# Data Visualization > 数据可视化专家，涵盖图表选择、色彩理论和标注最佳实践。支持图表类型（柱状图、折线图、散点图、热力图）、坐标轴规则和数据叙事。用于：图表、图形、仪表板、报告、演示文稿、信息图、数据故事。触发词：data visualization, chart, graph, data chart, bar chart, line chart, scatter plot, data viz, visualization, dashboard chart, infographic data, data presentation, chart design, plot, heatmap, pie chart alternative - Skill: `lza6/data-visualization` (Agent Skill) - Install (CLI): `npx skillmds@latest add lza6/data-visualization` - Raw SKILL.md: https://api.skillmd.com/api/skills/lza6/data-visualization/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: lza6 (https://skillmd.com/u/lza6) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/lza6/data-visualization --- # 数据可视化 通过 [inference.sh](https://inference.sh) CLI 创建清晰、有效的数据可视化。 ## 快速入门 > 需要 inference.sh CLI (`infsh`)。 [安装说明](https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md) ```bash infsh login # Generate a chart with Python infsh app run infsh/python-executor --input '{ "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nrevenue = [42, 48, 55, 61, 72, 89]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nax.bar(months, revenue, color=\"#3b82f6\", width=0.6)\nax.set_ylabel(\"Revenue ($K)\")\nax.set_title(\"Monthly Revenue Growth\", fontweight=\"bold\")\nfor i, v in enumerate(revenue):\n ax.text(i, v + 1, f\"${v}K\", ha=\"center\", fontweight=\"bold\")\nplt.tight_layout()\nplt.savefig(\"revenue.png\", dpi=150)\nprint(\"Saved\")" }' ``` ## 图表选择指南 ### 哪个图表代表哪个数据？ |数据关系|最佳图表|切勿使用 | |------------------|----------|------------| | **随时间变化** |折线图|饼图| | **比较类别** |条形图（许多类别的水平）|折线图| | **整体的一部分** |堆叠条形图、树状图 |饼图（有争议，但：条形图总是更清晰）| | **分布** |直方图、箱线图 |条形图| | **相关性** |散点图 |条形图| | **排名** |水平条形图|竖条、饼图 | | **地理** |等值线地图 |条形图| | **随着时间的推移组成** |堆积面积图 |多个饼图| | **单一指标** |大数字（KPI卡）|任何图表（大材小用）| | **流程/过程** |桑基图|条形图| ### 饼图问题 饼图几乎总是错误的选择： ``` ❌ Pie chart problems: - Hard to compare similar-sized slices - Can't show more than 5-6 categories - 3D pie charts are always wrong - Impossible to read exact values ✅ Use instead: - Horizontal bar chart (easy comparison) - Stacked bar (part of whole) - Treemap (hierarchical parts) - Just a table (if precision matters) ``` ## 设计规则 ### 轴 |规则|为什么 | |------|-----| | Y 轴始终从 0 开始（条形图）|防止视觉误导 | |折线图可以从 0 以上开始 |当显示变化时，而不是绝对值 | |标记两个轴 |读者不必猜测单位 | |删除不必要的网格线 |减少视觉噪音 | |使用水平标签|竖排文字难以阅读 | |按值对条形图排序 |除非有原因，否则不要使用字母顺序 | ＃＃＃ 颜色 |原理|应用 | |------------|------------| | **每张图表最多 5-7 种颜色** |更多内容变得难以阅读 | | **强调一件事** |灰色的一切，颜色的焦点| | **顺序**大小 |浅→暗为低→高| | **正/负的分歧** |红色←中性→蓝色| | **针对团体的分类** |不同的色调，相似的亮度| | **色盲安全** |避免仅使用红色/绿色 - 添加形状或标签 | | **含义一致** |如果蓝色=收入，那么到处都保持蓝色| ### 好的调色板 ```python # Sequential (low to high) sequential = ["#eff6ff", "#bfdbfe", "#60a5fa", "#2563eb", "#1d4ed8"] # Diverging (negative to positive) diverging = ["#ef4444", "#f87171", "#d1d5db", "#34d399", "#10b981"] # Categorical (distinct groups) categorical = ["#3b82f6", "#f59e0b", "#10b981", "#8b5cf6", "#ef4444"] # Colorblind-safe cb_safe = ["#0077BB", "#33BBEE", "#009988", "#EE7733", "#CC3311"] ``` ### 文本和标签 |元素|规则| |---------|------| | **标题** |陈述洞察力，而不是数据类型。 “第二季度收入翻倍”而不是“第二季度收入图表”| | **注释** |直接在图表上标注出关键数据点 | | **传奇** |尽可能避免 - 直接在图表线/条上标记 | | **字体大小** |演示文稿最小 12 像素、14 像素以上 | | **数字格式** |对于大数使用 K、M、B（42K 而不是 42,000）| | **数据标签** |当精确值很重要时添加到条形/点 | ## 图表食谱 ### 折线图（时间序列） ```bash infsh app run infsh/python-executor --input '{ "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(12, 6))\nfig.patch.set_facecolor(\"white\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\nthis_year = [120, 135, 148, 162, 178, 195, 210, 228, 245, 268, 290, 320]\nlast_year = [95, 102, 108, 115, 122, 130, 138, 145, 155, 165, 178, 190]\n\nax.plot(months, this_year, color=\"#3b82f6\", linewidth=2.5, marker=\"o\", markersize=6, label=\"2024\")\nax.plot(months, last_year, color=\"#94a3b8\", linewidth=2, linestyle=\"--\", label=\"2023\")\nax.fill_between(range(len(months)), last_year, this_year, alpha=0.1, color=\"#3b82f6\")\n\nax.annotate(\"$320K\", xy=(11, 320), fontsize=14, fontweight=\"bold\", color=\"#3b82f6\")\nax.annotate(\"$190K\", xy=(11, 190), fontsize=12, color=\"#94a3b8\")\n\nax.set_ylabel(\"Revenue ($K)\", fontsize=12)\nax.set_title(\"Revenue grew 68% year-over-year\", fontsize=16, fontweight=\"bold\")\nax.legend(fontsize=12)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.grid(axis=\"y\", alpha=0.3)\nplt.tight_layout()\nplt.savefig(\"line-chart.png\", dpi=150)\nprint(\"Saved\")" }' ``` ### 水平条形图（比较） ```bash infsh app run infsh/python-executor --input '{ "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(10, 6))\n\ncategories = [\"Email\", \"Social\", \"SEO\", \"Paid Ads\", \"Referral\", \"Direct\"]\nvalues = [12, 18, 35, 22, 8, 5]\ncolors = [\"#94a3b8\"] * len(values)\ncolors[2] = \"#3b82f6\" # Highlight the winner\n\n# Sort by value\nsorted_pairs = sorted(zip(values, categories, colors))\nvalues, categories, colors = zip(*sorted_pairs)\n\nax.barh(categories, values, color=colors, height=0.6)\nfor i, v in enumerate(values):\n ax.text(v + 0.5, i, f\"{v}%\", va=\"center\", fontsize=12, fontweight=\"bold\")\n\nax.set_xlabel(\"% of Total Traffic\", fontsize=12)\nax.set_title(\"SEO drives the most traffic\", fontsize=16, fontweight=\"bold\")\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nplt.tight_layout()\nplt.savefig(\"bar-chart.png\", dpi=150)\nprint(\"Saved\")" }' ``` ### KPI / 大数字卡 ```bash infsh app run infsh/html-to-image --input '{ "html": "

Monthly Revenue

$89K

↑ 23% vs last month

Active Users

12.4K

↑ 8% vs last month

Churn Rate

2.1%

↑ 0.3% vs last month

" }' ``` ### 热图 ```bash infsh app run infsh/python-executor --input '{ "code": "import matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(10, 6))\n\ndays = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\nhours = [\"9AM\", \"10AM\", \"11AM\", \"12PM\", \"1PM\", \"2PM\", \"3PM\", \"4PM\", \"5PM\"]\ndata = np.random.randint(10, 100, size=(len(hours), len(days)))\ndata[2][1] = 95 # Tuesday 11AM peak\ndata[2][3] = 88 # Thursday 11AM\n\nim = ax.imshow(data, cmap=\"Blues\", aspect=\"auto\")\nax.set_xticks(range(len(days)))\nax.set_yticks(range(len(hours)))\nax.set_xticklabels(days, fontsize=12)\nax.set_yticklabels(hours, fontsize=12)\n\nfor i in range(len(hours)):\n for j in range(len(days)):\n color = \"white\" if data[i][j] > 60 else \"black\"\n ax.text(j, i, data[i][j], ha=\"center\", va=\"center\", fontsize=10, color=color)\n\nax.set_title(\"Website Traffic by Day & Hour\", fontsize=16, fontweight=\"bold\")\nplt.colorbar(im, label=\"Visitors\")\nplt.tight_layout()\nplt.savefig(\"heatmap.png\", dpi=150)\nprint(\"Saved\")" }' ``` ## 用数据讲故事 ### 叙事弧线 |步骤|做什么 |示例| |------|------------|---------| | 1. **背景** |设置读者需要了解的内容 | “我们每月跟踪客户获取成本”| | 2. **紧张** |显示问题或更改 | “第三季度 CAC 增长 40%” | | 3. **决议** |展示见解或解决方案 | “但 LTV 增加了 80%，因此单位经济效益有所改善”| ### 标题为洞察力 ``` ❌ Descriptive titles (what the chart shows): "Q3 Revenue by Product Line" "Monthly Active Users 2024" "Customer Satisfaction Survey Results" ✅ Insight titles (what the chart means): "Enterprise product drives 70% of revenue growth" "User growth accelerated after the free tier launch" "Support response time is the #1 satisfaction driver" ``` ### 注释技术 |技术|何时使用 | |------------|------------| | **标注标签** |突出显示特定数据点（“峰值：320K”）| | **参考线** |显示目标/基准（“目标：100K”）| | **阴影区域** |标记时间段（“产品发布窗口”）| | **箭头+文字** |引起人们对趋势变化的关注 | | **行前/行后** |显示事件的影响 | ## 深色模式图表 ```bash infsh app run infsh/python-executor --input '{ "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\n# Dark theme\nplt.rcParams.update({\n \"figure.facecolor\": \"#0f172a\",\n \"axes.facecolor\": \"#0f172a\",\n \"axes.edgecolor\": \"#334155\",\n \"axes.labelcolor\": \"white\",\n \"text.color\": \"white\",\n \"xtick.color\": \"white\",\n \"ytick.color\": \"white\",\n \"grid.color\": \"#1e293b\"\n})\n\nfig, ax = plt.subplots(figsize=(12, 6))\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nvalues = [45, 52, 58, 72, 85, 98]\n\nax.plot(months, values, color=\"#818cf8\", linewidth=3, marker=\"o\", markersize=8)\nax.fill_between(range(len(months)), values, alpha=0.15, color=\"#818cf8\")\nax.set_title(\"MRR Growth: On track for $100K\", fontsize=18, fontweight=\"bold\")\nax.set_ylabel(\"MRR ($K)\", fontsize=13)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.grid(axis=\"y\", alpha=0.2)\n\nfor i, v in enumerate(values):\n ax.annotate(f\"${v}K\", (i, v), textcoords=\"offset points\", xytext=(0, 12), ha=\"center\", fontsize=11, fontweight=\"bold\")\n\nplt.tight_layout()\nplt.savefig(\"dark-chart.png\", dpi=150, facecolor=\"#0f172a\")\nprint(\"Saved\")" }' ``` ## 常见错误 |错误|问题 |修复| |---------|---------|-----| |饼图|难以比较，总是具有误导性 |使用条形图或树状图 | | Y 轴不从 0 开始（条形图）|夸大差异|条形从 0 开始，可以截断线条 | |颜色太多| Visual noise, confusing |最多 5-7 种颜色，仅突出显示重要的内容 | |无标题或通用标题 |读者不知其真知 |标题 = 要点，而不是数据类型 | | 3D 图表 |扭曲数据，看起来不专业 |始终使用 2D | |双 Y 轴 |具有误导性，难以阅读 |使用两个单独的图表 | |条形图上按字母顺序排序 |隐藏故事|按值排序（最大的在前）| |轴上没有标签 |读者无法解读|始终标有单位 | | Chartjunk（装饰元素）|分散数据注意力删除所有不传达信息的内容 | |红色/绿色仅用于颜色编码 |色盲用户无法阅读 |使用形状、图案或色盲安全调色板 | ## 相关技能 ```bash npx skills add inference-sh/skills@pitch-deck-visuals npx skills add inference-sh/skills@technical-blog-writing npx skills add inference-sh/skills@competitor-teardown ``` 浏览所有应用程序：`infsh 应用程序列表`