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Use when creating charts, designing dashboards, choosing visualization types, improving chart readability, or reviewing data presentations for clarity.","projectious-work\u002Fprocesskit","projectious-work","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F263889726?v=4","01M6N97TJJ118WQ35CHRV8RD0E","projectious.work","01M6N97TPWJPF0BMKYQ4PBY5W8",0,"MIT","https:\u002F\u002Fgithub.com\u002Fprojectious-work\u002Fprocesskit\u002Fblob\u002Fmain\u002Farchive\u002Fprocesskit-install-20261101\u002Fcontext\u002Ftemplates\u002Fprocesskit\u002Fv0.23.0\u002Fcontext\u002Fskills\u002Fdocuments\u002Fdata-visualization\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"data visualization","visualization","data","use","when","for","v1","types","color","chart","selection","dashboard design","chart selection","design","work","Charts","presentations","documents","accessibility","dashboard","2026-11-10 10:12:30",false,"---\nname: data-visualization\ndescription: |\n  Chart selection, color accessibility, annotation, and dashboard design. Use when creating charts, designing dashboards, choosing visualization types, improving chart readability, or reviewing data presentations for clarity.\nmetadata:\n  processkit:\n    apiVersion: processkit.projectious.work\u002Fv1\n    id: SKILL-data-visualization\n    version: \"1.1.0\"\n    created: 2026-04-06T00:00:00Z\n    category: documents\n---\n\n# Data Visualization\n\n## Intro\n\nA good chart matches the chart type to the data relationship,\nannotates the insight rather than the raw point, and stays readable\nfor colorblind viewers. The default fallbacks are bar (categorical)\nand scatter (continuous).\n\n## Overview\n\n### Chart type selection\n\nMatch chart to relationship, not to what looks impressive. Quick\nguide: comparison = bar, trend = line, distribution = histogram,\nrelationship = scatter. When unsure, default to bar (categorical) or\nscatter (continuous). Never use a pie chart with more than four\nslices — use a horizontal bar instead. Avoid 3D charts entirely\nunless you are visualizing actual three-dimensional data. Full\ndecision tree is in Level 3.\n\n### Color and accessibility\n\nUse colorblind-friendly palettes: seaborn `\"colorblind\"`, viridis,\ncividis. Never rely on color alone — add patterns, markers, or\nlabels. Sequential data (low-to-high) needs a single-hue gradient or\nviridis. Diverging data (deviation from center) needs coolwarm or\nRdBu centered at zero. Categorical data needs distinct hues with\nsufficient contrast. Limit to seven or fewer colors; group or facet\nbeyond that. Test with a colorblindness simulator before sharing.\n\n### Annotation\n\nAnnotate the insight, not just the data point. Always annotate\nmax\u002Fmin values that matter, threshold or target lines, and events\nthat explain trend changes. Use direct labels instead of legends\nwith 2–3 series. Arrows should point from label to data, not the\nreverse. Keep annotation text concise: \"Peak: 1,247 (Q3 launch)\" not\n\"The maximum value occurred in Q3.\"\n\n### Dashboard layout\n\nPlace the most important metric in the top-left (reading order).\nUse a consistent grid: 2–3 columns, cards of equal height per row.\nGroup related metrics visually. Filters live at the top, not buried\nin individual charts. Every dashboard needs a title, date range, and\ndata freshness indicator. Limit to 6–8 visualizations per dashboard\n— more causes cognitive overload.\n\n### Storytelling\n\nLead with the conclusion: \"Revenue grew 40% in Q3,\" not \"Here is a\nchart of revenue.\" Structure as context → finding → implication. Use\nprogressive disclosure: summary first, then supporting detail.\nHighlight the relevant; dim or remove the irrelevant. Annotate to\nguide the viewer's eye to the key insight.\n\n### Static vs interactive\n\nStatic charts (matplotlib, seaborn) for reports, papers,\npresentations. Interactive (plotly, Altair, D3) for dashboards and\nexploratory tools. Interactive needs tooltips, zoom for dense data,\nand filter controls. Static needs readability at print size (12pt+\nfonts, 300 dpi). Always provide a static fallback for interactive\ncharts.\n\n## Gotchas\n\nAgent-specific failure modes — provider-neutral pause-and-self-check items:\n\n- **Truncating the bar chart y-axis to make differences look larger.** A bar chart with a y-axis starting at 85% makes a 3-percentage-point difference look enormous. Bar charts must start at zero unless the baseline is explicitly labeled and well understood by the audience. Use a dot plot or connected line for before\u002Fafter comparisons where zero is not meaningful.\n- **Dual y-axes implying a relationship that doesn't exist.** Two series on dual axes can be made to look correlated or anti-correlated purely by scaling. The chart becomes a tool for misleading rather than informing. Use small multiples or index both series to a common baseline instead.\n- **Overplotting on scatter plots hiding the actual distribution.** With more than a few hundred points, overlapping markers make density invisible — a dense cluster and a sparse one look identical. Use alpha transparency, 2D density (hexbin, KDE), or aggregate before plotting when the point count is large.\n- **Pie charts with more than four slices.** Humans cannot accurately compare arc lengths or areas beyond four segments. Use a horizontal bar chart ordered by value; it is easier to read, easier to label, and easier to extend.\n- **Relying on color alone to encode information.** Color-blind viewers (roughly 8% of men) cannot distinguish red from green or blue from yellow. Always pair color with a second encoding — position, shape, pattern, or direct label — so the chart is readable in grayscale.\n- **Default titles that describe the chart, not the finding.** A title like \"Monthly Revenue by Region\" tells the viewer what data is shown but not what to conclude. A title like \"Northeast revenue grew 34% while all other regions declined\" tells them what to look for. Lead with the insight.\n- **Missing units on axes.** \"Revenue\" on the y-axis without a currency and scale (thousands? millions?) forces the viewer to guess. Every axis must have a label that includes units. Every chart must specify the time range if time is involved.\n\n## Full reference\n\n### Chart selection decision tree\n\n**Comparison across categories:**\n\n- **Vertical bar** — magnitudes across \u003C 15 categories. Avoid for\n  time series (use line) or > 15 categories (use horizontal).\n- **Horizontal bar** — long labels or > 10 categories. Avoid for\n  time-based or continuous x-axis.\n- **Grouped bar** — comparing 2–3 sub-groups within a category.\n  Avoid > 3 sub-groups (facet instead) or when showing totals\n  (stacked).\n\n**Trend over time:**\n\n- **Line** — continuous time axis, 1–5 series. Avoid with \u003C 5 time\n  points or > 7 lines.\n- **Area** — emphasizing total volume or magnitude. Avoid for\n  similar-magnitude series (lines occlude) or precise reading.\n\n**Distribution:**\n\n- **Histogram** — shape of one continuous variable.\n- **Box plot** — comparing distributions across groups.\n- **Violin** — like box plot but shows full shape; good for bimodal\n  data.\n\n**Relationship:**\n\n- **Scatter** — two continuous variables. Use hexbin or 2D density\n  for > 1000 points.\n- **Bubble** — scatter with size as a third variable. Avoid for\n  precise comparison (humans are bad at areas).\n- **Heatmap** — magnitude across two categorical or ordinal\n  dimensions. Avoid for sparse data or > 20 items per axis.\n\n**Composition (parts of a whole):**\n\n- **Stacked bar** — parts contributing to a total over time or\n  category. Avoid > 5–6 segments.\n- **Pie** — only with 2–4 slices and approximate proportions.\n  Almost always replaceable with a horizontal bar.\n- **Treemap** — hierarchical part-of-whole with many categories.\n  Avoid when precise comparison matters.\n\n**Geographic:**\n\n- **Choropleth** — values for defined regions.\n- **Point\u002Fdot map** — individual locations or events.\n\n| Relationship | First choice | Alternative | Avoid |\n|---|---|---|---|\n| Compare categories | Vertical bar | Horizontal bar, lollipop | Pie |\n| Trend over time | Line | Area | Bar (unless few points) |\n| Distribution | Histogram | Box, violin | Pie |\n| Two variables | Scatter | Hexbin, 2D density | Line |\n| Parts of whole | Stacked bar | Treemap | Pie (> 4 slices) |\n| Correlation matrix | Heatmap | Clustered pairs | Table of numbers |\n| Geographic | Choropleth | Point map | Non-geographic |\n\n### Universal rules\n\n1. Start with the simplest chart that answers the question.\n2. If the legend has > 7 entries, redesign.\n3. If a colleague cannot understand it in 10 seconds, simplify.\n4. When comparing, use the same axis scale across panels.\n5. Titles state the insight: \"Sales doubled in Q3,\" not \"Sales by\n   Quarter.\"\n\n### Common mistakes\n\n- Truncated y-axis on bar charts — bars must start at zero\n- Dual y-axes — misleading; use two panels\n- Overplotting — use alpha, jitter, hexbin, or density\n- Missing units on axes\n- Default titles (\"Figure 1\") instead of the finding\n- Legend far from the data it describes\n- Too many decimal places — round to meaningful precision\n\n### Export checklist\n\nBefore sharing, verify: title states the finding; both axes labeled\nwith units; legend present or direct labels used; colorblind-friendly\npalette; 300 dpi for print, SVG\u002FPDF for scalable; fonts readable at\nfinal display size.\n\n### Worked examples\n\n- **5 product lines × 24 months:** Multi-line chart, one line per\n  product, distinct colorblind-safe colors with direct end-of-line\n  labels instead of a legend. Title states the trend (\"Product A\n  overtook B in mid-2025\"), annotate the crossover point, light\n  gridlines on white background.\n- **15-chart dashboard is overwhelming:** Audit for redundancy,\n  group remaining charts by domain, move detail charts to a\n  drill-down page, keep 6 key metrics on the main view, add a\n  summary KPI row with sparklines at the top.\n- **Overplotted scatter:** Reduce marker size, set `alpha=0.3`,\n  switch dense regions to hexbin or 2D density, add marginal\n  histograms, label outlier clusters.\n","# Data Visualization\n\n## Intro\n\nA good chart matches the chart type to the data relationship,\nannotates the insight rather than the raw point, and stays readable\nfor colorblind viewers. The default fallbacks are bar (categorical)\nand scatter (continuous).\n\n## Overview\n\n### Chart type selection\n\nMatch chart to relationship, not to what looks impressive. Quick\nguide: comparison = bar, trend = line, distribution = histogram,\nrelationship = scatter. When unsure, default to bar (categorical) or\nscatter (continuous). Never use a pie chart with more than four\nslices — use a horizontal bar instead. Avoid 3D charts entirely\nunless you are visualizing actual three-dimensional data. Full\ndecision tree is in Level 3.\n\n### Color and accessibility\n\nUse colorblind-friendly palettes: seaborn `\"colorblind\"`, viridis,\ncividis. Never rely on color alone — add patterns, markers, or\nlabels. Sequential data (low-to-high) needs a single-hue gradient or\nviridis. Diverging data (deviation from center) needs coolwarm or\nRdBu centered at zero. Categorical data needs distinct hues with\nsufficient contrast. Limit to seven or fewer colors; group or facet\nbeyond that. Test with a colorblindness simulator before sharing.\n\n### Annotation\n\nAnnotate the insight, not just the data point. Always annotate\nmax\u002Fmin values that matter, threshold or target lines, and events\nthat explain trend changes. Use direct labels instead of legends\nwith 2–3 series. Arrows should point from label to data, not the\nreverse. Keep annotation text concise: \"Peak: 1,247 (Q3 launch)\" not\n\"The maximum value occurred in Q3.\"\n\n### Dashboard layout\n\nPlace the most important metric in the top-left (reading order).\nUse a consistent grid: 2–3 columns, cards of equal height per row.\nGroup related metrics visually. Filters live at the top, not buried\nin individual charts. Every dashboard needs a title, date range, and\ndata freshness indicator. Limit to 6–8 visualizations per dashboard\n— more causes cognitive overload.\n\n### Storytelling\n\nLead with the conclusion: \"Revenue grew 40% in Q3,\" not \"Here is a\nchart of revenue.\" Structure as context → finding → implication. Use\nprogressive disclosure: summary first, then supporting detail.\nHighlight the relevant; dim or remove the irrelevant. Annotate to\nguide the viewer's eye to the key insight.\n\n### Static vs interactive\n\nStatic charts (matplotlib, seaborn) for reports, papers,\npresentations. Interactive (plotly, Altair, D3) for dashboards and\nexploratory tools. Interactive needs tooltips, zoom for dense data,\nand filter controls. Static needs readability at print size (12pt+\nfonts, 300 dpi). Always provide a static fallback for interactive\ncharts.\n\n## Gotchas\n\nAgent-specific failure modes — provider-neutral pause-and-self-check items:\n\n- **Truncating the bar chart y-axis to make differences look larger.** A bar chart with a y-axis starting at 85% makes a 3-percentage-point difference look enormous. Bar charts must start at zero unless the baseline is explicitly labeled and well understood by the audience. Use a dot plot or connected line for before\u002Fafter comparisons where zero is not meaningful.\n- **Dual y-axes implying a relationship that doesn't exist.** Two series on dual axes can be made to look correlated or anti-correlated purely by scaling. The chart becomes a tool for misleading rather than informing. Use small multiples or index both series to a common baseline instead.\n- **Overplotting on scatter plots hiding the actual distribution.** With more than a few hundred points, overlapping markers make density invisible — a dense cluster and a sparse one look identical. Use alpha transparency, 2D density (hexbin, KDE), or aggregate before plotting when the point count is large.\n- **Pie charts with more than four slices.** Humans cannot accurately compare arc lengths or areas beyond four segments. Use a horizontal bar chart ordered by value; it is easier to read, easier to label, and easier to extend.\n- **Relying on color alone to encode information.** Color-blind viewers (roughly 8% of men) cannot distinguish red from green or blue from yellow. Always pair color with a second encoding — position, shape, pattern, or direct label — so the chart is readable in grayscale.\n- **Default titles that describe the chart, not the finding.** A title like \"Monthly Revenue by Region\" tells the viewer what data is shown but not what to conclude. A title like \"Northeast revenue grew 34% while all other regions declined\" tells them what to look for. Lead with the insight.\n- **Missing units on axes.** \"Revenue\" on the y-axis without a currency and scale (thousands? millions?) forces the viewer to guess. Every axis must have a label that includes units. Every chart must specify the time range if time is involved.\n\n## Full reference\n\n### Chart selection decision tree\n\n**Comparison across categories:**\n\n- **Vertical bar** — magnitudes across \u003C 15 categories. Avoid for\n  time series (use line) or > 15 categories (use horizontal).\n- **Horizontal bar** — long labels or > 10 categories. Avoid for\n  time-based or continuous x-axis.\n- **Grouped bar** — comparing 2–3 sub-groups within a category.\n  Avoid > 3 sub-groups (facet instead) or when showing totals\n  (stacked).\n\n**Trend over time:**\n\n- **Line** — continuous time axis, 1–5 series. Avoid with \u003C 5 time\n  points or > 7 lines.\n- **Area** — emphasizing total volume or magnitude. Avoid for\n  similar-magnitude series (lines occlude) or precise reading.\n\n**Distribution:**\n\n- **Histogram** — shape of one continuous variable.\n- **Box plot** — comparing distributions across groups.\n- **Violin** — like box plot but shows full shape; good for bimodal\n  data.\n\n**Relationship:**\n\n- **Scatter** — two continuous variables. Use hexbin or 2D density\n  for > 1000 points.\n- **Bubble** — scatter with size as a third variable. Avoid for\n  precise comparison (humans are bad at areas).\n- **Heatmap** — magnitude across two categorical or ordinal\n  dimensions. Avoid for sparse data or > 20 items per axis.\n\n**Composition (parts of a whole):**\n\n- **Stacked bar** — parts contributing to a total over time or\n  category. Avoid > 5–6 segments.\n- **Pie** — only with 2–4 slices and approximate proportions.\n  Almost always replaceable with a horizontal bar.\n- **Treemap** — hierarchical part-of-whole with many categories.\n  Avoid when precise comparison matters.\n\n**Geographic:**\n\n- **Choropleth** — values for defined regions.\n- **Point\u002Fdot map** — individual locations or events.\n\n| Relationship | First choice | Alternative | Avoid |\n|---|---|---|---|\n| Compare categories | Vertical bar | Horizontal bar, lollipop | Pie |\n| Trend over time | Line | Area | Bar (unless few points) |\n| Distribution | Histogram | Box, violin | Pie |\n| Two variables | Scatter | Hexbin, 2D density | Line |\n| Parts of whole | Stacked bar | Treemap | Pie (> 4 slices) |\n| Correlation matrix | Heatmap | Clustered pairs | Table of numbers |\n| Geographic | Choropleth | Point map | Non-geographic |\n\n### Universal rules\n\n1. Start with the simplest chart that answers the question.\n2. If the legend has > 7 entries, redesign.\n3. If a colleague cannot understand it in 10 seconds, simplify.\n4. When comparing, use the same axis scale across panels.\n5. Titles state the insight: \"Sales doubled in Q3,\" not \"Sales by\n   Quarter.\"\n\n### Common mistakes\n\n- Truncated y-axis on bar charts — bars must start at zero\n- Dual y-axes — misleading; use two panels\n- Overplotting — use alpha, jitter, hexbin, or density\n- Missing units on axes\n- Default titles (\"Figure 1\") instead of the finding\n- Legend far from the data it describes\n- Too many decimal places — round to meaningful precision\n\n### Export checklist\n\nBefore sharing, verify: title states the finding; both axes labeled\nwith units; legend present or direct labels used; colorblind-friendly\npalette; 300 dpi for print, SVG\u002FPDF for scalable; fonts readable at\nfinal display size.\n\n### Worked examples\n\n- **5 product lines × 24 months:** Multi-line chart, one line per\n  product, distinct colorblind-safe colors with direct end-of-line\n  labels instead of a legend. Title states the trend (\"Product A\n  overtook B in mid-2025\"), annotate the crossover point, light\n  gridlines on white background.\n- **15-chart dashboard is overwhelming:** Audit for redundancy,\n  group remaining charts by domain, move detail charts to a\n  drill-down page, keep 6 key metrics on the main view, add a\n  summary KPI row with sparklines at the top.\n- **Overplotted scatter:** Reduce marker size, set `alpha=0.3`,\n  switch dense regions to hexbin or 2D density, add marginal\n  histograms, label outlier clusters.\n",{"name":5,"metadata":54,"description":60},{"processkit":55},{"id":56,"created":57,"version":58,"category":46,"apiVersion":59},"SKILL-data-visualization",1775433600000,"1.1.0","processkit.projectious.work\u002Fv1","Chart selection, color accessibility, annotation, and dashboard design. 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