Python Dictionaries: Key-Value Pairs for Structured Data

PYTHON JOURNEY - Day 34 / 50..!! TOPIC – Python Lists (Basics) Today I explored Lists in Python — a powerful way to store multiple items in a single variable. 1. Creating a List Python fruits = ["Apple", "Banana", "Cherry"] print(fruits) 2. Accessing Items (Indexing) Python starts counting at 0! Python fruits = ["Apple", "Banana", "Cherry"] print(fruits[0]) # Output: Apple print(fruits[-1]) # Output: Cherry (Last item) 3. Adding & Removing Items Python fruits.append("Orange") # Adds to the end fruits.remove("Banana") # Removes specific item print(fruits) Why Use Lists? Keep related data organized They are "mutable" (you can change them after creation) Can hold different data types (Strings, Integers, etc.) Mini Task Write a program that: Creates a list of your 3 favorite movies. Adds one more movie to the list using .append(). Prints the first movie and the total length of the list using len(). #Python #PythonLearning #50DaysOfPython #DailyCoding #LearnPython #CodingJourney #PythonForBeginners #LinkedInLearning #DeveloperCommunity

Python Code Practice: Variables & Data Types Today I practiced the fundamentals that every Python programmer needs to know. Here are the four basic data types I worked with: 🔹 Integer (int) - Whole numbers like age, count, quantity 🔹 Float - Decimal numbers for precision like GPA, prices 🔹 String (str) - Text data enclosed in quotes 🔹 Boolean (bool) - True/False values for logical operations My practice code: python age = 22 cgpa = 3.75 name = "Iqra Munir" is_learning = True print(f"Name: {name}") print(f"Age: {age}") print(f"CGPA: {cgpa}") print(f"Learning Python: {is_learning}") Key takeaway: Understanding data types is more than just theory. It's about knowing which type to use when storing and processing information. Small, focused practice makes these concepts stick. What's one Python fundamental you wish you'd practiced more in the beginning? #Python #DataScience #LearningInPublic #CodingJourney

 PYTHON JOURNEY - Day 37 / 50..!! TOPIC – Python Dictionaries Today I explored Dictionaries — one of the most powerful and widely used data structures in Python. Unlike lists, dictionaries store data in Key:Value pairs! 1. Creating a Dictionary Think of it like a real-life dictionary: you look up a Word (Key) to find its Meaning (Value). Python student = { "name": "Srikanth", "course": "Python", "day": 37 } print(student["name"]) # Output: Srikanth 2. Adding & Updating Data Dictionaries are mutable, meaning you can change them easily. Python # Adding a new key-value pair student["status"] = "Learning" # Updating an existing value student["day"] = 38 3. Dictionary Methods Useful ways to extract information from your dictionary. Python print(student.keys()) # Gets all keys print(student.values()) # Gets all values print(student.items()) # Gets both in pairs Why Use Dictionaries? Fast Lookups: You don't need to know the index; just the name of the key. Organized Data: Perfect for representing real-world objects (like a user profile or a product's details). Readable: It makes your code look much more intuitive. Mini Task Write a program that: Creates a dictionary for a Smartphone (Brand, Model, and Price). Updates the price of the phone. Adds a new key called "Color". Prints the final dictionary using an f-string. #Python #PythonLearning #50DaysOfPython #DailyCoding #LearnPython #CodingJourney #PythonForBeginners #LinkedInLearning #DeveloperCommunity

Do you know that everything in Python is an Object? Even the integers and strings. Most beginners are taught that a variable is a "box" where you store data. In Python, that is fundamentally wrong. A variable is just a reference bound to an object. Your variable just points to it. The Anatomy of an Object Every single value in your code, whether it’s the number 5, a string, or a complex function, is a full-blown object. Each object carries three essential things - Identity: A unique memory address - Type: The class that defines its behavior - Value: The actual data stored within it. Why "Everything is an Object" Matters Because everything is an object, Python treats functions, modules, and even classes as first-class values. Functions can have attributes: You can literally attach data to a function, like my_func.metadata = "v1" Classes are objects too: Just as 5 is an instance of int, your class User is actually an instance of a metaclass called type. Uniformity: This design creates a single mental model where attributes and methods operate the same way across all values. The Performance Trade-off Python isn't "slow" by accident; it's a deliberate architectural choice. By making a simple integer a PyObject struct in C (complete with a reference count and type pointer), Python sacrifices raw speed for extreme flexibility and metaprogramming power. The Bottom Line: When you reassign x = 10, you aren't changing the value inside a box. You are moving the label "x" to a completely different object in memory. Understanding this "binding" behavior is the first step toward mastering memory management and avoiding accidental mutations in your code.

🐍 90 Days of Python – Day 10 Today, I focused on Python data structures, which play a key role in storing and organizing data efficiently. Choosing the right data structure can make programs simpler, faster, and easier to maintain. Some important data structures I revised and practiced today: • Lists – for ordered and mutable collections • Tuples – for fixed, immutable data • Sets – for storing unique elements • Dictionaries – for key-value based data storage Understanding how and when to use each data structure helps in writing clean and optimized code. I’m spending time strengthening these concepts because data structures are used everywhere — from simple scripts to large-scale applications. 📌 Day 10 completed. Organizing data before solving bigger problems. 👉 Which Python data structure do you find most useful in real-world tasks? #90DaysOfPython #PythonLearning #LearningInPublic #DataStructures #BTechCSE #MachineLearning

PySpark is often described as “Spark but in Python,” a convenient yet incomplete framing. For those working with large-scale data, grasping how PySpark operates under the hood can significantly enhance performance tuning, debugging, and design decisions. ❇️ PySpark Is Not Executing Python at Scale Your Python code does not run on worker nodes as many might assume. Here’s what actually occurs: - You write transformations in Python. - PySpark converts them into a logical plan. - Spark’s Catalyst Optimizer rewrites that plan. - The optimized plan is executed on the JVM across the cluster. 👉 This is crucial because: - Some Python constructs work effectively. - Others can silently degrade performance. - User Defined Functions (UDFs) behave very differently from built-in functions. #DataEngineering #ApacheSpark #PySpark

PYTHON PROGRAMMING – DAY 3 Python Data Type Features Topics covered: What is a Data Type and why it matters Classification of Python data types: Primitive: int, float, bool, str, complex Non-Primitive: list, tuple, set, dict How Python stores data internally (objects & references) Memory management and immutability concepts Detailed features of: int (including binary, octal, hexadecimal formats) float (decimal handling & scientific notation) bool (True/False behavior and logical operations) str (immutability, indexing, slicing, Unicode) complex (real & imaginary parts) list (mutable, ordered collections) tuple (immutable, fixed-size collections) set (unique values, hashing, set operations) dict (key–value pairs, hashing, fast lookup) Mutable vs Immutable data types How Python handles data creation, storage, and updates in memory Understanding how Python stores, manages, and handles data at a deeper level. Learning with structured guidance from Ajay Miryala at 10000 Coders. #Python #PythonProgramming #DataTypes #PythonInternals #CorePython #LearningPython #10000Coders

Python Dictionaries Key–Value Pairs in Python Ordered + Mutable + Unique Keys One of the most powerful features in Python is the dictionary. Unlike lists, dictionaries store data as key–value pairs, making them ideal for real-world use cases such as user profiles, configurations, and API responses. Here’s a simple example: employee = { 'name': 'Alice', 'role': 'Data Engineer', 'skills': ['Python', 'SQL', 'Spark'], 'active': True } # Safe access with .get() — avoids KeyError print(employee.get('salary', 'Not specified')) # Output: Not specified # Easy update employee['salary'] = 120000 print(employee['salary']) # Output: 120000 #Python

One of the most interesting parts of learning Python has been working with real data. Recently, I built a US States guessing game where I used CSV data and the Pandas library to manage and interact with information instead of hard-coding values. Through this project, I learned how to: Read and work with CSV files using Pandas Access and filter data programmatically Connect data to game logic in a meaningful way Use Python to turn raw data into something interactive What stood out to me was how powerful it feels to separate data from logic — once the data is structured properly, the program becomes easier to extend, debug, and improve. This project helped reinforce that Python isn’t just about writing code — it’s also about understanding and working with data effectively. Sharing this in case it’s useful or encouraging for anyone else learning Python and starting to explore libraries like Pandas. Project Link : https://lnkd.in/deEvzzTe #Python #DataAnalysis #Pandas #LearningInPublic #100DaysOfCode #LearningInPublic

🐍 90 Days of Python – Day 5 Today, I started learning about basic data structures in Python, which are essential for storing and organizing data efficiently. Understanding data structures is important because they decide how data is stored, accessed, and modified in a program. Some core data structures I explored today: • Lists – ordered and mutable collections • Tuples – ordered but immutable collections • Sets – unordered collections with unique elements • Dictionaries – key-value pairs for structured data Choosing the right data structure can make code simpler, faster, and more readable. I’m focusing on understanding when and why to use each one, instead of just memorizing syntax. 📌 Day 5 completed. Learning how data is structured before manipulating it. 👉 Which Python data structure do you use the most in your projects? #90DaysOfPython #PythonLearning #LearningInPublic #DataStructures #BTechCSE #MachineLearning