This guide explains ten common chart types—bar, line, area, numeric box, scatter, pie, tree, geographic map, table, and funnel—detailing their definitions, ideal use cases, related alternatives, and concrete best‑practice recommendations to help you choose the right visualization for your data story.
The article introduces d3blocks, a Python library that extends D3.js's interactive visualization capabilities to Python, demonstrating how to create interactive scatter, network, Sankey, and image slider charts with just a few lines of code and offering examples with code snippets and GIF illustrations.
Using AntV’s F2 4.0, the team revamped Alipay’s Fund Index section with a multi‑dimensional scatter‑plot tool that lets users explore returns vs volatility, featuring refined touch gestures, adaptive bubble sampling, lazy loading, and a layered M‑C‑V architecture for reusable components.
This guide walks you through installing Matplotlib, importing it with NumPy, and using its pyplot interface to create line, scatter, bar, histogram, pie, and box plots, complete with code examples and visual results for effective data visualization.
This tutorial explains how to use the Python Bokeh library to create interactive scatter, bar, and multi‑panel visualizations, covering installation, figure configuration, hover tooltips, data sources, color mapping, looping over categories, and linking plots with shared ranges.
This tutorial demonstrates how to install Plotly, import necessary modules, and create various visualizations—including scatter, bar, histogram, pie, 3D, and density map charts—using both the graph_objects and express APIs with sample CSV data and code snippets.
This tutorial demonstrates how to use the Python Pyecharts library to fetch Hangzhou walking route data, render it on a Baidu map heatmap, and build a gradient‑styled scatter plot comparing life expectancy and GDP for multiple countries in 1990 and 2015.
This article describes how the author overcame Jupyter setup issues and used Python with Plotly to generate scatter and line chart examples, providing the full source code and screenshots of the final visualizations.
This article walks through essential Matplotlib techniques—including creating scatter plots, applying polynomial fitting, generating multiple subplots with the Iris dataset, drawing reference lines, and enabling interactive updates—providing concise code snippets and visual examples for each step.
