Mastering Python Data Visualization
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Why does visualization require planning?

The whole process of visualization involves people with different skill sets and domain expertise. Data wranglers diligently collect data and analyze it. Mathematicians and statisticians understand the visual design principles and communicate their data using those principles. Designers or artists (in some cases, frontend developers) have the skills necessary for visualization, while business analysts look out for things like customer behavioral patterns, outliers, or a sudden unusual trend. However, it always starts with either acquiring or gathering data, and with the following steps:

  • Acquire or gather data from an external source, a website, or from a file on a disk
  • Parse and filter data using programming methods to parse, clean, and reduce the data
  • Analyze and refine to remove noise and unnecessary dimensions and find patterns
  • Represent and interact to present the data in ways that are more accessible and understandable

How much of this process is followed varies with different problems, and in some cases, there is more analysis done than filtering of data. As discussed in the previous chapter, in some instances, the analysis and visualization is done iteratively. In other words, the distribution of these steps is not always predictable and consistent.