Why memory management matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array of zeros with 5 elements.

NumPy

import numpy as np
arr = np.[1](5)

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

Azeros

Bempty

Cones

Dfull

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.ones instead of np.zeros, which creates an array of ones.

Using np.empty which creates an uninitialized array with random values.

The np.zeros function creates an array filled with zeros, which is useful for initializing memory efficiently.

2fill in blank

medium

Complete the code to check the memory size (in bytes) of a NumPy array.

NumPy

import numpy as np
arr = np.arange(10)
size_in_bytes = arr.[1]

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

Ashape

Bnbytes

Citemsize

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using size which returns the number of elements, not bytes.

Using itemsize which returns bytes per element, not total.

The nbytes attribute gives the total number of bytes consumed by the elements of the array, which helps understand memory usage.

3fill in blank

hard

Fix the error in the code to create a copy of a NumPy array to avoid modifying the original.

NumPy

import numpy as np
original = np.array([1, 2, 3])
copied = original.[1]()

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

Acopy

Bview

Cclone

Dduplicate

Attempts:

3 left

💡 Hint

Common Mistakes

Using view() which creates a shallow copy sharing the same data.

Using non-existent methods like clone() or duplicate().

The copy() method creates a new array with its own data, so changes to it don't affect the original array.

4fill in blank

hard

Fill both blanks to create a memory-efficient array of integers from 0 to 9 with 8-bit data type.

NumPy

import numpy as np
arr = np.arange(10, dtype=[1])
size = arr.[2]

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

Anp.int8

Bnp.int64

Cnbytes

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.int64 which uses more memory than needed.

Using size which returns number of elements, not bytes.

Using np.int8 reduces memory per element to 1 byte. The nbytes attribute shows total memory used.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping each word to its length only if length is greater than 3.

NumPy

words = ['data', 'science', 'is', 'fun']
lengths = { [1]: [2] for word in words if [3] }

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

Aword

Blen(word)

Clen(word) > 3

Dword.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.upper() as key which changes the word.

Using incorrect condition like len(word) < 3.

This dictionary comprehension maps each word to its length len(word) only if the length is greater than 3.

Practice

1. Why is memory management important when working with numpy arrays?

easy

A. It increases the size of arrays to store more data.

B. It makes the code look cleaner and easier to read.

C. It helps keep programs fast and stable by using memory efficiently.

D. It automatically fixes errors in the code.

Solution

1. Step 1: Understand memory use in numpy

   Numpy arrays use memory to store data, and managing this memory well keeps programs efficient.

2. Step 2: Connect memory management to performance

   Efficient memory use prevents slowdowns and crashes, making programs fast and stable.

3. Final Answer:

   It helps keep programs fast and stable by using memory efficiently. -> Option C

4. Quick Check:

   Memory management = fast and stable programs [OK]

Hint: Memory management keeps numpy fast and stable [OK]

Common Mistakes:

• Thinking memory management changes code readability
• Believing it fixes code errors automatically
• Assuming it increases array size by default

2. Which of the following numpy array declarations uses the least memory?

easy

A. np.array([1, 2, 3], dtype=np.int8)

B. np.array([1, 2, 3], dtype=np.int64)

C. np.array([1, 2, 3], dtype=np.float64)

D. np.array([1, 2, 3], dtype=np.float32)

Solution

1. Step 1: Compare data types by memory size

   int8 uses 1 byte per element, int64 uses 8 bytes, float32 uses 4 bytes, float64 uses 8 bytes.

2. Step 2: Identify smallest memory usage

   int8 uses the least memory because it stores numbers in 1 byte each.

3. Final Answer:

   np.array([1, 2, 3], dtype=np.int8) -> Option A

4. Quick Check:

   int8 = smallest memory type here [OK]

Hint: Smaller dtype means less memory used [OK]

Common Mistakes:

• Choosing float types thinking they use less memory
• Assuming int64 uses less memory than int8
• Ignoring dtype when creating arrays

3. What is the output of this code?

import numpy as np
arr = np.array([10, 20, 30], dtype=np.int16)
print(arr.nbytes)

medium

A. 3

B. 6

C. 12

D. 24

Solution

1. Step 1: Understand dtype size

   int16 means each element uses 2 bytes.

2. Step 2: Calculate total bytes

   3 elements x 2 bytes = 6 bytes total.

3. Step 3: Check nbytes output

   nbytes returns total bytes used by array data, which is 6.

4. Final Answer:

   6 -> Option B

5. Quick Check:

   3 elements x 2 bytes = 6 [OK]

Hint: Multiply elements by dtype byte size for nbytes [OK]

Common Mistakes:

• Confusing number of elements with bytes
• Using default int size instead of dtype size
• Mixing up bits and bytes

4. Find the error in this code that tries to check memory size:

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

medium

A. The attribute should be nbytes, not bytes.

B. The array must be created with a dtype to use bytes.

C. The print statement should be inside a function.

D. The array cannot be printed directly.

Solution

1. Step 1: Identify attribute for memory size

   The correct attribute to get memory size is nbytes, not bytes.

2. Step 2: Understand error cause

   Using bytes causes an AttributeError because it does not exist on numpy arrays.

3. Final Answer:

   The attribute should be nbytes, not bytes. -> Option A

4. Quick Check:

   Use arr.nbytes to get memory size [OK]

Hint: Use arr.nbytes, not arr.bytes [OK]

Common Mistakes:

• Using arr.bytes instead of arr.nbytes
• Thinking dtype is required for nbytes
• Misplacing print statement

5. You have a large numpy array of floats using float64 but want to reduce memory use without losing much precision. Which approach is best?

hard

A. Convert the array to int32 using arr.astype(np.int32).

B. Convert the array to object dtype to save memory.

C. Keep the array as float64 but slice it into smaller parts.

D. Convert the array to float32 using arr.astype(np.float32).

Solution

1. Step 1: Understand dtype memory sizes and precision

   float64 uses 8 bytes per element; float32 uses 4 bytes but keeps decimal precision.

2. Step 2: Evaluate conversion options

   Converting to float32 halves memory use with minimal precision loss; int32 loses decimal data; slicing doesn't reduce total memory; object dtype uses more memory.

3. Final Answer:

   Convert the array to float32 using arr.astype(np.float32). -> Option D

4. Quick Check:

   float32 halves memory, keeps decimals [OK]

Hint: Use float32 to save memory with decimals [OK]

Common Mistakes:

• Converting to int32 and losing decimals
• Thinking slicing reduces total memory
• Using object dtype which increases memory