[Tutor] weird lambda expression -- can someone help me understand how this works

[Amit Saha]

On Sat, Dec 14, 2013 at 12:29 PM, Amit Saha <amitsaha.in at gmail.com> wrote:
> On Sat, Dec 14, 2013 at 12:14 PM, Michael Crawford <dalupus at 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.


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