Baoyu Infographic

Baoyu Infographic is a community skill for generating infographics from data and text, covering layout generation, data visualization, icon integration, color scheme application, and automated infographic production for content marketing.

What Is This?

Overview

Baoyu Infographic provides patterns for automatically creating infographics from structured data and text content. It covers layout generation that arranges sections, statistics, and visual elements in a vertical scroll format, data visualization that creates charts, progress bars, and comparison graphics from numerical data, icon integration that places relevant icons alongside text sections for visual scanning, color scheme application that applies consistent palettes based on topic or brand guidelines, and automated production pipelines that generate infographics from structured input data. The skill enables teams to create infographics without design expertise.

Who Should Use This

This skill serves content marketers creating data-driven infographics for social media and blogs, report authors visualizing key findings as shareable graphics, and developers building infographic generation features into applications.

Why Use It?

Problems It Solves

Professional infographic design requires tools and skills most content teams lack. Data visualization within infographic layouts needs coordinated design decisions. Creating consistent infographic styles across a content series is difficult manually. Converting report data into visual formats is time-consuming without automation.

Core Highlights

Layout engine arranges content sections in visually balanced infographic compositions. Chart builder generates embedded visualizations from numerical data. Color system applies harmonious palettes across all infographic elements. Export renderer produces high-resolution PNG and SVG output.

How to Use It?

Basic Usage

from PIL import Image,\
  ImageDraw, ImageFont
from dataclasses\
  import dataclass

@dataclass
class InfoSection:
  title: str
  content: str
  stat: str = ''
  stat_label: str = ''

class InfographicBuilder:
  def __init__(
    self,
    width: int = 800,
    bg_color: str\
      = '#ffffff',
    accent: str\
      = '#2563eb',
    font_path: str = None
  ):
    self.width = width
    self.bg = bg_color
    self.accent = accent
    self.font_path =\
      font_path

  def build(
    self,
    title: str,
    sections:\
      list[InfoSection]
  ) -> Image.Image:
    section_h = 200
    header_h = 150
    total_h = header_h\
      + len(sections)\
      * section_h + 50

    img = Image.new(
      'RGB',
      (self.width, total_h),
      self.bg)
    draw = ImageDraw.Draw(
      img)

    # Header
    draw.rectangle(
      [0, 0,
       self.width, header_h],
      fill=self.accent)
    font = ImageFont.truetype(
      self.font_path, 36)\
      if self.font_path\
      else ImageFont.load_default()
    draw.text(
      (40, 50), title,
      fill='white',
      font=font)

    # Sections
    y = header_h + 20
    for sec in sections:
      self._draw_section(
        draw, sec, y)
      y += section_h
    return img

  def _draw_section(
    self, draw,
    sec: InfoSection,
    y: int
  ):
    if sec.stat:
      font_big =\
        ImageFont.truetype(
          self.font_path,
          48)\
        if self.font_path\
        else ImageFont\
          .load_default()
      draw.text(
        (40, y),
        sec.stat,
        fill=self.accent,
        font=font_big)

Real-World Examples

sections = [
  InfoSection(
    title='Users',
    content=\
      'Monthly active users',
    stat='2.4M',
    stat_label=\
      'monthly active'),
  InfoSection(
    title='Growth',
    content=\
      'Year over year growth',
    stat='+47%',
    stat_label=\
      'annual growth'),
  InfoSection(
    title='Retention',
    content=\
      '30-day user retention',
    stat='82%',
    stat_label=\
      'retention rate'),
]

builder = InfographicBuilder(
  width=800,
  accent='#10b981',
  font_path='fonts/Inter.ttf')

infographic = builder.build(
  'Q4 Performance Report',
  sections)
infographic.save(
  'report.png')

Advanced Tips

Use a modular section system where each data point gets its own visual block for easy rearrangement. Generate SVG output alongside PNG for scalable infographics that remain sharp at any size. Apply color contrast ratios that meet accessibility standards for text readability.

When to Use It?

Use Cases

Generate a performance report infographic from quarterly metrics data. Create social media infographics summarizing survey results with key statistics. Build a CMS integration that generates infographics from structured article data.

Related Topics

Infographic design, data visualization, content marketing, image generation, and visual communication.

Important Notes

Requirements

Pillow or similar image library for rendering. Font files for typography rendering. Structured data input with statistics and section content.

Usage Recommendations

Do: limit infographics to 5 to 7 key data points for readability. Use large bold statistics as visual anchors for each section. Test output at the target display size for text legibility.

Don't: overcrowd infographics with too many data points reducing visual clarity. Use inconsistent font sizes and colors that break visual hierarchy. Generate infographics wider than typical mobile screen widths for social media sharing.

Limitations

Programmatic layout cannot match the visual refinement of professional graphic design tools. Complex chart types like treemaps and sankey diagrams require dedicated visualization libraries. Generated infographics may need manual adjustment for optimal visual balance.