Memory usage analysis in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the module used for memory profiling.

Data Analysis Python

import [1]

Drag options to blanks, or click blank then click option'

Apandas

Bsys

Cos

Dmemory_profiler

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated modules like os or pandas.

Using sys which is for system-specific parameters, not memory profiling.

The memory_profiler module is used to analyze memory usage in Python.

2fill in blank

medium

Complete the code to get the current memory usage of the Python process in MB.

Data Analysis Python

import psutil
process = psutil.Process()
mem = process.[1]().rss / (1024 * 1024)
print(f"Memory usage: {mem:.2f} MB")

Drag options to blanks, or click blank then click option'

Amemory_percent

Bmemory_full_info

Cmemory_info

Dmemory_maps

Attempts:

3 left

💡 Hint

Common Mistakes

Using memory_percent which returns a percentage, not bytes.

Using memory_maps which returns memory mapped files info.

The memory_info() method returns memory usage details of the process.

3fill in blank

hard

Fix the error in the code to correctly measure memory usage of a function using a decorator.

Data Analysis Python

@memory_profiler.[1]
def my_function():
    a = [i for i in range(100000)]
    return sum(a)

Drag options to blanks, or click blank then click option'

Aprofile

Bprofiled

Cmeasure

Dmemory

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect decorator names like 'profiled' or 'measure'.

Forgetting the decorator symbol '@'.

The @memory_profiler.profile decorator measures memory usage of the decorated function.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps variable names to their memory size in bytes, filtering only variables larger than 1000 bytes.

Data Analysis Python

memory_usage = {var: sys.getsizeof([1]) for var in globals() if sys.getsizeof([2]) > 1000}

Drag options to blanks, or click blank then click option'

Aglobals()[var]

Blocals()[var]

Cvar

Dglobals()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'var' directly which is just the variable name string.

Using locals() when variables are in globals().

We use globals()[var] to get the value of each variable by name for size checking.

5fill in blank

hard

Fill all three blanks to create a list comprehension that collects sizes of all list variables in locals() larger than 5000 bytes.

Data Analysis Python

sizes = [sys.getsizeof([1]) for [2], [3] in locals().items() if isinstance([1], list) and sys.getsizeof([1]) > 5000]

Drag options to blanks, or click blank then click option'

Avalue

Bname

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using the variable name string instead of the value for size and type checks.

Mixing up the order of key and value in the loop.

We iterate over locals().items() with name, value. The variable value is used to check type and size.

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