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exploring_imessage.py
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import os
import sys
import sqlite3
from datetime import date, datetime
import pandas as pd
import matplotlib.pyplot as plt
import nltk
import json
USERNAME = os.popen('whoami').read().strip()
chat_db = '/Users/'+USERNAME+'/Library/Messages/chat.db'
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT name
FROM sqlite_master
WHERE type='table';
'''
df = pd.read_sql_query(query, conn)
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
PRAGMA table_info (message);
'''
df = pd.read_sql_query(query, conn)
pd.set_option('display.max_rows', 1000) # Display all columns
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT ROWID, date, text
FROM message;
'''
df = pd.read_sql_query(query, conn)
print(df[:5])
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT ROWID, text,
datetime(message.date/1000000000 + strftime("%s", "2001-01-01") ,"unixepoch","localtime") as date
FROM `message`;
'''
# Less than OSX 10.13 use this query
# query = '''
# SELECT ROWID, text,
# datetime(message.date + strftime("%s", "2001-01-01") ,"unixepoch","localtime") as date
# FROM `message`;
# '''
df = pd.read_sql_query(query, conn)
print(df[:5])
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT *
FROM `message`;
'''
df = pd.read_sql_query(query, conn)
df.describe()
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT count(ROWID) as count
FROM `message`;
'''
df = pd.read_sql_query(query, conn)
number_of_messages = df['count'][0]
print(number_of_messages)
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT
sum(case when is_from_me = 1 then 1 else 0 end) as from_me,
sum(case when is_from_me = 0 then 1 else 0 end) as from_others,
count(ROWID) as total
FROM `message`
'''
df = pd.read_sql_query(query, conn)
conn = sqlite3.connect(chat_db, check_same_thread =False)
date_time_conversion = 'datetime(message.date/1000000000 + strftime("%s", "2001-01-01") ,"unixepoch","localtime")'
# Less than OSX 10.13 use this query
# date_time_conversion = 'datetime(message.date + strftime("%s", "2001-01-01") ,"unixepoch","localtime")'
query = '''
SELECT min({}) as min_date, max({}) as max_date
FROM `message`;
'''.format(date_time_conversion, date_time_conversion)
df = pd.read_sql_query(query, conn)
min_date_string = df['min_date'][0]
min_date = datetime.strptime(min_date_string, "%Y-%m-%d %H:%M:%S")
max_date_string = df['max_date'][0]
max_date = datetime.strptime(max_date_string, "%Y-%m-%d %H:%M:%S")
delta = (max_date - min_date).days
average = number_of_messages / delta
print('I exchange {:.3f} messages on average per day.'.format(average))
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT ROWID, text,
datetime(message.date/1000000000 + strftime("%s", "2001-01-01"), "unixepoch","localtime") as date
FROM `message`;
'''
# Less than OSX 10.13 use this query
# query = '''
# SELECT ROWID, text,
# datetime(message.date + strftime("%s", "2001-01-01"), "unixepoch","localtime") as date
# FROM `message`;
# '''
df = pd.read_sql_query(query, conn)
df['Dates'] = df['date'].apply(lambda x: x[:10])
frequencies = df.groupby('Dates')['ROWID'].count()
frequencies.plot(kind='line', figsize=(15, 10))
plt.show()
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT count(handle.ROWID) as total, handle.id, message.text
FROM `handle` JOIN `message`
ON handle.ROWID = message.handle_id
GROUP BY handle.id
ORDER BY total DESC
LIMIT 10;
'''
df = pd.read_sql_query(query, conn)
# my_plot = df.plot(kind='bar', x=df['id']) # display who they are
my_plot = df.plot(kind='bar')
my_plot.set_xlabel("Contacts")
my_plot.set_ylabel("Exchanged messages")
plt.show()
conn = sqlite3.connect(chat_db, check_same_thread=False)
query = '''
SELECT
text
FROM `message`
WHERE is_from_me = 1
'''
my_imessage_df = pd.read_sql_query(query, conn)
my_imessage_df[:3]
# we need to account for empty messages# we nee
messages = [msg for msg in my_imessage_df["text"] if msg]
msg_tokens = []
for msg in messages:
new_set = nltk.word_tokenize(msg)
msg_tokens.extend(new_set)
text = nltk.Text(msg_tokens)
text.dispersion_plot([
"hungry", "happy", "sad", "sleepy", "jealous", "angry", "gutted"
])
text.dispersion_plot([
"breakfast", "brunch", "lunch", "dinner", "snack", "dessert"
])