**π‘ Problem Formulation:** When working with categorical data in pandas, it’s common to encounter scenarios where finding the maximum value of a dataset with an ordered CategoricalIndex is necessary. This article demonstrates how to retrieve the highest category level given a dataframe with an ordered categorical index. For instance, if the categories are [‘low’, ‘medium’, ‘high’] and our DataFrame contains a mix of these values, we seek the ‘high’ output as it represents the maximum category.

## Method 1: Using sort_values( ) and tail( )

This method involves sorting the DataFrame based on the categorical index in ascending order and then selecting the last value with the `tail()`

function. This works because pandas understands the order of a CategoricalIndex when sorting.

Here’s an example:

import pandas as pd # Create a categorical series with order category = pd.Categorical(['low', 'high', 'medium', 'high'], categories=['low', 'medium', 'high'], ordered=True) df = pd.DataFrame({'Category': category}) df_sorted = df.sort_values(by='Category').tail(1) print(df_sorted)

Output:

Category 1 high

This code first creates an ordered categorical pandas Series and then constructs a DataFrame from it. The DataFrame is sorted by the ‘Category’ column, which maintains the logical order of the categories. Applying `tail(1)`

to the sorted DataFrame returns the maximum value.

## Method 2: Using max() on Categorical data

The `max()`

function in pandas is designed to handle categorical data with ease. By calling `max()`

directly on a Series with ordered Categorical data, pandas returns the highest category.

Here’s an example:

max_category = df['Category'].max() print(max_category)

Output:

high

In this snippet, the maximum value of the ‘Category’ column is directly computed using the `max()`

function, which takes into account the order of the categories and returns the result ‘high’.

## Method 3: Using idxmax() to find the index of the max

The `idxmax()`

function will return the index of the first occurrence of the maximum value. In an ordered categorical data series, this corresponds to the maximum category’s index.

Here’s an example:

max_index = df['Category'].idxmax() print(df.loc[max_index])

Output:

Category high Name: 1, dtype: category Categories (3, object): ['low' < 'medium' < 'high']

The `idxmax()`

function finds the index of the maximum value in the ‘Category’ column, which we then use to obtain the corresponding DataFrame entry using `loc`

.

## Method 4: Using groupby() and tail()

If the DataFrame is not solely indexed by categories but includes other values, grouping by the categorical column and then using `tail()`

on each group can also yield the maximum category.

Here’s an example:

max_group = df.groupby('Category').tail(1).max() print(max_group)

Output:

Category high dtype: object

This code snippet groups the DataFrame by ‘Category’ and then selects the last entry in each group, which should correspond to the maximum value due to the categories being ordered. Calling `max()`

on the resulting DataFrame gives us the highest category.

## Bonus One-Liner Method 5: Use query( ) and max( )

A one-liner solution that combines `query()`

with `max()`

to swiftly extract the maximum category from a DataFrame, provided it has a single categorical column.

Here’s an example:

max_value = df.query('Category == Category.max()') print(max_value)

Output:

Category 1 high

This one-liner filters the DataFrame using a boolean condition that checks for the maximum category, which relies on the ordered nature of the CategoricalIndex to determine the correct maximum value.

## Summary/Discussion

**Method 1:**Sort and Tail. Strengths: Intuitive, can also be used to get n-largest values. Weaknesses: Potentially inefficient for large datasets.**Method 2:**Max Function. Strengths: Simple and direct. Weaknesses: Offers no indexing information.**Method 3:**Idxmax Function. Strengths: Provides index of maximum value. Weaknesses: Only returns first occurrence if multiple maxima exist.**Method 4:**Groupby and Tail. Strengths: Versatile, can be used with additional non-categorical data. Weaknesses: Overcomplicated for simple tasks.**Method 5:**Query and Max One-Liner. Strengths: Concise. Weaknesses: Can be less readable, especially for beginners.