Initial commit

helper.py

@@ -4,4 +4,178 @@ from collections import Counter
 import pandas as pd
 import emoji
 
-extractor = URLExtract()
+extractor = URLExtract()
+
+
+def fetch_states(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. Number of media
+    num_media_messages = df[df['message'] == '<Media omitted>\n'].shape[0]
+
+    # 4. Number of Links
+    links = []
+    for message in df['message']:
+        links.extend(extractor.find_urls(message))
+
+    return num_messages, len(words), num_media_messages, len(links)
+
+
+def monthly_timeline(selected_user, df):
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    df['month_num'] = df['date'].dt.month
+    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.rename(columns={'message': 'Message', 'time': '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()
+    daily_timeline = daily_timeline.rename(
+        columns={'only_date': 'Date', 'message': 'Message'})
+
+    return daily_timeline
+
+
+def week_activity_map(selected_user, df):
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    week_activity = df['day_name'].value_counts().reset_index()
+    week_activity = week_activity.rename(
+        columns={'day_name': 'Day', 'count': "Count"})
+
+    return week_activity
+
+
+def month_activity_map(selected_user, df):
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    month_activity = df['month'].value_counts().reset_index()
+    month_activity = month_activity.rename(
+        columns={'month': 'Month', 'count': "Count"})
+
+    return month_activity
+
+
+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)
+
+    user_heatmap = user_heatmap.rename_axis('Day', axis='index')
+    user_heatmap = user_heatmap.rename_axis('Time Period', axis='columns')
+
+    return user_heatmap
+
+
+def most_active_user(df):
+
+    temp = df[df['user'] != 'group_notification']
+
+    x = (temp['user'].value_counts().head()).reset_index().rename(
+        columns={'user': 'User', 'count': 'Count'})
+
+    per = round(((temp['user'].value_counts() / temp.shape[0]) * 100),
+                2).reset_index().rename(columns={'user': 'User', 'count': 'Percent(%)'})
+    return x, per
+
+
+def create_wordcloud(selected_user, df):
+    f = open('stop_ben-hin-eng.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_ben-hin-eng.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)).iloc[::-1].rename(columns={0: 'Message', 1: 'Count'})
+
+    return most_common_df
+
+
+def emoji_data(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))))
+
+    if emojis:
+        emoji_df = emoji_df.rename(columns={0: 'Emoji', 1: 'Count'})
+
+    return emoji_df
+
+
+def data_timeframe(df):
+
+    df_first = df.iloc[0]
+    df_last = df.iloc[-1]
+
+    timeframe = str(df_first['day']) + " " + str(df_first['month']) + " " + str(df_first['year']) + \
+        " to " + str(df_last['day']) + " " + \
+        str(df_last['month']) + " " + str(df_last['year'])
+
+    return timeframe