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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['user'] == selected_user]
# fetch the number of messages
num_messages = df.shape[0]
# fetch the total number of words
words = []
for message in df['message']:
words.extend(message.split())
# fetch number of media messages
num_media_messages = df[df['message'] == '<Media omitted>\n'].shape[0]
# fetch number of links shared
links = []
for message in df['message']:
links.extend(extract.find_urls(message))
return num_messages,len(words),num_media_messages,len(links)
def most_busy_users(df):
x = df['user'].value_counts().head()
df = round((df['user'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename(
columns={'index': 'name', 'user': '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['user'] == selected_user]
temp = df[df['user'] != 'group_notification']
temp = temp[temp['message'] != '<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['message'] = temp['message'].apply(remove_stop_words)
df_wc = wc.generate(temp['message'].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['user'] == selected_user]
temp = df[df['user'] != 'group_notification']
temp = temp[temp['message'] != '<Media omitted>\n']
words = []
for message in temp['message']:
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['message']:
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['user'] == selected_user]
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_timeline = df.groupby('only_date').count()['message'].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='message', aggfunc='count').fillna(0)
return user_heatmap
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