Take into account this: You might be tasked with analyzing numerical information from a prolonged PDF report consisting of textual content and tables. A colleague has already extracted the data utilizing Optical Character Recognition (see last week’s post).
Sadly, slightly than a structured dataset, this file is slightly messy — you discover redundant headers, extraneous footnotes, and irregular line breaks. Numbers are inconsistently formatted, and information descriptors are scattered all through, rendering any significant evaluation practically not possible with out vital preprocessing. It seems to be like you can be dealing with hours of tedious information cleansing at present.
Gladly, although, you’ve came upon Regex. Brief for “common expressions,” it’s a highly effective software 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 software for chopping by means of messy information.
This piece shall present a bit extra background on Regex, and the way it’s carried out in Python. We then dig deeper into the important Regex options for information cleansing, and supply a hands-on instance (that we very lately confronted at Wangari) for instance how this works in observe. When you…