Category codes and labels in Pandas - Step-by-Step Execution

Concept Flow - Category codes and labels

Create categorical data

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Assign categories with labels

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Access category codes

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Use codes for analysis or storage

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Map codes back to labels if needed

This flow shows how to create categorical data, assign labels, access their integer codes, and use them for efficient analysis.

Execution Sample

Pandas

import pandas as pd
colors = pd.Series(['red', 'blue', 'green', 'blue', 'red'])
cats = colors.astype('category')
codes = cats.cat.codes
labels = cats.cat.categories

This code creates a categorical series from color names, then extracts integer codes and category labels.

Execution Table

Step	Action	Data/Variable	Result/Value
1	Create Series with colors	colors	0:red,1:blue,2:green,3:blue,4:red
2	Convert to categorical	cats	Categories: ['blue', 'green', 'red'] with codes [2,0,1,0,2]
3	Access category codes	codes	[2,0,1,0,2]
4	Access category labels	labels	Index(['blue', 'green', 'red'])
5	Use codes for indexing or storage	codes	Integer array representing categories
6	Map codes back to labels	codes + labels	2->red,0->blue,1->green
7	End		All categories and codes assigned correctly

💡 All categorical codes and labels extracted and mapped successfully

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
colors	Empty	['red','blue','green','blue','red']	Same	Same	Same
cats	N/A	Categorical with categories ['blue','green','red']	Same	Same	Same
codes	N/A	N/A	[2,0,1,0,2]	Same	Same
labels	N/A	N/A	N/A	['blue','green','red']	Same

Key Moments - 2 Insights

Why do the category codes not match the original order of colors?

Category codes are assigned based on sorted unique categories (alphabetical order), not the original order. See execution_table step 2 where categories are ['blue', 'green', 'red'] sorted alphabetically.

How can I get the original color names back from the codes?

Use the category labels with the codes to map back. For example, code 0 corresponds to 'blue' as shown in execution_table step 6.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the code for the color 'green'?

A 1

B 2

C 0

D 3

💡 Hint

Check the codes array in step 3 and the category order in step 2.

At which step do we convert the original Series into categorical data?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look for the action 'Convert to categorical' in the execution_table.

If a new color 'yellow' is added, what happens to the category codes?

A All codes shift by one

B Codes remain the same for existing colors, 'yellow' gets a new code

C Codes become strings instead of integers

D Codes reset to zero for all colors

💡 Hint

Think about how pandas assigns codes only to new categories without changing existing ones.

Concept Snapshot

Category codes and labels in pandas:
- Convert a Series to 'category' dtype.
- Categories are sorted unique values.
- Codes are integer indexes for categories.
- Use .cat.codes for codes, .cat.categories for labels.
- Codes help efficient storage and analysis.

Full Transcript

We start by creating a pandas Series with color names. Then, we convert this Series to a categorical type, which organizes unique colors as categories sorted alphabetically. Each category gets an integer code starting from zero. We extract these codes using .cat.codes and the category labels using .cat.categories. These codes represent the original data efficiently and can be mapped back to the labels when needed. This process helps in saving memory and speeding up analysis when working with repeated categorical data.