1import{j as e,L as r}from"./vendor-react-CVijXI0n.js";import{S as d,b as h,h as m,a as p,c as x}from"./index-D0o2Sutv.js";import{C as u}from"./ChartBreadcrumb-BOBTh6lg.js";import{C as g,b as f}from"./ChartFAQ-DwXxlpIj.js";import{S as o,D as b,a as y,C as w,A as v,B as j,T as k,b as N,R as S}from"./References-CIdDEfJg.js";const i="https://www.visualizing.org",l=[{q:"What is a heatmap?",a:"A heatmap is a data visualization that represents values as colors in a two-dimensional grid. Each cell of the grid corresponds to a combination of two categorical or ordinal variables (rows and columns), and the cell's color encodes the magnitude of a third, quantitative variable on a sequential or diverging color scale."},{q:"When should you use a heatmap?",a:"Use a heatmap when you have a dense matrix of values (dozens to thousands of cells) and want readers to spot patterns, clusters, or outliers without reading every number. It is the right tool for correlation matrices, dayÃhour activity grids, gene-expression matrices, and any situation where the gestalt of the matrix matters more than precise per-cell values."},{q:"When should you avoid a heatmap?",a:"Avoid a heatmap when readers need precise values â color is the least precise visual encoding. Skip it when the matrix is small (a 3Ã3 grid is just a confusing table), when you only have one dimension (use a bar chart), or when you canât guarantee a colorblind-safe palette and a textual fallback."},{q:"What is the difference between a heatmap and a choropleth map?",a:"Both encode magnitude with color, but a choropleth colors geographic regions on a map (countries, states, ZIP codes) while a heatmap colors cells in an abstract rowÃcolumn grid. A choropleth lives in geographic space; a heatmap lives in tabular space. Both share the same accessibility and palette rules."},{q:"What is the difference between a heatmap and a scatter plot?",a:"A scatter plot encodes two continuous variables with point position to reveal correlation; a heatmap encodes a third value (often a count or a correlation coefficient) with color across a grid of two categorical or binned variables. Scatter plots are precise per-point; heatmaps are precise about pattern."},{q:"Sequential or diverging color scale â which should a heatmap use?",a:"Use a sequential palette (lightâdark in a single hue, e.g. Viridis or Blues) when values run from a meaningful low to a meaningful high. Use a diverging palette (two hues meeting at a neutral midpoint, e.g. RdBu) when values are signed around a meaningful zero, such as correlation coefficients (-1 to +1) or percent change."},{q:"Is a rainbow palette OK for a heatmap?",a:"No. Rainbow palettes (jet, hsv, spectral) are not perceptually uniform â the eye sees green steps as smaller than yellow steps, distorting your data. They also fail badly for colorblind readers. Use Viridis, Magma, Inferno, Cividis, or any ColorBrewer sequential or diverging scheme."},{q:"How do I make a heatmap accessible?",a:"Pair a colorblind-safe palette (Viridis, Cividis, ColorBrewer) with a textual data table, label cell values directly when there are fewer than ~50 cells, ensure the legend has 4.5:1 contrast for axis text, and provide an aria-label that summarizes the dominant pattern instead of just naming the chart."},{q:"Should I cluster the rows and columns?",a:"Often, yes. Re-ordering rows and columns by hierarchical clustering, by row sum, or by a meaningful sort key turns a noisy matrix into block patterns the eye can read. The same data with random row order looks chaotic; with sorted rows it tells a story."},{q:"What is a calendar heatmap?",a:"A calendar heatmap is a heatmap whose rows and columns are days of the week and weeks of the year. GitHubâs contribution graph is the canonical example. The grid layout makes seasonal patterns and individual outlier days obvious in a way a line chart of daily totals cannot."},{q:"How big can a heatmap be?",a:"A well-designed heatmap can hold thousands of cells if the cells stay at least 4â6 pixels wide and the palette is perceptually uniform. Past about 10,000 cells you start to fight Moiré patterns and rendering performance â consider downsampling, hierarchical clustering, or a focus-plus-context view instead."},{q:"What category of chart is a heatmap?",a:"A heatmap belongs to the Magnitude family of charts (and overlaps with the Correlation fam
1ily when used for correlation matrices). Charts in those families are designed to answer the same kind of question, so they often work as alternatives when one doesnât quite fit your data."},{q:"What is the best library for building heatmaps in code?",a:"For static publication-quality heatmaps, seabornâs sns.heatmap (Python) and ggplot2âs geom_tile (R) are the standard choices. For interactive web heatmaps, D3.js gives the most control while Observable Plot (Plot.cell), Plotly, ECharts, and Recharts are higher-level alternatives."}],T=f("/heatmap",l),M={"@type":"HowTo","@id":`${i}/heatmap/#howto-python`,name:"Make a heatmap in Python with seaborn",description:"Build a heatmap in Python by loading a 2D matrix into a pandas DataFrame, calling seaborn.heatmap with a perceptually uniform palette, and adding cell annotations.",totalTime:"PT6M",tool:["Python 3","pandas","seaborn","Matplotlib"],step:[{"@type":"HowToStep",position:1,name:"Install seaborn",text:"Run pip install seaborn pandas matplotlib so the plotting stack is available."},{"@type":"HowToStep",position:2,name:"Shape the data",text:"Pivot your long-format data into a 2D matrix (DataFrame) with rows and columns aligned to the two categorical dimensions."},{"@type":"HowToStep",position:3,name:"Pick a palette",text:"Choose cmap='viridis' for sequential data or cmap='RdBu_r' centered on zero for diverging data such as correlations."},{"@type":"HowToStep",position:4,name:"Call sns.heatmap",text:"Pass the matrix to sns.heatmap(matrix, cmap=..., annot=True, fmt='.2f', cbar_kws={'label': 'units'})."},{"@type":"HowToStep",position:5,name:"Polish",text:"Rotate tick labels, add a takeaway title, and call plt.tight_layout() before plt.show() or plt.savefig()."}]},A={"@type":"HowTo","@id":`${i}/heatmap/#howto-excel`,name:"Make a heatmap in Microsoft Excel",description:"Build a heatmap in Excel by laying out a 2D matrix and applying conditional formatting with a 3-color scale or color scale icon set.",totalTime:"PT4M",tool:["Microsoft Excel"],step:[{"@type":"HowToStep",position:1,name:"Lay out the matrix",text:"Put row labels in column A, column labels in row 1, and numeric values in the inner cells."},{"@type":"HowToStep",position:2,name:"Select the value range",text:"Highlight only the inner numeric cells â not the row or column headers."},{"@type":"HowToStep",position:3,name:"Apply a color scale",text:"Open Home â Conditional Formatting â Color Scales and pick a 3-color or 2-color scale (sequential for magnitudes, diverging for signed values)."},{"@type":"HowToStep",position:4,name:"Polish",text:"Reduce row height and column width to make the cells more square, hide cell values if you only want the pattern, and add a manual legend below the matrix."}]},c=[{title:"Pick the question the matrix has to answer",description:"A heatmap answers âwhere are the high and low values across these two dimensions, and what pattern do they form?â Write that question down. If you actually need precise per-cell values, a sortable table is better; if your data is one-dimensional, use a bar chart."},{title:"Reshape the data into a 2D matrix",description:"Pivot your long-format data into a wide matrix with one row per row-category, one column per column-category, and one numeric value per cell. Decide how to fill in missing cells (NA, zero, the row mean) before plotting â different fills tell different stories."},{title:"Choose sequential vs diverging",description:"If values run from low to high with no meaningful zero, use a sequential palette (lightâdark in one hue: Viridis, Blues). If values are signed around a meaningful zero (correlation, percent change, deviation from mean), use a diverging palette (two hues meeting at a neutral midpoint: RdBu, BrBG)."},{title:"Pick a perceptually uniform palette",description:"Use Viridis, Cividis, Magma, Inferno, or any ColorBrewer scheme. Avoid jet, rainbow, hsv, and spectral â they are not perceptually uniform and will distort your data. Cividis is the safest default for colorblind audiences while still looking attractive."},{title:"Set the color domain explicitly",description:"Donât let the library auto-scale silently. Decide whether the domain is [0, max], [min, max], or fixed across multiple panels. For a diverging scale, anch
1or the midpoint to zero and make the domain symmetric (e.g. [-1, +1]) so reds and blues are comparable."},{title:"Sort or cluster the rows and columns",description:"Random row order makes a heatmap look like noise. Sort rows by value, by a meaningful key (date, magnitude), or by hierarchical clustering. Re-order columns the same way. Block patterns, diagonal stripes, and clusters only emerge after sorting."},{title:"Decide whether to label cells",description:"If the grid is smaller than ~50 cells, print the numeric value inside each cell with text contrast that flips between dark and light against the cell color. Past 50 cells, hide the labels and lean on the legend; past 200 cells, hide the gridlines too."},{title:"Add a clear legend with units",description:"Every heatmap needs a labeled color bar. State the units (âcorrelation râ, âsessions per hourâ, â%â), show the numeric range, and use 5â7 visible tick marks. Position the legend close to the plot, not floating in a corner."},{title:"Write a takeaway title and ship",description:"âTraffic by day and hourâ is a label. âWeekday lunchtime drives our peak traffic â weekend evenings barely registerâ is a takeaway. Lead with the takeaway, put the descriptive label as a subtitle, and verify the chart still works at the size your readers will see it."}],C=x({slug:"/heatmap",id:"howto-steps",name:"How to make a heatmap",description:"Step-by-step guide to designing and shipping a heatmap that communicates your data clearly.",steps:c.map(t=>({name:t.title,text:String(t.description)}))}),D={"@context":"https://schema.org","@graph":[{"@type":"WebPage","@id":`${i}/heatmap/#webpage`,url:`${i}/heatmap`,name:"Heatmap â The Definitive Guide",description:"The complete reference guide to heatmaps: definition, anatomy, when to use, accessibility, code samples in Python, R and JavaScript, tool-specific instructions, and the most common comparisons.",isPartOf:{"@id":`${i}/#website`},breadcrumb:{"@id":`${i}/heatmap/#breadcrumb`},mainEntity:{"@id":`${i}/heatmap/#article`},inLanguage:"en"},h({slug:"/heatmap",caption:"Example heatmap",width:620,height:280}),{"@type":["Article","LearningResource"],"@id":`${i}/heatmap/#article`,mainEntityOfPage:{"@id":`${i}/heatmap/#webpage`},headline:"Heatmap: Definition, Anatomy, Code, and When to Use",description:"A complete reference guide to heatmaps â what they are, how to build one in Excel, Sheets, Python, R, JavaScript, Tableau, and Power BI, when to use them, and common mistakes to avoid.",image:{"@id":m("/heatmap")},datePublished:"2025-03-15",dateModified:"2026-04-28",publisher:{"@id":`${i}/#organization`},inLanguage:"en",educationalLevel:"intermediate",learningResourceType:"reference",teaches:["How to read and interpret heatmaps","When to use heatmaps for data visualization","How to build heatmaps in Excel, Google Sheets, Python, R, JavaScript, Tableau, and Power BI","How to make heatmaps accessible to all readers","How to choose between sequential and diverging color palettes","Common heatmap mistakes and how to avoid them"],competencyRequired:"Basic understanding of data matrices and color scales",timeRequired:"PT15M",isAccessibleForFree:!0,audience:{"@type":"EducationalAudience",educationalRole:"student"},about:{"@id":`${i}/heatmap/#term`},hasPart:[{"@id":`${i}/heatmap/#howto-steps`},{"@id":`${i}/heatmap/#howto-excel`},{"@id":`${i}/heatmap/#howto-python`},{"@id":`${i}/heatmap/#code-python`},{"@id":`${i}/heatmap/#code-r`},{"@id":`${i}/heatmap/#code-js`}],keywords:["heatmap","heat map","data visualization","color encoding","matrix visualization","correlation matrix","seaborn heatmap","ggplot2 geom_tile","d3 heatmap"]},C,{"@type":"DefinedTerm","@id":`${i}/heatmap/#term`,name:"Heatmap",description:"A data visualization chart type that uses color intensity to represent values in a two-dimensional matrix. 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The chart"},{id:"what-is-it",label:"02. What is it?"},{id:"when-to-use",label:"03. When to use it"},{id:"when-not-to-use",label:"04. When not to use it"},{id:"data-requirements",label:"05. Data requirements"},{id:"anatomy",label:"06. Anatomy"},{id:"step-by-step",label:"07. Step-by-step"},{id:"real-world-examples",label:"08. Examples"},{id:"variations",label:"09. Variations"},{id:"comparisons",label:"10. Comparisons"},{id:"common-mistakes",label:"11. Common mistakes"},{id:"accessibility",label:"12. Accessibility"},{id:"best-practices",label:"13. Best practices"},{id:"related-charts",label:"14. Alternatives"},{id:"tool-instructions",label:"15. Tool instructions"},{id:"code-examples",label:"16. Code examples"},{id:"faqs",label:"17. FAQs"},{id:"references",label:"18. References"}],B=[{name:"row",type:"string / categorical",description:"The first dimension of the matrix â days of the week, gene IDs, products, customer segments, hours of the day. Each unique value becomes one row of the grid. Order is significant: re-sorting rows often reveals patterns that random order hides.",required:!0},{name:"column",type:"string / categorical",description:"The second dimension of the matrix â hours, time periods, experiment conditions, regions. Each unique value becomes one column. Like rows, the order of columns can be sorted, clustered, or kept in a natural sequence (months, ordinal scales).",required:!0},{name:"value",type:"number (continuous)",description:"The quantity each cell encodes. Must share a single unit across cells (count, percent, dollars, correlation coefficient). For diverging palettes the value should have a meaningful zero point (e.g. signed change, correlation in [-1, +1]).",required:!0},{name:"annotation",type:"string (optional)",description:"Optional cell text used to print the numeric value (or a symbol such as * for significance) directly inside the cell. Useful for small grids; turn off for grids larger than ~50 cells where text becomes unreadable."}],H=[{title:"Use a colorblind-safe, perceptually uniform palette",wcag:"1.4.1",description:"Cividis, Viridis, and ColorBrewerâs sequential and diverging schemes are perceptually uniform and have been tested against the major forms of color-vision deficiency. Avoid redâgreen diverging palettes (Spectral, RdYlGn) for any audie
1nce that has not been screened."},{title:"Match the palette type to the data type",wcag:"1.4.1",description:"Sequential palettes for unsigned magnitudes (counts, durations). Diverging palettes for signed values around a meaningful midpoint (correlations in [-1, +1], year-over-year change). A diverging palette on unsigned data invents a midpoint that doesnât exist and misleads readers."},{title:"Provide a textual data table fallback",wcag:"1.1.1",description:"Place the underlying matrix in a screen-reader-friendly HTML table beneath or beside the chart, or expose it via a hidden table. Many readers cannot perceive color encoding at all and will navigate the data via the table or copy it to their own tools."},{title:"Write an aria-label that summarizes the pattern",wcag:"1.1.1",description:'Set the SVGâs role="img" and an aria-label that names the takeaway, not the chart type. âHeatmap of website trafficâ is weak; âTraffic peaks TuesdayâThursday between 10am and 2pm; weekend mornings are nearly emptyâ is strong.'},{title:"Meet contrast for legend text and cell labels",wcag:"1.4.3",description:"Axis text, legend ticks, and cell annotations must reach 4.5:1 contrast for body text or 3:1 for large text. When cells are labeled, switch label color between dark and light depending on the cellâs luminance so labels stay readable across the whole palette."},{title:"Encode meaning programmatically",wcag:"1.3.1",description:"Donât encode information only in color. Add gridlines, group separators, or printed values for the most important cells so the matrix structure is conveyed in the DOM (and to assistive technology) and not only as pixels."},{title:"Make the chart resizable and zoomable",wcag:"1.4.4",description:"Render the heatmap in a responsive SVG with a viewBox so it stays legible at 200% browser zoom and on small screens. Avoid baking cell pixel sizes into the markup â the cell aspect ratio should adapt to the container."},{title:"Keep cell tooltips keyboard-reachable",wcag:"2.1.1",description:"If cells expose tooltips with the precise value, every cell should be reachable with the Tab key, the tooltip should appear on focus (not only hover), and the cell should have a visible focus ring. Tooltip text must be selectable so screen readers can read it."},{title:"Respect prefers-reduced-motion",wcag:"2.3.3",description:"If cells fade or recolor on load or on filter, gate the animation behind a prefers-reduced-motion: no-preference media query so motion-sensitive readers see the final state immediately."}],R=[{kind:"do",title:"Use Viridis, Cividis, or ColorBrewer",description:"Default to a perceptually uniform palette designed for data: Viridis or Cividis for sequential, RdBu or BrBG for diverging. They were engineered by visualization researchers specifically to keep equal data steps perceptually equal across the whole range."},{kind:"dont",title:"Use rainbow / jet / spectral palettes",description:"Rainbow palettes look colorful but are not perceptually uniform: equal steps in the data become unequal steps in the eye. They also fail badly for the ~8% of men and ~0.5% of women with color-vision deficiency. Stop using them."},{kind:"do",title:"Sort or cluster rows and columns",description:"Re-order rows and columns by value, by a meaningful key, or by hierarchical clustering. The same data with sorted rows tells a story; with random rows it looks like noise. Sorting is often the single biggest improvement you can make."},{kind:"dont",title:"Use a heatmap for small data",description:"Below about 5Ã5 cells a heatmap is just a confusingly colored table. Use a small-multiple bar chart, a labeled table, or a dot plot instead â they communicate the same information far more precisely."},{kind:"do",title:"Anchor diverging scales at zero",description:"When the value has a meaningful zero (correlations, signed change), set the diverging paletteâs midpoint to exactly zero and make the domain symmetric (e.g. [âmax, +max]) so positive and negative values are visually comparable."},{kind:"dont",title:"Hide the legend",description:"Without a labeled color bar showing units and the numeric range, the heatmap is unreadable. Always include the legend close to the plot, with 5â7 visible tick marks and a units label."},{kind:"do",title:"Provide the data as a table too",description:"Pair every heatmap with the underlying values in an accessible HTML table. Readers will copy from it for analysis, screen readers will navigate it, and it gives anyone who canât see the colors a precise fallback."},{kind:"dont",title:"Cram thousands of unlabeled cells",description:"Past about 10,000 cells you start fighting Moiré patterns and rendering performance, and the matrix becomes impossible to navigate cell-by-cell. Down-sample, cluster, or build a focus-plus-context view instead."}],z=[{id:"vs-choropleth",heading:"Heatmap vs choropleth (geographic)",takeaway:"Both encode magnitude with color, but they live in different c
1oordinate systems. A choropleth colors geographic regions on a map; a heatmap colors cells in an abstract rowÃcolumn grid. Use whichever coordinate system actually carries the meaning of your data.",left:{name:"Heatmap (row à column)",summary:"Encodes a value with color across a grid of two non-spatial dimensions (dayÃhour, geneÃsample, productÃregion as labels, not as map polygons).",bullets:["Rows and columns are categorical or ordinal","Order can be sorted or clustered","No geographic interpretation"]},right:{name:"Choropleth map",summary:"Encodes a value with color across geographic polygons (countries, states, ZIPs, hex bins on a map). Spatial position is part of the meaning.",bullets:["Polygons follow real geography","Must normalize by area (rate, not count)","Often misleading without a cartogram alternative"]}},{id:"vs-scatter",heading:"Heatmap vs scatter plot (correlation)",takeaway:"When the question is âhow do these two variables relate?â a scatter plot answers it precisely for a few hundred points. When the question is âhow do these many variables correlate with each other?â a correlation heatmap is the right tool.",left:{name:"Heatmap (correlation matrix)",summary:"Each cell is the correlation between two variables. Color encodes r in [-1, +1], usually with a diverging palette anchored at zero. Reveals clusters of correlated variables at a glance.",bullets:["Compares many pairs at once","Loses individual point detail","Diverging palette anchored at 0"]},right:{name:"Scatter plot",summary:"Each point is one observation; position encodes two continuous variables. Reveals shape (linear, non-linear), clusters, and outliers at the row level. Best for one or a small number of variable pairs.",bullets:["Precise per-point","Hard to scan many variable pairs","Use scatter matrix for several pairs"]}},{id:"vs-table",heading:"Heatmap vs sortable data table",takeaway:"A table gives you precise per-cell values and unlimited interaction (sort, filter, copy). A heatmap gives you the gestalt â the pattern of highs and lows across the whole matrix. Pair them: heatmap above, table beneath.",left:{
1name:"Heatmap",summary:"Color encodes value, sacrificing precision for pattern recognition. Best when the audience needs to see structure, clusters, or outliers in a large matrix at once.",bullets:["Pattern-first, not value-first","Scales to thousands of cells","Color is the encoding"]},right:{name:"Sortable table",summary:"Numbers in cells, no color encoding. Best when readers will read or copy individual values, sort by columns, or filter to a subset.",bullets:["Value-first, not pattern-first","Hard to scan past ~30 rows","Always pair with a heatmap if matrix is large"]}},{id:"vs-calendar",heading:"Heatmap vs calendar heatmap",takeaway:"A calendar heatmap is a heatmap with the rows and columns fixed to days-of-week and weeks-of-year. Use it when the time dimension is the story; use a generic heatmap when rows and columns are anything else.",left:{name:"Generic heatmap",summary:"Rows and columns are arbitrary categorical or ordinal dimensions. Order can be sorted or clustered to reveal blocks.",bullets:["Flexible row/column meaning","Sorting reveals structure","Use for matrices, correlations, dayÃhour grids"]},right:{name:"Calendar heatmap",summary:"Rows are days of the week (or months), columns are weeks of the year. Layout mirrors a wall calendar so seasonal patterns and individual outlier days are obvious.",bullets:["Fixed calendar layout","Best for daily activity over months/years","GitHub contribution graph is the canonical example"]}}],P=[{id:"excel",tool:"Microsoft Excel",tagline:"Spreadsheet â ~4 min",steps:["Lay out the matrix with row labels in column A, column labels in row 1, and numeric values in the inner cells.","Highlight only the inner numeric cells â do not include the row or column header text.","Open Home â Conditional Formatting â Color Scales and pick a 3-color scale (sequential) or a RedâWhiteâBlue scale (diverging) anchored at zero.","Open Manage Rules â Edit Rule and set Minimum to Number â 0 (or your true min) and Maximum to a fixed number so the scale stays comparable across panels.","Reduce row height and column width to make cells roughly square; turn off cell borders for a cleaner grid.","Cluster or sort the rows by adding a helper column with the row sum, then sorting the table by that helper before re-applying the color scale."],caption:"Tip: Excelâs default 3-color scale is greenâyellowâred â swap to a colorblind-safe RedâWhiteâBlue or WhiteâBlue scale before sharing."},{id:"google-sheets",tool:"Google Sheets",tagline:"Spreadsheet â ~3 min",steps:["Lay out the matrix in the same shape as Excel: row labels, column labels, numeric inner cells.","Select the inner numeric range, then choose Format â Conditional formatting â Color scale.","Pick a preset (the WhiteâBlue preset is the closest to Viridis available natively) or set custom Min/Mid/Max colors.","For a diverging palette, set Midpoint to Number â 0 and choose a neutral midpoint color so positive and negative values are visually comparable.","Resize rows and columns to be roughly square so the grid reads as a chart, not a table.","Add a manual legend in cells beneath the matrix using the same color scale on a small reference range."],caption:"Sheets has no built-in clustering. If row/column order matters, sort the helper column first, then re-apply the conditional format."},{id:"python",tool:"Python (seaborn)",tagline:"Code â ~5 min",steps:["Install the stack with pip install pandas seaborn matplotlib if it isnât already in your environment.","Pivot your long-format data into a 2D DataFrame with df.pivot(index='row', columns='col', values='value').","Choose a palette: cmap='viridis' for sequential magnitudes, cmap='RdBu_r' centered on zero for diverging signed data.","Call sns.heatmap(matrix, cmap=..., annot=True, fmt='.0f', linewidths=0.5, cbar_kws={'label': 'units'}) for small matrices, or annot=False for large ones.","For diverging palettes, pass center=0 and vmin=-vmax, vmax=+vmax so the midpoint is anchored on zero with a symmetric domain.","Sort rows and columns by sum, by hierarchical clustering (sns.clustermap), or by a meaningful key before plotting.","Add plt.title() with a takeaway sentence, rotate ylabels with plt.yticks(rotation=0), and call plt.tight_layout() before plt.savefig()."],caption:"Use sns.clustermap() instead of sns.heatmap() to get hierarchical clustering on rows and columns for free â the dendrograms appear automatically on the margins."},{id:"r",tool:"R (ggplot2)",tagline:"Code â ~5 min",steps:["Install ggplot2 and viridisLite with install.packages(c('ggplot2', 'viridisLite')) and load them.","Reshape data into long format with three columns: row, column, value (use tidyr::pivot_longer if needed).","Build the plot with ggplot(df, aes(x = col, y = row, fill = value)) + geom_tile().","Add scale_fill_viridis_c(option = 'D', name = 'units') for sequential, or scale_fill_gradient2(low = '#2c7bb6', mid = '#ffffff', high = '#d7191c', midpoint = 0) for diverging.","Reorder factors with reorder(row, value) (or with hclust + cutree for true clustering) so blocks emerge.","Add coord_fixed() so cells are square, and theme_minimal() + theme(panel.grid = element_blank()) to drop default gridlines.","Polish with labs() for the takeaway title and axis labels, then ggsave() to a PNG or SVG."],caption:"Use the heatmaply package for an interactive ggplot-flavored heatmap with built-in row/column clustering and zoom â great for exploration before exporting a static publication chart."},{id:"javascript",tool:"JavaScript (D3.js)",tagline:"Code â ~10 min",steps:["Install D3 with npm i d3, or include the CDN script tag in your HTML.","Create an SVG container with a viewBox, set role='img', and add an aria-label that summarizes the dominant pattern.","Build d3.scaleBand() for the row and column axes, padded slightly so cell borders are visible.","Build the color scale with d3.scaleSequential(d3.interpolateViridis) for sequential or d3.scaleDiverging(d3.interpolateRdBu) anchored at zero.","Bind data with svg.selectAll('rect').data(data).join('rect') and set x, y, width, height from the band scales and fill from the color scale.","Render the axes with d3.axisBottom() and d3.axisLeft(), and append a color-bar legend using d3-color-legend or by stamping a gradient <rect>
1 with tick text.","For interactivity, add focus rings, keyboard navigation across cells, and tooltips that appear on focus (not only hover) so the chart works for keyboard and screen-reader users."],caption:"If you donât need full control, Observable Plotâs Plot.cell() mark gets you to a publication-quality D3 heatmap in about 10 lines."},{id:"tableau",tool:"Tableau",tagline:"BI â ~4 min",steps:["Drag the row dimension to Rows and the column dimension to Columns. Drag the value measure to the Color shelf inside the Marks card.","Change the Marks type to Square so the cells fill the grid (rather than the default circles).","Click the Color shelf and choose Edit Colors. Pick the Viridis palette (or BlueâTeal Sequential) for sequential data, or RedâBlue Diverging anchored at zero for signed data.","Tick âUse Full Color Rangeâ and, for diverging palettes, set the Center value to 0 to keep red and blue comparable.","Drag the value measure onto Label so each cell prints its value, then format the label so dark-cell text is white and light-cell text is dark.","Sort rows and columns by total or by a chosen field (right-click â Sort) so blocks of similar cells emerge instead of random colors."],caption:"Tableauâs default âAutomaticâ color scale is OrangeâBlue diverging â itâs pretty but it implies a midpoint even when your data has none. Switch to a sequential palette when in doubt."},{id:"power-bi",tool:"Power BI",tagline:"BI â ~4 min",steps:["Use the built-in Matrix visual: drag the row dimension to Rows, the column dimension to Columns, and the value to Values.","In the Format pane, expand Cell elements, set Apply to values = the measure, and toggle Background color on.","Click the fx button next to Background color, choose Format style = Gradient, and set Minimum, Center (for diverging), and Maximum colors using a colorblind-safe palette.","For diverging palettes set Center to a fixed Number = 0 so positive and negative values are visually comparable across slicers.","Reduce the column width and row height to make cells more square, and toggle off totals if they would dwarf the per-cell color scale.","For richer heatmaps, install the Custom Visual âHeatmapâ or âCorrelation plotâ from AppSource â they support clustering and dedicated legends out of the box."],caption:"Power BIâs default conditional formatting palette is redâyellowâgreen â the worst possible choice for colorblind users. Always swap to Cividis or a RedâWhiteâBlue diverging scheme."}],O=[{id:"python",language:"python",label:"Python â seaborn",filename:"heatmap.py",runner:"python heatmap.py",code:`import numpy as np 2import pandas as pd 3import seaborn as sns 4import matplotlib.pyplot as plt 5 6# Hours of the day x days of the week, with synthetic traffic counts. 7days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] 8hours = ["6am", "8am", "10am", "12pm", "2pm", "4pm", "6pm", "8pm", "10pm"] 9 10# rows = days, cols = hours, values = sessions 11values = np.array([ 12 [ 40, 220, 880, 940, 820, 360, 120, 90, 40], 13 [ 60, 360, 920, 1180, 980, 540, 200, 110, 50], 14 [ 50, 280, 860, 1020, 940, 420, 230, 120, 60], 15 [ 30, 300, 880, 1080, 920, 580, 260, 130, 70], 16 [ 50, 320, 720, 920, 720, 420, 320, 200, 90], 17 [ 30, 80, 200, 340, 220, 180, 360, 540, 280], 18 [ 30, 60, 180, 220, 200, 160, 280, 480, 200], 19]) 20 21matrix = pd.DataFrame(values, index=days, columns=hours) 22 23fig, ax = plt.subplots(figsize=(9, 4.5)) 24sns.heatmap( 25 matrix, 26 cmap="viridis", # perceptually uniform sequential palette 27 linewidths=0.5, 28 linecolor="#fff", 29 annot=False, 30 cbar_kws={"label": "Sessions"}, 31 ax=ax, 32) 33ax.set_title("Weekday lunchtime drives our peak traffic", loc="left", fontsize=14) 34ax.set_xlabel("Hour of day") 35ax.set_ylabel("Day of week") 36plt.yticks(rotation=0) 37plt.tight_layout() 38plt.savefig("heatmap.png", dpi=200) 39plt.show() 40`},{id:"r",language:"r",label:"R â ggplot2",filename:"heatmap.R",runner:"Rscript heatmap.R",code:`library(ggplot2) 41library(viridisLite) 42library(tidyr) 43 44days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") 45hours <- c("6am", "8am", "10am", "12pm", "2pm", "4pm", "6pm", "8pm", "10pm") 46 47values <- matrix(c( 48 40, 220, 880, 940, 820, 360, 120, 90, 40, 49 60, 360, 920, 1180, 980, 540, 200, 110, 50, 50 50, 280, 860, 1020, 940, 420, 230, 120, 60, 51 30, 300, 880, 1080, 920, 580, 260, 130, 70, 52 50, 320, 720, 920, 720, 420, 320, 200, 90,
53 30, 80, 200, 340, 220, 180, 360, 540, 280, 54 30, 60, 180, 220, 200, 160, 280, 480, 200 55), nrow = 7, byrow = TRUE) 56 57df <- as.data.frame(values) 58colnames(df) <- hours 59df$day <- factor(days, levels = days) 60 61long <- pivot_longer(df, -day, names_to = "hour", values_to = "sessions") 62long$hour <- factor(long$hour, levels = hours) 63 64ggplot(long, aes(x = hour, y = day, fill = sessions)) + 65 geom_tile(colour = "white", linewidth = 0.6) + 66 scale_fill_viridis_c(option = "D", name = "Sessions") + 67 coord_fixed() + 68 labs(title = "Weekday lunchtime drives our peak traffic", 69 subtitle = "Sessions per hour, last 7 days", 70 x = "Hour of day", y = NULL) + 71 theme_minimal(base_size = 12) + 72 theme(panel.grid = element_blank(), 73 plot.title.position = "plot") 74`},{id:"js",language:"javascript",label:"JavaScript â D3.js",filename:"heatmap.js",runner:"open index.html",code:`import * as d3 from "d3"; 75 76const days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]; 77const hours = ["6am", "8am", "10am", "12pm", "2pm", "4pm", "6pm", "8pm", "10pm"]; 78 79const raw = [ 80 [ 40, 220, 880, 940, 820, 360, 120, 90, 40], 81 [ 60, 360, 920, 1180, 980, 540, 200, 110, 50], 82 [ 50, 280, 860, 1020, 940, 420, 230, 120, 60], 83 [ 30, 300, 880, 1080, 920, 580, 260, 130, 70], 84 [ 50, 320, 720, 920, 720, 420, 320, 200, 90], 85 [ 30, 80, 200, 340, 220, 180, 360, 540, 280], 86 [ 30, 60, 180, 220, 200, 160, 280, 480, 200], 87]; 88 89const data = raw.flatMap((row, i) => 90 row.map((value, j) => ({ day: days[i], hour: hours[j], value })) 91); 92 93const width = 720; 94const height = 320; 95const margin = { top: 36, right: 24, bottom: 32, left: 56 }; 96 97const svg = d3.select("#chart") 98 .append("svg") 99 .attr("viewBox", \`0 0 \${width} \${height}\`) 100 .attr("role", "img") 101 .attr("aria-label", 102 "Heatmap of website sessions by day and hour: weekday traffic peaks Tuesday through Thursday around noon; weekend mornings are quietest."); 103 104const x = d3.scaleBand().domain(hours).range([margin.left, width - margin.right]).padding(0.05); 105const y = d3.scaleBand().domain(days).range([margin.top, height - margin.bottom]).padding(0.05); 106 107const color = d3.scaleSequential(d3.interpolateViridis) 108 .domain([0, d3.max(data, d => d.value)]); 109 110svg.append("g").selectAll("rect") 111 .data(data).join("rect") 112 .attr("x", d => x(d.hour)) 113 .attr("y", d => y(d.day)) 114 .attr("width", x.bandwidth()) 115 .attr("height", y.bandwidth()) 116 .attr("fill", d => color(d.value)) 117 .attr("rx", 2) 118 .append("title") 119 .text(d => \`\${d.day} \${d.hour}: \${d.value.toLocaleString()} sessions\`); 120 121svg.append("g") 122 .attr("transform", \`translate(0,\${margin.top})\`) 123 .call(d3.axisTop(x).tickSizeOuter(0)); 124 125svg.append("g") 126 .attr("transform", \`translate(\${margin.left},0)\`) 127 .call(d3.axisLeft(y).tickSizeOuter(0)); 128`}],F=[{source:"Wikipedia â Heat map",href:"https://en.wikipedia.org/wiki/Heat_map",description:"Encyclopedia entry covering the history, variants, and visual encoding of heatmaps. A solid neutral starting point with citations to primary sources.",kind:"Reference"},{source:"Toussaint Loua â Atlas statistique de la population de Paris (1873)",href:"https://gallica.bnf.fr/ark:/12148/bpt6k844290",description:"Louaâs 1873 statistical atlas, where shaded matrices to encode social data first appeared. Hosted by the Bibliothèque nationale de France.",kind:"Primary source"},{source:"Cynthia Brewer â ColorBrewer 2.0",href:"https://colorbrewer2.org/",description:"Cartographer Cynthia Brewerâs online tool for choosing colorblind-safe sequential, diverging, and qualitative palettes. The de facto standard for choropleth and heatmap palettes.",kind:"Tool"},{source:"Stefan van der Walt & Nathaniel Smith â A Better Default Colormap for Matplotlib (Viridis, SciPy 2015)",href:"https://bids.github.io/colormap/",description:"The talk and write-up that introduced the Viridis family of perceptually uniform colormaps and explained why jet is so harmful for data visualization.",kind:"Primary source"},{source:"Datawrapper Academy â What to consider when creating a heatmap",href:"https://academy.datawrapper.de/article/197-how-to-create-a-heatmap-in-datawrapper",description:"Hands-on tutorial with real published examples. Especially useful for sequential vs diverging palette choice and for handling missing cells.",kind:"Tutorial"},{source:"Edward Tufte â The Visual Display of Quantitative Information",href:"https://www.edwardtufte.com/book/the-visual-display-of-quantitative-information/",description:"Tufteâs foun
128dational text on data graphics. The chapters on data-ink ratio and small multiples explain why a sorted heatmap of small multiples often beats a single dense matrix.",kind:"Book"},{source:"Financial Times â Visual Vocabulary",href:"https://github.com/Financial-Times/chart-doctor/tree/main/visual-vocabulary",description:"Open-source poster categorizing chart types by intent. Heatmaps appear in both the Magnitude and Correlation families.",kind:"Reference"},{source:"WAI â Complex Images: Charts and Graphs",href:"https://www.w3.org/WAI/tutorials/images/complex/",description:"Web Accessibility Initiative guidance on making charts accessible: text alternatives, long descriptions, and data tables. Use this when building the accessibility checklist for your heatmap.",kind:"Accessibility"},{source:"seaborn documentation â sns.heatmap",href:"https://seaborn.pydata.org/generated/seaborn.heatmap.html",description:"Official API reference for seabornâs heatmap helper used in this guideâs Python sample. Includes guidance on cmap, center, vmin/vmax and annotations.",kind:"Docs"},{source:"ggplot2 reference â geom_tile",href:"https://ggplot2.tidyverse.org/reference/geom_tile.html",description:"Tidyverse documentation for the ggplot2 tile geometry. The standard way to draw a heatmap in R.",kind:"Docs"},{source:"Observable D3 â Heatmap",href:"https://observablehq.com/@d3/heatmap",description:"Maintained Observable notebook from the D3 community that mirrors the JavaScript code sample in this guide.",kind:"Docs"},{source:"Mike Bostock â Calendar View",href:"https://observablehq.com/@d3/calendar-view",description:"The classic D3 calendar heatmap notebook. Useful template for adapting heatmaps to time-series data with year/month/week structure.",kind:"Tutorial"}];function _(){return e.jsxs(e.Fragment,{children:[e.jsx(d,{title:"Heatmap: Definition, Best Practices & Examples",description:"Use a heatmap to compare data clearly: definition, when to use it, real-world examples, common mistakes, and tools (Excel, D3, Tableau).",path:"/heatmap",ogType:"article",jsonLd:[D]}),e.jsx(u,{slug:"/heatmap"}),e.jsxs("div",{className:"max-w-[1100px] mx-auto px-6 md:px-12",children:[e.jsxs("div",{className:"py-[56px] pb-[48px] border-b border-[var(--border-color)]",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-5",children:[e.jsx("span",{className:"font-mono text-[10px] tracking-[0.12em] uppercase bg-[var(--accent-light)] text-[var(--accent-color)] px-[10px] py-[4px] rounded-[3px] font-medium",children:"Magnitude"}),e.jsx("span",{className:"font-mono text-[10px] tracking-[0.10em] uppercase bg-[var(--bg-alt)] text-[var(--ink-muted)] px-[10px] py-[4px] rounded-[3px]",children:"Intermediate"})]}),e.jsx("h1",{className:"font-serif text-[42px] md:text-[52px] font-normal tracking-[-0.025em] leading-[1.1] text-[var(--ink)] mb-4",children:"Heatmap"}),e.jsx("p",{className:"text-[17px] md:text-[19px] font-light text-[var(--ink-muted)] leading-[1.6] max-w-[680px]",children:"A grid of colored cells where color intensity encodes magnitude. Heatmaps turn dense data matrices into instantly scannable patterns â the darker the cell, the higher the value â and have been used to spot structure in everything from social statistics to gene expression."})]}),e.jsxs("div",{className:"grid grid-cols-1 md:grid-cols-[1fr_280px] gap-16 pt-12 items-start",children:[e.jsxs("div",{className:"min-w-0",children:[e.jsxs("section",{className:"py-14 border-b border-[var(--border-color)]",id:"the-chart",children:[e.jsx(o,{number:"01",label:"The chart",title:"What it looks like"}),e.jsxs("div",{className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[12px] p-6 md:p-10 mt-8 mb-2",children:[e.jsxs("div",{className:"font-mono text-[10px] tracking-[0.10em] uppercase text-[var(--ink-faint)] mb-6 flex justify-between items-center",children:[e.jsxs("span",{children:["Example â ",e.jsx("em",{className:"not-italic text-[var(--accent-color)] font-medium",children:"Website traffic by day & hour"})]}),e.jsx("span",{children:"One week"})]}),e.jsxs("svg",{viewBox:"0 0 620 320",xmlns:"http://www.w3.org/2000/svg",className:"w-full block",role:"img","aria-label":"Heatmap of website sessions by day and hour: weekday traffic peaks Tuesday through Thursday around noon, with a darker red cell at Tuesday 12pm; weekend mornings are quietest.",children:[e.jsx("text",{x:"62",y:"48",textAnchor:"end",fill:"#6b6b67",fontSize:"11",fontFamily:"DM Mono, monospace",children:"Mon"}),e.jsx("text",{x:"62",y:"88",textAnchor:"end",fill:"#6b6b67",fontSize:"11",fontFamily:"DM Mono, monospace",children:"Tue"}),e.jsx("text",{x:"62",y:"128",textAnchor:"end",fill:"#6b6b67",fontSize:"11",fontFamily:"DM Mono, monospace",children:"Wed"}),e.jsx("text",{x:"62",y:"168",textAnchor:"end",fill:"#6b6b67",fontSize:"11",fontFamily:"DM Mono, monospace",children:"Thu"}),e.jsx("text",{x:"62",y:"208",textAnchor:"end",fill:"#6b6b67",fontSize:"11",fontFamily:"DM Mono, 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Fix vmin and vmax explicitly so colors are comparable across panels and across time."},{title:"Heatmap on a tiny matrix",desc:"Below about 5Ã5 cells, a heatmap is just a confusingly colored table. Use a labeled table, a small-multiple bar chart, or a dot plot instead. Heatmaps earn their keep when the matrix is too dense to read cell-by-cell."},{title:"Encoding everything in color, nothing in text",desc:"If color is the only encoding, anyone who canât see it (printed in grayscale, screen-reader users, colorblind viewers, low-contrast monitors) loses the entire chart. 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The GitHub-contribution-graph layout."}),e.jsx("span",{className:"inline-block mt-[10px] font-mono text-[9px] tracking-[0.1em] uppercase text-[var(--accent-color)] bg-[var(--accent-light)] px-2 py-[3px] rounded-[3px]",children:"Time series"})]}),e.jsxs(r,{href:"/correlogram",className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] p-6 px-5 hover:border-[var(--accent-border-hover)] hover:-translate-y-[2px] hover:shadow-[var(--shadow-card)] transition-all duration-150 no-underline block",children:[e.jsx("div",{className:"h-[72px] mb-4 flex items-center justify-center",children:e.jsx("svg",{viewBox:"0 0 100 60",width:"100",height:"60",children:[0,1,2,3,4].map(t=>[0,1,2,3,4].map(a=>{const s=Math.abs(t-a),n=["#9e3522","#c94a2e","#e8c4b8","#f5ede9","#f5ede9"];return e.jsx("rect",{x:t*18+6,y:a*11+2,width:"16",height:"9",rx:"1",fill:n[s]},`${t}-${a}`)}))})}),e.jsx("p",{className:"text-[14px] font-medium text-[var(--ink)] mb-1",children:"Correlogram"}),e.jsx("p",{className:"text-[12px] text-[var(--ink-muted)] font-light leading-[1.5]",children:"A heatmap specifically of a correlation matrix, usually with a diverging palette and a symmetric domain."}),e.jsx("span",{className:"inline-block mt-[10px] font-mono text-[9px] tracking-[0.1em] uppercase text-[var(--accent-color)] bg-[var(--accent-light)] px-2 py-[3px] rounded-[3px]",children:"Correlation"})]}),e.jsxs(r,{href:"/treemap",className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] p-6 px-5 hover:border-[var(--accent-border-hover)] hover:-translate-y-[2px] hover:shadow-[var(--shadow-card)] transition-all duration-150 no-underline block",children:[e.jsx("div",{className:"h-[72px] mb-4 flex items-center justify-center",children:e.jsxs("svg",{viewBox:"0 0 100 60",width:"100",height:"60",children:[e.jsx("rect",{x:"0",y:"0",width:"55",height:"40",fill:"#c94a2e"}),e.jsx("rect",{x:"55",y:"0",width:"45",height:"22",fill:"#e8c4b8"}),e.jsx("rect",{x:"55",y:"22",width:"45",height:"18",fill:"#9e3522"}),e.jsx("rect",{x:"0",y:"40",width:"35",height:"20",fill:"#f5ede9",stroke:"#c94a2e",strokeWidth:"0.5"}),e.jsx("rect",{x:"35",y:"40",width:"30",height:"20",fill:"#e8c4b8"}),e.jsx("rect",{x:"65",y:"40",width:"35",height:"20",fill:"#c94a2e",fillOpacity:"0.7"})]})}),e.jsx("p",{className:"text-[14px] font-medium text-[var(--ink)] mb-1",children:"Treemap"}),e.jsx("p",{className:"text-[12px] text-[var(--ink-muted)] font-light leading-[1.5]",children:"When the data is hierarchical and you need to show parts-of-a-whole as well as magnitude."}),e.jsx("span",{className:"inline-block mt-[10px] font-mono text-[9px] tracking-[0.1em] uppercase text-[var(--accent-color)] bg-[var(--accent-light)] px-2 py-[3px] rounded-[3px]",children:"Part-to-whole"})]}),e.jsxs(r,{href:"/density-plot",className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] p-6 px-5 hover:border-[var(--accent-border-hover)] hover:-translate-y-[2px] hover:shadow-[var(--shadow-card)] transition-all duration-150 no-underline block",children:[e.jsx("div",{className:"h-[72px] mb-4 flex items-center justify-center",children:e.jsxs("svg",{viewBox:"0 0 100 60",width:"100",height:"60",children:[e.jsx("path",{d:"M0,55 C20,50 30,10 50,10 C70,10 80,50 100,55 L100,60
128L0,60 Z",fill:"#c94a2e",fillOpacity:"0.6"}),e.jsx("line",{x1:"0",y1:"55",x2:"100",y2:"55",stroke:"#1a1a18",strokeWidth:"0.5",strokeOpacity:"0.2"})]})}),e.jsx("p",{className:"text-[14px] font-medium text-[var(--ink)] mb-1",children:"Density plot"}),e.jsx("p",{className:"text-[12px] text-[var(--ink-muted)] font-light leading-[1.5]",children:"When you want a smooth view of a single distribution rather than a binned 2D heatmap."}),e.jsx("span",{className:"inline-block mt-[10px] font-mono text-[9px] tracking-[0.1em] uppercase text-[var(--accent-color)] bg-[var(--accent-light)] px-2 py-[3px] rounded-[3px]",children:"Distribution"})]}),e.jsxs(r,{href:"/hexbin-plot",className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] p-6 px-5 hover:border-[var(--accent-border-hover)] hover:-translate-y-[2px] hover:shadow-[var(--shadow-card)] transition-all duration-150 no-underline block",children:[e.jsx("div",{className:"h-[72px] mb-4 flex items-center justify-center",children:e.jsx("svg",{viewBox:"0 0 100 60",width:"100",height:"60",children:[{x:20,y:20,c:"#e8c4b8"},{x:38,y:20,c:"#c94a2e"},{x:56,y:20,c:"#9e3522"},{x:74,y:20,c:"#e8c4b8"},{x:29,y:36,c:"#c94a2e"},{x:47,y:36,c:"#c94a2e"},{x:65,y:36,c:"#e8c4b8"},{x:38,y:52,c:"#f5ede9"},{x:56,y:52,c:"#e8c4b8"}].map((t,a)=>e.jsx("polygon",{points:`${t.x},${t.y-8} ${t.x+7},${t.y-4} ${t.x+7},${t.y+4} ${t.x},${t.y+8} ${t.x-7},${t.y+4} ${t.x-7},${t.y-4}`,fill:t.c},a))})}),e.jsx("p",{className:"text-[14px] font-medium text-[var(--ink)] mb-1",children:"Hexbin plot"}),e.jsx("p",{className:"text-[12px] text-[var(--ink-muted)] font-light leading-[1.5]",children:"A heatmap built from hexagonal bins of point data. Cures over-plotting in dense scatter plots."}),e.jsx("span",{className:"inline-block mt-[10px] font-mono text-[9px] tracking-[0.1em] uppercase text-[var(--accent-color)] bg-[var(--accent-light)] px-2 py-[3px] rounded-[3px]",children:"Correlation"})]})]})]}),e.jsx(k,{number:"15",id:"tool-instructions",intro:"Heatmaps live in every modern data tool, but the path to a clean, sorted, colorblind-safe heatmap differs from one to the next. The recipes below get you to a publication-ready chart in each of the most common platforms.",tools:P}),e.jsx(N,{number:"16",id:"code-examples",intro:"Three runnable snippets that produce the same chart â a sequential Viridis heatmap of weekly website traffic by day and hour. Copy, paste, and replace the data with yours.",samples:O}),e.jsx(g,{faqs:l,sectionNumber:"17"}),e.jsx(S,{number:"18",id:"references",intro:"Primary sources, reference texts, and the official documentation for the libraries and tools referenced throughout this guide.",items:F})]}),e.jsxs("aside",{className:"hidden md:block sticky top-[80px]",children:[e.jsxs("div",{className:"mt-12 mb-7",children:[e.jsx("p",{className:"font-mono text-[10px] tracking-[0.12em] uppercase text-[var(--ink-faint)] mb-[14px] pb-[10px] border-b border-[var(--border-color)]",children:"On this page"}),e.jsx("ul",{className:"list-none",children:q.map(t=>e.jsx("li",{className:"mb-[2px]",children:e.jsx("a",{href:`#${t.id}`,className:"block text-[12px] text-[var(--ink-muted)] no-underline py-[5px] pl-3 -ml-3 border-l-2 border-transparent hover:text-[var(--ink)] hover:border-l-[var(--accent-color)] transition-all duration-150 leading-[1.4]",children:t.label})},t.id))})]}),e.jsxs("div",{className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] overflow-hidden mb-5",children:[e.jsx("div",{className:"bg-[var(--ink)] px-[18px] py-[14px] font-mono text-[10px] tracking-[0.12em] uppercase text-[var(--white-muted)]",children:"// At a glance"}),e.jsx("div",{children:[{label:"Category",val:"Magnitude / Correlation",color:""},{label:"Difficulty",val:"Intermediate",color:"text-[var(--caution)]"},{label:"Encoding",val:"Color intensity per cell",color:""},{label:"Best palette",val:"Viridis / Cividis",color:""},{label:"Best matrix size",val:"5Ã5 â ~10,000 cells",color:""},{label:"Invented",val:"1873 (Loua) / 1991 (term)",color:""},{label:"Read time",val:"~15 min",color:""}].map((t,a)=>e.jsxs("div",{className:"px-[18px] py-[14px] border-b border-[var(--border-color)] last:border-b-0 flex flex-col gap-[2px]",children:[e.jsx("span",{className:"text-[11px] text-[var(--ink-faint)] font-mono tracking-[0.05em]",children:t.label}),e.jsx("span",{className:`text-[14px] text-[var(--ink)] ${t.color}`,children:t.val})]},a))})]}),e.jsxs("div",{className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] overflow-hidden mb-5",children:[e.jsx("div",{className:"bg-[var(--ink)] px-[18px] py-[14px] font-mono text-[10px] tracking-[0.12em] uppercase text-[var(--white-muted)]",children:"// Tags"}),e.jsx("div",{className:"px-[18px] py-4",children:e.jsx("div",{className:"flex flex-wrap gap-[6px]",children:["Magnitude","Correlation","Matrix","Color encoding","Pattern","Genomics","UX","Calendar"].map(t=>e.jsx("span",{className:"font-mono text-[10px] tracking-[0.06em] uppercase bg-[var(--bg-alt)] text-[var(--ink-muted)] px-[10px] py-1 rounded-[3px]",children:t},t))})})]}),e.jsxs("div",{className:"bg-[var(--bg-card)] border border-[var(--border-color)] rounded-[10px] overflow-hidden mb-5",children:[e.jsx("div",{className:"bg-[var(--ink)] px-[18px] py-[14px] font-mono text-[10px] tracking-[0.12em] uppercase text-[var(--white-muted)]",children:"// Similar charts"}),e.jsx("div",{className:"py-1",children:[{name:"Choropleth map",href:"/choropleth-map"},{name:"Calendar heatmap",href:"/calendar-heatmap"},{name:"Correlation matrix",href:"/correlogram"},{name:"Treemap",href:"/treemap"},{name:"Density plot",href:"/density-plot"}].map((t,a,s)=>e.jsxs(r,{href:t.href,className:`flex items-center justify-between px-[18px] py-3 text-[13px] text-[var(--ink-muted)] hover:text-[var(--accent-color)] transition-colors duration-150 cursor-pointer no-underline ${a<s.length-1?"border-b border-[var(--border-color)]":""}`,children:[t.name," ",e.jsx("span",{children:"â"})]},t.name))})]})]})]})]}),e.jsxs("div",{className:"border-t border-[var(--border-color)] bg-[var(--bg-card)] px-6 md:px-12 py-5 flex items-center justify-between",children:[e.jsx("div",{className:"flex gap-8",children:e.jsxs(r,{href:"/histogram",className:"text-[13px] text-[var(--ink-muted)] flex items-center gap-[6px] hover:text-[var(--ink)] transition-colors duration-150 no-underline",children:[e.jsx("span",{className:"text-[var(--accent-color)]",children:"â"})," Previous: Histogram"]})}),e.jsx("span",{className:"font-mono text-[11px] text-[var(--ink-faint)] hidden md:block",children:"1 of 80+ chart types"}),e.jsx("div",{className:"flex gap-8",children:e.jsxs(r,{href:"/choropleth-map",className:"text-[13px] text-[var(--ink-muted)] flex items-center gap-[6px] hover:text-[var(--ink)] transition-colors duration-150 no-underline",children:["Next: Choropleth map ",e.jsx("span",{className:"text-[var(--accent-color)]",children:"â"})]})})]})]})}export{_ as default};
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.