Pandas and CSV

 Hi all,

Create a csv file like given below

No,Name,Place

1,HUP,Kollam

2,Abc,Test

3,Raj,Klm

#Program 1

import pandas as pd

df=pd.read_csv('hup1.csv')

print(df.to_string())

#Program 2

import pandas as pd

df=pd.read_csv('hup1.csv')

for ind in df.index:

  print(df['1'][ind], df['HUP'][ind])

Remove stop words and predict using Naive Bayes Classifier

 Hi all,

Use this code for NBC which removes stop words

-------------------------------------------------------------------------------

from nltk import NaiveBayesClassifier as nbc

from nltk.tokenize import word_tokenize

from itertools import chain

import csv

from gensim.parsing.preprocessing import remove_stopwords

from nltk.tokenize import word_tokenize

with open('trainingdata.csv','r') as csvinput:

    reader=csv.reader(csvinput,delimiter=",")

    rownum = 0 

    training_data = []

    for row in reader:

      old=row[0]

      sent=remove_stopwords(row[0])

      row[0]=sent

     

      training_data.append (row)

      rownum += 1

      print('hup original ',old)

      print('hup new ',sent)

      print('----------------')

vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))

feature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]

classifier = nbc.train(feature_set)

with open('testdata.csv','r') as csvinput:

    with open('data.csv', 'w') as csvoutput:

        writer = csv.writer(csvoutput, lineterminator='\n')

        reader1 = csv.reader(csvinput)

        all = []

        row = next(reader1)

        

        for row in reader1:

            test_sentence = row[1]

            featurized_test_sentence =  {i:(i in word_tokenize(test_sentence.lower())) for i in vocabulary}

            print ("test_sent:",test_sentence)

            print ("tag:",classifier.classify(featurized_test_sentence))

            row.append(classifier.classify(featurized_test_sentence))

            all.append(row)

        writer.writerows(all)