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helper.py
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from urlextract import URLExtract
extract=URLExtract()
import nltk
from wordcloud import WordCloud
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import pandas as pd
from collections import Counter
import emoji
def fetch_stats(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
num_messages = df.shape[0]
words=[]
for message in df['messages']:
words.extend(message.split())
numberofmediamsgs=df[df['messages']=='<Media omitted>\n'].shape[0]
links=[]
for message in df['messages']:
links.extend(extract.find_urls(message))
return num_messages,len(words),numberofmediamsgs,len(links)
def most_busy_users(df):
mostbusyusers=df['user'].value_counts().head(30)
df=(round((df['user'].value_counts()/df.shape[0])*100,2)).reset_index().rename(columns={'user':'Name','count':'Percentage of contribution'})
return mostbusyusers,df
def create_wordcloud(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
temp=df[df['user']!='group_notification']
temp=temp[temp['messages']!='<Media omitted>\n']
def remove_stop_words(message):
y=[]
for word in message.lower().split():
if word not in stop_words:
y.append(word)
return " ".join(y)
wc=WordCloud(width=500,min_font_size=10)
temp['messages']=temp['messages'].apply(remove_stop_words)
df_wc=wc.generate(temp['messages'].str.cat(sep=" "))
return df_wc
def most_common_words(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
temp=df[df['user']!='group_notification']
temp=temp[temp['messages']!='<Media omitted>\n']
words=[]
for message in temp['messages']:
for word in message.lower().split():
if word not in stop_words:
words.append(word)
most_common_df=pd.DataFrame(Counter(words).most_common(20))
return most_common_df
def emoji_helper(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
emojis=[]
for message in df['messages']:
emojis.extend([c for c in message if c in emoji.EMOJI_DATA])
emojidf=pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
emojidf=emojidf.reset_index(drop=True)
emojidf.columns = ['Emoji', 'Count']
return emojidf
def monthly_timeline(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
timeline=df.groupby(['year','month_num','month','weekday','date']).count()['messages'].reset_index()
time=[]
for i in range(timeline.shape[0]):
time.append(timeline['month'][i]+"-"+str(timeline['year'][i]))
timeline['time']=time
return timeline
def daily_timeline(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
daily_timeline=df.groupby('only_date').count()['messages'].reset_index()
return daily_timeline
def week_activity_map(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
return df['day_name'].value_counts()
def month_activity_map(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
return df['month'].value_counts()
def activity_heatmap(selected_user,df):
if selected_user!="Overall":
df=df[df['user']==selected_user]
user_heatmap=df.pivot_table(index='day_name',columns='period',values='messages',aggfunc='count').fillna(0)
return user_heatmap