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helper.py
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import matplotlib.pyplot as plt
from wordcloud import WordCloud
import pandas as pd
import emoji
from collections import Counter
def fetch_stats(selected_user,df):
if selected_user != "Overall":
df = df[df['user'] == selected_user]
#fetch no of messages
num_messages=df.shape[0]
from urlextract import URLExtract
extractor = URLExtract()
#fetch no of words
words = []
links=[]
for message in df['message']:
words.extend(message.split())
links.extend(extractor.find_urls(message))
#fetch no of media_messages
num_media=df[df['message']=='<Media omitted>\n'].shape[0]
#fetch no of links shared
num_links=len(links)
return num_messages,len(words),num_media,num_links
def busiest_users(df):
x = df['user'].value_counts().head()
df = round(100 * (df['user'].value_counts().head() / df.shape[0]), 2).reset_index().rename(
columns={'index': 'name', 'user': 'percent'})
return x,df
def create_wordcloud(selected_user,df):
if selected_user != "Overall":
df = df[df['user'] == selected_user]
wc= WordCloud(width=500, height=500,min_font_size=10,background_color='white')
df_wc=wc.generate(df['message'].str.cat(sep=" "))
return df_wc
def most_common_words(selected_user,df):
if selected_user != "Overall":
df = df[df['user'] == selected_user]
# remove group notifications
temp = df[df['user'] != 'group notification']
# remove media omitted
temp = temp[temp['message'] != '<Media omitted>\n']
f=open('stop_hinglish.txt','r')
stop_words=f.read()
# remove stop words
words = []
for message in temp['message']:
for word in message.lower().split():
if word not in stop_words:
words.append(word)
from collections import Counter
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['message']:
emojis.extend([c for c in message if c in emoji.UNICODE_EMOJI['en']])
emoji_df=pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
return emoji_df
def monthly_timeline(selected_user,df):
if selected_user != "Overall":
df = df[df['user'] == selected_user]
df['month_num'] = df['date'].dt.month
timeline = df.groupby(['year', 'month_num', 'month']).count()['message'].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_time = df.groupby('only_date').count()['message'].reset_index()
return daily_time
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='message', aggfunc='count').fillna(0)
return user_heatmap