Today, I took a deeper dive into how Python manages memory internally by studying its Garbage Collection mechanism. As developers, we often rely on Python’s simplicity and assume memory management is completely automatic. However, behind the scenes, Python uses well-defined strategies to efficiently allocate and deallocate memory. 🔹 Reference Counting At the core of Python’s memory management is reference counting. Every object in Python maintains a count of how many references point to it. When this count drops to zero, the object is automatically removed from memory. This approach is efficient and works well in most cases. 🔹 The Limitation: Circular References However, reference counting alone is not sufficient. In cases where objects reference each other (circular references), the reference count may never reach zero - even if those objects are no longer in use. This can lead to memory not being released as expected. 🔹 Generational Garbage Collection To address this, Python uses a Generational Garbage Collector. Objects are categorized based on their lifespan: - Younger objects are checked more frequently - Older objects are checked less often This is based on the observation that most objects are short-lived. Day 5 of strengthening my Python and Machine Learning fundamentals 🚀 #Python #Programming #MachineLearning #coding #Learning #BuildinPublic
Python Memory Management: Reference Counting & Generational Garbage Collection
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Today’s Python lesson felt like learning how to write code in a smarter, cleaner way. 🐍 Day 13 of my #30DaysOfPython journey was all about list comprehension and lambda functions, and this one felt like a nice upgrade in how I think about Python. List comprehension is a compact way to create a list from a sequence. It is also faster and cleaner than writing the same logic with a full for loop. Syntax: [expression for i in iterable if condition] Then came lambda functions — tiny anonymous functions with no name. They can take any number of arguments, but only one expression. They are useful when you need a quick function inside another function. Syntax: lambda param1, param2: expression What stood out to me today was how Python gives you more than one way to solve the same problem. You can write it the long way, or you can write it in a tighter, more elegant way when the situation calls for it. One more day, one more topic, one more step toward writing code that feels sharper and more intentional. Which one clicked faster for you: list comprehension or lambda functions? #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney
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🔁 Python Revision – Lists & List Comprehension Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Lists (creation, indexing, slicing) ✔️ List methods (append, remove, sort, etc.) ✔️ Iterating through lists ✔️ List Comprehension Practiced working with lists to store and manipulate data efficiently, and explored list comprehension for writing cleaner and more concise code. Documented my practice in a Python Notebook and shared it as a PDF to track my progress. Learning how to handle data in Python step by step 📊 #Python #Revision #Lists #ListComprehension #Programming #DataAnalytics #LearningJourney
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🚀 Just Published My First Medium Article! I’m excited to share my first blog on Medium: 👉 Python Basics – Part 1 As a beginner, I started exploring Python and realized something important — strong fundamentals make everything easier later. In this article, I’ve covered: ✔️ Variables and data types ✔️ Basic syntax and operations I also tried to keep it simple and beginner-friendly, so anyone starting their coding journey can understand it easily. 💡 Learning is not about knowing everything at once — it’s about starting small and staying consistent. This is just the beginning of my journey into tech and self-improvement. 🔗 Read my full article here: https://lnkd.in/dfFnCFm2 I’d love to hear your feedback and suggestions! #Python #LearningJourney #Beginners #Coding #SelfImprovement #WomenInTech
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🚀 Day 12 of Python Coding Challenge 📌 Problem: Count Total Number of Characters in a File Understanding file handling is a fundamental skill in Python. Today’s task is to count the total number of characters in a given file. 💡 Approach: Open the file in read mode Read the file content Use len() to count characters 🧠 Python Code: def count_characters(file_path): try: with open(file_path, 'r') as file: content = file.read() return len(content) except FileNotFoundError: return "File not found." # Example usage file_path = "sample.txt" result = count_characters(file_path) print("Total characters in file:", result) ✅ Sample Output: Total characters in file: 12 🔍 Key Learnings: File handling using open() Using with statement for safe file operations Applying len() on strings 📢 Pro Tip: If you want to exclude spaces or newline characters, you can filter them before counting! 🔥 Keep Learning, Keep Growing! Follow along for more daily Python problems. #Python #CodingChallenge #Day12 #LearningJourney #30DaysOfCode
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At this point, Python is starting to feel less like a language… and more like a toolkit. Today’s Python MahaRevision 🧠 Chapter 13: Advanced Python (Part 2) This chapter introduced some really powerful and practical concepts: → Virtual environments → pip freeze (managing dependencies) → Lambda functions → bin() method → format() function → map, filter, reduce It’s interesting how these tools make code shorter, cleaner, and more efficient—once you understand how to use them properly. Practice set done: Worked on applying lambda functions, transforming data using map/filter, experimenting with reduce, and managing environments and dependencies. Some concepts felt a bit abstract at first (especially map/filter/reduce)… but with practice, they started making more sense. Biggest takeaway: Better tools don’t just make coding easier—they change how you think about solving problems. Still exploring, still improving. #Python #LearningInPublic #CodingJourney #Programming #AdvancedPython
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My Python Journey: Lists + Loops Today, I focused on building a strong foundation in Python with lists and loops. I practiced 10 essential problems, including: 🔹Printing all elements of a list 🔹Accessing elements at even indices 🔹Filtering even numbers 🔹Finding numbers greater than a threshold 🔹Calculating the sum of all elements (without sum()) 🔹Counting total elements (without len()) 🔹Counting numbers greater than 5 🔹Finding the smallest number (without min()) 🔹Printing a list in reverse (using loops) 🔹Creating a new list with squares of numbers 💡 Key takeaways: Loops are powerful for iteration and data manipulation Conditional checks inside loops make Python very flexible Practicing manually (without built-ins) strengthens problem-solving skills. Here’s a glimpse of my list of numbers I practiced on: nums = [14, 13, 27, 34, 20, 16, 23, 82, 49, 83] Feeling confident and ready for Day 2 challenges! 🔥 #Python #DataAnalytics #CodingJourney #100DaysOfCode #LearningByDoing #ProblemSolving
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🚀 Day 24/30 – Context Managers & with Statement in Python Today, I learned a cleaner and safer way to handle resources in Python. 📌 Context Managers They manage resources like files automatically — opening and closing them without extra code. 📌 with Statement Ensures proper setup and cleanup, even if an error occurs. Example: with open("data.txt", "r") as file: content = file.read() print(content) No need to manually close the file — Python handles it. 💡 Key Takeaway: Using with makes code cleaner, safer, and more professional. It’s a small change that improves reliability in real-world applications. Day 24 complete ✅ #Python #30DaysChallenge #LearningInPublic #ProgrammingJourney #Consistency #TechGrowth Aditya ChaturvediJECRC University
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One concept in Python that appears simple but carries deeper implications is mutability. On the surface, we categorize: - Lists and dictionaries as mutable - Strings and tuples as immutable However, the real impact becomes clear when considering memory and references. In Python, variables do not store values directly; they store references to objects. Thus, when you assign one variable to another, you are not copying data — you are pointing to the same object in memory. This distinction leads to very different behaviors between mutable and immutable types. With immutable objects, any modification results in the creation of a new object. In contrast, mutable objects allow the original object to be modified in place. This difference directly influences: - How functions behave - How data flows across modules - The emergence of subtle bugs in production Understanding this concept has aided me in debugging issues that initially seemed perplexing. It has also transformed my perspective on passing data between functions. Sometimes, the problem lies not in the logic but in how the data is being referenced. #Python #SoftwareEngineering #BackendDevelopment #Programming
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If Python variables still confuse you sometimes — lists behaving oddly, a global that won't update, `is` returning results that don't match `==` — it's almost always the same issue. You're thinking of variables as boxes that hold values. Python treats them as name tags attached to objects. Once that click happens, most of Python's "strange" behavior stops being strange. You can find a tutorial on PythonCodeCrack at the link below built entirely around that single idea. It starts from your first assignment and walks all the way through scope, mutability, type conversion, and even the CPython implementation details that surprise senior developers. Every section ends with a "thread marker" tying the concept back to the core model, and there are predict-before-you-run prompts at the moments where beginner intuition sometimes fails. Ends with a 10-question exam and a downloadable certificate for anyone who wants to mark the milestone and share their progress in building their Python programming skillset. Free. No signup. Just a solid foundation. https://lnkd.in/gA6nnmSJ #Python #LearnPython #PythonForBeginners #Coding #Programming
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Focusing more on understanding how things work internally rather than just using them 🙂