'seaborn' 검색 결과

Creates figures for machine learning papers from draft text or experiment data. Extracts components for Gemini-generated diagrams, chooses suitable charts with matplotlib or seaborn, and adjusts colors, fonts, and export formats for publication.

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• Turn a method section into an architecture diagram
• Create comparison charts from experiment results
• Plot training loss and accuracy over time

Create figures for machine learning papers from text or experiment data. Uses Gemini for architecture diagrams and matplotlib/seaborn for numerical charts, choosing suitable layouts and chart types while preparing consistent styling, export files, and reusable generation scripts.

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• Turning a methods section into an architecture diagram
• Charting model comparisons and ablation results
• Plotting training curves and confusion matrices

Prepare scientific figures for journal submission with consistent panel layouts, error bars showing uncertainty, statistical significance labels, and accessible colors. Use matplotlib, seaborn, or plotly to adjust figure dimensions and export publication formats.

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• Preparing figures for journal submission
• Combining multiple plots with consistent styling
• Making figures readable in color and grayscale

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

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• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Choose suitable charts for trends, comparisons, distributions, and relationships, then create them in Python with matplotlib, seaborn, or plotly. Apply guidance on colors, labels, scales, and accessibility to produce publication-quality figures or interactive charts.

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• Choose a chart type that fits the data and message
• Create figures for reports or publications
• Build interactive charts with Plotly

Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.

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• Exploring distributions and relationships in tabular data
• Comparing group differences with box or violin plots
• Visualizing correlation matrices as heatmaps

Create statistical charts from tabular data to explore distributions, relationships, and group differences. This skill guides plot selection, multi-panel layouts, and styling, including box plots, heatmaps, and charts with estimates and uncertainty bands.

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• Exploring data distributions and unusual values
• Comparing measurements across groups
• Visualizing correlations between variables

Apply seaborn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses seaborn or its strengths fit the task.

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Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

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SciPilot Skills 家族成员，负责科研数据可视化——但定位不是"画图工具"， 而是"可视化顾问"。先做数据剖析（列类型/样本量/分布/异常值/分组结构/相关性）， 再结合用户的论证目标推荐图型，主动拦截科研画图的经典错误（小样本画均值柱掩盖 分布、双 Y 轴、饼图、Y 轴不当截断、rainbow 色图、把分类点连成折线等），最后 产出 Nature / Science / IEEE / Elsevier / PNAS / 中文核心期刊级别的成图。 覆盖纯数据图：折线、柱状、散点、箱线 / 小提琴、热力图、误差棒、分布图（直方 图 / KDE）、相关性矩阵 / 散点矩阵、多面板组合。技术栈 matplotlib + seaborn + SciencePlots（静态）+ plotly（交互）。中英文双语，中文模式自动配置 Noto Sans CJK / Source Han Sans / SimHei 并修复负号方框，支持中文期刊"宋体正文 + Times New Roman 数字"混排。默认色盲安全配色 + 冗余编码 + 灰度预览。出图后做 "视觉自检闭环"：渲染 PNG 预览→程序自检缺字/文字裁切/刻度重叠→AI 读图复核遮盖 与子图对齐→回改重渲，直到通过。 当用户的任务涉及以下任何情况时主动触发：论文配图、科研画图、数据可视化、 不知道用什么图、怎么展示数据、用什么图好、期刊投稿图、figure、出版级图表、 matplotlib、seaborn、plotly、误差棒、显著性标注、色盲安全配色、矢量图导出、 中文论文图表、多面板。**即使用户只是给一批数据问"这个怎么画"或"用什么图 好"，也应使用本技能——本技能首要能力是"判断该用什么图"，其次才是绘制。** 不做示意图、流程图、架构图。

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Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

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Python visual creation and matplotlib/seaborn patterns for PBIR reports. Automatically invoke when the user mentions "Python visual", "matplotlib in Power BI", "seaborn in Power BI", "pythonVisual", or asks to "create a Python visual", "add a matplotlib chart", "write a Python visual script".

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Python (matplotlib, seaborn, plotly) でデータ可視化を行うスキル。 「グラフを作って」「チャート作成」「データを可視化して」等のリクエストで発動。 チャート選定、デザイン原則、アクセシビリティ対応も含む。

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Select the right Python charting library (seaborn, matplotlib, graphviz) and produce publication-quality static visualizations. Use when creating charts, plots, graphs, diagrams, heatmaps, visualizations from data, or when choosing between matplotlib/seaborn/graphviz. Also triggers for network diagrams, flowcharts, dependency trees, state machines, and entity-relationship diagrams. For interactive browser-rendered charts or uploaded data exploration, defer to charting-vega-lite instead.

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Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.

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SciPilot Skills 家族成员，负责科研数据可视化——但定位不是"画图工具"， 而是"可视化顾问"。先做数据剖析（列类型/样本量/分布/异常值/分组结构/相关性）， 再结合用户的论证目标推荐图型，主动拦截科研画图的经典错误（小样本画均值柱掩盖 分布、双 Y 轴、饼图、Y 轴不当截断、rainbow 色图、把分类点连成折线等），最后 产出 Nature / Science / IEEE / Elsevier / PNAS / 中文核心期刊级别的成图。 覆盖纯数据图：折线、柱状、散点、箱线 / 小提琴、热力图、误差棒、分布图（直方 图 / KDE）、相关性矩阵 / 散点矩阵、多面板组合。技术栈 matplotlib + seaborn + SciencePlots（静态）+ plotly（交互）。中英文双语，中文模式自动配置 Noto Sans CJK / Source Han Sans / SimHei 并修复负号方框，支持中文期刊"宋体正文 + Times New Roman 数字"混排。默认色盲安全配色 + 冗余编码 + 灰度预览。出图后做 "视觉自检闭环"：渲染 PNG 预览→程序自检缺字/文字裁切/刻度重叠→AI 读图复核遮盖 与子图对齐→回改重渲，直到通过。 当用户的任务涉及以下任何情况时主动触发：论文配图、科研画图、数据可视化、 不知道用什么图、怎么展示数据、用什么图好、期刊投稿图、figure、出版级图表、 matplotlib、seaborn、plotly、误差棒、显著性标注、色盲安全配色、矢量图导出、 中文论文图表、多面板。**即使用户只是给一批数据问"这个怎么画"或"用什么图 好"，也应使用本技能——本技能首要能力是"判断该用什么图"，其次才是绘制。** 不做示意图、流程图、架构图。

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Create scientific charts in Python with detailed control over axes, colors, labels, legends, and layout. Arrange multiple plots in one figure and export PNG, PDF, or SVG files for papers and presentations.

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• Create charts from experimental data
• Arrange multiple plots in one figure
• Adjust colors, labels, and annotations

Create quarterly earnings reports for companies already being followed. Compare results with market expectations, explain differences, revise forecasts, and assess the investment case in a Word document with charts, summary tables, and linked sources.

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• Writing a report after a quarterly earnings release
• Explaining results above or below market expectations
• Revising forecasts after new company guidance

Check single-cell RNA-seq data quality, filter low-quality cells using median absolute deviation, a measure of spread, and compare plots before and after filtering. Outputs include filtered data and the original dataset with quality annotations.

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• Assess single-cell RNA-seq data quality
• Filter low-quality cells and rarely detected genes
• Compare quality plots before and after filtering

Turn spreadsheets, query results, or pasted data into clear Python charts. Get help choosing chart types, formatting labels and colors, and producing static images or interactive charts with hover details and zoom.

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• Chart monthly revenue trends for a report
• Compare product scores in a ranked bar chart
• Explore data with hover details and zoom

Helps explore datasets, clean missing or inconsistent values, and explain patterns through statistics and charts. Uses Python and SQL to compare groups, examine relationships, and present findings in plain language with documented analysis steps.

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• Checking missing values, duplicates, and inconsistent formats
• Exploring trends and distributions with charts
• Comparing groups with statistical tests

Prepare a Python environment for Mixpanel analysis by installing required libraries and checking credentials and connectivity. Use it for first-time setup, missing dependencies, or guidance on service account and OAuth sign-in options.

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• Set up a new environment for Mixpanel analysis
• Install missing Python analysis libraries
• Configure service account or OAuth credentials

Prioritizes candidate genes from GWAS, studies linking genetic variants to traits. Combines locus predictions, gene expression evidence, and rare variant support into ranked results with source attribution, confidence labels, reproducible files, and optional figures.

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• Find candidate genes for a disease or trait
• Rank genes near specified lead variants
• Compare evidence and tissue support across genes

Turn percentage strings into numeric data, calculate the remaining share needed to reach 100%, and create stacked bar charts. Compare category composition across periods or groups and save the chart as a high-resolution image.

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• Charting monthly changes in category shares
• Comparing composition across groups
• Calculating an Other category from remaining percentages