Understanding Inner Joins in Pandas DataFrames

When working with dataframes in pandas, inner joins can be a powerful tool for merging two datasets based on common columns. However, understanding the intricacies of how these merges work is crucial to achieving the desired results.

In this article, we’ll delve into the world of pandas’ inner join functionality and explore why, in certain cases, the resulting merge can have more rows than either of the original dataframes.

The Problem

The question presented at stack overflow illustrates a common scenario where an inner join is performed, but the resulting dataframe has more rows than the original dataframes. This may seem counterintuitive at first glance, as one might expect the number of rows in the merge to be less than or equal to the number of rows in either of the original dataframes.

Let’s examine the example provided:

df_A = pd.DataFrame(dict(my_icon_number=[1, 2, 3, 4, 4, 4], other_column1=range(6)))
df_B = pd.DataFrame(dict(my_icon_number=[4, 4, 4, 5, 6, 7], other_column2=range(6)))

pd.merge(df_A, df_B, how='inner', on='my_icon_number')

When we run this code, we obtain the following output:

   my_icon_number  other_column1  other_column2
0               4              3              0
1               4              3              1
2               4              3              2
3               4              4              0
4               4              4              1
5               4              4              2
6               4              5              0
7               4              5              1
8               4              5              2

At first glance, this result appears to be correct. However, let’s examine the actual code being used in this example:

dfa = df_A.drop_duplicates(subset=['my_icon_number'])
dfb = df_B.drop_duplicates(subset=['my_icon_number'])

new_df = pd.merge(dfa, dfb, how='inner', on='my_icon_number')

Here, we’re using the drop_duplicates function to remove any duplicate rows from either dataframe before performing the inner join.

The Cause of the Problem

The key to understanding why this example results in more rows than expected lies in the fact that both dataframes have duplicates for the same value in their common column (‘my_icon_number’). In this case, pandas is essentially creating a new row for every combination of duplicate values from each dataframe.

This can be visualized by examining the output of df_A and df_B before removing any duplicates:

   my_icon_number  other_column1
0               4              3
1               4              3
2               4              3
3               4              4
4               4              4
5               4              4

my_icon_number    color      size
0               4              blue      small
1               4              red       large 
2               4              yellow    medium

With these duplicates in place, when pandas performs the inner join, it will create a new row for every possible combination of duplicate values. In this case, there are two unique values (‘4’ and ‘123’) that each appear three times in df_A and twice in df_B. As a result, we get 9 total rows for these duplicates, one for every combination.

A General Explanation

The problem arises because pandas performs an inner join by finding the intersection of the two dataframes based on their common columns. When there are duplicate values in either dataframe, this can lead to more rows being included in the merge than expected.

To illustrate this further, let’s examine another example:

df_A = pd.DataFrame(dict(my_icon_number=[1, 2, 3, 4, 5], other_column1=range(6)))
df_B = pd.DataFrame(dict(my_icon_number=[1, 1, 3, 4, 5], other_column2=range(7)))

pd.merge(df_A, df_B, how='inner', on='my_icon_number')

In this case, we have three unique values (‘1’, ‘3’, and ‘4’) that appear once in df_A and twice in df_B. When pandas performs the inner join, it will create a new row for every possible combination of these duplicate values. In this case, there are two combinations of duplicates (‘1-1’ and ‘1-3’), resulting in 2 total rows.

Removing Duplicate Values

To avoid this issue, you can remove any duplicate values from either dataframe before performing the inner join using drop_duplicates:

df_A = df_A.drop_duplicates(subset=['my_icon_number'], keep='first')
df_B = df_B.drop_duplicates(subset=['my_icon_number'], keep='first')

pd.merge(df_A, df_B, how='inner', on='my_icon_number')

In this example, we’re keeping only the first occurrence of each duplicate value. This ensures that there are no duplicate values present for any unique value in the common column.

Conclusion

Understanding how pandas performs inner joins is crucial to achieving the desired results when working with dataframes. By recognizing that duplicate values can lead to more rows than expected, you can take steps to remove these duplicates before performing the merge.

By following this process, you’ll be able to ensure that your inner joins produce accurate and reliable results.

Last modified on 2024-01-28