Converting to categorical in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does converting a column to categorical type in pandas do?

It changes the column to a special type that uses less memory and can speed up operations by storing data as categories instead of raw values.

beginner

How do you convert a pandas DataFrame column named 'color' to categorical?

Use df['color'] = df['color'].astype('category') to convert the 'color' column to categorical type.

beginner

Why might you want to convert a text column with repeated values to categorical?

Because it saves memory and can make filtering and grouping faster, just like organizing similar items into labeled boxes instead of keeping them loose.

intermediate

What is the difference between ordered and unordered categorical data?

Ordered categorical data has a meaningful order (like small < medium < large), while unordered categorical data has no order (like red, blue, green).

intermediate

How can you create an ordered categorical column in pandas?

Use df['size'] = pd.Categorical(df['size'], categories=['small', 'medium', 'large'], ordered=True) to create an ordered categorical column.

What is the main benefit of converting a column to categorical in pandas?

A Saves memory and speeds up operations

B Changes numbers to strings

C Deletes duplicate rows

D Sorts the data automatically

Converting to categorical saves memory and can speed up operations by storing repeated values efficiently.

Which pandas method converts a column to categorical type?

A df['col'].fillna()

B df['col'].to_string()

C df['col'].sort_values()

D df['col'].astype('category')

The astype('category') method converts a column to categorical type.

What does ordered=True do when creating a categorical column?

A Sorts the DataFrame

B Defines a meaningful order for categories

C Converts numbers to strings

D Removes duplicates

Setting ordered=True defines a meaningful order among categories.

Which of these is NOT a reason to use categorical data?

A Automatically fix missing data

B Reduce memory usage

C Speed up filtering and grouping

D Represent repeated text values efficiently

Categorical data does not automatically fix missing data.

If you have a column with values 'red', 'blue', 'green', what type is best to convert it to?

A Float

B Integer

C Categorical

D Boolean

Categorical type is best for repeated text values like colors.

Explain why and how you would convert a text column with repeated values to categorical in pandas.

Think about how repeated labels can be stored more efficiently.

Describe the difference between ordered and unordered categorical data and give an example of each.

Consider sizes for ordered and colors for unordered.

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