"Learning Python OOPs: Private, Protected, Class Methods"

🧠 Day 47 – Python OOPs: Private, Protected, and Class Method Concepts Today I learned how to use private and protected variables in Python classes and how name mangling helps access them safely. 🧩 Code Summary class cl_new: def __init__(self, name, age): self.__name = name # Private variable self._age = age # Protected variable def m_new(self): print(f"name = {self.__name}, age = {self._age}") @classmethod def m_cl_ndth(cls, name, age): return cls(name, age) def m_2(self): self.__m_new() # Call private method internally # Object 1 n = cl_new("siva", 27) n._cl_new__m_new() # Access private method via name mangling print(n._cl_new__name) # Access private variable using mangling print(n._age) # Object 2 n2 = cl_new("krishna", 45) n2.m_new() # Class method usage prxy = cl_new.m_cl_ndth("pawan", 60) prxy.m_new() # Object 3 n3 = cl_new("kalyan", 20) n3.m_new() 🧾 Concepts Explained 🔒 1. Private Variables (__var) Declared with double underscores (__). Not directly accessible outside the class. Accessed only using name mangling like: object._ClassName__variable Example: self.__name 🛡️ 2. Protected Variables (_var) Declared with a single underscore (_). Accessible within the class and subclasses, but should not be accessed directly from outside (by convention). Example: self._age 🧩 3. Private Methods Methods declared with __ prefix. Example: __m_new() Can be accessed inside the class or externally using name mangling. 🧠 4. Class Method Declared using @classmethod decorator. Takes cls as the first parameter. Used to create new objects using alternative constructors. 🖥️ Output Explanation name = siva, age = 27 name = siva, age = 27 siva 27 name = krishna, age = 45 name = pawan, age = 60 name = kalyan, age = 20 ✅ Demonstrates how to define and access private/protected members. ✅ Shows class method usage for creating objects dynamically. ✅ Explains encapsulation, one of the main OOPs principles. 💡 Key Takeaway Encapsulation in Python ensures data hiding and protects object integrity. Using private, protected, and class methods, we can control access to class data efficiently. ✨ Every day brings a deeper understanding of Object-Oriented Programming with Python! #Python #OOPs #Encapsulation #Day47 #LearningJourney #Programming #LinkedInLearning

#DAY-37 #TOPIC: OOPS concepts in python oops are classified into 4 types: 1.inheritance 2.polymorphism 3.abstraction 4.encapusulation #inheritances: Inheritance means one class (child class) can get the properties and methods of another class (parent class). Example-1: class RBI: #parent   cash=100000   @classmethod   def available_cash(cls):     print("available_cash is",cls.cash)     #print("available_cash is",RBI.cash) class SBI(RBI):#child-1   pass class HDFC(RBI):#child-2   cash=50000   @classmethod   def new_cash(cls):     print("new_cash is",cls.cash+cls.cash)     #print("new_cash is",cls.cash+RBI.cash) a=SBI() print(dir(a)) b=HDFC() print(dir(b)) b.available_cash() b.new_cash() #output: ['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'available_cash', 'cash'] ['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'available_cash', 'cash', 'new_cash'] available_cash is 50000 new_cash is 100000 #Task(Multiple inheritance) class Father:   def weight(self):     print("weight is 60kgs") class Mother:   def height(self):     print("height is 5.5 inches") class child:   def dob(self):     print("just born.....") a=Father() a.weight() b=Mother() b.height() c=child() c.dob() #output: weight is 60kgs height is 5.5 inches just born..... Example-2: class Bus:   def seats(self):     a = input("Enter total number of seats (e.g. 100): ")     print("Total seats:", a) class Women:   def seats(self):     b = input("Enter number of seats for women (e.g. 50): ")     print("Seats for women:", b) class Men(Bus, Women):    def seats(self):     c = input("Enter number of seats for men: ")     print("Seats for men:", c) # Create objects and call methods a = Bus() a.seats() b = Women() b.seats() c = Men() c.seats() #output: Enter total number of seats (e.g. 100): 100 Total seats: 100 Enter number of seats for women (e.g. 50): 50 Seats for women: 50 Enter number of seats for men: 40 Seats for men: 40 Codegnan Pooja Chinthakayala mam Saketh Kallepu sir co-founder Uppugundla Sairam sir -CEO

Finding Symmetric Difference In Python Sets (3 Ways) 🧮 ✨Since i was brusing up my logic building for python collections today, i understood how we can find symmetric difference in sets using three ways 🤓Use .symmetric_difference() 🤓Use XOR (^) Operator 🤓Use difference between union ( | ) and intersection (&) 🤔 I found all of them easy depends what is ur personal favorite or standard method to find symmetric difference in python sets ---------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythonlogicbuilding #pythoncollections #pythonsets #symmetricdifference #pythonforbeginners

🚀 **Day 34 of #100DaysOfPython – Date and Time in Python** 🕒 Python provides the **`datetime`** module to handle dates and times efficiently. Let’s explore the most useful features with simple examples 👇 --- ### 🧭 1️⃣ Importing the datetime Module ```python import datetime ``` This gives access to classes like `date`, `time`, `datetime`, and `timedelta`. --- ### 📅 2️⃣ Working with Dates ```python from datetime import date today = date.today() print("Today's date:", today) specific_date = date(2024, 2, 16) print("Specific date:", specific_date) ``` ✅ You can extract parts of a date: ```python print("Year:", today.year) print("Month:", today.month) print("Day:", today.day) ``` ✅ To find the weekday: ```python print("Weekday (0=Mon):", today.weekday()) print("ISO Weekday (1=Mon):", today.isoweekday()) ``` --- ### ⏰ 3️⃣ Working with Time ```python from datetime import time specific_time = time(14, 30, 15) print("Specific time:", specific_time) print("Hour:", specific_time.hour) print("Minute:", specific_time.minute) print("Second:", specific_time.second) ``` --- ### 📆 4️⃣ Date and Time Together ```python from datetime import datetime now = datetime.now() print("Current date and time:", now) specific_datetime = datetime(2024, 2, 16, 14, 30, 15) print("Specific date and time:", specific_datetime) ``` ✅ Extract individual parts: ```python print("Year:", now.year) print("Month:", now.month) print("Day:", now.day) print("Hour:", now.hour) print("Minute:", now.minute) print("Second:", now.second) ``` --- ### 🕓 5️⃣ Formatting Dates & Times ```python formatted_date = now.strftime("%Y-%m-%d") formatted_time = now.strftime("%H:%M:%S") formatted_datetime = now.strftime("%d-%b-%Y %I:%M %p") print("Formatted Date:", formatted_date) print("Formatted Time:", formatted_time) print("Formatted Date and Time:", formatted_datetime) ``` 📘 Example: `%Y` = Year, `%m` = Month, `%d` = Day, `%H` = Hour, `%M` = Minute, `%S` = Second, `%p` = AM/PM --- ### 🔁 6️⃣ Parsing Strings into Dates ```python from datetime import datetime date_string = "16-02-2024 14:30" parsed_date = datetime.strptime(date_string, "%d-%m-%Y %H:%M") print("Parsed Date and Time:", parsed_date) ``` --- ### ⏳ 7️⃣ Date and Time Arithmetic ```python from datetime import timedelta from datetime import date, datetime today = date.today() now = datetime.now() future_date = today + timedelta(days=7) past_date = today - timedelta(days=3) future_time = now + timedelta(hours=2) print("Date after 7 days:", future_date) print("Date 3 days ago:", past_date) print("Time after 2 hours:", future_time) ``` --- ✨ **In short:** - `date` → handles calendar dates - `time` → handles time only - `datetime` → combines both - `timedelta` → does date/time math 💬 What’s the most common date format you use in your projects? #Python #100DaysOfCode #PythonProgramming #LearningPython #DateTime

🚀 Day 6 of my 30 Days Python Challenge: Mastering Python starts with mastering lists! Here’s your ready reckoner with real code + outputs and use cases—just for you! A Python list is an ordered, dynamic, and mutable collection that can hold any type of data—like numbers, strings, or even other lists. 💡 Why use lists? Flexible for all data types Store, access, edit, or remove elements easily Used everywhere—from automation to data science! 🎯 Let’s see real list magic in Python: # 1️⃣ Create & check type my_list = [1, 2, 3, 4, 5] print(type(my_list)) # Output: <class 'list'> ###################################### # 2️⃣ List with multiple data types mixed = [10, "Krishna", 2.5, True] print(mixed) # Output: [10, 'Krishna', 2.5, True] ###################################### # 3️⃣ Access by index fruits = ['Apple', 'Banana', 'Mango'] print(fruits[0]) # Output: Apple print(fruits[2]) # Output: Mango ###################################### # 4️⃣ Slicing numbers = [1, 2, 3, 4, 5] print(numbers[1:4]) # Output: [2, 3, 4] ###################################### # 5️⃣ Change value numbers = [10, 20, 30, 40] numbers[2] = 99 print(numbers) # Output: [10, 20, 99, 40] ###################################### # 6️⃣ Add elements (append, insert, extend) nums = [1, 2, 3] nums.append(4) print(nums) # Output: [1, 2, 3, 4] nums.insert(1, 99) print(nums) # Output: [1, 99, 2, 3, 4] nums.extend([7, 8]) print(nums) # Output: [1, 99, 2, 3, 4, 7, 8] ###################################### # 7️⃣ Remove element (pop) colors = ['red', 'green', 'blue'] removed = colors.pop() print(removed) # Output: blue print(colors) # Output: ['red', 'green'] ###################################### # 8️⃣ Nested lists matrix = [ [1, 2, 3], [4, 5, 6] ] print(matrix[1][2]) # Output: 6 ###################################### # 9️⃣ List functions (len, max, min) data = [7, 12, 4, 9, 21] print(len(data)) # Output: 5 print(max(data)) # Output: 21 print(min(data)) # Output: 4 ###################################### # 🔟 Methods (reverse, copy, count) nums = [5, 2, 5, 7] nums.reverse() print(nums) # Output: [7, 5, 2, 5] copy_nums = nums.copy() print(copy_nums) # Output: [7, 5, 2, 5] print(nums.count(5)) # Output: 2 ###################################### 🔥 Real-world uses: Data science: holding survey results, experiment values Web: lists of products, users ML/AI: feature sets, predictions Automation: batch rename, organize files and folders ❓ Your turn: What’s the most creative way you used a Python list? Share your favorite tip, question, or challenge below 👇 Want more Python details? Comment "YES" & save this post! #Python #DevOps #LearnPython #CodingTips #Automation #DataScience #AIBasics #Programming #LinkedInLearning Follow for more actionable DevOps and Python tips with real-world examples!

🧮 Dissecting NumPy: Working With Intrinsic NumPy Objects For Array Creation 💪 It feels really exciting getting into the core of NumPy and seeing it unlocking its true strength infront of me! 🤔 Why NumPy Arrays Are Better Than Python Lists? - Fast Computation Of Large Datasets - Dont Require Loops - Easy Arithmetical Operations - Consume Less Memory ⚙️Today i digged a bit deeper into NumPy Array Creation With Intrinsic Objects like: - np.ones/np.zeros: gives arrays of 1s and 0s - np.arange(): gives a sequence of array unlike python range() that gives integers - np.linspace(): gives equally linear numbers in array between a start and stop value - np.reshape(): it can simply reshape a given array without changing its data, means generates a new array with a different number of rows and columns (as specified) But, listen up! ‼️One critical thing about NumPy Arrays is their Axis0(rows) and Axis1(coulums). ‼️It means we can perform some of the arithmetical ops on row elements and colum elements using their axis 💭 Its been a productive week by far getting into the world of NumPy and unlocking a new skill on the way of becoming a data scientist! 🫡 Until we meet again, my fellow coders! ------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- 🤓 NumPy (Beginner To Intermediate): 🧮Arrays: https://lnkd.in/ebghYRYE ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗 GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythonnumpy #NumPy #pythonlibraries #pythonfordatascience #datascience #machinelearning #artificialintelligence

🧠 String Methods in Python — Master Text Manipulation Brought to you by programmingvalley.com Credit: @allinpython Strings are one of the most common data types in Python — and mastering them can make your code cleaner and faster. Here are some must-know string methods every developer should learn: → "HELlo".lower() → converts to lowercase → hello → "hello".upper() → converts to uppercase → HELLO → "hello world".capitalize() → makes first letter uppercase → Hello world → "hello world".title() → capitalizes each word → Hello World → " hello ".strip() → removes spaces → hello → "Hello".startswith("He") → checks if string starts with “He” → True → "Hello".endswith("lo") → checks if string ends with “lo” → True → "one,three".replace(",", "|") → replaces characters → one|three → "one,three".split(", ") → splits string → ['one', 'three'] → "-".join(["a", "b", "c"]) → joins list into string → a-b-c → "hello".find("e") → returns position → 1 → "hello".index("e") → similar to find() but raises error if not found → "hello world".count("o") → counts occurrences → 2 → "12345".isnumeric() → checks if all characters are numeric → True 🎓 Learn Python the right way: Python for Data Science → https://lnkd.in/d5iyumu4 Google IT Automation with Python → https://lnkd.in/dyJ4mYs9 Microsoft Python Development Certificate → https://lnkd.in/dDXX_AHM #Python #Coding #DataScience #LearnPython #ProgrammingValley #PythonTips

Python Loops! 👀 Loops helps us avoid repetition in our code and consolidate large data into clean, short and readable code. 💭 Why Loops Matter? Loops allow programs to repeat actions efficiently without writing the same code multiple times. In my Jupyter Notebook, I explored both major types of loops: 🔹 for loops: perfect for iterating through sequences, lists, and ranges 🔹 while loops: great when repetition depends on a condition rather than a count 🔹 break / continue: for controlling flow precisely 🔹 range(start, stop, step): mastering how iteration sequences work 🔹 Nested loops: loops inside loops for multi-dimensional logic 🔹 Avoiding infinite loops: understanding when and how to end repetition safely 😊Good News: My Jupyter Notebook for Python Loops Is Up On My Github! Make sure to check it out and try to do mini projects for python loops that i made there! Good Luck Python Beginners! 😊What Is Coming Next?: Python Functions In detail for Beginners like me! --------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- #pythonconditionals #ifelifelseinpython #ternaryconditionals #nestedconditionals #conditionalswithoperators #pythonbasicknowledge #pythonbasicconcepts #pythonforbeginners #pythonprogramming #pythonfordatascience #pythonforaiml

🚀 Day 35 of #100DaysOfPython – Working with Date, Time & Calendar 🕒📅 Today’s topic helps us retrieve and manipulate the current date, time, and calendar using Python’s built-in modules — time and calendar. 🕐 1️⃣ Retrieving the Current Time Python provides the time() and localtime() functions to get system time. import time lt = time.localtime(time.time()) print(lt) 🧩 Output example: time.struct_time(tm_year=2022, tm_mon=4, tm_mday=14, tm_hour=10, tm_min=30, ...) 🔹 Attributes of time.struct_time: tm_year → Current year tm_mon → Current month tm_mday → Day of month tm_hour → Hour tm_min → Minute tm_sec → Second tm_wday → Weekday tm_yday → Day of year tm_isdst → Daylight saving flag 🕓 2️⃣ Formatted Time with asctime() The asctime() method returns the current time as a formatted string. import time lt = time.asctime(time.localtime(time.time())) print(lt) ✅ Example Output: Thu Apr 14 10:33:59 2022 🧮 3️⃣ Converting String to Time – strptime() Used to parse strings into time structures. import time tr = time.strptime("26 jun 14", "%d %b %y") print(tr) 📖 Example Output: time.struct_time(tm_year=2014, tm_mon=6, tm_mday=26, ...) 🧭 4️⃣ Formatting Time – strftime() Converts time into a specific string format. import time t = (2014, 6, 26, 17, 3, 38, 1, 48, -1) t = time.mktime(t) print(time.strftime("%d %m %y %H:%M:%S", time.gmtime(t))) ✅ Output: 26 06 14 11:33:38 📆 5️⃣ Python Calendar Module The calendar module allows working with dates, months, and years. import calendar print(calendar.prcal(2023)) 🧠 Common Calendar Functions: Method Description prcal(year) Prints full calendar for a year firstweekday() Returns first weekday (default Monday = 0) isleap(year) Checks if year is leap monthcalendar(year, month) Returns matrix of weeks in month leapdays(y1, y2) Counts leap years between y1 and y2 prmonth(year, month) Prints specific month 🗓️ Example: import calendar print(calendar.isleap(2020)) # True print(calendar.monthcalendar(2022, 6)) calendar.prmonth(2022, 5) ✨ In Short: time → retrieves and formats time strptime / strftime → convert between strings & time calendar → helps print and analyze calendars 💬 Which one do you use most often — datetime, time, or calendar? #Python #100DaysOfCode #LearningPython #PythonProgramming #DateTime #Calendar

#Day21 of #120DaysChallenge Python Functions Today, I learned about functions in Python — one of the most powerful features that help make code reusable, organized, and easier to understand. Here’s what I covered: What functions are and why we use them The basic syntax using the def keyword How to pass parameters to a function How to use the return statement to send back results Here’s a simple example I practiced def add(a,b): print(a+b) add(1,2) output: 3 ex2 : def calculate():   a=   print('Sum is',a+b)   print('Difference is',a-b)   calculate() calculate() def calculate():   a=   print('Sum is',a+b)   print('Difference is',a-b)   calculate() calculate() ''' ''' def add():   a=input('enter fname:')   b=input('enter lname:')   print(a+b) add()    ''' ''' def add():   a=input('enter fname:')   b=input('enter lname:')   print((a+''+b).title()) add()   ''' #using While loop while True:     def cal():         a=int(input('enter a value:'))         b=int(input('enter b value:'))         print('The sume is',a+b)         cal() # a function call itself its recursive     cal() #recursive function : a function call itself def cal():     a=int(input('enter a value:'))     b=int(input('enter b value:'))     print('The sume is',a+b)     cal() # a function call itself its recursive cal() #return def mul(a,b):     return a*b print(mul(1,3)) ''' #Difference Between Print and return ->Print just show the human user output in a console ->return is used to terminate the function and gives back a value from function ''' Key takeaway: Functions help break big problems into smaller, manageable pieces — and make code cleaner and reusable! Excited to keep learning and building step by step. #Python #LearningJourney #Coding #Functions #100DaysOfCode Pooja Chinthakayala Day 22 Challenge done Mam