Python Class Properties for Data Management

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

🚀 Day 15 – Python Database Connectivity (PDBC) Today I learned how Python connects with databases like MySQL. 🔹 Used DB-API (PEP 249 standard) 🔹 Performed database operations using cursor 🔹 Learned how to insert and fetch data 💡 Key Learning: Python becomes truly powerful when it interacts with databases — this is where real-world applications begin. 📌 Example: cursor.execute("SELECT * FROM employee") Ajay Miryala 10000 Coders #Python #Database #BackendDevelopment #CodingJourney #100DaysOfCode

Made a Data Visualization Dashboard only using Python! DataViz is a lightweight, terminal-based data visualization tool designed for quick and efficient analysis of structured data. It enables users to generate graphs directly from datasets while also supporting manual data entry, making it flexible. The tool provides seamless CSV import and export functionality, allowing users to easily load existing data and save processed results for further use. With its minimal setup and fast execution, DataViz offers a simple yet powerful way to visualize and manage data directly from the command line. I have uploaded the project on my git https://lnkd.in/d8VhW4VE Steps to access: 1. Go to repo 2. Click and access the latest release page 3. Download the .exe file(dataviz.exe) and All set! Make sure to give a star if you liked the project !

🚀 Day 16 – Exploring REST API Tools in Python Today I learned about tools and frameworks used to build REST APIs in Python, and how real-world APIs are structured. 🔹 Key Concepts Covered: • Understanding how APIs manage resources like countries (name, capital, area) • Designing endpoints such as /countries for handling data • Using JSON as a standard data format • Storing data temporarily using Python lists 🔹 Framework Explored: Flask • Lightweight Python framework for building APIs • Handles HTTP requests and routes them to functions • Built simple endpoints like: GET /countries → retrieve data POST /countries → add new data 🔹 What I understood: • How APIs are structured in real applications • How requests and responses work in backend systems • How Python can be used not just to consume APIs, but also to build them This was my first step into backend API development using Python, and it gave me a clear understanding of how data flows in real-world applications. Continuing to build my Data Engineering & API knowledge step by step. 🐍💻 #Python #DataEngineering #APIs #RESTAPI #BackendDevelopment #LearningJourney #SelfLearning

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

🚀 Project Completed: Expense Tracker using Python I developed a command-line application to track daily expenses using Python and CSV file handling. 🔹 Features: ✔ Add and store expenses ✔ View all transactions ✔ Calculate total spending 🔗 GitHub Repository: https://lnkd.in/gQ4nwR95. This project helped me understand file handling and build a real-world application. #Python #DataScience #BeginnerProject #Learning

Python Tuples — Quick Guide with Examples A tuple in Python is an ordered, immutable collection that allows duplicate values. Once created, you cannot modify its elements. Creating a Tuple t = (10, 20, 30) Single element tuple (comma is required) t = (5,) Accessing elements t = (10, 20, 30) print(t[0]) # 10 Tuple slicing t = (1, 2, 3, 4) print(t[1:3]) # (2, 3) Tuple concatenation t1 = (1, 2) t2 = (3, 4) print(t1 + t2) Tuple unpacking person = ("John", 25, "Analyst") name, age, role = person Key Features: ✔ Ordered ✔ Immutable ✔ Allows duplicates ✔ Faster than lists ✔ Can store multiple data types When to use tuples? Use tuples when data should not change — like coordinates, database records, fixed configurations, etc. #Python #PythonBasics #DataStructures #Tuple #Coding #LearnPython #Programming #PythonForBeginners

DB Administration in SQL through a Python package. Building out one piece of a larger data pipeline—establishing local database connections, creating structured tables, inserting records, and querying live data directly from Python. This is where application logic meets data infrastructure, turning code into systems that store, validate, and move real information. More to come as the system continues to expand. #fscj #aiprogram #python #ai #data

🚀 Python Variables Explained (Beginner Friendly) Today I practiced Python variables and naming conventions 1. Basic Variable Declaration name = "Ankaj" age = 36 salary = 50000 print("Name:", name) print("Age:", age) print("Salary:", salary) 2. Variable Naming Rules user_name = "Ankaj" age1 = 25 _salary = 30000 print(user_name) print(age1) print(_salary) 3. Constants Convention (Uppercase) PI = 3.14 MAX_SPEED = 120 COMPANY_NAME = "ABC Pvt Ltd" print(PI) print(MAX_SPEED) print(COMPANY_NAME) Key Learnings: * Variables store data values * Naming rules matter in clean code * Constants are written in UPPERCASE by convention I’m building my Python skills step by step Follow Ankaj Python Hub for my daily learning journey #Python #Coding #LearnPython #100DaysOfCode #Programming

🚀 Python Variables Explained (Beginner Friendly) Today I practiced Python variables and naming conventions 1. Basic Variable Declaration name = "Ankaj" age = 36 salary = 50000 print("Name:", name) print("Age:", age) print("Salary:", salary) 2. Variable Naming Rules user_name = "Ankaj" age1 = 25 _salary = 30000 print(user_name) print(age1) print(_salary) 3. Constants Convention (Uppercase) PI = 3.14 MAX_SPEED = 120 COMPANY_NAME = "ABC Pvt Ltd" print(PI) print(MAX_SPEED) print(COMPANY_NAME) Key Learnings: * Variables store data values * Naming rules matter in clean code * Constants are written in UPPERCASE by convention I’m building my Python skills step by step Follow Ankaj Python Hub for my daily learning journey #Python #Coding #LearnPython #100DaysOfCode #Programming