Logical Operators

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What are Logical Operators?

Logical operators are used to combine conditional statements (Boolean expressions). They help us make decisions in our code.

The Three Logical Operators

Operator	Description	Example
and	True if both are True	A and B
or	True if at least one is True	A or B
not	True if condition is False	not A

Step 1: Basic Setup and Introduction

a = True
b = False

Step 2: Using and

Explanation:

and returns True only if both sides are True.

print(a and b)  # False, because b is False
print(True and True)  # True
print(False and True)  # False

Practical Machine Learning Use Case:

Suppose you're checking if a data point is in range and labeled as valid:

feature_value = 0.75
is_labeled = True

if feature_value > 0.5 and is_labeled:
    print("Use this data point")
else:
    print("Ignore this data point")

Step 3: Using or

Explanation:

or returns True if at least one side is True.

print(a or b)  # True, because a is True
print(False or False)  # False

ML Example – Feature Checks:

is_missing = False
is_corrupted = True

if is_missing or is_corrupted:
    print("Drop this sample")
else:
    print("Keep it")

Step 4: Using not

Explanation:

not inverts the boolean value.

print(not a)  # False, because a is True
print(not b)  # True, because b is False

ML Example – Filtering Data:

is_outlier = False

if not is_outlier:
    print("Include in training")
else:
    print("Exclude from training")

Step 5: Combine Logical Operators

You can mix and, or, and not in one condition.

accuracy = 0.91
precision = 0.88
is_outlier = False

if accuracy > 0.90 and (precision > 0.85 or not is_outlier):
    print("Model is acceptable")
else:
    print("Needs improvement")

Truth Table (Great for Intuition)

A	B	A and B	A or B	not A
True	True	True	True	False
True	False	False	True	False
False	True	False	True	True
False	False	False	False	True

Common Use Cases in Machine Learning:

Situation	Example
Data validation	if age > 18 and income > 30000:
Missing/Invalid values	if missing or corrupted:
Feature combinations	if (x1 > 0.5 and x2 < 1.2):
Filter training data	if not is_outlier:
Hyperparameter logic	if lr < 0.01 and batch_size > 32:

Step-by-Step Mini Project (Example)

Let’s use logical operators to filter valid rows in a dataset:

data = [
    {"name": "Alice", "age": 25, "score": 88, "valid": True},
    {"name": "Bob", "age": 17, "score": 75, "valid": False},
    {"name": "Carol", "age": 30, "score": 95, "valid": True}
]

for row in data:
    if row["age"] > 18 and row["valid"]:
        print(f"{row['name']} is eligible for training.")

Summary

Operator	Usage	When to Use
and	Both must be True	Data must meet multiple strict conditions
or	At least one is True	One of several conditions is enough
not	Reverses condition	Filter out a certain group

Keywords

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