Okabe-Ito color palette hex codes: Complete Guide

Complete Okabe-Ito color palette hex codes and RGB values

The complete Okabe-Ito color palette hex codes must be entered exactly if you want consistent results across plotting software, slide applications, spreadsheets, and illustration tools. The palette has eight colors: Black #000000 (0,0,0); Orange #E69F00 (230,159,0); Sky Blue #56B4E9 (86,180,233); Bluish Green #009E73 (0,158,115); Yellow #F0E442 (240,228,66); Blue #0072B2 (0,114,178); Vermillion #D55E00 (213,94,0); and Reddish Purple #CC79A7 (204,121,167). Do not replace these with visually similar browser colors, software presets, or rounded values.

Hex values are most convenient in plotting code and web-based figure tools. RGB values are useful when a desktop application provides three separate input fields. RGB channels here use a 0–255 scale. If an application asks for a different color representation, use its built-in conversion feature rather than guessing, and confirm the result against the official guidance for that application.

The listed sequence is a reference, not a requirement that every figure use all eight colors. A two-series experiment normally needs only two colors, while a five-condition study needs five. Using fewer colors reduces legend-search time and leaves room to reserve black for text, reference lines, outlines, or a control group.

• Black — #000000 — RGB (0,0,0)
• Orange — #E69F00 — RGB (230,159,0)
• Sky Blue — #56B4E9 — RGB (86,180,233)
• Bluish Green — #009E73 — RGB (0,158,115)
• Yellow — #F0E442 — RGB (240,228,66)
• Blue — #0072B2 — RGB (0,114,178)
• Vermillion — #D55E00 — RGB (213,94,0)
• Reddish Purple — #CC79A7 — RGB (204,121,167)

What each of the eight colors is best used for

Color meaning is contextual, so the palette does not impose universal semantic labels. Nevertheless, some assignments are more practical than others. Blue and Sky Blue work well for related conditions, measured data, or neutral categories. Orange and Vermillion are visually prominent and can emphasize interventions, warnings, treated groups, or high-priority series. Bluish Green is useful for a separate comparison group, while Reddish Purple provides another distinct categorical choice without relying on a conventional red-versus-green pairing.

Black should be assigned carefully. It is a strong option for a control condition, observed data plotted against colored model estimates, or an overall summary. It is also the normal choice for axes, labels, and annotations, so a black data series may blend conceptually with the figure framework. Differentiate it with a heavier line, a specific marker, or an explicit direct label if it represents data.

Yellow is the palette color that most often needs additional treatment. Against a white background, it can be weak as a thin line, small point, unbolded text, or unoutlined bar. Use it for a sufficiently large filled area, add a dark border to yellow markers or bars, choose a non-white plotting area when appropriate, or reinforce the category with a marker shape. Do not silently substitute a darker yellow, because that would no longer be the specified palette value.

• Use Blue and Sky Blue for separate categories, not merely as two shades of one continuous quantity.
• Use Orange or Vermillion when a series needs visual emphasis, but avoid implying danger unless that meaning is intended.
• Use Bluish Green and Reddish Purple as distinct categorical accents in multi-series figures.
• Reserve Black for controls, summaries, observed values, outlines, or reference information when that hierarchy fits the figure.
• Use Yellow mainly for larger marks or fills, and support it with an outline, label, or shape on light backgrounds.

How to use Okabe-Ito color palette hex codes in matplotlib

In matplotlib, define the colors once as a named dictionary or ordered list instead of repeating literal values throughout a notebook. A clear dictionary is: `okabe_ito = {'black': '#000000', 'orange': '#E69F00', 'sky_blue': '#56B4E9', 'bluish_green': '#009E73', 'yellow': '#F0E442', 'blue': '#0072B2', 'vermillion': '#D55E00', 'reddish_purple': '#CC79A7'}`. You can then write expressions such as `color=okabe_ito['blue']`, making the semantic intent easier to audit.

For repeated categorical plots, create an ordered list and pass it to the relevant plotting call or set it as an axes color cycle. Keep the category-to-color mapping stable across panels: if the control is Blue in panel A, it should not become Orange in panel B. Stability is especially important in a thesis or paper containing several figures because readers build color associations as they move through the document.

Do not rely only on color for overlapping lines. Combine each color with a marker or line pattern, such as circles versus triangles or solid versus dashed lines. Increase line width and marker size enough for the final output dimensions, not merely for the enlarged notebook preview. Export a test image at the intended physical size and inspect it at 100% zoom; exact resolution and format requirements should follow the official journal, course, or institutional guidelines.

• Define the eight colors once in a dictionary or module shared by the project.
• Assign colors through category names rather than row positions that may change after sorting.
• Add `marker`, `linestyle`, direct labels, or separate panels when curves overlap.
• Check both the interactive display and the exported file because rendering can differ.
• Keep a white-background test specifically for Yellow #F0E442 and thin Sky Blue marks.

Using the palette in R and ggplot-style workflows

In R, create a named character vector so that each experimental level has an explicit color. For example: `okabe_ito <- c(Black='#000000', Orange='#E69F00', SkyBlue='#56B4E9', BluishGreen='#009E73', Yellow='#F0E442', Blue='#0072B2', Vermillion='#D55E00', ReddishPurple='#CC79A7')`. A named vector is safer than an unnamed list because you can connect colors to factor levels intentionally rather than depending on their accidental alphabetical order.

For a categorical color aesthetic, supply the appropriate named subset to a manual color scale, such as `scale_color_manual(values=c(Control=okabe_ito['Blue'], Treatment=okabe_ito['Vermillion']))`. For filled bars, areas, or box plots, use the equivalent manual fill scale. If a plot maps one variable to line color and another to point fill, document both mappings and avoid making readers decode too many simultaneous legends.

Before plotting, set factor levels in the same logical order used in the caption and legend. Check for missing or newly introduced levels after filtering or joining data; otherwise, a category may receive no color or a different color than expected. If you use Yellow as a fill, add a visible border where possible. For line charts, prefer one of the darker colors for the thinnest or most important curve and reinforce group identity with `linetype` or `shape`.

• Store the palette as a named vector in a reusable script.
• Match names to factor levels explicitly in manual color or fill scales.
• Use the same mapping in every figure belonging to the same project.
• Pair color with line type or point shape for grayscale printing and dense overlaps.
• Review legends after data filtering to catch unused, missing, or reordered categories.

Entering the eight colors in Excel

In Excel, select the specific series or data point, open its formatting controls, choose a custom color, and enter the RGB channels separately. For example, Blue requires Red 0, Green 114, and Blue 178; Orange requires Red 230, Green 159, and Blue 0. Repeat the process for each category using the exact values in the reference section. Menu wording and available controls can vary by version and operating system, so use the official application guide if the interface differs.

Avoid clicking a visually similar color from the standard theme palette. Theme colors can change when a workbook is opened with another template, copied into a presentation, or reformatted. After assigning custom colors, save a clean chart or workbook as a reusable template if your software version supports that workflow. Keep a small mapping table beside the data so another student, teacher, or collaborator can reproduce the chart.

Spreadsheet charts also require non-color editing. Increase line widths, enlarge data markers, and remove unnecessary gradients, shadows, bevels, and transparent effects that alter the intended colors. For bars containing Yellow, apply a dark outline. For a line chart, use different dash patterns or markers, particularly when the chart may be printed in grayscale or projected in a bright classroom.

• Open the custom color controls for each series and select RGB input.
• Enter the three channel values exactly; do not estimate colors by eye.
• Disable decorative effects that change the apparent fill or line color.
• Preserve a written category-to-color key in the workbook.
• Export or print one test at its actual delivery size before submission or teaching.

When the Okabe-Ito palette should not be used

The Okabe-Ito palette is categorical, meaning its colors are suited to separate groups without an inherent numeric order. It is not the right tool for a continuous heatmap, elevation map, probability field, temperature surface, or any other variable that progresses smoothly from low to high. A continuous variable requires a perceptually ordered sequential or diverging scale. Selecting several Okabe-Ito colors and placing them in numeric order can create false boundaries and uneven visual jumps.

It is also a poor fit for an ordered set such as low, medium, high, and very high if the color sequence is expected to communicate increasing magnitude. The eight colors do not form a consistent lightness progression. Readers should not have to infer whether Orange is greater than Blue or whether Reddish Purple falls between Bluish Green and Yellow. Use position, length, or an appropriate ordered scale for magnitude, and reserve these colors for group identity.

More than eight categories present another limit. Repeating colors creates ambiguous legends, while inventing extra look-alike colors defeats the purpose of using a defined eight-color palette. Consider grouping minor categories, labeling lines directly, splitting the graphic into panels, highlighting only selected groups while muting the remainder, or replacing the chart with a table. The palette may also be unnecessary for monochrome publication, a single-series plot, or a figure governed by mandatory institutional colors; exact requirements are always subject to the official guidelines.

• Do not use the palette as a continuous gradient.
• Do not treat the colors as an ordered low-to-high sequence.
• Do not create unofficial ninth or tenth colors and call them Okabe-Ito colors.
• Do not use color alone when categories overlap or marks are extremely small.
• Do not override mandatory publisher, course, or institutional specifications.

A practical accessibility and pre-submission workflow

Start by deciding what the figure must communicate before choosing colors. List the categories, identify the most important comparison, and decide whether any group should be visually dominant. Choose only the required Okabe-Ito colors, then add redundant encodings such as marker shape, line style, labels, or panel position. This approach is more reliable than styling the plot first and trying to repair accessibility at the end.

Next, test the figure under realistic conditions. View it at the final display or print size, not only at a large editing size. Check a grayscale version, inspect thin lines and small points, and verify that labels remain legible against their backgrounds. A color-vision simulation can identify potential problems, but simulation should supplement rather than replace checks of contrast, spacing, direct labeling, and visual hierarchy.

Finally, verify consistency across the complete document. The same category should keep the same color in the main text, supplemental figures, slides, and exported diagrams. Confirm that all eight values, when used, match the official reference exactly and that conversion or compression has not introduced unexpected changes. Ask a colleague to interpret the figure without being told the mapping; if the main conclusion is not apparent, revise the encoding rather than merely enlarging the legend.

• Define the message, categories, and hierarchy before assigning colors.
• Use no more colors than the data actually require.
• Add shape, line style, borders, text labels, or faceting as redundant cues.
• Test at final size, in grayscale, and with a color-vision simulation.
• Check category-to-color consistency across every figure and slide.
• Follow official submission guidance for dimensions, resolution, file format, and typography.

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