| from urlextract import URLExtract
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| from wordcloud import WordCloud
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| import pandas as pd
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| from collections import Counter
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| import emoji
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| extract = URLExtract()
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| def fetch_stats(selected_user,df):
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| num_messages = df.shape[0]
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| words = []
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| for message in df['message']:
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| words.extend(message.split())
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| num_media_messages = df[df['message'] == '<Media omitted>\n'].shape[0]
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| links = []
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| for message in df['message']:
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| links.extend(extract.find_urls(message))
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|
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| return num_messages,len(words),num_media_messages,len(links)
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|
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| def most_busy_users(df):
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| x = df['user'].value_counts().head()
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| df = round((df['user'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename(
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| columns={'index': 'name', 'user': 'percent'})
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| return x,df
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|
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| def create_wordcloud(selected_user,df):
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| f = open('stop_hinglish.txt', 'r')
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| stop_words = f.read()
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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|
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| temp = df[df['user'] != 'group_notification']
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| temp = temp[temp['message'] != '<Media omitted>\n']
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|
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| def remove_stop_words(message):
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| y = []
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| for word in message.lower().split():
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| if word not in stop_words:
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| y.append(word)
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| return " ".join(y)
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|
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| wc = WordCloud(width=500,height=500,min_font_size=10,background_color='white')
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| temp['message'] = temp['message'].apply(remove_stop_words)
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| df_wc = wc.generate(temp['message'].str.cat(sep=" "))
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| return df_wc
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| def most_common_words(selected_user,df):
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|
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| f = open('stop_hinglish.txt','r')
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| stop_words = f.read()
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|
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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|
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| temp = df[df['user'] != 'group_notification']
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| temp = temp[temp['message'] != '<Media omitted>\n']
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| words = []
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| for message in temp['message']:
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| for word in message.lower().split():
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| if word not in stop_words:
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| words.append(word)
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| most_common_df = pd.DataFrame(Counter(words).most_common(20))
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| return most_common_df
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|
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| def emoji_helper(selected_user,df):
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| emojis = []
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| for message in df['message']:
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| emojis.extend([c for c in message if c in emoji.UNICODE_EMOJI['en']])
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| emoji_df = pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
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|
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| return emoji_df
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| def monthly_timeline(selected_user,df):
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|
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| timeline = df.groupby(['year', 'month_num', 'month']).count()['message'].reset_index()
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| time = []
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| for i in range(timeline.shape[0]):
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| time.append(timeline['month'][i] + "-" + str(timeline['year'][i]))
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| timeline['time'] = time
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| return timeline
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| def daily_timeline(selected_user,df):
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| daily_timeline = df.groupby('only_date').count()['message'].reset_index()
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| return daily_timeline
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| def week_activity_map(selected_user,df):
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| return df['day_name'].value_counts()
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| def month_activity_map(selected_user,df):
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| return df['month'].value_counts()
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| def activity_heatmap(selected_user,df):
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| if selected_user != 'Overall':
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| df = df[df['user'] == selected_user]
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| user_heatmap = df.pivot_table(index='day_name', columns='period', values='message', aggfunc='count').fillna(0)
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| return user_heatmap
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