Parses data out of your Google Takeout (History, Activity, Youtube, Locations, etc...)

Overview

google_takeout_parser

  • parses both the Historical HTML and new JSON format for Google Takeouts
  • caches individual takeout results behind cachew
  • merge multiple takeouts into unique events

Parses data out of your Google Takeout (History, Activity, Youtube, Locations, etc...)

This doesn't handle all cases, but I have yet to find a parser that does, so here is my attempt at parsing what I see as the most useful info from it. The Google Takeout is pretty particular, and the contents of the directory depend on what you select while exporting. Unhandled files will warn, though feel free to PR a parser or create an issue if this doesn't parse some part you want.

This can take a few minutes to parse depending on what you have in your Takeout (especially while using the old HTML format), so this uses cachew to cache the function result for each Takeout you may have. That means this'll take a few minutes the first time parsing a takeout, but then only a few seconds every subsequent time.

Since the Takeout slowly removes old events over time, I would recommend periodically (personally I do it once every few months) backing up your data, to not lose any old events and get data from new ones. To use, go to takeout.google.com; For Reference, once on that page, I hit Deselect All, then select:

  • Chrome
  • Google Play Store
  • Location History
    • Select JSON as format
  • My Activity
    • Select JSON as format
  • Youtube and Youtube Music
    • Select JSON as format
    • In options, deselect music-library-songs, music-uploads and videos

The process for getting these isn't that great -- you have to manually go to takeout.google.com every few months, select what you want to export info for, and then it puts the zipped file into your google drive. You can tell it to run it at specific intervals, but I personally haven't found that to be that reliable.

This was extracted out of my HPI modules, which was in turn modified from the google files in karlicoss/HPI

Installation

Requires python3.7+

To install with pip, run:

pip install git+https://github.com/seanbreckenridge/google_takeout_parser

Usage

CLI Usage

Can be access by either google_takeout_parser or python -m google_takeout_parser. Offers a basic interface to list/clear the cache directory, and/or parse a takeout and interact with it in a REPL:

To clear the cachew cache: google_takeout_parser cache_dir clear

To parse a takeout:

$ google_takeout_parser parse ~/data/Unpacked_Takout --cache
Parsing...
Interact with the export using res

In [1]: res[-2]
Out[1]: PlayStoreAppInstall(title='Hangouts', device_name='motorola moto g(7) play', dt=datetime.datetime(2020, 8, 2, 15, 51, 50, 180000, tzinfo=datetime.timezone.utc))

In [2]: len(res)
Out[2]: 236654

Also contains a small utility command to help move/extract the google takeout:

$ google_takeout_parser move --from ~/Downloads/takeout*.zip --to-dir ~/data/google_takeout --extract
Extracting /home/sean/Downloads/takeout-20211023T070558Z-001.zip to /tmp/tmp07ua_0id
Moving /tmp/tmp07ua_0id/Takeout to /home/sean/data/google_takeout/Takeout-1634993897
$ ls -1 ~/data/google_takeout/Takeout-1634993897
archive_browser.html
Chrome
'Google Play Store'
'Location History'
'My Activity'
'YouTube and YouTube Music'

Library Usage

Assuming you maintain an unpacked view, e.g. like:

 $ tree -L 1 ./Takeout-1599315526
./Takeout-1599315526
├── Google Play Store
├── Location History
├── My Activity
└── YouTube and YouTube Music

To parse one takeout:

from pathlib import Path
from google_takeout.path_dispatch import TakeoutParser
tp = TakeoutParser(Path("/full/path/to/Takeout-1599315526"))
# to check if files are all handled
tp.dispatch_map()
# to parse without caching the results in ~/.cache/google_takeout_parser
uncached = list(tp.parse())
# to parse with cachew cache https://github.com/karlicoss/cachew
cached = list(tp.cached_parse())

To merge takeouts:

from pathlib import Path
from google_takeout.merge import cached_merge_takeouts
results = list(cached_merge_takeouts([Path("/full/path/to/Takeout-1599315526"), Path("/full/path/to/Takeout-1634971143")]))

The events this returns is a combination of all types in the models.py (to support easy serialization with cachew), to filter to a particular just do an isinstance check:

>> len(locations) 99913 ">
from google_takeout_parser.models import Location
takeout_generator = TakeoutParser(Path("/full/path/to/Takeout")).cached_parse()
locations = list(filter(lambda e: isinstance(e, Location), takeout_generator))
>>> len(locations)
99913

I personally exclusively use this through my HPI google takeout file, as a configuration layer to locate where my takeouts are on disk, and since that 'automatically' unzips the takeouts (I store them as the zips), i.e., doesn't require me to maintain an unpacked view

Contributing

Just to give a brief overview, to add new functionality (parsing some new folder that this doesn't currently support), you'd need to:

  • Add a model for it in models.py, which a key property function which describes each event uniquely (used to merge takeout events); add it to the Event Union
  • Write a function which takes the Path to the file you're trying to parse and converts it to the model you created (See examples in parse_json.py). If its relatively complicated (e.g. HTML), ideally extract a div from the page and add a test for it so its obvious when/if the format changes.
  • Add a regex match for the file path to the DEFAULT_HANDLER_MAP

Tests

git clone 'https://github.com/seanbreckenridge/google_takeout_parser'
cd ./google_takeout_parser
pip install '.[testing]'
mypy ./google_takeout_parser
pytest
Owner
Sean Breckenridge
:)
Sean Breckenridge
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
CRISP: Critical Path Analysis of Microservice Traces

CRISP: Critical Path Analysis of Microservice Traces This repo contains code to compute and present critical path summary from Jaeger microservice tra

Uber Research 110 Jan 06, 2023
DataPrep — The easiest way to prepare data in Python

DataPrep — The easiest way to prepare data in Python

SFU Database Group 1.5k Dec 27, 2022
Making the DAEN information accessible.

The purpose of this repository is to make the information on Australian COVID-19 adverse events accessible. The Therapeutics Goods Administration (TGA) keeps a database of adverse reactions to medica

10 May 10, 2022
Very basic but functional Kakuro solver written in Python.

kakuro.py Very basic but functional Kakuro solver written in Python. It uses a reduction to exact set cover and Ali Assaf's elegant implementation of

Louis Abraham 4 Jan 15, 2022
Bamboolib - a GUI for pandas DataFrames

Community repository of bamboolib bamboolib is joining forces with Databricks. For more information, please read our announcement. Please note that th

Tobias Krabel 863 Jan 08, 2023
Pypeln is a simple yet powerful Python library for creating concurrent data pipelines.

Pypeln Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines. Main Features Simple: Pypeln

Cristian Garcia 1.4k Dec 31, 2022
Provide a market analysis (R)

market-study Provide a market analysis (R) - FRENCH Produisez une étude de marché Prérequis Pour effectuer ce projet, vous devrez maîtriser la manipul

1 Feb 13, 2022
A distributed block-based data storage and compute engine

Nebula is an extremely-fast end-to-end interactive big data analytics solution. Nebula is designed as a high-performance columnar data storage and tabular OLAP engine.

Columns AI 131 Dec 26, 2022
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Jan 02, 2023
Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment

Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment Brief explanation of PT Bukalapak.com Tbk Bukalapak was found

Najibulloh Asror 2 Feb 10, 2022
API>local_db>AWS_RDS - Disclaimer! All data used is for educational purposes only.

APIlocal_dbAWS_RDS Disclaimer! All data used is for educational purposes only. ETL pipeline diagram. Aim of project By creating a fully working pipe

0 Apr 25, 2022
This repository contains some analysis of possible nerdle answers

Nerdle Analysis https://nerdlegame.com/ This repository contains some analysis of possible nerdle answers. Here's a quick overview: nerdle.py contains

0 Dec 16, 2022
For making Tagtog annotation into csv dataset

tagtog_relation_extraction for making Tagtog annotation into csv dataset How to Use On Tagtog 1. Go to Project Downloads 2. Download all documents,

hyeong 4 Dec 28, 2021
A CLI tool to reduce the friction between data scientists by reducing git conflicts removing notebook metadata and gracefully resolving git conflicts.

databooks is a package for reducing the friction data scientists while using Jupyter notebooks, by reducing the number of git conflicts between different notebooks and assisting in the resolution of

dataroots 86 Dec 25, 2022
A notebook to analyze Amazon Recommendation Review Dataset.

Amazon Recommendation Review Dataset Analyzer A notebook to analyze Amazon Recommendation Review Dataset. Features Calculates distinct user count, dis

isleki 3 Aug 22, 2022
WithPipe is a simple utility for functional piping in Python.

A utility for functional piping in Python that allows you to access any function in any scope as a partial.

Michael Milton 1 Oct 26, 2021
Performance analysis of predictive (alpha) stock factors

Alphalens Alphalens is a Python Library for performance analysis of predictive (alpha) stock factors. Alphalens works great with the Zipline open sour

Quantopian, Inc. 2.5k Jan 09, 2023
An Integrated Experimental Platform for time series data anomaly detection.

Curve Sorry to tell contributors and users. We decided to archive the project temporarily due to the employee work plan of collaborators. There are no

Baidu 486 Dec 21, 2022