Python2 vs Python3: 36 Key Differences

🧠 “Variables forget. Files don’t. And today’s lesson was all about that power.” 💻 Day 64 of #100DaysOfCode — Python File I/O Unlocked 📂🐍 Today I completed the File I/O lecture from CS50’s Python course — and it was one of those topics that feels simple at first, but suddenly makes your programs more real and more powerful. Here’s what clicked today: 📄 Reading & Writing Files Understood how to read text files line-by-line, write new content, and append to existing data — turning Python scripts into tools that interact with real stored information. 🔐 Why with Matters Learned how the with keyword automatically handles opening and closing files, preventing corruption, memory issues, and unexpected behavior. A tiny keyword with massive reliability impact. 📚 Working with CSVs Explored structured data using: csv.reader → raw lists csv.DictReader → clean, readable key–value pairs This makes working with datasets far more intuitive and scalable. 🔎 Real-World Perspective Logs, user data, configs, analytics, exports — File I/O is what separates a toy script from actual software that remembers, stores, and interacts with the world outside RAM. Every concept today felt like adding permanence to my code — a shift from “running something” to “building something.” Step by step, leveling up. 🚀 #100DaysOfCode #Python #CS50 #FileIO #DataProcessing #LearningInPublic #BuildInPublic #SoftwareEngineering #CleanCode #BackendDevelopment #DeveloperLife #CodingJourney #TechSkills #ProblemSolving #ProgrammingFundamentals

Speed up your Frappe queries with frappe.get_cached_value() If you’re repeatedly fetching the same field value from the database, don’t use frappe.db.get_value() every time — it hits the database on each call. Instead, use frappe.get_cached_value() . It stores the result in memory (cache) and returns it faster on the next request.  When to use it? Use frappe.get_cached_value() when :- Fetching non-changing fields like settings, configs, defaults You want better performance with fewer DB calls Accessing single fields from large DocTypes When NOT to use :- When the data changes frequently When you need fresh DB values every time When fetching multiple rows — use frappe.get_all() or get_list() instead Use caching smartly — small optimizations add up in big Frappe apps 💪 #Frappe #ERPNext #Backend #Performance #OpenSource #Python Rushabh MehtaHussain NagariaEjaaz KhanAditya HaseSherin K RRitvik SardanaManish DipankarFrappeefeone

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 𝐟𝐨𝐫 𝐀𝐜𝐭𝐮𝐚𝐫𝐢𝐞𝐬: 𝐖𝐡𝐲 𝐒𝐲𝐧𝐭𝐚𝐱 𝐈𝐬 𝐍𝐨𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 Most actuaries who create models are familiar with some coding—whether in Excel, Python, R, or various vendor tools. However, here's the truth: 👉 Knowing syntax is like knowing the alphabet; it doesn't make you an author. To build robust actuarial systems, we need more than just "code that works." We need: ✅ 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 & 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 – Understanding lists, dictionaries, arrays, and efficient logic is essential. ✅ 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 – Utilizing tools like NumPy and Numba. ✅ 𝐒𝐦𝐚𝐫𝐭 𝐋𝐢𝐛𝐫𝐚𝐫𝐲 𝐂𝐡𝐨𝐢𝐜𝐞𝐬 – With Python, R, and C++ offering thousands of options, how do you know which one to choose? Mastering these concepts takes time. It's not just about memorizing commands; it's about understanding how to structure and optimize your code to tackle real-world actuarial challenges effectively. 💬 What's your experience? Do you believe actuaries should invest in these fundamentals, or should they rely on vendor tools?

There’s a difference between code that works and code that’s Pythonic. Small language features can make your code cleaner, clearer, and easier to maintain. But many developers overlook them. Here are 4 Python tricks that instantly improve readability: 1/ next() → get the first matching item without manual loops 2/ all() / any() → simplify multi-condition filtering 3/ Tuple unpacking → remove temporary variables and noise 4/ dict.get() → handle missing keys cleanly and safely These patterns are subtle, but they add up. Especially in data workflows, ETL pipelines, and notebooks. Read the article: https://lnkd.in/dAa7gF88

🚀 𝑨𝒖𝒕𝒐𝒎𝒂𝒕𝒊𝒏𝒈 𝑫𝒂𝒕𝒂 𝑰𝒎𝒑𝒐𝒓𝒕𝒔 — 𝑶𝒏𝒍𝒚 𝑾𝒉𝒂𝒕’𝒔 𝑵𝒆𝒆𝒅𝒆𝒅! 🧠📊 Over the last 10 years, I’ve accumulated a huge collection of performance files in my system. Each file, fortunately, contains its own date — which gave me an idea 💡. Instead of manually selecting which files to import every quarter, I wrote a small yet powerful Python script that automatically 𝐢𝐦𝐩𝐨𝐫𝐭𝐬 𝐨𝐧𝐥𝐲 𝐭𝐡𝐞 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐪𝐮𝐚𝐫𝐭𝐞𝐫’𝐬 𝐝𝐚𝐭𝐚 from my archive. Here’s how it works 🧾 ✅ Scans the target folder for all .zip files ✅ Extracts the date (in DDMMYYYY format) from filenames ✅ Identifies the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐪𝐮𝐚𝐫𝐭𝐞𝐫 𝐚𝐧𝐝 𝐲𝐞𝐚𝐫 dynamically ✅ Filters and imports only the files that fall within that rang This small automation saves time ⏱️, reduces mistakes ❌, and keeps the data pipeline clean and focused on the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐪𝐮𝐚𝐫𝐭𝐞𝐫’𝐬 𝐊𝐏𝐈𝐬. 🔹 Function name: get_current_quarter_files() 🔹 Output: A list of .zip files belonging to the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐪𝐮𝐚𝐫𝐭𝐞𝐫 (𝐞.𝐠., 𝐐𝟒 𝟐𝟎𝟐𝟓) Python continues to be my go-to tool for streamlining repetitive data engineering tasks — one function at a time 🐍⚙️ #Python #DataAutomation #QuarterlyData #KPIs #DataEngineering #Productivity #Automation

Stop using lists when you don’t need them. Here’s why Python generators might quietly be one of the most powerful features in the language. ⚡ A generator doesn’t store data. It creates data — one item at a time, only when you need it. That means: ✅ Near-zero memory usage ✅ Faster for large datasets ✅ Cleaner, more readable code 3 ways to create a generator: # 1. Function with 'yield' def numbers():   for i in range(5):     yield i # 2. Generator expression g = (n for n in range(3, 5)) next(g) # 3 # 3. Class-based iterator class Numbers:   def __iter__(self): ...   def __next__(self): ... In practice, the function way win 99% of the time, less code, more clarity. Where it shines: - Reading massive log files - Streaming API data - Processing large DB results - Building data pipelines Tip: Generators are lazy, they produce values only when needed. That’s why they’re fast and memory-efficient. Because sometimes… the best optimization isn’t to store everything, but to create just what you need. #Python #CodingTips #BackendDevelopment #Performance #CleanCode

Comments, Docstrings & Input: Talking to Your Code 💬🧠 Imagine reading a coffee recipe with no explanations - you’d have no clue what’s going on! 😅 That’s what code without comments feels like. Let’s learn how to make Python talk - to you and your users! 💬 Comments - Notes for Humans Comments are ignored by Python - they’re just for us. Use # to write one-liners. # This line prints a message print("Coffee is ready! ☕") ✅ Shortcut (VS Code / PyCharm): Ctrl + / → toggle comment. 🧾 Multi-line Comments Use triple quotes for longer notes or explanations. ''' This program greets the user and asks for their coffee preference. ''' print("Welcome to Python Café ☕") 🧠 Docstrings — Notes for Developers Docstrings describe what a function or file does. They appear in help() or tooltips. def make_coffee(drink): """Prepares the given drink.""" print("Making", drink, "coffee ☕") help(make_coffee) ✅ Tip: Type """ + Enter in VS Code to auto-format. 🎤 Taking Input - Talking to Your User input() lets your program ask questions! name = input("Enter your name: ") drink = input("Your favorite drink? ") print("Hey", name + "!", "Here’s your", drink, "☕") 🧮 Converting Input to Numbers By default, input() gives text. Use int() or float() for numbers. cups = int(input("How many cups? ")) print("Preparing", cups, "cups ☕") 🧠 Today’s takeaway: Comments explain your logic. Docstrings explain your functions. Input connects your code with people. Together, they make Python clear, friendly, and human! 🤝 #PythonWithKeshav #LearnPython #PythonBasics #PythonComments #Docstrings #PythonInput #CodingJourney #ProgrammingForBeginners #STEMEducation #CodeSmart #PythonLearning

𝘚𝘵𝘰𝘱 𝘞𝘳𝘪𝘵𝘪𝘯𝘨 𝘖𝘯𝘦 𝘋𝘈𝘎 𝘱𝘦𝘳 𝘗𝘪𝘱𝘦𝘭𝘪𝘯𝘦 A common anti-pattern: 50 pipelines → 50 hand-written DAGs. It scales poorly, creates drift, and forces teams to maintain orchestration instead of improving their actual data platform. A better approach: a 𝗗𝗔𝗚 𝗙𝗮𝗰𝘁𝗼𝗿𝘆. You build one Python module that: Reads a folder of YAML configs Generates DAGs dynamically Enforces retries, naming, SLOs, owners, security, and alert patterns automatically A new pipeline becomes a new YAML file. No extra Python. No drift. No bespoke orchestration logic hiding in someone’s repo. This is usually where engineers realize they’re not building pipelines anymore — they’re building a platform others can reliably use.

I spent 3 days debugging one whitespace. I used to ignore the "Golden Rule" of Python strings. It cost me hours of frustration until I realized: Strings are immutable. I was writing `text.strip()` thinking it cleaned my data. But the variable remained dirty because I wasn't assigning it back. Once I fixed my workflow, I discovered the 3 tools that actually separate pros from beginners: 1. The Janitor: Data is rarely clean. [cite_start]`.strip()` removes the hidden spaces that break your code, while `.zfill()` perfectly pads your IDs 2. The Power Duo: `.split()` and `.join()` are the most powerful text processing team. [cite_start]They turn messy CSV strings into structured lists instantly 3. The Modern Standard: Stop using `.format()`. [cite_start]F-strings are cleaner, faster, and the absolute standard for injecting variables . Stop fighting your data. Start formatting like a pro. --- #Python #DataScience #CodingTips #EdTech #TechSkills #DigitalTransformation #DeveloperLife 💡 What is the one coding error you keep making? Share below!

🐍 Python Roadmap — Your Complete Learning Path Here’s how to master Python from zero to advanced 👇 🔹 Basics Start with the foundation: • Syntax and Variables • Data Types • Conditionals and Loops • Functions and Exceptions • Lists, Tuples, Sets, Dictionaries 🔹 Advanced Concepts Build depth in programming: • List Comprehensions • Generators and Iterators • Regex • Decorators and Closures • Functional Programming (map, reduce, filter) • Threading and Magic Methods 🔹 Object-Oriented Programming (OOP) • Classes • Inheritance • Methods 🔹 Web Frameworks • Django • Flask • FastAPI 🔹 Data Science Libraries • NumPy • Pandas • Matplotlib • Seaborn • Scikit-learn • TensorFlow • PyTorch 🔹 Testing • Unit Testing • Integration and Load Testing 🔹 Automation • File and Web Automation • GUI and Network Automation 🔹 Data Structures & Algorithms (DSA) • Arrays, Linked Lists, Stacks, Queues • Trees, Recursion, Sorting, Hash Tables 🔹 Package Managers • pip • conda 🎓 Learn Python for Free: 🔗 https://lnkd.in/d5iyumu4 🔗 https://lnkd.in/dkK-X9Vx 🔗 https://lnkd.in/dMF3xSmJ 🔗 https://lnkd.in/dmBDSuHH #Python #Programming #DataScience #MachineLearning #Django #Flask #AI #ProgrammingValley