How to use Python 3.14's t-strings for secure SQL queries

Did you know you can read and write files in Python without using Pandas? Well, I only discovered this recently while exploring Python more deeply and it has been eye-opening. Python has what is known as file modes, and they are the little helpers that let you handle files directly. Some of these modes are: - 'r' → for reading - 'w' → for writing - 'a' → for appending - Add 'b' → to handle binary files (e.g., 'rb', 'wb', 'ab') I recently worked on a project where I wrote and read a CSV file using these modes. Attached to this post is a snapshot of part of the code, and you can find the full script on my GitHub via the link below: https://lnkd.in/dG88jyAa #Python #DataEngineering #Programming #GitHub #Data

✅ * Python - Loops, Conditionals & Lists* Today I explored the core building blocks of Python logic and data handling: 🔁 *Loops* - `for` and `while` loops for iteration - `break`, `continue`, and `range()` in practice 🔀 *Conditional Statements* - Used `if`, `elif`, and `else` to control program flow - Practiced with different conditions and logical operators 📋 *Lists and Operations* - Created and accessed list elements - Performed operations like `append()`, `insert()`, `remove()`, `pop()`, `sort()`, `reverse()`, slicing, and looping through lists Excited to keep building up my Python foundation! #Python #LearningPython #100DaysOfCode #Loops #Conditionals #Lists #CodingJourney

PYTHON JOURNEY..Day - 9/50 TOPIC : Logical Operators in Python Logical operators are used to combine conditional statements and make smarter decisions in our code. There are 3 Logical Operators: 1. and → Returns True if both conditions are true 2. or → Returns True if any one condition is true 3. not → Reverses the result (True → False, False → True) Example: a = 10 b = 5 c = 15 # AND operator print(a > b and a < c) # True # OR operator print(a > b or a > c) # True # NOT operator print(not(a > b)) # False Output: True True False Quick Tip: Use logical operators to combine multiple conditions in loops or if-statements — they make your code cleaner and smarter! --- #Python #50DaysOfCode #PythonLearning #LogicalOperators #LearnPython #LinkedInLearning

100 Days of Python Challenge – Day 1 💻 Python Basics: Variables & Data Types Variables are like containers that store data values, which you can use and manipulate code. Data types define the type of data a variable can hold: int – whole numbers (10, -5) float – decimal numbers (3.14, 0.5) str – text ("Hello", "Python") bool – True/False values (True, False) Understanding variables and data types is the first step to writing Python programs! 💡 #PythonBasics #LearnPython #100DaysOfPython #CodingJourney

 Understanding Python Data Collections — The “Fantastic Four” 🐍 In Python, we often deal with data collections — but choosing the right one makes all the difference! 🔧 Common Methods List: append(), remove(), sort(), pop() Tuple: count(), index() Dictionary: keys(), values(), items(), update() Set: add(), remove(), union(), intersection() Pro Tip: Use List → when data changes often Tuple → when data stays fixed Dict → when you need lookup by keys Set → when uniqueness matters #Python #Coding #DataStructures #Developers #ProgrammingTips #LearningPython

🚀 Day 41 of #50daysofpython 💻 File Handling in Python — Simple & Powerful! Have you ever wanted your Python program to save data permanently, even after it stops running? That’s where File Handling comes into play! 🐍 🔹 What is File Handling? File handling means reading from or writing to files using Python code. It allows your program to store data on your computer — like saving user info, logs, or reports. 🔹 Basic Operations in File Handling: 👉 Open a file using open() 👉 Read data using read() or readline() 👉 Write or append data using write() 👉 Close the file using close() But Python makes this even easier with the with statement — it automatically closes the file for you! ✅ "w" → write mode (overwrites the file) ✅ "a" → append mode (adds new data) ✅ "r" → read mode (reads existing data) 💬 Have you tried working with files in Python yet? What’s the first thing you’d store in a text file? #Python #FileHandling #LearnPython #CodingForBeginners #ProgrammingTips #50DaysOfCode #PythonLearningJourney

Dear #LinkedInFamily I am happy to announce that my Python Library "pyedahelper", short for Python Exploratory Data Analysis Helper is available for your use on Notebooks and IDEs. It's here on Python Package Index: https://lnkd.in/d3SV3FBJ Small Intro: Cramming Python syntaxes when doing Data Analysis is no longer necessary, call on #pyedahelper to provide the syntaxes while you focus on the goal of your analysis. How to: First you need to install the latest version: ~ pip install pyedahelper==1.0.2, then import: ~ import edahelper (not pyedahelper) as anything (I prefer "eda"), next is to: ~ eda.show(), and you have everything. If you are totally new to Python and don't know when to use any of the syntaxes you have from above or what they mean, just type: ~ eda.core().show(), this will also tell you what they do, mean and when to use. Please share this post to any #Python #Data Analyst, as well as academicians using Python for data #analysis.

𝗣𝘆𝘁𝗵𝗼𝗻 𝟯.𝟭𝟰 𝗺𝗮𝗱𝗲 𝘁𝘆𝗽𝗲 𝗮𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗹𝗮𝘇𝘆 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁’𝘀 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗶𝘁 𝗹𝗼𝗼𝗸𝘀 In earlier versions, Python would evaluate type hints as soon as a function or class was defined. That caused issues when you tried to reference a class that wasn’t defined yet, like in recursive or linked data structures.  • With Python 3.14, this behavior is now lazy by default.  • Type hints are stored and resolved only when needed.  • This small change removes one of the most common pain points in large projects.  • It helps avoid circular import errors and makes type hints easier to maintain across multiple modules. 💡 My view This is a great quality-of-life improvement for developers. It keeps code cleaner, reduces boilerplate, and makes type hints a lot more practical in everyday Python development. #Python #TypeAnnotations #CleanCode #SoftwareEngineering #Developers #Python314

Python’s built-in data structures — List, Tuple, Set, and Dictionary — are designed to make data handling simple, fast, and memory-efficient. Each structure has ready-to-use methods (like .append(), .remove(), .union(), .get(), etc.) This reduces the need to write complex algorithms from scratch, saving both time and memory. Python handles memory allocation and garbage collection automatically — so when you modify or delete items, it efficiently manages the memory behind the scenes.

💻 Day 43 of #100DaysOf100Projects – “Top 10 Posts in 4chan” 🌐 Today’s project dives into real-time API data fetching! Using Python’s requests library, I built a script that retrieves the Top 10 most active threads from any 4chan board in real time. 🔍 It fetches: Thread titles Reply counts Direct thread links 📚 Concepts covered: ✅ REST API usage ✅ JSON parsing ✅ Sorting logic ✅ Clean CLI interface This project helped me understand how major discussion platforms structure and serve public data via APIs — a perfect exercise for exploring data handling and API integration in Python. ⚙️ Tools Used: Python, requests, JSON GitHub Link: https://lnkd.in/gjQW-z2b #Python #100DaysOfCode #100DaysOf100Projects #APIs #DataScience #Developer #CodingJourney #Automation #LearningByDoing #OpenSource #R_Dinesh