GroupBy and Order Groups based on max value in each group using Pandas

In this article, we will explore how to achieve the desired output by grouping a Pandas DataFrame by one column, sorting the groups based on the maximum value of another column, and then ordering the rows within each group.

Introduction

The Pandas library is widely used for data manipulation and analysis in Python. When working with large datasets, it’s common to want to group the data by certain columns and perform operations on specific subsets of the data. In this article, we’ll focus on how to achieve a specific output by grouping a DataFrame by one column, sorting the groups based on the maximum value of another column, and then ordering the rows within each group.

Example Use Case

Suppose we have a Pandas DataFrame containing tracks, scores, and other columns. We want to group the data by “tracks” and sort the groups based on the maximum score in descending order, while also keeping all rows from each group intact.

tracks       score
20            2.2
20            1.5
25            3.5
24            1.2
24            5.5

We expect the output to be:

tracks       score
24            5.5
              1.2
25            3.5
20            2.2
              1.5

In this example, we first sort all values in descending order based on the “score” column.

sub_df = sub_df.sort_values("score")

However, this approach doesn’t meet our requirements because it sorts all rows within each group instead of grouping them by “tracks”.

Solution

To achieve the desired output, we can use the GroupBy.transform function to create a helper column containing the maximum score for each group. Then, we sort the DataFrame by this helper column and finally drop it.

sub_df['max'] = sub_df.groupby('tracks')['score'].transform('max')

This creates a new column ‘max’ which contains the maximum value of the “score” column for each unique “tracks” value in the group.

Next, we sort the DataFrame by this ‘max’ column and also by the “score” column. The ascending=False argument ensures that the groups are sorted in descending order based on the maximum score.

sub_df = sub_df.sort_values(["max","score"], ascending=[False,True]).drop('max', axis=1)

Finally, we drop the ‘max’ helper column to obtain our desired output:

if necessary sorting also by tracks column
sub_df = sub_df.sort_values(["max","tracks","score"], ascending=[False,True]).drop('max', axis=1)
print (sub_df)
   tracks  score
4      24    5.5
3      24    1.2
2      25    3.5
0      20    2.2
1      20    1.5

Explanation

The key to this solution is the use of the GroupBy.transform function, which creates a new series containing the maximum value for each group.

sub_df['max'] = sub_df.groupby('tracks')['score'].transform('max')

Without the transform method, grouping by “tracks” and applying the max() function to the “score” column would create a Series with the same index as the DataFrame, containing the maximum value for each group. However, this series would not be tied to any specific row in the DataFrame.

Using the transform method instead creates a new column in the DataFrame that contains the maximum value for each unique “tracks” value in the group.

We then sort the DataFrame by the ‘max’ column and also by the “score” column. The ascending=[False,True] argument ensures that the groups are sorted in descending order based on the maximum score, while keeping all rows from each group intact.

Finally, we drop the ‘max’ helper column to obtain our desired output.

Code

Here is the complete code example:

import pandas as pd

# Create a sample DataFrame
data = {
    "tracks": [20, 20, 25, 24, 24],
    "score": [2.2, 1.5, 3.5, 1.2, 5.5]
}

df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Sort all values in descending order based on the 'score' column
sub_df = df.sort_values("score")

print("\nSorted DataFrame (all rows sorted):")
print(sub_df)

# Create a helper column containing the maximum score for each group
sub_df['max'] = sub_df.groupby('tracks')['score'].transform('max')

# Sort the DataFrame by the 'max' column and also by the 'score' column
sub_df = sub_df.sort_values(["max","score"], ascending=[False,True]).drop('max', axis=1)

print("\nDataFrame sorted by 'max' column and then 'score' column:")
print(sub_df)

This code creates a sample DataFrame, sorts all rows in descending order based on the “score” column, and then uses the GroupBy.transform method to create a helper column containing the maximum score for each group. Finally, it sorts the DataFrame by this ‘max’ column and also by the “score” column.

Last modified on 2023-10-30