NLP - Python - Conditional Frequency Distribution

I am trying to solve a question in hackerrank, which determine conditional frequency distribution of all the words(lowercase and removing stop words) for the given category 'cfdconditions', and events 'cfdevents'. Also compute conditional frequency distribution of category 'cfdconditions' and events ending with 'ing' or 'ed'. And then display frequency modal for both distributions.

My code is -

def calculateCFD(cfdconditions, cfdevents):
    # Write your code here
    from nltk.corpus import brown
    from nltk import ConditionalFreqDist
    from nltk.corpus import stopwords
    stopword = set(stopwords.words('english'))
    cdev_cfd = [ (genre, word.lower()) for genre in cfdconditions for word in brown.words(categories=genre) if word.lower() not in stopword]
    cdev_cfd = [list(x) for x in cdev_cfd]
    cdev_cfd = nltk.ConditionalFreqDist(cdev_cfd)
    a = cdev_cfd.tabulate(condition = cfdconditions, samples = cfdevents)
    inged_cfd = [ (genre, word.lower()) for genre in cfdconditions for word in brown.words(categories=genre) if (word.lower().endswith('ing') or word.lower().endswith('ed')) ]
    inged_cfd = [list(x) for x in inged_cfd]
    for wd in inged_cfd:
        if wd[1].endswith('ing') and wd[1] not in stopword:
            wd[1] = 'ing'
        elif wd[1].endswith('ed') and wd[1] not in stopword:
            wd[1] = 'ed'

    inged_cfd = nltk.ConditionalFreqDist(inged_cfd)    
    b = inged_cfd.tabulate(cfdconditions, samples = ['ed','ing'])
    return(a,b)

But result is still failing for 2 test cases, for which my output is -

                 many years 
      adventure    24    32 
        fiction    29    44 
science_fiction    11    16 
                  ed  ing 
      adventure 3281 1844 
        fiction 2943 1767 
science_fiction  574  293 

and

                  good    bad better 
      adventure     39      9     30 
        fiction     60     17     27 
        mystery     45     13     29 
science_fiction     14      1      4 
                  ed  ing 
      adventure 3281 1844 
        fiction 2943 1767 
        mystery 2382 1374 
science_fiction  574  293 

If anyone can help me for the solution, it will be of great help.

Try this code and see if it works.

from nltk.corpus import brown,stopwords
def calculateCFD(cfdconditions, cfdevents):


# Write your code here
stopword = set(stopwords.words('english'))
cdev_cfd = nltk.ConditionalFreqDist([(genre, word.lower()) for genre in brown.categories() for word in brown.words(categories=genre) if not word.lower()  in stopword])
cdev_cfd.tabulate(conditions = cfdconditions, samples = cfdevents)
inged_cfd = [ (genre, word.lower()) for genre in brown.categories() for word in brown.words(categories=genre) if (word.lower().endswith('ing') or word.lower().endswith('ed')) ]
inged_cfd = [list(x) for x in inged_cfd]
for wd in inged_cfd:
    if wd[1].endswith('ing') and wd[1] not in stopword:
        wd[1] = 'ing'
    elif wd[1].endswith('ed') and wd[1] not in stopword:
        wd[1] = 'ed'
#print(inged_cfd)
inged_cfd = nltk.ConditionalFreqDist(inged_cfd)
#print(inged_cfd.conditions())    
inged_cfd.tabulate(conditions=cfdconditions, samples = ['ed','ing'])