abhishek-shrm

recommend_percentage=pd.DataFrame(((df.groupby('name')['reviews.doRecommend'].sum()*100)/df.groupby('name')['reviews.doRecommend'].count()).sort_values(ascending=True))

plt.figure(figsize=(16,8))
plt.xlabel('Recommend Percentage')
plt.ylabel('Products')
plt.title('Percentage of reviewers recommended a product')
recommend_graph=plt.barh(np.arange(len(recommend_percentage.index)),recommend_percentage['reviews.doRecommend'],color='green')

# Writing product names on bar
for bar,product in zip(recommend_graph,recommend_percentage.index):

abhishek-shrm

import textstat
df['dale_chall_score']=df['reviews.text'].apply(lambda x: textstat.dale_chall_readability_score(x))
df['flesh_reading_ease']=df['reviews.text'].apply(lambda x: textstat.flesch_reading_ease(x))
df['gunning_fog']=df['reviews.text'].apply(lambda x: textstat.gunning_fog(x))

print('Dale Chall Score of upvoted reviews=>',df[df['reviews.numHelpful']>1]['dale_chall_score'].mean())
print('Dale Chall Score of not upvoted reviews=>',df[df['reviews.numHelpful']<=1]['dale_chall_score'].mean())

print('Flesch Reading Score of upvoted reviews=>',df[df['reviews.numHelpful']>1]['flesh_reading_ease'].mean())
print('Flesch Reading Score of not upvoted reviews=>',df[df['reviews.numHelpful']<=1]['flesh_reading_ease'].mean())

abhishek-shrm

df['text_standard']=df['reviews.text'].apply(lambda x: textstat.text_standard(x))

print('Text Standard of upvoted reviews=>',df[df['reviews.numHelpful']>1]['text_standard'].mode())
print('Text Standard of not upvoted reviews=>',df[df['reviews.numHelpful']<=1]['text_standard'].mode())

abhishek-shrm

df['reading_time']=df['reviews.text'].apply(lambda x: textstat.reading_time(x))

print('Reading Time of upvoted reviews=>',df[df['reviews.numHelpful']>1]['reading_time'].mean())
print('Reading Time of not upvoted reviews=>',df[df['reviews.numHelpful']<=1]['reading_time'].mean())

abhishek-shrm

df['cleaned']=df['reviews.text'].apply(lambda x: x.lower())

abhishek-shrm

df['cleaned']=df['cleaned'].apply(lambda x: re.sub('\w*\d\w*','', x))

abhishek-shrm

df['cleaned']=df['cleaned'].apply(lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))

abhishek-shrm

import folium
m=folium.Map(location=[28.644800, 77.216721])
m

abhishek-shrm

from branca.element import Figure
fig=Figure(width=550,height=350)

abhishek-shrm

m1=folium.Map(width=550,height=350,location=[28.644800, 77.216721],zoom_start=11,min_zoom=8,max_zoom=14)
fig.add_child(m1)
m1