March 2024
Intermediate to advanced
814 pages
22h 10m
English
Often, many data cleansing tasks involve carrying out complex searches and substitutions between strings. The usual search and replace tools are sometimes not enough to get the desired results. For instance, let’s suppose you need to match strings, not in an exact way (for instance, via equality conditions) but using similar criteria between them. Knowing how to use tools such as regular expressions (alias regex) can make all the difference in projects that require high-quality data. To illustrate the power of regular expressions in data cleansing, consider a scenario involving a dataset of user names with inconsistent formatting. Some usernames contain spaces, numbers, special characters, or a mix of ...
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