Categorical data type optimization in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a categorical data type in data analysis?

A categorical data type is a way to store data that has a limited set of possible values, like colors or categories, which helps save memory and speeds up analysis.

beginner

Why should you convert string columns with repeated values to categorical type?

Because categorical type stores each unique value only once and uses codes for the data, reducing memory use and making operations faster.

intermediate

How does pandas represent categorical data internally?

Pandas stores categorical data as integer codes that point to a list of unique categories, which saves space compared to storing full strings repeatedly.

beginner

What is a common method to convert a column to categorical in pandas?

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

intermediate

What is a potential downside of using categorical data type?

If the column has many unique values (high cardinality), converting to categorical might not save memory and can add overhead.

What does converting a column to categorical type mainly help with?

A Increasing the number of unique values

B Reducing memory usage

C Changing data to numbers only

D Removing missing values

Categorical type reduces memory by storing unique values once and using codes.

Which pandas method converts a column to categorical?

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

B df['col'].to_numeric()

C df['col'].fillna()

D df['col'].unique()

astype('category') changes the column type to categorical.

What is stored internally for categorical data in pandas?

A Boolean values

B Full strings repeated

C Integer codes and categories list

D Only numbers

Pandas stores integer codes pointing to unique categories.

When might categorical type NOT save memory?

A When there are many unique values

B When data is numeric

C When data has missing values

D When data is already numeric

High cardinality means many unique values, reducing memory savings.

Which of these is a benefit of categorical data type?

A Converting numbers to strings

B More precise floating point calculations

C Automatic data cleaning

D Faster comparisons and grouping

Categorical data speeds up operations like grouping and comparisons.

Explain what categorical data type is and why it is useful in data analysis.

Think about how repeated strings can be stored more efficiently.

Describe how to convert a string column to categorical in pandas and when you should do it.

Focus on the pandas method and the type of data that benefits most.