Optimizing Backend System with Pandas and NumPy

🚀 Day 12/20 — Python for Data Engineering Filtering & Selecting Data (Pandas) Now that we know what a DataFrame is… 👉 The real work starts here: getting only the data you need 🔹 Selecting Columns df["name"] 👉 Select a single column df[["name", "salary"]] 👉 Select multiple columns 🔹 Filtering Rows df[df["salary"] > 50000] 👉 Get rows based on condition 🔹 Multiple Conditions df[(df["salary"] > 50000) & (df["age"] < 30)] 👉 Combine conditions 🔹 Why This Matters Reduce unnecessary data Focus on relevant records Improve performance 🔹 Real-World Use 👉 Raw Data → Filter → Useful Data 💡 Quick Summary Selecting = columns Filtering = rows 💡 Something to remember You don’t need all the data… You need the right data. #Python #DataEngineering #DataAnalytics #LearningInPublic #TechLearning #Databricks

Python Loops: Iteration Simplified 🔁 Ever felt like you're repeating yourself in code? That’s where Python Loops come to the rescue. Understanding the logic between FOR and WHILE loops is a fundamental step for any data professional looking to automate their workflow. The Breakdown: • FOR Loops: These are your go-to when you have a definite number of iterations. Whether you're iterating through a list of column names or a specific range of values, the for loop handles the sequence beautifully. • WHILE Loops: These are all about conditions. The code keeps running as long as a specific condition remains True. This is perfect for scenarios where you don't know exactly how many times you'll need to run the logic until a certain threshold is met. Why this matters for Data Analysts: While we often rely on vectorized operations in Python (like Pandas), understanding the raw logic of loops helps when: 1. Automating API calls that require pagination. 2. Web scraping through multiple pages. 3. Building complex logic inside custom Power BI transformations or advanced SQL stored procedures. Mastering these flowcharts is the key to writing cleaner, more efficient scripts! #Python #CodingLogic #DataAnalytics #Automation #ProgrammingBasics #PythonLoops #SQL #PowerBI #Codebasics

I started using Pandas last week. After a month of Python and NumPy, I thought I was ready. First impression: it feels like Excel. But smarter. In code. NumPy gave me arrays—rows of numbers I could analyze mathematically. Pandas gives me DataFrames—full tables with column names, mixed data types, and the ability to ask real questions of real data. The difference hit me immediately: With NumPy I was working with arrays I created myself. With Pandas I loaded an actual CSV file. Real column names. Real messy data. Real supply chain numbers. And in 3 lines of code: pd.read_csv() df.head() df.info() I could already see which suppliers had missing data, what their delivery rates looked like, and which columns needed cleaning. That's not practice anymore. That's actual analysis. This is where Python stops being theoretical and starts being useful. #Python #Pandas #LearningInPublic #SupplyChain #DataAnalytics

The pipeline is live. The data is accumulating. Instead of overwriting state, it captures every meaningful change over time, allowing repository activity to be analyzed as a sequence rather than a snapshot. Core design decision: versioned state over snapshot storage All changes are tracked using SCD Type 2 modeling in dbt, preserving full historical state of repository attributes. This enables questions such as: -how repository popularity evolves over time -when growth begins or slows down -what distinguishes sustained momentum from short-term spikes Stack: Python · Prefect · Postgres (Supabase) · dbt · Streamlit The value isn’t in ingesting data, it’s in what becomes possible once data is treated as a record of change rather than a static snapshot. Live dashboard: https://lnkd.in/gU77tVF9

🚀 New Webinar: Fabric Data Engineering with Python Notebooks 📅 April 2, 2026 | 12:00–1:30 PM EDT | Online If you’re building on Microsoft Fabric and looking to do more with less, this session is going to be a game‑changer. Python notebooks are quickly becoming the most cost‑efficient and flexible way to engineer data in Fabric—especially for small teams and organizations watching capacity consumption closely. In this webinar, we’ll explore how to design smarter pipelines using modern libraries like Polars, Delta Lake, DuckDB, and MS SQL, and how to evaluate cost tradeoffs using the Capacity Metrics app. 🎤 Speaker: John Miner Senior Data Architect at Insight Digital Innovation 10x Microsoft MVP | 30+ years of data engineering expertise John will walk through practical patterns, real‑world examples, and cost‑optimized design strategies you can apply immediately. 💡 You’ll learn: - Why Spark notebooks and Dataflows Gen2 can be more expensive than Python notebooks - How to build efficient ETL pipelines using modern Python data libraries - How to compare engineering designs using Fabric’s Capacity Metrics - How small companies can maximize value with minimal capacity 🔗 Register here: https://lnkd.in/dnm6irSM FutureDataDriven CloudDataDriven #microsoftfabric #dataengineering #python

🅳🅰🆃🅰 🆃🆈🅿🅴🆂 📦 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬? Definition: Data types represent the kind of value that tells Python how we intend to use the data. It determines what operations we can perform on that data. In simple terms? If a variable is a box, the Data Type is the nature of the item inside. We wouldn't treat a glass vase the same way we treat a pile of clothes, right? Python feels the same way about data! 🏠 The Real-World Example: The Kitchen Pantry Imagine we are organizing our kitchen. We have different types of containers for different types of food: 👉 Integers (int): Think of Whole Eggs. We can have 1, 6, or 12 eggs. We never have 1.5 eggs in a carton. These are whole numbers without decimals. 👉 Floating Point (float): Think of Milk. We measure it in liters like 1.5L or 0.75L. These are numbers with decimal points. 👉 Strings (str): Think of the Labels on our jars like "Sugar" or "Salt". In Python, these are pieces of text wrapped in quotes. Booleans (bool): Think of the Light Switch in the pantry. It’s either True (On) or False (Off). No middle way. #python #datatypes #pythonforeveryone #easylearning

𝗬𝗼𝘂𝗿 𝗣𝗮𝗻𝗱𝗮𝘀 𝗶𝘀𝗻’𝘁 𝘀𝗹𝗼𝘄. 𝗬𝗼𝘂𝗿 𝗰𝗼𝗱𝗲 𝗶𝘀. If your Python script "hangs" the moment you load a 1GB file, you don't need to go out and buy a 128GB RAM Macbook. You just need to stop treating Pandas like an Excel spreadsheet and start treating it like a Matrix. Here are 4 simple switches that can turn a 10-minute wait into a 10-seconds win: 𝟭. 𝗧𝗵𝗲 𝗟𝗼𝗼𝗽𝘀 Using loops or "iterrows()" is like asking a delivery driver to go back to the warehouse for every single package. It’s exhausting and slow. The Fix: Use NumPy-backed operations (like df['a'] + df['b']) The Magic: This uses something called SIMD, which lets your CPU process a whole "block" of data at once instead of one row at a time. 𝟮. 𝗧𝗵𝗲 "𝗮𝗽𝗽𝗹𝘆()" A lot of people think ".apply()" is fast. It’s not. It’s just a loop wearing a fancy suit. The Hack: Always check for "Accessors" first. Example: Don't use a lambda to capitalize text. Use ".str.upper()". These are built in C and run at lightning speed. 𝟯. 𝗧𝗵𝗲 𝗗𝗼𝘄𝗻𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Pandas is "pessimistic." It defaults to the biggest data sizes (like "int64"), even if your numbers are small. Change "Object" columns (strings) to "category". The Result: You can often shrink your memory usage by 90% just by changing the data type. 𝟰. 𝗨𝘀𝗲 𝗡𝘂𝗺𝗯𝗮 𝗳𝗼𝗿 𝗜𝗺𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗟𝗼𝗴𝗶𝗰 Sometimes your math is too complex for standard Pandas functions. Instead of going back to slow loops, use the "numba" library. Pro Move: Adding a simple "@jit" decorator compiles your Python code into "machine code" while it runs. It’s basically giving your script a jet engine. #DataScience #Python #Pandas #BigData

How I bypassed the Pandas "Object Tax" to process 10 Million rows 8x faster with 78% less RAM. 🏎️💨 Standard Python data pipelines are bleeding compute cash. When you run pd.read_csv() on a massive file, Python loads the entire thing into memory and wraps every single value in a heavy Python object. This "Object Tax" is what causes your server to spike in cost and eventually crash with an "Out of Memory" (OOM) error. The Baseline (10 Million Rows / ~400MB CSV): ❌ Standard Pandas: 10.61 seconds | 1,738 MB RAM The Solution: I built Axiom-CSV, a custom C-extension for Python that uses memory mapping (mmap) and pointer arithmetic. It scans the raw bytes directly from the disk and calculates aggregations on the fly, entirely bypassing the Python heap. The Axiom Benchmark: ✅ Axiom-CSV (C-Bridge): 1.34 seconds | 375 MB RAM The ROI (Why this matters): By dropping the memory footprint by 78%, you can process enterprise-level datasets on a $5/month AWS t2.micro instead of a $40/month high-memory instance. You don't need "more RAM." You need better architecture. The Proof & Code: https://lnkd.in/gd-FBdvB DM me: I am conducting 2 architecture audits this week for teams hitting performance walls in their Python pipelines. Let’s translate your latency into balance sheet savings. #Python #DataEngineering #PerformanceEngineering #CProgramming #SystemsArchitecture #CloudOptimization #Pandas #ZeroLatency

I Tracked My Expenses Using Python & NumPy — Here's What ₹38,940 Taught Me About My Spending Habits I built a Personal Finance Tracker using just Python and NumPy — no Pandas, no fancy libraries. Here's what I discovered about my own spending 👇 The project started simple: a CSV file with 50 transactions across 3 months. But when I ran the numbers through NumPy, the insights hit different. What the data revealed: • Shopping eats 40% of my budget — with just 6 transactions • My Top 5 purchases alone = 36% of total spending • Average spend (₹779) vs Median (₹465) — proof that a few big buys skew everything • 56% of money goes to just 11 "high-tier" transactions What I actually built: → Read raw CSV data using Python's csv module → Converted everything to NumPy arrays for fast computation → Used np.sum(), np.mean(), np.max(), np.median(), np.std() → Boolean masking to filter by category & month → np.argsort() to rank top expenses → np.percentile() for distribution analysis → A formatted summary report printed right to the console. Key takeaway: You don't need complex tools to get powerful insights. NumPy + a CSV file + curiosity = real, actionable data about your life. Watch the screen recording below to see the full report output! This is Week 1 of my Python data journey. Next stop: Pandas & Matplotlib. #NumPy #DataAnalysis #PersonalFinance #LearningInPublic #PythonProjects #BuildInPublic #Python #DataScience #CodeNewbie #Programming #TechTwitter #DataDriven #100DaysOfCode #FinanceTracker

Built an Automated Data Profiling & Insight Generation API, turning raw CSV data into meaningful insights in seconds! As part of my data analytics journey, I developed a scalable system using FastAPI that simplifies the entire data analysis workflow — from upload to insights 📊 🔍 What it does: • Processes CSV datasets and generates automated insights like statistical summaries & correlation matrices • Handles datasets with 50K+ rows & 20+ columns efficiently • Performs data cleaning (missing values, duplicates, type normalization), improving data quality by ~35% • Uses optimized Pandas operations to reduce execution time by ~40% • Built with a modular architecture (routes, services, utils) for scalability ⚙️ Tech Stack: Python | FastAPI | Pandas | NumPy | SQL | Matplotlib | Postman | Render 🌐 Deployed the API on Render and tested endpoints using Postman 🎥 Also created a YouTube video explaining the complete project & workflow This project reflects my focus on building practical, scalable data solutions that can be used in real-world analytics scenarios. GitHub Link: https://lnkd.in/dXyY-ty4 Streamlit: https://lnkd.in/d6bjPKuW Live Link: https://lnkd.in/dru34GKa YouTube link: https://lnkd.in/dxzfpvpq Would love to connect with professionals and recruiters in the data space 🤝 #DataAnalytics #DataAnalyst #Python #FastAPI #DataScience #MachineLearning #Pandas #NumPy #SQL #DataProjects #PortfolioProject