Connecting to a Database with Python

Python Series – Day 27: SQL with Python (Connect Python with Databases!) Yesterday, we learned JSON in Python📄 Today, let’s learn how Python works with databases: SQL with Python What is SQL with Python? SQL is used to store, manage, and retrieve data from databases. When combined with Python, you can: ✔️ Insert data ✔️ Read data ✔️ Update records ✔️ Delete records ✔️ Automate database tasks Why It Matters? Almost every company stores data in databases. 👉 Customer data 👉 Sales records 👉 Employee data 👉 Product inventory Python + SQL = Powerful combo 🔥 Example: Connect SQLite Database import sqlite3 conn = sqlite3.connect("students.db") print("Connected Successfully") Output: Connected Successfully Example: Create Table cursor = conn.cursor() cursor.execute(""" CREATE TABLE students( id INTEGER, name TEXT, marks INTEGER ) """) Example: Insert Data cursor.execute( "INSERT INTO students VALUES (1,'Ali',85)" ) conn.commit() Example: Read Data cursor.execute("SELECT * FROM students") print(cursor.fetchall()) Output: [(1, 'Ali', 85)] 🎯 Why SQL with Python is Important? ✔️ Used in Data Analysis ✔️ Used in Web Apps ✔️ Used in Automation ✔️ Important for Data Science jobs Pro Tip Learn both SQL queries + Python libraries for better career growth. One-Line Summary SQL + Python = Manage databases using code 📌 Tomorrow: Excel Automation with Python (Save Hours of Manual Work!) Follow me to master Python step-by-step 🚀 #Python #SQL #SQLite #Database #DataScience #Automation #Coding #Programming #LearnPython #MustaqeemSiddiqui

Working with Python and SQL together — a few things that made a difference for me In most projects, SQL handles data well, and Python helps in controlling the flow and processing around it. While working with both, a few patterns consistently worked better. 🔹 Always push filtering to SQL Instead of fetching everything and filtering in Python: rows = cursor.execute("SELECT * FROM orders") filtered = [row for row in rows if row["status"] == "COMPLETE"] Better to push it into SQL: SELECT * FROM orders WHERE status = 'COMPLETE'; 🔹 Use parameterized queries Avoid building queries using string formatting: query = f"SELECT * FROM emp WHERE emp_id = {emp_id}" Use bind variables instead: cursor.execute(   "SELECT * FROM emp WHERE emp_id = :1",   [emp_id] ) 🔹 Fetch data in manageable batches Instead of loading everything at once: rows = cursor.fetchall() Fetch in batches: rows = cursor.fetchmany(1000) 🔹 Let SQL handle data, Python handle flow cursor.execute("SELECT dept_id, COUNT(*) FROM emp GROUP BY dept_id") for row in cursor:   process(row) SQL does aggregation, Python handles the next step. 💡 What worked for me Using Python and SQL together is less about replacing one with the other, and more about letting each do what it does best. Curious to know — how do you usually split work between SQL and Python in your projects? #Python #SQL #DataEngineering #OracleSQL #DatabaseDevelopment #CodingPractices

🚀 Day 14: Database Connectivity in Python In real-world applications, data needs to be stored, managed, and retrieved efficiently. 👉 That’s where databases come in. Python allows us to connect with databases and perform operations like storing, updating, and retrieving data. 🔹 Common Databases: ✔ SQLite (lightweight, built-in) ✔ MySQL (widely used in web applications) 🔹 Basic Operations: ✔ Insert data ✔ Fetch data ✔ Update records ✔ Delete records 💡 Example (SQLite): import sqlite3 conn = sqlite3.connect("students.db") cursor = conn.cursor() cursor.execute("CREATE TABLE IF NOT EXISTS students (name TEXT)") cursor.execute("INSERT INTO students (name) VALUES ('Ali')") conn.commit() conn.close() 📌 Why it matters? Every application from small apps to large systems depends on databases. ✔ User data storage ✔ Authentication systems ✔ Transaction records Frameworks like Django make database handling even more powerful using ORM. 💡 A strong developer not only writes logic but also manages data efficiently. 📈 Step by step, building real-world backend skills. #Python #Database #BackendDevelopment #Programming #Developers #Django #SQL #LearningJourney

💻 Learning by Doing: MySQL with Python Recently, I worked on a hands-on practice project using Python and MySQL where I explored some core database concepts.10000 CodersAjay Miryala Here’s what I implemented: 🔹 Created a table (emp_details) in MySQL 🔹 Applied constraints like NOT NULL to ensure data integrity 🔹 Inserted multiple records dynamically using user input 🔹 Used the WHERE clause to filter data (salary > 2000) While building this, I also realized the importance of writing efficient and secure code—like avoiding repeated database connections and using proper query methods. This exercise helped me understand not just how to write queries, but also how to write them better. 📈 Looking forward to improving further and building more real-world database-driven applications! #Python #MySQL #SQL #DatabaseManagement #CodingJourney #Learning #Developers #100DaysOfCode

🚀Python Series – Day 26: JSON in Python (Handle API Data Like a Pro!) Yesterday, we learned APIs in Python🌐 Today, let’s learn how Python works with the most common data format used in APIs: JSON What is JSON? JSON stands for JavaScript Object Notation It is a lightweight format used to store and exchange data. 📌 JSON is easy for humans to read and easy for machines to understand. 🔹 Where is JSON Used? ✔️ APIs ✔️ Web applications ✔️ Config files ✔️ Data exchange between systems 💻 Example of JSON Data { "name": "Mustaqeem", "age": 24, "skills": ["Python", "SQL", "Power BI"] } 💻 Convert JSON to Python Dictionary import json data = '{"name":"Ali","age":22}' result = json.loads(data) print(result) print(result["name"]) 🔍 Output: {'name': 'Ali', 'age': 22} Ali 💻 Convert Python Dictionary to JSON import json student = { "name": "Sara", "age": 23 } json_data = json.dumps(student) print(json_data) 🔍 Output: {"name": "Sara", "age": 23} 🎯 Why JSON is Important? ✔️ Used in almost every API ✔️ Easy data exchange format ✔️ Important for Web Development ✔️ Must-know for Data Science projects ⚠️ Pro Tip 👉 Learn dictionary concepts well, because JSON looks similar to Python dictionaries. 🔥 One-Line Summary 👉 JSON = Standard format to store and exchange data 📌 Tomorrow: SQL with Python (Connect Python with Databases!) Follow me to master Python step-by-step 🚀 #Python #JSON #API #WebDevelopment #DataScience #Coding #Programming #LearnPython #MustaqeemSiddiqui

🚀 Built a Lightweight File-Based Database in Python Over the past few days, I worked on a project called mini-DB — a simple yet powerful key-value database built from scratch using Python. 🔹 What it does: Stores data in JSON files 📁 Supports basic operations: create, get, set, update Uses file-level locking to handle concurrent access 🔒 Provides a clean CLI interface using argparse ⚡ 🔹 Why I built this: I wanted to understand how databases actually work under the hood — especially concepts like: Data persistence Concurrency control Storage abstraction Instead of just using tools like Redis or MongoDB, I tried building a minimal version myself. 🔹 Key learnings: Designing layered architecture (Storage → Connector → Interface) Handling race conditions with file locks Building CLI tools that feel like real-world systems Example usage: python tool.py master set user Jashan python tool.py master get user python tool.py master update user Singh 🔗 GitHub: https://lnkd.in/gXduQnez This project may look simple, but it gave me a deeper appreciation of how real databases manage data safely and efficiently. 💡 Next step: applying these concepts to more complex systems and scaling ideas further. Would love feedback or suggestions! #Python #BackendDevelopment #SystemDesign #Databases #CLI #LearningByBuilding

I know that YOU know the Python Fundamentals. - But bro, that’s just the START. In order to build scalable, production-grade solutions, YOU NEED TO KNOW A LOT MORE. First thing first, have you ever worked with UTILITIES inside PySpark/Pandas? If no, then lemme tell you that you need STRONG OOP knowledge in order to do that (and we use utils on a daily basis) ➡️ AND, What if I say that Python can’t perform real DATA VALIDATION natively? You can’t just hope the data is correct. You have to learn PYDANTIC to enforce schemas and ensure your pipelines don't crash mid-way. ➡️ One more, what about writing TEST CASES using Python while building data pipelines..... And YKW? There are so many other "Engineering-Grade" areas you should KNOW if you’re working with Python in the Data Domain. I’ve put together a LATEST VIDEO where I give you the full, detailed guide on what to learn after the fundamentals. This is for every single person who wants to move from "Script Writer" to "Data Engineer." 📍 Find the LINK in the COMMENTS.

PydanTable 1.17.0 has been released, and MongoDB is now officially part of the story. This release introduces an optional MongoDB execution engine, allowing work to remain on the MongoDB database side when supported. This means you can materialize data only when it is actually needed in the application, rather than pulling full result sets into Python first. Additionally, this version adds integration with Beanie, a popular Python ODM (object-document mapper) for MongoDB built on Pydantic. If your application already models MongoDB documents with Beanie, PydanTable can seamlessly integrate with that layer, ensuring your document models and typed, table-shaped workflow remain aligned without the need for a parallel schema. For more details, check out the documentation and release notes: - PyPI: https://lnkd.in/ez4NZMjT - Documentation: https://lnkd.in/eV4RTqZQ - Repository: https://lnkd.in/eVpjrcRX #Python #Pydantic #MongoDB #DataEngineering #OpenSource

SQL, Python, and Power BI get compared too often as if one of them should win. That is usually a sign that the work itself has been collapsed too loosely. They are not competing for the same job. The confusion starts when very different parts of analytics get treated as if they are one thing. Getting the data is one kind of work. Shaping and extending the logic is another. Building something other people can explore and use is another. Once you separate the work properly, the tools stop looking interchangeable. SQL sits closest to structured data. Python becomes more useful when the work needs flexibility, repeated transformation, or custom logic. Power BI matters when the analysis needs to be modeled, explored, and used for decisions beyond the analyst. The real mistake is usually not tool choice. It is forcing one tool to carry a layer of work it was not built for.

In this lecture, we begin our SQL journey by introducing PostgreSQL, pgAdmin, and the fundamentals of SQL in the context of a Python and SQL course. You’ll learn what databases are, how relational databases work, why PostgreSQL is widely used in data and backend workflows, and how pgAdmin helps you interact with your database through a graphical interface. We also walk through the core SQL concepts every beginner needs: creating a database, creating a table, inserting data, and querying data with SELECT, WHERE, and ORDER BY. This lecture is designed for beginners who want a practical, structured introduction before moving into Python and SQL integration. What you’ll learn: What a database and DBMS are What PostgreSQL is and why it matters How pgAdmin is used to manage PostgreSQL databases What SQL is and how it is used How to create a database and a table How to run basic SQL queries with SELECT, WHERE, and ORDER BY https://lnkd.in/gihAUFki