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!

• Top 10 Recipients
• Top 10 Senders
• Thread Durations
• Thread Sizes
• Activity by Day of the Week
• Activity by Hour of the Day
• Label Usage
• Subject Word Cloud

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 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 …

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 -

• Chat vs. Email Usage
• Chat vs. Email Usage (Cumulative)
• Top 10 Chatters
• Chat Clients
• Chat Times
• Chat (vs. Email) Days …

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

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

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.

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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 …