Bar Plot (Python)

🤖 AI Skill — Download the Bizard unified skill for your AI assistant ⬇️ Download Skill ZIP

🔌 MCP Server — Let AI agents (Claude Code, Cursor, …) query Bizard live: claude mcp add bizard -- npx @openbiox/bizard-mcp — or the free hosted instance (no install, faster from mainland China): claude mcp add --transport http bizard http://biotree.top:38123/bizard-mcp/mcp 📖 MCP Docs

📖 Cite Bizard — if Bizard helps your research, please cite: Li, K., Zheng, H., Huang, K., et al. (2026). Bizard: A Community-Driven Platform for Accelerating and Enhancing Biomedical Data Visualization. iMetaMed, e70038. 🔗 DOI / BibTeX

A bar plot compares quantities across discrete categories by their height. In biomedical research, bar plots are ubiquitous for reporting mean responses across treatment groups, cell-type composition per sample, and cohort summaries. Python’s matplotlib and seaborn libraries provide grouped, stacked, and percentage-stacked variants with publication-quality output.

Example

Setup

• System Requirements: Cross-platform (Linux/MacOS/Windows)
• Programming Language: Python
• Dependencies: matplotlib, seaborn, pandas, numpy

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

Data Preparation

We simulate a drug-dose response experiment (four groups, replicate measurements) and a cell-type composition matrix (six samples profiled by flow cytometry).

np.random.seed(42)

# Dose-response: raw replicate values per group
groups = ['Vehicle', 'Low dose', 'Mid dose', 'High dose']
raw = pd.DataFrame({
    'Group': pd.Categorical(np.repeat(groups, 40), categories=groups, ordered=True),
    'Response': np.concatenate([
        np.random.normal(2.0, 0.9, 40),
        np.random.normal(3.1, 0.9, 40),
        np.random.normal(4.6, 0.9, 40),
        np.random.normal(6.2, 0.9, 40),
    ])
})

# Cell-type composition per sample (rows sum to 1)
samples = [f'S{i}' for i in range(1, 7)]
cell_types = ['T cells', 'B cells', 'Macrophages', 'NK cells']
composition = pd.DataFrame(
    np.random.dirichlet([2, 1.5, 1, 0.8], size=6),
    index=samples, columns=cell_types
)
composition.index.name = 'Sample'

Visualization

Basic Bar Plot with Error Bars

When bar heights summarize grouped measurements, always attach an error bar (SEM or 95% CI) — seaborn computes both the mean and the interval automatically.

plt.figure(figsize=(6, 4.2))
ax = sns.barplot(data=raw, x='Group', y='Response',
                 errorbar=('se', 1), capsize=0.15,
                 color='#8da0cb', edgecolor='0.25', linewidth=0.8)
ax.set_xlabel('Treatment group')
ax.set_ylabel('Mean response (a.u.)')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 1: Bar plot of mean response by dose group with SEM error bars

Grouped Bar Plot

A second factor (e.g., sex or batch) is mapped to hue, producing side-by-side bars within each category.

grouped = raw.copy()
grouped['Sex'] = np.tile(np.repeat(['Female', 'Male'], 20), 4)
summary = grouped.groupby(['Group', 'Sex'], observed=True)['Response'].mean().reset_index()

plt.figure(figsize=(6.5, 4.2))
ax = sns.barplot(data=summary, x='Group', y='Response', hue='Sex',
                 palette={'Female': '#e78ac3', 'Male': '#8da0cb'},
                 edgecolor='0.25', linewidth=0.8)
ax.set_xlabel('Treatment group')
ax.set_ylabel('Mean response (a.u.)')
ax.legend(frameon=False, title=None)
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 2: Grouped bar plot with a second experimental factor

Stacked Bar Plot

Stacked bars show composition per category. With pandas, the composition matrix plots directly via plot.bar(stacked=True).

colors = ['#66c2a5', '#8da0cb', '#e78ac3', '#a6d854']
ax = composition.plot.bar(stacked=True, color=colors,
                          edgecolor='0.25', linewidth=0.6,
                          figsize=(6.5, 4.2), rot=0)
ax.set_xlabel('Sample')
ax.set_ylabel('Cell fraction')
ax.legend(frameon=False, bbox_to_anchor=(1.02, 0.75))
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 3: Stacked bar plot of cell-type composition per sample

100% Stacked Bar Plot

Normalizing each bar to 100% makes composition comparable across samples with unequal totals.

composition_pct = composition.div(composition.sum(axis=1), axis=0) * 100

ax = composition_pct.plot.bar(stacked=True, color=colors,
                              edgecolor='0.25', linewidth=0.6,
                              figsize=(6.5, 4.2), rot=0)
ax.set_xlabel('Sample')
ax.set_ylabel('Composition (%)')
ax.legend(frameon=False, bbox_to_anchor=(1.02, 0.75))
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 4: 100% stacked bar plot of relative cell-type composition

References

1. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95.
2. Waskom, M. L. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021.
3. Weissgerber, T. L., et al. (2015). Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biology, 13(4), e1002128.