Built a Python workflow using requests, pandas, and numpy.

I learned from the course on using Python with Excel. I'm grateful to Dr. Antoinette Smith, PhD, CPA, for introducing me to the foundation of automating in Excel. It was very useful to me, knowing I had a base knowledge of concepts such as ranges, indexes, for loops, and if-else statements. This was definitely helpful as an experience, with how Python can intersect with Excel worksheets. 1.) One takeaway from this section of the course was how naming and structuring data in Excel, just like in Python, can make our workflow cleaner, more readable, and far easier to automate. In the lesson, the instructor demonstrates how to use Openpyxl, which is used to define named ranges and tables directly within Excel workbooks. This is very useful when moving data between Python and Excel, maybe it can be used when working with pandas dataframes. The process begins with extracting unique values from a dataset. After, openpyxl allows you to create a named range, assigning a descriptive name to a specific group of cells. I used table functions to define a range. Once it is saved, the result is an Excel file where both ranges and formatted tables are accessible. 2.)I explored how to create Excel plots in Python. In the lesson, I worked with population data. Sort it by size from high to low first. After, you would send the pandas data to Excel. using a specific dataframe function. Then, using the Barchart function to make a bar chart. Once this is done, I used the reference function to add data and categories together. This reference function is what brings out the column values. Essentially, now the Workbook is done, and these is Excel bar chart created. The Excel chart looks exactly as one one would make it manually instead of utilizing python. 3. Conditional formatting is a powerful tool; it turns numbers into insights you can see. I learned how to create color-coded excel reports directly from Python using pandas and Openpyxl, meaning no manual clicking through Excel menus. I created a custom Python function to highlight cells, and expanded the idea to highlight entire rows based on value ranges. Comparing Excel’s manual formatting, these rules are somewhat more reproducible. If your data changes, you rerun the script, and the updated, colored report is ready efficiently. This approach used design, making reports faster to build and easier to read. Overall, I think utilizing Python and other software languages should be essential for anyone who wants to take Excel automation to the next level. https://lnkd.in/eVEPvJBk Just finished the course “Using Python with Excel! hashtag #python #c #excel

You're wondering why Python is often the go-to choice for machine learning (ML) models, even when working with .NET Core. 🤔 Here are a few reasons: - ML Ecosystem: Python has a rich ecosystem of libraries and frameworks like TensorFlow, Keras, and scikit-learn, which make building and training ML models a breeze. These libraries are widely adopted and well-maintained. - Ease of Use: Python's syntax is simple and intuitive, making it easier to focus on the ML logic rather than wrestling with the language. - Rapid Prototyping: Python's dynamic nature and extensive libraries enable rapid prototyping and experimentation, which is perfect for ML development. - Integration: While .NET Core is a great framework, integrating Python-based ML models can be beneficial. You can leverage Python for ML tasks and .NET Core for the rest of your application. Python's dominance in the ML space makes it a popular choice. 💻

Ever wondered how Python keeps your data unique and organized with zero duplicates? Meet Sets — Python’s hidden gems for clean data management! 💎🐍 Sets are one of Python’s most efficient data structures. They automatically remove duplicates and make operations like union, intersection, and difference lightning fast ⚡ — perfect for handling large or messy datasets! Here’s why Python Sets are a powerhouse: 🔥 Unique Elements – Automatically discard duplicates ⚙️ Mutable & Dynamic – Add or remove elements anytime 📚 Unordered – Elements don’t follow a fixed sequence 🚀 Optimized for Math Operations – Perform unions, intersections, and more with simple syntax Whether you’re cleaning data, comparing lists, or ensuring uniqueness, Python Sets make it simple and blazing fast! ⚡ Keep your data clean, efficient, and duplicate-free with Python Sets! 🧠✨ ----- 💾 Save this post to revisit when practicing Python data structures. 📢 Note: My free 1000+ page Python tutorial PDF is coming soon — covering everything from the basics to advanced topics. Stay tuned to grab your copy first! 🚀

Ever wondered how Python keeps your data unique and organized with zero duplicates? Meet Sets — Python’s hidden gems for clean data management! 💎🐍 Sets are one of Python’s most efficient data structures. They automatically remove duplicates and make operations like union, intersection, and difference lightning fast ⚡ — perfect for handling large or messy datasets! Here’s why Python Sets are a powerhouse: 🔥 Unique Elements – Automatically discard duplicates ⚙️ Mutable & Dynamic – Add or remove elements anytime Python Sets

When I first started learning Python, I thought I was just learning to code. Now I realize — I was learning how to connect ideas, people, and possibilities. At work, it helped me automate processes and save hours. In side projects, it turned messy data into meaningful insights. But beyond that — it connected me to a community of builders who think in logic and create with empathy. One day, I shared a small Python script that cleaned CSV files. I didn’t think much of it. A week later, someone messaged: “This saved me hours. Thank you.” That’s when it clicked — the smallest solutions can have the biggest ripple effect. We don’t just write code — we write impact, one script at a time. Python isn’t about syntax. It’s about synergy — between logic and creativity, structure and imagination. When you share what you build, you don’t just grow — you help others grow too. What’s the one piece of code you wrote that made you feel proud — not because it was perfect, but because it helped someone?

If you are a data killer, then you should be understanding about 🐍 Python vs Python Libraries — What’s the Difference? Many beginners ask: “Isn’t everything in Python just… Python?” Not quite 😄 — here’s the simple breakdown 👇 💡 Python (the language) ➡️ The foundation — syntax, logic, loops, data types, classes, and functions. ➡️ You use it to write logic and control flow from scratch. 📘 Python Libraries (modules/packages) ➡️ Pre-written, reusable code built on top of Python. ➡️ Help you work faster — no need to reinvent the wheel. 🧠 Example: Without library (pure Python): numbers = [10, 20, 30, 40] avg = sum(numbers) / len(numbers) print(avg) With library (NumPy): import numpy as np numbers = np.array([10, 20, 30, 40]) print(np.mean(numbers)) Same goal ✅ — but the second one is faster, cleaner, and scalable. 📊 In short: 🔹 Python = The engine 🔹 Libraries = The turbo boosters 🚀 Whether you’re into data science, AI, or web development — Mastering the core language first makes understanding any library much easier later. If it is helpful, please repost and follow Roshan Jha #Python #Coding #Programming #Learning #Developers #AI #DataScience #WebDevelopment #JroshanCode #CodeJroshan #Google #Microsoft #Amazon #Flipkart #Swiggy #Mintra #Software

🚀Day 10 of my python journey🐍💪 . 🚀 Your Ultimate Python Roadmap to Mastery in 2025!🐍 . . “Consistency beats intensity — a little progress every day adds up to big results!”💯 . . Learning Python can seem overwhelming at first… but with the right roadmap, it becomes an exciting and achievable journey!💪 Here’s a simple step-by-step path to master Python from beginner to advanced: 1️⃣ Basic Syntax – Learn the building blocks: variables, strings, keywords. 2️⃣ Loops & Conditionals – Master control flow with if-else and loops. 3️⃣ Data Types – Understand lists, tuples, sets, and dictionaries. 4️⃣ Functions & Modules – Write reusable and efficient code. 5️⃣ Functional Programming – Level up with lambda, map, filter, and comprehensions. 6️⃣ OOPS & Regex – Grasp the power of Object-Oriented Programming. 7️⃣ Frameworks – Explore Django, Flask, Numpy, and Pandas. 8️⃣ Projects – Apply your knowledge and build real-world solutions!💻 . . Venkata Krishna Komaragiri . #Python #PythonProgramming #DataScience #MachineLearning #AI #Programming #CodingJourney #FullStackDeveloper #WebDevelopment #DeveloperCommunity #100DaysOfCode #PythonRoadmap #LearnCoding #TechTrends #CodeNewbie #SoftwareEngineering #Innovation #FutureSkills #LinkedInLearning #CodeLife

📚 Data Structures Learning Roadmap (Using Python) 🚀 To strengthen my backend & problem-solving skills, I am consistently learning Data Structures in Python. Here’s the structured topic roadmap I am following 👇 📌 Core Data Structure Topics ✅ Basics Variables, Memory, Time & Space Complexity Data Types (int, float, str, bool) Python Collections Overview ✅ Linear Data Structures List Tuple Dictionary Set Stack Queue Deque ✅ Linked Data Structures Singly Linked List Doubly Linked List Circular Linked List ✅ Trees & Graphs Binary Tree Binary Search Tree Heap Trie Graph (BFS, DFS) ✅ Searching & Sorting Linear Search Binary Search Bubble / Selection / Insertion Sort Merge Sort Quick Sort ✅ Advanced Topics Hashing Recursion Dynamic Programming Basics Greedy Concepts Backtracking 🎯 Goal Improve backend coding efficiency, logic building, and system thinking. Strong DSA = Strong Backend Foundations ✅ I'll be sharing short notes, problems, and solutions. Let’s grow together! 🚀🔥 Suggestions & guidance are always welcome 🤝 #Python #DataStructures #DSA #BackendDevelopment #LearningJourney #Programming #ProblemSolving

🚀 Day 37 of Python Learning – Object-Oriented Programming System (OOPS) OOPS is one of the most powerful concepts in Python. It helps us structure our code in a more reusable and organized way. Main Principles: 🔹 Classes & Objects 🔹 Encapsulation 🔹 Abstraction 🔹 Inheritance 🔹 Polymorphism 🧱 Class: A class is a blueprint or template that defines the structure and behavior (methods & attributes) of objects. class Person: def name(self): print("My name is John") def desg(self): print("Software Trainer") p = Person() p.name() p.desg() 🎯 Object: An object is an instance of a class — it represents something real and tangible. 🔸 Encapsulation Combining data and methods into a single unit (class). Helps in data protection and reusability. 🔸 Abstraction Hiding unnecessary details and showing only essential features to the user. 🔸 Inheritance Allows a new class to use features of an existing class. 🔸 Polymorphism Means “many forms” — same method or function behaves differently based on input or context. 🏗️ Constructor in Python A constructor initializes object variables. It is automatically called when an object is created. Default Constructor class Person: def __init__(self): self.name = "Sushma" self.desg = "Trainer" def display(self): print("Name:", self.name) print("Designation:", self.desg) p = Person() p.display() Parameterized Constructor class Person: def __init__(self, name, desg): self.name = name self.desg = desg def display(self): print("Name:", self.name) print("Designation:", self.desg) p1 = Person("Pooja", "HR") p2 = Person("Harsha", "Admin") p1.display() p2.display() 💡 In short: OOPS helps to make code modular, flexible, and maintainable. #Python #OOPS #Programming #LearningJourney #Day37 #PythonDeveloper

Mastering Python in 2025 — The Smartest Skill You Can Learn Today Why Python Still Rules in 2025 Despite dozens of new programming languages, Python remains the most widely used and versatile. From AI and data science to web development and automation, its simple syntax and massive community support make it the go-to language for both beginners and experts. In fact, recent studies show that Python is used by over 80% of AI developers worldwide, making it the foundation of modern machine learning and automation systems. Top Areas Where Python Excels AI & Machine Learning: With frameworks like TensorFlow, PyTorch, and Scikit-learn. Data Analytics: Pandas, NumPy, and Matplotlib simplify big data tasks. Web Development: Django and Flask make backend development fast and efficient. Automation & Scripting: Automate workflows, file management, and even SEO tasks. How to Start Learning Python Master the Basics: Variables, loops, and conditionals form the foundation. Work on Mini Projects: Try building calculators, weather apps, or web scrapers. Learn Through https://lnkd.in/gqeKkBgz