python-dataviz-guide

Python Data Visualization Guide

Overview

Data visualization is how researchers communicate quantitative findings. A well-designed figure can convey complex relationships instantly, while a poor one buries the signal in clutter. Python's visualization ecosystem -- anchored by matplotlib, seaborn, and plotly -- provides everything needed to produce publication-quality figures for journals, conferences, and presentations.

This guide covers the three major Python visualization libraries, their strengths and trade-offs, and concrete recipes for the chart types researchers use most frequently. Each example is designed to be copy-paste ready and customizable for your specific dataset and venue requirements.

The emphasis is on producing figures that meet journal standards: correct DPI, appropriate font sizes, accessible color palettes, and vector-format exports. We also cover interactive visualization with plotly for exploratory analysis and supplementary materials.

Matplotlib: The Foundation

Matplotlib is the most flexible Python plotting library. Nearly every other visualization tool in the Python ecosystem builds on it.

Setting Up Publication Defaults

import matplotlib.pyplot as plt
import matplotlib as mpl

# Publication-quality defaults
plt.rcParams.update({
    'figure.figsize': (6, 4),
    'figure.dpi': 150,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
    'font.size': 11,
    'font.family': 'serif',
    'font.serif': ['Times New Roman'],
    'axes.labelsize': 12,
    'axes.titlesize': 13,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'lines.linewidth': 1.5,
    'lines.markersize': 6,
    'axes.grid': True,
    'grid.alpha': 0.3,
})

Line Plot with Error Bands

import numpy as np

epochs = np.arange(1, 51)
acc_mean = 1 - 0.5 * np.exp(-epochs / 10)
acc_std = 0.03 * np.exp(-epochs / 20)

fig, ax = plt.subplots()
ax.plot(epochs, acc_mean, label='Our Method', color='#2563EB')
ax.fill_between(epochs, acc_mean - acc_std, acc_mean + acc_std,
                alpha=0.2, color='#2563EB')
ax.set_xlabel('Epoch')
ax.set_ylabel('Accuracy')
ax.set_ylim(0.4, 1.0)
ax.legend(frameon=False)
fig.savefig('accuracy_curve.pdf')  # Vector format for papers

Multi-Panel Figures

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

for ax, dataset, color in zip(axes, ['CIFAR-10', 'ImageNet', 'COCO'],
                                ['#2563EB', '#DC2626', '#16A34A']):
    x = np.random.randn(200)
    ax.hist(x, bins=30, color=color, alpha=0.7, edgecolor='white')
    ax.set_title(dataset)
    ax.set_xlabel('Score Distribution')

axes[0].set_ylabel('Count')
plt.tight_layout()
fig.savefig('multi_panel.pdf')

Seaborn: Statistical Visualization

Seaborn excels at statistical graphics with minimal code. It handles data frames natively and produces polished output by default.

Comparison Bar Chart with Significance

import seaborn as sns
import pandas as pd

data = pd.DataFrame({
    'Method': ['Baseline', 'Baseline', 'Ours', 'Ours', 'Ours+FT', 'Ours+FT'],
    'Metric': ['BLEU', 'ROUGE'] * 3,
    'Score': [34.2, 45.1, 41.8, 52.3, 48.5, 58.7]
})

fig, ax = plt.subplots(figsize=(8, 5))
sns.barplot(data=data, x='Metric', y='Score', hue='Method',
            palette=['#94A3B8', '#3B82F6', '#EF4444'], ax=ax)
ax.set_ylabel('Score')
ax.legend(title='Method', frameon=False)
fig.savefig('comparison.pdf')

Correlation Heatmap

corr_matrix = pd.DataFrame(
    np.random.randn(8, 8),
    columns=[f'Feature {i}' for i in range(8)]
).corr()

fig, ax = plt.subplots(figsize=(8, 7))
sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='RdBu_r',
            center=0, square=True, linewidths=0.5, ax=ax)
ax.set_title('Feature Correlation Matrix')
fig.savefig('heatmap.pdf')

Violin Plot for Distribution Comparison

df = pd.DataFrame({
    'Group': np.repeat(['Control', 'Treatment A', 'Treatment B'], 100),
    'Value': np.concatenate([
        np.random.normal(50, 10, 100),
        np.random.normal(55, 8, 100),
        np.random.normal(60, 12, 100)
    ])
})

fig, ax = plt.subplots(figsize=(8, 5))
sns.violinplot(data=df, x='Group', y='Value', palette='Set2',
               inner='box', ax=ax)
ax.set_ylabel('Measurement')
fig.savefig('violin.pdf')

Plotly: Interactive Visualization

Plotly is ideal for exploratory analysis and HTML-based supplementary materials.

import plotly.express as px

df = px.data.gapminder().query("year == 2007")
fig = px.scatter(df, x="gdpPercap", y="lifeExp",
                 size="pop", color="continent",
                 hover_name="country",
                 log_x=True, size_max=60,
                 title="GDP vs Life Expectancy (2007)")
fig.write_html("interactive_scatter.html")
fig.write_image("scatter.pdf")  # Requires kaleido

Chart Type Selection Guide

Best Practices

• Export as PDF or SVG for print, PNG at 300 DPI as fallback. Never submit JPEG figures to journals.
• Use colorblind-safe palettes. sns.color_palette("colorblind") or use tools like ColorBrewer.
• Label everything. Axes, legends, and units should be readable without referring to the caption.
• Avoid chartjunk. Remove unnecessary gridlines, borders, and decorative elements.
• Match the figure width to the journal column width. Single-column is typically 3.3 inches; double-column is 6.9 inches.
• Use consistent styling across all figures in a paper. Define a style dictionary once and reuse it.
• Include error bars or confidence intervals. Raw point estimates without uncertainty are incomplete.

References

• matplotlib Documentation -- Official reference
• seaborn Documentation -- Statistical visualization
• plotly Documentation -- Interactive charts
• Scientific Visualization: Python + Matplotlib -- Nicolas Rougier
• Ten Simple Rules for Better Figures -- Rougier et al.