Logical Operators

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What Are Logical Operators?

Logical operators are used to combine or modify conditional statements — conditions that return either True or False. They help programs make decisions, similar to how we reason in real life.

For example:

“If it’s sunny and warm, I’ll go for a walk.” Here, both conditions (sunny and warm) must be True.

The Three Logical Operators in Python

Operator	Description	Example	Returns
and	True only if both sides are True	A and B	True when both A and B are True
or	True if at least one side is True	A or B	True when A or B (or both) are True
not	Reverses a condition’s truth value	not A	True when A is False

Step 1: Basic Setup

a = True
b = False

Here,

• a is True

• b is False

We’ll use these to explore how logical operators work.

Step 2: Using and

Explanation:

and returns True only if both conditions are True.

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

Think of it like:

“You can enter the lab if you’re a student and wearing a badge.”

Both must be true for access.

Machine Learning Example:

Imagine checking if a data point is within range and correctly labeled.

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

Think of it like:

“You can enter if you have a key or a passcode.” You only need one to get in.

Machine Learning Example:

Checking for invalid data — drop if it’s missing or corrupted.

is_missing = False
is_corrupted = True

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

Another example:

is_hot = True
is_humid = False

if is_hot or is_humid:
    print("Activate cooling system")

Step 4: Using not

Explanation:

not reverses a Boolean value. If something is True, not makes it False (and vice versa).

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

Think of it like:

“You can play outside if it’s not raining.”

Machine Learning Example:

Filtering out outliers.

is_outlier = False

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

Step 5: Combining Logical Operators

You can combine and, or, and not in a single expression.

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")

Explanation:

• accuracy > 0.90 ✅

• precision > 0.85 or not is_outlier ✅ → Both groups are True, so the model passes.

Truth Table (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	Description
Data validation	Check if both conditions hold before using data
Missing or invalid values	Handle incomplete data
Feature combinations	Combine multiple feature-based rules
Filter training data	Exclude unwanted samples
Hyperparameter logic	Conditional model configuration

Video Tutorial

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Keywords

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