Export chrome history

export_chrome_history.py

import sqlite3
import pandas as pd
from datetime import datetime, timedelta

# Path to your Chrome history database [Select one based on your os and chrome history path]
history_path = "C:\\Users\\[USER_NAME]\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\History" # Windows
history_path = "~/Library/Application Support/Google/Chrome/Default/History" # MAC
history_path = "~/.config/google-chrome/Default/History" # Linux

# Connect to the database
conn = sqlite3.connect(history_path)
cursor = conn.cursor()

# Get the start of today in microseconds since epoch
start_of_today = datetime.combine(datetime.today(), datetime.min.time())
start_timestamp = int((start_of_today - datetime(1601, 1, 1)).total_seconds() * 1000000)

# Query to get all available fields by joining `urls` and `visits` tables
query = """
SELECT 
    urls.url AS URL, 
    urls.title AS Title, 
    urls.visit_count AS Visit_Count, 
    urls.typed_count AS Typed_Count, 
    urls.last_visit_time AS Last_Visit_Time, 
    visits.visit_time AS Visit_Time, 
    visits.from_visit AS Referrer_URL_ID, 
    visits.transition AS Transition_Type
FROM urls
JOIN visits ON urls.id = visits.url
WHERE urls.last_visit_time >= ?
ORDER BY urls.last_visit_time DESC;
"""

# Execute query
cursor.execute(query, (start_timestamp,))
rows = cursor.fetchall()

# Convert timestamp and load data into a DataFrame
data = []
epoch_start = datetime(1601, 1, 1)
for row in rows:
    url, title, visit_count, typed_count, last_visit_time, visit_time, referrer_url_id, transition_type = row
    last_visit_timestamp = epoch_start + timedelta(microseconds=last_visit_time)
    visit_timestamp = epoch_start + timedelta(microseconds=visit_time)
    data.append((url, title, visit_count, typed_count, last_visit_timestamp, visit_timestamp, referrer_url_id, transition_type))

# Create DataFrame
df = pd.DataFrame(data, columns=[
    'URL', 'Title', 'Visit Count', 'Typed Count', 
    'Last Visit Time', 'Visit Time', 'Referrer URL ID', 'Transition Type'
])

# Closing the connection
conn.close()

# Analyze or save your data
print(df.head())

# Save the df to CSV
df.to_csv('output.csv', index=False)