Unlocking Data Potential with Python Libraries

sum() vs NumPy vs math.fsum(): Which One Is Faster? Simulation script is available here: https://lnkd.in/ec9ecZxx I benchmarked four ways to sum 1,000,000 floats stored in a Python list: - sum() - np.sum() - np.add.reduce() - math.fsum() Each function was executed 1000 times (after warm-up), and I compared the mean execution time. Result - math.fsum() - fastest - sum() - slightly slower - np.add.reduce() - slower - np.sum() - slowest Surprising? A bit. Why NumPy Lost Here Because the data is a Python list. When calling np.sum(list), NumPy first converts the list into an array. That conversion overhead dominates the runtime. Meanwhile: > sum() works directly with the list > math.fsum() is a C-optimized implementation with better numerical stability The Takeaway NumPy is extremely fast - when working with NumPy arrays. But if your data is already a list and you just need a single aggregation, plain Python may be faster. Performance always depends on context: - Data structure - Memory layout - Conversion cost Benchmark in your real setup - not in theory. #python #numpy #sum #math #fsum

𝗪𝗲𝗲𝗸 𝟱 of my 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 & 𝘔𝘓 journey with ParoCyber. Here's what I learned: ☑️ Pandas Series: creating a one-dimensional data structure from a Python list. ☑️ DataFrames – organizing data into rows and columns, similar to a spreadsheet or table. ☑️ Creating DataFrames from dictionaries with columns like Name, Age, and City. ☑️ NumPy Operations: performing mathematical operations on arrays and exploring indexing. I have learnt that NumPy helps with fast numerical calculations, while Pandas makes it easier to organize and explore datasets. Also, dataFrames make data much easier to understand because everything is structured in rows and columns. It almost feels like working with Excel, but using Python. Seeing how simple lists and dictionaries can be turned into structured datasets made me realize how Python is slowly preparing us to work with real-world data. #DataScience #MachineLearning #Python #ParoCyber

Python Data Visualization Quick Guide V1.0 📊 What’s inside: • Distribution plots (Histogram, KDE, Box, Violin) • Categorical analysis (Bar, Count, Pie) • Relationship plots (Scatter, Regression, Bubble) • Time series visualizations (Line, Area) • Multivariate exploration (Heatmaps, Pairplots) • Hierarchical charts (Sunburst, Treemap) • Geographic maps with Plotly • Faceting and subplot layouts • A Visualization Selection Guide to help choose the right chart quickly 🔗 Notebook link: https://lnkd.in/daHNQpdq I’d love to hear your feedback and suggestions for improving it further. #Python #DataScience #DataVisualization #EDA #MachineLearning #Plotly #Seaborn #Matplotlib

Excited to share my Pandas Data Analysis Guide! This practical PDF contains essential commands and techniques for working with DataFrames: filtering, aggregations, sorting, ranking, conditional columns, and more. It’s a quick reference for aspiring Data Analysts or anyone practicing Python data analysis — perfect to keep handy while exploring real datasets. Tip: Use it as a learning companion to speed up your workflow and strengthen your Pandas skills! #Python #Pandas #DataAnalysis #DataAnalytics #DataScience #LearningResource

🐍 Day 72 — Mean (Average) Day 72 of #python365ai ➗ The mean is the average value of a dataset. Example: import numpy as np data = [10, 20, 30] print(np.mean(data)) 📌 Why this matters: The mean helps describe the central tendency of data. 📘 Practice task: Calculate the mean of five numbers. #python365ai #Mean #Statistics #Python

📊 Pandas vs NumPy — Most beginners use both, but few understand when to use which. If you're working with data in Python, chances are you've used Pandas and NumPy. 🔹 NumPy → Best for numerical computations and handling large multi-dimensional arrays. 🔹 Pandas → Best for data manipulation, analysis, and working with structured/tabular data. In simple terms: ➡️ Use NumPy for fast mathematical operations ➡️ Use Pandas for data cleaning, transformation, and analysis Both libraries complement each other and form the backbone of the Python data ecosystem. Which one do you use more in your projects? 👇 #Python #Pandas #NumPy #PythonProgramming #DataScience #DataAnalyst #DataAnalytics #DataAnalysis #MachineLearning #ArtificialIntelligence #Analytics #DataScientist #DataAnalyst #Programming #Coding #BigData #LearnPython #TechCommunity #DataEngineering

📊 Data Visualization Practice – Frequency of Diagnoses Today I worked on creating a bar plot to visualize the frequency of different diagnoses using Python and Matplotlib in Google Colab. 🔹 Added meaningful titles and axis labels 🔹 Rotated tick labels for better readability 🔹 Used `tight_layout()` for clean formatting 🔹 Exported the visualization as a PNG file This exercise reinforced the importance of clear labeling and presentation in data visualization. A well-structured graph makes insights easier to understand and communicate. Continuing to strengthen my skills in: #Python #DataVisualization #Matplotlib #DataAnalytics #GoogleColab #LearningJourney

𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈’𝐦 𝐧𝐨𝐭𝐢𝐜𝐢𝐧𝐠 𝐰𝐡𝐢𝐥𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 While practicing Python and SQL lately, one thing is becoming very clear, data analysis is not just about tools. Most of the time actually goes into understanding the data itself. Looking at patterns, asking the right questions, and figuring out what the numbers really represent. Even small exercises start getting interesting when you try to interpret the results instead of just writing code. Still early in the journey, but slowly getting more comfortable working with data and thinking more analytically. #DataAnalytics #Python #SQL #LearningJourney

Stop writing slow for loops! 🛑⏱️ As I’ve been diving deeper into Data Science, I’ve realized that while Python is easy to write, it can be slow if you don't use the right techniques. The game-changer? Vectorization. Instead of processing data one item at a time, vectorization allows you to perform operations on entire arrays simultaneously. It’s like using a power washer instead of a toothbrush to clean a driveway. Why it matters: 🚀 Speed: It’s significantly faster for large datasets. 📉 Cleanliness: It makes your code more readable and professional. 🛠️ Numpy Power: It leverages optimized functions designed for high-performance computing. If you’re working with Big Data, you can’t afford to ignore this! What’s your favorite NumPy trick to speed up your workflow? Let’s share in the comments! 👇 #DataScience #Python #NumPy #Vectorization #CodingTips #TechCommunity #MachineLearning

A few days ago I posted about how SQL forces you to think in layers. What I didn't mention is how differently it feels compared to Python, which I've been learning for a while now. I came across an article by Benn Stancil that finally put it into words for me: SQL is like a basic Lego set. Limited pieces, but they always fit together predictably. You know what you're building. Data rolls downhill like a snowball, collecting and compressing until you get your answer. Python is more like specialized Lego sets. Seaborn, Pandas, Scikit-learn — each library is its own world. Together they can build almost anything, but sometimes you just have to trust the result. Data branches out like a web. I'm still figuring out which way of thinking I prefer honestly. But I'm starting to see why people say you need both. If you're learning both, which one are you finding harder to wrap your head around? #SQL #Python #DataAnalytics