Line 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 line plot displays the trend of a continuous variable over an ordered axis such as time. In biomedical research, line plots are the standard figure for growth curves, pharmacokinetic profiles, and longitudinal omics time courses, usually with an uncertainty band around the group mean. Python’s seaborn aggregates replicates into a mean line with a confidence band in a single call, while matplotlib provides fine-grained styling.

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 preclinical tumor-growth study: 12 mice per arm (vehicle vs treatment) measured weekly for 8 weeks, using a two-phase exponential growth model with mouse-to-mouse variability.

np.random.seed(21)

weeks = np.arange(0, 9)
n_mice = 12
records = []
for arm, growth_rate in [('Vehicle', 0.32), ('Treatment', 0.18)]:
    for mouse in range(n_mice):
        size = 100 * np.exp(growth_rate * np.random.normal(1, 0.08) * weeks)
        records.append(pd.DataFrame({
            'Week': weeks,
            'Size': size,
            'Arm': arm,
            'Mouse': f'{arm[:2]}_{mouse:02d}'
        }))
tumor = pd.concat(records, ignore_index=True)

Visualization

Basic Line Plot

Aggregate first, then draw: one line per group with distinct markers keeps small summaries readable.

mean_size = tumor.groupby(['Arm', 'Week'], as_index=False)['Size'].mean()

plt.figure(figsize=(6, 4.2))
ax = sns.lineplot(data=mean_size, x='Week', y='Size', hue='Arm',
                  style='Arm', markers=['o', 's'], markersize=6,
                  palette={'Vehicle': '#457b9d', 'Treatment': '#e63946'},
                  linewidth=1.8)
ax.set_xlabel('Week')
ax.set_ylabel('Tumor volume (mm$^3$)')
ax.legend(frameon=False, title=None)
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 1: Mean tumor size per week for the two treatment arms

Line Plot with Confidence Band

Passing the raw replicate measurements to seaborn.lineplot with errorbar=('ci', 95) bootstraps a 95% confidence band around each group mean — the default uncertainty display in preclinical figures.

plt.figure(figsize=(6, 4.2))
ax = sns.lineplot(data=tumor, x='Week', y='Size', hue='Arm',
                  errorbar=('ci', 95), err_kws=dict(alpha=0.2),
                  palette={'Vehicle': '#457b9d', 'Treatment': '#e63946'},
                  linewidth=1.8)
ax.set_xlabel('Week')
ax.set_ylabel('Tumor volume (mm$^3$)')
ax.legend(frameon=False, title=None)
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 2: Group mean with bootstrapped 95% confidence band

Spaghetti Plot with Population Mean

Showing every subject as a thin line behind the bold group mean communicates both the trend and the animal-to-animal variability — preferred by many journals over a band alone.

fig, ax = plt.subplots(figsize=(6, 4.2))
sns.lineplot(data=tumor, x='Week', y='Size', hue='Arm', units='Mouse',
             estimator=None, alpha=0.25, linewidth=0.9, legend=False,
             palette={'Vehicle': '#457b9d', 'Treatment': '#e63946'}, ax=ax)
mean_size = tumor.groupby(['Arm', 'Week'], as_index=False)['Size'].mean()
sns.lineplot(data=mean_size, x='Week', y='Size', hue='Arm',
             palette={'Vehicle': '#457b9d', 'Treatment': '#e63946'},
             linewidth=2.4, ax=ax)
ax.set_xlabel('Week')
ax.set_ylabel('Tumor volume (mm$^3$)')
ax.legend(frameon=False, title=None)
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 3: Individual growth curves (thin) with the group mean (bold)

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.