Vectorization over loops in NumPy - Mini Project: Build & Apply

Vectorization over loops

📖 Scenario: You work in a small bakery that tracks daily sales of three types of bread. You have the sales data for a week and want to calculate the total sales for each bread type.

🎯 Goal: Build a program that calculates total sales for each bread type using vectorized operations with numpy instead of loops.

📋 What You'll Learn

Create a numpy array with daily sales data for three bread types over 7 days.

Create a variable to hold the number of days.

Use vectorized numpy operations to calculate total sales for each bread type.

Print the total sales array.

💡 Why This Matters

🌍 Real World

Vectorized operations are used in data science to quickly process large datasets like sales, sensor data, or images without slow loops.

💼 Career

Data scientists and analysts use vectorization to write faster and cleaner code for data aggregation and analysis.

1

Create the sales data array

Create a numpy array called sales with the exact values: [[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]].

NumPy

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

Hint

Use np.array([...]) with the exact nested list of sales values.

2

Create the days variable

Create a variable called days and set it to the number of rows in the sales array using sales.shape[0].

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]])
# Create the variable days with the number of rows in sales
# Your code here

Hint

Use sales.shape[0] to get the number of rows (days).

3

Calculate total sales using vectorization

Create a variable called total_sales and set it to the sum of sales over the rows (axis 0) using np.sum(sales, axis=0).

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]])
days = sales.shape[0]
# Calculate total_sales by summing sales over axis 0
# Your code here

Hint

Use np.sum with axis=0 to sum each bread type's sales over all days.

4

Print the total sales

Write a print statement to display the total_sales array.

NumPy

import numpy as np
sales = np.array([[10, 15, 20], [12, 18, 22], [11, 14, 19], [13, 17, 21], [14, 16, 23], [15, 19, 20], [16, 20, 24]])
days = sales.shape[0]
total_sales = np.sum(sales, axis=0)
# Print the total_sales array
# Your code here

Hint

Use print(total_sales) to display the result.

Practice

1. What is the main advantage of using vectorization in numpy instead of loops?

easy

A. It makes the code run faster by doing many operations at once.

B. It uses more memory but slows down the code.

C. It makes the code harder to read and understand.

D. It requires writing more lines of code.

Solution

1. Step 1: Understand vectorization concept

   Vectorization means applying operations on whole arrays at once, not one element at a time.

2. Step 2: Compare speed and simplicity

   This approach uses optimized numpy functions that run faster than explicit loops.

3. Final Answer:

   It makes the code run faster by doing many operations at once. -> Option A

4. Quick Check:

   Vectorization = faster code [OK]

Hint: Vectorization speeds up code by handling arrays at once [OK]

Common Mistakes:

• Thinking vectorization slows code
• Believing vectorization needs more code
• Confusing vectorization with loops

2. Which of the following numpy codes correctly replaces a loop to add 5 to each element in array a?

easy

A. a + 5

B. for i in a: i = i + 5

C. np.add(a, 5, out=a)

D. a.append(5)

Solution

1. Step 1: Identify vectorized addition

   Using np.add(a, 5, out=a) adds 5 to every element in array a efficiently.

2. Step 2: Check other options

   a + 5 works but returns a new array, for i in a: i = i + 5 does not change a, and a.append(5) is invalid for numpy arrays.

3. Final Answer:

   np.add(a, 5, out=a) -> Option C

4. Quick Check:

   np.add = vectorized add [OK]

Hint: Use numpy functions like np.add for vectorized operations [OK]

Common Mistakes:

• Using loops that don't modify array
• Trying list methods on numpy arrays
• Confusing array addition with append

3. What is the output of this code?

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 2
print(result)

medium

A. [2 4 6]

B. [1 2 3 1 2 3]

C. [3 4 5]

D. Error: cannot multiply array by int

Solution

1. Step 1: Understand array multiplication

   Multiplying a numpy array by a number multiplies each element by that number.

2. Step 2: Calculate element-wise multiplication

   Each element in [1, 2, 3] is multiplied by 2, resulting in [2, 4, 6].

3. Final Answer:

   [2 4 6] -> Option A

4. Quick Check:

   Array * number = element-wise multiply [OK]

Hint: Array * number multiplies each element [OK]

Common Mistakes:

• Thinking it concatenates arrays
• Expecting an error on multiplication
• Confusing element-wise with matrix multiply

4. Identify the error in this code that tries to add 10 to each element of arr using a loop:

import numpy as np
arr = np.array([1, 2, 3])
for i in arr:
    i = i + 10
print(arr)

medium

A. Syntax error in for loop.

B. Loop variable i does not modify original array elements.

C. Array arr is not defined.

D. Cannot add integer to numpy array elements.

Solution

1. Step 1: Understand loop variable behavior

   Variable i is a copy of each element, so changing i does not change arr.

2. Step 2: Explain why array remains unchanged

   The original array arr stays the same because the loop does not assign back to array elements.

3. Final Answer:

   Loop variable i does not modify original array elements. -> Option B

4. Quick Check:

   Loop var copy ≠ array change [OK]

Hint: Loop vars are copies; assign to array to change [OK]

Common Mistakes:

• Assuming loop changes array automatically
• Thinking syntax error exists
• Believing numpy disallows int addition

5. You have a numpy array data = np.array([1, 2, 3, 4, 5]). You want to create a new array where each element is squared if it is even, otherwise doubled. Which vectorized code achieves this?

hard

A. data * 2 if data % 2 else data ** 2

B. [x**2 if x%2==0 else x*2 for x in data]

C. for i in range(len(data)): data[i] = data[i]**2 if data[i]%2==0 else data[i]*2

D. np.where(data % 2 == 0, data**2, data*2)

Solution

1. Step 1: Understand vectorized conditional with np.where

   np.where(condition, x, y) returns array choosing x where condition is true, else y.

2. Step 2: Apply condition for even and odd elements

   Condition data % 2 == 0 checks even numbers; squares them, else doubles.

3. Step 3: Check other options

   [x**2 if x%2==0 else x*2 for x in data] uses list comprehension (not vectorized), C uses loop (not vectorized), D is invalid syntax.

4. Final Answer:

   np.where(data % 2 == 0, data**2, data*2) -> Option D

5. Quick Check:

   np.where = vectorized if-else [OK]

Hint: Use np.where for vectorized if-else conditions [OK]

Common Mistakes:

• Using loops or list comprehensions instead of vectorization
• Writing invalid syntax for conditional operations
• Confusing element-wise operations with scalar