The concat() function of pandas can be used to concatenate pandas dataframes along a particular axis. You can check the list of arguments of this function in the link given under concat().
The parameter 'axis' is used to determine the axis to concatenate along: {0 - concatenate as new rows, 1-concatenate as new columns}. The default is 0.
Here is an example:
import pandas as pd
df1 = pd.DataFrame({"name": ['AA', 'BB', 'CC', 'DD', 'EE', 'HH', 'II'], "age": [34, 12, 56, 43, 23, 41, 52]})
df2 = pd.DataFrame({"name": ['AA', 'BB', 'CC', 'DD', 'EE', 'FF', 'GG'], "income": [3434, 1122, 2156, 4334, 54523, 4321, 6541]})
df = pd.concat([df1, df2], axis=0)
print(df)
The above code will print the following output. Since df1 and df2 don't have the same columns, it created a union of columns from both dataframes and put 'NaN' for the missing values.
name age income
0 AA 34.0 NaN
1 BB 12.0 NaN
2 CC 56.0 NaN
3 DD 43.0 NaN
4 EE 23.0 NaN
5 HH 41.0 NaN
6 II 52.0 NaN
0 AA NaN 3434.0
1 BB NaN 1122.0
2 CC NaN 2156.0
3 DD NaN 4334.0
4 EE NaN 54523.0
5 FF NaN 4321.0
6 GG NaN 6541.0
import pandas as pd
df1 = pd.DataFrame({"name": ['AA', 'BB', 'CC', 'DD', 'EE', 'HH', 'II'], "age": [34, 12, 56, 43, 23, 41, 52]})
df2 = pd.DataFrame({"name": ['AA', 'BB', 'CC', 'DD', 'EE', 'FF', 'GG'], "income": [3434, 1122, 2156, 4334, 54523, 4321, 6541]})
df = pd.concat([df1, df2], axis=1)
print(df)
The above code will print the following output. Since df1 and df2 have a common column, it printed duplicate columns.
name age name income
0 AA 34 AA 3434
1 BB 12 BB 1122
2 CC 56 CC 2156
3 DD 43 DD 4334
4 EE 23 EE 54523
5 HH 41 FF 4321
6 II 52 GG 6541
