Ordered categories in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an ordered category in pandas?

An ordered category is a type of categorical data where the categories have a meaningful order or ranking, like 'low' < 'medium' < 'high'.

beginner

How do you create an ordered categorical column in pandas?

Use pd.Categorical() with the ordered=True parameter and specify the category order with the categories argument.

beginner

Why use ordered categories instead of strings or numbers?

Ordered categories save memory and allow meaningful comparisons like <, >, and sorting based on the order you define.

intermediate

What happens if you compare two ordered categorical values in pandas?

Pandas compares them based on the order you set, not alphabetically or numerically. For example, 'low' < 'high' if 'low' is before 'high' in the category order.

intermediate

How can you change the order of categories in an existing ordered categorical column?

Use the cat.reorder_categories() method with the new order list and ordered=True to update the order.

Which parameter makes a pandas categorical ordered?

A order='asc'

B ordered=True

C sort=True

D rank=True

The parameter ordered=True tells pandas the categories have a meaningful order.

What is the output type when you use pd.Categorical(['low', 'high'], categories=['low', 'medium', 'high'], ordered=True)?

A A pandas Series

B A numpy array

C A list

D An ordered categorical object

This creates an ordered categorical object with the specified categories and order.

If 'medium' is between 'low' and 'high' in order, what is the result of 'medium' > 'low'?

A True

B False

C Error

D Depends on data type

Because 'medium' comes after 'low' in the order, the comparison returns True.

How do ordered categories help in sorting data?

A They convert categories to numbers before sorting.

B They sort data randomly.

C They allow sorting based on the defined order, not alphabetically.

D They do not affect sorting.

Ordered categories let pandas sort data according to the category order you set.

Which method changes the order of categories in a pandas categorical column?

A cat.reorder_categories()

B cat.set_order()

C cat.sort_categories()

D cat.change_order()

The cat.reorder_categories() method updates the order of categories.

Explain what ordered categories are and why they are useful in pandas.

Think about how you rank things in real life, like sizes or ratings.

Describe how to create and modify an ordered categorical column in pandas.

Focus on the parameters and methods pandas provides.

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