Building a List from User Input & Basic Stats in Python

Hey there, data fam! 👋 I’m kicking off a new series today to help you master Python in most simplified form. 🆅🅰🆁🅸🅰🅱🅻🅴🆂 🅰🅽🅳 🅽🅰🅼🅸🅽🅶 🆁🆄🅻🅴🆂.🐍 📦 What is a Variable? In Python, variables are symbolic names that act as references to objects stored in memory.  In the simplest terms a variable is just a container for storing data values. You can think of it as a labeled box where you put something important so you can find it later. 🏠 The Real-World Example: Imagine you are moving into a new apartment. You have dozens of cardboard boxes. If you put your kitchen plates in a box but you forgot label it, you'll be digging through 50 boxes just to eat dinner. But if you write "Kitchen_Plates" on the side, you know exactly what is inside and where to find it. Simply said , In Python, the Label is the variable name, and the Plates are the data. 📝 𝐓𝐡𝐞 𝐍𝐚𝐦𝐢𝐧𝐠 𝐑𝐮𝐥𝐞𝐬 We can't just name a variable anything. Python has some ground rules to keep things organized. 👉 Start with a letter or underscore: name or _name is fine. 1name is a big no-no. 👉 No Spaces: Python hates spaces in names. Use an underscore instead (user_age, not user age). 👉 Case Sensitive: Age, age, and AGE are three completely different ! 👉 No Special Characters: Keep @, $, %, and & is no - no . 👉 Reserved Words: We can't use words Python already uses for itself, like if, else, or print. #python #variables #namingrules #pythonsimplified

🚀 Day 8/30 of My LeetCode Journey (Python + SQL) Staying consistent and leveling up every day! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Find Customer Referee* Given a `Customer` table, write a query to find the names of customers who are either: • Not referred by customer with id = 2 • OR not referred by anyone 💡 *Key Concept:* Filtering with conditions (`!= 2` OR `IS NULL`). 🔹 **Python Problem of the Day** 👉 *Merge Sorted Array* Given two sorted arrays, merge them into a single sorted array in-place without returning a new array. 💡 *Key Concept:* Two-pointer approach from the end for efficient in-place merging. Every problem is helping me think more efficiently and write better code ⚡ Day 8 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning

🚀 Day 66 – Exploring Pandas Series Today’s focus was on understanding one of the core building blocks of data analysis in Python — the Pandas Series. A Series is essentially a one-dimensional labeled array that can hold any data type — integers, strings, floats, or even Python objects. You can think of it as a single column in a spreadsheet or a database table, but with powerful capabilities built in. Here’s what I explored today 👇 🔹 Creating a Series Learned how to create a Series from lists, dictionaries, and NumPy arrays — the foundation of working with Pandas. 🔹 Accessing Elements Understood how to retrieve values using index labels and positions, making data handling intuitive and flexible. 🔹 Binary Operations on Series Discovered how operations like addition, subtraction, and comparisons work seamlessly across Series — even with mismatched indices. 🔹 Pandas Series Index Methods Explored index-related functions that help in labeling, aligning, and managing data efficiently. 🔹 Creating a Series from an Array Practiced converting arrays into Series, reinforcing how Pandas integrates smoothly with NumPy. 💡 Key Takeaway: Pandas Series are simple yet incredibly powerful — mastering them is a crucial step toward effective data analysis and manipulation. On to Day 67! 🔥 #Python #Pandas #DataScience #DataAnalysis #Coding

Day 15/365: Merging Two Dictionaries with Summed Values in Python 🧮🔗 Today I worked on a very common real-world task: merging two dictionaries where overlapping keys should have their values added together. 🧠 What this code does: I start with two dictionaries: d1 = {1: 10, 2: 20, 3: 30} d2 = {3: 40, 5: 50, 6: 60} Each key can represent something like: a product ID with its total sales, a student ID with total marks, a user ID with total points. The goal is to combine d2 into d1: If a key from d2 already exists in d1, I add the values. If the key doesn’t exist in d1, I insert it. Step by step: I loop over each key i in d2: for i in d2: For each key: If i is already a key in d1: I update d1[i] by adding d2[i] to it. Otherwise: I create a new entry in d1 with that key and its value from d2. After the loop finishes, d1 contains the merged result. For the given dictionaries: Key 3 exists in both, so its values are added: 30 + 40 = 70. Keys 5 and 6 only exist in d2, so they are added as new keys. Final output: {1: 10, 2: 20, 3: 70, 5: 50, 6: 60} 💡 What I learned: How to merge two dictionaries manually using a loop and conditions. How to update values in a dictionary when keys overlap. How this pattern appears in real data tasks like: combining monthly reports, merging user activity stats, aggregating counts from multiple sources. Next, I’d like to explore: Handling much larger dictionaries efficiently. Using dictionary methods like update() or Counter from collections to compare approaches. Trying the same logic with string keys (like product names) instead of numbers. Day 15 done ✅ 350 more to go. Got any other dictionary + loop problems (like counting frequencies from multiple sources or merging configs)? Drop them in the comments—I’d love to try them next. #100DaysOfCode #365DaysOfCode #Python #Dictionaries #DataStructures #LogicBuilding #CodingJourney #LearnInPublic #AspiringDeveloper

🚀 Day 1/30 of again starting My LeetCode Journey (Python + SQL) Starting a 30-day challenge to sharpen my problem-solving skills with a mix of Python and SQL! 💻📊 🔹 **SQL Problem of the Day** 👉 *Question:* Write a query to report the first name, last name, city, and state of each person. If a person doesn’t have an address, still include them in the result. 💡 *Key Concept:* LEFT JOIN to ensure all records from the PERSON table are included. 🔹 **Python Problem of the Day** 👉 *Question:* Write a function to find the **longest common prefix** string among an array of strings. If there is no common prefix, return an empty string. 💡 *Key Concept:* Iterative prefix reduction & string comparison. Done and dusted both the question in a reasonable time Consistency > Motivation. Excited to grow one problem at a time! 🔥 #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #ProblemSolving #DataStructures #LearningInPublic

Day 8 of #30DaysOfPython ✅ Until today, every single thing I built vanished the moment I closed the terminal. Run the script, see the output, close it — gone. No trace. No memory. Nothing. File handling changed that completely. I can now write a script that creates a file, puts data inside it, and saves it to my computer — permanently. I can open that same file an hour later and read every line back. My code has storage now. That shift in what Python can do hit harder than I expected. The concept itself isn't complicated. But the details will get you. I spent 20 minutes wondering why my file kept getting wiped every time I ran my script. Turns out I was opening it in "w" mode — which doesn't add to a file. It replaces it. Completely. Every. Single. Time. Switched to "a" for append. Problem solved. Data survived. Lesson learned permanently. 😅 The other habit I picked up today: always use with open() instead of plain open(). It automatically closes the file when you're done — even if your code crashes midway. I didn't think it mattered until I read about what happens to files that never get closed properly. Now I will never skip it. What I covered today: "r" to read, "w" to write, "a" to append with open() as the safe, correct way to handle files .read(), .readlines(), and looping line by line Checking if a file exists before trying to open it Day 8 done. My code has a memory now. 🧠 👇 What was the first script you wrote that actually saved data to a file? I'd love to know what made file handling click for you — drop it below! #Python #30DaysOfPython #FileHandling #BuildInPublic #PythonProjects

🚀 Day 16/30 of My LeetCode Journey (Python + SQL) Consistency is compounding… feeling more confident with every problem solved 💻🔥 🔹 **SQL Problems of the Day** 👉 *Swap Salary* Swap all `'m'` and `'f'` values in the `sex` column using a single update statement. 💡 *Key Concept:* Using `CASE WHEN` inside an UPDATE. 👉 *Not Boring Movies* Find movies with odd-numbered IDs and a description that is not "boring", sorted by rating in descending order. 💡 *Key Concept:* Filtering + ORDER BY with conditions. 🔹 **Python Problems of the Day** 👉 *Contains Duplicate* Check if any value appears at least twice in the array. 💡 *Key Concept:* Using HashSet for O(n) time. 👉 *Majority Element* Find the element that appears more than ⌊n/2⌋ times. 💡 *Key Concept:* Boyer-Moore Voting Algorithm. From basic logic to optimized approaches… growth is visible 📈 Day 16 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning #Algorithms

🚀 Day 3/30 of My LeetCode Journey (Python + SQL) Showing up daily and building consistency, one problem at a time! 💻🔥 🔹 **Python Problems of the Day** 👉 *1. Move Zeroes* Given an integer array, move all 0’s to the end while maintaining the relative order of non-zero elements. Do it in-place without making a copy. 💡 *Key Concept:* Two-pointer technique for efficient in-place rearrangement. 👉 *2. Remove Element* Given an array and a value, remove all occurrences of that value in-place and return the number of remaining elements. 💡 *Key Concept:* In-place filtering using pointer overwrite approach. 🔹 **SQL Problem of the Day** 👉 *Find Duplicate Emails* Given a `Person` table with an email column, write a query to report all duplicate emails. 💡 *Key Concept:* GROUP BY with HAVING COUNT > 1. Small steps daily = Big progress over time 📈 Staying consistent and enjoying the process! #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #LearnInPublic

🚀 Day 9 of #100DaysOfCode — Finding the Sum of the Smallest Numbers in Python Today I explored two different approaches to solve a simple but important problem: 👉 Find the sum of the smallest N numbers in a list ✅ Approach 1: Pythonic & Efficient numbers = [5, 2, 9, 1, 7] n = 2 result = sum(sorted(numbers)[:n]) print(result)🔹 How it works: sorted(numbers) → sorts the list[:n] → picks the smallest n elementssum() → adds them up💡 Clean, readable, and perfect for most use cases. ✅ Approach 2: Manual Logic (Without Built-ins) arr = [5, 2, 9, 4, 3, 5] N = 2 total = 0 data = len(arr) for k in range(N): min_index = 0 for i in range(1, data): if arr[i] < arr[min_index]: min_index = i total += arr[min_index] arr[min_index] = float('inf') print("Sum of smallest", N, "numbers:", total)🔹 How it works: Repeatedly finds the smallest elementAdds it to totalMarks it as used (by setting to infinity)💡 Great for understanding core logic and algorithm design 🔍 Key Takeaways ✔️ Built-in functions save time and reduce complexity ✔️ Manual approach helps strengthen problem-solving skills ✔️ Always balance readability vs control 💬 Best Comment Insight “Don’t just learn shortcuts — understand what’s happening under the hood. That’s where real growth happens.” #Python #CodingJourney #30DaysOfCode #LearnToCode #Programming #Developers #ProblemSolving #PythonBasics

💡 Rearranging a Linked List In-Place — A Clean Trick I Learned Today Worked on the Odd-Even Linked List problem today, and it turned out to be a great reminder that pointer manipulation doesn’t have to be messy. The task sounds simple: 👉 Group all odd-indexed nodes together followed by even-indexed nodes But the challenge is to do it: In-place (no extra space) In one pass 🔑 Approach that clicked for me: Maintain two pointers: odd → tracks odd-position nodes even → tracks even-position nodes Keep a reference to the head of the even list (even_head) Rewire pointers while traversing Finally, connect odd list to even list ✨ What I liked: No extra data structures Pure pointer manipulation O(n) time and O(1) space Python code : https://lnkd.in/gCB9ZGaq 🧠 Biggest takeaway: Linked list problems often look tricky, but once you clearly track pointers and their roles, the solution becomes surprisingly elegant. Have you faced a linked list problem that looked hard but turned out simple after breaking it down? 👇 #LinkedList #DataStructures #CodingInterview #Python #ProblemSolving #LeetCode Rajan Arora