Python for Data Science - Data Visualization

2021-06-11 10:04

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Python for Data Science - Data Visualization

Three Different Data Visualization Types

  • Data storytelling - for presentations to organizational decision makers
    • Make it easy for the audience to get the point
    • Your data visualization should be:
      • Clutter-free
      • Highly focused
    • Intended audience:
      • Nonanalysts
      • Nontechnical business managers
    • Product types:
      • Static images
      • Simple interactive dashboards
    • Optimal graphics:
      • Area Charts
      • Bar Charts
      • Line Charts
      • Pie Charts
      • Cloropleths
      • Point Maps
  • Data showcasing - for presentations to analysts, scientist, mathematicians, and engineers
    • Showcase lots of data so your audience members can think for themselves
    • Your data visualization should be:
      • Highly contextual
      • Open ended
    • Intended audience:
      • Analysts, quants
      • Engineers, mathematicians, scientists
    • Product types:
      • Static images
      • Interactive dashboards
    • Optimal graphics:
      • Area Charts
      • Bar Charts
      • Line Charts
      • Cloropleths
      • Point Maps
      • Histograms
      • Scatter Plots
      • Scatter Plot Matrices
      • Raster Maps
  • Data art - for presentations to activists or to the general public
    • Use your data visualization to make a statement
    • Your data visualization should be:
      • Attention getting
      • Creative controversial
    • Intended audience:
      • Idealists, dreamers, artists
      • Social activists
    • Product types:
      • Static images
    • Optimal graphics:
      • Line Charts
      • Graph Networks
      • Cloropleths
      • Something Weird and Artistic...

Communicating with color and context

Color should be used:

  • Strategically
  • Sparingly
  • Consistently

You want to use color to draw attention to the parts of the visualization that matter, and away from the parts that don‘t

Creating Context

  • How: add data on additional metrics that are relevant to the datasets you‘re showing, trendlines, colors, and annotations.
  • Why: meant to give audience some deeper perspective and insight into what‘s happening
  • When :useful in data showcasing

Python for Data Science - Data Visualization

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原文地址:https://www.cnblogs.com/keepmoving1113/p/14226155.html


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