Memory usage analysis in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the memory_usage() method in pandas do?

It shows the amount of memory used by each column in a DataFrame or by a Series. This helps understand how much space your data takes in memory.

intermediate

How can you get the total memory usage of a pandas DataFrame including the index?

Use df.memory_usage(deep=True).sum(). The deep=True option counts the actual memory usage of objects like strings.

beginner

Why is it useful to analyze memory usage in pandas?

It helps you find which columns use the most memory. Then you can optimize data types or drop unnecessary columns to save memory and speed up your program.

intermediate

What does the deep=True parameter do in memory_usage()?

It makes pandas calculate the full memory usage of object types like strings, not just the pointer size. This gives a more accurate memory size.

intermediate

How can you reduce memory usage after analyzing it in pandas?

You can convert columns to smaller data types (like int8 instead of int64), or convert strings to categories if they have few unique values.

Which pandas method shows memory usage of each column in a DataFrame?

A memory_usage()

B info()

C describe()

D head()

memory_usage() returns memory used by each column. info() shows summary but less detail on memory.

What does setting deep=True in memory_usage() do?

A Ignores index memory

B Compresses data in memory

C Only counts numeric columns

D Counts full memory of object types like strings

With deep=True, pandas counts actual memory used by objects like strings, not just pointers.

How do you get total memory usage of a DataFrame including index?

A df.info()

B df.memory_usage(deep=True).sum()

C df.describe()

D df.head()

Summing memory_usage(deep=True) gives total memory including index.

Why might you convert a string column to category after memory analysis?

A To make it numeric

B To increase memory usage

C To save memory if few unique values

D To delete the column

Categorical data uses less memory when there are few unique values.

Which data type change can reduce memory usage for integer columns?

A int64 to int8

B int8 to int64

C float64 to object

D object to string

Using smaller integer types like int8 reduces memory compared to int64.

Explain how to check memory usage of a pandas DataFrame and why it is important.

Think about how much space your data takes and how to find it.

Describe two ways to reduce memory usage in pandas after analyzing it.

Focus on changing data types and removing data.