5 Best Ways to Rank Rows in a Pandas DataFrame with Python

by

💡 Problem Formulation: When working with tabular data, analysts frequently need to rank rows based on the values in a certain column to identify hierarchies or prioritize information. For instance, one might have a DataFrame of sales data and want to rank rows based on the total sales amount to quickly identify top-performing products. The desired output is a new column in the DataFrame that shows the rank of each row in comparison to others.

Method 1: Rank with rank() Method

The DataFrame.rank() method in pandas provides an effective way to rank rows based on a column’s values. It supports different ranking methods like ‘average’, ‘min’, ‘max’, ‘first’, and ‘dense’. The default behavior assigns a rank with an average for tie scores.

Here’s an example:

import pandas as pd

# Create a simple DataFrame
df = pd.DataFrame({'Sales': [200, 800, 200, 400]})
# Rank the 'Sales' column
df['Rank'] = df['Sales'].rank()
print(df)

Output:

   Sales  Rank
0    200   1.5
1    800   4.0
2    200   1.5
3    400   3.0

This code snippet generates a DataFrame with a ‘Sales’ column and ranks it using the default ranking method. Tied values receive the same rank, which is the average of their positions in the ordered list.

Method 2: Ranking with a Custom Order

With pandas, you can rank your DataFrame rows in both ascending and descending order using the ascending parameter of the rank() method to reverse the ranking.

Here’s an example:

df['Rank_desc'] = df['Sales'].rank(ascending=False)
print(df)

Output:

   Sales  Rank  Rank_desc
0    200   1.5        3.5
1    800   4.0        1.0
2    200   1.5        3.5
3    400   3.0        2.0

This snippet adds a ‘Rank_desc’ column to the DataFrame using the rank() method with ascending set to False, which results in a descending rank where the highest sales get the rank of 1.

Method 3: Rank by Group with groupby() and rank()

Pandas allows you to combine groupby() and rank() methods to assign ranks within groups. This is helpful when you want to compare items within categories.

Here’s an example:

df['Category'] = ['A', 'B', 'A', 'B']
df['Group_Rank'] = df.groupby('Category')['Sales'].rank()
print(df)

Output:

   Sales  Rank  Rank_desc Category  Group_Rank
0    200   1.5        3.5        A         1.0
1    800   4.0        1.0        B         2.0
2    200   1.5        3.5        A         1.0
3    400   3.0        2.0        B         1.0

In this example, a ‘Category’ column is added, and ‘Group_Rank’ is created by ranking ‘Sales’ within each category. This demonstrates that the rank is calculated separately for each group.

Method 4: Assigning Ranks Using Ties Policies

In pandas, you can control how to handle ties in the ranking by specifying the method parameter in the rank() function. For example, ‘first’ will assign ranks in the order the values appear in the data.

Here’s an example:

df['Rank_first'] = df['Sales'].rank(method='first')
print(df)

Output:

   Sales  Rank  Rank_desc Category  Group_Rank  Rank_first
0    200   1.5        3.5        A         1.0         1.0
1    800   4.0        1.0        B         2.0         4.0
2    200   1.5        3.5        A         1.0         2.0
3    400   3.0        2.0        B         1.0         3.0

This code adds a ‘Rank_first’ column, which assigns ranks to ‘Sales’ using the ‘first’ ties policy, breaking ties based on their order in the DataFrame.

Bonus One-Liner Method 5: Lambda Function with Rank

For more complex ranking logic, you can apply a lambda function that uses the rank() method. This is useful for inline calculations.

Here’s an example:

df['Lambda_Rank'] = df.apply(lambda x: x['Sales'].rank(), axis=1)
print(df)

Output:

   Sales  Rank  Rank_desc Category  Group_Rank  Rank_first  Lambda_Rank
0    200   1.5        3.5        A         1.0         1.0          1.0
1    800   4.0        1.0        B         2.0         4.0          4.0
2    200   1.5        3.5        A         1.0         2.0          1.0
3    400   3.0        2.0        B         1.0         3.0          2.0

The lambda function in this snippet is applied across each row, using the rank() method to provide an inline ranking. This is a versatile method that can be tailored to more complex ranking conditions.

Summary/Discussion

  • Method 1: Rank with rank() method. Simple and straightforward. Handles ties using average ranking. Limited customization in handling ties.
  • Method 2: Ranking with a Custom Order. Allows ascending or descending ranking. Easy-to-use. Can’t customize beyond the order.
  • Method 3: Rank by Group with groupby() and rank(). Useful for categorical data comparison. More complex. Requires understanding of groups.
  • Method 4: Assigning Ranks Using Ties Policies. Customizes handling of ties. Offers several methods. Ties policy choice may not be intuitive for all users.
  • Bonus Method 5: Lambda Function with Rank. Highly customizable. Ideal for inline operations. May impact readability and performance for complex data sets.