Memory usage analysis in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is memory usage analysis in data science?

Memory usage analysis is the process of checking how much computer memory (RAM) your data and programs use. It helps you understand if your data fits in memory or if you need to optimize it.

beginner

How can you check the memory usage of a pandas DataFrame?

You can use the memory_usage() method on a DataFrame. For example, df.memory_usage(deep=True) shows memory used by each column including object types.

intermediate

Why is it important to use deep=True in memory_usage()?

Using deep=True tells pandas to include the actual memory used by objects like strings, not just the pointers. This gives a more accurate memory usage.

intermediate

What Python library can help analyze memory usage of variables in detail?

The sys library with sys.getsizeof() can show memory size of objects. Also, memory_profiler is a tool to track memory usage over time.

intermediate

How can reducing data types help with memory usage?

Using smaller or more efficient data types (like int8 instead of int64) reduces the memory each value uses, saving overall memory and speeding up processing.

Which pandas method shows memory usage of a DataFrame?

A describe()

B info()

C memory_usage()

D head()

The memory_usage() method returns memory used by each column in a DataFrame.

What does deep=True do in memory_usage()?

A Excludes numeric columns

B Shows memory in megabytes

C Compresses the data

D Includes memory of objects like strings

Setting deep=True includes the actual memory used by object types such as strings.

Which Python function gives the size of an object in bytes?

A sys.getsizeof()

B len()

C type()

D memory_usage()

sys.getsizeof() returns the memory size of a Python object in bytes.

Why reduce data types in a DataFrame?

A To save memory and speed up processing

B To increase file size

C To make data harder to read

D To add more columns

Smaller data types use less memory and can make data processing faster.

Which tool helps track memory usage over time in Python?

A matplotlib

B memory_profiler

C numpy

D pandas

memory_profiler is a Python tool to monitor memory usage during program execution.

Explain how to check and interpret memory usage of a pandas DataFrame.

Think about how pandas stores different data types.

Describe ways to reduce memory usage when working with large datasets.

Consider what takes up space in your data.