# When Do I Send Emails?

Work and the holidays have been distracting me from blogging and fun side projects for a couple of months, so I'm easing back into it with a really quick and easy Gmail data-wrangling post.

When I first started playing around with my Gmail data, I mentioned that I wanted to get some of the stats that Xobni used to provide before they were swallowed by the Yahoo! black hole. A couple of the simpler stats to compile are "what days of the week do I send most of my email?" and "what time of day do I send the most emails?".

In order to do any time-based analysis on my emails, I'm going to need the dates and times they were sent. So I've taken the PowerShell script from a while back and made a slight modification; in the $props hash I'm adding a field called SentDate: $props = @{
Id = $mimeMessage.MessageId To =$mimeMessage.To.ToString()
From = $mimeMessage.From.ToString() FromEmail =$fromEmail
Subject = $mimeMessage.Subject Body =$bodyText
Format = $actualFormat SentDate =$mimeMessage.Date.ToUniversalTime()
}


MimeKit provides the sent date of the email as a DateTimeOffset; to keep things consistent, I'm converting everything to UTC at this stage.

From there, I import the data into pandas as per usual and filter it down to just the emails sent by me:

import pandas as pd
import numpy as np
import humanfriendly
import matplotlib.pyplot as plt
plt.style.use('ggplot')

df = pd.read_csv('../times.csv', header = 0)

fromMe = df.query('FromEmail == "[my email]"')


It turns out that indexing your data by date/time in pandas is pretty easy; you just create a DateTimeIndex:

temp = pd.DatetimeIndex(fromMe['SentDate'], tz = 'UTC').tz_convert('America/Denver')


Here I'm telling pandas to create an index by the SentDate field, and that the field is already in UTC. Then I'm converting all of those dates and times to my local timezone so that the data makes sense from my local perspective. This mostly works, because I mostly live in the Mountain timezone. Some of my data will be a little skewed because of emails sent while traveling and a few months where I lived in the Eastern time zone, but not so much that I care. In a later post I might look at how this data changes over time, which is more interesting (I might even be able to identify when and where I was traveling based on that data).

But for now, let's break down the data in temp and shove it back into the original dataset:

fromMe['DayOfWeek'] = temp.dayofweek
fromMe['Hour'] = temp.hour
fromMe['Year'] = temp.year


Now for each email from me, I've got a column that tells me what hour of the day and day of the week it was sent. From there, aggregating it and charting it are a snap:

# Number of emails sent by day of week
sentDayOfWeek = fromMe.groupby(['DayOfWeek']).agg({'Id' : 'count'})
sentDayOfWeek['Id'].plot(kind='bar', figsize=(6, 6), title='Emails Sent By Day Of Week')
plt.show()

# Number of emails sent by hour of day
sentHourOfDay = fromMe.groupby(['Hour']).agg({'Id' : 'count'})
sentHourOfDay['Id'].plot(kind='bar', figsize=(6, 6), title='Emails Sent By Hour Of Day')
plt.show()

The data is about what I'd expect; more emails on Monday than any other day (0 == Monday on this chart) and the majority of emails sent during the workday (with a dip around lunch).

Aggregating by year provides a bit of mystery, though:

sentYear = fromMe.groupby(['Year']).agg({'Id' : 'count'})
sentYear['Id'].plot(kind='bar', figsize=(6, 6), title='Emails Sent By Year')
plt.show()
The numbers vary quite a bit more than I'd expect. 2004 makes sense; I only started using Gmail in July of that year. And the next couple of years shows me using Gmail more and more over my old Lycos account. The spike in 2011 also seems reasonable, as that's when I stopped working at an office with an Exchange server, so my day-to-day email load shifted. But the dips in 2012 and 2015? No idea. I'll have to dig further into those.
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# How Long Would It Take To Read All My Email?

This is part of a series on mining your own Gmail data.

For this post I want to tackle a fun question: how long would it take to read all of my email if that's all I did, 24/7? It's one of those questions that should interest anyone who's concerned about information overload or is looking to pare down their information consumption: "Just how much of my time is theoretically committed to my inbox?"

First, the obvious: nobody actually does this. No one actually reads every email they receive from start to finish (as anyone who's dealt with email in a corporate environment knows all too well). Most of us have filters (both electronic and mental) set up to glean the info we need and skip the rest.

And I'll bet that a lot of email is written without any expectation that the whole thing will be read; the author may be well aware that different portions of the email are relevant to different recipients, or that the email itself will only be interesting to a subset of the mailing list (e.g., many marketing emails).

So it's not the one super-relevant data point that should make people completely re-think their information consumption habits or anything like that. But it is fun to think about, and as one data point among many others it might prove interesting or useful.

On to the fun part - actually coming up with a number!

Like most people, I'm getting new emails all the time. So technically I should be taking into account all the new emails I receive while I'm still reading through my old ones. But that's hard, so I'm not going to bother. Instead, I'm just going to assume I've stopped getting emails at all while I'm reading. Which means that getting a basic number is easy - I just have to count all the words in all my emails, divide that by the number of words per minute I read, and I've got the number of minutes it would take to read everything.

The first thing I need to do is go back to my PowerShell script and pull in the body of each email. This is where we hit the first snag - HTML emails.

For doing word counts, I really don't want to look at HTML emails, because there's a ton of junk in there which a human won't be reading. Luckily, most email clients which send HTML emails also include a text version; in those cases, we'll just extract that text portion of the email and ignore the HTML. Unfortunately, this isn't always the case; when there's not a text version available, we'll just have to get the HTML and figure out how to deal with it later.

As usual, MimeKit will be doing most of the work. This version of the script is pretty similar to our previous ones, except that we have to loop through the possible body formats for each message to figure out which formats are available. We always check for the 'Text' format first, because that's the one we really want. If that's not available, we run through the others until we find one that works.

The relevant changes are the hash of the possible formats, which we use for iteration and for tracking the number of emails of each type:

$formats = @{ [MimeKit.Text.TextFormat]::Text = 0; [MimeKit.Text.TextFormat]::Flowed = 0; [MimeKit.Text.TextFormat]::Html = 0; [MimeKit.Text.TextFormat]::Enriched = 0; [MimeKit.Text.TextFormat]::RichText = 0; [MimeKit.Text.TextFormat]::CompressedRichText = 0 }  And the section where we determine what the actual format is and store it: $bodyText = $null$actualFormat = $null # Run through all the enumeration values # The pipe through sort ensures that we check them in the enum order, # which is great because we prefer text over flowed over HTML, etc.$formats.Keys | sort | % {
# try each Format until we find one that works
if($actualFormat -eq$null) {
# Try to get the body in the current format
$bodyText =$mimeMessage.GetTextBody($_) if($bodyText) {
$actualFormat =$_
}
}
}

if($actualFormat -eq$null) {
$unknownFormat += 1;$actualFormat = "Unknown"
} else {
$formats[$actualFormat] += 1;
}


You can find the full script here.

A couple of notes:

1. This isn't perfect; sometimes MimeKit can't really figure out what the format is. For example, I have some Skype notification emails which MimeKit thinks are HTML only, but are in fact text. I'm not sure why MimeKit gets confused (probably incorrect headers in the original emails), but out of about 43,000 emails only a couple dozen seem to have issues, so I'm not going to worry about it.
2. In all of my emails, the only two formats returned were Text and HTML. This might have something to do with what Gmail supports; I've seen some posts that suggest Gmail doesn't support Flowed, though those may be outdated. In any case, I'm only really dealing with Text and HTML in my word counts.

Once we've got the data, we can load it up in pandas and do some counting. Doing a naive count of the words in the plain text emails is trivial; we just define a method that uses Python's split method with None as the delimiter argument, and then look at the length of the returned list. Here's what textWordCount looks like:

def textWordCount(text):
if not(isinstance(text, str)):
return 0

return len(text.split(None))


But the HTML emails are problematic because most of the content is markup that the user will never actually read. So we need to strip all that markup out and just count the words in the text portions of the HTML. To do that, we create another method which parses the HTML email content using the amazing Beautiful Soup library, strips away the style, script, head, and title parts, and extracts the text from what's left using get_text(). Once we've got the actual human-readable text, we can run it through our usual word counting method:

def htmlWordCount(text):
if not(isinstance(text, str)):
return 0

soup = bsoup(text, 'html.parser')

if soup is None:
return 0

stripped = soup.get_text(" ", strip=True)

[s.extract() for s in soup(['style', 'script', 'head', 'title'])]

stripped = soup.get_text(" ", strip=True)

return textWordCount(stripped)


I took a couple of online tests to get an idea of how fast I read and came up with 350 words per minute. With that bit of data, we can now add some more columns to our data and figure out the total time to read all the emails:

def wordCount(row):

if(row['Format'] == 'Html'):
return htmlWordCount(row['Body'])

return textWordCount(row['Body'])

averageWordsPerMinute = 350

# Count the words in each message body
emails['WordCount'] = emails.apply(wordCount, axis=1)
emails['MinutesToRead'] = emails['WordCount'] / averageWordsPerMinute

# Get total number of minutes required to read all these emails
totalMinutes = emails['MinutesToRead'].sum()

# And convert that to a more human-readable timespan
timeToRead = humanfriendly.format_timespan(totalMinutes * 60)


The full script is here, if you're playing at home.

Running that against all of my Gmail gives me:

>>> timeToRead
'2 weeks, 6 days and 18 hours'


So if I sat down and read at my fastest speed 24/7 for three weeks straight with no breaks, no sleep, and never slowing down, I could finish reading every word of every email I've ever received in my Gmail account. If I only read them 8 hours a day, it'd take me about 9 weeks to finish.

That's actually less than I expected, though "two whole months of your life spent just reading your email" is a still a bit sobering.

Sobering enough that I'm not going to try to compute this for my other four email accounts, anyway.

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# Mining Your Gmail Data - Part 6

First off, let's take a look at the second question that came up at the end of the last post: ignoring the Media Type (the 'application/', 'video/', etc.) from the MIME type.

That turns out to be pretty easy - the script from last time already collected that data, because MimeKit already made it available. We just need to adjust our pandas script to group on 'MediaSubtype' instead of 'MimeType':

types = notFromMe.groupby(['MediaSubtype'])


That cleaned things up a lot. But we still have the second question from the last post: what's behind octet-stream?

Application/octet-stream is basically the generic binary file option; most likely the original client which uploaded the file didn't specify the type. But we can make an educated guess about the type based on the file name extension, where we have it. So we'll write a quick function which takes a row of data and, if the Media Subtype is 'octet-stream', returns the file name extension from the FileName column:

import os.path

...

def filetype(row):
if not(isinstance(row['ContentTypeName'], str)):
return ''
if row['MediaSubtype'] == 'octet-stream':
return os.path.splitext(row['ContentTypeName'])[1]
return row['MediaSubtype']


We can run that function against our data and put the results in a new column which we'll call 'FileType':

notFromMe['FileType'] = notFromMe.apply(lambda row: filetype(row), axis = 1)


Now, instead of grouping by MediaSubtype, we just group by FileType. This isn't perfect - some of our data is getting discarded because there's not enough info between Media Subtype and FileName to figure out what kind of attachment it is. But the data is mostly good, and gives us a much more useful chart:

I'm also running this chart with a threshold of 0.02 for the 'other' section, to clean up the less-frequent file types. The whole script can be found here.

So, if I'm looking to downsize my Gmail backup, I should probably concentrate on JPEGs, videos (wmv and mpeg), and PDFs.

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# Mining Your Gmail Data - Part 5

Last time I talked about mining my Gmail data, I figured out that most of my received attachments (by size) were from friends and family. This time I'm going to break down the data a little more so I can figure out what types of data are in those attachments. On a practical level, I could reduce my Gmail backup size by deleting old messages with photos I already have backed up elsewhere. In reality, I just want to screw around with the charting capabilities in pandas.

This time I need all the data broken down by individual attachments. The script for this is similar to the last one, but instead of aggregating the attachments per message, I'm keeping them separate. I'm also grabbing some additional data, like the MIME type of each attachment and the file name. And because I'll need counts of individual attachments, I need to make sure I've got a unique ID for them. I can't use the message ID, because a single message can have multiple attachments. And I can't use the file name, because that may be repeated across messages. So I'm concatenating the two into a new field called AttachmentId. I'm also ignoring all the emails without any attachments. Here's the new script:

$emails = @()$gmvaultdb = "C:\Users\hartez\gmvault-db\db"
$total = (Get-ChildItem$gmvaultdb -Recurse -Filter *.eml | measure).Count
Add-Type -Path "MimeKit.1.2.10.0\lib\net45\MimeKit.dll"

Get-ChildItem $gmvaultdb -Recurse -Filter *.eml | ForEach-Object {$i=0} {
Write-Host "Processing $_ ($i of $total)"$mimeMessage = [MimeKit.MimeMessage]::Load($_.FullName)$attachments = @()

$mimeMessage.Attachments | % {$attachment = @{
Id = $mimeMessage.MessageId +$_.ContentDisposition.FileName
FileName = $_.ContentDisposition.FileName ContentTypeName =$_.ContentType.Name
MimeType = $_.ContentType.MimeType MediaType =$_.ContentType.MediaType
MediaSubtype = $_.ContentType.MediaSubtype Length =$_.ContentObject.Stream.Length
ContentType = $_.ContentType }$attachments += (New-Object PSObject -Property $attachment) }$mimeMessage.From.ToString() -match '"\s<(.*)>$' | Out-Null;$fromEmail = $Matches[1] if($attachments.Count -gt 0) {
$attachments | % {$props = @{
Id = $mimeMessage.MessageId To =$mimeMessage.To.ToString()
From = $mimeMessage.From.ToString() FromEmail =$fromEmail
AttachmentId = $_.Id FileName =$_.FileName
ContentTypeName = $_.ContentTypeName MimeType =$_.MimeType
MediaType = $_.MediaType MediaSubtype =$_.MediaSubtype
Size = $_.Length ContentType =$_.ContentType
}
}

$emails += (New-Object PSObject -Property$props)
}

$i++ }$emails | Select Id, To, From, FromEmail, FileName, AttachmentId, ContentTypeName, MimeType, MediaType, MediaSubtype, Size, ContentType | Export-Csv attachments2.csv -NoTypeInformation


In pandas, I'm doing the usual import of the data and filtering down to messages which aren't from myself. Then I group the data by MIME type:

    types = notFromMe.groupby(['MimeType'])


and create aggregate columns for the total number of attachments of each type and the total size of those attachments:

    types = types.agg({'AttachmentId' : 'count', 'Size' : 'sum'})


I'll also need the total number of attachments and the total size of all attachments so I can calculate percentages:

    totalCount = types['AttachmentId'].sum()
totalSize = types['Size'].sum()


With that in place, adding the percentage columns is easy:

    types['percentCount'] = types['AttachmentId'] / totalCount
types['percentSize'] = types['Size'] / totalSize


At the top of the script I've already imported matplotlib:

    import matplotlib.pyplot as plt
plt.style.use('ggplot')


Now we're all set to plot this data so I can see the relative sizes of each file type. I'll start with 'percentSize':

    types['percentSize'].plot(kind='pie', figsize=(6, 6))
plt.show()


Wow. That's ... pretty ugly. Let's see if we can un-clutter that a bit by combining all of the really low-percentage stuff into a slice called "other". While we're at it, we'll create a method that we can re-use when we want to graph the percentages by count:

    def combinedPlot(df, col, cutoff):

# Just get the mime types which are 1% or more
overCutoff = df[col]
overCutoff = df.query(col + '>' + str(cutoff))

# Fill in the 'other' section
remaining = 1 - (major[col].sum())
other = pd.DataFrame({col : pd.Series([remaining], index = ['other'])})

# Add the 'other' section to our main data
both = overCutoff.append(other)

# Plot it
both[col].plot(kind='pie', figsize=(6, 6))
plt.show()


To use it, we just drop in our types data frame, the column we want to graph, and where we want to cut off the data:

    combinedPlot(types, 'percentSize', 0.01)


That's much better. But it raises a couple of questions:

1. What's with application/octet-stream? That could be any sort of file; can we dig down into that?
2. Can we get some more succinct labels? We don't really need the 'application/; and 'video/' prefixes.

This is already long; we'll get to those questions next time.

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