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How to drop columns from a Pandas DataFrame — with examples.
I use Pandas — and Python in general — for any type of scripting. Having grown to loathe redundant menial tasks, especially with CSV’s, I lean more and more on this powerful library. Since I manipulate and use them (CSV’s) daily at work, if I perform a routine 3 times, it finds its way into a Python script with pandas leading the charge. More often than not, when analyzing CSV data, they tend to be messy. Likely they have columns you don’t need or care about. Pandas DataFrames have a function that allows you to get rid of those columns and keep only the ones you need. As always, I learn best by example so keep reading and learn with me…
OS, Database, and software used:
- OpenSuse Leap 15.0
- Python 3.7.2
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Far from in shape, I still walk a lot. Typically I walk or hike at least an hour a day, 6 days a week. I track certain stats from my walks for a month in — you guessed it — a CSV file. The example CSV below contains some columns I want to keep and others I don’t. How can I get rid…
