Using defaultdict function in verb tags counting

Question

Could you please explain why the defaultdict function does not define verbs in the code following? Googling tells me that the syntax is fine. Please help! Thanks in advance :)

My code

import spacy
from collections import defaultdict
nlp = spacy.load("en_core_web_sm")
!pip install pandas==0.24.2 --user
import pandas as pd

def calculate_the_word_types(df):
  verbs = defaultdict(calculate_the_word_types)

for i, row in df.iterrows():
  doc = nlp(row["text"])


for v in map(lambda x: x.lemma_, filter(lambda x: (x.pos_ == 'AUX') | (x.pos_ == 'VERB'), doc)):
  verbs[v] += 1
  df.at(i, "nr_verb", len(list(map(lambda x: x.text,
                                                 filter(lambda x: (x.pos_ == 'AUX') | (x.pos_ == 'VERB'), doc)))))

return df
verbs

Error code

NameError                                 Traceback (most recent call last)
<ipython-input-32-7e7c626bb331> in <module>()
     13 
     14 for v in map(lambda x: x.lemma_, filter(lambda x: (x.pos_ == 'AUX') | (x.pos_ == 'VERB'), doc)):
---> 15   verbs[v] += 1
     16   df.at(i, "nr_verb", len(list(map(lambda x: x.text,
     17                                                  filter(lambda x: (x.pos_ == 'AUX') | (x.pos_ == 'VERB'), doc)))))

NameError: name 'verbs' is not defined

Answer 1

set_value() function is deprecated.

As replacement, you can use ".at" like this: df.at["YOURINDEX", "YOURCOLUMN"] = "YOURVALUE". Also, you have issue on this line verbs = defaultdict(calculate_the_word_types) initialize with 0 since it will act as counter.

And fix your indentation

working code -

import spacy
from collections import defaultdict
nlp = spacy.load("en_core_web_sm")
!pip install pandas==0.24.2 --user
import pandas as pd

def calculate_the_word_types(df):
    verbs = defaultdict(lambda: 0)

    # count all tokens, but not the punctuations
    for i, row in df.iterrows():
        doc = nlp(row["text"])

        # count only the verbs
        for v in map(lambda x: x.lemma_, filter(lambda x: (x.pos_ == 'AUX') | (x.pos_ == 'VERB'), doc)):
            verbs[v] += 1
        df.at[i, "nr_verb"] = len(list(map(lambda x: x.text, 
                                        filter(lambda x: (x.pos_ == 'AUX') | (x.pos_ == 'VERB'), doc)))) 

    return df


# dataframe
df = pd.DataFrame({'text':['hello there', 'I love Tatooine', 'I hate sands']})

# print the dataframe with verb count
print(calculate_the_word_types(df))

Output -

        text      nr_verb
0   hello there     0.0
1   I love Tatooine 1.0
2   I hate sands    1.0