visualization

name	visualization
description	Data and information visualization
license	MIT
metadata	{"audience":"developers","category":"data-science"}

What I do

• Create effective data visualizations
• Select appropriate chart types
• Design interactive dashboards
• Handle large datasets efficiently
• Apply color and typography
• Tell stories with data

When to use me

When presenting data, building dashboards, or creating visual reports.

Key Concepts

Chart Selection

Comparison: Bar, Column, Radar
Distribution: Histogram, Box plot
Relationship: Scatter, Bubble
Composition: Pie, Stacked area
Trend: Line, Area
Part-to-whole: Treemap, Sunburst

Data Visualization Libraries

# Python
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from bokeh.plotting import figure

# JavaScript
import * as d3 from 'd3';
import Chart from 'chart.js/auto';
import { Vega } from 'vega';

Design Principles

1. Show data, not decoration
2. Maximize data-ink ratio
3. Avoid 3D effects
4. Use color purposefully
5. Include clear labels
6. Provide context

Interactive Dashboards

// Plotly Dash example
import dash
from dash import dcc, html

app = dash.Dash(__name__)
app.layout = html.Div([
    dcc.Graph(id='chart'),
    dcc.Interval(interval=5000)
])

Accessibility in Visualization

• Colorblind-safe palettes
• Patterns + colors
• Screen reader support
• High contrast mode
• Tooltips for detail

Best Practices

• Start with clean data
• Choose the simplest chart
• Label axes clearly
• Add legends
• Provide context
• Test with real users