Memory savings with categoricals in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a categorical data type in pandas?

A categorical data type in pandas is a way to store data that has a limited set of possible values, called categories. It saves memory by storing the data as integers internally and mapping them to the categories.

beginner

How does using categorical data types save memory compared to using strings?

Using categorical data types saves memory because it stores each unique value only once and uses integer codes to represent the data, instead of storing full strings repeatedly.

beginner

Which pandas method converts a column to a categorical type?

The pandas method to convert a column to categorical type is astype('category').

beginner

What is one common use case for converting data to categorical type?

A common use case is when a column has repeated values from a small set, like days of the week or product categories, to reduce memory and speed up operations.

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True or False: Categorical data types can only be used with text data.

False. Categorical data types can be used with any data that has a limited set of values, including numbers and strings.

What does pandas store internally when using a categorical data type?

A Floating point numbers

B Integer codes representing categories

C Full string values for each row

D Boolean values

Pandas stores integer codes internally for categorical data, mapping them to the category values.

Which pandas function converts a column to categorical?

A astype('category')

B to_categorical()

C convert_category()

D category_convert()

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

Why do categorical types save memory?

A They remove missing values

B They compress data using zip

C They store unique values once and use codes

D They convert data to integers only

Categorical types save memory by storing unique values once and using integer codes for data.

Which data is best suited for categorical type?

A Unique IDs with many different values

B Continuous numerical data

C Random floating point numbers

D Repeated values from a small set

Categorical types are best for repeated values from a small set, like categories.

Can categorical types be used for numeric data?

A Yes, if the numbers represent categories

B No, only for text data

C Only for floating point numbers

D Only for boolean data

Categorical types can be used for numeric data if the numbers represent categories.

Explain how converting a pandas column to categorical type helps save memory.

Think about how repeated values are stored in normal strings versus categorical.

Describe a real-life example where using categorical data type would be beneficial.

Consider data with many repeated labels.