data-viz-plots

Data Visualization (Universal)

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Install skill "data-viz-plots" with this command: npx skills add microck/ordinary-claude-skills/microck-ordinary-claude-skills-data-viz-plots

Data Visualization (Universal)

Overview

This skill enables you to create professional scientific visualizations including scatter plots, line charts, heatmaps, violin plots, and more. Unlike cloud-hosted solutions, this skill uses the matplotlib and seaborn Python libraries and executes locally in your environment, making it compatible with ALL LLM providers including GPT, Gemini, Claude, DeepSeek, and Qwen.

When to Use This Skill

  • Create publication-quality figures for papers and presentations

  • Generate exploratory data analysis (EDA) plots

  • Visualize gene expression, QC metrics, or clustering results

  • Create multi-panel figures combining different plot types

  • Export high-resolution images for reports

  • Customize plot aesthetics (colors, fonts, styles)

How to Use

Step 1: Import Required Libraries

import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np from matplotlib import gridspec import matplotlib.patches as mpatches

Set style for publication-quality plots

sns.set_style("whitegrid") plt.rcParams['figure.dpi'] = 150 plt.rcParams['savefig.dpi'] = 300 plt.rcParams['font.size'] = 10

Step 2: Basic Scatter Plot

Create figure and axis

fig, ax = plt.subplots(figsize=(6, 5))

Scatter plot

ax.scatter(x_data, y_data, s=20, alpha=0.6, c='steelblue', edgecolors='k', linewidths=0.5)

Labels and title

ax.set_xlabel('Gene Expression (log2)', fontsize=12) ax.set_ylabel('Cell Count', fontsize=12) ax.set_title('Expression vs. Cell Count', fontsize=14, fontweight='bold')

Grid and styling

ax.grid(alpha=0.3) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False)

Save figure

plt.tight_layout() plt.savefig('scatter_plot.png', dpi=300, bbox_inches='tight') plt.show() print("✅ Scatter plot saved to: scatter_plot.png")

Step 3: Line Plot with Multiple Series

fig, ax = plt.subplots(figsize=(8, 5))

Plot multiple lines

ax.plot(time_points, group1_values, marker='o', label='Group 1', color='#E74C3C', linewidth=2) ax.plot(time_points, group2_values, marker='s', label='Group 2', color='#3498DB', linewidth=2) ax.plot(time_points, group3_values, marker='^', label='Group 3', color='#2ECC71', linewidth=2)

Styling

ax.set_xlabel('Time Point', fontsize=12) ax.set_ylabel('Expression Level', fontsize=12) ax.set_title('Gene Expression Over Time', fontsize=14, fontweight='bold') ax.legend(frameon=True, loc='best', fontsize=10) ax.grid(alpha=0.3, linestyle='--')

plt.tight_layout() plt.savefig('line_plot.png', dpi=300, bbox_inches='tight') plt.show()

Step 4: Box Plot and Violin Plot

Prepare data (long-form DataFrame)

df should have columns: 'cluster', 'expression', 'gene', etc.

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

Box plot

sns.boxplot(data=df, x='cluster', y='expression', palette='Set2', ax=ax1) ax1.set_title('Box Plot: Expression by Cluster', fontsize=12, fontweight='bold') ax1.set_xlabel('Cluster', fontsize=11) ax1.set_ylabel('Expression Level', fontsize=11) ax1.tick_params(axis='x', rotation=45)

Violin plot

sns.violinplot(data=df, x='cluster', y='expression', palette='muted', ax=ax2, inner='quartile') ax2.set_title('Violin Plot: Expression Distribution', fontsize=12, fontweight='bold') ax2.set_xlabel('Cluster', fontsize=11) ax2.set_ylabel('Expression Level', fontsize=11) ax2.tick_params(axis='x', rotation=45)

plt.tight_layout() plt.savefig('box_violin_plot.png', dpi=300, bbox_inches='tight') plt.show()

Step 5: Heatmap

Prepare data matrix (rows=genes, columns=samples or clusters)

gene_expression_matrix: pandas DataFrame or numpy array

fig, ax = plt.subplots(figsize=(8, 6))

Create heatmap

sns.heatmap( gene_expression_matrix, cmap='viridis', cbar_kws={'label': 'Expression'}, xticklabels=True, yticklabels=True, linewidths=0.5, linecolor='gray', ax=ax )

ax.set_title('Gene Expression Heatmap', fontsize=14, fontweight='bold') ax.set_xlabel('Samples', fontsize=12) ax.set_ylabel('Genes', fontsize=12)

plt.tight_layout() plt.savefig('heatmap.png', dpi=300, bbox_inches='tight') plt.show()

Step 6: Bar Plot with Error Bars

fig, ax = plt.subplots(figsize=(7, 5))

Data

categories = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3'] means = [120, 85, 200, 150] errors = [15, 10, 25, 20]

Bar plot

bars = ax.bar(categories, means, yerr=errors, capsize=5, color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12'], edgecolor='black', linewidth=1.2, alpha=0.8)

Labels

ax.set_ylabel('Cell Count', fontsize=12) ax.set_title('Cell Counts by Cluster', fontsize=14, fontweight='bold') ax.set_ylim(0, max(means) * 1.3)

Add value labels on bars

for bar, mean in zip(bars, means): height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height + 5, f'{mean}', ha='center', va='bottom', fontsize=10)

plt.tight_layout() plt.savefig('bar_plot.png', dpi=300, bbox_inches='tight') plt.show()

Advanced Features

Multi-Panel Figure

Create complex layout

fig = plt.figure(figsize=(12, 8)) gs = gridspec.GridSpec(2, 3, figure=fig, hspace=0.3, wspace=0.3)

Panel A: Scatter

ax1 = fig.add_subplot(gs[0, :2]) ax1.scatter(x_data, y_data, c=cluster_labels, cmap='tab10', s=10, alpha=0.6) ax1.set_title('A. UMAP Projection', fontsize=12, fontweight='bold', loc='left') ax1.set_xlabel('UMAP1') ax1.set_ylabel('UMAP2')

Panel B: Violin

ax2 = fig.add_subplot(gs[0, 2]) sns.violinplot(data=df, y='expression', palette='Set2', ax=ax2) ax2.set_title('B. Expression', fontsize=12, fontweight='bold', loc='left')

Panel C: Heatmap

ax3 = fig.add_subplot(gs[1, :]) sns.heatmap(matrix, cmap='coolwarm', center=0, ax=ax3, cbar_kws={'label': 'Z-score'}) ax3.set_title('C. Gene Expression Heatmap', fontsize=12, fontweight='bold', loc='left')

plt.savefig('multi_panel_figure.png', dpi=300, bbox_inches='tight') plt.show()

Custom Color Palette

Define custom colors

custom_palette = ['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6']

Use in seaborn

sns.set_palette(custom_palette)

Or create color dict for specific mapping

color_dict = { 'T cells': '#E74C3C', 'B cells': '#3498DB', 'Monocytes': '#2ECC71', 'NK cells': '#F39C12' }

Use in scatter plot

for cell_type, color in color_dict.items(): mask = df['celltype'] == cell_type ax.scatter(df.loc[mask, 'x'], df.loc[mask, 'y'], c=color, label=cell_type, s=20, alpha=0.7) ax.legend()

Density Plot

from scipy.stats import gaussian_kde

fig, ax = plt.subplots(figsize=(8, 6))

Calculate density

xy = np.vstack([x_data, y_data]) z = gaussian_kde(xy)(xy)

Sort points by density for better visualization

idx = z.argsort() x, y, z = x_data[idx], y_data[idx], z[idx]

Scatter with density colors

scatter = ax.scatter(x, y, c=z, s=20, cmap='viridis', alpha=0.6, edgecolors='none') plt.colorbar(scatter, ax=ax, label='Density')

ax.set_xlabel('UMAP1', fontsize=12) ax.set_ylabel('UMAP2', fontsize=12) ax.set_title('Density Scatter Plot', fontsize=14, fontweight='bold')

plt.tight_layout() plt.savefig('density_plot.png', dpi=300, bbox_inches='tight') plt.show()

Common Use Cases

QC Metrics Visualization

Assuming adata.obs has QC columns: n_genes, n_counts, percent_mito

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

Plot 1: Histogram of genes per cell

axes[0].hist(adata.obs['n_genes'], bins=50, color='steelblue', edgecolor='black', alpha=0.7) axes[0].axvline(adata.obs['n_genes'].median(), color='red', linestyle='--', label='Median') axes[0].set_xlabel('Genes per Cell', fontsize=11) axes[0].set_ylabel('Frequency', fontsize=11) axes[0].set_title('Genes per Cell Distribution', fontsize=12, fontweight='bold') axes[0].legend()

Plot 2: Scatter UMI vs Genes

axes[1].scatter(adata.obs['n_counts'], adata.obs['n_genes'], s=5, alpha=0.5, c='coral') axes[1].set_xlabel('UMI Counts', fontsize=11) axes[1].set_ylabel('Genes Detected', fontsize=11) axes[1].set_title('UMIs vs Genes', fontsize=12, fontweight='bold')

Plot 3: Violin plot of mitochondrial percentage

sns.violinplot(y=adata.obs['percent_mito'], ax=axes[2], color='lightgreen') axes[2].axhline(y=20, color='red', linestyle='--', label='20% threshold') axes[2].set_ylabel('Mitochondrial %', fontsize=11) axes[2].set_title('Mitochondrial Content', fontsize=12, fontweight='bold') axes[2].legend()

plt.tight_layout() plt.savefig('qc_metrics.png', dpi=300, bbox_inches='tight') plt.show()

UMAP/tSNE Visualization

Assuming adata.obsm['X_umap'] exists and adata.obs['clusters'] exists

fig, ax = plt.subplots(figsize=(8, 7))

Get unique clusters

clusters = adata.obs['clusters'].unique() n_clusters = len(clusters)

Generate colors

colors = plt.cm.tab20(np.linspace(0, 1, n_clusters))

Plot each cluster

for i, cluster in enumerate(clusters): mask = adata.obs['clusters'] == cluster ax.scatter( adata.obsm['X_umap'][mask, 0], adata.obsm['X_umap'][mask, 1], c=[colors[i]], label=f'Cluster {cluster}', s=10, alpha=0.7, edgecolors='none' )

ax.set_xlabel('UMAP1', fontsize=12) ax.set_ylabel('UMAP2', fontsize=12) ax.set_title('UMAP Projection by Cluster', fontsize=14, fontweight='bold') ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=True, fontsize=9)

plt.tight_layout() plt.savefig('umap_clusters.png', dpi=300, bbox_inches='tight') plt.show()

Gene Expression Dot Plot

genes: list of gene names

clusters: list of cluster IDs

Create matrix: rows=genes, columns=clusters with mean expression and % expressing

fig, ax = plt.subplots(figsize=(10, 6))

Prepare data

from matplotlib.colors import Normalize

dot_size_matrix: % cells expressing (0-100)

color_matrix: mean expression level

for i, gene in enumerate(genes): for j, cluster in enumerate(clusters): # Size proportional to % expressing size = dot_size_matrix[i, j] * 5 # Scale factor # Color by expression level color_val = color_matrix[i, j]

    ax.scatter(j, i, s=size, c=[color_val], cmap='Reds',
               vmin=0, vmax=color_matrix.max(),
               edgecolors='black', linewidths=0.5)

Labels

ax.set_xticks(range(len(clusters))) ax.set_xticklabels(clusters, rotation=45, ha='right') ax.set_yticks(range(len(genes))) ax.set_yticklabels(genes) ax.set_xlabel('Cluster', fontsize=12) ax.set_ylabel('Gene', fontsize=12) ax.set_title('Marker Gene Expression', fontsize=14, fontweight='bold')

Colorbar

norm = Normalize(vmin=0, vmax=color_matrix.max()) sm = plt.cm.ScalarMappable(cmap='Reds', norm=norm) sm.set_array([]) cbar = plt.colorbar(sm, ax=ax, pad=0.02) cbar.set_label('Mean Expression', rotation=270, labelpad=15)

plt.tight_layout() plt.savefig('gene_dotplot.png', dpi=300, bbox_inches='tight') plt.show()

Volcano Plot (DEG Analysis)

Assuming deg_df has columns: gene, log2FC, pvalue

fig, ax = plt.subplots(figsize=(8, 7))

Calculate -log10(pvalue)

deg_df['-log10_pvalue'] = -np.log10(deg_df['pvalue'])

Classify genes

deg_df['significant'] = 'Not Significant' deg_df.loc[(deg_df['log2FC'] > 1) & (deg_df['pvalue'] < 0.05), 'significant'] = 'Up-regulated' deg_df.loc[(deg_df['log2FC'] < -1) & (deg_df['pvalue'] < 0.05), 'significant'] = 'Down-regulated'

Plot

for category, color in zip(['Not Significant', 'Up-regulated', 'Down-regulated'], ['gray', 'red', 'blue']): mask = deg_df['significant'] == category ax.scatter(deg_df.loc[mask, 'log2FC'], deg_df.loc[mask, '-log10_pvalue'], c=color, label=category, s=20, alpha=0.6, edgecolors='none')

Threshold lines

ax.axvline(x=1, color='black', linestyle='--', linewidth=1, alpha=0.5) ax.axvline(x=-1, color='black', linestyle='--', linewidth=1, alpha=0.5) ax.axhline(y=-np.log10(0.05), color='black', linestyle='--', linewidth=1, alpha=0.5)

Labels

ax.set_xlabel('log2 Fold Change', fontsize=12) ax.set_ylabel('-log10(p-value)', fontsize=12) ax.set_title('Volcano Plot: Differential Expression', fontsize=14, fontweight='bold') ax.legend(frameon=True, loc='upper right')

plt.tight_layout() plt.savefig('volcano_plot.png', dpi=300, bbox_inches='tight') plt.show()

Best Practices

  • Figure Size: Use appropriate dimensions for target medium (papers: 6-8 inches wide, posters: larger)

  • DPI: Save at 300 DPI for publications, 150 DPI for presentations

  • Colors: Use colorblind-friendly palettes (e.g., viridis , Set2 , tab10 )

  • Fonts: Keep font sizes readable (titles: 12-14pt, labels: 10-12pt, ticks: 8-10pt)

  • Transparency: Use alpha for overlapping points to show density

  • Layout: Always call plt.tight_layout() before saving to prevent label clipping

  • File Format: PNG for general use, SVG for vector graphics (editable in Illustrator)

  • Close Figures: Call plt.close() after saving to free memory when generating many plots

Troubleshooting

Issue: "Figure too cluttered with many points"

Solution: Use transparency and smaller point sizes

ax.scatter(x, y, s=5, alpha=0.3, edgecolors='none')

Issue: "Legend overlaps with data"

Solution: Place legend outside the plot area

ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

Issue: "Labels are cut off in saved figure"

Solution: Use bbox_inches='tight'

plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Issue: "Colors don't match between plots"

Solution: Define color palette once and reuse

PALETTE = {'Group A': '#E74C3C', 'Group B': '#3498DB'}

Use PALETTE in all plots

Issue: "Heatmap text too small"

Solution: Adjust figure size or font size

fig, ax = plt.subplots(figsize=(12, 10)) sns.heatmap(data, ax=ax, annot_kws={'fontsize': 8})

Technical Notes

  • Libraries: Uses matplotlib and seaborn (widely supported, stable)

  • Execution: Runs locally in the agent's sandbox

  • Compatibility: Works with ALL LLM providers (GPT, Gemini, Claude, DeepSeek, Qwen, etc.)

  • File Formats: Supports PNG, PDF, SVG, JPEG

  • Performance: Typical plot generation takes <1 second for standard plots, 2-5 seconds for complex multi-panel figures

  • Memory: Keep figure count reasonable; close figures after saving if generating many plots

References

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data-viz-plots | V50.AI