Contiguous memory layout concept in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a contiguous NumPy array from a Python list.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5], [1]='C')
print(arr.flags['C_CONTIGUOUS'])

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

Aorder

Bcopy

Cdtype

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'copy' instead of 'order' will not control memory layout.

Confusing 'dtype' with memory layout parameter.

The order parameter controls the memory layout. Setting order='C' creates a contiguous array in row-major order.

2fill in blank

medium

Complete the code to check if a NumPy array is stored contiguously in memory.

NumPy

import numpy as np
arr = np.arange(10)
is_contiguous = arr.flags.[1]
print(is_contiguous)

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AF_CONTIGUOUS

BWRITEABLE

COWNDATA

DC_CONTIGUOUS

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'F_CONTIGUOUS' checks for Fortran-style layout, not C-style.

Confusing 'OWNDATA' with memory contiguity.

The C_CONTIGUOUS flag indicates if the array is stored in contiguous C-style (row-major) memory layout.

3fill in blank

hard

Fix the error in the code to make the array contiguous after slicing.

NumPy

import numpy as np
arr = np.arange(10)
sliced = arr[::2]
contiguous = sliced.[1]()
print(contiguous.flags['C_CONTIGUOUS'])

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Aflatten

Bcopy

Ccontiguous

Dreshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'flatten' returns a copy but also changes shape to 1D.

Using 'reshape' without copying may fail if memory is not contiguous.

Slicing with steps creates a non-contiguous view. Using copy() creates a new contiguous array.

4fill in blank

hard

Fill both blanks to create a 2D contiguous array and check its memory layout.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]], [1]='[2]')
print(arr.flags['C_CONTIGUOUS'])

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

Aorder

Bcopy

CC

DF

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'F' instead of 'C' creates Fortran-style layout.

Using 'copy' as parameter name is incorrect.

Setting order='C' creates a contiguous array in row-major order.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

NumPy

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

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Aword

Blen(word)

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' is invalid because words are strings.

Using 'len(word)' as key instead of value.

The dictionary comprehension uses the word as key, its length as value, and filters words with length greater than 3.