For the sake of curiosity is there any practical difference between getting the column names of a DataFrame (let's say df) by using df.columns or df.keys()?
I've checked the outs by type and it seems to be exactly the same. Am I missing something or these two methods are just as redundant as it seems? Is one more appropriate to use than the other?
Thanks.
one difference I noticed That you can Use .keys() with Series but You can not use .columns with Series .
Doesn't look like there's a practical difference and if there is, I'd really like to know what it is. You probably saw in the documentation that DataFrame.columns has the column labels and it is an axis property and DataFrame.keys gets the info axis. I would think that since the former is an attribute or reference and the latter a callable method, the method takes a little more time to execute. I have not tested this but I'm pretty sure that, even if there's a difference, it is not significant. Also they both return the same type:
>>> type(data.columns)
<class 'pandas.core.indexes.base.Index'>
>>> type(data.keys())
<class 'pandas.core.indexes.base.Index'>