Python REST API Development with Flask

Day 7 of my Python Full Stack journey. ✅ Today's topic: Dictionaries — the most important data structure in Python. A dictionary stores data as key-value pairs. Think of it like a real dictionary — word (key) and its meaning (value). Here's what I typed today: student = { "name": "Punith", "age": 24, "course": "Python Full Stack" } # Access print(student["name"]) # Punith # Add / Update student["city"] = "Bangalore" # Loop through for key, value in student.items(): print(f"{key}: {value}") Why this matters for Django: Every Django model, every API response, every JSON data — all dictionary-like. If you understand dictionaries, Django will make sense. 60 minutes done. Pushed to GitHub. Day 8 tomorrow. One week and 2 days in. Still showing up. 💪 #PythonFullStack #Day7 #Dictionaries #BuildingInPublic #100DaysOfCode #Bangalore

Starting my journey into databases with Python 🐍 One of the first things I’m learning is how to connect Python to a database and begin interacting with data using SQL. To make this easier, I’m using SQLite a simple and lightweight database alongside SQLAlchemy, which helps Python communicate with different types of databases. Here’s what I’ve learned so far: Import create engine from SQLAlchemy Create a database engine by specifying the database type and name. Use the engine to connect and interact with the database. Explore the database by retrieving table names using engine.table_names() It’s a small step, but an important foundation for querying and analyzing data. Small steps, big growth 🚀 #Python #SQL #DataEngineering #LearningJourney #TechGrowth

Is it time to ditch uv + pipx for Pixi? I’ve been a uv fan for a long time, but Pixi is starting to change the conversation—especially for data engineers. The choice is simpler than it looks: Stay with uv if you’re doing standard Python development. If you're mainly installing packages from PyPI or building your own tools to share with the community, uv is the fastest, most standard tool for the job.. Switch to Pixi the moment you have "Python-plus" problems. If your project needs specific C++ binaries, CUDA, or system-level dependencies to run, Pixi handles the whole system—not just the Python parts. The rule of thumb: If your "it works on my machine" issues are caused by things outside of Python, Pixi is worth the move. Read the full breakdown here: https://lnkd.in/eMeEJsPX #Python #DataEngineering #MachineLearning #Pixi

Today I explored one of the most powerful data structures in Python – Dictionaries 🐍 📌 Key Takeaways: 🔹 Dictionaries store data in key-value pairs 🔹 Keys are unique, but values can be duplicated 🔹 Easy data access using keys 🔹 Efficient for storing structured data 💡 Important Operations Covered: ✔️ Creating dictionaries using {} and dict() ✔️ Accessing values using keys and .get() ✔️ Removing elements using del, .pop(), .clear() ✔️ Understanding dictionary length using len() ✔️ Using .popitem() to remove the last inserted item 📊 Dictionaries are widely used in real-world applications like: ➡️ JSON data handling ➡️ APIs ➡️ Database-like structures Learning dictionaries strengthens the foundation for real-world Python development 💻 🔥 Consistency is the key — one step closer to mastering Python! Global Quest Technologies ✨ #GlobalQuestTechnologies #GQT #Python #PythonProgramming #100DaysOfCode #CodingJourney #LearnPython #DataStructures #Programming #Developer #CodingLife #TechLearning #SoftwareDevelopment #PythonBasics #CareerGrowth

Day 6 of my Python Full Stack journey. ✅ Week 2 starts today — Data Structures. First up: Lists — the most used data structure in Python. A list stores multiple values in one variable. And Python gives you powerful built-in methods to work with them. Here's what I typed today: skills = ["Python", "Django", "React"] # Add skills.append("PostgreSQL") # Remove skills.remove("React") # Access by index print(skills[0]) # Python # Loop through for skill in skills: print(skill) Biggest lesson today: List index starts at 0, not 1. I know it sounds obvious. But every beginner makes this mistake at least once 😄 60 minutes done. Pushed to GitHub. Back tomorrow. What's the data structure you use the most in your day to day coding? Drop it below 👇 #PythonFullStack #Day6 #Week2 #BuildingInPublic #100DaysOfCode #Bangalore

📘 Python Dictionaries — Quick Guide A dictionary in Python stores data in key–value pairs. It’s useful when you want to map one value to another, like name → grade or product → price. 🔹 Creating a dictionary student_grades = { "Anu": "A", "Durga": "B", "Keerthi": "A" } 🔹 Accessing values student_grades["Anu"] # Output: 'A' 🔹 Adding / Updating values student_grades["Rama"] = "B" # Add student_grades["Durga"] = "A" # Update 🔹 Loop through dictionary for name, grade in student_grades.items(): print(name, grade) 🔹 Key features ✔ Stores data as key–value pairs ✔ Keys must be unique ✔ Mutable (can add/update/remove) ✔ Fast lookup using keys Dictionaries are widely used in real-world tasks like APIs, data analysis, and configuration handling. #Python #DataStructures #PythonBasics #Coding #LearningPython

🐍📰 Start using Polars .group_by() to make sense of your data. This tutorial shows you how to group, aggregate, and reveal hidden insights with hands-on examples. #python

Day 4 – Python: Files, Data Formats, Functional Tools & Recursion** The series continues with 15 programs covering practical file handling, structured data, functional programming concepts, and an introduction to recursion and decorators. **Focus areas for Day 4:**   Working with the filesystem via `os` and `sys`, reading/writing `csv` and `json`, iteration tools like `enumerate` and `zip`, functional constructs `map`, `filter`, `lambda`, plus recursion fundamentals and a first look at decorators. **Day 4 program list:** | Concept | File | | --- | --- | | Filesystem basics | `01_os_basics.py` | | Cross-platform paths | `02_path_join.py` | | File modes: write vs append | `03_write_append_file.py` | | Line-by-line reading | `04_read_lines.py` | | CSV read/write | `05_csv_read_write.py` | | JSON read/write | `06_json_read_write.py` | | `enumerate()` and `zip()` | `07_enumerate_zip.py` | | `map()` and `filter()` | `08_map_filter.py` | | Lambda for sorting | `09_lambda_sort.py` | | Dictionary methods | `10_dict_methods.py` | | List methods | `11_list_methods.py` | | Recursion: factorial | `12_recursion_factorial.py` | | Recursion: Fibonacci | `13_recursion_fibonacci.py` | | Command-line arguments | `14_command_line_args.py` | | Decorator basics | `15_simple_decorator.py` | **How to use:**   All scripts use only the Python standard library. Python 3 required.   Run individually: `python 06_json_read_write.py`   Run the full set: `python Day4Files.py` **Series progression:**   Day 1 → Syntax, I/O, basic logic   Day 2 → Functions, lists, dicts, file I/O   Day 3 → Exceptions, modules, OOP basics   Day 4 → File systems, data formats, functional tools, recursion This stage bridges the gap between writing scripts and working with real data. These are the tools you’ll use daily when automating tasks, processing files, or building CLI utilities. #Python #SoftwareEngineering #DataEngineering #Programming #FileIO #ComputerScience #FunctionalProgramming #Recursion #TechEducation #OpenSource #GitHub #PythonProgramming Threads Link:- https://lnkd.in/gVf8wrpY

🚀 Ever wondered what really happens when you run a Python program? Most beginners just write code and hit “Run” — but under the hood, Python follows a powerful internal workflow 👇 🔍 Internal Structure & Working of Python 1️⃣ Source Code (Your .py file) You write human-readable code using Python syntax. 2️⃣ Compilation to Bytecode Python doesn’t directly convert your code into machine language. Instead, it compiles it into bytecode — an intermediate, platform-independent form. 3️⃣ Python Virtual Machine (PVM) The bytecode is executed by the PVM, which acts as the engine of Python. 👉 This is what makes Python portable across systems. 4️⃣ Execution & Output The PVM interprets the bytecode line-by-line and produces the final output. 💡 Why this matters? ✔️ Helps you debug smarter ✔️ Improves performance understanding ✔️ Makes you a better developer beyond just syntax 📌 In Simple Terms: Python = Code → Bytecode → PVM → Output Mastering this flow = leveling up from beginner to pro 🔥 --- 💬 What part of Python do you find most confusing — syntax, logic, or internals? Drop your thoughts 👇 --- #Python #Programming #Coding #Developer #SoftwareEngineering #Tech #AI #MachineLearning #DeepLearning #DataScience #CodingLife #LearnPython #PythonDeveloper #ProgrammingLife #TechCareer #CollegeLife #GenZ #FutureTech #CodeNewbie #100DaysOfCode

Headline: Python is evolving. Are you? 🐍 I published a quick guide on the "Modern Python Trinity" that every dev should be using in 2026: ✅ The Walrus (:=) – Clean up your loops. ✅ Match-Case – Destroy those nested if-elif chains. ✅ Parenthesized Ctx Managers – No more messy backslashes (\). #Python #CleanCode #Programming #SoftwareDevelopment #Tips