I have a data frame like this: I want o apply a function on pos and save the result in a new column. So the output would look like this: So the function return a list for each tuple in the list (but the implementation of the function is not the point here, for that I just call get_sentiment). I

how to do nested loop using apply in pandas

I have a data frame like this:

text,                pos
No thank you.        [(No, DT), (thank, NN), (you, PRP)]
They didn't respond  [(They, PRP), (didn't, VBP), (respond, JJ)]

I want o apply a function on pos and save the result in a new column. So the output would look like this:

text,                pos                                           score
No thank you.        [(No, DT), (thank, NN), (you, PRP)]        [[0.0, 0.0, 1.0], [], [0.5, 0.0, 0.45]]
They didn't respond  [(They, PRP), (didn, VBP), (respond, JJ)]  [[0.0, 0.0, 1.0], [], [0.75, 0.0, 0.25]]

So the function return a list for each tuple in the list (but the implementation of the function is not the point here, for that I just call get_sentiment). I can do it using the nested loop but I didn’t like it. I want to do it using a more pythonic and Pandas Dataframe way:

This is what I have tried so far:

df['score'] = df['pos'].apply(lambda k: [get_sentiment(x,y) for j in k for (x,y) in j])

However, it raises this error:

ValueError: too many values to unpack (expected 2)

There is a couple of question in so but the answers was in R.

for more clarity:

get_sentiment function is a function in NLTK that assigns a list of score to each word (The list is [positive score, negative score, objectivity score]). Overall, I need to apply that function on top of the pos column of my Dataframe.

Answer

In your case

df['score'] = df['pos'].apply(lambda k: [get_sentiment(j[0],j[1]) for j in k ])

Advertisement

Answer