{"id":1363,"date":"2026-07-27T02:28:09","date_gmt":"2026-07-27T02:28:09","guid":{"rendered":"https:\/\/www.vpascode.com\/ja\/docs\/uncategorized\/echarts-playbook\/parallel-chart-multi-dimensional-data-analysis\/"},"modified":"2026-07-30T01:28:15","modified_gmt":"2026-07-30T01:28:15","slug":"parallel-chart-multi-dimensional-data-analysis-guide","status":"publish","type":"ld_docs","link":"https:\/\/www.vpascode.com\/ja\/docs\/vpascode-docs\/echarts-playbook\/parallel-chart-multi-dimensional-data-analysis-guide\/","title":{"rendered":"Parallel Chart: Multi-Dimensional Data Analysis"},"content":{"rendered":"

When comparing multivariate data points across many numerical or categorical axes simultaneously, the Parallel Coordinates Chart<\/strong> is an essential visual tool. Instead of mapping coordinates to perpendicular horizontal and vertical axes, parallel plots place all dimensions side-by-side as parallel lines. Individual data entries are rendered as continuous polyline paths intersecting each axis, making it easy to identify multi-attribute clusters, trade-offs, correlations, and outliers<\/em>\u2014such as comparing car models across horsepower, weight, fuel efficiency, price, and safety rating.<\/p>\n

The Mechanics of Parallel Charts<\/h2>\n

In Apache ECharts, parallel coordinates plots rely on a dedicated parallelAxis<\/code> configuration block that defines each parallel scale (its axis index, dimension name, and data type). The chart series sets type: 'parallel'<\/code> and maps rows of multi-element value arrays to each corresponding axis index.<\/p>\n

1. Essential Setup<\/h3>\n

To construct a basic parallel chart, define your dimensions in the parallelAxis<\/code> array and pass corresponding coordinate arrays inside your series data<\/code>:<\/p>\n

\r\n
\r\n ECharts<\/span>\r\n \r\n <\/path><\/path><\/svg>\r\n Edit ECharts in VPasCode<\/span>\r\n <\/a>\r\n <\/div>\r\n
\r\n 
option = {\r\n    parallelAxis: [\r\n        { dim: 0, name: 'Price ($)' },\r\n        { dim: 1, name: 'Rating' },\r\n        { dim: 2, name: 'Weight (kg)' },\r\n        { dim: 3, name: 'Power (HP)' }\r\n    ],\r\n    series: [\r\n        {\r\n            type: 'parallel',\r\n            lineStyle: {\r\n                width: 2\r\n            },\r\n            data: [\r\n                [1299, 4.5, 1.2, 120],\r\n                [899, 4.0, 1.5, 95],\r\n                [1599, 4.8, 1.1, 150],\r\n                [499, 3.5, 2.0, 80],\r\n                [2100, 4.9, 0.9, 180]\r\n            ]\r\n        }\r\n    ]\r\n};<\/code><\/pre>                <\/div>\r\n                
\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n            <\/div>\r\n            \n
\"\"<\/p>\n
 <\/p>\n
Advanced Structural Techniques<\/h2>\n
Parallel charts can be combined with visual color gradients or mixed categorical scales to evaluate complex trade-offs across distinct product categories.<\/p>\n
1. Visual Map Color Gradients Across Dimensions<\/h3>\n
Pair a visualMap<\/code> component with a key axis dimension (e.g., mapping line color to performance ratings) to instantly highlight top-tier performers across all parallel axes:<\/p>\n            
\r\n                                
\r\n                    ECharts<\/span>\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n                                
\r\n                    
option = {\r\n    parallelAxis: [\r\n        { dim: 0, name: 'CPU Cores' },\r\n        { dim: 1, name: 'RAM (GB)' },\r\n        { dim: 2, name: 'Storage (TB)' },\r\n        { dim: 3, name: 'Monthly Cost ($)', min: 0, max: 200 }\r\n    ],\r\n    visualMap: {\r\n        show: true,\r\n        type: 'continuous',\r\n        min: 0,\r\n        max: 200,\r\n        dimension: 3, \/\/ Color line paths based on dimension index 3 (Monthly Cost)\r\n        inRange: {\r\n            color: ['#91cc75', '#fac858', '#ee6666'] \/\/ Green (Low Cost) to Red (High Cost)\r\n        },\r\n        orient: 'horizontal',\r\n        top: '2%',\r\n        left: 'center'\r\n    },\r\n    parallel: {\r\n        top: '18%',\r\n        bottom: '10%'\r\n    },\r\n    series: [\r\n        {\r\n            type: 'parallel',\r\n            lineStyle: {\r\n                width: 3,\r\n                opacity: 0.8\r\n            },\r\n            data: [\r\n                [4, 8, 0.5, 25],\r\n                [8, 16, 1.0, 50],\r\n                [16, 32, 2.0, 110],\r\n                [32, 64, 4.0, 185],\r\n                [12, 24, 1.5, 80]\r\n            ]\r\n        }\r\n    ]\r\n};<\/code><\/pre>                <\/div>\r\n                
\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n            <\/div>\r\n            \n
\"\"<\/p>\n
 <\/p>\n
Fine-Tuning Layout and Interactive Filtering<\/h2>\n
Adding smooth curves and line opacity prevents dense, crisscrossing data threads from overwhelming the user interface.<\/p>\n
1. Smooth Curve Paths and Low-Opacity Threads<\/h3>\n
Set smooth: true<\/code> and lower line opacity to make high-density data overlaps and clusters naturally visible:<\/p>\n            
\r\n                                
\r\n                    ECharts<\/span>\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n                                
\r\n                    
option = {\r\n    parallelAxis: [\r\n        { dim: 0, name: 'Score A' },\r\n        { dim: 1, name: 'Score B' },\r\n        { dim: 2, name: 'Score C' },\r\n        { dim: 3, name: 'Score D' },\r\n        { dim: 4, name: 'Score E' }\r\n    ],\r\n    parallel: {\r\n        left: '10%',\r\n        right: '10%',\r\n        bottom: '12%',\r\n        top: '12%'\r\n    },\r\n    series: [\r\n        {\r\n            type: 'parallel',\r\n            smooth: true, \/\/ Curves line paths for cleaner visuals\r\n            lineStyle: {\r\n                color: '#5470c6',\r\n                width: 2,\r\n                opacity: 0.45\r\n            },\r\n            data: [\r\n                [85, 92, 78, 90, 88],\r\n                [70, 65, 80, 72, 75],\r\n                [95, 88, 92, 96, 94],\r\n                [60, 75, 68, 62, 70],\r\n                [88, 90, 85, 89, 91],\r\n                [78, 82, 74, 80, 83]\r\n            ]\r\n        }\r\n    ]\r\n};<\/code><\/pre>                <\/div>\r\n                
\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n            <\/div>\r\n            \n
\"\"<\/p>\n
 <\/p>\n
Strategic Best Practices<\/h2>\n
    \n
  • Arrange Axes Thoughtfully:<\/strong> Place strongly correlated or directly comparable metrics on adjacent axes so relationships and cross-over trends are easier to spot.<\/li>\n
  • Use Lower Opacity for High Data Volume:<\/strong> When plotting dozens or hundreds of lines, reduce lineStyle.opacity<\/code> to 0.2<\/code>\u20130.5<\/code> so overlapping trends create darker, high-density bands naturally.<\/li>\n
  • Enable smooth: true<\/code> for Complex Datasets:<\/strong> Applying subtle curvature to line paths reduces sharp visual angles when threads cross multiple parallel axes in quick succession.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
    When comparing multivariate data points …<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":1254,"menu_order":19,"template":"","meta":{"inline_featured_image":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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