Relational Operators

Nerd Cafe | نرد کافه

Step 1: What Are Relational Operators?

Relational operators (also called comparison operators) are used to compare two values. Each comparison returns a Boolean result — either:

• True

• 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: Relational operations always return either True or False.

Step 4: 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) values, not dictionary order.

Step 5: Applications in Machine Learning

Relational operators are widely used in ML for:

• Filtering datasets

• Conditional logic

• Feature selection

• Performance evaluation

Example 3: Thresholding Predictions

prediction = 0.72

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

Output: Class 1

Example 4: Filtering Data with Pandas

import pandas as pd

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

df = pd.DataFrame(data)

# Filter rows where Score >= 70
passed = df[df['Score'] >= 70]
print(passed)

Output:

     Name  Score
0    Anna     85
2  Charlie     73

Note: In Pandas, relational operators work element-wise.

Step 6: Combining Relational and Logical Operators

x = 80

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

Output: Good score

This kind of condition is often used in ML for model evaluation:

Step 7: Using Relational Operators with NumPy Arrays

import numpy as np

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

# Create a boolean mask
mask = arr > 0.5
print(mask)       # [False  True False  True]

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

Use Case: Commonly used for filtering predictions, feature values, and other numeric conditions.

Summary Table (Quick Reference)

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)

Video Tutorial

Support Our Work

If you find this post helpful and would like to support my work, you can send a donation via TRC-20 (USDT). Your contributions help us keep creating and sharing more valuable content.

TRC-20 Address: TAAVVf9ZxUpbyvTa6Gd5SGPmctBdy4PQwf

Thank you for your generosity! 🙏

Keywords

relational operators, comparison operators, python operators, boolean values, true false, equality operator, inequality operator, greater than, less than, logical conditions, conditional statements, python basics, data filtering, pandas comparison, numpy mask, machine learning conditions, thresholding, string comparison, feature selection, boolean logic, nerd cafe , نرد کافه

Channel Overview

🌐 Website: www.nerd-cafe.ir

📺 YouTube: @nerd-cafe

🎥 Aparat: nerd_cafe

📌 Pinterest: nerd_cafe

📱 Telegram: @nerd_cafe

📝 Blog: Nerd Café on Virgool

💻 GitHub: nerd-cafe