More often than not, datasets will be messy and hard to visualize right away. They will have missing values, dates in different formats, text in numeric-only columns, multiple items in the same columns, various spellings of the same name, and other unexpected things. See Figure 4-1 for inspiration. Don’t be surprised if you find yourself spending more time cleaning up data than you do analyzing and visualizing it.
Figure 4-1. More often than not, raw data looks messy.
In this chapter, you’ll learn about different tools to help you make decisions about which one to use to clean up your data efficiently. We’ll start with basic cleanup methods using Google Sheets in “Smart Cleanup with Google Sheets”, “Find and Replace with Blank”, “Transpose Rows and Columns”, “Split Data into Separate Columns”, and “Combine Data into One Column”. While we feature Google Sheets in our examples, many of these principles (and in some cases, the same formulas) apply to Microsoft Excel, LibreOffice Calc, Mac’s Numbers, or other spreadsheet packages. Next, you’ll learn how to extract table data from text-based PDF documents with Tabula, a free tool used by data journalists and researchers worldwide to analyze spending data, health reports, and all sorts of other datasets that get trapped in PDFs (see “Extract Tables from PDFs with Tabula”). Finally, we will introduce ...
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