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scrapedata.py
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scrapedata.py
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import requests
import urllib
from bc4 import Blackcoffer
import time
import sys
import xlwt
import xlrd
import xlsxwriter
from xlwt import Workbook
# Give the location of the file
loc = ('/home/anushka/cik_list.xlsx')
locw = ('/home/anushka/OutputDataStructure.xlsx')
# To open Workbook
wb = xlrd.open_workbook(loc)
sheet = wb.sheet_by_index(0)
workbook = xlsxwriter.Workbook('Example.xlsx')
sheet1 = workbook.add_worksheet()
sys.setrecursionlimit(100000)
# For row 0 and column 0
for i in range(1, 152):
a = sheet.cell_value(i, 5)
url = ('https://www.sec.gov/Archives/'+a)
response = requests.get(url)
#scraping the pages one by one
coffee = Blackcoffer(response.text, "html.parser")
txt = str(coffee)
text = txt.lower()
doc_lenght = len(text)
#function to extract mda text
def mdafc(text, doc_lenght):
first_instance_part1 = text.find(
"management's discussion and")+1
if first_instance_part1 != 0: # index page reference
new_text = text[(first_instance_part1)*3:doc_lenght] #skipping the idex
second_instance_part1 = new_text.find(
"management's discussion and") #title reference
text_forFlag = text[(first_instance_part1)*3 +
second_instance_part1:doc_lenght]
flag = text_forFlag.find("item ")
try:
mda1 = (text_forFlag[flag+5]) # to check if its a title not a word
mda = (text_forFlag[:flag])
return mda
except IndexError:
return 0
else:
return 0
#function to extract qqdmr text
def qqdmr(text, doc_lenght):
first_instance_part2 = text.find(
"quantitative and qualitative disclosures")+1
if first_instance_part2 != 0:
new_text = text[(first_instance_part2)*3:doc_lenght]
second_instance_part2 = new_text.find(
"management's discussion and")
text_forFlag = text[(first_instance_part2)*3 +
second_instance_part2:doc_lenght]
flag = text_forFlag.find("item ")
try:
qqd1 = (text_forFlag[flag+5])
qqd = (text_forFlag[:flag])
return qqd
except IndexError:
return 0
else:
return 0
def rf(text, doc_lenght):
first_instance_part3 = text.find("risk factors")+1
if first_instance_part3 != 0:
new_text = text[(first_instance_part3)*3:doc_lenght]
second_instance_part3 = new_text.find(
"management's discussion and")
text_forFlag = text[(first_instance_part3)*3 +
second_instance_part3:doc_lenght]
flag = text_forFlag.find("item ")
rfs = (text_forFlag[:flag])
return rfs
else:
return 0
def cleanText(Ctext):
"""
removes punctuation, stopwords and returns lowercase text in a list of single words
"""
Ctext = Ctext.lower()
from bc4 import Blackcoffer
Ctext = Blackcoffer(Ctext, features="lxml").get_text()
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'\w+')
Ctext = tokenizer.tokenize(Ctext)
from nltk.corpus import stopwords
clean = [
word for word in Ctext if word not in stopwords.words('english')]
return clean
def loadPositive():
"""
loading positive dictionary
"""
myfile = open('/home/anushka/positive.csv', "r")
positives = myfile.readlines()
positive = [pos.strip().lower() for pos in positives]
return positive
def loadNegative():
"""
loading positive dictionary
"""
myfile = open('/home/anushka/negative.csv', "r")
negatives = myfile.readlines()
negative = [neg.strip().lower() for neg in negatives]
return negative
def loadConstrain():
"""
loading constraining dictionary
"""
myfile = open('/home/anushka/constrain.csv', "r")
constrains = myfile.readlines()
constrain = [con.strip().lower() for con in constrains]
return constrain
def loadUncertain():
"""
loading uncertainity dictionary
"""
myfile = open('/home/anushka/uncertain.csv', "r")
uncertains = myfile.readlines()
uncertain = [un.strip().lower() for un in uncertains]
return uncertain
def countNeg(cleantext, negative):
"""
counts negative words in cleantext
"""
negs = [word for word in cleantext if word in negative]
return len(negs)
def countPos(cleantext, positive):
"""
counts negative words in cleantext
"""
pos = [word for word in cleantext if word in positive]
return len(pos)
def countCons(cleantext, constrain):
"""
counts negative words in cleantext
"""
con = [word for word in cleantext if word in constrain]
return len(con)
def countUn(cleantext, uncertain):
"""
counts negative words in cleantext
"""
un = [word for word in cleantext if word in uncertain]
return len(un)
def getSentiment(cleantext, negative, positive):
"""
counts negative and positive words in cleantext and returns a score accordingly
"""
positive = loadPositive()
negative = loadNegative()
return ((countPos(cleantext, positive) - countNeg(cleantext, negative))/(countPos(cleantext, positive) + countNeg(cleantext, negative) + 0.000001))
def complex_word(words):
ctr = 0
for word in words:
if len(word) > 2:
ctr += 1
return ctr
#initializing
mda = mdafc(text, doc_lenght)
qqd = qqdmr(text, doc_lenght)
rfs = rf(text, doc_lenght)
positive = loadPositive()
negative = loadNegative()
constrain = loadConstrain()
uncertain = loadUncertain()
total_cons = countCons(text, constrain)
#considering all the cases(i made a mistake while reading the docs)
if mda != 0 and qqd == 0 and rfs == 0:
print(1)
obj_clean = cleanText(mda)
mda_positive_count = countPos(obj_clean, positive)
mda_negative_count = countNeg(obj_clean, negative)
mda_constrain_count = countCons(obj_clean, constrain)
mda_uncertain_count = countUn(obj_clean, uncertain)
mda_polarity = getSentiment(obj_clean, negative, positive)
mda_word_count = len(obj_clean)
mda_avg_sentence_lenght = len(mda.split())/mda.count('.')
mda_complex_words = complex_word(obj_clean)
mda_percentage_complex_words = (mda_complex_words/mda_word_count)*100
mda_fog_idex = 0.4*(mda_avg_sentence_lenght +
mda_percentage_complex_words)
mda_positive_ratio = mda_positive_count/mda_word_count
mda_negative_ratio = mda_negative_count/mda_word_count
mda_uncertain_ratio = mda_uncertain_count/mda_word_count
mda_constrain_ratio = mda_constrain_count/mda_word_count
qqd_positive_count = ""
qqd_negative_count = ""
qqd_constrain_count = ""
qqd_uncertain_count = ""
qqd_polarity = ""
qqd_word_count = ""
qqd_avg_sentence_lenght = ""
qqd_complex_words = ""
qqd_percentage_complex_words = ""
qqd_fog_idex = ""
qqd_positive_ratio = ""
qqd_negative_ratio = ""
qqd_uncertain_ratio = ""
qqd_constrain_ratio = ""
rf_positive_count = ""
rf_negative_count = ""
rf_constrain_count = ""
rf_uncertain_count = ""
rf_polarity = ""
rf_word_count = ""
rf_avg_sentence_lenght = ""
rf_complex_words = ""
rf_percentage_complex_words = ""
rf_fog_idex = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
elif mda == 0 and qqd != 0 and rfs == 0:
print(2)
obj_clean = cleanText(qqd)
qqd_positive_count = countPos(obj_clean, positive)
qqd_negative_count = countNeg(obj_clean, negative)
qqd_constrain_count = countCons(obj_clean, constrain)
qqd_uncertain_count = countUn(obj_clean, uncertain)
qqd_polarity = getSentiment(obj_clean, negative, positive)
qqd_word_count = len(obj_clean)
qqd_avg_sentence_lenght = len(qqd.split())/qqd.count('.')
qqd_complex_words = complex_word(obj_clean)
qqd_percentage_complex_words = (qqd_complex_words/qqd_word_count)*100
qqd_fog_idex = 0.4*(qqd_avg_sentence_lenght +
qqd_percentage_complex_words)
qqd_positive_ratio = qqd_positive_count/qqd_word_count
qqd_negative_ratio = qqd_negative_count/qqd_word_count
qqd_uncertain_ratio = qqd_uncertain_count/qqd_word_count
qqd_constrain_ratio = qqd_constrain_count/qqd_word_count
rf_positive_count = ""
rf_negative_count = ""
rf_constrain_count = ""
rf_uncertain_count = ""
rf_polarity = ""
rf_word_count = ""
rf_avg_sentence_lenght = ""
rf_complex_words = ""
rf_percentage_complex_words = ""
rf_fog_idex = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
mda_positive_count = ""
mda_negative_count = ""
mda_constrain_count = ""
mda_uncertain_count = ""
mda_polarity = ""
mda_word_count = ""
mda_avg_sentence_lenght = ""
mda_complex_words = ""
mda_percentage_complex_words = ""
mda_fog_idex = ""
mda_positive_ratio = ""
mda_negative_ratio = ""
mda_uncertain_ratio = ""
mda_constrain_ratio = ""
elif mda == 0 and qqd == 0 and rfs != 0:
print(3)
obj_clean = cleanText(rfs)
rf_positive_count = countPos(obj_clean, positive)
rf_negative_count = countNeg(obj_clean, negative)
rf_constrain_count = countCons(obj_clean, constrain)
rf_uncertain_count = countUn(obj_clean, uncertain)
rf_polarity = getSentiment(obj_clean, negative, positive)
rf_word_count = len(obj_clean)
if rfs.count('.') == 0:
rf_avg_sentence_lenght = 0
else:
rf_avg_sentence_lenght = len(rfs.split())/rfs.count('.')
rf_complex_words = complex_word(obj_clean)
if rf_word_count == 0:
rf_percentage_complex_words = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
rf_fog_idex = ""
else:
rf_percentage_complex_words = (rf_complex_words/rf_word_count)*100
rf_positive_ratio = rf_positive_count/rf_word_count
rf_negative_ratio = rf_negative_count/rf_word_count
rf_uncertain_ratio = rf_uncertain_count/rf_word_count
rf_constrain_ratio = rf_constrain_count/rf_word_count
rf_fog_idex = 0.4*(rf_avg_sentence_lenght +
rf_percentage_complex_words)
mda_positive_count = ""
mda_negative_count = ""
mda_constrain_count = ""
mda_uncertain_count = ""
mda_polarity = ""
mda_word_count = ""
mda_avg_sentence_lenght = ""
mda_complex_words = ""
mda_percentage_complex_words = ""
mda_fog_idex = ""
mda_positive_ratio = ""
mda_negative_ratio = ""
mda_uncertain_ratio = ""
mda_constrain_ratio = ""
qqd_positive_count = ""
qqd_negative_count = ""
qqd_constrain_count = ""
qqd_uncertain_count = ""
qqd_polarity = ""
qqd_word_count = ""
qqd_avg_sentence_lenght = ""
qqd_complex_words = ""
qqd_percentage_complex_words = ""
qqd_fog_idex = ""
qqd_positive_ratio = ""
qqd_negative_ratio = ""
qqd_uncertain_ratio = ""
qqd_constrain_ratio = ""
elif mda != 0 and qqd != 0 and rfs == 0:
print(4)
obj_clean_mda = cleanText(mda)
obj_clean_qqd = cleanText(qqd)
mda_positive_count = countPos(obj_clean_mda, positive)
qqd_positive_count = countPos(obj_clean_qqd, positive)
qqd_negative_count = countNeg(obj_clean_qqd, negative)
mda_constrain_count = countCons(obj_clean_mda, constrain)
qqd_constrain_count = countCons(obj_clean_qqd, constrain)
mda_uncertain_count = countUn(obj_clean_mda, uncertain)
qqd_uncertain_count = countUn(obj_clean_qqd, uncertain)
mda_polarity = getSentiment(obj_clean_mda, negative, positive)
qqd_polarity = getSentiment(obj_clean_qqd, negative, positive)
mda_word_count = len(obj_clean_mda)
qqd_word_count = len(obj_clean_qqd)
mda_avg_sentence_lenght = len(qqd.split())/mda.count('.')
qqd_avg_sentence_lenght = len(mda.split())/qqd.count('.')
mda_complex_words = complex_word(obj_clean_mda)
qqd_complex_words = complex_word(obj_clean_qqd)
mda_percentage_complex_words = (mda_complex_words/mda_word_count)*100
qqd_percentage_complex_words = (qqd_complex_words/qqd_word_count)*100
mda_fog_idex = 0.4*(mda_avg_sentence_lenght +
mda_percentage_complex_words)
qqd_fog_idex = 0.4*(qqd_avg_sentence_lenght +
qqd_percentage_complex_words)
qqd_positive_ratio = qqd_positive_count/qqd_word_count
qqd_negative_ratio = qqd_negative_count/qqd_word_count
qqd_uncertain_ratio = qqd_uncertain_count/qqd_word_count
qqd_constrain_ratio = qqd_constrain_count/qqd_word_count
mda_positive_ratio = mda_positive_count/mda_word_count
if isinstance( mda_word_count, ( int) ) and isinstance( mda_negative_count, ( int) ):
mda_negative_ratio = mda_negative_count/mda_word_count
else:
mda_negative_ratio = ""
mda_uncertain_ratio = mda_uncertain_count/mda_word_count
mda_constrain_ratio = mda_constrain_count/mda_word_count
rf_positive_count = ""
rf_positive_count = ""
rf_negative_count = ""
rf_constrain_count = ""
rf_uncertain_count = ""
rf_polarity = ""
rf_word_count = ""
rf_avg_sentence_lenght = ""
rf_complex_words = ""
rf_percentage_complex_words = ""
rf_fog_idex = ""
rf_fog_idex = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
elif mda != 0 and qqd == 0 and rfs != 0:
print(5)
obj_clean_mda = cleanText(mda)
obj_clean_rf = cleanText(rfs)
obj_clean = obj_clean_mda.extend(obj_clean_rf)
mda_positive_count = countPos(obj_clean_mda, positive)
rf_positive_count = countPos(obj_clean_rf, positive)
mda_negative_count = countNeg(obj_clean_mda, negative)
rf_negative_count = countNeg(obj_clean_rf, negative)
mda_constrain_count = countCons(obj_clean_mda, constrain)
rf_constrain_count = countCons(obj_clean_rf, constrain)
mda_uncertain_count = countUn(obj_clean_mda, uncertain)
rf_uncertain_count = countUn(obj_clean_rf, uncertain)
mda_polarity = getSentiment(obj_clean_mda, negative, positive)
rf_polarity = getSentiment(obj_clean_rf, negative, positive)
mda_word_count = len(obj_clean_mda)
rf_word_count = len(obj_clean_rf)
if rfs.count('.') == 0:
rf_avg_sentence_lenght = 0
else:
rf_avg_sentence_lenght = len(rfs.split())/rfs.count('.')
mda_avg_sentence_lenght = len(mda.split())/mda.count('.')
mda_complex_words = complex_word(obj_clean_mda)
rf_complex_words = complex_word(obj_clean_rf)
mda_percentage_complex_words = (mda_complex_words/mda_word_count)*100
mda_fog_idex = 0.4*(mda_avg_sentence_lenght +
mda_percentage_complex_words)
mda_positive_ratio = mda_positive_count/mda_word_count
mda_negative_ratio = mda_negative_count/mda_word_count
mda_uncertain_ratio = mda_uncertain_count/mda_word_count
mda_constrain_ratio = mda_constrain_count/mda_word_count
if rf_word_count == 0:
rf_percentage_complex_words = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
rf_fog_idex = ""
else:
rf_percentage_complex_words = (rf_complex_words/rf_word_count)*100
rf_positive_ratio = rf_positive_count/rf_word_count
rf_negative_ratio = rf_negative_count/rf_word_count
rf_uncertain_ratio = rf_uncertain_count/rf_word_count
rf_constrain_ratio = rf_constrain_count/rf_word_count
rf_fog_idex = ""
qqd_positive_count = ""
qqd_negative_count = ""
qqd_constrain_count = ""
qqd_uncertain_count = ""
qqd_polarity = ""
qqd_word_count = ""
qqd_avg_sentence_lenght = ""
qqd_complex_words = ""
qqd_percentage_complex_words = ""
qqd_fog_idex = ""
qqd_positive_ratio = ""
qqd_negative_ratio = ""
qqd_uncertain_ratio = ""
qqd_constrain_ratio = ""
elif mda == 0 and qqd != 0 and rfs != 0:
print(6)
obj_clean_qqd = cleanText(qqd)
obj_clean_rf = cleanText(rfs)
obj_clean = obj_clean_qqd.extend(obj_clean_rf)
qqd_positive_count = countPos(obj_clean_qqd, positive)
rf_positive_count = countPos(obj_clean_rf, positive)
qqd_negative_count = countNeg(obj_clean_qqd, negative)
rf_negative_count = countNeg(obj_clean_rf, negative)
qqd_constrain_count = countCons(obj_clean_qqd, constrain)
rf_constrain_count = countCons(obj_clean_rf, constrain)
qqd_uncertain_count = countUn(obj_clean_qqd, uncertain)
rf_uncertain_count = countUn(obj_clean_rf, uncertain)
qqd_polarity = getSentiment(obj_clean_qqd, negative, positive)
rf_polarity = getSentiment(obj_clean_rf, negative, positive)
qqd_word_count = len(obj_clean_qqd)
rf_word_count = len(obj_clean_rf)
if rfs.count('.') == 0:
rf_avg_sentence_lenght = ""
else:
rf_avg_sentence_lenght = len(rfs.split())/rfs.count('.')
qqd_avg_sentence_lenght = len(qqd.split())/qqd.count('.')
qqd_complex_words = complex_word(obj_clean_qqd)
rf_complex_words = complex_word(obj_clean_rf)
qqd_percentage_complex_words = (qqd_complex_words/qqd_word_count)*100
qqd_fog_idex = 0.4*(qqd_avg_sentence_lenght +
qqd_percentage_complex_words)
if rf_word_count == 0:
rf_fog_idex = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
rf_percentage_complex_words = ""
else:
rf_fog_idex = ""
rf_positive_ratio = rf_positive_count/rf_word_count
rf_negative_ratio = rf_negative_count/rf_word_count
rf_uncertain_ratio = rf_uncertain_count/rf_word_count
rf_constrain_ratio = rf_constrain_count/rf_word_count
rf_percentage_complex_words = (rf_complex_words/rf_word_count)*100
qqd_positive_ratio = qqd_positive_count/qqd_word_count
qqd_negative_ratio = qqd_negative_count/qqd_word_count
qqd_uncertain_ratio = qqd_uncertain_count/qqd_word_count
qqd_constrain_ratio = qqd_constrain_count/qqd_word_count
mda_positive_count = ""
mda_negative_count = ""
mda_constrain_count = ""
mda_uncertain_count = ""
mda_polarity = ""
mda_word_count = ""
mda_avg_sentence_lenght = ""
mda_complex_words = ""
mda_percentage_complex_words = ""
mda_fog_idex = ""
mda_positive_ratio = ""
mda_negative_ratio = ""
mda_uncertain_ratio = ""
mda_constrain_ratio = ""
elif mda != 0 and qqd != 0 and rfs != 0:
print(7)
obj_clean_mda = cleanText(mda)
obj_clean_qqd = cleanText(qqd)
obj_clean_rf = cleanText(rfs)
temp = obj_clean_qqd.extend(obj_clean_rf)
mda_positive_count = countPos(obj_clean_mda, positive)
qqd_positive_count = countPos(obj_clean_qqd, positive)
rf_positive_count = countPos(obj_clean_rf, positive)
mda_negative_count = countNeg(obj_clean_mda, negative)
rf_negative_count = countNeg(obj_clean_rf, negative)
qqd_negative_count = countNeg(obj_clean_qqd, negative)
mda_constrain_count = countCons(obj_clean_mda, constrain)
qqd_constrain_count = countCons(obj_clean_qqd, constrain)
rf_constrain_count = countCons(obj_clean_rf, constrain)
mda_uncertain_count = countUn(obj_clean_mda, uncertain)
qqd_uncertain_count = countUn(obj_clean_qqd, uncertain)
rf_uncertain_count = countUn(obj_clean_rf, uncertain)
mda_polarity = getSentiment(obj_clean_mda, negative, positive)
qqd_polarity = getSentiment(obj_clean_qqd, negative, positive)
rf_polarity = getSentiment(obj_clean_rf, negative, positive)
mda_word_count = len(obj_clean_mda)
qqd_word_count = len(obj_clean_qqd)
rf_word_count = len(obj_clean_rf)
mda_avg_sentence_lenght = len(mda.split())/mda.count('.')
qqd_avg_sentence_lenght = len(qqd.split())/qqd.count('.')
if rfs.count('.') == 0:
rf_avg_sentence_lenght = ""
else:
rf_avg_sentence_lenght = len(rfs.split())/rfs.count('.')
if rf_word_count == 0:
rf_fog_idex = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
rf_percentage_complex_words = ""
else:
rf_fog_idex = ""
rf_positive_ratio = rf_positive_count/rf_word_count
rf_negative_ratio = rf_negative_count/rf_word_count
rf_uncertain_ratio = rf_uncertain_count/rf_word_count
rf_constrain_ratio = rf_constrain_count/rf_word_count
rf_percentage_complex_words = ""
mda_complex_words = complex_word(obj_clean_mda)
qqd_complex_words = complex_word(obj_clean_qqd)
rf_complex_words = complex_word(obj_clean_rf)
mda_percentage_complex_words = (mda_complex_words/mda_word_count)*100
qqd_percentage_complex_words = (qqd_complex_words/qqd_word_count)*100
mda_fog_idex = 0.4*(mda_avg_sentence_lenght +
mda_percentage_complex_words)
qqd_fog_idex = 0.4*(qqd_avg_sentence_lenght +
qqd_percentage_complex_words)
qqd_positive_ratio = qqd_positive_count/qqd_word_count
qqd_negative_ratio = qqd_negative_count/qqd_word_count
qqd_uncertain_ratio = qqd_uncertain_count/qqd_word_count
qqd_constrain_ratio = qqd_constrain_count/qqd_word_count
mda_positive_ratio = mda_positive_count/mda_word_count
mda_negative_ratio = mda_negative_count/mda_word_count
mda_uncertain_ratio = mda_uncertain_count/mda_word_count
mda_constrain_ratio = mda_constrain_count/mda_word_count
else:
mda_positive_count = ""
mda_negative_count = ""
mda_constrain_count = ""
mda_uncertain_count = ""
mda_polarity = ""
mda_word_count = ""
mda_avg_sentence_lenght = ""
mda_complex_words = ""
mda_percentage_complex_words = ""
mda_fog_idex = ""
mda_positive_ratio = ""
mda_negative_ratio = ""
mda_uncertain_ratio = ""
mda_constrain_ratio = ""
qqd_positive_count = ""
qqd_negative_count = ""
qqd_constrain_count = ""
qqd_uncertain_count = ""
qqd_polarity = ""
qqd_word_count = ""
qqd_avg_sentence_lenght = ""
qqd_complex_words = ""
qqd_percentage_complex_words = ""
qqd_fog_idex = ""
qqd_positive_ratio = ""
qqd_negative_ratio = ""
qqd_uncertain_ratio = ""
qqd_constrain_ratio = ""
rf_positive_count = ""
rf_negative_count = ""
rf_constrain_count = ""
rf_uncertain_count = ""
rf_polarity = ""
rf_word_count = ""
rf_avg_sentence_lenght = ""
rf_complex_words = ""
rf_percentage_complex_words = ""
rf_fog_idex = ""
rf_positive_ratio = ""
rf_negative_ratio = ""
rf_uncertain_ratio = ""
rf_constrain_ratio = ""
""" writing on to excel """
sheet1.write(i, 6, mda_positive_count)
sheet1.write(i, 7, mda_negative_count)
sheet1.write(i, 8, mda_avg_sentence_lenght)
sheet1.write(i, 9, mda_percentage_complex_words)
sheet1.write(i, 10, mda_fog_idex)
sheet1.write(i, 11, mda_complex_words)
sheet1.write(i, 12, mda_word_count)
sheet1.write(i, 13, mda_uncertain_count)
sheet1.write(i, 14, mda_constrain_count)
sheet1.write(i, 15, mda_positive_ratio)
sheet1.write(i, 16, mda_negative_ratio)
sheet1.write(i, 17, mda_constrain_ratio)
sheet1.write(i, 18, qqd_positive_count)
sheet1.write(i, 19, qqd_negative_count)
sheet1.write(i, 20, qqd_avg_sentence_lenght)
sheet1.write(i, 21, qqd_percentage_complex_words)
sheet1.write(i, 22, qqd_fog_idex)
sheet1.write(i, 23, qqd_complex_words)
sheet1.write(i, 24, qqd_word_count)
sheet1.write(i, 25, qqd_uncertain_count)
sheet1.write(i, 26, qqd_constrain_count)
sheet1.write(i, 27, qqd_positive_ratio)
sheet1.write(i, 28, qqd_negative_ratio)
sheet1.write(i, 29, qqd_constrain_ratio)
sheet1.write(i, 30, rf_positive_count)
sheet1.write(i, 31, rf_negative_count)
sheet1.write(i, 32, rf_avg_sentence_lenght)
sheet1.write(i, 33, rf_percentage_complex_words)
sheet1.write(i, 34, rf_fog_idex)
sheet1.write(i, 35, rf_complex_words)
sheet1.write(i, 36, rf_word_count)
sheet1.write(i, 37, rf_uncertain_count)
sheet1.write(i, 38, rf_constrain_count)
sheet1.write(i, 39, rf_positive_ratio)
sheet1.write(i, 41, rf_negative_ratio)
sheet1.write(i, 42, rf_constrain_ratio)
sheet1.write(i, 43, total_cons)
workbook.close()