It answered it. I had forgotten that you could pass functions around in python.
Thanks, Mike On Dec 13, 2013, at 9:31 PM, Amit Saha <amitsaha...@gmail.com> wrote: > On Sat, Dec 14, 2013 at 12:29 PM, Amit Saha <amitsaha...@gmail.com> wrote: >> On Sat, Dec 14, 2013 at 12:14 PM, Michael Crawford <dalu...@gmail.com> wrote: >>> I found this piece of code on github >>> >>> https://gist.github.com/kljensen/5452382 >>> >>> def one_hot_dataframe(data, cols, replace=False): >>> """ Takes a dataframe and a list of columns that need to be encoded. >>> Returns a 3-tuple comprising the data, the vectorized data, >>> and the fitted vectorizor. >>> """ >>> vec = DictVectorizer() >>> mkdict = lambda row: dict((col, row[col]) for col in cols) >>> #<<<<<<<<<<<<<<<<<< >>> vecData = pandas.DataFrame(vec.fit_transform(data[cols].apply(mkdict, >>> axis=1)).toarray()) >>> vecData.columns = vec.get_feature_names() >>> vecData.index = data.index >>> if replace is True: >>> data = data.drop(cols, axis=1) >>> data = data.join(vecData) >>> return (data, vecData, vec) >>> >>> I don't understand how that lambda expression works. >>> For starters where did row come from? >>> How did it know it was working on data? >> >> Consider this simple example: >> >>>>> l = lambda x: x**2 >>>>> apply(l, (3,)) >> 9 >> >> A lambda is an anonymous function. So, when you use apply(), the >> lambda, l gets the value 3 in x and then returns x**2 which is 9 in >> this case. > > Argh, no sorry, that doesn't answer your question. Sorry, my bad. I > should have read your query properly. > > > -- > http://echorand.me
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