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.

Practice

1. What does the memory_usage(deep=True) method in pandas do?

easy

A. It counts the number of rows in the DataFrame.

B. It deletes unused memory from the DataFrame.

C. It compresses the DataFrame to use less memory.

D. It shows detailed memory usage of each column including object types.

Solution

1. Step 1: Understand memory_usage method

   The memory_usage method reports memory used by each column in a DataFrame.

2. Step 2: Effect of deep=True parameter

   Using deep=True makes it include memory used by object types like strings, giving detailed info.

3. Final Answer:

   It shows detailed memory usage of each column including object types. -> Option D

4. Quick Check:

   memory_usage(deep=True) = detailed memory info [OK]

Hint: Remember deep=True reveals full memory use including strings [OK]

Common Mistakes:

• Thinking it deletes or compresses data
• Confusing it with counting rows
• Ignoring deep=True effect

2. Which of the following is the correct syntax to get memory usage of a pandas DataFrame df including deep analysis?

easy

A. memory_usage(df, deep=True)

B. df.memoryusage(deep=True)

C. df.memory_usage(deep=True)

D. df.get_memory_usage(deep=True)

Solution

1. Step 1: Recall pandas method name

   The correct method is memory_usage with underscore, called on DataFrame.

2. Step 2: Check method call syntax

   It is called as df.memory_usage(deep=True). Other options have wrong method names or syntax.

3. Final Answer:

   df.memory_usage(deep=True) -> Option C

4. Quick Check:

   Correct method name and call = df.memory_usage(deep=True) [OK]

Hint: Use exact method name: memory_usage with underscore [OK]

Common Mistakes:

• Misspelling method as memoryusage
• Using function style instead of method
• Adding get_ prefix incorrectly

3. Given the following code, what is the output?

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2, 3],
  'B': ['x', 'y', 'z']
})
print(df.memory_usage(deep=True))

medium

A. Error because deep=True is invalid

B. Index memory + int64 column + object column memory sizes

C. Total memory size as a single number

D. Only memory size of numeric columns

Solution

1. Step 1: Understand DataFrame columns

   Column 'A' is integers, 'B' is strings (object type), and index is default integer index.

2. Step 2: memory_usage(deep=True) output

   This method returns a Series showing memory used by index and each column separately, including deep memory for object column 'B'.

3. Final Answer:

   Index memory + int64 column + object column memory sizes -> Option B

4. Quick Check:

   memory_usage(deep=True) lists memory per column [OK]

Hint: memory_usage(deep=True) shows memory per column including index [OK]

Common Mistakes:

• Expecting a single total number output
• Ignoring index memory
• Thinking deep=True causes error

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'col': ['a', 'b', 'c']})
print(df.memory_usage(deep=False))

medium

A. It does not show full memory usage of string data.

B. The method memory_usage does not exist.

C. deep=False causes a syntax error.

D. DataFrame must have numeric columns to use memory_usage.

Solution

1. Step 1: Check memory_usage with deep=False

   By default or with deep=False, memory_usage does not count full memory used by object columns like strings.

2. Step 2: Effect on output

   This means the reported memory for 'col' will be underestimated, missing actual string memory.

3. Final Answer:

   It does not show full memory usage of string data. -> Option A

4. Quick Check:

   deep=False skips detailed object memory [OK]

Hint: Use deep=True to get full string memory usage [OK]

Common Mistakes:

• Thinking memory_usage method is missing
• Assuming deep=False causes error
• Believing numeric columns are required

5. You have a large DataFrame with many string columns. You want to reduce memory usage without losing data. Which approach helps most based on memory usage analysis?

hard

A. Convert string columns to categorical type after checking memory usage.

B. Drop all string columns to save memory.

C. Use memory_usage(deep=False) to estimate memory and ignore strings.

D. Convert all columns to float type regardless of original data.

Solution

1. Step 1: Analyze memory usage of string columns

   Using memory_usage(deep=True) shows string columns use a lot of memory.

2. Step 2: Convert strings to categorical

   Changing string columns to categorical reduces memory by storing unique values once.

3. Step 3: Avoid data loss

   Converting to categorical keeps data but uses less memory, unlike dropping or wrong type conversion.

4. Final Answer:

   Convert string columns to categorical type after checking memory usage. -> Option A

5. Quick Check:

   Convert strings to categorical for memory saving [OK]

Hint: Convert strings to categorical to save memory without losing data [OK]

Common Mistakes:

• Dropping important columns blindly
• Ignoring deep=True memory insights
• Converting all columns to float causing errors