Ordered categories in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an ordered categorical type with levels 'low', 'medium', 'high'.

Pandas

import pandas as pd
levels = ['low', 'medium', 'high']
cat_type = pd.CategoricalDtype(categories=levels, ordered=[1])

Drag options to blanks, or click blank then click option'

ATrue

B0

CNone

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting ordered=False when order matters

Using None instead of True for ordered

Setting ordered=True creates an ordered categorical type.

2fill in blank

medium

Complete the code to convert a column 'priority' in DataFrame df to the ordered categorical type cat_type.

Pandas

df['priority'] = df['priority'].astype([1])

Drag options to blanks, or click blank then click option'

Alist

Bstr

Cint

Dcat_type

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'str' instead of the categorical type

Using 'int' or 'list' which are not categorical types

Use astype(cat_type) to convert the column to the ordered categorical type.

3fill in blank

hard

Fix the error in the code to compare if 'priority' is greater than 'medium'.

Pandas

result = df['priority'] [1] 'medium'

Drag options to blanks, or click blank then click option'

A>

B!=

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality instead of comparison

Using less than or equal which checks the wrong condition

Ordered categorical types support comparison operators like >.

4fill in blank

hard

Fill both blanks to create a dictionary of counts for categories greater than 'low'.

Pandas

counts = {cat: sum(df['priority'] [1] cat) for cat in cat_type.categories if cat [2] 'low'}

Drag options to blanks, or click blank then click option'

A>

B==

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality or not equal operators instead of greater than

Mixing different comparison operators in the two blanks

We count how many rows have priority greater than each category, and only for categories greater than 'low'.

5fill in blank

hard

Fill both blanks to create a filtered DataFrame with priority between 'medium' and 'high' inclusive.

Pandas

filtered = df[(df['priority'] [1]= 'medium') & (df['priority'] [2]= 'high')]

Drag options to blanks, or click blank then click option'

A>=

B<=

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators that exclude boundaries

Using != which excludes values

Use >= for the lower bound and <= for the upper bound.

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