Memory usage analysis in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check the memory usage of a DataFrame.

Pandas

import pandas as pd

df = pd.DataFrame({'A': range(1000), 'B': range(1000, 2000)})
memory = df.[1]()
print(memory)

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

Ahead

Binfo

Cdescribe

Dmemory_usage

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.info() which shows summary but not detailed memory usage.

Using df.describe() which shows statistics, not memory.

Using df.head() which shows data rows, not memory.

The memory_usage() method shows the memory used by each column in the DataFrame.

2fill in blank

medium

Complete the code to get the total memory usage of the DataFrame in bytes.

Pandas

import pandas as pd

df = pd.DataFrame({'A': range(500), 'B': range(500, 1000)})
total_mem = df.memory_usage([1]).sum()
print(total_mem)

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

Adeep=False

Bindex=True

Cdeep=True

Dindex=False

Attempts:

3 left

💡 Hint

Common Mistakes

Using index=False which excludes index memory but not detailed object memory.

Using deep=False which gives less accurate memory usage.

Using index=True which includes index but not deep memory.

Using deep=True gives a more accurate memory usage including object types.

3fill in blank

hard

Fix the error in the code to correctly print the memory usage of column 'A'.

Pandas

import pandas as pd

df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': [1, 2, 3]})
mem_col = df['A'].[1](deep=True)
print(mem_col)

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

Amemory

Bmemory_usage

Cmemory_use

Dmem_usage

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like memory or mem_usage.

Forgetting parentheses after method name.

The correct method to get memory usage of a Series is memory_usage().

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using less than '<' instead of greater than '>'.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values for words longer than 4 characters.

Pandas

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
result = [1]: [2] for w in words if len(w) [3] 4}
print(result)

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

Aw.upper()

Bw

C>

Dlen(w)

Attempts:

3 left

💡 Hint

Common Mistakes

Using length as value instead of the word.

Using less than '<' instead of greater than '>'.

Not converting keys to uppercase.

The dictionary comprehension uses uppercase words as keys, original words as values, and filters words longer than 4 characters.

Practice

1. What does the memory_usage() function in pandas primarily show?

easy

A. The amount of memory used by each column and the index in a DataFrame

B. The total number of rows in a DataFrame

C. The data types of each column in a DataFrame

D. The number of missing values in each column

Solution

1. Step 1: Understand the purpose of memory_usage()

   This function reports memory consumption of each column and the index in a pandas DataFrame.

2. Step 2: Compare with other options

   Options A, B, and D describe different DataFrame properties unrelated to memory usage.

3. Final Answer:

   The amount of memory used by each column and the index in a DataFrame -> Option A

4. Quick Check:

   memory_usage() = memory per column and index [OK]

Hint: memory_usage() shows memory per column and index [OK]

Common Mistakes:

• Confusing memory_usage() with shape or info()
• Thinking it shows data types or missing values
• Ignoring that it includes index memory

2. Which of the following is the correct syntax to get detailed memory usage of a DataFrame df including object types?

easy

A. df.memory_usage()

B. df.memory_usage(True)

C. df.memory_usage(deep=True)

D. df.memory_usage(deep=False)

Solution

1. Step 1: Recall the parameter for detailed memory info

   The deep=True argument tells pandas to include full memory usage for object columns.

2. Step 2: Check syntax correctness

   df.memory_usage(deep=True) uses the correct keyword argument deep=True. df.memory_usage(True) uses positional argument incorrectly. Options A, B, and C do not provide detailed memory info.

3. Final Answer:

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

4. Quick Check:

   Use deep=True for detailed memory [OK]

Hint: Use deep=True to get full memory info on objects [OK]

Common Mistakes:

• Passing True without keyword (positional argument error)
• Not using deep=True and missing object memory
• Using deep=False which is default and less detailed

3. Given the following code:

import pandas as pd

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

What will be the output type of print(df.memory_usage(deep=True))?

medium

A. A pandas Series showing memory usage per column and index

B. A single integer showing total memory usage

C. A dictionary with column names as keys and memory usage as values

D. A DataFrame with memory usage details

Solution

1. Step 1: Understand the return type of memory_usage()

   The memory_usage() method returns a pandas Series with index as column names plus the index, and values as memory usage in bytes.

2. Step 2: Check the code output type

   Since deep=True is used, it includes detailed memory for object columns but still returns a Series, not a dict or DataFrame.

3. Final Answer:

   A pandas Series showing memory usage per column and index -> Option A

4. Quick Check:

   memory_usage() returns Series [OK]

Hint: memory_usage() returns Series with memory per column [OK]

Common Mistakes:

• Expecting total memory as single integer
• Thinking it returns a dictionary or DataFrame
• Confusing output type with other pandas methods

4. You run df.memory_usage(deep=True) but get an error: TypeError: memory_usage() got an unexpected keyword argument 'deep'. What is the likely cause?

medium

A. You forgot to import pandas as pd

B. You are using an older pandas version that does not support the 'deep' argument

C. You passed 'deep' as a positional argument instead of keyword

D. The DataFrame is empty

Solution

1. Step 1: Analyze the error message

   The error says 'unexpected keyword argument deep', meaning the function does not recognize 'deep'.

2. Step 2: Identify cause based on pandas version

   Older pandas versions did not support the 'deep' argument in memory_usage(). This is the most common cause.

3. Final Answer:

   You are using an older pandas version that does not support the 'deep' argument -> Option B

4. Quick Check:

   Old pandas versions lack deep argument [OK]

Hint: Check pandas version if 'deep' argument causes error [OK]

Common Mistakes:

• Assuming import error causes this
• Thinking positional argument fixes it
• Blaming empty DataFrame for this error

5. You have a large DataFrame with many object columns. You want to reduce memory usage by converting some columns to categorical type. Which approach helps you analyze memory before and after conversion?

hard

A. Use df.memory_usage() without deep argument before conversion

B. Use df.info() only after conversion

C. Use df.describe() before conversion

D. Use df.memory_usage(deep=True) before and after converting columns to 'category' dtype

Solution

1. Step 1: Understand memory usage measurement

   Using memory_usage(deep=True) gives detailed memory info including object columns, which is important before conversion.

2. Step 2: Compare memory before and after conversion

   By running this method before and after converting columns to 'category', you can see the memory savings clearly.

3. Final Answer:

   Use df.memory_usage(deep=True) before and after converting columns to 'category' dtype -> Option D

4. Quick Check:

   memory_usage(deep=True) shows detailed memory changes [OK]

Hint: Compare memory_usage(deep=True) before and after changes [OK]

Common Mistakes:

• Using info() which shows less detailed memory
• Using describe() which summarizes stats, not memory
• Using memory_usage() without deep misses object memory