I searched the FSL mailing list archives, found the summary page for April 2014 and copied and pasted the table of contents into a text file. That file is saved on GitHub and can be accessed with the urllib2 python library.

In [1]:

```
import urllib2
archive_file = urllib2.urlopen('https://raw.githubusercontent.com/HappyPenguin/OpenScience/master/FSLmailinglist_archive_April2014.txt')
archive_lines = archive_file.readlines()
archive_lines = [ line.rstrip() for line in archive_lines]
```

The first few lines of this file are:

In [2]:

```
for line in archive_lines[:10]:
print line
```

So the first thing we can find out are how many distinct email subjects were used in the month of April:

In [3]:

```
len(archive_lines)
```

Out[3]:

But, to be honest, we don't really care about the subject lines; we want to know how many emails were sent in total. And that requires a little parsing of this text file. Specifically we'll split each line at the *last* '(' character, discard everything to the left of that, and strip out the constant string ' messages)' from the part that's left.

In [4]:

```
archive_messages_n = [ x.rsplit('(',1)[1].rstrip(' messages)') for x in archive_lines ]
print archive_messages_n[:10]
```

We can convert those values to integers and save them in a numpy array data frame so we can get a total value!

In [5]:

```
import numpy as np
array = np.array(archive_messages_n, dtype=np.int)
print array.sum()
```

**871 emails in one month! Crazy days!**

Thank you FMRIB ;)