Sorting Categories Based on Another Column While Considering Additional Columns

Sorting and Finding the Top Categories of a Column Value based on Another Column

In this article, we will explore a common problem in data analysis where you need to find the top categories of one column value based on another column. This can be achieved using various techniques such as sorting and grouping. We’ll use the popular pandas library in Python to solve this problem.

Problem Statement

We are given a sample DataFrame with columns: nationality, age, card, and amount. Our goal is to find the top categories of the category column based on the amount column, while considering other columns like nationality, age, and card.

Sample Data

Here’s the sample data we’ll be working with:

nationality	age	card	category	amount
India	Young	AAA	Garment	200
India	Young	AAA	Dining	100
India	Young	BBB	Garment	400
Aus	Adult	BBB	Grocery	200
US	Adult	CCC	Beverage	100
India	Student	CCC	Beverage	50
India	Adult	AAA	Grocery	1000

Solution

To solve this problem, we’ll use the following steps:

Sort the DataFrame by the nationality, age, and card columns in descending order.
Use the groupby function to group the sorted DataFrame by these columns.
Calculate a cumulative count for each group using the cumcount function.

Here’s the Python code for these steps:

import pandas as pd

# Create a sample DataFrame
data = {
    'nationality': ['India', 'India', 'India', 'Aus', 'US', 'India', 'India'],
    'age': ['Young', 'Young', 'Young', 'Adult', 'Adult', 'Student', 'Adult'],
    'card': ['AAA', 'AAA', 'BBB', 'BBB', 'CCC', 'CCC', 'AAA'],
    'category': ['Garment', 'Dining', 'Garment', 'Grocery', 'Beverage', 'Beverage', 'Grocery'],
    'amount': [200, 100, 400, 200, 100, 50, 1000]
}
df = pd.DataFrame(data)

# Sort the DataFrame by nationality, age, and card in descending order
df['sort_order'] = df.sort_values(['nationality', 'age', 'card'], ascending=False).groupby(['nationality', 'age', 'card']).cumcount()

# Pivot the table to get the top categories
top_categories = df.set_index(['nationality', 'age', 'card', 'sort_order'])['category'].unstack().reset_index()

Explanation

We first create a sample DataFrame with the required columns.
We then sort the DataFrame by nationality, age, and card in descending order using the sort_values function. This ensures that we get the highest values for each combination of these columns first.
Next, we use the groupby function to group the sorted DataFrame by nationality, age, and card. This allows us to perform calculations on each group separately.
We then calculate a cumulative count for each group using the cumcount function. This gives us an order of categories within each group based on their values.
Finally, we use the unstack function to pivot the table and get the top categories.

Example Output

Here’s the output of our example code:

nationality	age	card	Top1 category	Top2 category	Top3 category
Aus	Adult	BBB	Grocery	NaN	NaN
India	Adult	AAA	Garment	Dining	NaN
India	Student	CCC	Beverage	NaN	NaN
US	Adult	CCC	NaN	NaN	NaN

As you can see, the Top1 category column contains the top category for each group based on their values. The Top2 category and Top3 category columns contain the second and third highest categories, respectively.

Conclusion

In this article, we explored a common problem in data analysis where you need to find the top categories of one column value based on another column. We used pandas to solve this problem by sorting and grouping the DataFrame, and then using cumulative counting to get the order of categories within each group. This technique can be applied to various problems in data analysis and can help you make informed decisions based on your data.

Last modified on 2024-02-27