Prevent Data Analysis Errors: Sort Your Data First

Most people default to Pandas. Works fine… until your data scales. That’s where Polars wins: > Similar syntax for most operations > Faster execution > Lazy evaluation (big performance boost) Don’t ditch Pandas. But ignoring Polars now? That’s a mistake. Learn both. Use what fits. Found Insightful? ♻️ Repost in your network and follow Sahil Alam for more. #DataEngineering #Python #Pandas #Polars #BigData #DataAnalyticsSahil Alam for more.

After three years of relying on pandas as my daily driver, I finally dipped my toes into Polars today. At first glance, the semantics feel comfortably familiar. But once you look under the hood, it’s clear that the underlying philosophy is a total departure from the "eager" execution we’re used to in Python. In fact, it feels more like returning to the tidyverse in R. It’s refreshing to see data manipulation evolving toward this "query engine" mindset. I believe if you’re coming from a background in R or SQL, Polars might just feel like coming home. #DataScience #Python #Polars #Rust #Pandas #DataEngineering #MachineLearning

𝐀 𝐬𝐦𝐚𝐥𝐥 𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐩𝐚𝐧𝐝𝐚𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞𝐝 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐭𝐢𝐦𝐞 While working with a dataset in Python today, I came across something simple but very useful — value_counts() in pandas. Instead of writing multiple filters or loops just to see how frequently different values appear in a column, value_counts() gives a quick frequency breakdown instantly. For example, if you want to see how many records belong to each category, city, or product type, one line can show the entire distribution. It’s a small function, but it makes exploring a new dataset much faster. Slowly realizing that data analysis is really about knowing these small but powerful tools. #Python #Pandas #DataAnalytics #LearningJourney

Still Googling Pandas syntax every time you work on a project? . . . . I created a one-page Pandas Cheat Sheet covering the most used commands: read_csv() • groupby() • merge() • fillna() • drop_duplicates() Save this before your next project Which topic should I cover next: NumPy / Statistics / ML Metrics ? #Pandas #Python #DataAnalytics #DataScience #MachineLearning #Analytics #InterviewPreparation

𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞𝐝 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬 While exploring a dataset in Python recently, I noticed how often real datasets contain missing values. At first it seems like a small issue, but it can actually affect the entire analysis. Using pandas functions like isnull() and fillna() made it easier to detect and handle those gaps before doing any calculations or visualizations. It made me realize that a big part of data analysis isn’t just analyzing the data — it’s preparing the data properly so the results actually make sense. Still learning, but these small steps are starting to make the workflow clearer. #Python #Pandas #DataAnalytics #DataCleaning

𝗧𝗵𝗲 𝗴𝗿𝗮𝗽𝗵 𝗹𝗼𝗼𝗸𝗲𝗱 𝘀𝗶𝗺𝗽𝗹𝗲. 𝗧𝗵𝗲 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝗯𝘂𝗶𝗹𝘁 𝗶𝘁 𝗱𝗶𝗱𝗻'𝘁. Day 22 of #1000DaysOfLearning 🗓️ Today I plotted my first graph in matplotlib — a 𝘀𝗰𝗮𝘁𝘁𝗲𝗿 𝗽𝗹𝗼𝘁. 📊 What I worked through: → plt.scatter() vs plt.plot() — and what each communicates → Controlling 𝗺𝗮𝗿𝗸𝗲𝗿 𝘀𝗶𝘇𝗲, 𝗰𝗼𝗹𝗼𝗿, 𝗹𝗮𝗯𝗲𝗹𝘀, 𝘁𝗶𝘁𝗹𝗲𝘀, 𝗮𝗻𝗱 𝗹𝗲𝗴𝗲𝗻𝗱𝘀 → Grouping data points using slicing and color lists The code gets long for what looks like a simple output. But 𝘁𝗵𝗮𝘁 𝗹𝗲𝗻𝗴𝘁𝗵 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 — every label, every color, every legend entry is a deliberate line. Matplotlib assumes nothing. 🎯 Also noticed that 𝘇𝗶𝗽 𝗮𝗻𝗱 𝘁𝘂𝗽𝗹𝗲 𝘂𝗻𝗽𝗮𝗰𝗸𝗶𝗻𝗴, which felt less useful in regular Python, come up naturally when working with coordinate data. Made more sense here than any time I saw them before. 💡 #Python #DataScience #Matplotlib #DataVisualization #LearningInPublic

My first ML project is live on GitHub. Built a Random Forest model trained on 1,460 real house sales that predicts sale prices with a Mean Absolute Error of ~$17,000. Used SHAP values to explain which features drive predictions — turns out overall quality and living area matter most. Tech used: Python, pandas, scikit-learn, SHAP https://lnkd.in/gC4DhQbg #DataScience #MachineLearning #Python #Portfolio

Today’s Python breakthrough: Rethinking the Fibonacci sequence. I started with a Recursive (Top-Down) approach—it looks clean but recalculates the same values repeatedly. It's fine for small numbers, but a nightmare for scaling. I moved to Tabulation (Bottom-Up). By using a list to store results as I go: ✅ Time complexity dropped from O(2 n) to O(n). ✅ No more redundant calculations. ✅ The code actually scales. As an economist moving into Data Science, these efficiency wins are what I love most. It’s not just about getting the right answer; it’s about the most effective way to get there. Check out the clean code here: https://lnkd.in/dgw_sRVM #Python #DataScience #BuildInPublic #DynamicProgramming #WomenInTech

Data cleaning shouldn't be a headache. 🐍💻 Most of a Data Analyst's time isn't spent building models—it’s spent cleaning the mess. I’ve put together a minimalist Data Cleaning in Python Cheat sheet covering the essential steps to get your datasets "analysis-ready" in minutes. What’s inside: ✅ Standardizing formats & strings ✅ Handling duplicates & missing values ✅ Filtering outliers with the IQR method ✅ Quick data exploration commands Whether you're using Pandas for the first time or just need a quick syntax refresher, keep this one bookmarked.  #DataScience #DataAnalytics #Python #Pandas #DataCleaning #CodingTips #MachineLearning

Today, I decided to put my notes on Data wrangling into a simple visual for a quick look-up for anyone struggling with this topic using Python. Data rarely comes clean, and that’s where real analysis begins. I created this quick visual to break down some essential data wrangling techniques in Python (pandas) that I regularly use to clean, transform, and prepare datasets for analysis. From handling missing values to transforming data types and creating meaningful features, these steps are critical for turning raw data into reliable insights. If you're starting out in data analytics, mastering data wrangling is one of the highest-leverage skills you can build. #DataAnalytics #Python #Pandas #DataWrangling #DataScience #LearningInPublic