Building a Recommendation Engine from Scratch with Python

🚀 Exploring Python Lists – A Powerful Data Structure Recently, I learned how Python lists work in real-world scenarios, and it completely changed how I think about handling data in Python. 📌 Summary: Python lists allow us to store, manage, and manipulate multiple values efficiently. From basic operations to advanced techniques like list comprehensions, they make coding faster and more readable. 💡 Key Learnings: Lists are dynamic and can store different data types Methods like append(), remove(), and sort() make data handling easy List comprehensions help write clean and efficient code 🌍 Real-world use: Lists are widely used in applications like shopping carts, user data storage, and data analysis. 🔗 I’ve also written a detailed blog on this topic: 👉 https://lnkd.in/gT_FGa97 Excited to share my learning on Python Lists 🚀 Thanks to Mr.Vishwanath Nyathani, Mr.Raghu Ram Aduri, Mr.Kanav Bansal, Mr.Mayank Ghai, Mr.@Harsha M. Also inspired by Innomatics Research Labs learning resources #Python #Learning  #Python #DataStructures #MachineLearning #AI #LearningInPublic #Coding #Tech

Your 2020 Python skills are becoming a 2026 bottleneck. I’ve seen brilliant analysts struggle with memory errors and 10-minute wait times for simple joins. The problem isn't their logic; it’s their toolkit. The "Modern Python Stack" for Analysts has fundamentally shifted. If you are still relying 100% on Pandas and Matplotlib, you are leaving performance and interactivity on the table. I’ve fact-checked the production environments of top data teams this year. Here is the Save-Worthy 2026 Python for Analysts Cheat Sheet. 🚀 Polars: The multi-threaded engine that handles 10GB+ datasets on a laptop. 🦆 DuckDB: Run high-speed SQL directly on your local Parquet files. 📊 Plotly Express: Interactive charts that stakeholders can actually explore. ✅ Pydantic V2: Automated data cleaning that's 20x faster than traditional methods. 👇 The Big Debate: Is it finally time to retire import pandas as pd for good, or is it still the king of small-scale EDA? Let’s settle it in the comments. #Python #DataAnalytics #Polars #DuckDB #DataScience #MicrosoftFabric #2026Trends #Coding

Everyone asks whether they should learn Python or Excel. Wrong question. Both tools solve the same problems. The difference is scale, speed, and how repeatable you need the work to be. If you know Excel, you already understand the logic behind Python's most used functions. You just do not know the syntax yet. A pivot table is groupby().  XLOOKUP is merge().  Remove Duplicates is drop_duplicates().  Go To Special to find blanks is dropna(). The logic is identical. The syntax is new. That is all. The cheatsheet below maps 10 tasks every analyst does regularly across both tools, with real examples. 𝐏.𝐒. I share your thoughts and follow for more 👉Sagar Sharma #DataAnalytics #DataScience #Excel #Python #MySQL #PowerBI

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

Rethinking Data in 2025: Are you leveraging Python effectively for your data analysis? The power of libraries like Pandas and NumPy can transform how you clean, analyze, and visualize data. Data isn't just numbers and figures; it's the foundation of insightful decision-making. With the right tools, you can uncover trends and patterns that drive strategy and create value. Pandas provides intuitive data structures, while NumPy offers fast array computations that make data manipulation seamless. One common misconception is that data analysis requires complex programming skills. In reality, using Python libraries can simplify the process. By mastering these tools, you can handle large datasets with ease and extract insights more efficiently. Imagine deriving actionable insights from your business data in a fraction of the time it currently takes. This not only boosts productivity but enhances your organization's agility in a fast-paced market. Curious about hands-on techniques to elevate your data skills? Learn it hands-on with us → https://lnkd.in/gjTSa4BM) #Python #Pandas #DataAnalysis #DataScience #DataVisualization

Your Python Code Consuming Too Much Memory? Today, I explored a fundamental concept in NumPy that many of us often overlook: manual data type (dtype) . While NumPy is naturally more efficient than standard Python arrays, the way we define our data plays a massive role in actual performance. I recently followed a lecture by Respected Sir Zafar Iqbal on this topic, and it changed how I look at memory management in Data Science/ML. Here are my three key takeaways from today's practice: 1. The "Default" Memory Waste When we create an array without specifying a data type, Python often assigns the maximum possible size, such as int64, by default. If your data consists of small numbers (like 1 to 100), using int64 is a waste of resources. By simply defining dtype=np.int8, you can perform the same operations while using significantly less memory. 2. The Out-of-Bounds Trap Every data type has a specific boundary. For instance, int8 can only store values between -128 and 127. If you try to store a number like 130 in an int8 array, you will encounter an "out of bounds" error. In such cases, moving to int16 or int32 provides the necessary range while still being more efficient than the 64-bit default. 3. The Cost of "Object" Flexibility NumPy allows us to mix different types, like strings, integers, and floats, by using dtype=object. While this offers flexibility, it comes at a price: you lose the famous speed advantage that makes NumPy so powerful. For high-performance computing, keeping your data homogeneous is essential. Pro Tip: When working with large datasets, always use the .nbytes attribute to check exactly how much memory your array is consuming. Making small adjustments to your data types can transform a heavy, slow program into a super-efficient one. I am curious to hear from other data professionals: Do you usually stick with the default settings, or do you prefer manual control over your memory usage? Let me know in the comments. #Python #DataScience #NumPy #CodingLife #LearningEveryday #MachineLearning #Efficiency

🚀 Day 13/30 of My LeetCode Journey (Python + SQL) Showing up every day and pushing my limits a little more! 💻🔥 🔹 SQL Problem of the Day 👉 Customer with Most Orders Given an Orders table, write a query to find the customer_number who has placed the highest number of orders. 💡 Key Concept: GROUP BY + COUNT() with ordering/aggregation to find maximum. 🔹 Python Problem of the Day 👉 Subarray Sum Equals K Given an array and an integer k, return the total number of subarrays whose sum equals k. 💡 Key Concept: Prefix Sum + HashMap for optimizing from O(n²) → O(n). Learning how to optimize brute force solutions into efficient ones is a big win ⚡ Day 13 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #PrefixSum #Learning

🧠 Python Concept: get() method in dictionary Avoid key errors like a pro 😎 ❌ Traditional Way data = {"name": "Alice", "age": 25} print(data["city"]) 👉 KeyError (crashes if key not found) ❌ Old Safe Way if "city" in data: print(data["city"]) else: print("Not found") 👉 Too many lines ✅ Pythonic Way data = {"name": "Alice", "age": 25} print(data.get("city")) 👉 Output: None (no crash ✅) 🧒 Simple Explanation Think of get() like a safe search 🔍 ➡️ If key exists → returns value ➡️ If not → returns None (or default) 💡 Why This Matters ✔ Prevents crashes ✔ Cleaner code ✔ Useful in APIs & real data ✔ Handles missing keys easily ⚡ Bonus Example data = {"name": "Alice"} print(data.get("city", "Unknown")) 👉 Output: "Unknown" 🐍 Don’t let missing keys break your code 🐍 Use get() smartly #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

9 favorite websites to practice coding exercises until you're a MASTER: 9. Mode (SQL) 8. DataLemur (SQL) 7. LeetCode (Python) 6. Codewars (Python) 5. Stratascratch (SQL) 4. HackerRank (Python) 3. Kaggle (Data Science) 2. W3 Resource (pandas) 1. bnomial (Machine Learning)

Technical post: I've been posting some graphs on here, talking about functions and "equivalence". This was all started by working on porting an MLOPs framework from python 3.10 to 3.12, and all the "dependency hell" one has to go through. Then naturally the question arose "What are the boundaries of one project to another, in terms of functions being called etc.,?" This led me down the rabbit hole (not too deep) of what happens when I do something like python -m <module> <somescript>. Specifically, what is a "no op" module, and what kind of ops can we inject, thanks to python being an interpreted language. A few years ago I'd worked on something along similar lines called TracePath, which provided a decorator to do something similar (e.g. who called who, how long it took, etc.). So I merged these two ideas (avoid decorating every function, have an "inspector" module) and ran this on a simple pandas dataframe creation. The resulting function invocation graph is the image attached to this post. When I ran it across the whole workflow (create, load, transform data etc.,), the graph had ~9000 connections. The nice thing is I can specify which modules (e.g. only pandas, or pandas and numpy) should be added to the graph etc. What do you think is the next logical thing to do with something like this? What kind of graphs would well structured software produce? How about badly written software? #graphs #swe #dependencyhell #python