You can use the following basic syntax to perform an inner join in pandas:
import pandas as pd df1.merge(df2, on='column_name', how='inner')
The following example shows how to use this syntax in practice.
Example: How to Do Inner Join in Pandas
Suppose we have the following two pandas DataFrames that contains information about various basketball teams:
import pandas as pd #create DataFrame df1 = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'], 'points': [18, 22, 19, 14, 14, 11, 20, 28]}) df2 = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'G', 'H'], 'assists': [4, 9, 14, 13, 10, 8]}) #view DataFrames print(df1) team points 0 A 18 1 B 22 2 C 19 3 D 14 4 E 14 5 F 11 6 G 20 7 H 28 print(df2) team assists 0 A 4 1 B 9 2 C 14 3 D 13 4 G 10 5 H 8
We can use the following code to perform an inner join, which only keeps the rows where the team name appears in both DataFrames:
#perform left join
df1.merge(df2, on='team', how='inner')
team points assists
0 A 18 4
1 B 22 9
2 C 19 14
3 D 14 13
4 G 20 10
5 H 28 8
The only rows contained in the merged DataFrame are the ones where the team name appears in both DataFrames.
Notice that two teams were dropped (teams E and F) because they didn’t appear in both DataFrames.
Note that you can also use pd.merge() with the following syntax to return the exact same result:
#perform left join
pd.merge(df1, df2, on='team', how='inner')
team points assists
0 A 18 4
1 B 22 9
2 C 19 14
3 D 14 13
4 G 20 10
5 H 28 8
Notice that this merged DataFrame matches the one from the previous example.
Note: You can find the complete documentation for the merge function here.
Additional Resources
The following tutorials explain how to perform other common operations in pandas:
How to Do a Left Join in Pandas
How to Merge Pandas DataFrames on Multiple Columns
Pandas Join vs. Merge: What’s the Difference?
