Best of Data Visualization — April 2025

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    Video
    Avatar of TechWithTimTech With Tim·1y

    How To Automate Your Finances with Python - Full Tutorial (Pandas, Streamlit, Plotly & More)

    The post provides a step-by-step tutorial for building a personal finance automation tool using Python with libraries such as Pandas, Streamlit, and Plotly. The tool can upload bank statements in CSV format, categorize transactions, and summarize expenses using visualizations. It also explains how to convert bank statements into a format conducive to analysis and the importance of utilizing structured, project-based learning resources like Data Camp for Python and finance fundamentals.

  2. 2
    Article
    Avatar of tinybirdTinybird·1y

    Using LLMs to generate user-defined real-time data visualizations

    Developers are increasingly using Tinybird to track LLM usage, costs, and performance in AI applications. A new app template called the LLM Performance Tracker allows users to generate real-time data visualizations. The core components include a Tinybird datasource, a Tinybird pipe, a React component, and an AI API route. The backend processes user input to generate chart parameters, while the frontend visualizes the data. This approach emphasizes the importance of performant analytics backends and cautious LLM usage for secure and scalable data visualization.

  3. 3
    Article
    Avatar of logrocketLogRocket·1y

    Best React chart libraries (2025 update): Features, performance & use cases

    The post compares top React chart libraries in 2025, including Recharts, react-chartjs-2, Victory, Nivo, React ApexCharts, Ant Design Charts, Apache ECharts, visx, and MUI X Charts. It evaluates these libraries based on features, documentation, community adoption, and customizability. It also discusses their specific strengths, such as support for real-time data updates, rendering methods, responsiveness, and integration with mobile applications.

  4. 4
    Article
    Avatar of syncfusionSyncfusion·1y

    Top 5 React Stock Chart Libraries for 2025

    Discover key advancements in modern React stock chart libraries, including improved performance, mobile-first design, and advanced customization. The post reviews top libraries like Syncfusion, Highcharts, amCharts, AnyChart, and CanvasJS, detailing their features to help build powerful stock market visualizations in 2025.

  5. 5
    Article
    Avatar of detlifeData Engineer Things·1y

    Power BI? Tableau? No Thanks. I’d Rather Use This AI Tool for Charts

    Quadratic is an AI-powered tool designed to simplify chart creation for non-technical users across various fields such as marketing, sales, education, and healthcare. It allows users to generate insightful visualizations quickly and effortlessly using natural language prompts, eliminating the need for complex tools and reliance on data analysts.

  6. 6
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    11 Most Important DS Plots

    This post provides an overview of the 11 most important and essential plots in data science, highlighting their uses and significance. It covers plots like the KS Plot, SHAP Plot, ROC Curve, Precision-Recall Curve, QQ Plot, and others, explaining their role in data analysis, model performance evaluation, and interpretability.

  7. 7
    Article
    Avatar of planetpythonPlanet Python·1y

    Matplotlib Practice Online: Free Exercises

    Explore a comprehensive set of Matplotlib exercises and practice using an online Matplotlib compiler. Learn how to analyze trends, build machine learning models, and explore datasets with various plot types such as bar charts, scatter plots, histograms, and more. The exercises include practical examples to help you understand and visualize data using Python, making it easier for you to focus on mastering data visualization.

  8. 8
    Article
    Avatar of mlnewsMachine Learning News·1y

    Complete Guide: Working with CSV/Excel Files and EDA in Python

    This tutorial provides a comprehensive guide to working with CSV/Excel files and performing exploratory data analysis (EDA) using Python. It covers importing, cleaning, and preprocessing data, exploring data through statistics and visualization, and deriving insights from business data using libraries such as pandas, NumPy, matplotlib, and seaborn. The guide uses a realistic e-commerce dataset to demonstrate the entire workflow, including merging datasets and handling data quality issues.

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