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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
extractor = URLExtract()
def fetch_stats(selected_user, df):
if selected_user != 'Overall':
df = df[df['User'] == selected_user]
# 1. number of messages
num_messages = df.shape[0]
# 2. number of words
words = []
for message in df['Message']:
words.extend(message.split())
# 3.
num_media_msg = df[df['Message'] == '<Media omitted>'].shape[0]
links = []
for messages in df['Message']:
links.extend(extractor.find_urls(message))
return num_messages, len(words), num_media_msg, len(links)
def most_busy_user(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):
file = open('stop_word.txt', 'r')
stop_words = file.read()
if selected_user != 'Overall':
df = df[df['User'] == selected_user]
temp = df[df['User'] != 'Notification']
temp = temp[temp['Message'] != '<Media omitted>']
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, 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_word(selected_user, df):
if selected_user != 'Overall':
df = df[df['User'] == selected_user]
temp = df[df['User'] != 'Notification']
temp = temp[temp['Message'] != '<Media omitted>']
words = []
file = open('stop_word.txt', 'r')
stop_words = file.read()
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])
emojis_df = pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
return emojis_df
def monthly_timeline(selected_user,df):
if selected_user != 'Overall':
df = df[df['User'] == selected_user]
timeline = df.groupby(['Year', 'Month_Number', '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'].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