This entry is part of the Chart Index, the reference library for the Chart Design Field Guide.
Parallel coordinates is the form that lets you visualise many continuous variables at once. Each variable becomes a vertical axis; each observation becomes a polyline crossing every axis at its value. Where line patterns cluster, you find sub-populations; where lines cross between two axes, you find inverse relationships; where lines bundle, you find correlated variables.
The form was introduced by Alfred Inselberg in the 1980s as a way to visualise high-dimensional data in a 2D plane. It is dense, abstract, and uniquely powerful for multivariate exploration. It is also the chart most often misused — one of the easiest forms to make beautiful and impossible to read at the same time.
What it is
A parallel coordinates plot maps N continuous variables to N parallel vertical axes, equally spaced along the x-axis. Each observation is rendered as a polyline that visits each axis at the value of that observation's corresponding variable. With many observations, the polylines form a dense fabric — the shape of which reveals relationships, clusters, and outliers across all dimensions simultaneously.
Six vertical axes, twenty-four lines crossing them. The eye picks up patterns: heavy cars cluster with high displacement and high horsepower (bundled lines on the left); efficient cars cluster on the right (high MPG, low displacement). Lines crossing between axes (e.g., between weight and MPG) indicate inverse relationships.
When to use it
Parallel coordinates is the right choice when:
- You have 3 or more continuous variables to compare across observations.
- The reader's question is "what relationships exist among these variables, and are there sub-populations?"
- You have many observations (50–5,000) — the fabric of overlapping lines is the form's power.
- The audience is willing to learn the form or already trained.
- You want multivariate pattern discovery rather than precise reading.
When not to use it
- One or two variables. Use a scatter plot.
- Discrete categories. Parallel coordinates needs continuous values per axis. For mixed types, use coordinated multiple views.
- Audiences unfamiliar with the form. The reading task is non-obvious. Reserve for analytical contexts or expect to teach the form.
- Precise value reading. The form shows patterns; specific numbers require tooltips.
Design principles
Order axes deliberately
The axes' arrangement determines what relationships are visible. Place variables that you expect to be related adjacent to each other. For exploratory work, allow the reader to reorder axes interactively; for editorial, choose a permanent order and explain it.
Use opacity for many lines
A parallel coordinates plot with 500 fully-opaque lines is a black wall. Reduce opacity to 0.05–0.15 so dense regions become visible without losing individual lines. The polylines aggregate into a fabric.
Highlight categories or sub-groups
When you know there are sub-populations (e.g., car classes, customer segments), colour the lines by group. The eye picks out the bundling of each group across the axes; one group of lines may bunch high on one axis and low on another, telling a story no single bivariate plot would.
Normalise axes thoughtfully
Each axis can be scaled to its own min-max (relative) or to a common scale (absolute). Relative scaling reveals patterns within each variable's range; absolute scaling shows how variables differ in magnitude. Most parallel coordinates use per-axis min-max; document the choice.
Add small histograms beside each axis
A small histogram or density curve beside each axis shows the marginal distribution of that variable. The reader gets both the multivariate pattern (the polylines) and the per-variable distribution (the histograms).
Annotate notable lines or clusters
If the chart's story is here is the cluster of efficient cars or here is the one outlier model, highlight those lines or groups with colour and a label. The dense fabric otherwise has no narrative direction.
Avoid overwhelming the form
The form's complexity tempts addition: 12 axes, 5,000 lines, three colour encodings, animated transitions. Most of those additions degrade legibility. Be ruthless about what dimensions and observations the chart actually needs.
Anatomy
A parallel coordinates plot's anatomy is a row of vertical axes, polylines crossing them, axis labels at the bottom, and optional marginal histograms or category-colour encoding. The form's information density is high; its design needs to remain quiet around the polylines.
Related types
- Scatter plot matrix (SPLOM) — all pairwise bivariate scatters in a grid. The conventional alternative for multivariate exploration.
- Radar / spider chart — radial parallel coordinates. Different reading task.
- Heatmap (small multiples) — for the same N-variable comparison without crossings.
- Andrews curves — function-based visualisation of high-dimensional data; statistical cousin.
- Chord / arc diagram — for relationships among categorical variables.
Reading list
- Inselberg, A. (1985). The Plane with Parallel Coordinates. The original paper.
- Inselberg, A. (2009). Parallel Coordinates: Visual Multidimensional Geometry and Its Applications. The definitive reference.
- Heinrich, J. & Weiskopf, D. (2013). State of the Art of Parallel Coordinates. A survey of variants and developments.