Using regular expressions and simple Python scripts to produce clearly formatted data for processing and visualization. I presently use regex and Python for text cleaning when editing business copy, and for data cleaning (specifically of NOAA's weather buoy data sets). Here's an example of a regular expression I use every week. In a text with numbers formatted as "$1,234", and the desired output of "$1234" (no comma), I used this regex to find the offending instances: (\s)([1-9]),([0-9]{3}) and replaced it in Textwrangler with "\ \2\3" to yield the desired result over several hundred cases in a book. In another instance, I used regex to reformat NOAA weather data, processing dates and times that were tab delimited into a single column that could be read by a Python script for identification purposes.