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concept map or mind map titled "DATA STRUCTURE IN PYTHON." It visually outlines and connects various concepts related to data structures in Python, particularly focusing on Lists. Here is a breakdown of the key concepts and relationships shown: Data Structures and Collections Collections are described as the most popular type of data structure, leading to Lists. Other types of collections include Sets, Dictionaries, and Arrays. Tuples are also shown as related to Collections but are specifically labeled as "are not" Mutable. Lists Lists are Mutable (meaning they can be changed). They are iterated by Loops. Lists are ordered groups of Elements. They are created using syntax like myList = []. Lists have Methods, such as .sort() and .append(). Elements, Indexes, and Loops Elements are the contents of the lists. They are reviewed sequentially by Loops. Elements are located by Indexes. Elements can be of type Int, string, list, etc. Elements can also be Pointers. Indexes are needed for Assignment (using syntax like myList[ind] = X). Indexes can be used to run forward from 0 to length-1 or run backward from -1 to length. Indexes use syntax like myList[ ind ]. Loops often follow Idioms such as for elem in myList.

🐍 Python Trick Question: Can You Guess the Output? 🐍 Here’s a classic loop puzzle that often surprises even experienced Python devs 👇 nums = [1, 2, 3, 4] squares = [lambda: n**2 for n in nums] print([f() for f in squares]) 🤔 What do you think this prints? Most people expect: [1, 4, 9, 16] But the actual output is: [16, 16, 16, 16] 😲 Why? Because the lambda inside the list comprehension captures the variable n, not its value at each iteration. By the time the lambdas run, n equals the last value (4) — so each lambda returns 4**2. ✅ Fix it: Bind the variable in the lambda’s default argument: squares = [lambda n=n: n**2 for n in nums] print([f() for f in squares]) # Output: [1, 4, 9, 16] 💡 Lesson: In Python, closures capture references, not values! #Python #CodingInterview #ProgrammingTips #LearnPython #CodeTricks #Developers

#Day26 of #120Days Challenge of Python Generators in Python : What is a Generator? A generator is a special type of function in Python that returns values one at a time using the keyword yield instead of return. Generators don’t store all values in memory—they generate them when needed. This makes them faster and memory-efficient. Example 1: Normal function (using return) def numbers_list(n): result = [] for i in range(1, n+1): result.append(i) return result print(numbers_list(5)) Output: [1, 2, 3, 4, 5] All values returned at once. Example 2: Generator function (using yield) def numbers_gen(n): for i in range(1, n+1): yield i for num in numbers_gen(5): print(num) Output: 1 2 3 4 5 Values generated one by one. Example 3: Infinite Generator def infinite_numbers(): num = 1 while True: yield num num += 1 When to Use Generators Use generators when: Working with large datasets Reading large files Need streams of data Don’t want to store entire list in memory #Day27 Challenge done Pooja Chinthakayala Mam

Tuples in python oop: A tuple in Python is an ordered and immutable collection of elements. It is one of Python's built-in data types used to store collections of data, alongside lists, sets, and dictionaries. Key Characteristics of Tuples: Ordered: The items in a tuple have a defined order, and this order will not change. This means you can access elements by their index. Immutable (Unchangeable): Once a tuple is created, you cannot modify its elements, meaning you cannot add, remove, or change items within the tuple. Heterogeneous: Tuples can contain elements of different data types (e.g., integers, strings, booleans, or even other tuples). Creating Tuples: Tuples are typically created by enclosing a sequence of items, separated by commas, within parentheses ().# An empty tuple empty_tuple = () # A tuple with various data types my_tuple = ("apple", 10, True, 3.14) # A tuple with a single item (note the trailing comma) single_item_tuple = ("banana",)

🔹 Dictionary Methods & Functions in Python A Dictionary in Python is a collection of key-value pairs. It’s one of the most powerful and flexible data structures used to store data in a structured way. 🧩 Common Dictionary Methods & Functions: 🔸dict() - Creates a new dictionary 🔸clear() - Removes all items 🔸copy() - Returns a shallow copy 🔸fromkeys() - Creates a new dict from keys 🔸get() - Returns value of a key 🔸items() - Returns list of (key, value) pairs 🔸keys() - Returns all keys 🔸values() - Returns all values 🔸pop() - Removes item by key 🔸popitem() - Removes last inserted item 🔸setdefault() - Returns value if key exists; else adds key with default value 🔸update() - Updates dictionary with another dict ✅ Dictionaries are fast, flexible, and great for structured data — use them when you need key-based access. #Python #PythonLearning #Coding #LearnPython #PythonBasics #DataStructures #DictionaryInPython

A Python rule of thumb: Use a dictionary for a single object or when the structure is dynamic. Use a dataclass when you want to create multiple structured objects with the same fields. By Jessica Wachtel

Indexing and splitting strings in Python 🐍 How to solve the Python string slicing problem Step 1: Understand string slicing In Python, the slice operator [:] is used to get a substring from a string. The syntax is str[start:end], where the substring starts at the start index and ends before the end index. Step 2: Apply the slicing to the problem The problem asks for the result of str[1:3] on the string str = "anne". The start index is 1. The end index is 3. The slice will include the character at index 1 and the character at index 2, but it will not include the character at index 3. Step 3: Determine the characters The character at index 1 is 'n'. The character at index 2 is 'n'. The character at index 3 is 'e', but it is not included in the slice. The substring is formed by the characters at indices 1 and 2. Answer: The result of str[1:3] is 'nn'.

➡️➡️➡️Logical Operators in Python: Logical operators help you make decisions in your code by combining conditions. They are use with conditioner statement. There are three main logical operators: 1. AND ('and'): Both conditions must be true. Example: 'x >(greater than) 5 and x < (less than)10' (x is between 5 and 10) 2. OR ('or'): At least one condition must be true. Example: 'x > 5 or x < 0' (x is either greater than 5 or less than 0) 3. NOT ('not'): Reverses the condition.Example: 'not x > 5' (x is not greater than 5) These operators help you write more complex and flexible conditions in your code. ➡️➡️➡️A CONDITIONAL EXPRESSION A conditional expression is a short way to make a decision and choose between two options based on a condition. It is also a one line statrment for if and else statement. formular Print('x' if condition else 'y') #Codind #Python #Pythonprogramminglanguage #logicaloperators #conditionalexpression

🐍📰 Using Python Optional Arguments When Defining Functions Use Python optional arguments to handle variable inputs. Learn to build flexible function and avoid common errors when setting defaults https://lnkd.in/dFGqkyz3

Concurrency vs Parallelism in Python My understanding of concurrency and parallelism was very crucial, and it helped me formulate the right solution to the problems caused by the single-threaded and synchronous nature of Python. This article explains my understanding of concurrency and parallelism. Read article here: https://lnkd.in/gXJiwT9i #Python #ConcurrencyParallelism