Contiguous arrays and stride tricks in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of array flags after slicing

What is the output of the following code snippet regarding the contiguity flags of the sliced array?

NumPy

import numpy as np
arr = np.arange(10)
sliced = arr[2:8]
print(sliced.flags['C_CONTIGUOUS'], sliced.flags['F_CONTIGUOUS'])

A True True

B False True

C True False

D False False

💡 Hint

Slicing a 1D numpy array usually returns a view that is still contiguous in memory.

The sliced array is a view of the original 1D array. For 1D arrays, both C_CONTIGUOUS and F_CONTIGUOUS flags are True since there is no distinction between row- and column-major order.

❓ data_output

intermediate

Shape and strides of a reshaped array

Given the code below, what are the shape and strides of the reshaped array?

NumPy

import numpy as np
arr = np.arange(12)
reshaped = arr.reshape((3,4))
print(reshaped.shape, reshaped.strides)

A (3, 4) (32, 8)

B (3, 4) (8, 32)

C (4, 3) (32, 8)

D (4, 3) (8, 32)

💡 Hint

Each element is 8 bytes (int64), and strides indicate bytes to step in each dimension.

The array has shape (3,4). The stride for the first dimension is 4 elements * 8 bytes = 32 bytes, and for the second dimension is 8 bytes (one element).

🔧 Debug

advanced

Why does this stride trick code raise an error?

Consider this code using numpy stride tricks. Why does it raise an error?

NumPy

import numpy as np
from numpy.lib.stride_tricks import as_strided
arr = np.arange(5)
window = as_strided(arr, shape=(3,3), strides=(8,8))
print(window)

A It raises a TypeError because strides must be integers, but 8 is a float.

B It raises a ValueError because the requested shape and strides exceed the original array's memory.

C It raises an IndexError because the shape is larger than the array length.

D It raises no error and prints a 3x3 window.

💡 Hint

Check if the strides and shape fit inside the original array's memory buffer.

The as_strided function creates a view with given shape and strides without bounds checking. Here, the maximum memory offset is 32 bytes (within the 40-byte buffer), so no error occurs. It prints [[0 1 2] [1 2 3] [2 3 4]].

❓ visualization

advanced

Visualizing memory layout with strides

Which option correctly describes the memory layout visualization of a 2D numpy array with shape (2,3) and strides (24,8)?

NumPy

import numpy as np
arr = np.array([[1,2,3],[4,5,6]], dtype=np.int64)
print(arr.strides)

A Each element is 24 bytes apart; array is 1D in memory.

B Each row is 8 bytes apart, each column 24 bytes apart; elements are stored column-wise.

C Each row is 24 bytes apart, each column 8 bytes apart; elements are stored row-wise.

D Strides indicate the number of elements, not bytes, between rows and columns.

💡 Hint

Stride values are in bytes, and int64 elements are 8 bytes each.

Strides (24,8) mean to move 24 bytes to go to next row (3 elements * 8 bytes) and 8 bytes to go to next column. This matches row-major order.

🚀 Application

expert

Using stride tricks to create sliding windows

Which option correctly creates a sliding window view of size 3 over a 1D numpy array using stride tricks?

NumPy

import numpy as np
from numpy.lib.stride_tricks import as_strided
arr = np.arange(5)

A as_strided(arr, shape=(3,), strides=(8,))

B as_strided(arr, shape=(3, 3), strides=(8, 8))

C as_strided(arr, shape=(3, 3), strides=(8, 24))

D as_strided(arr, shape=(3, 3), strides=(8, 8))[:,:]

💡 Hint

Sliding window of size 3 over 1D array should have shape (3, window_size) with correct strides.

To create sliding windows of size 3 over a 1D array of length 5, the shape should be (3,3) and strides (8,8) to move one element at a time for both dimensions. This produces [[0,1,2], [1,2,3], [2,3,4]]. Option B does this.

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