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helper.py
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helper.py
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from urlextract import URLExtract
from wordcloud import WordCloud
import pandas as pd
from collections import Counter
import emoji
extract=URLExtract()
def fetch_stats(selected_user,df):
if selected_user!= 'Overall':
df = df[df['users'] == selected_user]
# 1. number of Messages
num_messages = df.shape[0]
# 2. number of words
words = []
for message in df['messages']:
words.extend(message.split())
# 3. Fetching number of media shared
num_media_messages=df[df['messages']=="<Media omitted>\n"].shape[0]
# 4. Fetching the number of links shared
links=[]
for message in df['messages']:
links.extend(extract.find_urls(message))
return num_messages, len(words),num_media_messages,len(links)
def most_busy_user(df):
x=df['users'].value_counts().head()
df=round(df['users'].value_counts() / df.shape[0] * 100, 2).reset_index().rename(
columns={"users": 'name', 'count': 'percent'})
return x , df
def create_wordcloud(selected_user,df):
f = open("stop_hinglish.txt", 'r')
stop_words = f.read()
if selected_user != 'Overall':
df = df[df['users'] == selected_user]
temp = df[df['users'] != '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,height=500,min_font_size=10,background_color="white")
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):
f = open("stop_hinglish.txt", 'r')
stop_words = f.read()
if selected_user!= 'Overall':
df = df[df['users'] == selected_user]
temp = df[df['users'] != '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['users'] == selected_user]
emojis = []
for message in df['messages']:
emojis.extend([c for c in message if c in emoji.EMOJI_DATA])
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['users'] == selected_user]
timeline = df.groupby(['year', 'month_num', 'month']).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['users'] == selected_user]
daily_timeline = df.groupby(['date']).count()['messages'].reset_index()
return daily_timeline
def week_activity_map(selected_user,df):
if selected_user!= 'Overall':
df = df[df['users'] == selected_user]
return df['day_name'].value_counts()
def month_activity_map(selected_user,df):
if selected_user!= 'Overall':
df = df[df['users'] == selected_user]
return df['month'].value_counts()
def activity_heatmap(selected_user,df):
if selected_user!= 'Overall':
df = df[df['users'] == selected_user]
user_heatmap=df.pivot_table(index="day_name", columns='period', values='messages', aggfunc='count').fillna(0)
return user_heatmap