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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 | |