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)

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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]

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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]()

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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]

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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] }

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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.