Ordered categories in Pandas - Step-by-Step Execution

Concept Flow - Ordered categories

Create categorical data

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Define category order

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Assign ordered categories

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Use categories in analysis

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Compare or sort using order

We start with data, define an order for categories, assign this order, then use it to compare or sort data.

Execution Sample

Pandas

import pandas as pd
cats = pd.Categorical(['low', 'medium', 'high', 'medium'],
                      categories=['low', 'medium', 'high'],
                      ordered=True)
print(cats > 'low')

This code creates an ordered categorical variable and compares each value to 'low'.

Execution Table

Step	Action	Input/Condition	Result/Output
1	Create categorical data	['low', 'medium', 'high', 'medium']	Categorical object with values ['low', 'medium', 'high', 'medium']
2	Define categories and order	categories=['low', 'medium', 'high'], ordered=True	Categories set with order: low < medium < high
3	Assign ordered categories	Assign to variable 'cats'	cats is ordered categorical with given categories
4	Compare cats > 'low'	Compare each element to 'low'	[False, True, True, True]
5	Print result	Output comparison array	[False, True, True, True]

💡 Comparison done element-wise using the defined order, execution ends after print.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
cats	None	['low', 'medium', 'high', 'medium']	Ordered categories set	Assigned to cats variable	Compared to 'low'	Ordered categorical with given categories

Key Moments - 3 Insights

Why does comparing categories like 'cats > "low"' work?

Because 'cats' is an ordered categorical with a defined order low < medium < high, pandas can compare each element to 'low' using this order (see execution_table step 4).

What happens if the categories are not ordered?

Without ordering, comparisons like 'cats > "low"' will raise an error because pandas cannot determine which category is greater (not shown in execution_table but important to know).

Why do we need to specify categories explicitly?

Specifying categories and their order ensures pandas knows the full set and the order, even if some categories don't appear in data (see execution_table step 2).

Visual Quiz - 3 Questions

Look at the execution table at step 4, what is the comparison result of 'cats > "low"' for the first element?

A False

B True

C Error

D None

💡 Hint

Check the 'Result/Output' column in step 4 of the execution_table.

At which step in the execution table is the order of categories defined?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look for where 'Categories set with order' is mentioned in the execution_table.

If we remove 'ordered=True' from the code, what will happen when comparing 'cats > "low"'?

A Comparison returns all True

B Raises an error

C Comparison returns all False

D Comparison ignores order and works

💡 Hint

Recall the key moment about comparisons without ordering.

Concept Snapshot

Ordered categories in pandas:
- Use pd.Categorical(data, categories=[...], ordered=True)
- Defines a fixed order for categories
- Enables comparisons and sorting
- Without ordered=True, comparisons fail
- Useful for data with natural order like ratings

Full Transcript

This example shows how to create ordered categorical data in pandas. We start by creating a categorical variable with values like 'low', 'medium', and 'high'. We explicitly define the categories and set ordered=True to tell pandas the order low < medium < high. This allows us to compare each category value to another, for example checking which are greater than 'low'. The comparison returns a boolean array showing True for 'medium' and 'high' and False for 'low'. This ordering is important because without it, pandas cannot compare categories. Specifying categories also ensures pandas knows all possible categories and their order, even if some are missing in the data. This technique is useful for data science tasks where categories have a natural order, like ratings or sizes.