type_of_label = set(data_train['label'])
# stop = stopwords.words('english')
# stop.append("the")
# stop.append("company")
# stop_words=set(stop)
label_company = dict()
label_other = dict()
index = 0
for s in type_of_companies:
    label_company[index]=s
    label_other[s]=index
    index+=1


for s in type_of_companies:
    df=data_train[data_train['label'] == s]
    email=''
    for i in df.index: 
        email+=df["text"][i]
    tokenizer = RegexpTokenizer(r'\w+')
    filtered_sentence=[]
    word_tokens = tokenizer.tokenize(email)
    for w in word_tokens:
        if w.lower() not in stop:
            filtered_sentence.append(w.lower())

    fdist2 = FreqDist(filtered_sentence)
    fdist2.plot(10,cumulative=False,title='Frequency for '+str(s))