def visualizeWordDistribution(inputFile, outputImage):
#write your code here
a = 0
b = 0
c = 0
m = 0
e = 0
q = 0
g = 0
h = 0
i = 0
j = 0
k = 0
f = []
zinput = str("<row")
with open(inputFile, 'r') as file:
for line in file:
if zinput in line:
f.append(line)
for row in f:
result = preprocessData.preprocessLine(row)
parser.Parser(result)
for value in parser.dict_vocab.values():
if (value>= 0 and value < 10):
a +=1
elif (value>=10 and value <20 ):
b+=1
elif (value>=20 and value <30 ):
c+=1
elif (value>=30 and value <40 ):
m+=1
elif (value>=40 and value <50 ):
e+=1
elif (value>=50 and value <60 ):
q+=1
elif (value>=60 and value <70 ):
g+=1
elif (value>=70 and value <80 ):
h+=1
elif (value>=80 and value <90 ):
i+=1
elif (value>=90 and value <100 ):
j+=1
elif (value>=100):
k+=1
df = pd.DataFrame({'Labels' : ['0-10', '10-20','20-30','30-40','40-50','50-60','60-70','70-80','80-90','90-100','Others'], 'Values':[a,b,c,m,e,q,g,h,i,j,k]})
print(df)
df.plot(kind='bar', x='Labels', y='Values', color='red')
plt.savefig(outputImage)
plt.clf()
def visualizePostNumberTrend(inputFile, outputImage):
q1 = 0
a1 = 0
q2 = 0
a2 = 0
q3 = 0
a3 = 0
q4 = 0
a4 = 0
f = []
zinput = str("<row")
with open(inputFile, 'r') as file:
for line in file:
if zinput in line:
f.append(line)
for row in f:
result = preprocessData.preprocessLine(row)
parser.Parser(result)
for (value), (type) in zip(parser.dict_quarter.values(), parser.dict_type.values()):
if int(value) == 1 and int(type) == 1:
q1+=1
if int(value) == 1 and int(type) == 2:
a1+=1
if int(value) == 2 and int(type) == 1:
q2+=1
if int(value) == 2 and int(type) == 2:
a2+=1
if int(value) == 3 and int(type) == 1:
q3+=1
if int(value) == 3 and int(type) == 2:
a3+=1
if int(value) == 4 and int(type) == 1:
q4+=1
if int(value) == 4 and int(type) == 2:
a4+=1
df1 = pd.DataFrame(
{'Quarters': ['1','2','3','4'],
'Questions': [q1,q2,q3,q4],
'Answers':[a1,a2,a4,a4]})
print(df1)
print(df1)
ax = plt.gca()
df1.plot(kind='line', x='Quarters', y='Questions', ax=ax)
df1.plot(kind='line', x='Quarters', y='Answers', color='red', ax=ax)
plt.savefig(outputImage)
plt.clf()
def getVocabularySize(self):
if self.if_parse() is True: # checks parse condition
# line is first preprocessed using preprocessLine() method of class Parser
# all the punctuations are removed
# line is stripped to remove redundant spaces, then converted to lower case and finally split into words.
# set will consist of unique words of the line and its length is returned
# [^\w\s] will not consider words and whitespaces
return len(
set(
re.sub(r'[^\w\s]', '',
preprocessData.preprocessLine(
self.inputString)).strip().lower().split()))
else:
return None # return none if not parsable
def getCleanedBody(self):
self.inputString = preprocessData.preprocessLine(self.inputString)
def getCleanedBody(self): # using the function created on the previous script return preprocessLine(self.inputString)
def getCleanedBody(self):
if self.if_parse() is True: # checks for parse condition if true
return preprocessData.preprocessLine(
self.inputString) # returns clean body using preprocessLine()
else:
return None # returns None if string cannot be parsed