Ordered categories in Pandas - Mini Project: Build & Apply

Ordered categories

📖 Scenario: You work in a store that sells clothes. You have a list of T-shirt sizes from customers. The sizes are small, medium, and large. You want to organize these sizes in order so you can analyze them better.

🎯 Goal: Create a pandas Series with T-shirt sizes as an ordered category. Then, sort the sizes in the correct order: small, medium, large.

📋 What You'll Learn

Create a pandas Series called sizes with the values: 'medium', 'small', 'large', 'medium', 'small'.

Create an ordered categorical type called size_order with categories: 'small', 'medium', 'large' in this order.

Convert the sizes Series to use the ordered categorical type size_order.

Sort the sizes Series by the ordered categories.

Print the sorted sizes Series.

💡 Why This Matters

🌍 Real World

Stores often have product sizes or ratings that have a natural order. Using ordered categories helps sort and analyze such data correctly.

💼 Career

Data analysts and scientists use ordered categories to prepare and clean data for reports and machine learning models.

1

Create the sizes Series

Import pandas as pd. Create a pandas Series called sizes with these exact values in this order: 'medium', 'small', 'large', 'medium', 'small'.

Pandas

import pandas as pd
# Create the sizes Series with the given values
# Your code here

Hint

Use pd.Series and pass the list of sizes exactly as given.

2

Create the ordered category type

Create an ordered categorical type called size_order using pd.CategoricalDtype with categories 'small', 'medium', 'large' in this order. Make sure it is ordered.

Pandas

import pandas as pd
sizes = pd.Series(['medium', 'small', 'large', 'medium', 'small'])
# Create the ordered categorical type size_order
# Your code here

Hint

Use pd.CategoricalDtype with the categories list and ordered=True.

3

Convert sizes to ordered category

Convert the sizes Series to use the ordered categorical type size_order by using astype(size_order) and assign it back to sizes.

Pandas

import pandas as pd
sizes = pd.Series(['medium', 'small', 'large', 'medium', 'small'])
size_order = pd.CategoricalDtype(categories=['small', 'medium', 'large'], ordered=True)
# Convert sizes to ordered categorical type
# Your code here

Hint

Use sizes.astype(size_order) to convert the Series.

4

Sort and print the sizes

Sort the sizes Series using sort_values() and print the sorted Series.

Pandas

import pandas as pd
sizes = pd.Series(['medium', 'small', 'large', 'medium', 'small'])
size_order = pd.CategoricalDtype(categories=['small', 'medium', 'large'], ordered=True)
sizes = sizes.astype(size_order)
# Sort sizes and print the result
# Your code here

Hint

Use sizes.sort_values() to sort and then print() to show the sorted Series.

Practice

1. What does setting ordered=True do when creating a pandas categorical variable?

easy

A. It converts categories to numeric values automatically.

B. It sorts the categories alphabetically.

C. It defines a natural order for the categories, allowing comparisons.

D. It removes duplicate categories from the list.

Solution

1. Step 1: Understand the role of ordered categories

   Ordered categories allow pandas to know the sequence of categories, so it can compare them meaningfully.

2. Step 2: Effect of ordered=True

   Setting ordered=True tells pandas to treat the categories as having a specific order, enabling operations like greater than or less than.

3. Final Answer:

   It defines a natural order for the categories, allowing comparisons. -> Option C

4. Quick Check:

   ordered=True means categories have order [OK]

Hint: Ordered=True means categories can be compared by order [OK]

Common Mistakes:

• Thinking it sorts categories automatically
• Assuming it converts categories to numbers
• Believing it removes duplicates

2. Which of the following is the correct way to create an ordered categorical variable in pandas with categories 'low', 'medium', 'high' in that order?

easy

A. pd.Categorical(['low', 'medium', 'high'], categories=['low', 'medium', 'high'], ordered=True)

B. pd.Categorical(['low', 'tiny', 'high'], categories=['low', 'medium', 'high'], ordered=True)

C. pd.Categorical(['low', 'medium', 'high'], ordered=True, categories=['low', 'medium', 'high'])

D. pd.Categorical(['low', 'medium', 'high'], ordered=False, categories=['low', 'medium', 'high'])

Solution

1. Step 1: Check parameter order and names

   pd.Categorical takes data first, then categories, then ordered flag as keyword arguments.

2. Step 2: Identify correct syntax

   pd.Categorical(['low', 'medium', 'high'], categories=['low', 'medium', 'high'], ordered=True) correctly passes data, categories list, and ordered=True in correct order and names.

3. Final Answer:

   pd.Categorical(['low', 'medium', 'high'], categories=['low', 'medium', 'high'], ordered=True) -> Option A

4. Quick Check:

   Correct syntax uses categories= and ordered=True [OK]

Hint: Use categories= first, then ordered=True keyword [OK]

Common Mistakes:

• Passing ordered=True before categories
• Using ordered=False for ordered categories
• Not matching categories list with data

3. What will be the output of this code?

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

medium

A. [False, True, True]

B. [False, False, True]

C. [True, True, False]

D. [True, False, True]

Solution

1. Step 1: Understand the categories and order

   Categories are ordered as low < medium < high.

2. Step 2: Compare each element to 'low'

   'medium' > 'low' is True, 'low' > 'low' is False, 'high' > 'low' is True.

3. Final Answer:

   [True, False, True] -> Option D

4. Quick Check:

   medium and high are greater than low [OK]

Hint: Compare each category to 'low' using order [OK]

Common Mistakes:

• Confusing category order
• Assuming string comparison instead of categorical
• Mixing True/False positions

4. The following code raises an error. What is the cause?

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

medium

A. Comparison operator '>' is not supported for categorical data.

B. The categories list is missing 'high', causing invalid comparison.

C. Ordered=True cannot be used with categories parameter.

D. The data list has duplicate values.

Solution

1. Step 1: Check categories vs data

   Data contains 'high' but categories only include 'low' and 'medium'.

2. Step 2: Understand effect on comparison

   'high' is not in categories, so comparison with 'medium' fails causing error.

3. Final Answer:

   The categories list is missing 'high', causing invalid comparison. -> Option B

4. Quick Check:

   Data must match categories exactly [OK]

Hint: Ensure all data values are in categories list [OK]

Common Mistakes:

• Thinking ordered=True disallows categories
• Believing '>' is unsupported for ordered categories
• Ignoring missing categories in list

5. You have a DataFrame with a column 'size' containing values 'small', 'medium', 'large', and 'extra large'. You want to sort the DataFrame by 'size' in the natural order small < medium < large < extra large. Which code correctly achieves this?

hard

A. df['size'] = pd.Categorical(df['size'], categories=['small', 'medium', 'large', 'extra large'], ordered=True)
df.sort_values('size')

B. df['size'] = pd.Categorical(df['size'], ordered=True)
df.sort_values('size')

C. df['size'] = pd.Categorical(df['size'], categories=['extra large', 'large', 'medium', 'small'], ordered=True)
df.sort_values('size')

D. df.sort_values('size') # No need to set categories

Solution

1. Step 1: Define ordered categories with correct order

   Set categories=['small', 'medium', 'large', 'extra large'] with ordered=True to define natural order.

2. Step 2: Sort DataFrame by the ordered categorical column

   Use df.sort_values('size') to sort according to the defined order.

3. Final Answer:

   df['size'] = pd.Categorical(df['size'], categories=['small', 'medium', 'large', 'extra large'], ordered=True) df.sort_values('size') -> Option A

4. Quick Check:

   Set ordered categories then sort [OK]

Hint: Set ordered categories with correct order before sorting [OK]

Common Mistakes:

• Not specifying categories order
• Using wrong category order
• Trying to sort without ordered categories