Groupby and Mean for Fast Data Comparison

Pandas Advanced – Part 7 🐼📊 This video focuses on how analysts think, not just syntax. Instead of jumping into code, we learn how to: Clean data correctly Avoid misleading insights Ask better analytical questions If you’re learning Pandas for real-world data analysis, this part is important. ▶️ Watch: https://lnkd.in/gT2xC4EE 📁 GitHub: https://lnkd.in/gdzNcMaT #Pandas #DataAnalysis #Python #Analytics #LearningInPublic #PyAIHub

Pandas GroupBy is powerful — but only when you understand how it actually works. In Pandas Advanced – Part 6, I break down: GroupBy internals (split → apply → combine) When to use apply, agg, and transform How analysts think while writing Pandas code Why some GroupBy code feels slow in real projects 🎥 Full video: https://lnkd.in/gyw2KAyC 📂 Code & learning notes: https://lnkd.in/gdzNcMaT #pyaihub #Pandas #DataAnalysis #Python #LearningInPublic

📊 Data analysis isn’t always about charts and visuals. Sometimes, it can feel a bit less exciting than graphs and dashboards. Today, I started working on text analysis, focusing on quick and practical methods to move efficiently through the process. Simple exercises like this help build strong foundations and keep progress steady—step by step. Here I shaw step by step the path to make correctly the process, and the correspondent code. The most important thing is to understand what the business is asking, and translate to python language. #DataAnalysis #Python #TextAnalysis #LearningByDoing #ContinuousImprovement

𝗽𝗮𝗻𝗱𝗮𝘀 𝟯.𝟬: 𝗧𝗵𝗲 𝗘𝗻𝗱 𝗼𝗳 𝗦𝗲𝘁𝘁𝗶𝗻𝗴𝗪𝗶𝘁𝗵𝗖𝗼𝗽𝘆𝗪𝗮𝗿𝗻𝗶𝗻𝗴 New Feature: new default string dtype 🤖Problem When you filter a DataFrame and modify the result, you expect the original to stay unchanged. But sometimes pandas modified your original data anyway, triggering the SettingWithCopyWarning. 🌝Solution pandas 3.0 fixes this. Filtering now always creates a separate copy, so modifying the result never affects your original data. Upgrade to pandas 3.0 with “pip install -U pandas”. #data #dataanalysis #Pandas3 #datascience #tech #python

Pandas 3.0 is here! 🎉https://lnkd.in/dfAUP2bH - Copy-on-Write (CoW) fully implemented: SettingWithCopyWarning is gone ✅. No more debugging mysterious copies - chained assignments just work - pd.col() syntax: Clean column references in assign() and loc() without messy lambdas. E.g., df.assign(c=pd.col('a') + pd.col('b')) - Faster UDFs 🚀: No more "slow as molasses" user-defined functions - major perf boosts via better optimization (full Arrow backend didn't land, but it's solid) I made a Kaggle notebook to try https://lnkd.in/d-SsfryV #Pandas #DataScience #Python #DataAnalysis #MachineLearning

𝐅𝐢𝐫𝐬𝐭 𝐓𝐢𝐦𝐞 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐚 𝐌𝐞𝐬𝐬𝐲 𝐃𝐚𝐭𝐚𝐬𝐞𝐭📊 Today I worked on a raw dataset in Python using pandas — and honestly, cleaning the data took more effort than analyzing it. Dealt with missing values, inconsistent formats, duplicate entries, and weird column names. It made me realize that real-world data is rarely clean, and most of the work actually happens before any “analysis” begins. Still learning, but this felt like a real step forward from just theory. #Python #Pandas #DataAnalytics #RealLearning

Headline: Stop guessing, start modeling: Using NumPy for Polynomial Regression 📈 Linear regression is great, but real-world data is rarely a straight line. When your data curves, Least Squares Polynomial Fit is your best friend. By minimizing the squared distance between your data points and the functional curve, you can uncover patterns that a simple linear model would miss. Here’s how I streamline the process using Python: The Discovery: Use np.polyfit(x, y, deg) to determine the optimal parameters for your independent and dependent variables. The Evaluation: Pass those coefficients into np.polyval() to generate your estimation. The Validation: Always plot your polyval results against your raw data. If the "residuals" (the gap between the dot and the line) are too large, it’s time to adjust your degree. Pro-tip: Be careful with the deg (degree) parameter. A degree too high leads to overfitting—where you're modeling the noise, not the signal! #DataScience #Python #Numpy #QuantitativeAnalysis #MachineLearning

Today, I took a deep dive into the heart of Python's data ecosystem. I transformed a messy raw text file into a structured, professional dashboard using NumPy and Pandas. Key takeaways from today's session: ✅ Data Parsing: Turning strings into meaningful dictionaries. ✅ Vectorization: Performing complex math across thousands of rows instantly with NumPy. ✅ Analysis: Filtering and reporting critical insights with Pandas. The goal isn't just to write code; it's to turn raw noise into actionable intelligence. Onwards to Day! What are your favorite Python libraries for data handling? Let's discuss below! 👇 #Python #DataScience #DataAnalytics #Pandas #Numpy #CodingJourney #GlobalTech #LearningEveryday

Today, I worked on understanding and implementing Linear Regression from scratch using Python and scikit-learn. I focused not just on writing code, but on understanding why the model works. One key insight I learned today is the importance of model evaluation. Metrics like MAE, MSE, and RMSE help quantify how wrong a model’s predictions are, rather than relying on visual plots or intuition alone. This matters because in real-world data science, a model that looks good can still fail if it doesn’t generalize well. Learning to evaluate models properly is just as important as building them. I documented the full process and code here on GitHub: https://lnkd.in/gdM6rcRx Looking forward to building stronger foundations step by step. #MachineLearning #DataScience #LearningInPublic #Python #StudentDeveloper

Why aren’t my Matplotlib tick labels behaving? Let’s ask Cameron Riddell! In this week’s Cameron’s Corner, Cameron digs into Matplotlib’s ticker system and shows how small choices can make your charts much clearer (or much more confusing). Learn: ✅ How major and minor tickers work ✅ When to use AutoLocator, MultipleLocator, and custom formatters ✅ Tips for clean, readable axes that communicate your message Read here: https://lnkd.in/g5hkw8ua Ever wrestled with cluttered tick labels? Drop your best Matplotlib tip below 👇 #Python #Matplotlib #DataViz #CameronsCorner #DontUseThisCode