Contiguous arrays and stride tricks in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NumPy

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

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Aorder

Bcopy

Cdtype

Dndmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'copy' instead of 'order' does not control contiguity.

Confusing 'dtype' with memory layout parameter.

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

2fill in blank

medium

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

NumPy

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

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Acontiguous

Bis_contiguous()

Cis_c_contiguous

Dflags['C_CONTIGUOUS']

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to call a method that does not exist.

Using incorrect attribute names.

The flags attribute shows memory layout flags. flags['C_CONTIGUOUS'] returns True if the array is contiguous in C order.

3fill in blank

hard

Fix the error in the code to create a sliding window view using stride tricks.

NumPy

import numpy as np
from numpy.lib.stride_tricks import [1]
arr = np.arange(5)
windows = [1](arr, window_shape=3)

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Asliding_window_view

Bas_strided

Cbroadcast_to

Dreshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'as_strided' directly can cause errors if not used carefully.

Trying to use 'reshape' or 'broadcast_to' which do not create sliding windows.

The function sliding_window_view creates a view of sliding windows over the array.

4fill in blank

hard

Fill both blanks to create a 2D sliding window view of size 2x2 on a 3x3 array.

NumPy

import numpy as np
from numpy.lib.stride_tricks import [1]
arr = np.arange(9).reshape(3, 3)
windows = [1](arr, window_shape=[2])

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Asliding_window_view

Bas_strided

C(2, 2)

D(3, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong function name.

Using the wrong window shape size.

Use sliding_window_view with window_shape=(2, 2) to get 2x2 windows sliding over the 3x3 array.

5fill in blank

hard

Fill all three blanks to create a contiguous array, then create a sliding window view of size 3.

NumPy

import numpy as np
from numpy.lib.stride_tricks import [1]
arr = np.arange(10)[[2]]
contiguous_arr = np.ascontiguousarray(arr)
windows = [1](contiguous_arr, window_shape=[3])

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Asliding_window_view

Bslice(0, 10)

C3

Dslice(1, 11)

Attempts:

3 left

💡 Hint

Common Mistakes

Using an incorrect slice range.

Forgetting to make the array contiguous before sliding window.

Use slice(0, 10) to select elements, create a contiguous array with np.ascontiguousarray, then use sliding_window_view with window size 3.