Python Fundamentals: Variables and Data Types

Polars is quietly becoming one of the most exciting tools in the modern Python data stack. Most of us have hit the limits of traditional DataFrame workflows: slow group‑bys, memory issues with medium‑large datasets, and complex pipelines that are hard to optimize. Polars tackles all of that head‑on with a fresh design. Docs: https://docs.pola.rs/

 Day 9/100: Mastering Python Dictionaries & Data Nesting! Today was all about organizing complex data. I moved from simple Lists to Dictionaries, which allow for much more powerful data management using Key-Value pairs. What I covered today: Dictionary Basics: Creating, editing, and wiping dictionaries. The "Secret" of Nesting: Placing Lists inside Dictionaries and even Dictionaries inside Dictionaries! Looping: How to efficiently iterate through keys and values. Daily Project: Secret Auction (Bidding System) I built a blind auction program where multiple users can enter their names and bids. The program keeps the bids secret and automatically identifies the highest bidder at the end. This project was a great test of my ability to manage user input within nested data structures. Check out the "Secret Auction" code here: https://lnkd.in/edbJz2bW #Python #100DaysOfCode #DataStructures #Dictionaries #CodingJourney #BackendDevelopment

Day 2/100: Mastering Data Types and Logic Today was all about how Python handles information. I dived deep into the mechanics of data and mathematical operations. What I tackled today: Data Types: Understanding Integers, Floats, and Booleans. Subscripting: Pulling specific characters out of a string (like a pro!). The len() Function: Measuring the length of data. Type Conversion: Converting data (e.g., String to Integer) to make calculations possible. Math in Python: Using mathematical operators, the round() function, and assignment operators. Daily Project: Tip/Bill Calculator I created a program that calculates how much each person should pay when splitting a bill, including the tip. It’s a real-world tool built with just a few lines of code! Step by step, I'm getting more comfortable with the logic. 🚀 #Python #100DaysOfCode #DataTypes #CodingCommunity #SoftwareDevelopment

Insert Interval (LeetCode 57) - Medium I explored a more optimized way to handle intervals when the input is already sorted. Instead of re-sorting everything, I learned how to process the intervals in a single linear pass. Key Learnings: * Linear Scan: Since the input is sorted, we can divide the problem into three logical parts: Before overlap, During overlap (merge), and After overlap. * In-place Merging: For the overlapping part, we simply update the start to the min and the end to the max of the conflicting intervals. * Efficiency: No sorting means we save time! This approach is much faster for pre-sorted data. Complexity: ⏱️ Time Complexity: O(N) — because we only iterate through the list once. 📂 Space Complexity: O(N) — to store the result list. Consistency is the key #LeetCode #CodingJourney #Blind75 #SDEPrep #DataStructures #Python #ProblemSolving #TechCommunity

🚀 Day 2/100 — Introducing Control Flow 🧠 “Logic transforms static code into a decision-making system.” Every functional system depends on its ability to evaluate conditions and respond accordingly. Today, I focused on understanding how Python enables programs to make decisions based on dynamic inputs and defined conditions. ⚙️ 🔧 Today’s focus areas: 🔀 Conditional Statements — Implementing if, elif, and else 🧩 Logical Operators — Combining multiple conditions 🎯 Decision Logic — Controlling execution flow based on evaluation 🖥️ Practical Exercises — Building programs with dynamic behavior 🎯 The objective was to enable programs to respond intelligently — forming the core of backend validations and control systems. ✅ Day 2 complete: Decision-making logic established. ▶️ Day 3: Structuring repeated execution using loops. Step by step. The system evolves. 🏗️ #100DaysOfCode #Python #FullStackDevelopment #SoftwareEngineering #BackendDevelopment #DeveloperJourney

Troubleshooting my way into Pandas! 🐼 Today, I took a deep dive into Python's most powerful data tool: pandas. Like every developer's journey, it started with a "ModuleNotFoundError," but that was just the first step! Key takeaways from my session: Installation: How to "invite pandas to the party" manually when it's not pre-installed. The DataFrame vs. df: I learned that DataFrame is the blueprint (the class), while df is the specific variable where we store our data. Series vs. DataFrames: A Series is a single column (1D), while a DataFrame is the entire tabular structure (2D). Case Sensitivity: A big lesson—pd.DataFrame() works, but pd.dataframe() does not! Next stop: Cleaning even messier datasets! 🚀 #Python #Pandas #DataScience #LearningJourney #DataAnalytics

🚀 Solved the “Two Sum” Problem | Data Structures & Algorithms Practice Today I solved the classic Two Sum problem—a fundamental question in data structures & algorithms. 🔹 Problem: 1 Given an array of integers and a target value, return the indices of two numbers such that they add up to the target. ⏱️ Complexity: Time Complexity: O(n) Space Complexity: O(n) 🔗 GitHub Repository (more DSA problems inside): https://lnkd.in/gdrbnQDF #DSA #ProblemSolving #Python #CodingJourney #SoftwareEngineering #LeetCode

Today I used Cortex Code to run evaluations on my Cortex Agent and the workflow is surprisingly smooth. Cortex Code can create eval datasets, configure metrics, kick off evaluation runs, and analyze results -- all from the CLI. No context-switching between Snowsight tabs. You describe what you want to test, it writes the SQL  and Python, and you iterate from there. I was able to compare accuracy and groundedness across different prompt configs in about 15 minutes. The traces show exactly where the agent went off track, which makes debugging way faster than staring at raw logs. If you're building Cortex Agents, pair them with Cortex Code for evals. Works just as well for production monitoring as it does for continual testing as you tweak prompts and configs. What's your workflow for evaluating agents as they evolve?  #Snowflake #CortexCode #CortexAI #AIAgents #AIObservability

🚀 Day 5/100 — Working with Persistent Storage 🧠 “Persistence transforms execution into continuity.” Systems become meaningful when they retain and retrieve information reliably. Today, I learned how Python interacts with files to store and retrieve persistent data. ⚙️ 🔧 Today’s focus areas: 📂 File Reading — Accessing stored data 📝 File Writing — Persisting new information 🔄 File Modes — Managing read and write operations 🎯 Data Persistence — Ensuring continuity across executions 🎯 The objective was to enable programs to maintain state beyond runtime. ✅ Day 5 complete: Persistent data handling established. ▶️ Day 6: Strengthening reliability through exception handling. Step by step. The system evolves. 🏗️ #Python #BackendDevelopment #100DaysOfCode #SoftwareEngineering

🚀 Day 10/30 | LeetCode Problem: Reverse Linked List (206) Problem: Given the head of a singly linked list, reverse the list and return the new head. 💡 Approach (Iterative – Three Pointers) To reverse a linked list, we carefully change the direction of pointers. We use three variables: prev → previous node current → current node next_node → temporarily stores next node Steps: Store next node Reverse the current node’s pointer Move prev and current one step forward Repeat until the list ends Finally, prev becomes the new head. ⏱ Complexity Time Complexity: O(n) Space Complexity: O(1) 🧠 Python Code class Solution: def reverseList(self, head): prev = None current = head while current: nxt = current.next current.next = prev prev = current current = nxt return prev 📌 Example Input: [1,2,3,4,5] Output: [5,4,3,2,1] 🎯 Key Takeaway Linked List problems are all about pointer manipulation. Understanding pointer flow is more important than memorizing code. ✅ Accepted 🔖 Hashtags #LeetCode #30DaysOfLeetCode #Day10 #Python #LinkedList #DataStructures #Algorithms #ProblemSolving #CodingJourney #SoftwareEngineering