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')

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

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]

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

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)

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

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])

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

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])

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

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.

Practice

1. What does it mean when a NumPy array is contiguous?

easy

A. It has more than one dimension.

B. It contains only positive numbers.

C. Its data is stored in one continuous block of memory.

D. It is a copy of another array.

Solution

1. Step 1: Understand contiguous memory

   A contiguous array stores all its elements in one continuous block of memory, which helps with fast access and efficient computation.

2. Step 2: Compare with other options

   Options B, C, and D describe other properties unrelated to memory layout.

3. Final Answer:

   Its data is stored in one continuous block of memory. -> Option C

4. Quick Check:

   Contiguous = continuous memory block [OK]

Hint: Contiguous means data is stored without gaps [OK]

Common Mistakes:

• Confusing contiguous with array shape
• Thinking contiguous means sorted data
• Assuming contiguous means a copy

2. Which of the following is the correct way to create a view using NumPy stride tricks?

easy

A. np.array(arr, copy=True)

B. np.copy(arr).reshape(3,3)

C. arr.flatten()

D. np.lib.stride_tricks.as_strided(arr, shape=(3,3), strides=(8,8))

Solution

1. Step 1: Identify stride tricks usage

   The function np.lib.stride_tricks.as_strided creates a view with a new shape and strides without copying data.

2. Step 2: Check other options

   Options B, C, and D create copies or flatten arrays, not views with stride tricks.

3. Final Answer:

   np.lib.stride_tricks.as_strided(arr, shape=(3,3), strides=(8,8)) -> Option D

4. Quick Check:

   Stride tricks use as_strided() [OK]

Hint: Use as_strided() to create views with custom strides [OK]

Common Mistakes:

• Using copy() instead of as_strided()
• Flattening instead of changing strides
• Confusing reshape with stride tricks

3. What will be the output of this code?

import numpy as np
arr = np.arange(6)
view = np.lib.stride_tricks.as_strided(arr, shape=(3,), strides=(2*arr.itemsize,))
view[0] = 100
print(arr)

medium

A. [100 1 2 3 4 100]

B. [100 1 2 3 4 5]

C. [100 1 100 3 4 5]

D. [0 1 2 3 4 5]

Solution

1. Step 1: Understand stride and view

   The view selects elements with stride 2*itemsize, so it accesses arr[0], arr[2], arr[4]. Changing view[0] changes arr[0], changing view[1] changes arr[2], etc.

2. Step 2: Analyze the assignment and print

   view[0] = 100 sets arr[0] = 100. The print shows arr with arr[0] changed, arr[2] and arr[4] unchanged.

3. Final Answer:

   [100 1 2 3 4 5] -> Option B

4. Quick Check:

   Stride view modifies original array [OK]

Hint: Stride selects spaced elements; modifying view changes original [OK]

Common Mistakes:

• Assuming view is a copy, not a view
• Ignoring stride size effect
• Expecting all elements to change

4. Identify the error in this code snippet:

import numpy as np
arr = np.arange(4)
view = np.lib.stride_tricks.as_strided(arr, shape=(3,3), strides=(8,8))
print(view)

medium

A. The shape is incompatible with the original array size.

B. The strides are incorrect for the array's data type.

C. as_strided cannot be used on 1D arrays.

D. The code will run without errors and print a 3x3 view.

Solution

1. Step 1: Check shape vs original array size

   The original array has 4 elements, but the requested view shape is (3,3) = 9 elements, which is larger than original data.

2. Step 2: Understand stride_tricks constraints

   as_strided creates views without copying, so shape must fit within original data size. Here, it does not, causing invalid memory access.

3. Final Answer:

   The shape is incompatible with the original array size. -> Option A

4. Quick Check:

   View shape must fit original array size [OK]

Hint: View shape must not exceed original array size [OK]

Common Mistakes:

• Ignoring array size vs view shape
• Assuming as_strided copies data
• Misunderstanding stride meaning

5. You have a large 1D NumPy array representing sensor data. You want to create a 2D sliding window view of size 4 without copying data. Which approach correctly uses stride tricks to achieve this?

hard

A. Use np.lib.stride_tricks.as_strided with shape=(len(arr)-3, 4) and strides=(arr.itemsize, arr.itemsize).

B. Use arr.reshape(-1, 4) to create windows.

C. Use np.copy(arr).reshape(-1, 4) to avoid modifying original data.

D. Use arr.flatten() and then slice manually.

Solution

1. Step 1: Understand sliding window with stride tricks

   Sliding window views use as_strided with shape=(number_of_windows, window_size) and strides that move one element at a time for rows and one element for columns.

2. Step 2: Check options for correct usage

   Use np.lib.stride_tricks.as_strided with shape=(len(arr)-3, 4) and strides=(arr.itemsize, arr.itemsize). correctly sets shape and strides to create overlapping windows without copying. Options B and C reshape but do not create sliding windows. Use arr.flatten() and then slice manually. flattens but does not create windows.

3. Final Answer:

   Use np.lib.stride_tricks.as_strided with shape=(len(arr)-3, 4) and strides=(arr.itemsize, arr.itemsize). -> Option A

4. Quick Check:

   Sliding windows need as_strided with correct shape and strides [OK]

Hint: Sliding windows need as_strided with overlapping strides [OK]

Common Mistakes:

• Using reshape instead of stride tricks
• Copying data unnecessarily
• Incorrect stride values causing wrong windows