Dictionaries in Python: Fast and Flexible Data Storage

💻 Everyone learns Python data types. But few ask how much memory they actually use. As part of my AI diploma, I looked into it — and the answer isn’t as simple as you’d expect. Here’s what I found 👇 🔍 Short answer: It depends. Unlike languages like C or Java, Python doesn’t fix memory sizes. It uses dynamic typing and flexible memory management. 📊 How common types behave: int → No fixed size Can grow as large as memory allows (no traditional overflow) float → Typically 64-bit Similar to C’s double bool → Subclass of int Stored as an object (not just 1 byte) str → Variable size Uses flexible internal encoding depending on characters list / tuple → Store references Not the actual values directly 💡 Why does Python work this way? Flexibility. No need to declare types No need to manage memory manually Easier and safer for developers ⚠️ The trade-off More flexibility = more memory usage A Python int can take around 28 bytes while a C int takes only 4 bytes Same value — very different cost. My biggest takeaway? Python hides memory complexity — but understanding it makes you a better programmer. 💬 Did this surprise you? Thank you Eng. Jana Hatem for pushing us to look deeper❤️ #Python #Programming #DataTypes #ComputerScience #LearningInPublic #TechExplained

Fundamentals… Many of us are avid users of the Python Pandas library. The read_csv function is the basic step in reading data. After data is read, the most basic model to create is a simple linear regression model. While we (including myself) deal with the abstracted form of these functions, in a learning curve, it is imperative we understand what happens at the basic level. And when dealing with programming at the basic level, then it is not far fetched to bring the C language into discussion. Learning C is fun, it tires you out sometimes, specially when you’ve learnt C++, Python and Java beforehand, C is still important. While writing this subroutine, I was able to think from a fundamental perspective as to how, FILE input is read, how to escape string delimiters, how to read integer input, perform operations on it to build a function, we use so widely. Read the code, and you’ll understand, how memory allocation is being done depending on the file, how operations are formed and stored to be reused, what is scope and what would happen in variables are accessed outside of scope. Learning the fundamentals not only helps you in becoming better engineers but also lets you think in a manner where you can solve problems at the basic level without a lot of overhead. P.S( The code might replicate the initial functionality, but it still doesn’t work a optimally, but it does provide a fundamental layout, suggestions and improvements are always welcome). Link: https://lnkd.in/ggPaeRx2 #opensource #C #programming #fundamentals #learninpublic

If You Understand This, Dictionaries Become Your Fastest Tool in Python Think of a dictionary like a real-world phonebook. You don’t search every page. You go directly to the name and get the number instantly. Think like this: • Key → Person’s name • Value → Phone number • Adding data → Saving a new contact • Updating → Changing someone’s number • Deleting → Removing a contact • Accessing → Direct lookup using name • get() → Safe lookup without errors • keys(), values(), items() → Different views of your contacts • Nested dictionary → Contact groups inside groups • Dictionary comprehension → Auto-generating contacts with logic Most important: Search in dictionary → Direct lookup Not scanning the whole list That’s why it’s fast. The difference: Lists search. Dictionaries locate. Once you understand this, you stop looping over data and start accessing it intelligently #Python #PythonProgramming #DataStructures #Coding #Programming #LearnPython #TechLearning #SoftwareEngineering #Developers

Python Learning Journey - Dictionaries Deep Dive Dictionaries are one of the most powerful and flexible data structures in Python. Today, I explored some important functions that every developer should know Core Dictionary Functions: len() - Returns number of key-value pairs clear() - Removes all elements get() - Access values safely without errors pop() - Removes specific key and returns its value popitem() - Removes last inserted key-value pair keys() - Returns all keys items() - Returns key-value pairs copy() - Creates a shallow copy setdefault() - Returns value of key (adds if not present) update() - Updates dictionary with new key-value pairs Advanced Concept: Dictionary Comprehension - A concise way to create dictionaries in a single line Example: {x: x*x for x in range(5)} Mastering dictionaries helps in writing efficient and clean code, especially when working with real-world data. #Globalquesttechnologies #GR Narendra Reddy #Python #Coding Journey #100DaysOfCode #Programming #Software Development #PythonBasics #Learning

🚀 Python Basics to Advanced Learning Series – Day 14 Today, I dived into one of the most important data structures in Python — Dictionaries. I explored how dictionaries work as key-value pairs, making data storage and retrieval efficient and structured. 🔍 Key Concepts I Learned: 🔹 Dictionaries store data in key → value format 🔹 Insertion order is preserved 🔹 Mutable data type (can be modified) 🔹 Supports heterogeneous data (both keys & values) 🔹 No indexing or slicing 🔹 Keys are unique, but values can be duplicated ⚙️ Operations I Practiced: ✅ Accessing values using keys ✅ Deleting elements using: del dict[key] dict.clear() del dict 🛠️ Built-in Functions Explored: dict(), len(), clear(), get(), pop(), popitem(), keys(), values(), items(), copy(), setdefault(), update() 💡 Dictionaries are extremely powerful when working with real-world data, APIs, and structured datasets. Grateful to Global Quest Technologies for the continuous guidance and support 🙏 Excited to keep learning and building every single day! 🔥 Keep Practicing. Keep Learning. Keep Growing. G.R NARENDRA REDDY #Python #PythonLearning #LearningJourney #Day14 #Dictionary #DataStructures #Coding #Programming #Developers #TechSkills #100DaysOfCode #SoftwareDevelopment #TechLearning #Developers #GlobalQuestTechnologies #PythonDeveloper #CodeNewbie

“If You Understand This, Python Lists Become Effortless” Think of a Python list like a toolbox. You don’t just store tools… you organize, access, replace, and remove them based on your need. Think like this: • Creating list → Setting up your toolbox • Indexing → Picking a specific tool instantly • Slicing → Taking a subset of tools for a task • Append / Insert → Adding new tools • Remove / Pop → Taking out what you don’t need • Sorting → Arranging tools in order • Searching → Finding the right tool quickly • List comprehension → Building tools automatically with logic • Nested lists → Toolbox inside a toolbox • Complexity → Knowing how fast you can access or modify tools Same list. Different operations. The difference: Beginners use lists to store data. Smart developers use lists to manipulate and control data efficiently. Once you understand this, Python lists stop being syntax and start becoming a powerful system #Python #PythonProgramming #Coding #Programming #LearnPython #DataStructures #CodingTips #TechLearning #Developers #SoftwareEngineering

9 favorite websites to practice coding exercises until you're a MASTER: 9. Mode (SQL) 8. DataLemur (SQL) 7. LeetCode (Python) 6. Codewars (Python) 5. Stratascratch (SQL) 4. HackerRank (Python) 3. Kaggle (Data Science) 2. W3 Resource (pandas) 1. bnomial (Machine Learning)

🚀 Python Practice – Standard Library Modules Continuing my Python learning journey by exploring the powerful Standard Library 🐍 In this session, I worked with some commonly used modules: ✔️ array – handling collections of elements ✔️ math – mathematical operations ✔️ random – generating random values ✔️ os – file & directory operations ✔️ json – data conversion (dict ↔ JSON) ✔️ csv – reading and writing CSV files ✔️ datetime & time – working with date and time ✔️ re (Regular Expressions) – pattern matching in text Practiced using these modules to perform real-world tasks like file handling, data conversion, and basic data processing. Understanding the Standard Library is helping me write more efficient code without relying on external libraries 📊 A big thanks to Krish Naik for his amazing guidance and clear explanations 🙌 Documented all my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Learning how to use built-in tools to solve real problems step by step 💡 #Python #StandardLibrary #DataAnalytics #LearningJourney #Coding

🚀 Just completed structured notes on Python fundamentals! Covered key concepts like: ✔️ Operators (Logical, Bitwise, Membership) ✔️ Data Structures (Lists & Strings) ✔️ Conditional Statements & Loops ✔️ Functions & Scope ✔️ File Handling & Exception Handling ✔️ Basics of OOP (Classes, Inheritance, Encapsulation) Diving into Object-Oriented Programming (OOP)! OOP helps structure code in a smarter way using: ✔️ Classes & Objects ✔️ Encapsulation (data hiding) ✔️ Inheritance (code reusability) ✔️ Polymorphism (flexibility) It makes programs more scalable, reusable, and easy to manage 🚀 Building strong fundamentals one step at a time 💡 Consistency > Perfection. Feel free to Repost & Follow Himansh S. for more helpful resources. #Python #Programming #CodingJourney #LearnPython #TechSkills #DataStructures #OOP #Developers #StudentLife #OOP #Programming #Python #Coding #SoftwareDevelopment #TechLearning #Developers #LearnToCode

😊❤️ Todays topic: Topic: Class and Object in Python: ================ Object-Oriented Programming (OOP) helps organize code using real-world concepts. Class: A class is a blueprint for creating objects. class Student: def __init__(self, name, age): self.name = name self.age = age Object: An object is an instance of a class. s1 = Student("John", 20) print(s1.name) print(s1.age) Output: John 20 Understanding init: It is a constructor Automatically runs when object is created Used to initialize data Understanding self: Refers to the current object Used to access variables inside the class Adding a method: class Student: def __init__(self, name): self.name = name def greet(self): print("Hello", self.name) s1 = Student("John") s1.greet() Output: Hello John Key Points: Class → blueprint Object → real instance init → initializes object data self → refers to current object Interview Insight: OOP improves code reusability, structure, and scalability in large applications. Quick Question: What will happen if we remove self from method parameters? #Python #Programming #Coding #InterviewPreparation #Developers