How can two dataframes be combined by matching a shared column while retaining duplicate values?

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To combine two dataframes by matching a shared column while retaining duplicate values, you can use the merge() function in pandas. By default, merge() performs an inner join and drops any rows with missing values. However, you can change the type of join and specify the appropriate parameter to keep the duplicates. Here's an example:

import pandas as pd # create two dataframes with duplicate values df1 = pd.DataFrame({'id': [1, 2, 3, 3, 4], 'name': ['Alice', 'Bob', 'Charlie', 'Charlie', 'David']}) df2 = pd.DataFrame({'id': [3, 4, 5], 'age': [30, 40, 50]}) # merge the two dataframes on the 'id' column while keeping duplicates df_combined = pd.merge(df1, df2, on='id', how='outer') print(df_combined) 

Output:

   id     name   age
0   1    Alice   NaN
1   2      Bob   NaN
2   3  Charlie  30.0
3   3  Charlie  30.0
4   4    David  40.0
5   5      NaN  50.0

In this example, we merged df1 and df2 on the 'id' column using an outer join. This kept all the rows from both dataframes and filled in missing values with NaN. You can see that the duplicate value for id=3 is retained in the resulting dataframe.