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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]
#Fetiching the number of messages
num_messages = df.shape[0]
#Fetching the number of words
words = []
for message in df['messages']:
words.extend(message.split())
#Fetching the media messages
num_media_messages = df[df['messages'] == '<Media omitted>\n'].shape[0]
#Fetching the links in messages
links = []
for message in df['messages']:
links.extend(extract.find_urls(message))
return num_messages, len(words), num_media_messages, len(links)
def fetch_most_busy_users(df):
x = df['users'].value_counts().head()
df = round((df['users'].value_counts() * 100) / df.shape[0], 2).reset_index().rename(
columns={'index': 'Name', 'user': 'Percentage'})
return x, df
def create_wordcloud(selected_user, df):
f = open('Hinglish.txt', 'r')
stopwords = f.read()
if selected_user != "Overall":
df = df[df['users'] == selected_user]
# Removing group notifications
temp = df[df['users'] != 'group notifications']
# Removing media omitted
temp = temp[temp['messages'] != "<Media omitted>\n"]
def remove_stop_words(message):
y=[]
for word in message.lower().split():
if word not in stopwords:
y.append(word)
return " ".join(y)
wc = WordCloud(width=500, height=300, 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):
#Importing stopwords
f = open('Hinglish.txt', 'r')
stopwords = f.read()
if selected_user != "Overall":
df = df[df['users'] == selected_user]
#Removing group notifications
temp = df[df['users'] != 'group notifications']
#Removing media omitted
temp = temp[temp['messages'] != "<Media omitted>\n"]
words = []
for message in temp['messages']:
for word in message.lower().split():
if word not in stopwords:
words.extend(message.split())
most_common_df = pd.DataFrame(Counter(words).most_common(20))
return most_common_df
def emoji_counter(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('datetimeline').count()['messages'].reset_index()
return daily_timeline
def week_activity_map(selected_user, df):
if selected_user != "Overall":
df = df[df['users'] == selected_user]
week_activity = df['day_name'].value_counts()
return week_activity
def month_activity_map(selected_user, df):
if selected_user != "Overall":
df = df[df['users'] == selected_user]
month_activity = df['Month'].value_counts()
return month_activity
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