Does it support advanced plots like UMAP or Heatmaps?

Yes, the skill includes specific implementation patterns for complex visualizations like UMAP projections, genomic heatmaps, and volcano plots.

Can I export plots for academic journals?

Yes, the skill includes configurations for high-resolution output (300 DPI) and vector-friendly formatting suitable for publication.

Does this skill require a specific cloud environment?

No, this skill is designed to run locally in your own Python environment, making it compatible with any local dataset and ensuring data privacy.

Which Python libraries are required?

You will need matplotlib, seaborn, pandas, and numpy installed in your environment to use these visualization patterns.

Data Visualization Plots

Name: Data Visualization Plots
Author: Microck

byMicrock

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데이터 과학 및 ML

Generates publication-quality scientific visualizations and data plots locally using Python's Matplotlib and Seaborn libraries.

소개

This skill empowers Claude to create professional-grade figures, ranging from basic scatter and line plots to complex multi-panel heatmaps and volcano plots. By executing Python code locally within the user's environment, it bypasses the limitations of cloud-hosted sandboxes, allowing for high-resolution exports and detailed customization of aesthetics, fonts, and styles. It is particularly valuable for researchers and data scientists performing Exploratory Data Analysis (EDA) or preparing figures for academic publication and technical presentations.

주요 기능

Comprehensive plot library: Scatter, line, bar, box, violin, and heatmaps.
Publication-ready quality: High-DPI (300+) export settings and professional styling templates.
Scientific domain support: Specific templates for UMAP/tSNE, QC metrics, and Volcano plots.
Local environment execution: Full control over data privacy and integration with local CSV/Parquet files.
Advanced layouts: Support for multi-panel figures using Matplotlib GridSpec.
81 GitHub stars

사용 사례

Automating Exploratory Data Analysis (EDA) to visualize gene expression or QC metrics.
Creating high-resolution figures for research papers and academic presentations.
Generating multi-panel dashboards to visualize clustering results and differential expression.

소개

주요 기능

Comprehensive plot library: Scatter, line, bar, box, violin, and heatmaps.
Publication-ready quality: High-DPI (300+) export settings and professional styling templates.
Scientific domain support: Specific templates for UMAP/tSNE, QC metrics, and Volcano plots.
Local environment execution: Full control over data privacy and integration with local CSV/Parquet files.
Advanced layouts: Support for multi-panel figures using Matplotlib GridSpec.
81 GitHub stars

사용 사례

Automating Exploratory Data Analysis (EDA) to visualize gene expression or QC metrics.
Creating high-resolution figures for research papers and academic presentations.
Generating multi-panel dashboards to visualize clustering results and differential expression.