Mastering Python Strings: Techniques for Text Manipulation

🚀 Mastering Python String manipulation (Indexing, Slicing, and Methods): #### 1. String Indexing : Indexing allows you to access individual characters within a string based on their position. Python uses zero-based indexing, meaning the first character is at position 0. Positive Indexing : Starts from 0 at the beginning of the string and increases. Example: For the string "Madhav", index 0 is M, 1 is a, 2 is d, and so on. Negative Indexing : Starts from -1 at the very end of the string and decreases towards the beginning. Example: For "Madhav", index -1 is v, -2 is a, and so on. Syntax Example: variable[index] python name = "Madhav" print(name[0]) # Output: M print(name[-1]) # Output: v #### 2. String Slicing : Slicing allows you to extract a subset or a range of characters from a string. Syntax: string[start:end:step] start: The index to begin slicing (inclusive). end: The index to stop slicing (exclusive). step: The increment value (default is 1). Basic Slicing : python name = "Madhav" print(name[0:3]) # Output: Mad (Characters at 0, 1, 2) Reversing a String : Using a step of -1 to reverse the entire string. python print(name[::-1]) # Output: vahdaM #### 3. String Methods : These built-in functions allow for common string modifications covers commonly used built-in methods to modify or analyze strings 1. len(): Returns the total number of characters in a string, including spaces. Example: len('Hello World') returns 11. 2. upper() & lower(): Converts string case for normalization. Example: 'python'.upper() becomes 'PYTHON'. 3. strip(): Removes leading and trailing whitespace, crucial for data cleaning. Example: ' data '.strip() becomes data. 4. find(): Locates the index (position) of a specific character or substring. Example: 'linkedin'.find('k') returns 4. 5. replace(): Swaps old substrings with new ones. Example: 'Hello Rishabh'.replace('Rishabh', 'User') becomes 'Hello User'. 6. split(): Breaks a string into a list based on a delimiter, great for parsing text data. Example: 'apple,banana'.split(',') returns ['apple', 'banana']. 7. join(): Combines a list of strings into a single string using a specified separator. Example: ' - '.join(['A', 'B', 'C']) returns 'A - B - C'. #Python #DataScience #Coding #Learning #TechTips

🔥 Part-1: Tuples in Python (Complete Guide) 🔥 Tuples are one of the most important data structures in Python — simple, fast, and immutable! Let’s break it down in an easy and practical way 👇 📌 What is a Tuple? 👉 An ordered, immutable collection 👉 Allows duplicate values 👉 Written using parentheses () Example: fruits = ("apple", "orange", "cherry", "apple") print(fruits) # Output: ('apple', 'orange', 'cherry', 'apple') ✨ Ways to Create Tuples 🔹 1. Using Parentheses fruits = ("apple", "orange", "cherry") 🔹 2. Without Parentheses (Tuple Packing) numbers = 1, 2, 3, 4 print(type(numbers)) # Output: <class 'tuple'> 🔹 3. Single Element Tuple (Important ⚠️) single_tuple = ("apple",) print(type(single_tuple)) # Output: <class 'tuple'> 👉 Without comma, it becomes a string! 🎯 Accessing Elements 🔹 Indexing colors = ("red", "green", "blue", "orange") print(colors[0]) # red print(colors[-1]) # orange 🔹 Slicing nums = (10, 20, 30, 40, 50) print(nums[1:4]) # (20, 30, 40) ⚙️ Tuple Operations 🔹 Concatenation (+) print((1, 2) + (3, 4)) # (1, 2, 3, 4) 🔹 Repetition (*) print(("apple",) * 3) # ('apple', 'apple', 'apple') 🔁 Iteration in Tuple 🔹 Using for loop colors = ("red", "green", "blue") for color in colors: print(color) 🔹 Using while loop fruits = ("apple", "mango", "cherry") i = 0 while i < len(fruits): print(fruits[i]) i += 1 🛠️ Useful Methods & Functions 🔹 count() nums = (1, 2, 3, 2, 2) print(nums.count(2)) # 3 🔹 index() colors = ("red", "green", "blue") print(colors.index("green")) # 1 🔹 sorted() (returns list) nums = (3, 1, 2) print(sorted(nums)) # [1, 2, 3] 🔹 Built-in Functions numbers = (10, 20, 30, 40) print(len(numbers)) # 4 print(sum(numbers)) # 100 print(min(numbers)) # 10 print(max(numbers)) # 40 🎁 Packing & Unpacking 🔹 Unpacking coordinates = (10, 20, 30) x, y, z = coordinates print(y) # 20 🔹 Packing a = "Madhav" b = 21 c = "Engineer" tuple_pack = (a, b, c) print(tuple_pack) # ('Madhav', 21, 'Engineer') 🚀 Key Takeaways ✔ Tuples are immutable (cannot change) ✔ Faster than lists ✔ Useful for fixed data & security ✔ Supports indexing, slicing & iteration 💬 Stay tuned for Part-2 (Advanced Tuple Concepts) #Python #PythonProgramming #DataStructures #CodingTutorial #DataScience #SoftwareDevelopment

Week 2, Day 1 of 180 — Strings in Python are way more powerful than I thought! 😲 Last week I learned Python's basics — variables, operators, input. This week? We went deeper. Way deeper. 🕳️ Today was ALL about Strings — and honestly, this topic alone has a LOT going on. 👇 🤔 First — What even IS a String? Simply put — any text wrapped in quotes is a String! python name = "Python" msg = 'Hello World' ``` But here's the wild part — a String is actually an **array of characters** under the hood. 🤯 Which means you can access each letter individually! --- **📍 String Indexing — Every character has an address!** ``` P y t h o n 0 1 2 3 4 5 ← positive index -6 -5 -4 -3 -2 -1 ← negative index python s = "Python" print(s[0]) # P (first character) print(s[-1]) # n (last character) print(s[2]) # t (third character) Positive index = left to right starting at 0 Negative index = right to left starting at -1 ✂️ String Slicing — The coolest feature! Syntax → string[start : stop : step] python s = "Python" print(s[0:3]) # "Pyt" → index 0 to 2 print(s[2:]) # "thon" → index 2 to end print(s[-3:]) # "hon" → last 3 chars print(s[::-1]) # "nohtyP" → REVERSED! 🔄 That [::-1] trick to reverse a string? Genuinely mind-blowing. 😂 ➕ String Operations: python "Hi " + "Ravi" # "Hi Ravi" → Concatenation "Ha" * 3 # "HaHaHa" → Repetition 😂 "Py" in "Python" # True → Membership check len("Python") # 6 → Length 🛠️ Common String Methods: python "hello".upper() # "HELLO" "HELLO".lower() # "hello" " hi ".strip() # "hi" "Hi".replace("i", "ey") # "Hey" "a,b,c".split(",") # ['a', 'b', 'c'] "Python".find("t") # 2 🔒 The BIG Rule — Strings are IMMUTABLE! You CANNOT change a string directly. All methods return a brand new string. The original? Completely untouched. 🐱 python s = "hello" s.upper() # Does NOT change s! print(s) # Still "hello" 😅 s = s.upper() # NOW it works ✅ print(s) # "HELLO" 🎉 💡 Week 2 Vibe so far: Last week felt like learning to walk. 🚶 This week feels like learning to run. 🏃💨 📅 Week 2 Day 1 / 180 — Done! ✅ Still on track with my 6-Month GenAI & Agentic AI Certificate Program 🏛️ IIT Patna Certified print("Strings? Understood. Let's go!" ) 💪 💬 Did the [::-1] trick surprise you too? What's YOUR favourite string method? 👇 ♻️ Repost to help someone finally understand string slicing! #Python 🐍 #Strings #StringSlicing #Week2 #GenAI 🤖 #IITPatna 🏛️ #LearningInPublic #100DaysOfCode #PythonBasics #CodingJourney #AgenticAI #NeverStopLearning 🚀 #BuildInPublic #AIForDevelopers

Making Python Interactive – Input, Strings & Indexing! ⌨️🔤 Programs get really powerful when they can talk back and forth with users. This session introduced me to taking input, manipulating strings, and accessing individual characters—skills that make coding dynamic and interactive! Here's what I learned: --- ➕ String Concatenation – Adding Strings Together Just like numbers, strings can be "added"—but instead of math, they get joined together. ```python a = "1" + "2" print(a) # Output: "12" (not 3!) ``` ✅ This is called String Concatenation Real example: ```python username = "Ravi" print("Hi " + username) # Output: Hi Ravi ``` --- ⚠️ Important: Can't Mix Types! ```python print("*" + 10) # ERROR! ``` ❌ TypeError: can only concatenate str (not "int") to str You can only add string to string—numbers need conversion or different approach. --- 🔁 String Repetition – Multiply Strings! Want 10 stars? Just multiply the string: ```python print("*" * 10) # Output: ********** ``` ✅ Called String Repetition Cool example: ```python s = "Python" s = ("*" * 3) + s + ("*" * 3) print(s) # Output: ***Python*** ``` --- 📏 Length of String – len() ```python username = "Ravi" length = len(username) print(length) # Output: 4 ``` len() counts all characters, including spaces and special chars. --- ⌨️ Taking User Input – input() Instead of hardcoding values, let the user type! ```python username = input() # User types something print("Hello " + username) ``` ✅ input() always returns a string Multiple inputs: ```python name = input() age = input() print(name + " is " + age + " years old") ``` Input: ``` Ravi 10 ``` Output: Ravi is 10 years old --- 🔢 String Indexing – Accessing Characters Strings are like sequences—each character has a position number (index) starting from 0. ``` String: R a v i Index: 0 1 2 3 ``` ```python username = "Ravi" print(username[0]) # Output: R print(username[1]) # Output: a print(username[2]) # Output: v print(username[3]) # Output: i ``` ✅ Index = position - 1 (first character is at 0) --- ✅ Key Takeaways: Concept Syntax Example String Concatenation str + str "Hi " + "Ravi" String Repetition str * number "*" * 10 String Length len(str) len("Ravi") User Input input() name = input() String Indexing str[index] name[0] --- 💬 Let's Discuss: What's the first interactive program you ever wrote? A greeting? A calculator? Or something more creative? Share your memories below! 👇 --- 🔖 #Python #CodingBasics #LearnToCode #StringManipulation #UserInput #ProgrammingForBeginners #NXTWave #TechJourney

🚀 I Learned Two Python Concepts Today That Instantly Made My Code Better Today I practiced two important Python concepts: • While Loops • Format Specifiers (Python f-string formatting) Both are heavily used in real programs for input validation and clean output formatting. 🔁 1. While Loop — Repeating Code Until a Condition Changes A while loop repeatedly executes code as long as a condition is true. age = int(input("Enter your age: ")) while age < 0: print("Age cannot be negative") age = int(input("Enter your age: ")) print(f"You are {age} years old") 📌 What Happens 1️⃣ User enters age. 2️⃣ If the value is negative, an error appears. 3️⃣ The program asks again until a valid value is entered. 🌍 Real‑world uses • Input validation • Login retry systems • Game loops • Menu programs For example, an ATM keeps asking for a PIN until it is correct. 🔄 2. Infinite Loop + Break (Common Pattern) while True: principal = float(input("Enter principal amount: ")) if principal < 0: print("Principal cannot be negative") else: break 🧠 Logic • while True creates a continuous loop • Invalid input → error message • Valid input → break stops the loop This pattern is widely used for reliable user input handling. 🎯 3. Format Specifiers — Clean Output Formatting Python f-strings allow precise formatting of numbers. General syntax {value:flags} Example value price = 3000.1459 🔢 Round to specific decimals print(f"{price:.2f}") Output 3000.15 .2f → round to 2 decimal places (very common in finance). 0️⃣ Zero padding / fixed width print(f"{price:010}") Example 0003000.1459 Useful for IDs, invoices, or structured tables. 📊 Number alignment Left {value:<10} Right {value:>10} Center {value:^10} Helpful when printing tables or reports. 💰 Sign and comma formatting print(f"{price:+,.2f}") Output +3,000.15 Meaning: • + show sign • , thousands separator • .2f two decimals Common in financial dashboards and reports. 🧮 Example: Compound Interest total = principal * pow((1 + rate/100), time) print(f"Balance after {time} years: ${total:.2f}") This demonstrates: • while-loop validation • calculation logic • formatted output 📚 What I Practiced Today • While loops • Infinite loop pattern • Input validation • Python format specifiers • Clean numeric output Programming is about controlling logic and handling real data correctly. 🔗 Code Repository GitHub: https://lnkd.in/dFtwyqEw #python #pythonlearning #codingjourney #programming #softwaredeveloper #learnpython #developers #100daysofcode #computerscience

✅ Week 2 of 180 — Python Lists are basically a Swiss Army knife! 🔪 Last session was Strings — fixed, immutable, locked. 🔒 Today? We unlocked something FAR more powerful. 🔓 Lists in Python — and honestly this changes EVERYTHING. 👇 🤔 So what IS a List? A List is an ordered, mutable collection of items that can hold multiple values — including different data types all in one place! fruits = ["apple", "banana", "cherry"] mixed = ["Alice", 30, True, 3.14] # yes, all in ONE list! 😲 ✅ 4 Key Properties: 📌 Ordered — every item has a fixed index position ✏️ Mutable — you can change, add or remove items freely 🔁 Allows Duplicates — same value can appear multiple times 🎲 Mixed Types — strings, ints, booleans all together! 📍 List Indexing — works exactly like Strings! nums = [10, 20, 30, 40, 50] # 0 1 2 3 4 ← positive index # -5 -4 -3 -2 -1 ← negative index print(nums[0]) # 10 → first item print(nums[-1]) # 50 → last item If you understood String indexing last session — congrats, you already know List indexing! 😄 ✂️ List Slicing — same syntax as Strings! nums = [10, 20, 30, 40, 50] print(nums[1:4]) # [20, 30, 40] → index 1 to 3 print(nums[::-1]) # [50, 40, 30, 20, 10] → REVERSED! 🔄 Python really said: "Learn it once, use it everywhere." 😂 🛠️ List Methods — the full toolkit: fruits = ["apple", "banana", "cherry"] fruits.append("kiwi") # adds to END fruits.insert(1, "mango") # inserts at index 1 fruits.remove("banana") # removes first occurrence fruits.pop(2) # removes & returns at index 2 fruits.sort() # sorts ascending fruits.reverse() # reverses the list fruits.clear() # removes ALL items new = fruits.copy() # creates a copy 🔥 The BIG difference from Strings: Strings → IMMUTABLE → methods return a NEW string Lists → MUTABLE → methods change the original list directly! # Strings - must reassign s = "hello" s = s.upper() # must save it back ✅ # Lists - changes happen in place! fruits.append("kiwi") # original list is updated automatically ✅ This tripped me up at first. Now it makes total sense! 😄 💡 Week 2 Summary so far: → Strings = read-only text with powerful methods 📖 → Lists = flexible, editable collections of anything 🗂️ Both use the same indexing and slicing — Python is beautifully consistent! 🐍 📅 Week 2 / 180 — Lists: DONE! ✅ Still going strong on my 6-Month GenAI & Agentic AI Certificate Program 🏛️ IIT Patna Certified print("Lists understood. Building momentum! 🚀") 💪 💬 Which List method do you use the most in your projects? Drop it below! 👇 ♻️ Repost if this helped you understand Python Lists better! #Python 🐍 #Lists #ListMethods #Week2 #GenAI 🤖 #IITPatna 🏛️ #LearningInPublic #100DaysOfCode #PythonBasics #DataStructures #CodingJourney #AgenticAI #NeverStopLearning 🚀 #BuildInPublic #AIForDevelopers

🚀 Python for Data Analyst- Advanced Set Concepts in Python (Part 3)-(Post 9) These are small concepts individually, but together they make sets very powerful in real-world Python work. 1️⃣ issubset() Checks whether all elements of one set are present in another. s1 = {1, 2, 3, 4, 5} s2 = {4, 5} print(s2.issubset(s1)) Output: True More examples: A = {1, 2, 3} B = {1, 2, 3, 4, 5} C = {1, 2, 4, 5} print(A.issubset(B)) # True print(B.issubset(A)) # False print(A.issubset(C)) # False print(C.issubset(B)) # True 2️⃣ issuperset() Checks whether one set contains all elements of another. A = {4, 1, 3, 5} B = {6, 0, 4, 1, 5, 3} print(A.issuperset(B)) print(B.issuperset(A)) Output: False True 3️⃣ isdisjoint() Checks whether two sets have no common elements. s1 = {1, 2, 3} s2 = {4, 5, 6} print(s1.isdisjoint(s2)) Output: True If there is at least one common value: set1 = {2, 4, 5, 6} set3 = {1, 2} print(set1.isdisjoint(set3)) Output: False It also works with: list tuple dictionary string Important: For dictionaries, only keys are checked. 4️⃣ copy() Creates a shallow copy of a set. set1 = {1, 2, 3, 4} set2 = set1.copy() print(set2) Using copy() is useful because direct assignment: set2 = set1 makes both variables point to the same set. With copy(), modifications in the copied set do not affect the original: first = {'g', 'e', 'k', 's'} second = first.copy() second.add('f') print(first) print(second) 5️⃣ frozenset A frozenset is like a set, but immutable. Once created: cannot add cannot remove cannot update Example: fs = frozenset([1, 2, 3, 4, 5]) print(fs) Output: frozenset({1, 2, 3, 4, 5}) Useful when you need a set-like structure that should not change. 6️⃣ Typecasting into Sets The set() constructor can convert: list tuple string range dictionary Examples: print(set([1, 2, 2, 3])) print(set((1, 1, 2, 3))) print(set("GeeksforGeeks")) print(set(range(3, 8))) print(set({'x': 1, 'y': 2})) Important: When converting a dictionary to a set, only keys are included. 7️⃣ set() Function Summary Syntax: set(iterable) removes duplicates automatically creates empty set if no argument is passed accepts only iterables Examples: set() set([4, 5, 5, 6]) set((1, 1, 2, 3)) set("hello") 8️⃣ min() and max() with Sets You can find minimum and maximum values in a set. s1 = {4, 12, 10, 9, 13} print(min(s1)) print(max(s1)) Output: 4 13 ⚠️ For heterogeneous sets like {"Geeks", 11}, min() and max() raise TypeError because Python cannot compare different types. 9️⃣ Using sorted() with Sets sorted() works on any iterable, including sets. It returns a new sorted list and does not modify the original set. s = {5, 3, 9, 1, 7} sorted_s = sorted(s) print(sorted_s) Output: [1, 3, 5, 7, 9] Descending order print(sorted(s, reverse=True)) Sorting strings in a set A = {'ab', 'ba', 'cd', 'dz'} print(sorted(A)) #Python #PythonLearning #DataAnalytics #Sets #LearningInPublic

Python3: Mutable, Immutable… Everything is an Object! Introduction : In Python, everything is an object. This fundamental idea shapes how variables behave, how memory is managed, and how data flows through your programs. Understanding the difference between mutable and immutable objects is essential for writing predictable and efficient code. In this post, I’ll walk through object identity, types, mutability, and how Python handles function arguments—with concrete examples. Id and Type : Every Object in Python has: an identity (its memory address) - a type (what kind of object it is) - a value You can inspect these using id() and type(): x=10 print(id(x)) # unique identifier (memory address) print(type(x)) # <class 'int'> Example Output : 140734347123456 <class 'int'> Two variables can point to the same object: a = 5 b = a print(id(a)) print(id(b)) Both a and b will have the same id, meaning they reference the same object. MUTABLE OBJECTS : Mutable objects can be changed after they are created without changing their identity. Common mutable types: List , dict , set Example: my_list = [1, 2, 3] print(id(my_list)) my_list.append(4) print(my_list) print(id(my_list)) # same id! Output: [1, 2, 3, 4] The content changed, but the memory address stayed the same. Another example with dictionaries: d = {"a": 1} d["b"] = 2 print(d) # {'a': 1, 'b': 2} IMMUTABLE OBJECTS: Immutable objects cannot be modified after creation. Any "change" actually creates a new object. Common immutable types: int , float , str , tuple Example: x = 10 print(id(x)) x = x + 1 print(id(x)) # different id! Output: 140734347123456 140734347123999  A new object is created instead of modifying the old one. String example: s = "hello" print(id(s)) s += " world" print(id(s)) Again, a new object is created. Why does it matter? Understanding mutability helps avoid unexpected bugs. Example problem: list1 = [1, 2, 3] list2 = list1 list2.append(4) print(list1) # [1, 2, 3, 4] Both variables changed because they reference the same object. To avoid this: list2 = list1.copy() Now they are independent. HOW ARGUMENTS ARE PASSED TO FUNCTIONS Python uses pass-by-object-reference (or “call by sharing”). With immutable objects: def add_one(x):   x += 1   print("Inside:", x) a = 5 add_one(a) print("Outside:", a) Output: Inside: 6 Outside: 5 The original value is unchanged. With mutable objects: def add_item(lst):   lst.append(4)   print("Inside:", lst) my_list = [1, 2, 3] add_item(my_list) print("Outside:", my_list) Output: Inside: [1, 2, 3, 4] Outside: [1, 2, 3, 4] The original object is modified. IMPORT IMPLICATION If you don’t want a function to modify your data: def safe_modify(lst):   lst = lst.copy()   lst.append(4)   return lst Understanding mutable vs immutable objects is crucial in Python because it directly affects: memory behavior , variable assignment , function side effects

1: Everything is an object? In the world of Python, (an integer, a string, a list , or even a function) are all treated as an objects. This is what makes Python so flexible but introduces specific behaviors regarding memory management and data integrity that must be will known for each developer. 2: ID and type: Every object has 3 components: identity, type, and value. - Identity: The object's address in memory, it can be retrieved by using id() function. - Type: Defines what the object can do and what values could be hold. *a = [1, 2, 3] print(id(a)) print(type(a)) 3: Mutable Objects: Contents can be changed after they're created without changing their identity. E.x. lists, dictionaries, sets, and byte arrays. *l1 = [1, 2, 3] l2 = l1 l1.append(4) print(l2) 4: Immutable Objects: Once it is created, it can't be changed. If you try to modify it, Python create new object with a new identity. This includes integers, floats, strings, tuples, frozensets, and bytes. *s1 = "Holberton" s2 = s1 s1 = s1 + "school" print(s2) 5: why it matters? and how Python treats objects? The distinction between them dictates how Python manages memory. Python uses integer interning (pre-allocating small integers between -5 and 256) and string interning for performance. However, it is matter because aliasing (two variables pointing to the same object) can lead to bugs. Understanding this allows you to choose the right data structure. 6: Passing Arguments to Functions: "Call by Assignment." is a mechanism used by Python. When you pass an argument to a function, Python passes the reference to the object. - Mutable: If you pass a list to a function and modify it inside, the change persists outside because the function operated on the original memory address. - Immutable: If you pass a string and modify it inside, the function creates a local copy, leaving the original external variable untouched. *def increment(n, l): n += 1 l.append(1) val = 10 my_list = [10] increment(val, my_list) print(val) print(my_list) *: Indicates an examples. I didn't involve the output, you can try it!

Understanding Relational Operators – Making Decisions in Python! ⚖️🐍 Programs aren't just about calculations—they need to compare values and make decisions. This session introduced me to Relational Operators, which help Python compare things and return True or False. The foundation of all logic in programming! Here's what I learned: --- 🔍 What Are Relational Operators? Relational operators compare two values and give a boolean result (True or False). Operator Meaning Example Result > Greater than 5 > 3 True < Less than 5 < 3 False >= Greater than or equal 5 >= 5 True <= Less than or equal 5 <= 3 False == Equal to 5 == 5 True != Not equal to 5 != 3 True --- ⚠️ BIGGEST Beginner Mistake! ```python print(3 = 3) # SyntaxError! ``` ❌ Wrong: Single = is for assignment, not comparison. ✅ Correct: ```python print(3 == 3) # Output: True ``` 🔹 = → Assigns a value 🔹 == → Checks if values are equal --- 📊 Comparing Numbers ```python print(10 > 5) # True print(2.53 >= 2.55) # False print(7 != 7) # False ``` --- 📝 Comparing Strings Strings can also be compared! ```python print("apple" == "apple") # True print("apple" == "Apple") # False ``` 🔹 Python is case-sensitive – "X" and "x" are different! Lexicographic ordering (dictionary order): ```python print("apple" < "banana") # True (a comes before b) ``` --- 🎯 Real Example: Check First Three Characters ```python str1 = input() str2 = input() result = str1[:3] == str2[:3] print(result) ``` Input: ``` programming program ``` Output: True (first 3 chars "pro" match) --- ✅ Key Takeaways: 🔹 Relational operators always return True or False 🔹 == checks equality, = assigns values – don't mix them! 🔹 Can compare numbers and strings 🔹 String comparison is case-sensitive 🔹 Used everywhere in conditional logic (if, loops, etc.) --- 💬 Let's Discuss: What's the funniest bug you've encountered because of using = instead of ==? Or have you ever been surprised by how Python compares strings? Share your stories below! 👇 --- 🔖 #Python #RelationalOperators #CodingBasics #LearnToCode #ProgrammingLogic #NXTWave #TechJourney #PythonTips