Contents

Chapter 1

Features

This chapter covers the core features and capabilities of Data Visualizer.

Features

  • Four chart types — Bar, line, pie, and scatter plots
  • Pure SVG output — Scalable vector graphics, works in any browser or image viewer
  • Auto-detection — Picks sensible column defaults from your data
  • Nice axis ticks — Automatically rounds tick values for clean labels (100, 200, 300 not 97, 194, 291)
  • Dark theme — Designed for dark backgrounds with high-contrast colors
  • Multi-series line charts — Compare multiple data series on one chart
  • Smart number formatting — 1,500,000 becomes 1.5M, 2,300 becomes 2.3K
  • 10-color palette — Accessibility-tested colors with enough contrast
  • CLI-friendly — One command to generate a chart

Requirements

  • Python 3.10+
  • No external dependencies (stdlib only)
Chapter 2

Quick Start

Follow this guide to get Data Visualizer up and running in your environment.

Quick Start

bash
# Bar chart from CSV
python src/data_visualizer.py --data examples/sample_chart_data.csv --chart bar --x month --y revenue

# Line chart
python src/data_visualizer.py --data examples/sample_chart_data.csv --chart line --x month --y revenue

# Pie chart
python src/data_visualizer.py --data examples/sample_chart_data.csv --chart pie --label month --value revenue

# Scatter plot
python src/data_visualizer.py --data data.csv --chart scatter --x users --y revenue --title "Users vs Revenue"

# Custom output and color
python src/data_visualizer.py --data data.json --chart bar --output dashboard_chart.svg --color "#FF3366"

Chart Types

TypeBest ForRequired Columns
barCategorical comparisons (revenue by month)x (labels), y (values)
lineTime series trends (growth over time)x (labels), y (values)
piePart-of-whole (market share, budget)label, value
scatterCorrelation analysis (users vs revenue)x (numeric), y (numeric)
Chapter 3
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Input Format

Chapter 4
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