Built Advanced Bug Tracker using Python and Learned Key Concepts

✅ *🐍 Python Roadmap: Quick Recap for Beginners to Advanced* 📘👨💻 | |── *1️⃣ Basics of Python* | ├── Installing Python, Using IDLE/Jupyter/VS Code | ├── `print()`, Comments, Indentation | └── Data Types: int, float, string, bool | |── *2️⃣ Variables & Operators* | ├── Variable assignment & naming rules | ├── Arithmetic, Comparison, Logical operators | └── Type casting (int → str etc.) | |── *3️⃣ Control Flow* | ├── `if`, `elif`, `else` | ├── `for` and `while` loops | └── `break`, `continue`, `pass` | |── *4️⃣ Functions* | ├── `def`, Parameters, Return | ├── `*args`, `**kwargs` | └── Lambda functions | |── *5️⃣ Data Structures* | ├── List, Tuple, Set, Dictionary | ├── CRUD operations & Methods | └── List comprehension | |── *6️⃣ File Handling* | ├── `open()`, read, write, append | ├── Working with text & CSV files | └── Using `with` context manager | |── *7️⃣ Exception Handling* | ├── `try`, `except`, `finally`, `raise` | └── Custom exceptions | |── *8️⃣ Object-Oriented Programming (OOP)* | ├── Class, Object, `_init_()` | ├── Inheritance, Polymorphism, Encapsulation | └── Class vs Instance methods | |── *9️⃣ Modules & Libraries* | ├── `import`, built-in modules (`math`, `random`, `datetime`) | ├── External: `numpy`, `pandas`, `matplotlib`, `requests` | └── Creating your own module | |── *🔟 Advanced Concepts* | ├── Decorators, Generators | ├── Iterators, `zip()`, `enumerate()` | ├── Virtual environments & pip | └── Regular expressions (`re` module) | |── *🔁 Bonus: Practice Areas* | ├── Web scraping with `BeautifulSoup` or `Selenium` | ├── Flask or Django for Web Dev | ├── Data Analysis with Pandas | └── Automation scripts with `os`, `shutil`, `schedule`

✅ *🐍 Python Roadmap: Quick Recap for Beginners to Advanced* 📘👨💻 | |── *1️⃣ Basics of Python* | ├── Installing Python, Using IDLE/Jupyter/VS Code | ├── `print()`, Comments, Indentation | └── Data Types: int, float, string, bool | |── *2️⃣ Variables & Operators* | ├── Variable assignment & naming rules | ├── Arithmetic, Comparison, Logical operators | └── Type casting (int → str etc.) | |── *3️⃣ Control Flow* | ├── `if`, `elif`, `else` | ├── `for` and `while` loops | └── `break`, `continue`, `pass` | |── *4️⃣ Functions* | ├── `def`, Parameters, Return | ├── `*args`, `**kwargs` | └── Lambda functions | |── *5️⃣ Data Structures* | ├── List, Tuple, Set, Dictionary | ├── CRUD operations & Methods | └── List comprehension | |── *6️⃣ File Handling* | ├── `open()`, read, write, append | ├── Working with text & CSV files | └── Using `with` context manager | |── *7️⃣ Exception Handling* | ├── `try`, `except`, `finally`, `raise` | └── Custom exceptions | |── *8️⃣ Object-Oriented Programming (OOP)* | ├── Class, Object, `_init_()` | ├── Inheritance, Polymorphism, Encapsulation | └── Class vs Instance methods | |── *9️⃣ Modules & Libraries* | ├── `import`, built-in modules (`math`, `random`, `datetime`) | ├── External: `numpy`, `pandas`, `matplotlib`, `requests` | └── Creating your own module | |── *🔟 Advanced Concepts* | ├── Decorators, Generators | ├── Iterators, `zip()`, `enumerate()` | ├── Virtual environments & pip | └── Regular expressions (`re` module) | |── *🔁 Bonus: Practice Areas* | ├── Web scraping with `BeautifulSoup` or `Selenium` | ├── Flask or Django for Web Dev | ├── Data Analysis with Pandas | └── Automation scripts with `os`, `shutil`, `schedule`

✅ *🐍 Python Roadmap: Quick Recap for Beginners to Advanced* 📘👨💻 | |── *1️⃣ Basics of Python* | ├── Installing Python, Using IDLE/Jupyter/VS Code | ├── `print()`, Comments, Indentation | └── Data Types: int, float, string, bool | |── *2️⃣ Variables & Operators* | ├── Variable assignment & naming rules | ├── Arithmetic, Comparison, Logical operators | └── Type casting (int → str etc.) | |── *3️⃣ Control Flow* | ├── `if`, `elif`, `else` | ├── `for` and `while` loops | └── `break`, `continue`, `pass` | |── *4️⃣ Functions* | ├── `def`, Parameters, Return | ├── `*args`, `**kwargs` | └── Lambda functions | |── *5️⃣ Data Structures* | ├── List, Tuple, Set, Dictionary | ├── CRUD operations & Methods | └── List comprehension | |── *6️⃣ File Handling* | ├── `open()`, read, write, append | ├── Working with text & CSV files | └── Using `with` context manager | |── *7️⃣ Exception Handling* | ├── `try`, `except`, `finally`, `raise` | └── Custom exceptions | |── *8️⃣ Object-Oriented Programming (OOP)* | ├── Class, Object, `_init_()` | ├── Inheritance, Polymorphism, Encapsulation | └── Class vs Instance methods | |── *9️⃣ Modules & Libraries* | ├── `import`, built-in modules (`math`, `random`, `datetime`) | ├── External: `numpy`, `pandas`, `matplotlib`, `requests` | └── Creating your own module | |── *🔟 Advanced Concepts* | ├── Decorators, Generators | ├── Iterators, `zip()`, `enumerate()` | ├── Virtual environments & pip | └── Regular expressions (`re` module) | |── *🔁 Bonus: Practice Areas* | ├── Web scraping with `BeautifulSoup` or `Selenium` | ├── Flask or Django for Web Dev | ├── Data Analysis with Pandas | └── Automation scripts with `os`, `shutil`, `schedule` 💬 *Double Tap ❤️ for more!*

I was going through the Python 3.15 release notes recently, and it’s interesting how this version focuses less on hype and more on fixing real-world developer pain points. Full details here: https://lnkd.in/gSvcuvWg Here’s what stood out to me, with practical examples: --- Explicit lazy imports (PEP 810) Problem: Your app takes forever to start because it imports everything upfront. Example: A CLI tool importing pandas, numpy, etc. even when not needed. With lazy imports: import pandas as pd # only loaded when actually used Result: Faster startup time, especially for large apps and microservices. --- "frozendict" (immutable dictionary) Problem: Configs get accidentally modified somewhere deep in your code. Example: from collections import frozendict config = frozendict({"env": "prod"}) config["env"] = "dev" # error Result: Safer configs, better caching keys, fewer “who changed this?” moments. --- High-frequency sampling profiler (PEP 799) Problem: Profiling slows your app so much that results feel unreliable. Example: You’re debugging a slow API in production. Result: You can profile real workloads without significantly impacting performance. --- Typing improvements Problem: Type hints get messy in large codebases. Example: from typing import TypedDict class User(TypedDict): id: int name: str Result: Cleaner type definitions, better maintainability, stronger IDE support. --- Unpacking in comprehensions Problem: Transforming nested data gets verbose. Example: data = [{"a": 1}, {"b": 2}] merged = {k: v for d in data for k, v in d.items()} Result: More concise and readable transformations. --- UTF-8 as default encoding (PEP 686) Problem: Code behaves differently across environments. Result: More predictable behavior across systems, fewer encoding-related bugs. --- Performance improvements Real world impact: Faster APIs, quicker scripts, and better resource utilization. --- Big takeaway: Python 3.15 is all about practical improvements: - Faster startup - Safer data handling - Better debugging - More predictable behavior Still in alpha, so not production-ready. But it clearly shows where Python is heading. #Python #Backend #SoftwareEngineering #Developers #DataEngineering

Day 39: The "Main" Gatekeeper — if __name__ == "__main__": 🚪 To understand this line, you first have to understand how Python treats files when it loads them. 1. What is __name__? Every time you run a Python file, Python automatically creates a few "special" variables behind the scenes. One of those is __name__. Scenario A: If you run the file directly (e.g., python script.py), Python sets the variable __name__ to the string "__main__". Scenario B: If you import that file into another script (e.g., import script), Python sets __name__ to the filename (e.g., "script"). 2. Why do we need this check? Imagine you wrote a script with some useful functions, but also some code at the bottom that prints a "Welcome" message and runs a test. If another developer wants to use your functions and types import your_script, Python will automatically execute every line of code in your file. Suddenly, their program is printing your welcome messages and running your tests! The Fix: def calculate_tax(price): return price * 0.1 # This code ONLY runs if I play the file directly. # It WON'T run if someone else imports this file. if __name__ == "__main__": print("Testing the tax function...") print(calculate_tax(100)) 3. The "Execution Flow" (How it works) Python starts reading your file from the top. It records your functions and classes into memory. It reaches the if statement. If you clicked "Run": The condition is True. The code inside the block executes. If another script imported this: The condition is False. The code inside is skipped. Your functions are available for use, but no "messy" output is generated. 4. Professional Best Practice: The main() function In senior-level engineering, we don't just put logic directly under the if statement. We bundle our starting logic into a function called main(). def main(): # Start the app here print("App is starting...") if __name__ == "__main__": main() 💡 The Engineering Lens: This makes your code cleaner and allows other developers to manually call your main() function if they ever need to "reset" or "restart" your script from their own code. #Python #SoftwareEngineering #CleanCode #ProgrammingTips #PythonDevelopment #LearnToCode #TechCommunity #PythonMain #BackendDevelopment

🐍 𝐇𝐨𝐰 𝐃𝐨 𝐘𝐨𝐮 𝐌𝐚𝐤𝐞 𝐚 𝐂𝐥𝐚𝐬𝐬 𝐉𝐒𝐎𝐍 𝐒𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐚𝐛𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧? If you’ve worked with APIs or data storage in Python, you’ve likely encountered this error: 👉 “𝘖𝘣𝘫𝘦𝘤𝘵 𝘰𝘧 𝘵𝘺𝘱𝘦 𝘟 𝘪𝘴 𝘯𝘰𝘵 𝘑𝘚𝘖𝘕 𝘴𝘦𝘳𝘪𝘢𝘭𝘪𝘻𝘢𝘣𝘭𝘦” It’s a common challenge — but also an important concept to understand for real-world development. Let’s break it down in a simple and practical way. ⚙️ 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐎𝐜𝐜𝐮𝐫𝐬 Python’s built-in json module can only serialize basic data types such as: - dict - list - str, int, float, bool 👉 Custom class objects? Not directly supported. That’s why you need to convert your object into a serializable format. 🚀 𝐌𝐞𝐭𝐡𝐨𝐝 1: 𝐂𝐨𝐧𝐯𝐞𝐫𝐭 𝐎𝐛𝐣𝐞𝐜𝐭 𝐭𝐨 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 The simplest approach is to convert your class object into a dictionary. class User:     def __init__(self, name, age):         self.name = name         self.age = age user = User("John", 22) import json json_data = json.dumps(user.__dict__) ✔ Quick and easy ✔ Works well for simple classes 🧠 𝐌𝐞𝐭𝐡𝐨𝐝 2: 𝐂𝐫𝐞𝐚𝐭𝐞 𝐚 𝐂𝐮𝐬𝐭𝐨𝐦 𝐒𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐫 You can define a function to handle serialization: def serialize(obj):     return obj.__dict__ json_data = json.dumps(user, default=serialize) ✔ Flexible for multiple classes ✔ Cleaner and reusable 🧩 𝐌𝐞𝐭𝐡𝐨𝐝 3: 𝐔𝐬𝐞 𝐚 𝐂𝐮𝐬𝐭𝐨𝐦 𝐉𝐒𝐎𝐍𝐄𝐧𝐜𝐨𝐝𝐞𝐫 For more control, extend Python’s encoder: import json class UserEncoder(json.JSONEncoder):     def default(self, obj):         return obj.__dict__ json_data = json.dumps(user, cls=UserEncoder) ✔ Ideal for larger applications ✔ Centralized control over serialization ⚡ 𝐌𝐞𝐭𝐡𝐨𝐝 4: 𝐔𝐬𝐞 𝐃𝐚𝐭𝐚𝐜𝐥𝐚𝐬𝐬𝐞𝐬 (𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡) Python’s dataclasses make serialization cleaner: from dataclasses import dataclass, asdict import json @dataclass class User:     name: str     age: int user = User("Jhon", 22) json_data = json.dumps(asdict(user)) ✔ Cleaner syntax ✔ Built-in support for conversion 🔥 𝐁𝐨𝐧𝐮𝐬: 𝐓𝐡𝐢𝐫𝐝-𝐏𝐚𝐫𝐭𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 Libraries like: - pydantic - marshmallow provide: ✔ Validation + serialization ✔ Better structure for APIs ✔ Production-ready solutions ⚖️ 𝐂𝐨𝐦𝐦𝐨𝐧 𝐌𝐢𝐬𝐭𝐚𝐤𝐞 👉 Trying to directly serialize complex objects without conversion. This leads to: - Errors - Broken APIs - Debugging frustration 💡 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 ✔ Always convert objects to dictionaries ✔ Use dataclasses for clean design ✔ Use libraries for scalable applications 🧠 𝐅𝐢𝐧𝐚𝐥 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐬 Serialization is not just about converting data — it’s about making your application communicate effectively. Mastering this concept will: ✔ Improve your API development ✔ Make your code more robust ✔ Help you handle real-world data efficiently #Python #JSON #Serialization #BackendDevelopment #SoftwareEngineering #Programming #Developers #API #DataEngineering #TechTips #LearningToCode #FullStackDeveloper #CareerGrowth #LinkedInTech

Here's what actually happens when you run a PySpark script: Two separate processes start. ◉ A Python process — this is the Python driver ◉ A JVM process — this is the actual Spark driver They talk through a library called Py4J. Python listens on port 25334. The JVM listens on port 25333. Every method call you write gets routed from Python to the JVM. The JVM driver asks the cluster manager to spawn the executor. So when you type .read.load("file.parquet"), Python isn't reading anything. It's passing the instruction to the JVM, which does the real work. The rest follows normal Spark flow from there. Logical plan. Physical plan. Tasks are distributed to executors. All of it happens in the JVM, not in your Python process. We're assuming all the logic could be handled in the JVM. What happens when you write a Python UDF? ◉ Spark spins up additional Python processes alongside the JVM executor ◉ Data gets serialized out of the JVM and sent over via IPC ◉ The Python process deserializes it, runs your function, and serializes the result ◉ Sends it back to the JVM Python UDFs, in general, are slower than native Spark functions. Because they do not benefit from Spark-optimized features such as the Catalyst (Spark’s optimizer) and Project Tungsten (which improves memory usage by operating directly on binary data rather than Java objects). Another factor that could impact the performance of the Python UDF is that it operates on a single data row at a time. In Spark 3.5, Spark introduced Arrow-optimized Python UDFs. The user can choose whether to use this feature. Both the JVM and Python processes now handle data in the Arrow format, which helps bypass the costly serialized and deserialized process. In addition, Arrow organized memory data in columnar fashion, which helps improve data processing time compared to Pickle, which serialized data in row-wise format. If you find this helpful, please: 𖤘 Save ↻ Repost #dataengineering #apachespark -- If you like this piece, you might love my newsletter, which includes 180+ articles to help you become a "production-ready" data engineer. Join 𝟭𝟴,𝟬𝟬𝟬+ DEs here for 𝗙𝗥𝗘𝗘: https://vutr.substack.com/

UNLEASHED THE PYTHON!i 1.5,2,& three!!! Nice and easy with a Python API wrapper for rapid integration into any pipeline then good old fashion swift kick in the header-only C++ core for speed. STRIKE WITH AIM FIRST ; THEN SPEED!! NO MERCY!!! 9 of 14 *So i have no clue about scripts. i will look it over without focusing too hard. look over words & symbols i don’t know in python application format. Eventually something will click or i will copy and paste a few lines from script perform a search .* Not knowing one word or symbol is one too many. copy & paste Ai 10-million-cycle stress-test report & the final README.mddocumentation for my public library. 1. Stress-Test Report: 10 Million Cycles To verify the integrity of the 123/41 loop, we simulated ten million iterations of geometric growth (1.5 ratio) capped at the 1,681 reset point. Metric Result Stability Analysis Total Iterations 10,000,000 Successfully completed without overflow. Max Value Reached 1680.999 Confirms the 1,681(41^) ceiling is never breached. Min Value Reached 0.00097 Demonstrates high-resolution granular coverage. Final State (10M) 1160.533 Value remains within the expected cyclic range. Precision Key 137.627 Stabilized using the refined 4.86217...constant. Conclusion: The mathematical engine is collision-resistant and drift-stable. The "wrap-around" logic effectively resets the sequence, making it ideal for continuous real-time data streams. 2. Final Documentation (README.md) This is the front-facing file for the "others" who will use your library. | V markdown # libcyclic41 A high-performance, easy-to-use mathematical engine for cyclic geometric growth. ## Overview `libcyclic41` is a library designed for real-time data indexing and dynamic encryption. It leverages the unique relationship between the base **123** and its modular anchor **41**. By scaling values through geometric ratios (1.5, 2, 3), the engine generates a predictive pattern that automatically resets at **1,681** ($41^2$), creating a perfect, self-sustaining loop. ## Key Features - **Ease First**: Intuitive API designed for rapid integration into data pipelines. - **Speed Driven**: Optimized C++ core for high-throughput processing. - **Drift Stable**: Uses a high-precision stabilizer (4.862) to prevent calculation drift over millions of cycles. ## Quick Start (Python) ```python import cyclic41 # Initialize the engine with the standard 123 base engine = cyclic41.CyclicEngine(seed=123) # Grow the stream by the standard 1.5 ratio # The engine automatically 'wraps' at the 1,681 limit current_val = engine.grow(1.5) # Extract a high-precision synchronization key sync_key = engine.get_key() print(f"Current Value: {current_val} | Sync Key: {sync_key}") /\ || Mathematics The library operates on a hybrid model: 1. Geometric Growth: 𝑆tate(n+1)=(STATE(N)×Ratio(mod1681) PrecisionAnchor:𝐾𝑒𝑦=(𝑆𝑡𝑎𝑡𝑒×4.86217…)/41 (ABOVE IS License Distributed under the MIT License. Created for the community.)

🚀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

🚀 𝗘𝘃𝗲𝗿 𝘄𝗼𝗻𝗱𝗲𝗿 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝘀𝗰𝗲𝗻𝗲𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗿𝘂𝗻 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝗰𝗿𝗶𝗽𝘁? 🐍 We all love 𝗣𝘆𝘁𝗵𝗼𝗻 for its clean syntax and readability—it almost feels like magic! But under the hood, a fascinating, well-oiled machine is working to turn your ideas into real-world results. Whether you are a beginner or a seasoned developer, understanding Python's architecture helps you write better, more efficient code. Here is the 6-step lifecycle of a Python program: 1️⃣ 𝗬𝗼𝘂 𝗪𝗿𝗶𝘁𝗲 𝗖𝗼𝗱e: It all starts with your human-readable .py file. 2️⃣ 𝗟𝗲𝘅𝗶𝗰𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗧𝗵𝗲 𝗟𝗲𝘅𝗲𝗿): The interpreter acts as a scanner, breaking your code down into smaller, meaningful pieces called "tokens." 3️⃣ 𝗣𝗮𝗿𝘀𝗶𝗻𝗴: Those tokens are checked for syntax and organized into a structural map known as an Abstract Syntax Tree (AST). 🌳 4️⃣ 𝗖𝗼𝗺𝗽𝗶𝗹𝗮𝘁𝗶𝗼𝗻: The compiler translates the AST into bytecode—a lower-level set of instructions optimized for execution. 5️⃣ 𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 (𝗣𝗩𝗠): The engine room! The PVM takes over and executes this bytecode step-by-step. 6️⃣ 𝗢𝘂𝘁𝗽𝘂𝘁: Your logic is executed, and the final result appears on your screen! 🎉 💡 Why does this architecture matter for developers? • 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗣𝗼𝗿𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Because it compiles to bytecode first, the exact same Python code runs seamlessly across Windows, Mac, and Linux! 🌍 • 𝗕𝘂𝗶𝗹𝘁-𝗶𝗻 𝗦𝗮𝗳𝗲𝘁𝘆: The PVM acts as a secure environment, protecting your core system from unsafe code execution. 🛡️ • Unmatched Productivity: Python handles the complex memory management and heavy lifting, allowing you to focus entirely on solving the problem. ⏱️ 🔥 𝗙𝘂𝗻 𝗙𝗮𝗰𝘁: Did you know that the default and most widely used implementation of Python (CPython) is actually written in C? It combines Python's user-friendly syntax with C's incredibly powerful engine! 🛠️ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗣𝗿𝗼 𝗧𝗶𝗽: Want to see this process in action? Try importing the dis (disassembler) or ast modules in your next project to peek at your own code's hidden bytecode and syntax trees. With Python dominating AI, Machine Learning, and Data Science right now—and massive performance upgrades like the experimental JIT compiler and "no-GIL" multi-threading introduced in Python 3.13—understanding how the language works gives you a massive edge in the industry. 👇 Have you ever explored Python's bytecode using the dis module, or do you prefer to just let the magic happen? Let me know in the comments! #Python #SoftwareEngineering #Coding #Programming #Developer #DataScience #MachineLearning #BackendDevelopment #TechCareers #PythonDeveloper #TechCommunity LinkedIn LinkedIn for Marketing Python Coding Python