Think about this: You’re tasked with analyzing numerical knowledge from a prolonged PDF report consisting of textual content and tables. A colleague has already extracted the knowledge utilizing Optical Character Recognition (see final week’s put up).
Sadly, moderately than a structured dataset, this file is moderately messy — you discover redundant headers, extraneous footnotes, and irregular line breaks. Numbers are inconsistently formatted, and knowledge descriptors are scattered all through, rendering any significant evaluation practically unimaginable with out vital preprocessing. It seems like you may be going through hours of tedious knowledge cleansing immediately.
Gladly, although, you’ve chanced on Regex. Quick for “common expressions,” it’s a highly effective device for sample matching in textual content. It sounds easy, however permitting customers to outline, search, and manipulate particular patterns inside textual content makes it a wonderful device for chopping by messy knowledge.
This piece shall present a bit extra background on Regex, and the way it’s applied in Python. We then dig deeper into the important Regex options for knowledge cleansing, and supply a hands-on instance (that we very lately confronted at Wangari) as an example how this works in observe. When you…