Upload helper.py

helper.py

@@ -0,0 +1,134 @@
+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 PIL.Image
+import numpy as np
+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