The merge() function of pandas can be used to merge two dataframes. You can check the list of arguments of this function in the link given under merge().
The parameter "on" is used to provide the name of the common column. Since this function works like database-style join, you can specify one of these values {'left', 'right', 'outer', 'inner', 'cross'} for the parameter "how".
Here is an example with different values of the parameter "how".
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 = df1.merge(df2, how='inner', on='name')
print(df)
The output of the above code with how='inner', i.e., inner-join. It uses the intersection of keys from both dataframes.
name age income
0 AA 34 3434
1 BB 12 1122
2 CC 56 2156
3 DD 43 4334
4 EE 23 54523
df = df1.merge(df2, how='outer', on='name')
The output of the above code with how='outer', i.e., outer-join. It uses the union of keys from both dataframes.
name age income
0 AA 34.0 3434.0
1 BB 12.0 1122.0
2 CC 56.0 2156.0
3 DD 43.0 4334.0
4 EE 23.0 54523.0
5 HH 41.0 NaN
6 II 52.0 NaN
7 FF NaN 4321.0
8 GG NaN 6541.0
df = df1.merge(df2, how='left', on='name')
The output of the above code with how='left', i.e., left-join. It uses only keys from the left dataframe.
name age income
0 AA 34 3434.0
1 BB 12 1122.0
2 CC 56 2156.0
3 DD 43 4334.0
4 EE 23 54523.0
5 HH 41 NaN
6 II 52 NaN
df = df1.merge(df2, how='right', on='name')
The output of the above code with how='right', i.e., right-join. It uses only keys from the right dataframe.
name age income
0 AA 34.0 3434
1 BB 12.0 1122
2 CC 56.0 2156
3 DD 43.0 4334
4 EE 23.0 54523
5 FF NaN 4321
6 GG NaN 6541
