Excel Skills Gateway to Python Programming

Day 1/6 Python Is Not Hard, You’re Just New Quick reminder for anyone learning Python for data analytics: Python isn’t hard. It just feels hard because it’s new, and new things always feel uncomfortable at first. At first, learning Python appears to be very simple. Like this: name = "Data Analyst" print(name) Here’s what’s happening: name is called a variable A variable is simply a name we assign to a piece of data so we can use it later "Data Analyst" is the value stored inside that variable print() is a built-in function that tells Python to display the result on the screen Nothing complex, just understanding how data is stored and shown. Now this: numbers = [10, 20, 30] print(sum(numbers)) What’s happening here: numbers is a list, meaning a collection of values sum() is another built-in function that adds all the values together print() shows us the final result This is data thinking. Small code. Simple logic. Real progress. You don’t start data analytics with dashboards or machine learning. You start by understanding how data is stored, calculated, and interpreted. If your code breaks sometimes, that’s okay. If you get errors, that’s normal. It means you’re learning. 👉 Follow me for Day 2 👉 Comment “I’m in” if you’re starting Python for data analytics Let’s keep it simple and consistent #Python #LearningPython #DataAnalytics #PythonForDataAnalysis #BeginnerInTech #LearningInPublic

Understanding the Uniqueness of Python Sets Sets in Python are powerful data structures that effectively manage collections of unique items. When you create a set, any duplicates in the input are automatically discarded, ensuring that each element appears exactly once. This property is vital when you need to maintain distinct values, such as usernames, tags, or product IDs, without the clutter of repetitions. The uniqueness feature of sets helps enhance memory efficiency and data integrity. For instance, when you add an item like "grape," Python checks if it’s already in the set. If "banana" is added again, it remains unaffected, showcasing sets' ability to prevent redundancy. Consequently, using sets results in cleaner applications as you eliminate unnecessary data duplication. Sets include methods like `add()` for introducing new elements and `remove()` for deleting existing items. However, using `remove()` can raise a `KeyError` if the item you're trying to delete isn't found, which can be a common pitfall for beginners. To prevent this error, it’s recommended to use `discard()`, which simply ignores the removal if an item is missing, allowing for safer manipulations. Understanding the performance benefits of sets is crucial. Operations such as membership testing—checking if an item exists—are significantly faster with sets compared to lists, thanks to their underlying hash table structure. This efficiency makes sets an optimal choice for scenarios requiring frequent checks for unique items or lookups. Quick challenge: Why might using `discard()` be preferred over `remove()` when manipulating sets? #WhatImReadingToday #Python #PythonProgramming #DataStructures #LearnPython #Programming

💥Day 3 of my 70 Days Python Learning Challenge💥 Today, I learned about data types in Python. This wasn’t completely new to me because I’ve worked with data types before using MIT App Inventor, but seeing how Python handles them was helpful. In Python, some basic data types include: Integer (int): These are whole numbers, both positive and negative. Python can handle very large integers as long as there is enough memory. Examples: 5, -17, 286479, 10 String (str): Strings are used to store text. They are written using single quotes, double quotes, or triple quotes for multiple lines. Once created, strings cannot be changed. Examples: "Hello", 'Python', "Welcome" Float (float): These are numbers with decimal points. They are used when more precision is needed. Integers can be converted to floats. Examples: 3.5, 0.25, 10.0 Boolean (bool): Booleans represent truth values. They can only be "True" or "False". They are commonly used in conditions and decision-making. Examples: True, False Understanding data types is important because they determine what kind of operations the computer can perform. Using the wrong data type can lead to errors, so choosing the right one matters. Making progress one day at a time! On to Day 4✌️ #70dayschallenge #python #programming #datatypes

📌 Python Variable Scope Explained (The Right Way) I recently deep-dived into Python Variable Scope, and here’s a clean mental model that finally made everything click 👇 🔹 Python follows the LEGB rule Local → Enclosing → Global → Built-in 🔹 You can read global variables inside a function 🔹 But you must explicitly use global to modify them ⚠️ The real twist most beginners miss: Mutation ≠ Assignment ✅ This works (mutable objects): my_list.append(10) ❌ This doesn’t (reassignment): my_list = [1, 2, 3] → needs global 🧠 Golden Rule I learned: Mutation changes the object, assignment changes the reference Understanding this cleared up: ✔️ confusing bugs ✔️ scope errors ✔️ interview trick questions Sharing this in case it helps another Python learner 🚀 If you’re learning Python too, let’s connect 🤝 #Python #LearningPython #Programming #Coding #SoftwareDevelopment #BeginnerToPro Krish Naik Monal S.

Mastering the basics is the fastest way to write complex code! 🐍💻 If you're just starting your Python journey, understanding how the language handles Numbers is a fundamental first step. Python is "dynamically typed," meaning it's smart enough to know what kind of number you're using without you having to tell it. Here’s a quick breakdown of the three main numeric types: 🔹 Int (Integer): Whole numbers, positive or negative, without decimals. (e.g., age = 25) 🔹 Float (Floating Point): Numbers containing one or more decimals. (e.g., price = 19.99) 🔹 Complex: Numbers written with a "j" as the imaginary part. Great for advanced science and engineering! (e.g., c = 3 + 5j) 💡 Why this matters: Precision: Choosing the right type ensures your calculations stay accurate. Performance: Integers are generally faster and use less memory. Automation: Python handles the heavy lifting, allowing you to focus on solving problems. Which data type do you find yourself using the most in your projects? Let me know in the comments! 👇

Today’s focus is on Python Strings — combinations of numbers, symbols, and letters enclosed in quotations. Strings are one of the most powerful data types in Python, and mastering them is key to writing clean, efficient code. #Various String Functions: a = "Learning is a continues process" 1) To check the length of String b=len(a) print("The length of the string is ",b) Output: The length of the string is 31 2) Check the count of a particular alphabet in a string b = a.count("a") print("The count of alphabet A is ",b) The count of alphabet A is 2 3) To Convert a string into upper case b= a.upper() print(b) Output: LEARNING IS A CONTINUES PROCESS 4) To Capitalize a given string b=a.capitalize() print(b) Output: Learning is a continues process Few other String Functions are: # isalpha: Returns TRUE if all characters in the string are alphanumeric # isdigit: Returns TRUE if all the characters in the string are digits Strings may look simple, but they’re the foundation of text processing, data cleaning, and even building user interfaces.

⭐ Python List Pre-Defined Function 1️⃣ append(item) Adds one element to the end of the list. 2️⃣ insert(index, item) Inserts an element at a specified position in the list. 3️⃣ extend(iterable) Adds multiple elements from another iterable (list, tuple, set, etc.) to the end of the list. 4️⃣ remove(item) Removes the first occurrence of the specified element from the list. 5️⃣ pop() Removes and returns the last element of the list. 6️⃣ pop(index) Removes and returns the element at the given index. 👉 Error if index is out of range 7️⃣ clear() Removes all elements from the list and makes it empty. 8️⃣ index(item) Returns the index position of the first occurrence of the specified element. 👉Error if item not found. 9️⃣ count(item) Returns the number of times the specified element appears in the list. 🔟 sort() Sorts the list elements in ascending order by default. 11. reverse() Reverses the order of elements in the list. 12. copy() Creates and returns a shallow copy of the list. ⭐ the Python programming language, there are 12 predefined list methods. Each method has its own characteristics, behavior, and specific use cases. These predefined list functions play a vital role when working with lists, as they help in efficiently adding, removing, searching, sorting, and managing data. #PythonProgramming #PythonLearning #PythonLists #ListMethods #CodingLife #ProgrammingBasics #LearnPython #PythonDevelopers #CodeNewbie #SoftwareDevelopment #ComputerScience #TechEducation #ProgrammingStudents #ITStudents #LinkedInLearning Dated:23jan26

#Python has become the lingua franca of #optimization. 6 years ago, if you were building serious optimization models, C++ was the default. Today, Python dominates the field. Why the shift? - Ease of Use: Clean syntax that shortens development cycles and lowers barriers to entry. - Rich Ecosystem: Seamless integration with data (Pandas), visualization (Plotly), and ML (Scikit-learn) for end-to-end decision intelligence pipelines. - Community: Python is what students are learning. It's democratizing optimization. But there are trade-offs to watch: ⚠️ Performance: Python is slower than C++. For large-scale applications, this matters. ⚠️ Efficiency: Know your bottlenecks. Most practitioners focus on solve time when model build time is the real culprit. The solution? Write efficient Python code: ✅ Use NumPy arrays and vectorization ✅ Leverage list comprehension instead of explicit loops ✅ Avoid nested for loops that kill performance ✅ Use the right data structures FICO Xpress's Python API makes this easy with native support for NumPy arrays, efficient problem building with addVariables(), and seamless integration with the full optimization suite. Link in the comments for some Xpress Numpy examples. The move to Python is democratizing optimization. More people than ever are building powerful decision models. Are you leveraging Python for your optimization projects? #DecisionIntelligence #DataScience #Xpress

industrial strength optimization requires a shell that enables regular looking tables to dynamically personalized each specific model. see work by milne and Orzell. knowing when and when not to use nest arrays goes back to work by Jim brown, ibm