Logical operations (and, or, not) in NumPy - Mini Project: Build & Apply

Logical Operations with NumPy Arrays

📖 Scenario: Imagine you work in a store that tracks daily sales of two products. You want to find days when certain sales conditions are met using logical operations.

🎯 Goal: You will create two NumPy arrays representing daily sales of two products. Then, you will use logical and, or, and not operations to find days matching specific sales conditions.

📋 What You'll Learn

Create two NumPy arrays named product_a_sales and product_b_sales with exact daily sales values.

Create a threshold variable named threshold with the exact value 50.

Use logical operations np.logical_and, np.logical_or, and np.logical_not on the sales arrays.

Print the final results arrays exactly as specified.

💡 Why This Matters

🌍 Real World

Stores and businesses often analyze sales data to find days when multiple products perform well or need attention.

💼 Career

Data analysts and scientists use logical operations on arrays to filter and analyze large datasets efficiently.

1

Create sales data arrays

Import NumPy as np. Create two NumPy arrays called product_a_sales and product_b_sales with these exact values: [30, 60, 45, 80, 55] and [20, 70, 50, 40, 65] respectively.

NumPy

import numpy as np
# Create product_a_sales and product_b_sales arrays with given values
# Your code here

Hint

Use np.array([...]) to create the arrays with the exact numbers given.

2

Set sales threshold

Create a variable called threshold and set it to the integer value 50.

NumPy

import numpy as np
product_a_sales = np.array([30, 60, 45, 80, 55])
product_b_sales = np.array([20, 70, 50, 40, 65])
# Create threshold variable with value 50
# Your code here

Hint

Just assign the number 50 to the variable named threshold.

3

Apply logical operations

Create three variables: both_above using np.logical_and to find days when both products have sales above threshold, either_above using np.logical_or to find days when either product has sales above threshold, and not_a_above using np.logical_not to find days when product A sales are NOT above threshold.

NumPy

import numpy as np
product_a_sales = np.array([30, 60, 45, 80, 55])
product_b_sales = np.array([20, 70, 50, 40, 65])
threshold = 50
# Create both_above, either_above, and not_a_above using logical operations
# Your code here

Hint

Use comparisons like product_a_sales > threshold inside the logical functions.

4

Print the results

Print the variables both_above, either_above, and not_a_above each on a separate line in this order.

NumPy

import numpy as np
product_a_sales = np.array([30, 60, 45, 80, 55])
product_b_sales = np.array([20, 70, 50, 40, 65])
threshold = 50
both_above = np.logical_and(product_a_sales > threshold, product_b_sales > threshold)
either_above = np.logical_or(product_a_sales > threshold, product_b_sales > threshold)
not_a_above = np.logical_not(product_a_sales > threshold)
# Print both_above, either_above, and not_a_above
# Your code here

Hint

Use three separate print() statements for each variable in the order given.

Practice

1. Which NumPy function is used to perform a logical AND operation element-wise on two arrays?

easy

A. numpy.logical_and

B. numpy.and_

C. numpy.bitwise_and

D. numpy.logical_or

Solution

1. Step 1: Understand logical AND operation in NumPy

   Logical AND combines two boolean arrays and returns True only where both are True.

2. Step 2: Identify the correct NumPy function

   numpy.logical_and is the function designed for element-wise logical AND on boolean arrays.

3. Final Answer:

   numpy.logical_and -> Option A

4. Quick Check:

   AND operation = numpy.logical_and [OK]

Hint: AND operation in NumPy uses logical_and function [OK]

Common Mistakes:

• Confusing bitwise_and with logical_and
• Using logical_or instead of logical_and
• Using and_ which is not a NumPy function

2. Which of the following is the correct syntax to apply a logical NOT operation on a NumPy array a?

easy

A. numpy.logical_not[]

B. numpy.not(a)

C. numpy.logical_not(a)

D. not numpy.logical(a)

Solution

1. Step 1: Recall the correct function for logical NOT in NumPy

   The function is numpy.logical_not(array) to invert boolean values element-wise.

2. Step 2: Check syntax correctness

   numpy.logical_not(a) uses correct function and parentheses syntax; others are invalid or incorrect.

3. Final Answer:

   numpy.logical_not(a) -> Option C

4. Quick Check:

   NOT operation = numpy.logical_not(array) [OK]

Hint: Use logical_not with parentheses for NOT operation [OK]

Common Mistakes:

• Using numpy.not which does not exist
• Using square brackets instead of parentheses
• Trying to negate with not keyword on arrays

3. What is the output of the following code?

import numpy as np
x = np.array([True, False, True])
y = np.array([False, False, True])
result = np.logical_or(x, y)
print(result)

medium

A. [False False False]

B. [False True True]

C. [True True True]

D. [True False True]

Solution

1. Step 1: Understand logical OR operation element-wise

   logical_or returns True if either element in the pair is True.

2. Step 2: Apply logical_or to each pair in arrays x and y

   Pairs: (True, False) -> True, (False, False) -> False, (True, True) -> True.

3. Final Answer:

   [True False True] -> Option D

4. Quick Check:

   logical_or([True,False,True],[False,False,True]) = [True,False,True] [OK]

Hint: OR is True if any element is True in pair [OK]

Common Mistakes:

• Confusing logical_or with logical_and
• Misreading boolean values in arrays
• Assuming all False if one element is False

4. Identify the error in the following code snippet:

import numpy as np
arr = np.array([True, False, True])
result = arr and np.array([False, True, False])
print(result)

medium

A. np.array should be np.logical_and

B. Using 'and' operator directly on NumPy arrays causes an error

C. Missing parentheses around arrays

D. Arrays must be converted to lists before logical operations

Solution

1. Step 1: Understand Python 'and' operator with arrays

   'and' is a Python keyword that does not work element-wise on arrays; it expects single boolean values.

2. Step 2: Identify correct way to do element-wise logical AND

   Use numpy.logical_and(arr1, arr2) for element-wise AND on arrays.

3. Final Answer:

   Using 'and' operator directly on NumPy arrays causes an error -> Option B

4. Quick Check:

   Python 'and' fails on arrays; use logical_and [OK]

Hint: Use numpy.logical_and, not Python 'and' for arrays [OK]

Common Mistakes:

• Using Python 'and' instead of numpy.logical_and
• Trying to use 'and' without parentheses
• Converting arrays to lists unnecessarily

5. Given two NumPy arrays:

a = np.array([1, 0, 3, 0, 5])
b = np.array([0, 2, 3, 4, 0])

Which code snippet correctly creates a boolean array that is True where a is non-zero AND b is zero?

hard

A. np.logical_and(a != 0, b == 0)

B. np.logical_or(a != 0, b == 0)

C. np.logical_not(np.logical_and(a == 0, b != 0))

D. np.logical_and(a == 0, b == 0)

Solution

1. Step 1: Translate the condition into logical expressions

   We want True where a is not zero AND b is zero, so conditions are a != 0 and b == 0.

2. Step 2: Use numpy.logical_and to combine conditions element-wise

   np.logical_and(a != 0, b == 0) returns True only where both conditions hold.

3. Final Answer:

   np.logical_and(a != 0, b == 0) -> Option A

4. Quick Check:

   AND of (a != 0) and (b == 0) = np.logical_and(a != 0, b == 0) [OK]

Hint: Combine conditions with logical_and for AND filtering [OK]

Common Mistakes:

• Using logical_or instead of logical_and
• Negating conditions incorrectly
• Checking for zeros instead of non-zeros