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🐍📰 Sending Emails With Python In this tutorial, you'll learn how to send emails using Python. Find out how to send plain-text and HTML messages, add files as attachments, and send personalized emails to multiple people #python

✍️ New post introducing profiling-explorer, a tool for exploring Python profiling data (pstats files). Use it with the classic cProfile (now called profiling.tracing) or Python 3.15’s new sampling profiler, Tachyon (profiling.sampling). https://lnkd.in/eZ6D8ZMD #Python

Automate Excel Tasks with Python for Efficiency Boost your Excel productivity with Python. Automate repetitive tasks like data cleaning, merging files, and generating reports. Save time and minimize errors....

Automate Microsoft Word Tasks with Python Automate Microsoft Word tasks with Python! Turn hours of manual editing, copying, and formatting into seconds. Learn how to clean, fill templates, and combine documents efficiently with `python-docx`....

🐍📰 Altair: Declarative Charts With Python Build interactive Python charts the declarative way with Altair. Map data to visual properties and add linked selections. No JavaScript required https://lnkd.in/ghbuFY3H

I wrote a short guide on using `uv` for Python dependency management. It’s helped clean up my local environments a bit, so I thought I’d share it in case it’s useful to anyone else. https://lnkd.in/dwqduhp9 #Python #uv

Your Django app went from 200MB to 8GB RAM usage in three weeks. Memory leaks don't crash dramatically—they creep up slowly until your servers start swapping and alerts start screaming. This guide shows you how to profile Python applications in production using memory_profiler and tracemalloc without causing downtime or performance impact. Learn to catch circular references, global variable accumulation, and resource leaks before they kill your application. #Python #DevOps #PerformanceOptimization #Django Learn More: https://lnkd.in/eWe2bRhT

🐍📰 Variables in Python: Usage and Best Practices Explore Python variables from creation to best practices, covering naming conventions, dynamic typing, variable scope, and type hints with examples https://lnkd.in/dUFf5QGE

Most Python code works. That’s not the problem. The problem is - most of it doesn’t scale past the person who wrote it. You’ve probably seen code like this: • full of comments explaining what’s happening • try/finally blocks everywhere • repeated logic for caching, logging, auth • functions doing 5 things at once It works. Until it doesn’t. The shift that changed how I think about Python: 👉 Stop writing logic 👉 Start using language-level patterns Once you start seeing it this way: • with replaces cleanup logic • decorators replace repeated behavior • generators replace unnecessary data structures • dunder methods make your objects feel native The result? Code that explains itself without comments, removes entire classes of bugs, and actually scales across teams. I wrote a deep dive on this - not surface-level tips, but how these patterns actually work, when to use them, and how they reshape your code. 👉 Read the full article: https://lnkd.in/g_9GZDRk Curious — what’s one Python concept that only clicked after real-world experience? For me, it was realizing generators aren’t about syntax - they’re about thinking in streams instead of collections. #Python #CleanCode #SoftwareEngineering #ScalableSystems #DesignPatterns #AdvancedPython #BackendDevelopment

🚀 Python Packages & Pip #Day25 Understanding packages and pip is absolutely essential. 📦 What Are Python Packages? Python packages are reusable collections of code that extend Python’s capabilities. Instead of writing everything from scratch, you can simply install and use packages built by others 💡 👉In simple terms: Module = Single .py file Package = Folder containing multiple modules 🎯 Why Packages Are Important In real-world projects, packages help you: ✅Handle data efficiently ✅Perform complex calculations ✅Create stunning visualizations 📊 ✅Save time by reusing existing code ✅Build scalable and professional projects 🧠 Common Types of Packages 🔹Built-in Packages Pre-installed with Python Examples: math, os, sys 🔹Third-party Packages Installed using pip Examples: numpy, pandas, matplotlib 🔹User-defined Packages Created by developers for custom needs ⚙️What Is Pip? Pip is Python’s official package manager 📥 It allows you to install and manage packages from the Python Package Index (PyPI). 💻 Installing Python Packages Using Pip To install any package, use this command in terminal or command prompt: pip install package_name 👉Example: pip install numpy This downloads and installs the package so you can use it in your Python programs 🚀 ⚠️If Pip Command Doesn’t Work Sometimes, due to system or environment issues, pip may not run directly. 👉In that case, use: python -m pip install package_name ✔️This ensures pip runs with the correct Python version installed on your system 🔍 Verifying Installation After installing a package, you can check it by: ✅Importing it in Python import numpy ✅Or listing installed packages pip list 🔥 Pro Tips for Beginners 💡Always use virtual environments for projects 💡Keep packages updated using pip install --upgrade 💡Explore PyPI for thousands of useful packages 🎓 Final Thoughts Mastering Python packages and pip will make your journey in data analytics and development much smoother. Once you understand this, you unlock the true power of Python’s ecosystem 🌍✨ #DataAnalystics #DataAnalysts #DataAnalysis #Excel #PowerBI #MicrosoftExcel #MicrosoftPowerBI #Tableau #PythonProgramming #Python #SQL #CodeWithHarry #Learning #LearningJourney #DataModels #DataCleaning #DataVisualization #DataCollection