E 9
df2 1 6 F 10
2 7 G 11
3 8 H 12
pd.concat([df1, df2], keys = ['First df','Second df'])
Output >>>
ID Name Class
0 1 A 5
First df 1 2 B 6
2 3 C 7
3 4 D 8
0 5 E 9
Second df 1 6 F 10
2 7 G 11
3 8 H 12
pd.concat([df1, df2], axis = 1, keys = ['First df','Second df'])
Output >>>
First df Second df
ID Name Class ID Name Class
0 1 A 5 5 E 9
1 2 B 6 6 F 10
2 3 C 7 7 G 11
3 4 D 8 8 H 12
df1 = pd.DataFrame({'ID': [1,2,3,4],
'Name': ['A', 'B', 'C','D'],
'Class': [5,6,7,8]})
df1
Output >>>
ID Name Class
0 1 A 5
1 2 B 6
2 3 C 7
3 4 D 8
df2 = pd.DataFrame({'Marks': [40, 63, 91, 34]})
df2
Output >>>
Marks
0 40
1 63
2 91
3 34
pd.concat([df1, df2])
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.
To accept the future behavior, pass 'sort=False'.
To retain the current behavior and silence the warning, pass 'sort=True'.
"""Entry point for launching an IPython kernel.
Output >>>
ID Name Class Marks
0 1.0 A 5.0 NaN
1 2.0 B 6.0 NaN
2 3.0 C 7.0 NaN
3 4.0 D 8.0 NaN
0 NaN NaN NaN 40.0
1 NaN NaN NaN 63.0
2 NaN NaN NaN 91.0
3 NaN NaN NaN 34.0
pd.concat([df1, df2], sort = False)
Output >>>
ID Name Class Marks
0 1.0 A 5.0 NaN
1 2.0 B 6.0 NaN
2 3.0 C 7.0 NaN
3 4.0 D 8.0 NaN
0 NaN NaN NaN 40.0
1 NaN NaN NaN 63.0
2 NaN NaN NaN 91.0
3 NaN NaN NaN 34.0
Download Jupyter file of Pandas Concat Function source code
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