How to Use Python for Real-World Applications

In 2025, Microsoft Excel power users will be using Python in Excel. Here are 5 reasons why (with business scenarios): 1. Visualizing Many Variables Excel charts are great for one or two columns. But what if you want to analyze 4, 5, or more columns simultaneously? With Python in Excel, you can create: - Bar charts that drill deep into your data - Scatter plots that show hidden correlations - Boxplots to spot outliers across multiple columns Business Scenario: A marketing manager wants to understand what drives high customer spend. With Python in Excel, they create boxplots of customer spend. Each spend boxplot is segmented by the interaction of 5 other columns in the table. The high level of segmentation allows the marketing manager to see new interactions for improved digital ad targeting. 2. K-means Clustering for Segmentation Grouping by “category” or “region” is just the start. With Python, you can run k-means clustering to segment: - Products by sales patterns - Customers by behavior - Patients by risk factors Business Scenario: A healthcare professional segments patients using k-means clustering on 100 patient attributes and behaviors, discovering at-risk groups for proactive care. Something impossible to do with just PivotTables. 3. Market Basket Analysis Excel can count, but Python can uncover relationships. Use market basket analysis (association rules) to find: - Which products are bought together - Customer purchasing journeys - Cross-sell opportunities Business Scenario: A retail team uses Python in Excel to analyze product combos across dozens of SKUs, finding patterns in multi-item purchases. These insights fuel smarter marketing, product recommendations, store endcaps, etc. 4. Machine Learning for Prediction Python in Excel unlocks real ML models: - Predict customer churn - Forecast demand - Spot fraud Business Scenario: An insurance professional uses ten customer features in a Python-driven logistic regression to predict fraud risk, leveraging the full complexity of the data. 5. Automating Repeatable Analytics No more “copy-paste-refresh-pray” workflows. Python in Excel can automate: - Data sourcing - Data cleaning and transformation - Data visualizations and advanced analytics Business Scenario: A finance professional sets up an automated forecast that: - Sources data from workbook tables and Power Query - Combines, cleans, and transforms the data - Trains a machine learning model - Makes forecasts All of which is easily shared with management.

10 years ago, when I was at Steadfast, I spent 200+ hours learning Python to automate some parts of our budgeting process. We went from 5,000 to 30,000 units in just 8 years. So we had to budget at scale. After 200+ hours and weeks of trial and error, this is how our budgeting process looked: 1) Download reports: Manually download all the reports from Yardi and from the property managers, which include all the financials, the rent rolls, the in-place rent numbers, and the budgets. 2) Compile + transform: The Python script read 100s of files in 5 seconds, compiled them, transformed the data, and auto-filled the budget template with formulas activated and everything in place. 3) Bulk update assumptions: Need to increase occupancy across Dallas properties? Click a few times. The script would open every file, make the updates, save, close, and move to the next one, on loop. 4) Analyze + visualize: The script would read all the completed budgets and build a Power BI dashboard with key visuals: average occupancy, rent assumptions, and YoY growth rates. Before: - An army of accountants managed the budgeting process in Excel. - Team of ~10 people, each assigned 5 properties. - Rolling up properties via Excel macro took 12 hours. After: - The Python script did the rollup in 45 seconds. - 0 constant manual labor and meetings. But it wasn't a straight line. One project even completely flopped, and we had to start from scratch. There were a ton of missing gaps that needed fixing. But that' exactly what led me to build Vizibly. Vizibly today is the high-end, sophisticated version of the Python system I built years ago. Except for the fact that, at Vizibily, we’ve added a real UI. And no one has to live inside a spreadsheet again. My discovery got twice as good each time. But it all started with a scrappy Python code.

Furthermore, my obsession with Python has led me to discover incredible real-world applications. My fiancé, a Film Marketing Director, used to spend hours manually checking YouTube views for her company’s trailers across multiple channels, regions, and languages. I thought, "Why not automate this?" So, I built a Python scraper to handle the task effortlessly. Now, she can search her YouTube videos across all her company's YouTube channels with the click of a button. The script leverages tools like yt-dlp for YouTube data extraction, Google Cloud Translation for multilingual support, and Flask for a simple web interface. Here’s a couple code snippets from what I built: 1. Search Functionality with yt-dlp - Automates YouTube searches efficiently using Python's yt-dlp library. 2. Translation Integration - Supports multilingual queries by integrating the Google Cloud Translation API. 3. Refinement and Parallel Processing - Enhances performance with parallel processing using ThreadPoolExecutor. Considering making this a Chrome Extension...🤔 #lifelonglearner #problemsolver #automateallthings #python

🔧 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐄𝐧𝐞𝐫𝐠𝐲 𝐃𝐚𝐭𝐚 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 𝐅𝐫𝐨𝐦 𝐌𝐚𝐧𝐮𝐚𝐥 𝐂𝐡𝐚𝐨𝐬 𝐭𝐨 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 ⚡ Thrilled to share a 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 where I engineered an 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐝𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that eliminated the manual, error-prone processes analysts dreaded, transforming how energy sales and capability data flows within the organization. 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Every quarter, analysts spent days 𝐰𝐫𝐚𝐧𝐠𝐥𝐢𝐧𝐠 𝐟𝐫𝐚𝐠𝐦𝐞𝐧𝐭𝐞𝐝 .𝐜𝐬𝐯 𝐚𝐧𝐝 𝐧𝐞𝐬𝐭𝐞𝐝 .𝐣𝐬𝐨𝐧 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 to derive sales and capability insights for various energy types across regions. Beyond being time-consuming, this bottleneck delays strategic decision-making. 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐭: An end-to-end automated data pipeline designed to replace a tedious manual process where energy analysts previously spent days retrieving and cleaning quarterly sales and capability data. 𝐒𝐤𝐢𝐥𝐥𝐬 𝐈’𝐯𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐓𝐡𝐢𝐬 𝐏𝐫𝐨𝐣𝐞𝐜𝐭: 𝟏) 𝐄𝐓𝐋 𝐃𝐞𝐬𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝 𝐏𝐚𝐧𝐝𝐚𝐬: Transforming raw .csv datasets and handling deeply nested .json files 𝟐) 𝐉𝐒𝐎𝐍 𝐏𝐚𝐫𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:  Extracting structured, analysis-ready tables from complex JSON formats 𝟑)𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: Ensuring accuracy, consistency, and readiness for downstream use 𝟒)𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Building scalable, repeatable processes for monthly data ingestion 𝟓)𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: Making pipelines resilient and production-ready 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐖𝐨𝐫𝐥𝐝: 1)Businesses run on 𝐭𝐢𝐦𝐞𝐥𝐲, 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚; manual processes delay insights and increase the risk of errors 2)Automating pipelines 𝐫𝐞𝐝𝐮𝐜𝐞𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐨𝐯𝐞𝐫𝐡𝐞𝐚𝐝 and frees teams to focus on strategic analysis 3)Data Engineers play a crucial role in building these systems, ensuring data is clean, trusted, and delivered efficiently This project has been a critical step in developing the technical foundation needed to work as a Data Engineer, combining Python, Pandas, and real-world data problems to create 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, impactful solutions. Looking forward to diving deeper into building 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 and driving value through reliable data engineering! 🚀 #DataEngineering #ETL #DataPipelines #Python #Pandas #JSONParsing #Automation #CareerInTech #DataTransformation #EnergyData #RealWorldSkills 𝐖𝐨𝐫𝐤𝐛𝐨𝐨𝐤: https://lnkd.in/dRhBrUY5