Vectorize pandas.Series.apply by aman-thakral · Pull Request #355 · pandas-dev/pandas (original) (raw)
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When profiling my code I noticed that numpy.vectorize was a bit faster than pandas.Series.apply.
Execution Times:
numpy.vectorize: 3.3 seconds
pandas.Series.apply: 4.4 seconds.
hey, be careful to add features in branches and not the master branch :) I will take a look at the apply patch, looks like a no brainer. You may consider resetting your fork to be exactly wesm/master since you've got a couple of merge commits
Definitely a good idea for me to reset my fork. Also, I guess the same reasoning could apply to Series.map. Unless you have some use case distinctions for map and apply in mind (besides map also accepting a dict). It would be good to document this distinction as well :).
np.vectorize has some string handling problems (changing Series.map and apply to vectorize caused unit test failures). I'll patch this separately very quickly (maybe writing a Cython function) and make sure the speed is comparable
wesm added a commit that referenced this pull request
see above commit...added a new cython function that's about 25% faster than np.vectorize and gets the types right. thanks for pointing this out!
dan-nadler pushed a commit to dan-nadler/pandas that referenced this pull request
Update .travis.yml
Update setup.py
dan-nadler pushed a commit to dan-nadler/pandas that referenced this pull request
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