🚀 Day 49 Today I explored Python’s HTMLParser and learned how to extract meaningful information from HTML snippets. 🔍 Key takeaways: • How to handle single-line and multi-line comments using handle_comment() • How to process text data inside HTML tags using handle_data() • The importance of ignoring unnecessary data like empty lines ('\n') • Understanding how parsers read content sequentially from top to bottom 💡 What I built: A Python program that reads HTML input and prints: ✔️ Single-line comments ✔️ Multi-line comments ✔️ Data content This task improved my understanding of how web data is structured and how parsers interpret it — a small step toward mastering web scraping and data processing! Consistency > Perfection. See you on Day 50 💻🔥 #Python #CodingJourney #LearningEveryday #HTMLParser #DeveloperLife

Python has easy ways to make your text, numbers, and dates look clear and professional. Here are 4 tricks you should try: 1. f-Strings :The easiest way to put variables straight into your text. Fast, readable, perfect for quick outputs. name = "Mayar" age = 22 print(f"My name is {name} and I am {age} years old.") 2. Alignment & Width :Keep tables, reports, or lists neat by aligning text and numbers. Left, center, or right—your choice! print("{:<10} | {:^10} | {:>10}".format("Name", "Age", "Score")) print("{:<10} | {:^10} | {:>10}".format("Mayar", 22, 95)) 3. Template Strings :Create reusable text templates and fill in values later. Makes your code cleaner and easier to manage. from string import Template t = Template("Hello $name, your score is $score.") print(t.substitute(name="Mayar", score=95)) 4. Date & Time Formatting :Show dates and times in a clear, readable way. Useful for reports, logs, or messages. from datetime import datetime now = datetime.now() print(f"Date: {now:%d-%m-%Y} Time: {now:%H:%M:%S}") CodeAcademy_om Kulsoom Shoukat Ali Sultan AL-Yahyai #Python #Coding #PythonTips #Developer #LearnPython #TechSkills #CodeBetter #DateTime

🧠 Python Concept: strip(), lstrip(), rstrip() Clean your strings like a pro 😎 ❌ Problem text = " Hello Python " print(text) 👉 Output: " Hello Python " 😵💫 (extra spaces) ❌ Traditional Way text = " Hello Python " text = text.replace(" ", "") print(text) 👉 Removes ALL spaces ❌ (not correct) ✅ Pythonic Way text = " Hello Python " print(text.strip()) # both sides print(text.lstrip()) # left only print(text.rstrip()) # right only 🧒 Simple Explanation Think of it like cleaning dust 🧹 ➡️ strip() → clean both sides ➡️ lstrip() → clean left ➡️ rstrip() → clean right 💡 Why This Matters ✔ Clean user input ✔ Avoid bugs in comparisons ✔ Very useful in real-world apps ✔ Cleaner string handling ⚡ Bonus Example text = "---Python---" print(text.strip("-")) 👉 Output: "Python" 🐍 Clean data, clean code 🐍 Small functions, big impact #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

Built a Web Scraper with Pagination using Python. Features: 1.Scrapes quotes and authors from multiple pages 2.Implements pagination to fetch data beyond a single page 3.Allows user to control the number of results 4.Handles invalid inputs and stops when pages end This helped me understand: 1.How pagination works in web scraping 2.Loop-based data collection across pages 3.Structuring scraped data for better readability.Cognifyz Technologies

From Raw Websites to Structured Data I recently worked on a project where I extracted real-time data from websites using Python. What I did: - Collected data using BeautifulSoup - Parsed HTML content - Converted unstructured data into a clean dataset using Pandas Why it matters: Data collection is the first step in any data analysis process. Without data, there are no insights! Curious — what kind of data would you scrape? #DataAnalytics #Python #WebScraping #Learning

⛓️💥 A silent patch in Airflow metaclass breaks Python MRO. This is something I was not expecting as an Airflow plugin maintainer. Recently I received a pull request to my airflow-clickhouse-plugin GitHub repo. A contributor found out that some plugin functionality is unusable because of a strange error: >>> ClickHouseSQLExecuteQueryOperator(task_id='id', sql='SELECT 1') ❌ TypeError: Invalid arguments were passed to ClickHouseSQLExecuteQueryOperator (task_id: id). Invalid arguments were: **kwargs: {'sql': 'SELECT 1'} Like, whaaaat? 😱 The only purpose of this operator is to literally execute SQL. The class definition is: class ClickHouseSQLExecuteQueryOperator( ClickHouseBaseDbApiOperator, sql.SQLExecuteQueryOperator, ): pass __init__ method is not defined in ClickHouseBaseDbApiOperator. So, it was pretty expected that it should call __init__ of SQLExecuteQueryOperator which has that sql parameter! It turned out that the reason was in BaseOperatorMeta. A metaclass that is used for every Airflow operator class to be created. Because of a dirty way to check “Does this class override __init__?” it was injecting an unexpected __init__ into the chain. Funny thing, this issue can be resolved as simply as adding a boilerplate __init__ method calling super().__init__(**kwargs). I have shared details of this investigation in my Medium post. What a sneaky bug it was! See it in the first comment. 💭 Any sneaky bugs you remember? #Airflow #Python #PythonMRO

Been building CallFlow Tracer, an open-source Python library that traces function call flows and visualizes them as interactive graphs. Just shipped v0.4.1 with some major improvements: Fixed critical security vulnerabilities (command injection, code injection in the old extension) Added Content Security Policy headers to all webviews What CallFlow Tracer does in this new version: OpenTelemetry export for production observability (Jaeger, OTLP) SLA/SLO tracking with error budgets and canary analysis Framework integrations: FastAPI, Flask, Django, SQLAlchemy Fixed imports and made it more modular and extensible. Would love feedback from anyone working on observability, profiling, or developer tooling. below is the link https://lnkd.in/drUQspvv #Python #OpenSource #DeveloperTools #Observability #OpenTelemetry #VSCode #TypeScript #SoftwareEngineering

🐍 Python List Operations – The Only Cheat Sheet You'll Need Master lists with these 25+ essential operations: 🔍 Accessing & Finding • list[i] → Get single item by index • list[start:end] → Get multiple items (slicing) • a, b, c = list → Unpack all items into variables • list.index(x) → Find position of first item with value x • x in list → Check if value x exists (True/False) 📊 Analyzing & Counting • len(list) → Total number of items • list.count(x) → Count how many times value x appears • max(list) / min(list) → Find highest/lowest values ✏️ Modifying Lists • list.append(x) → Add item x to the end • list.insert(i, x) → Insert item x at index i • list.extend(other_list) → Add items from another list • list[index] = new_value → Change item at specific index 🗑️ Removing Items • list.pop(i) → Remove and return item at index i (default last) • list.remove(x) → Remove first occurrence of value x • list.clear() → Remove all items 🔄 Sorting & Copying • list.sort() → Sort list in place (ascending) • list.reverse() → Flip order in place • new_list = sorted(list) → Get sorted copy • copy_list = list.copy() → Create a shallow copy ⚙️ Iteration & Processing • enumerate(list) → Iterate with index and value • [fn(x) for x in list if condition] → List comprehension (filter + transform in one line) • zip(list_a, list_b) → Pair items from two lists 💡 Pro tip: List comprehension is the most elegant Python feature. Master it and you'll write cleaner, faster code. #Python #PythonLists #CodingCheatSheet #DataStructures #LearnPython

🔹 *args and **kwargs in Python — Flexible Function Design Ever wondered how Python functions handle dynamic inputs? That’s where *args and **kwargs come in 👇 💡 *args (Non-keyword arguments) Allows you to pass a variable number of positional arguments. def add_numbers(*args): return sum(args) print(add_numbers(1, 2, 3)) # 6 print(add_numbers(5, 10, 15, 20)) # 50 💡 **kwargs (Keyword arguments) Allows you to pass a variable number of named arguments. def print_details(**kwargs): for key, value in kwargs.items(): print(f"{key} = {value}") print_details(name="Nittu", role="QA", skill="Playwright") ⚡ Combined usage: def demo(*args, **kwargs): print("Args:", args) print("Kwargs:", kwargs) demo(1, 2, 3, name="Tester", tool="Playwright") 🚀 Key takeaway: Use *args when you don’t know how many positional inputs you’ll get. Use **kwargs when you want flexibility with named inputs. #Python #Coding #Automation #QA #SoftwareTesting #Developers #TechLearning

🚀Today I explored another important concept in Python — Strings 💻 🔹 What is a String? A string is a sequence of characters used to store text data. Anything written inside quotes (' ' or " ") is considered a string in Python. 🔹 How Strings Work: 1️⃣ Each character has a position (index) 2️⃣ We can access characters using indexing 3️⃣ We can extract parts of a string using slicing 4️⃣ We can modify output using built-in methods 👉 Flow: Text → Access/Manipulate → Output 🔹 Operations I explored: ✔️ Indexing Accessing individual characters using position ✔️ Slicing Extracting a part of the string ✔️ String Methods Using built-in functions like upper(), lower(), replace() 🔹 Example 1: Indexing & Slicing text = "Python" print(text[0]) # P print(text[-1]) # n print(text[0:4]) # Pyth 🔹 Example 2: String Methods msg = "hello world" print(msg.upper()) print(msg.replace("world", "Python")) 🔹 Key Concepts I Learned: ✔️ Indexing (positive & negative) ✔️ Slicing ✔️ Built-in string methods ✔️ Immutability (strings cannot be changed directly) 🔹 Why Strings are Important: 💡 Used in user input 💡 Data processing 💡 Text manipulation in real-world applications 🔹 Real-life understanding: Strings are everywhere — from usernames and passwords to messages and data handling in applications Learning step by step and gaining deeper understanding every day 🚀 #Python #CodingJourney #Strings #Programming