Data is sometimes present but inconsistently represented in manners that can confound analysis. Tracking down these sorts of inconsistencies is complicated by the fact that data can be inconsistent in many different ways and that there are no general or automated methods of identifying these issues.
Examples include:
Inconsistent Date Formats (MM/DD/YYYY vs DD-MM-YYYY)
Variations in Categorical Data (“Male” vs “M”)
Missing Data or Different Null Representations (“NA”, “N/A”, “NULL”)
Inconsistent Use of Abbreviations and Acronyms (“St.” vs “Street”)
The corresponding exercise will provide you with an opportunity to further practice cleaning and handling inconsistent data, using the skills introduced in exercise 3.8.