Memory usage analysis in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Memory usage of DataFrame columns

What is the output of the following code showing memory usage of each column in bytes?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': np.arange(1000, dtype='int64'),
    'B': np.random.rand(1000),
    'C': ['text']*1000
})
mem_usage = df.memory_usage(deep=True)
print(mem_usage)

A

Index    128
A      8000
B      8000
C     24000
dtype: int64

B

Index    128
A      8000
B      8000
C     4000
dtype: int64

C

Index    128
A      8000
B      8000
C     32000
dtype: int64

D

Index    128
A      1000
B      8000
C     24000
dtype: int64

💡 Hint

Remember that strings with deep=True count the actual string memory, not just pointers.

The 'C' column contains 1000 identical strings 'text'. Each string takes about 24 bytes including overhead, so total is about 24000 bytes. Numeric columns use fixed sizes.

❓ data_output

intermediate

Total memory usage of DataFrame

What is the total memory usage in bytes of the DataFrame below, including index and deep memory?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'X': np.random.randint(0, 100, 5000),
    'Y': ['a']*5000
})
total_mem = df.memory_usage(deep=True).sum()
print(total_mem)

A 90000

B 50000

C 100128

D 40000

💡 Hint

Consider index memory plus each column's memory with deep=True.

Index of 5000 int64 uses 40000 bytes, 'X' column 40000 bytes, 'Y' column 20128 bytes (strings). Total is 100128 bytes.

🔧 Debug

advanced

Why does memory usage not decrease after dropping a column?

Given the code below, why does the memory usage of df not decrease after dropping column 'B'?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': np.arange(10000),
    'B': ['text']*10000
})
print(df.memory_usage(deep=True).sum())
df.drop('B', axis=1, inplace=True)
print(df.memory_usage(deep=True).sum())

A Because drop with inplace=True does not free memory immediately due to pandas internal caching.

B Because the 'B' column is still referenced elsewhere in the code, so memory is not freed.

C Because memory_usage(deep=True) always returns the same value regardless of columns.

D Because drop does not remove columns, it only hides them temporarily.

💡 Hint

Think about how pandas manages memory internally and when garbage collection happens.

pandas may keep memory allocated internally even after dropping columns due to caching and delayed garbage collection. Memory may not reduce immediately.

🧠 Conceptual

advanced

Effect of categorical dtype on memory usage

Which statement best describes the effect of converting a string column to category dtype on memory usage?

A It converts strings to integers but increases memory due to overhead.

B It increases memory usage because categories store extra metadata.

C It has no effect on memory usage but speeds up computations.

D It decreases memory usage by storing unique values once and using integer codes.

💡 Hint

Think about how categories store repeated values efficiently.

Converting to category stores unique strings once and replaces column with integer codes, reducing memory usage significantly.

🚀 Application

expert

Optimize memory usage of a large DataFrame

You have a DataFrame with 1 million rows and columns of various types. Which approach will most effectively reduce memory usage without losing data?

A Convert all integer columns to <code>int8</code> and all floats to <code>float16</code> regardless of value range.

B Convert object columns with few unique values to <code>category</code> and downcast numeric columns to smallest suitable subtype.

C Drop all columns with missing values to reduce memory usage.

D Convert all columns to string type to unify data and compress memory.

💡 Hint

Consider safe downcasting and categorical conversion for repeated strings.

Downcasting numeric columns to smallest subtype that fits data and converting low-cardinality object columns to category reduces memory without data loss. Blind conversion or dropping columns can cause data loss or inefficiency.

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