Excel Functions in Python Library Updated

𝙎𝙩𝙤𝙥 𝙨𝙩𝙧𝙪𝙜𝙜𝙡𝙞𝙣𝙜 𝙬𝙞𝙩𝙝 𝙋𝙮𝙩𝙝𝙤𝙣, 𝙩𝙝𝙞𝙨 𝙘𝙝𝙚𝙖𝙩 𝙨𝙝𝙚𝙚𝙩 𝙬𝙞𝙡𝙡 𝙘𝙝𝙖𝙣𝙜𝙚 𝙩𝙝𝙖𝙩! Learning Python doesn’t have to be confusing. I found this Python Cheat Sheet that covers everything you actually use from loops and functions to data types and syntax all in one place. No long tutorials. No unnecessary stuff. Just the important things you’ll need when working on real data projects. Perfect for anyone learning Python for data analytics or data engineering. Keep it handy it’ll save you a ton of time when coding! Doc Credit - Rajat Jain ♻️ Repost if you found this useful 🤝 Follow Sattari Sateesh Kumar for more  👨💻 For 1:1 guidance → https://topmate.io/sateesh #python #pyspark #pysparklearning

Learning Python step by step and had a small “aha!” moment today while comparing Python lists with NumPy arrays. 👩💻 Here’s the simple way I started thinking about it: 🔹 Python Lists Great for general use Flexible (can hold different data types) But when doing calculations, you usually need loops… which can get slow and a bit tiring for large data. 🔹 NumPy Arrays Designed for numerical operations Much faster for calculations Works naturally with multi-dimensional data (matrices, vectors, etc.) Lets you perform operations on entire arrays at once without writing loops. 💡 My beginner takeaway: If you're just storing data → lists are totally fine. If you're doing heavy calculations or working with numerical data → NumPy becomes a game changer. Still learning and connecting the dots every day, but moments like this make Python even more fun to explore. 🚀 #Python #NumPy #PythonLearning #CodingJourney #BeginnerProgrammer

🚀 Think Lists are Simple in Python? Think Again. Most beginners learn lists… But very few understand how powerful they become when you master CRUD. Create ✔️ Read ✔️ Update ✔️ Delete ✔️ That’s not just database terminology — it’s core Python logic. If you truly understand CRUD on lists, you're not just writing code… You’re controlling data. Which operation do you use the most in your projects — Create, Update, or Delete? 👀 Drop it in the comments 👇 #Python #LearnPython #DataAnalytics #DataAnalyst #WomenInTech

Python Starters Day 13 Foundation Nugget Strings are data, too Strings behave like lists of characters. word = "Python Starter Hub" print(word[0]) That prints P. You can slice strings just like lists. Understanding that everything in Python is structured data, which unlocks the flexibility of this language. Text isn’t just text. It is an ordered character. Many beginners treat strings as basic, and they are not; they power logins, search, messaging, and more. Follow the Python 🐍 Starters Hub: WhatsApp: https://lnkd.in/dbjAFv52 LinkedIn: https://lnkd.in/dkJE3tZq

👋 Welcome back! 📅 Python Learning – Day 50 Today we continue exploring data structures with Queues. A queue follows a simple rule: First In, First Out (FIFO). The first element that enters the queue is the first one to leave. You can imagine it like a line at a ticket counter. The person who arrives first gets served first. 📘 In this lesson, I’ve explained: 🚶 What a queue is and how it works ➕ How to add (enqueue) and remove (dequeue) elements in Python ⚠️ Common beginner mistakes when implementing queues Queues are widely used in scheduling systems, task processing, and many real-world applications. Understanding queues helps you build programs that manage tasks in an organized way. 🔗 Tutorial link is in the comments. ⏭️ Tomorrow: Python Linked Lists #PythonQueues #FIFOConcept #QueueDataStructure #LearnPythonConcepts #CodingForBeginners #AlgorithmLearning #DeveloperJourney #TechSkillsDevelopment #codepractice #learnpython #pythonlearning #python #codewithconfidence

Day 5 – Understanding Lists in Python Today I focused on one of the most important data structures in Python: Lists. Lists allow us to store and manage multiple values in a single variable.....something that closely resembles how datasets are structured in analytics. What I learned today: • Creating lists • Accessing elements using indexing • Updating list values • Adding elements using append() • Removing elements using remove() • Looping through lists • Calculating totals using list data Why Lists Matter in Data Analytics: In real-world scenarios, lists can represent: •Sales transactions •Customer IDs •Product prices •Profit values •Performance metrics Lists are the foundation for working with structured data before moving into advanced libraries like Pandas. Strong data structures lead to strong analysis. GitHub Repository:https://lnkd.in/gdD4yAvR #Python #DataAnalytics #LearningInPublic #DataAnalystJourney #ProgrammingBasics #CareerGrowth

Python Dictionaries – Storing Data with Key-Value Pairs Dictionaries are one of the most powerful data structures in Python. They store data in **key-value pairs**, making them fast and efficient for lookups. In this post, I’ve covered: ✔️ Creating dictionaries in different ways ✔️ Adding and updating values ✔️ Deleting and retrieving data safely using `get()` and `pop()` ✔️ Important dictionary methods like `keys()`, `values()`, `items()`, and `update()` 💡 Dictionaries are widely used in real-world applications such as APIs, databases, configuration settings, and JSON data handling. Mastering dictionaries improves your ability to manage structured data effectively. Keep learning and strengthening your Python fundamentals 🚀 #Python #Programming #Coding #PythonBasics #DataStructures #LearningJourney

✅Day 6 – Understanding Lists in Python Today I learned about Lists in Python. A list is used to store multiple values in a single variable. Example: numbers = [10, 20, 30, 40] ✅Lists are very useful because they allow us to: --Store multiple data points -- Access specific elements -- Modify or update values easily -- Why Lists Matter in Data Analytics ✅In real-world datasets, data often comes in collections such as: -- Sales records -- Customer IDs -- Product prices -- Lists help us organize and manage such data efficiently. ✅Today’s takeaway: -- Learning how data is stored is the first step toward analyzing it. -- Continuing the journey, one concept at a time. #Python #DataAnalytics #LearningJourney #BusinessAnalytics #Consistency

🚀 Day 15/30 – Dictionaries & Dictionary Methods in Python Today, I learned about Dictionaries, one of the most useful data structures in Python. A dictionary stores data in key–value pairs, making it easy to organize and access information. Example: student = { "name": "Rahul", "course": "Python", "day": 15 } print(student["name"]) 📌 Dictionary Methods I practiced: • .keys() → Get all keys • .values() → Get all values • .items() → Get key-value pairs • .get() → Access values safely • .update() → Modify dictionary data 💡 Key Takeaway: Dictionaries help manage structured data like user details, product information, or records efficiently. Understanding them feels like moving closer to building real-world applications. Day 15 complete ✅ #Python #30DaysChallenge #LearningInPublic #ProgrammingJourney #Consistency #TechGrowth Aditya ChaturvediJECRC UniversityArpit AgrawalRaj Gehlot