pandas loc vs. iloc vs. ix vs. at vs. iat?

python pandas performance indexing lookup
scribbles picture scribbles · Feb 27, 2015 · Viewed 109.1k times · Source

Recently began branching out from my safe place (R) into Python and and am a bit confused by the cell localization/selection in Pandas. I've read the documentation but I'm struggling to understand the practical implications of the various localization/selection options.

  • Is there a reason why I should ever use .loc or .iloc over the most general option .ix?
  • I understand that .loc, iloc, at, and iat may provide some guaranteed correctness that .ix can't offer, but I've also read where .ix tends to be the fastest solution across the board.
  • Please explain the real-world, best-practices reasoning behind utilizing anything other than .ix?

Answer

lautremont picture lautremont · May 4, 2015

loc: only work on index
iloc: work on position
ix: You can get data from dataframe without it being in the index
at: get scalar values. It's a very fast loc
iat: Get scalar values. It's a very fast iloc

http://pyciencia.blogspot.com/2015/05/obtener-y-filtrar-datos-de-un-dataframe.html

Note: As of pandas 0.20.0, the .ix indexer is deprecated in favour of the more strict .iloc and .loc indexers.

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