Python Iterators vs Generators Explained

I’ve just published my first blog post on Medium, diving into a Python concept I recently explored: 👉 Mutable vs Immutable Objects Before this, I used to think variables simply “store values.” But in Python, they actually reference objects in memory. Having learned C previously really helped me grasp this concept more quickly, especially when it comes to understanding how memory and references work. In this post, I break down: • The difference between == and is • Mutable vs immutable objects (with clear examples) • Why l1 = l1 + [4] and l1 += [4] behave differently • How Python passes arguments to functions Writing about what I learn pushes me to go deeper and truly understand the concepts. If you’re learning Python or preparing for technical interviews, this is definitely a topic worth mastering. 📖 Read here: https://lnkd.in/guVnYN3Q #Python #SoftwareEngineering #Programming #LearningJourney #Tech

There are multiple data types in Python, and it's pivotal to understand them so that you can build Python skills from the ground up. Here is a cheat sheet that lists the most commonly used types, what they are, and an example of what their output would look like: - String: Text data that is wrapped in quotes (e.g. "Hello World" or 'Hello World') - Integer: Positive or negative whole numbers (e.g. 15, -15) - Float: Positive or negative decimal numbers (e.g. 3.14, -1.5) - Boolean: Used for true or false evaluation (e.g. True or False) - List: Ordered, mutable collection of values held within []. Allows duplicates (e.g. [1,2,2,3]) - Tuple: Ordered, immutable collection of values held within () that cannot be changed after creation. Allows duplicates (e.g. (1,2,2,3)) - Set: Unordered, mutable collection of values held within {}. Values are unique and immutable within the set itself (e.g. {1,2,3}) - Dictionary: Key-value pairs where the key is a unique identifier for the value, held within {} (e.g. {"name":"John","age":25}) The application for the different data types is endless, such as converting a list to a set in order to remove duplicates and store a unique set of values from the original list. For example: og_list = [1,2,2,3,4,4,5] new_set = set(og_list) print(new_set) # Output: {1, 2, 3, 4, 5} Once you are familiar with the different data types, you have a foundation that helps you move on to creating more advanced scripts! #Python #PythonTips #LearnPython #Programming #DataEngineering #DataScience #AnalyticsEngineering

🐍 Python Tips & Tricks to Write Cleaner Code (Save this 🔖) If you're learning Python, these small tricks can make a BIG difference 👇 🔹 1. List Comprehension Write cleaner loops in one line squares = [x**2 for x in range(10)] 🔹 2. Swap Variables (No temp variable!) a, b = b, a 🔹 3. zip() Function Loop through multiple lists together for name, age in zip(names, ages): 🔹 4. enumerate() Get index + value easily for i, val in enumerate(data): 🔹 5. Dictionary Comprehension my_dict = {x: x**2 for x in range(5)} 🔹 6. Lambda Function (Quick functions) square = lambda x: x**2 🔹 7. Join Strings Efficiently " ".join(words) 🔹 8. Check Multiple Conditions if x in [1, 2, 3]: 💡 Writing clean code = Better readability + Faster development I’m sharing daily Python tips, Data Science projects & learning insights 🚀 👉 Follow me for more! #Python #CodingTips #Programming #DataScience #Developers #LearnPython #Tech #100DaysOfCode #AI #MachineLearning

🧠 Python Concept: set() for Removing Duplicates ✨ Sometimes lists contain repeated values. ✨ Python provides a simple way to remove them. Example numbers = [1, 2, 2, 3, 4, 4, 5] unique_numbers = list(set(numbers)) print(unique_numbers) Output [1, 2, 3, 4, 5] 🧠 What Happens? set() stores only unique values, so duplicates automatically disappear. 🧒 Simple Explanation 🍎 Imagine a basket of fruits 🍎 If you put two apples in a set basket, only one apple remains. ⚠️ Important Note set() does not preserve order. If order matters: numbers = [1, 2, 2, 3, 4, 4, 5] unique_numbers = list(dict.fromkeys(numbers)) print(unique_numbers) Output [1, 2, 3, 4, 5] 💡 Why This Matters ✔ Removes duplicates easily ✔ Cleaner data processing ✔ Very common in data handling ✔ Simple and Pythonic 🐍 Python often gives you simple tools for common problems 🐍 set() is one of the easiest ways to remove duplicates from a list. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

Day 9: Python Functions as First-Class Citizens ⚙️ Mastering neat, organized code is critical for Machine Learning pipelines. Today, I did a deep dive into Python Functions, focusing on how to organize code and how Python uses computer memory: Functional Programming: Functions behave like regular data (numbers or strings). I practiced storing them in variables, giving them as inputs to other functions, and having functions create new functions. This makes processing data in steps much easier. Decomposition & Abstraction: Moving past one giant block of code to build separate "boxes" for specific tasks (like separate sections for loading data, cleaning it, and training the AI model). I focused on writing clear instructions (docstrings) inside each one. Scoping & Frame Stack: Learned exactly how Python keeps track of where variables "live." A variable created inside a function is kept separate from variables outside, preventing accidental mistakes and data mix-ups. ⚡ Arbitrary Arguments (*args): Used *args to create super flexible functions that can accept any amount of inputs. This is crucial when you don't know exactly how much data you will get, ensuring the script doesn't crash. Moving from code that "works" to code that is neat, well-documented, and ready for production. 📈 #Python #LearningInPublic #ArtificialIntelligence #SoftwareEngineering #DataPipelines #Modularity #100DaysOfCode

Most people still debate Python vs R. In real projects, that’s the wrong question. At Sankhya, we don’t choose one we use both 👇 • Python→ data pipelines, ML models, APIs, deployment • R→ statistical modeling, deep analysis, dashboards, reporting And today, they integrate seamlessly: • reticulate → run Python inside R • rpy2 → run R inside Python Here’s what this looks like in practice: Clean data in Python → Model & analyze in R → Deploy back in Python No friction. Just flow. So the mindset shifts: Not Python vs R But Python + R 👉 Right tool for the right stage 👉 Focus on the problem, not the language Because the real edge isn’t the language you pick it’s how well you combine them. #DataScience #Python #RStats #Analytics #MachineLearning #Sankhya

🔹 Python Learning – Working with Dictionaries Efficiently 🔹 Today I practiced how to access and filter data from Python dictionaries 🐍 Here’s what I explored: ✔️ Iterating through dictionary keys ✔️ Fetching specific key-value pairs ✔️ Writing cleaner and more efficient code 💡 Example: bdict={'a':'10','b':'40','c':'50','d':'praveen','e':'fun','f':'joy'} for key, value in bdict.items(): if key == 'd': print(key, value) 📌 Key takeaway: While loops help in understanding data flow, direct access (dict[key]) is often more efficient when you already know the key. 🚀 Improving my Python fundamentals step by step and focusing on writing cleaner code! #Python #Learning #Programming #DevOps #Automation #CodingJourney

Day 2 of learning Pandas for Data Analysis 📊 Today I practiced how to read a CSV file using Pandas in Python. In most data analysis projects, datasets are usually stored in CSV (Comma Separated Values) format, so learning how to load them into Python is one of the first and most important steps. In Pandas, we use the read_csv() function to read a CSV file and convert it into a DataFrame. Simply put, read_csv() reads data from a CSV file and loads it into a table-like structure (DataFrame) so that we can easily explore and analyze it. While practicing this, I also learned: • head() – shows the first few rows of the dataset • tail() – shows the last few rows • shape – tells the number of rows and columns in the dataset • describe() – gives summary statistics like count, mean, min, max, etc. These functions help in quickly understanding the dataset before starting deeper analysis. Here’s the small example I tried: #Python #Pandas #DataAnalytics #LearningInPublic #DataAnalysis

Exploring Python Syntax: Your Foundation for Data & Automation. Python isn’t just a programming language it’s the language that powers data analysis, machine learning, and automation across industries. 🌐 During my journey learning Python, what's stood out to me: 1️⃣ Variables & Data Types: From integers to strings, every object in Python has a purpose. Naming matters descriptive names like student_name save hours of debugging later. 2️⃣ Functions & Conditional Logic: Reusable blocks of code (def) and conditional statements (if/elif/else) make programs flexible and powerful. 3️⃣ Operators & Expressions: Simple symbols like +, -, %, // carry immense power when you combine them creatively. 4️⃣ The Zen of Python: Beautiful is better than ugly. Readability counts. Simplicity wins. These guiding principles turn messy code into clean, maintainable solutions. 💡 Key Takeaway: Syntax + semantics are the heart of coding. The more you practice, the easier it becomes to communicate instructions to computers and to solve real world data problems efficiently. 📌 Bookmark PEP 8 and keep coding daily. Even small exercises compound into big skills over time. #Python #DataAnalytics #GrowWithGoogle #AI #Coding #LearnToCode #JupyterNotebook #DataAnalysis #TechCareers #LinkedInLearning #OOP #ZenOfPython #PythonTips #CareerGrowth #DataScience

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 #CodingJourney #100DaysOfCode #Programming #SoftwareDevelopment #PythonBasics #Learning