Data Visualization

Use this skill for creating effective visualizations: choosing the right library, chart type, and interactivity level for your data and audience.

When to use this skill

• Choosing a visualization library for a project

• Creating exploratory charts during EDA

• Building interactive dashboards

• Producing publication-quality figures

• Understanding tradeoffs between libraries

Library selection guide (2026)

Library Best For Interactivity Learning Curve

Matplotlib Publication-quality static plots, fine control Static Moderate

Seaborn Statistical visualization, quick EDA Static Easy

Plotly Interactive web charts, dashboards High Easy

Altair Declarative statistical charts, large datasets Medium Easy

hvPlot/HoloViz Large data, linked brushing, geospatial High Moderate

Bokeh Custom interactive web apps High Moderate

Quick decision tree

Static publication figure? → Matplotlib (full control) or Seaborn (quick statistical)

Interactive web/dashboard? → Plotly (easiest), Dash (full apps) → Panel/HoloViz (complex linked views) → Bokeh (custom web apps)

Large datasets (100k+ points)? → hvPlot + Datashader (automatic rasterization) → Altair (smart aggregation with Vega-Lite)

Declarative grammar preferred? → Altair (Vega-Lite) or Plotly Express

Already using Pandas? → df.plot() → Matplotlib → df.hvplot() → HoloViz → px.scatter(df) → Plotly

Core principles

1. Match chart to data and question

Question Chart Type

Distribution? Histogram, KDE, boxplot, violin

Relationship? Scatter, line, heatmap (correlation)

Composition? Pie (avoid), stacked bar, treemap

Comparison? Bar, grouped bar, dot plot

Trend over time? Line, area, candlestick

Geographic? Choropleth, scatter map, heatmap

2. Maximize data-ink ratio

• Remove unnecessary gridlines, borders, backgrounds

• Use color purposefully (not decoration)

• Label directly when possible

• One message per visualization

3. Choose interactivity appropriately

Audience Interactivity Level

Paper/report Static (Matplotlib/Seaborn)

Presentation Limited (Plotly static export)

Exploratory analysis High (zoom, pan, filter, hover)

Stakeholder dashboard Medium (linked views, drill-down)

Quick examples

Matplotlib (fine control)

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6)) ax.scatter(x, y, c=colors, alpha=0.6, edgecolors='none') ax.set_xlabel('Feature X', fontsize=12) ax.set_ylabel('Target Y', fontsize=12) ax.set_title('Relationship Analysis', fontsize=14, fontweight='bold') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) plt.tight_layout()

Seaborn (statistical)

import seaborn as sns

Distribution with KDE

sns.histplot(data=df, x='value', hue='category', kde=True, bins=30)

Correlation heatmap

corr = df.corr() sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', center=0)

Categorical comparison

sns.boxplot(data=df, x='category', y='value', palette='viridis')

Plotly (interactive web)

import plotly.express as px

Scatter with marginal distributions

fig = px.scatter(df, x='x', y='y', color='category', size='size', marginal_x='histogram', marginal_y='rug', hover_data=['label']) fig.show()

Faceted small multiples

fig = px.line(df, x='date', y='value', facet_col='category', facet_col_wrap=3, height=800) fig.show()

Altair (declarative, large data)

import altair as alt

Smart aggregation for large datasets

chart = alt.Chart(df).mark_circle().encode( x=alt.X('x:Q', bin=alt.Bin(maxbins=50)), y=alt.Y('y:Q', bin=alt.Bin(maxbins=50)), size='count()' ).interactive()

chart.save('chart.html') # Self-contained HTML

hvPlot/HoloViz (large data, linked views)

import hvplot.pandas import panel as pn

Linked brushing

scatter = df.hvplot.scatter(x='x', y='y', c='category', tools=['box_select'], width=400, height=400) hist = df.hvplot.hist(y='y', width=400, height=200)

layout = pn.Row(scatter, hist) layout.servable()

Bokeh (custom web apps)

from bokeh.plotting import figure, show from bokeh.models import ColumnDataSource, HoverTool

source = ColumnDataSource(df)

p = figure(title="Interactive Plot", tools="pan,wheel_zoom,box_select") p.circle('x', 'y', source=source, size=10, alpha=0.6)

hover = HoverTool(tooltips=[("X", "@x"), ("Y", "@y"), ("Label", "@label")]) p.add_tools(hover)

show(p)

Anti-patterns

• ❌ Pie charts with many slices (use bar charts)

• ❌ Dual y-axes (hard to read, try normalization or small multiples)

• ❌ 3D charts (distorts perception)

• ❌ Rainbow colormaps (use perceptually uniform: viridis, plasma)

• ❌ Missing labels, titles, or units

• ❌ Overplotting without handling (sampling, alpha, or Datashader)

Common issues and solutions

Problem Solution

Overplotting (100k+ points) Use Datashader (rasterization), hexbin, or 2D histogram

Slow interactivity Reduce data points, use WebGL (Plotly), or pre-aggregate

Large file size Save as JSON (Plotly/Altair) or use static images

Color blindness Use colorblind-friendly palettes (viridis, colorbrewer)

Progressive disclosure

• references/matplotlib-advanced.md — Subplots, annotations, custom styles

• references/seaborn-statistical.md — Complex statistical plots

• references/plotly-dash.md — Full dashboards with callbacks

• references/altair-grammar.md — Vega-Lite transformations

• references/holoviz-datashader.md — Large data visualization

• references/bokeh-server.md — Real-time streaming apps

Related skills

• @data-science-eda — Exploration patterns

• @data-science-interactive-apps — Dashboard deployment

• @data-science-notebooks — Notebook-specific visualization

References

• Matplotlib Documentation

• Seaborn Documentation

• Plotly Python

• Altair Documentation

• HoloViz Tutorial

• Bokeh Documentation

• Python Graph Gallery (examples by chart type)