Python Data Visualization

Python data visualization with matplotlib and seaborn for creating publication-quality figures, statistical graphics, and exploratory visualizations.

When to use each library

Matplotlib is the foundational plotting library. Use it for:

• Fine-grained control over every plot element
• Custom layouts with GridSpec or subplot_mosaic
• 3D visualizations
• Animations
• Embedding plots in GUI applications
• When you need low-level customization

Seaborn builds on matplotlib for statistical visualization. Use it for:

• Statistical plots with automatic aggregation and confidence intervals
• Dataset-oriented plotting from DataFrames
• Faceted multi-panel figures (small multiples)
• Distribution visualization (KDE, histograms, violin plots)
• Correlation matrices and clustered heatmaps
• Publication-ready aesthetics with minimal code

Combined approach: Use seaborn for the main visualization, then customize with matplotlib.

Core concepts

Matplotlib hierarchy

1. Figure - Top-level container for all plot elements
2. Axes - Actual plotting area (one Figure can have multiple Axes)
3. Artist - Everything visible (lines, text, ticks, patches)
4. Axis - The x/y number lines with ticks and labels

Two matplotlib interfaces

Object-oriented interface (recommended):

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y, linewidth=2, label='data')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.legend()
plt.savefig('figure.png', dpi=300, bbox_inches='tight')

pyplot interface (quick exploration only):

plt.plot(x, y)
plt.xlabel('X Label')
plt.show()

Always use the object-oriented interface for production code.

Seaborn function levels

Axes-level functions plot to a single matplotlib Axes:

• Accept ax= parameter for placement
• Return Axes object
• Examples: scatterplot, histplot, boxplot, heatmap

Figure-level functions manage entire figures with faceting:

• Use col, row parameters for small multiples
• Return FacetGrid, JointGrid, or PairGrid objects
• Cannot be placed in existing figures
• Examples: relplot, displot, catplot, lmplot, jointplot, pairplot

import seaborn as sns

# Axes-level: integrates with matplotlib
fig, axes = plt.subplots(1, 2)
sns.scatterplot(data=df, x='x', y='y', ax=axes[0])
sns.histplot(data=df, x='x', ax=axes[1])

# Figure-level: automatic faceting
sns.relplot(data=df, x='x', y='y', col='category', hue='group')

Seaborn semantic mappings

Map data variables to visual properties automatically:

• hue - Color encoding
• size - Point/line size
• style - Marker/line style
• col, row - Facet into subplots

sns.scatterplot(data=df, x='x', y='y',
                hue='category',      # Color by category
                size='importance',   # Size by value
                style='type')        # Different markers

Quick start workflow

1. Import libraries

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

2. Set theme (optional)

sns.set_theme(style='whitegrid', context='paper', font_scale=1.1)

3. Create the plot

# Simple seaborn plot
fig, ax = plt.subplots(figsize=(10, 6))
sns.scatterplot(data=df, x='total_bill', y='tip', hue='day', ax=ax)

# Or figure-level with faceting
g = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')

4. Customize with matplotlib

ax.set_xlabel('Total Bill ($)', fontsize=12)
ax.set_ylabel('Tip ($)', fontsize=12)
ax.set_title('Restaurant Tips', fontsize=14)
ax.legend(title='Day', bbox_to_anchor=(1.05, 1))

5. Save the figure

plt.savefig('figure.png', dpi=300, bbox_inches='tight')
plt.savefig('figure.pdf')  # Vector format for publications

Plot type selection

Data Type	Recommended	Alternatives
Distribution (1 variable)	histplot, kdeplot	boxplot, violinplot
Relationship (2 continuous)	scatterplot	regplot, hexbin
Time series	lineplot	plot with dates
Categorical comparison	barplot, boxplot	violinplot, stripplot
Correlation matrix	heatmap	clustermap
Pairwise relationships	pairplot	PairGrid
Bivariate with marginals	jointplot	JointGrid

For detailed plot type examples, see references/plot-types.md.

Best practices

Interface and layout

1. Use object-oriented interface - Explicit control, easier debugging
2. Use constrained_layout=True - Prevents overlapping elements
3. Set figsize at creation - fig, ax = plt.subplots(figsize=(10, 6))
4. Close figures explicitly - plt.close(fig) to prevent memory leaks

Data preparation

1. Use tidy/long-form data - Each variable a column, each observation a row
2. Use meaningful column names - Seaborn uses them as axis labels
3. Pass DataFrames - Not raw arrays, to preserve semantic information

Color and accessibility

1. Use perceptually uniform colormaps - viridis, plasma, cividis
2. Avoid rainbow colormaps - jet is not perceptually uniform
3. Consider colorblind users - Use viridis, cividis, or colorblind palette
4. Use diverging colormaps for centered data - coolwarm, RdBu for data with meaningful zero

Export

1. Use 300 DPI for publications - dpi=300
2. Use vector formats for print - PDF, SVG
3. Use bbox_inches='tight' - Removes excess whitespace
4. Set explicit figure size - Control dimensions in inches

Statistical plots

1. Understand automatic aggregation - Seaborn computes means and CIs by default
2. Specify error representation - errorbar='sd', errorbar=('ci', 95)
3. Show individual data points - Combine stripplot with boxplot

Common patterns

Multi-panel figure

fig, axes = plt.subplots(2, 2, figsize=(12, 10), constrained_layout=True)
sns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])
sns.histplot(data=df, x='x', ax=axes[0, 1])
sns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])
sns.heatmap(corr_matrix, ax=axes[1, 1], cmap='coolwarm', center=0)

Publication figure

sns.set_theme(style='ticks', context='paper', font_scale=1.1)
fig, ax = plt.subplots(figsize=(8, 6))

sns.boxplot(data=df, x='treatment', y='response', ax=ax)
sns.stripplot(data=df, x='treatment', y='response', color='black', alpha=0.3, ax=ax)

ax.set_xlabel('Treatment Condition')
ax.set_ylabel('Response (units)')
sns.despine()

plt.savefig('figure.pdf', dpi=300, bbox_inches='tight')

Faceted exploration

g = sns.relplot(
    data=df, x='x', y='y',
    hue='treatment', style='batch',
    col='timepoint', col_wrap=3,
    kind='line', height=3, aspect=1.5
)
g.set_axis_labels('X Variable', 'Y Variable')
g.set_titles('{col_name}')

Scripts

This skill includes helper scripts:

• scripts/plot_template.py - Template demonstrating various plot types
• scripts/style_configurator.py - Interactive style configuration utility

References

For detailed information, load specific references:

oaps skill context python-dataviz --references <name>

Reference	Content
matplotlib-fundamentals	Core matplotlib concepts, hierarchy, common operations
seaborn-fundamentals	Seaborn design, data structures, function categories
plot-types	Comprehensive plot type guide with examples
styling	Colormaps, palettes, themes, typography
api-reference	Quick reference for common functions and parameters
troubleshooting	Common issues and solutions
seaborn-objects	Modern seaborn.objects declarative interface