Delete helper.py

helper.py

@@ -1,132 +0,0 @@
-import matplotlib.pyplot as plt
-from urlextract import URLExtract
-from collections import Counter
-from wordcloud import WordCloud, STOPWORDS ,ImageColorGenerator
-import pandas as pd
-import matplotlib.pylab as plt
-import emoji
-
-extract=URLExtract()
-def fetch_stats(selected_user,df):
-
-    if selected_user!= "Group analysis":
-        df=df[df['users']==selected_user]
-    num_messages = df.shape[0]
-    words = []
-    for message in df['message']:
-        words.extend(message.split())
-
-
-    links=[]
-    for message in df['message']:
-        links.extend(extract.find_urls(message))
-
-    return num_messages, len(words),len(links)
-
-def most_busy_users(df):
-    x = df['users'].value_counts().head()
-    df=round((df['users'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename(
-        columns={'index': 'name', 'user': 'percent'})
-    return x,df
-
-def most_common_words(selected_user,df):
-    f = open('stop_hinglish.txt', 'r')
-    stop_words = f.read()
-
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-    temp = df[df['users'] != '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(30))
-    return most_common_df
-
-def word_cloud(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    stopwords = set('STOPWORDS')
-
-        # wordcloud
-    wordcloud = WordCloud(stopwords=stopwords, background_color="Black").generate(''.join(df['message']))
-    plt.figure(figsize=(10, 8), facecolor='k')
-    plt.imshow(wordcloud, interpolation='bilinear')
-    plt.show()
-
-    return wordcloud
-
-def emoji_helper(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-    emojis = []
-    for message in df['message']:
-        emojis.extend([c for c in message if c in emoji.EMOJI_DATA.keys()])
-    emoji_df=pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
-
-    return emoji_df
-
-def monthly_timeline(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    timeline = df.groupby(['year', 'Month_name', 'Month']).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 != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    daily_timeline = df.groupby('Date').count()['message'].reset_index()
-
-    return daily_timeline
-
-def week_activity_map(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-    return df['Day_name'].value_counts()
-
-def month_activity_map(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-    return df['Month_name'].value_counts()
-
-def activity_heatmap(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    Activity_heatmap= df.pivot_table(index='Day_name', columns='period', values='message', aggfunc='count').fillna(0)
-    return Activity_heatmap
-
-def pos_words(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    pos_word = df[df['vader_Analysis'] == 'Positive']
-    pos_word = pos_word.pop('message')
-    return pos_word
-
-def neg_words(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    neg_word = df[df['Analysis'] == 'Negative']
-    neg_word = neg_word.pop('message')
-    return neg_word
-
-def neu_words(selected_user,df):
-    if selected_user != "Group analysis":
-        df = df[df['users'] == selected_user]
-
-    neu_word = df[df['vader_Analysis'] == 'Neutral']
-    neu_word = neu_word.pop('message')
-    return neu_word