Converting to categorical in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of converting a column to categorical

What is the output of this code snippet that converts a DataFrame column to categorical and then prints the categories?

Pandas

import pandas as pd

df = pd.DataFrame({'color': ['red', 'blue', 'green', 'blue', 'red']})
df['color'] = df['color'].astype('category')
print(df['color'].cat.categories.tolist())

A ['blue', 'red', 'green']

B ['red', 'blue', 'green']

C ['blue', 'green', 'red']

D ['red', 'green', 'blue']

💡 Hint

Categories are sorted by default in pandas categorical dtype.

When converting a column to categorical, pandas sorts the unique values alphabetically by default. So the categories are ['blue', 'green', 'red'].

❓ data_output

intermediate

Number of categories after conversion

After converting the 'grade' column to categorical, how many categories does it have?

Pandas

import pandas as pd

df = pd.DataFrame({'grade': ['A', 'B', 'A', 'C', 'B', 'A']})
df['grade'] = df['grade'].astype('category')
print(len(df['grade'].cat.categories))

A 3

B 6

C 4

D 2

💡 Hint

Count unique values in the column before conversion.

The unique grades are 'A', 'B', and 'C', so there are 3 categories after conversion.

🔧 Debug

advanced

Identify the error in categorical conversion

What error does this code raise when trying to convert a column to categorical with specified categories?

Pandas

import pandas as pd

df = pd.DataFrame({'size': ['S', 'M', 'L', 'XL']})
df['size'] = pd.Categorical(df['size'], categories=['S', 'M', 'L'])

A TypeError: categories must be a list

B No error, conversion succeeds with 'XL' as NaN category

C ValueError: 'XL' not in categories

D KeyError: 'size'

💡 Hint

Check how pandas handles values not in specified categories.

Pandas converts values not in the specified categories to NaN without raising an error.

🚀 Application

advanced

Effect of ordered categorical on sorting

Given this DataFrame with an ordered categorical column, what is the output after sorting by 'priority'?

Pandas

import pandas as pd

df = pd.DataFrame({'task': ['task1', 'task2', 'task3'], 'priority': ['high', 'low', 'medium']})
priority_type = pd.CategoricalDtype(categories=['low', 'medium', 'high'], ordered=True)
df['priority'] = df['priority'].astype(priority_type)
sorted_df = df.sort_values('priority')
print(sorted_df['task'].tolist())

A ['task1', 'task3', 'task2']

B ['task2', 'task1', 'task3']

C ['task3', 'task2', 'task1']

D ['task2', 'task3', 'task1']

💡 Hint

Ordered categorical sorts according to category order, not alphabetically.

The order is low < medium < high, so sorting by 'priority' puts 'low' first (task2), then 'medium' (task3), then 'high' (task1).

🧠 Conceptual

expert

Memory usage difference with categorical dtype

Which statement best describes the memory usage difference when converting a large text column to categorical dtype in pandas?

A Memory usage decreases because pandas stores categories as integers internally

B Memory usage increases because categories add overhead for mapping

C Memory usage stays the same because data is unchanged

D Memory usage decreases only if categories are sorted alphabetically

💡 Hint

Think about how categorical data is stored compared to strings.

Categorical dtype stores data as integer codes with a separate category list, reducing memory compared to storing repeated strings.

Practice

1. What is the main benefit of converting a text column to a categorical type in pandas?

easy

A. It removes duplicate rows from the DataFrame.

B. It changes the text to numbers automatically.

C. It saves memory and speeds up analysis.

D. It sorts the DataFrame by that column.

Solution

1. Step 1: Understand what categorical type does

   Converting text to categorical stores repeated values efficiently, saving memory.

2. Step 2: Recognize impact on analysis speed

   Operations on categorical data are faster because pandas uses codes internally.

3. Final Answer:

   It saves memory and speeds up analysis. -> Option C

4. Quick Check:

   Converting to categorical = Save memory and speed [OK]

Hint: Categorical = less memory + faster analysis [OK]

Common Mistakes:

• Thinking it changes text to numbers visibly
• Assuming it sorts data automatically
• Confusing with removing duplicates

2. Which of the following is the correct syntax to convert a pandas DataFrame column df['color'] to categorical?

easy

A. df['color'] = df['color'].astype('category')

B. df['color'] = pd.to_categorical(df['color'])

C. df['color'] = df['color'].convert('category')

D. df['color'] = df['color'].category()

Solution

1. Step 1: Recall pandas method for type conversion

   The correct method is astype('category') to convert column type.

2. Step 2: Check each option

   df['color'] = df['color'].astype('category') uses astype('category'), which is correct. Others are invalid pandas syntax.

3. Final Answer:

   df['color'] = df['color'].astype('category') -> Option A

4. Quick Check:

   Use astype('category') to convert [OK]

Hint: Use astype('category') to convert columns [OK]

Common Mistakes:

• Using non-existent methods like convert() or category()
• Trying pd.to_categorical which is not a pandas function
• Missing quotes around 'category'

3. Given the code:

import pandas as pd
df = pd.DataFrame({'size': ['S', 'M', 'L', 'M', 'S']})
df['size'] = pd.Categorical(df['size'], categories=['S', 'M', 'L'], ordered=True)
print(df['size'].dtype)

What will be the output?

medium

A. category

B. object

C. string

D. int64

Solution

1. Step 1: Understand pd.Categorical usage

   Using pd.Categorical converts the column to categorical dtype with order.

2. Step 2: Check dtype after conversion

   The dtype of the column becomes category, not object or string.

3. Final Answer:

   category -> Option A

4. Quick Check:

   pd.Categorical sets dtype to category [OK]

Hint: pd.Categorical sets dtype to category [OK]

Common Mistakes:

• Expecting dtype to remain object
• Confusing category with string dtype
• Thinking dtype becomes int64

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'grade': ['A', 'B', 'C', 'B', 'A']})
df['grade'] = pd.Categorical(df['grade'], categories=['A', 'B', 'C'])
df['grade'] = df['grade'].astype('category', ordered=True)

medium

A. DataFrame column cannot be converted twice.

B. astype() does not accept ordered=True argument.

C. pd.Categorical requires categories to be sorted alphabetically.

D. Missing import statement for pandas.

Solution

1. Step 1: Check astype() method parameters

   The astype() method does not accept an ordered parameter.

2. Step 2: Identify correct way to set ordered

   Ordering must be set when creating the categorical with pd.Categorical(..., ordered=True), not via astype.

3. Final Answer:

   astype() does not accept ordered=True argument. -> Option B

4. Quick Check:

   ordered=True only in pd.Categorical, not astype [OK]

Hint: ordered=True only works in pd.Categorical, not astype [OK]

Common Mistakes:

• Trying to set ordered in astype()
• Assuming categories must be sorted alphabetically
• Thinking multiple conversions cause error

5. You have a DataFrame df with a column 'status' containing repeated text values: 'low', 'medium', 'high'. You want to convert it to a categorical type with the order low < medium < high, and then get the counts of each category in order. Which code snippet correctly does this?

hard

A. df['status'] = df['status'].astype('category', ordered=True)
print(df['status'].value_counts(sort=False))

B. df['status'] = df['status'].astype('category')
print(df['status'].value_counts())

C. df['status'] = pd.Categorical(df['status'])
print(df['status'].value_counts(sort=True))

D. df['status'] = pd.Categorical(df['status'], categories=['low', 'medium', 'high'], ordered=True)
print(df['status'].value_counts(sort=False))

Solution

1. Step 1: Convert with ordered categories

   Use pd.Categorical with categories=['low', 'medium', 'high'] and ordered=True to set order.

2. Step 2: Get counts without sorting

   Use value_counts(sort=False) to get counts in category order.

3. Final Answer:

   df['status'] = pd.Categorical(df['status'], categories=['low', 'medium', 'high'], ordered=True) print(df['status'].value_counts(sort=False)) -> Option D

4. Quick Check:

   pd.Categorical + ordered + value_counts(sort=False) = correct [OK]

Hint: Use pd.Categorical with ordered and value_counts(sort=False) [OK]

Common Mistakes:

• Using astype('category') without order
• Not setting ordered=True for category order
• Using value_counts() without sort=False loses order