File size: 6,549 Bytes
dfe57e4 ab8371c dfe57e4 ab8371c dfe57e4 ab8371c dfe57e4 ab8371c dfe57e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
from urlextract import URLExtract
from wordcloud import WordCloud
from collections import Counter
import pandas as pd
import emoji
extract = URLExtract()
def fetch_stats(selected_users, df):
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
# fetch the number of messages
num_messages = df.shape[0]
# fetch the number of words
words = []
for i in df['message']:
words.extend(i.split())
# fetch the number of media messages
num_media_messages = df[df['message'] == 'voice omitted'].shape[0]
# fetch the number of link shared
links = []
for i in df['message']:
links.extend(extract.find_urls(i))
return num_messages, len(words), num_media_messages, len(links)
def first_last_msg (df):
first_last_msg = df.drop(columns=['date', 'month_num'], axis=1)[1:]
first_last_msg = first_last_msg[~first_last_msg['user'].str.startswith('Group')]
first_last_msg = first_last_msg[~first_last_msg['message'].str.endswith('group')]
first_last_msg = first_last_msg[~first_last_msg['message'].str.endswith('contact')]
first_last_msg = first_last_msg[~first_last_msg['message'].str.endswith('you')]
first_last_msg ['date'] = first_last_msg [['year', 'month', 'day']].astype(str).agg('-'.join, axis=1)
first_last_msg ['time'] = first_last_msg [['hour', 'minute']].astype(str).agg(':'.join, axis=1)
first_last_msg = first_last_msg [['user', 'message', 'date', 'time']]
return first_last_msg
def connected_days(df):
first_date = pd.to_datetime(df['date'].min().date())
last_date = pd.to_datetime(df['date'].max().date())
connected_days = (last_date - first_date).days
return connected_days
def avg_messages_per_day(selected_users,df):
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
first_date = pd.to_datetime(df['date'].min().date())
last_date = pd.to_datetime(df['date'].max().date())
num_days = (last_date - first_date).days
num_messages = df.shape[0]
avg_messages_per_day = num_messages / num_days
return round(avg_messages_per_day)
def max_active_day(df):
df['only_date'] = df['date'].dt.date
daily_timeline = df.groupby(['only_date']).count()['message'].reset_index().rename(columns={'only_date':'Date','message':'Message Counts'})
max_active_day = daily_timeline.nlargest(1, 'Message Counts')
return max_active_day
def max_active_month(selected_users, df):
timeline = monthly_timeline_msg(selected_users, df)
max_month = timeline.loc[timeline['Message Count'].idxmax()]
year = max_month['Year']
month = max_month['Month']
message_count = max_month['Message Count']
max_active_month = pd.DataFrame({'Year': [year], 'Month': [month], 'Message Count': [message_count]})
return max_active_month
def most_busy_users(df):
df['user'] = df['user'].astype(str) # Convert 'user' column to string type
df['user'] = df['user'].str.strip() # Remove leading/trailing whitespaces if necessary
x = df['user'].value_counts().head()
df = round((df['user'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename(
columns={'user': 'Name', 'index': 'User'}
)
df = df[~df['User'].str.startswith('Group')]
return x, df
def create_wordcloud(selected_users, df):
f = open('stop_hinglish.txt', 'r')
stop_words = f.read()
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
temp = df[df['message'] != 'group_notification']
temp = temp[temp['message'] != '<Media_omitted>\n']
def remove_stopwords(message):
y = []
for i in message.lower().split():
if i not in stop_words:
y.append(i)
return " ".join(y)
wc = WordCloud(width=500, height=500, min_font_size=10,
background_color='White')
temp['message'] = temp['message'].apply(remove_stopwords)
df_wc = wc.generate(temp['message'].str.cat(sep=" "))
return df_wc
def most_common_words(selected_users, df):
f = open('stop_hinglish.txt', 'r')
stop_words = f.read()
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
temp = df[df['message'] != 'group_notification']
temp = temp[temp['message'] != '<Media_omitted>\n']
words = []
for i in temp['message']:
for word in i.lower().split():
# Exclude words that contain emojis
if word not in stop_words and not any(c for c in word if c in emoji.UNICODE_EMOJI['en']):
words.append(word)
most_common_words = pd.DataFrame(Counter(words).most_common(10)).rename(columns={0:'Words',1:'Counts'}).add_suffix(' ')
return most_common_words
def emoji_helper(selected_users, df):
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
emojis = []
for message in df['message']:
emojis.extend([c for c in message if c in emoji.UNICODE_EMOJI['en']])
emoji_counts = Counter(emojis)
top_5_emojis = emoji_counts.most_common(5)
emoji_df = pd.DataFrame(top_5_emojis, columns=['Emoji ', 'Count '])
return emoji_df
def monthly_timeline_msg(selected_users, df):
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
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 = timeline.drop(columns=['month_num'],axis=1)
timeline = timeline.rename(columns={'year': 'Year', 'month': 'Month', 'message': 'Message Count'})
return timeline
def monthly_timeline(selected_users, df):
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
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
timeline = timeline.drop(columns=['month_num'],axis=1)
return timeline
def daily_message(selected_users, df):
if selected_users != 'Overall':
df = df[df['user'] == selected_users]
df['only_date'] = df['date'].dt.date
daily_message = df.groupby(['only_date']).count()['message'].reset_index().rename(columns={'only_date':'Date','message':'Message Counts'})
return daily_message
|