helper.py

import pandas as pd
from jedi.api.refactoring import extract
from urlextract import URLExtract
from wordcloud import WordCloud
from collections import Counter
import emoji
import seaborn as sns

extract = URLExtract()
def fetch_stats(selected_user,df):
    if selected_user!='Overall':
        df = df[df['user'] == selected_user]
    num_messages=df.shape[0]#fetch number of messages
    words=[]
    #fetch total number of words
    for message in df['message']:
        words.extend(message.split())
        #fetch number of media messages
    num_media_messages=df[df['message'] == "<Media omitted> "].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('stopwords.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> ']
    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('stopwords.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> ']
    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(25))
    return most_common_df

def emoji_analy(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]
    timeline= df.groupby(['year', 'month_num', 'month_name']).count()['message'].reset_index()
    time=[]
    for i in range(timeline.shape[0]):
        time.append(timeline['month_name'][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_name'].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