Monitoring memory usage in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

Memory usage of a NumPy array

What is the output of the following code that checks the memory usage of a NumPy array?

NumPy

import numpy as np
arr = np.arange(1000, dtype=np.int32)
print(arr.nbytes)

A 4000

B 8000

C 1000

D 2000

💡 Hint

Each int32 element uses 4 bytes. Multiply by number of elements.

The array has 1000 elements, each 4 bytes (int32), so total memory is 4000 bytes.

🧠 Conceptual

intermediate

Understanding memory views in NumPy

Which statement correctly describes the memory usage when creating a slice view of a NumPy array?

A The slice creates a new array and copies all data, increasing memory usage.

B The slice compresses the data to save memory.

C The slice deletes the original array to free memory.

D The slice shares the original data buffer, so no extra memory is used for data.

💡 Hint

Think about whether slicing copies data or not.

NumPy slices create views sharing the same data buffer, so no extra memory is used for the data itself.

❓ Troubleshoot

advanced

Diagnosing high memory usage in NumPy arrays

You notice your Python program using too much memory when working with large NumPy arrays. Which of the following is the most likely cause?

A Using arrays with dtype float64 instead of float32 unnecessarily.

B Using array views instead of copies.

C Using np.arange instead of np.linspace for array creation.

D Using np.zeros instead of np.empty to initialize arrays.

💡 Hint

Consider how data type affects memory size per element.

float64 uses 8 bytes per element, double the memory of float32 which uses 4 bytes.

✅ Best Practice

advanced

Reducing memory footprint of large NumPy arrays

Which approach is best to reduce memory usage when storing large integer arrays in NumPy?

A Convert arrays to Python lists to save memory.

B Use dtype=np.int64 for maximum precision.

C Use dtype=np.int8 if values fit in 8 bits.

D Use object dtype to store integers flexibly.

💡 Hint

Choose the smallest integer type that fits your data.

Using smaller integer types like int8 reduces memory per element significantly.

🔀 Workflow

expert

Monitoring memory usage dynamically in a NumPy-based application

Which Python tool or method is best suited to monitor memory usage of a running Python program that uses NumPy arrays?

A Using the time module to measure execution time.

B Using the memory_profiler module with @profile decorator.

C Using the os.system('free -m') command inside the script.

D Using print statements to output arr.nbytes frequently.

💡 Hint

Look for a tool designed to measure memory usage line-by-line.

memory_profiler is a Python module that tracks memory usage of code lines, ideal for monitoring NumPy memory.