Understanding array memory layout in NumPy - Practice Questions & Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of array strides in NumPy

What is the output of the following code showing the strides of a 2D NumPy array?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
print(arr.strides)

A (12, 4)

B (3, 1)

C (8, 4)

D (4, 12)

💡 Hint

Remember that strides show the number of bytes to step in each dimension.

The array has shape (2, 3) with dtype int32 (4 bytes). The stride for the first dimension is 3 elements * 4 bytes = 12 bytes. The stride for the second dimension is 4 bytes.

❓ data_output

intermediate

Effect of transpose on array strides

What is the output of the strides attribute after transposing the array?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32)
arr_t = arr.T
print(arr_t.strides)

A (4, 12)

B (12, 4)

C (3, 1)

D (8, 4)

💡 Hint

Transposing swaps the dimensions and their strides.

Transposing swaps the shape and strides. Original strides were (12, 4), so after transpose they become (4, 12).

❓ visualization

advanced

Visualizing memory layout of a reshaped array

Which option correctly describes the memory layout visualization of the following reshaped array?

NumPy

import numpy as np
arr = np.arange(6, dtype=np.int32)
arr_reshaped = arr.reshape((2, 3))
print(arr_reshaped.strides)

A The array is stored non-contiguously with gaps between elements.

B The array is stored with strides (4, 12), columns stored one after another.

C The array is stored in a contiguous block with strides (12, 4), rows stored one after another.

D The array is stored in Fortran order with strides (4, 8).

💡 Hint

Check the strides and how reshape affects memory layout.

Reshape does not change the memory layout if possible. The array is contiguous with strides (12, 4) meaning rows are stored one after another.

🧠 Conceptual

advanced

Understanding C-contiguous vs F-contiguous arrays

Which statement correctly describes the difference between C-contiguous and F-contiguous arrays in NumPy?

A C-contiguous arrays are always 1D; F-contiguous arrays are always 2D.

B C-contiguous arrays store columns sequentially; F-contiguous arrays store rows sequentially.

C Both C-contiguous and F-contiguous arrays store data randomly in memory.

D C-contiguous arrays store rows sequentially in memory; F-contiguous arrays store columns sequentially.

💡 Hint

Think about how multi-dimensional arrays are stored in C and Fortran languages.

C-contiguous means rows are stored one after another (row-major order). F-contiguous means columns are stored one after another (column-major order).

🔧 Debug

expert

Identifying cause of unexpected array memory layout

Given the code below, why does arr.flags['C_CONTIGUOUS'] return False?

NumPy

import numpy as np
arr = np.arange(12).reshape((3,4))
arr_sliced = arr[:, ::2]
print(arr_sliced.flags['C_CONTIGUOUS'])

A Because the array dtype is not compatible with C-contiguity.

B Because slicing with a step creates a non-contiguous view in memory.

C Because the array has only one dimension.

D Because reshape always returns a non-contiguous array.

💡 Hint

Consider how slicing with steps affects memory layout.

Slicing with a step (like ::2) creates a view that skips elements, breaking the contiguous memory layout.

Practice

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

easy

A. The array is stored as a list of Python objects.

B. The array is stored in column-major order, meaning columns are stored one after another in memory.

C. The array is stored in row-major order, meaning rows are stored one after another in memory.

D. The array is stored in a compressed format to save memory.

Solution

1. Step 1: Understand memory layout types

   C-contiguous means the array is stored in row-major order, where rows are stored sequentially in memory.

2. Step 2: Compare with other layouts

   Fortran-contiguous arrays store columns sequentially, not rows.

3. Final Answer:

   The array is stored in row-major order, meaning rows are stored one after another in memory. -> Option C

4. Quick Check:

   C-contiguous = row-major order [OK]

Hint: C-contiguous means rows stored sequentially in memory [OK]

Common Mistakes:

• Confusing C-contiguous with Fortran-contiguous
• Thinking C-contiguous means compressed storage
• Assuming C-contiguous means column-major order

2. Which of the following NumPy commands correctly checks if an array a is stored in Fortran-contiguous order?

easy

A. np.isfortran(a)

B. np.iscontiguous(a)

C. a.is_fortran()

D. a.flags.f_contiguous

Solution

1. Step 1: Recall NumPy flags for memory layout

   NumPy arrays have a flags attribute with f_contiguous to check Fortran order.

2. Step 2: Identify correct syntax

   a.flags.f_contiguous returns True if array a is Fortran-contiguous.

3. Final Answer:

   a.flags.f_contiguous -> Option D

4. Quick Check:

   Check Fortran order with a.flags.f_contiguous [OK]

Hint: Use a.flags.f_contiguous to check Fortran order [OK]

Common Mistakes:

• Using non-existent functions like np.isfortran
• Confusing C-contiguous and Fortran-contiguous flags
• Trying to call methods that don't exist on arrays

3. Consider the following code:

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
transposed = arr.T
print(transposed.flags.c_contiguous)

What will be the output?

medium

A. True

B. False

C. Raises an AttributeError

D. None

Solution

1. Step 1: Understand the original array layout

   Original array arr is C-contiguous by default (row-major).

2. Step 2: Analyze the transpose operation

   Transposing arr.T changes memory layout to non-C-contiguous because rows and columns swap.

3. Step 3: Check the flag after transpose

   transposed.flags.c_contiguous will be False since the transpose breaks row-major order.

4. Final Answer:

   False -> Option B

5. Quick Check:

   Transpose breaks C-contiguity = False [OK]

Hint: Transpose usually breaks C-contiguity, so flag is False [OK]

Common Mistakes:

• Assuming transpose keeps C-contiguity True
• Confusing c_contiguous with f_contiguous
• Expecting an error instead of a boolean

4. You have this code:

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

Why might sliced.flags.c_contiguous be False?

medium

A. Because slicing can create views that are not stored continuously in memory.

B. Because slicing always returns a copy, which is never contiguous.

C. Because reshape changes the memory layout to Fortran order.

D. Because arange creates arrays that are never contiguous.

Solution

1. Step 1: Understand slicing behavior

   Slicing often returns a view, which may not be stored continuously in memory.

2. Step 2: Check memory layout after slicing

   The slice arr[:, 1:3] selects columns 1 and 2, which may not be contiguous in row-major order.

3. Step 3: Explain why c_contiguous is False

   Because the slice skips some elements in memory, the resulting view is not C-contiguous.

4. Final Answer:

   Because slicing can create views that are not stored continuously in memory. -> Option A

5. Quick Check:

   Slicing can break contiguity = False [OK]

Hint: Slicing can create non-contiguous views, so flag may be False [OK]

Common Mistakes:

• Thinking slicing always returns a copy
• Assuming reshape changes memory layout to Fortran order by default
• Believing arange arrays are never contiguous

5. You want to create a large 2D NumPy array with shape (1000, 1000) that is stored in Fortran order for faster column-wise operations. Which code snippet correctly creates such an array?

hard

A. np.zeros((1000, 1000), order='F')

B. np.zeros((1000, 1000), order='C')

C. np.zeros((1000, 1000)).T.copy()

D. np.zeros((1000, 1000), fortran=True)

Solution

1. Step 1: Recall how to specify memory order in NumPy

   Use the order parameter with value 'F' to create Fortran-contiguous arrays.

2. Step 2: Analyze each option

   np.zeros((1000, 1000), order='F') uses order='F', which is correct. np.zeros((1000, 1000), order='C') uses 'C' (row-major). np.zeros((1000, 1000)).T.copy() transposes a C-contiguous array and copies it into a new C-contiguous array by default. np.zeros((1000, 1000), fortran=True) uses an invalid parameter.

3. Final Answer:

   np.zeros((1000, 1000), order='F') -> Option A

4. Quick Check:

   Use order='F' to create Fortran order arrays [OK]

Hint: Use order='F' in array creation for Fortran order [OK]

Common Mistakes:

• Using order='C' instead of 'F'
• Trying to transpose to get Fortran order
• Using invalid parameters like fortran=True