Contiguous memory layout concept in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Check if a NumPy array is contiguous

Given the code below, what will be the output of the print statement?

NumPy

import numpy as np
arr = np.arange(12).reshape(3,4)
print(arr.flags['C_CONTIGUOUS'])

A True

B False

C None

D Raises an error

💡 Hint

Think about how reshape affects memory layout by default.

The reshape method by default returns a view with a contiguous memory layout in C order (row-major). So the C_CONTIGUOUS flag is True.

❓ Predict Output

intermediate

Effect of transpose on memory contiguity

What will be the output of this code snippet?

NumPy

import numpy as np
arr = np.arange(6).reshape(2,3)
arr_t = arr.T
print(arr_t.flags['C_CONTIGUOUS'])

A True

B None

C Raises ValueError

D False

💡 Hint

Transpose changes the order of elements in memory.

Transposing an array returns a view with a different memory layout, which is not contiguous in C order. Hence, C_CONTIGUOUS is False.

❓ data_output

advanced

Memory layout after slicing

Consider the following code. How many elements does the resulting array have, and is it contiguous in memory?

NumPy

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

A 5, False

B 10, False

C 10, True

D 5, True

💡 Hint

Slicing with a step creates a view but may break contiguity.

The slice arr[::2] selects every second element, resulting in 5 elements. This view is not contiguous in memory, so C_CONTIGUOUS is False.

❓ visualization

advanced

Visualizing memory layout flags

Which option correctly shows the output of the following code?

NumPy

import numpy as np
arr = np.arange(6).reshape(2,3)
arr_slice = arr[:, 1:3]
print(arr_slice.flags)

A

  C_CONTIGUOUS : True
  F_CONTIGUOUS : True
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

B

  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

C

  C_CONTIGUOUS : False
  F_CONTIGUOUS : False
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

D

  C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

💡 Hint

Slicing columns keeps row-major order but may break contiguity.

The slice arr[:, 1:3] returns a view that is contiguous in C order because columns are contiguous in each row. So C_CONTIGUOUS is True and F_CONTIGUOUS is False. The array does not own its data (OWNDATA is False).

🧠 Conceptual

expert

Understanding memory layout impact on performance

Which statement best explains why contiguous memory layout improves performance in NumPy?

A Contiguous arrays automatically compress data to save memory space.

B Contiguous arrays reduce the number of CPU cores needed to process data.

C Contiguous arrays allow faster access because data is stored sequentially in memory, improving cache usage.

D Contiguous arrays enable parallel processing without any synchronization.

💡 Hint

Think about how computers read memory and the role of CPU cache.

Contiguous memory means data is stored one after another. This helps the CPU cache to load data efficiently, speeding up computations. Other options are incorrect because they describe unrelated or false effects.