12 Years of Gmail, Part 5: Mail

Posted on 05 December 2016 in Technology • Tagged with 12 years of gmail, email, graphing, plotly, python, takeout inspector, wordcloud

This post is part of my series, 12 Years of Gmail, taking a look at the data Google has accumulated on me over the past 12 years of using various Google services and documenting the learning experience developing an open source Python project (Takeout Inspector) to analyze that data.

After taking a look at the chat data in my export, I am finally ready to move on to some of the actual mail! Much of what I will look at here is pretty similar to what I was able to turn up with chat data. I tried to branch out a bit, bringing in a new package to create word clouds, and also refactored some of the Takeout Inspector code to form the beginning of a more "formal" report generating process (instead of just spitting out a single HTML file with only a certain subset of the data). Hopefully I can continue to improve this to a point allowing for easier report generation for any user. Anyway, on to the mail data!

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Loading Plotly Graphs on Demand with Waypoints

Posted on 23 November 2016 in Technology • Tagged with graphing, javascript, plotly, waypoints

In my last post, 12 Years of Gmail, Part 4: Chat, I included eight Plotly graphs on a single page. All the graphs worked correctly, but the page was taking almost four seconds to render any content at all and up to 6-8 seconds to load completely without cached elements. By contrast, the landing page of chrxs.net takes less than a second to load with visual content rendering almost immediately. The site is intentionally designed to be light weight and uses very few resources on a standard load. But Plotly graphs require a big (1MB+ uncompressed) JavaScript file in order to load with all the bells and whistles. What can be done to improve this slow load time, particularly when many graphs are on a single page?

film strip before Film strip before optimization (webpagetest.org)

The page load film strip above shows almost three whole seconds before any content is rendered. The obvious first step was to move the loading of Plotly's large JavaScript file from the page head (which loads before content is rendered) to the end of the page body, theoretically allowing the page's content to be partially loaded and rendered earlier. However, doing this created a bit of …


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12 Years of Gmail, Part 4: Chat

Posted on 18 November 2016 in Technology • Tagged with 12 years of gmail, chat, graphing, plotly, python, takeout inspector

This post is part of my series, 12 Years of Gmail, taking a look at the data Google has accumulated on me over the past 12 years of using various Google services and documenting the learning experience developing an open source Python project (Takeout Inspector) to analyze that data.

With the Finishing Touches in place, it's finally time to start looking at some of the data in my Google Takeout Mail export file. What better to start with than the Google Talk (or Google Chat, as I will refer to it) content stored within!

I am starting with Chat because I was surprised to find it all stored in the export file. It makes sense as chat history is accessible from the Chats link in the old Gmail interface (I couldn't find an equivalent in Inbox). My surprise led to curiosity and my curiosity led to obsession with trying to figure how the Chat data is stored and what information each messages contains. It turns out there are quite a few things that can be gleaned from these chat messages -


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12 Years of Gmail, Part 3: Finishing Touches

Posted on 12 November 2016 in Technology • Tagged with 12 years of gmail, configparser, names, graphing, plotly, python, takeout inspector

This post is part of my series, 12 Years of Gmail, taking a look at the data Google has accumulated on me over the past 12 years of using various Google services and documenting the learning experience developing an open source Python project (Takeout Inspector) to analyze that data.

After spending last week Bootstrapping things and, somewhat related, working my way around Pelican, today I have tried to tie up loose ends so I can start spending more time thinking about what information I can get from all this data. While the package is far from complete, these "finishing touches" ended up being the three themes of this morning's work -

  1. Implementing a settings file
  2. Customising Plotly graphs
  3. Generating random names

Implementing a Settings File

While thinking about how to customize graphs (more on that below) and allow for changes to styles without too much effort, it struck me that there is likely some common ("Pythonic") way to handle settings. And, of course, there is - it's called ConfigParser and it's extremely handy.

To get my feet wet, I created a settings.cfg file with the following contents:

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;settings.cfg
[mail]
anonymize = False
db_file = data/email.db …

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12 Years of Gmail, Part 2: Bootstrapping

Posted on 08 November 2016 in Technology • Tagged with 12 years of gmail, mailbox, graphing, plotly, python, sqlite, takeout inspector

This post is part of my series, 12 Years of Gmail, taking a look at the data Google has accumulated on me over the past 12 years of using various Google services and documenting the learning experience developing an open source Python project (Takeout Inspector) to analyze that data.

Jumping back in to Python has been just as fun as my first experiences with it. After brushing off some of the dust, I have managed to put together a (very) small package that does a couple of basic things with a Google Takeout Mail (mbox) file:

  1. Parses and standardizes the format of email addresses;
  2. Imports key messages data in to an sqlite database;
  3. Produces simple graphs of top recipients and senders.

Parsing Email Addresses

The mailbox Python module makes it very simple to get an mbox file in to Python and play around using the mailbox.Mailbox and email.Message classes. Here is an example using my mbox file:

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import mailbox
email = mailbox.mbox('/path/to/email.mbox')

# The number of emails in the mbox file.
print len(email.keys())
114407

# The "Delivered-To" header of the first email.
print email …

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