Python Fundamentals: Variables & Strings

Spent some time today revisiting something I used to completely overlook in Python — how objects actually behave behind the scenes. Earlier I used to memorize outputs. Now I’m trying to understand why they happen. A few things finally clicked for me: Variables don’t hold values, they point to objects. Lists and dictionaries change in place, integers and strings don’t. += behaves differently depending on the type — with lists it usually modifies the same object, but with strings it creates a completely new object. Most “tricky” interview questions are really about mutation vs reassignment. Shallow copy and deep copy make sense once you think in terms of references instead of values. Many Python surprises aren’t magic — they come from not understanding how references and objects work internally. Still learning, still fixing gaps, but this kind of clarity feels very different from just finishing tutorials. If you’re preparing for Python interviews, try predicting outputs instead of running code immediately. That exercise alone teaches a lot. #Python #LearningInPublic #BackendDevelopment #InterviewPreparation #

Every Tuesday and Thursday, I send 2 tips to help you discover useful Python tools for data and AI. Recent tips: • PydanticAI: Type-safe LLM outputs with auto-validation • Polars: Stream million-row exports without memory spikes • Narwhals: One function for pandas, Polars, and DuckDB • uv: Switch Python versions without rebuilding environments It's free on Substack. 📬 Subscribe here: https://bit.ly/46fdOPl #Python #DataEngineering #AI #OpenSource #PythonTips

Python Learning Progress | 29th Jan Today’s Python class was focused on Regular Expressions (Regex) — a really powerful module used for pattern matching and text manipulation. We practiced concepts like: ✅ compile() ✅ search() ✅ findall() ✅ split() ✅ sub() It was interesting to see how efficiently Python can handle text processing with Regex. Looking forward to practicing more and strengthening these skills step by step. 🚀 #Python #Regex #Learning #CodingJourney #Programming Pooja Chinthakayala

Today I learned about Regular Expressions (Regex) in Python 🐍 Regular expressions (pattern matching) are a powerful tool for working with text in Python. They help you: ✅ Search for patterns inside a string ✅ Extract specific parts of a string ✅ Replace or clean text To use regex in Python, we import the module: import re I also learned about some important metacharacters, like: 🔹 [] set of characters 🔹 . any single character 🔹 \ escape special characters 🔹 | either/or 🔹 ^ start of string 🔹 $ end of string 🔹 {} specific number of occurrences 🔹 () grouping patterns 🔹 * zero or more occurrences 🔹 + one or more occurrences Regex feels confusing at first, but it’s extremely useful in real-world data cleaning and text processing. Learning one concept at a time 🚀 #Python #Regex #DataScience #LearningInPublic #Programming #100DaysOfCode #CareerSwitch

Learning Update | Python for Generative AI Today, I revisited key Python concepts essential for Machine Learning and Generative AI and organized my progress into a structured GitHub repository. The repository covers Python libraries, statistical analysis (univariate, bivariate, multivariate), and core Python concepts from an ML/GenAI perspective. I’m looking forward to continuously learning and updating this repository as I grow in the field. Sharing my learning progress here: 🔗 GitHub repository link https://lnkd.in/gHaZa3Zf #Python #MachineLearning #GenerativeAI #LearningInPublic #GitHub

👋 Welcome back! 📅 Python Learning – Day 40 Today is about working with text in a more powerful way: regular expressions Python RegEx. Sometimes simple string methods are not enough. You may need to search, validate, or extract patterns from text. That’s where RegEx becomes incredibly useful. 📘 In this lesson, I’ve explained: 🔍 What regular expressions are and when to use them 🧩 How pattern matching works in Python ⚠️ Common beginner mistakes with complex patterns RegEx may look intimidating at first, but once you understand the basics, it becomes a powerful text-processing tool. This skill is especially useful for validation, parsing, and data cleaning. 🔗 Tutorial link is in the comments. #PythonRegex #TextProcessing #LearnPythonDaily #PatternMatching #PythonForBeginners #DataCleaning #CodingConcepts #DeveloperSkills #codepractice #pythonlearning #python #computerscience #learnpython #pythonprogramming

🚀 Python with DSA – Day 51 Today’s focus: Recursion & Backtracking (Subsets – Basics) 🧠🐍 ✅ Revised recursion fundamentals (base case & recursive call) ✅ Understood backtracking as try → explore → undo ✅ Solved the Subsets problem using include/exclude strategy ✅ Debugged a common mistake: parameter mismatch in recursive calls ✅ Learned how to explain recursion clearly in interviews 💡 Key takeaway: For every element, we have two choices – include or exclude. This simple idea builds powerful solutions in backtracking. 📌 On to the next step: handling duplicates & advanced backtracking problems. Consistency > Motivation. One day at a time. 💪 #Python #DSA #Recursion #Backtracking #LearningInPublic #LeetCode #ProblemSolving #SoftwareEngineering

Exploring Ensemble Learning: BaggingClassifier vs RandomForestClassifier (Python) Recently, I experimented with BaggingClassifier and RandomForestClassifier using Scikit-learn. ·      Created a dataset with make_classification ·      Trained both models ·      Visualized decision trees using plot_tree ·      Compared how Bagging uses random data while Random Forest uses random data + random features Key takeaway: Random Forest provides better generalization by reducing overfitting more effectively. This hands-on practice helped me understand ensemble learning and model interpretability at a deeper level. Always learning, always building #MachineLearning #Python #DataAnalytics #ScikitLearn #RandomForest #Bagging #LearningByDoing

Python Sets — Why They’re More Useful Than You Think In simple words: A set in Python is a collection that: • Stores only unique values. • Doesn’t maintain order. • Allows fast membership checks. Why it matters: - Removing duplicates becomes easy. - 'in' operations are much faster than lists. - Set operations like union & intersection are powerful in real-world logic. If you're serious about writing cleaner Python code, sets are essential. Which set operation do you use most in real projects? #Python #LearnPython #DataStructures #BackendDevelopment #PythonDeveloper #Coding