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

Recall & Review

beginner

What is data type optimization in data analysis?

Data type optimization means choosing the best data types for your data to save memory and make analysis faster.

beginner

Why should you convert float64 columns to float32 when possible?

Because float32 uses less memory than float64, which helps your program run faster and use less RAM.

intermediate

How does converting object columns with few unique values to category type help?

It reduces memory use by storing the unique values once and using smaller codes for each row.

beginner

What is a simple way to check memory usage of a DataFrame in pandas?

Use the DataFrame.memory_usage(deep=True) method to see how much memory each column uses.

intermediate

What can happen if you choose a data type that is too small for your data?

You might get errors or wrong results because the data type cannot hold the values correctly.

Which data type uses the least memory for storing categorical text data in pandas?

A category

B object

C int64

D float64

The 'category' type stores unique values once and uses small codes, saving memory compared to 'object'.

What is the main benefit of optimizing data types in a DataFrame?

A Data becomes more accurate

B More columns can be added

C Faster calculations and less memory use

D Data is automatically cleaned

Optimizing data types reduces memory and speeds up calculations.

Which pandas method helps you convert a column to a smaller integer type?

A to_numeric()

B astype()

C convert_dtypes()

D memory_usage()

astype() lets you change the data type of a column, for example to 'int8' or 'int16'.

If a column has only 3 unique text values, which type is best to save memory?

A category

B object

C float64

D int64

Category type is best for columns with few unique text values.

What does the 'deep=True' option do in pandas memory_usage()?

A Compresses the data

B Shows only index memory

C Ignores memory of categorical data

D Calculates memory including object data

'deep=True' counts the full memory used by objects like strings.

Explain how converting object columns to category type can improve memory usage in pandas.

Think about how repeated text values are stored.

Describe the steps you would take to optimize data types in a large DataFrame.

Start by measuring memory, then change types carefully.