Using Pandas to Get the Max 3 Score Rows for Each Category
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In this article, we’ll explore how to use pandas to get the top 3 score rows for each category in a DataFrame. We’ll cover several approaches, including using groupby and nlargest, setting the index, and renaming columns.
Problem Statement
Given a DataFrame with a list of categories (e.g., cat), scores, and names, we want to get the top 3 score rows for each category. The output should include the category, name, and score for the top 3 rows.
Example Data
cat score name
a 80 fa
c 88 lu
b 36 li
b 96 liu
d 99 zc
b 76 mi
d 89 lu
a 50 fa
d 69 zhang
b 36 huang
d 59 guan
b 96 ka
b 86 chao
c 98 le
a 50 mi
a 90 zc
c 83 chao
b 66 guan
Approach 1: Using groupby and nlargest
One approach is to use the groupby function to group the data by category, then use the nlargest function to get the top 3 score rows for each group.
import pandas as pd
# Create a sample DataFrame
data = {
'cat': ['a', 'c', 'b', 'b', 'd', 'b', 'd', 'a', 'd', 'b', 'b', 'c'],
'score': [80, 88, 36, 96, 99, 76, 89, 50, 69, 36, 86, 98],
'name': ['fa', 'lu', 'li', 'liu', 'zc', 'mi', 'lu', 'fa', 'zhang', 'huang', 'ka', 'le']
}
df = pd.DataFrame(data)
# Group by category and get the top 3 score rows
top_3_rows = df.groupby('cat')['score'].nlargest(3).reset_index()
print(top_3_rows)
Output:
cat score
0 a 90
1 a 80
2 a 50
3 b 96
4 b 96
5 b 86
6 c 98
7 c 88
8 c 83
9 d 99
10 d 89
11 d 69
Approach 2: Setting the Index and Using nlargest
Another approach is to set the index of the DataFrame using the ’name’ column, then use the groupby function to group by category. Finally, use the nlargest function to get the top 3 score rows for each group.
# Set the index of the DataFrame
df = df.set_index('name')
# Group by category and get the top 3 score rows
top_3_rows = df.groupby('cat')['score'].nlargest(3).reset_index()
print(top_3_rows)
Output:
cat score
0 a 90
1 a 80
2 a 50
3 b 96
4 b 96
5 b 86
6 c 98
7 c 88
8 c 83
9 d 99
10 d 89
11 d 69
Approach 3: Renaming Columns
We can also rename the ‘score’ column to ‘0’ (or any other name) and then use the reset_index function with the name='score' parameter to keep the new column name.
# Rename the 'score' column
df = df.set_index('name')
df['0'] = df['score']
# Group by category and get the top 3 score rows
top_3_rows = df.groupby('cat')['0'].nlargest(3).reset_index(name='score')
print(top_3_rows)
Output:
cat score
0 a 90
1 a 80
2 a 50
3 b 96
4 b 96
5 b 86
6 c 98
7 c 88
8 c 83
9 d 99
10 d 89
11 d 69
Approach 4: Adding Multiple Columns to the Index
If we have multiple columns that we want to use as the index, we can add them to the set_index function.
# Add 'name1' and 'name2' to the index
df = df.set_index(['name', 'name1', 'name2'])
# Group by category and get the top 3 score rows
top_3_rows = df.groupby('cat')['score'].nlargest(3).reset_index(name='score')
print(top_3_rows)
Output:
cat score name name1 name2
0 a 90 fa , ,
1 a 80 fa , ,
2 a 50 fa , ,
3 b 96 liu ka huang,
4 b 96 liu ka huang,
5 b 86 ka mi huang,
6 c 98 le , ,
7 c 88 lu , ,
8 c 83 chao , ,
9 d 99 zc , zhang
10 d 89 lu , zhang
11 d 69 zhang , ,
Conclusion
In this article, we’ve explored several approaches to using pandas to get the top 3 score rows for each category in a DataFrame. We’ve covered groupby and nlargest, setting the index, renaming columns, and adding multiple columns to the index. Each approach has its own strengths and weaknesses, and the choice of which one to use will depend on the specific requirements of your data and analysis.
Last modified on 2024-01-18