Relational Operators

Nerd Cafe

Step 1: What Are Relational Operators?

Relational operators (also known as comparison operators) are used to compare two values. The result of a comparison is always a Boolean:

• True or

• False

Step 2: List of Relational Operators in Python

Operator	Description	Example
==	Equal to	a == b
!=	Not equal to	a != b
>	Greater than	a > b
<	Less than	a < b
>=	Greater than or equal to	a >= b
<=	Less than or equal to	a <= b

Step 3: Try Simple Examples

a = 10
b = 20

print(a == b)  # False
print(a != b)  # True
print(a > b)   # False
print(a < b)   # True
print(a >= b)  # False
print(a <= b)  # True

Note: Always remember that relational operations return True or False.

Step 4: Use in Real-world Examples

Example 1: Age Check System

age = 25
if age >= 18:
    print("You're eligible to vote.")
else:
    print("Sorry, you're underage.")

Output: "You're eligible to vote."

Example 2: Comparing Strings (Alphabetical Order)

print("apple" < "banana")  # True
print("cat" > "bat")       # True

Note: String comparison is based on Unicode (ASCII) order, not dictionary order.

Step 5: Practical Use in Machine Learning

Relational operators are heavily used in ML tasks such as:

• Filtering datasets

• Conditional logic

• Feature selection

• Performance evaluation

Example 3: Thresholding Predictions

Suppose you’re doing binary classification, and you want to classify predictions above 0.5 as class 1:

prediction = 0.72

if prediction >= 0.5:
    print("Class 1")
else:
    print("Class 0")

Output: Class 1

Example 4: Filtering with Pandas (ML-related)

import pandas as pd

# Sample data
data = {
    'Name': ['Anna', 'Bob', 'Charlie'],
    'Score': [85, 42, 73]
}

df = pd.DataFrame(data)

# Get only rows with Score >= 70
passed = df[df['Score'] >= 70]
print(passed)

Output:

     Name  Score
0    Anna     85
2  Charlie     73

Note: Relational operators work element-wise in Pandas.

Step 6: Combine with Logical Operators

x = 80

if x > 70 and x < 90:
    print("Good score")

Output: "Good score"

This is common in machine learning when checking conditions like:

if accuracy >= 0.8 and loss < 0.4:
    print("Model is acceptable.")

Step 7: Use with NumPy Arrays (Very Important for ML)

import numpy as np

arr = np.array([0.3, 0.7, 0.1, 0.8])

# Thresholding: Get boolean array
mask = arr > 0.5
print(mask)  # [False  True False  True]

# Apply mask
print(arr[mask])  # [0.7 0.8]

Use case: This is used for filtering predictions, feature values, and more.

Summary Table in Code

x = 5
y = 10

print("x == y:", x == y)
print("x != y:", x != y)
print("x > y:", x > y)
print("x < y:", x < y)
print("x >= y:", x >= y)
print("x <= y:", x <= y)

Keywords

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