Monitoring memory usage in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the module used for monitoring memory usage.

NumPy

import [1]

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

Apsutil

Bos

Csys

Dtime

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'os' which does not provide detailed memory info.

Using 'sys' which is for system-specific parameters.

The psutil module provides functions to monitor system resources like memory usage.

2fill in blank

medium

Complete the code to get the current process memory usage in bytes.

NumPy

process = psutil.Process()
memory_info = process.[1]()

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

Amemory_info_ex

Bmemory_full_info

Cmemory_info

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'memory_full_info' which may not be available on all platforms.

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

3fill in blank

hard

Fix the error in the code to print the resident set size (RSS) memory in MB.

NumPy

print(f"Memory usage: {memory_info.[1] / (1024 * 1024):.2f} MB")

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

Avms

Brss

Cuss

Dshared

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'vms' which is virtual memory size, not physical.

Using 'uss' which is unique set size and may not be available everywhere.

The rss attribute gives the resident set size, which is the non-swapped physical memory used by the process.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps array names to their memory usage in bytes.

NumPy

memory_usage = {name: arr.[1] * arr.[2] for name, arr in arrays.items()}

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

Anbytes

Bitemsize

Csize

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'nbytes' which already gives total bytes, so multiplying again causes error.

Using 'shape' which is a tuple, not a number.

The total memory used by a NumPy array is size * itemsize, where size is the number of elements and itemsize is the bytes per element.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters arrays with memory usage greater than 1MB.

NumPy

large_arrays = {name: arr for name, arr in arrays.items() if arr.[1] * arr.[2] [3] 1024 * 1024}

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

Asize

Bitemsize

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which would select smaller arrays.

Using 'nbytes' instead of size and itemsize multiplication.

This comprehension selects arrays where size * itemsize is greater than 1MB (1024 * 1024 bytes).

Practice

1. What does the nbytes attribute of a NumPy array represent?

easy

A. The size of each element in the array

B. The total memory in bytes used by the array data

C. The number of elements in the array

D. The number of dimensions of the array

Solution

1. Step 1: Understand what nbytes means

   The nbytes attribute gives the total number of bytes consumed by the elements of the array.

2. Step 2: Differentiate from other attributes

   Other attributes like size give element count, itemsize gives size per element, and ndim gives dimensions.

3. Final Answer:

   The total memory in bytes used by the array data -> Option B

4. Quick Check:

   nbytes = total bytes used [OK]

Hint: Remember: nbytes = total bytes used by array data [OK]

Common Mistakes:

• Confusing nbytes with size (number of elements)
• Thinking nbytes is size of one element
• Mixing nbytes with number of dimensions

2. Which of the following is the correct way to get the memory usage of a NumPy array arr?

easy

A. arr.memsize

B. arr.memory()

C. arr.size()

D. arr.nbytes

Solution

1. Step 1: Recall the attribute for memory usage

   The correct attribute to get total memory used by array data is nbytes, accessed without parentheses.

2. Step 2: Check other options for correctness

   memory() and memsize do not exist; size() is a method but returns element count, not memory.

3. Final Answer:

   arr.nbytes -> Option D

4. Quick Check:

   Use nbytes attribute, no parentheses [OK]

Hint: Use arr.nbytes without parentheses to get memory [OK]

Common Mistakes:

• Using parentheses with nbytes (it's an attribute, not a method)
• Confusing size() method with memory usage
• Trying non-existent methods like memory()

3. What is the output of the following code?

import numpy as np
arr = np.array([1, 2, 3, 4], dtype=np.int32)
print(arr.nbytes)

medium

A. 8

B. 32

C. 16

D. 4

Solution

1. Step 1: Calculate number of elements and size per element

   The array has 4 elements, each of type int32 which uses 4 bytes.

2. Step 2: Multiply elements by bytes per element

   Total bytes = 4 elements * 4 bytes = 16 bytes.

3. Final Answer:

   16 -> Option C

4. Quick Check:

   4 elements x 4 bytes = 16 bytes [OK]

Hint: Multiply element count by itemsize for nbytes [OK]

Common Mistakes:

• Assuming int32 uses 8 bytes
• Confusing nbytes with number of elements
• Forgetting to multiply by element size

4. You run this code but get an error:

import numpy as np
arr = np.array([1, 2, 3])
print(arr.nbytes())

What is the cause of the error?

medium

A. nbytes is an attribute, not a method; remove parentheses

B. Array must be created with dtype specified

C. nbytes only works on 2D arrays

D. You must import nbytes separately

Solution

1. Step 1: Identify the error cause

   The code calls arr.nbytes() with parentheses, but nbytes is an attribute, not a function.

2. Step 2: Correct usage

   Access nbytes without parentheses: arr.nbytes.

3. Final Answer:

   nbytes is an attribute, not a method; remove parentheses -> Option A

4. Quick Check:

   Attributes have no parentheses [OK]

Hint: Remember: attributes like nbytes have no parentheses [OK]

Common Mistakes:

• Adding parentheses to attributes
• Thinking dtype is required for nbytes
• Believing nbytes only works on certain array shapes

5. You have a large NumPy array big_arr with 1 million elements of type float64. You want to reduce memory usage without losing much precision. Which approach helps monitor and reduce memory usage effectively?

hard

A. Check big_arr.nbytes and convert to float32 if memory is high

B. Use big_arr.size to reduce elements by half

C. Delete big_arr and recreate with zeros

D. Convert big_arr to Python list to save memory

Solution

1. Step 1: Monitor memory usage with nbytes

   Use big_arr.nbytes to see current memory consumption in bytes.

2. Step 2: Reduce memory by changing data type

   Converting from float64 (8 bytes) to float32 (4 bytes) halves memory usage with minimal precision loss.

3. Final Answer:

   Check big_arr.nbytes and convert to float32 if memory is high -> Option A

4. Quick Check:

   Use nbytes + dtype change to reduce memory [OK]

Hint: Check nbytes then downcast dtype to save memory [OK]

Common Mistakes:

• Using size instead of nbytes to measure memory
• Converting to Python list increases memory
• Deleting array without monitoring memory