Memory layout (C-order vs Fortran-order) in NumPy - Hands-On Comparison

Memory layout (C-order vs Fortran-order)

📖 Scenario: Imagine you have a small table of numbers representing sales data for 3 products over 4 days. You want to store this data in a way that computers can understand and process efficiently.

🎯 Goal: You will create a 2D array with sales data, then create two copies of this array with different memory layouts: C-order (row-major) and Fortran-order (column-major). Finally, you will print these arrays to see how the data is stored differently.

📋 What You'll Learn

Create a 2D numpy array with exact values

Create a copy of the array with C-order memory layout

Create a copy of the array with Fortran-order memory layout

Print both arrays to compare

💡 Why This Matters

🌍 Real World

Understanding memory layout helps when working with large datasets or optimizing code for speed, especially in scientific computing and machine learning.

💼 Career

Data scientists and engineers often need to optimize data storage and processing. Knowing memory layout concepts helps write efficient numpy code.

1

Create the sales data array

Create a numpy array called sales with these exact values: [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]

NumPy

import numpy as np
# Create the numpy array called sales with the given values
# Your code here

Need a hint?

Use np.array() and pass the list of lists exactly as shown.

2

Create C-order and Fortran-order copies

Create two new arrays: sales_c as a copy of sales with order='C', and sales_f as a copy of sales with order='F'.

NumPy

import numpy as np
sales = np.array([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]])
# Create sales_c as a C-order copy of sales
# Create sales_f as a Fortran-order copy of sales
# Your code here

Need a hint?

Use np.array() with the order parameter set to 'C' or 'F'.

3

Check the memory layout flags

Print the flags['C_CONTIGUOUS'] and flags['F_CONTIGUOUS'] properties for both sales_c and sales_f to see their memory layouts.

NumPy

import numpy as np
sales = np.array([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]])
sales_c = np.array(sales, order='C')
sales_f = np.array(sales, order='F')
# Print the C_CONTIGUOUS and F_CONTIGUOUS flags for sales_c and sales_f
# Your code here

Need a hint?

Use array.flags['C_CONTIGUOUS'] and array.flags['F_CONTIGUOUS'] to check memory layout.

4

Print the arrays to compare

Print the arrays sales_c and sales_f to see their contents.

NumPy

import numpy as np
sales = np.array([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]])
sales_c = np.array(sales, order='C')
sales_f = np.array(sales, order='F')
print('sales_c C_CONTIGUOUS:', sales_c.flags['C_CONTIGUOUS'])
print('sales_c F_CONTIGUOUS:', sales_c.flags['F_CONTIGUOUS'])
print('sales_f C_CONTIGUOUS:', sales_f.flags['C_CONTIGUOUS'])
print('sales_f F_CONTIGUOUS:', sales_f.flags['F_CONTIGUOUS'])
# Print sales_c and sales_f arrays
# Your code here

Need a hint?

Use print() to display the arrays.