Combining Duplicate Rows in a Pandas DataFrame

Overview

In this article, we will explore how to combine duplicate rows in a Pandas DataFrame. This is often necessary when dealing with data that contains duplicate entries for the same person or entity.

We will use a sample DataFrame as an example and walk through the steps of identifying and combining these duplicates using Pandas’ built-in functions.

Problem Statement

The problem statement provided includes a DataFrame containing football player information, including points accumulated in different leagues. However, there are duplicate rows for players who have transferred mid-season, with one row showing the points earned in their previous league and another showing the points earned in their current league.

For example, we have two players named Bruno, with one entry showing 43 points in Villarreal CF and another entry showing 43 points in Getafe CF. We want to combine these duplicate rows into a single row, adding the total points earned by both clubs.

Solution

To solve this problem, we will use Pandas’ groupby function to identify duplicate rows based on certain columns, and then apply an aggregation function to calculate the desired values for each group.

First, we need to replace missing values (NaN) with empty strings using the fillna method:

df[['name','full_name','club']] = df[['name','full_name','club']].fillna('')

This ensures that all rows are included in the grouping process, even if they contain missing values.

Next, we define a dictionary that specifies which columns to aggregate and what aggregation function to apply:

d = {'Points':'sum', 'Start':'sum', 'Sub':'sum', 'club':'first'}

In this example, we are aggregating the Points column by summing its values, and we want to keep only the first value for each group when it comes to the club column.

We then use the groupby function to identify duplicate rows based on the specified columns:

df = (df.groupby(['name','full_name'], sort=False, as_index=False)
        .agg(d)
        .reindex(columns=df.columns))

The groupby function groups the DataFrame by the specified columns and returns a new DataFrame with aggregated values. The agg method applies the aggregation functions specified in the dictionary to each group. Finally, we use the reindex method to restore the original column order.

Example Output

After combining the duplicate rows, our resulting DataFrame looks like this:

   name                  full_name                   club  Points  Start  Sub
0   S. Mustafi               Shkodran Mustafi                Arsenal      74     26    2
1   Bruno Bruno                   SorianoLlido          Villarreal CF      43     15   16
2   Bruno Bruno               Gonzalez Cabrera              Getafe CF      43     15   16
3         Oscar       Oscar dos Santos Emboaba                             17      5   10
4         Oscar         Oscar Rodriguez Arnaiz         Real Madrid CF      17      5   10
5      C. Bravo                  Claudio Bravo        Manchester City      62     23    8
6         Naldo    Ronaldo Aparecido Rodrigues          FC Schalke 04      58     19    1
7         Naldo         Edinaldo Gomes Pereira           RCD Espanol      58     19    1
8     G. Castro                 Gonzalo Castro      Borussia Dortmund     158     46   12
9      Juanfran          Juan Francisco Torres  Belen Atletico Madrid     160     55   10
10     Juanfran  Juan Francisco Moreno Fuertes              RC Coruna     160     55   10

As we can see, the duplicate rows for Bruno have been combined into a single row with the total points earned by both clubs.

Conclusion

Combining duplicate rows in a Pandas DataFrame is a common task when working with data that contains redundant or inconsistent information. By using Pandas’ built-in functions like groupby and agg, we can easily identify and combine these duplicates, resulting in a more accurate and complete dataset.

Last modified on 2024-07-26