simple loop with append in Python

Question

I want to get a loop, so instead of writing me code such:

tfidf = vectorizer.fit_transform([ data[0]['body'] , data[1]['body'] , data[2]['body'] , data[3]['body'] ....  ])

I get it in a loop. So far I have tried:

for i in range(len(data) - 1):
    tfidf = vectorizer.fit_transform([ append(data[i]['body']) , data[i+1]['body']) ])

Any idea on how to make it work? I get the following error:

name 'append' is not defined

append() must be called on a list, it doesn't exist as a global scope function. — Matt C
– Matt C, Commented Apr 27, 2016 at 19:19

Salias · Accepted Answer · 2016-04-28 13:35:53Z

1

I guess you want to extract the 'body' content from the json data and have a list of text elements to then pass it to the feature extractor (make sure you have preprocessed the text before).

Try this:

    tfidf = vectorizer.fit_transform([d['body'] for d in data])

Or, if it is more clear to you, you can create the list first and then pass it to the function:

bodies = [d['body'] for d in data]
tfidf = vectorizer.fit_transform(bodies)

Hope it helps :)

Happy coding !!!

P.D: I haven't tested the code, but I think the idea is clear.

answered Apr 28, 2016 at 13:35

Salias

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2 Comments

Economist_Ayahuasca Over a year ago

great answer @Salias ! I looks really good, wha tod you mean by processes the text before?

Salias Over a year ago

If you want to extract tf-idf matrix of your collection, you should have been removed stopwords, punctuation, etc.... before. Otherwise, you will have a very noisy matrix with meaningless words like "to" or "and" as the most repeated, and thus, the most important. The pre-processing filters depend on the task you want to accomplish. Have a look at researchgate.net/publication/…

Albert Rothman · Accepted Answer · 2016-04-27 18:40:31Z

1

You need to call append() on a list. eg MyList.append(element)

answered Apr 27, 2016 at 18:40

Albert Rothman

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1 Comment

Economist_Ayahuasca Over a year ago

true, I tried to define a list before but the vectorizer.fit_transform does not allow it.... any other alternatives?

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