Data Visualization Libraries

“Show me your data plot!” That was the first thing the professor said when I tried to explain my ML model in graduate school at MIT. Not the accuracy. Not the loss curve. Not the architecture. The plot. Over time, I realized, visualization is not the final step of machine learning. It is the first one. Before we build anything we need to understand what we are working with. And to understand it, we need to see it. This week, I taught a lecture on data visualization for ML using Matplotlib, Seaborn, and Plotly on Vizuara's YouTube channel: https://lnkd.in/dQTQYccT We walked through a complete exploratory data analysis (EDA) pipeline, starting with foundational charts and ending with interactive, dynamic visualizations. And through that, I was reminded of a principle I often forget: A good plot does not just summarize your data. A good plot changes what you believe about your data. You are not always building models for yourself. You are building for a client, reviewer or even a policymaker. They will not read your code. They may not understand your metrics. But they will look at your plots. Visualization is what makes machine learning interpretable - not only to others, but to you. And that matters more than ever. -A boxplot reveals whether a feature is skewed. -A scatterplot shows whether it separates your classes. -A correlation heatmap tells you what is redundant. -A violin plot raises questions about fairness. The stack: Matplotlib, Seaborn, Plotly Each of the three libraries plays a different role in the data visualization journey. 1) matplotlib: The bedrock. Sometimes verbose, but it gives you full control. Perfect for plotting model metrics, trends, and comparisons. Think of it as the NumPy of plotting. 2) seaborn: Statistical plotting done right. One-liner plots that look beautiful and convey distributions, relationships, and groups instantly. Use it for EDA - where every plot leads to a new hypothesis. 3) plotly: The bridge to interaction. If you want to share a story, demo a dataset, or explore it dynamically - this is the tool. Interactive histograms, 3D scatter plots, tooltips on hover. Especially powerful for explaining your work to non-technical stakeholders. Data visualization is not about being fancy. It is about being thoughtful. If you cannot explain your dataset visually, you are not ready to model it. If you cannot explain your model’s results visually, you are not ready to defend it. No one ever changed their mind because of an F1-score. But stunning plot? Those make people pause. Those change narratives. As ML gets more complex - with deeper models, larger datasets, and higher stakes - our ability to communicate clearly will matter more, not less. So if you are starting out in ML - start here. Learn to see before you try to predict. The plots will tell you where to go.

Matplotlib and Seaborn are two essential tools for data visualization in Python. Matplotlib serves as the foundational plotting library, offering full control over every element, making it ideal for creating basic charts and intricate figures. It is best suited for scenarios where customization is crucial, despite requiring more lines of code for execution. On the other hand, Seaborn, built on top of Matplotlib, focuses on simplicity and elegance. It simplifies statistical visualizations with predefined themes and color palettes, making it perfect for quick EDA and generating beautiful visuals with minimal coding effort. When to use each: - Matplotlib: Opt for Matplotlib when you require meticulous control over labels, annotations, or subplots. - Seaborn: Choose Seaborn for swift and clean visuals, especially when dealing with grouped data or statistical summaries. For optimal results, consider combining both tools. Seaborn seamlessly integrates with Matplotlib, allowing you to leverage Seaborn's simplicity and then fine-tune details using Matplotlib, providing a versatile approach to data visualization. Understanding the strengths of Matplotlib and Seaborn is key to effectively communicating insights through dashboards, reports, or data exploration. Let's enhance our data visualization skills together! #DataVisualization #DataScience #Analytics #EDA #DataAnalystTools #Matplotlib #Seaborn #Python

Best LLM-based Open-Source tool for Data Visualization, non-tech friendly CanvasXpress is a JavaScript library with built-in LLM and copilot features. This means users can chat with the LLM directly, with no code needed. It also works from visualizations in a web page, R, or Python. It’s funny how I came across this tool first and only later realized it was built by someone I know—Isaac Neuhaus. I called Isaac, of course: This tool was originally built internally for the company he works for and designed to analyze genomics and research data, which requires the tool to meet high-level reliability and accuracy. ➡️Link https://lnkd.in/gk5y_h7W As an open-source tool, it's very powerful and worth exploring. Here are some of its features that stand out the most to me: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐆𝐫𝐚𝐩𝐡 𝐋𝐢𝐧𝐤𝐢𝐧𝐠: Visualizations on the same page are automatically connected. Selecting data points in one graph highlights them in other graphs. No extra code is needed. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: - Filtering data like in Spotfire. - An interactive data table for exploring datasets. - A detailed customizer designed for end users. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐮𝐝𝐢𝐭 𝐓𝐫𝐚𝐢𝐥: Tracks every customization and keeps a detailed record. (This feature stands out compared to other open-source tools that I've tried.) ➡️Explore it here: https://lnkd.in/gk5y_h7W Isaac's team has also published this tool in a peer-reviewed journal and is working on publishing its LLM capabilities. #datascience #datavisualization #programming #datanalysis #opensource

𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗦𝗲𝗺𝗶𝗼𝘁𝗶𝗰 𝟯: 𝗔 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜 https://lnkd.in/gJfMrbzJ Recently, I decided to use AI to optimize the data visualization library I released ten years ago at Netflix. I found out I could do so much more than that with it. After less than two weeks with Claude, I'm releasing Semiotic 3: a React #datavisualization library that covers charts, networks, streaming data, and coordinated views in a canvas-first framework with realtime streaming charts as a first-class citizen. Semiotic was always designed as a library for projects that outgrow standard charting libraries. Version 3 maintains that vision and takes it further by rebuilding the architecture for streaming data, adding AI integration across the library, and shipping the developer experience features that modern teams expect. 𝗞𝗲𝘆 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗰𝗵𝗮𝗿𝘁𝘀. Every chart is now static or streaming. Send a static array of data? Get a static chart. Send a push reference? Get a streaming chart. And you can style those streaming charts with the realtime encodings I outlined at Current. 𝟯𝟬 𝗰𝗵𝗮𝗿𝘁 𝘁𝘆𝗽𝗲𝘀 𝘄𝗶𝘁𝗵 𝗼𝗻𝗲 𝗔𝗣𝗜 𝗽𝗮𝘁𝘁𝗲𝗿𝗻. From line charts to bar charts, connected scatterplots and sankey diagrams, every chart is a React component with the same prop pattern: data, accessors, and optional frameProps for full control. 𝗕𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗔𝗜 This is where v3 diverges from every other charting library. 𝗘𝘃𝗲𝗿𝘆 𝗰𝗵𝗮𝗿𝘁 𝗲𝗺𝗶𝘁𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 𝗲𝘃𝗲𝗻𝘁𝘀 𝘃𝗶𝗮 𝗼𝗻𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻. useChartObserver aggregates these across linked charts. Foundational for AI agents that watch how users explore and generate insights. Built-in chart state serialization means an AI agent can manipulate chart state programmatically. 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿. 𝚗𝚙𝚡 𝚜𝚎𝚖𝚒𝚘𝚝𝚒𝚌-𝚖𝚌𝚙 exposes every chart as a tool that renders to static SVG. Claude, Cursor, Windsurf, and any MCP client can generate and verify charts. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. CLAUDE.md, .cursorrules, copilot-instructions.md, .windsurfrules, .clinerules, llms-full.txt. `npx semiotic-ai --doctor` validates component props from the command line. 𝗗𝗲𝘃-𝗺𝗼𝗱𝗲 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗳𝗶𝘅 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻𝘀. When an AI agent (or human) passes the wrong setting, the console is explicit about so the developer or agent self-corrects in one turn. 𝗧𝗿𝘆 𝗶𝘁 Tell your coding agent to check out 𝘀𝗲𝗺𝗶𝗼𝘁𝗶𝗰.𝗻𝘁𝗲𝗿𝗮𝗰𝘁.𝗶𝗼 with a dataset (or several) in mind and it will build you an entire dashboard or interactive analytical application. Or, if you’d prefer the way they artisanal UI developers do it in Colonial San Jose: 𝚗𝚙𝚖 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚜𝚎𝚖𝚒𝚘𝚝𝚒𝚌 GitHub: https://lnkd.in/gttSV5pG AI docs: https://lnkd.in/gRe88XTd

Python Libraries Every Analyst Should Know (and what they’re actually used for) When I started using Python for data work, I kept hearing "learn pandas, matplotlib, etc." But nobody really told me what functions mattered most or how they help in real analysis. So here’s a quick cheat sheet pandas – For cleaning, transforming, and analyzing tabular data .read_csv() – load your data .groupby() – segment and summarize .merge() – combine datasets like SQL joins .isnull().sum() – spot missing values .apply() – custom row/column logic matplotlib + seaborn – For visualization plt.plot() or sns.lineplot() – trends over time sns.barplot() – comparisons sns.heatmap() – correlation matrix (my fav for EDA!) numpy – For fast numerical operations np.mean(), np.std() – quick stats np.where() – conditional logic openpyxl / xlsxwriter – If you’re exporting to Excel a lot Style formatting, add formulas, automate reports scikit-learn – For basic predictive modeling train_test_split() – prep your data LinearRegression(), KMeans() – get started with ML This is the toolbox I keep coming back to- for dashboards, KPIs, reporting, and even interview take-home assignments. #python #dataanalytics #businessanalysis #pandas #visualization

📊 Ever wish Python had ggplot2? Turns out, it does — it’s called Plotnine. While recording my latest #NIRs project, I paused to explore how chemists (and data scientists) can use Plotnine to build clean, publication-ready figures… without fighting Matplotlib defaults. This tutorial shows how I: Reshaped spectral data for plotting Built layered line plots with confidence intervals Faceted spectra by material class (wood vs. vinyl) Applied themes for a clean, consistent style Sometimes, the right visualization library makes the difference between “squiggly lines” and hidden patterns you can actually see. 💡 Curious to see it in action? Full video link in the comments 👇 #python #datavisualization #plotnine #chemistry #spectroscopy #pythonforchemists

Struggling to visualize your graphs? NVL changes everything The technology behind Bloom, Neo4j’s flagship visualization product, has been transformed into a powerful, open library called NVL. This puts enterprise-grade graph visualization capabilities into developers' hands. This represents a significant shift in how teams can build graph applications. 🚀 NVL delivers unmatched performance through GPU acceleration and WebGL rendering. The library handles massive graphs with smooth interactions and real-time layout calculations that previously required specialized tools. 🧩 The library's modular architecture gives you complete control. Start with the core visualization engine for custom solutions, or use pre-built React components to accelerate development. Development teams can now build everything from basic graph displays to complex interactive applications. 💪 NVL already powers mission-critical tools like Neo4j Explore (Bloom), Query, and Data Importer. It represents years of development within Neo4j's own products. Whether you're building new applications or enhancing existing ones, NVL provides the foundation for next-generation graph experiences. 💬 What would you build with enterprise-grade graph visualization? Share your ideas below. ♻️ Know someone working with graph visualization? Share this post with them. 🔔 Follow me Daniel Bukowski for more insights about building with graph technology."