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.