Over the years, I’ve downloaded a lot of old emails to my laptop, which I saved as .eml files and then converted to .docx, .odt, and .txt files (mostly the last two).
The unfortunate habit of almost everyone (including myself, but no longer!) of quoting the original message as part of an email exchange has left me with text file(s) full of repeated sentences and paragraphs. What I’ve tended to do is to dump all the text from a one on one correspondence into a single file (“Dad-Erinaceus Complete Correspondence.odt,” for example) and then try cleaning it up and re-ordering the messages by date.
Apart from the emails, I have I guess what you’d call a “journal” which is a very long .odt file that runs to about 300 pages or so. Much of this has the same sentences and paragraphs over and over again, but sometimes with slight variations that I would like to keep. So far, in either .odt or .txt files, I’ve started by searching for the first sentence, deleting subsequent appearances of it, and then going on to the next sentence, and so forth. Very time consuming! Is there a faster (and safe) way to do this?
There is quite possibly a very simple solution to this that I haven’t thought of, but I’d be much obliged for any suggestions.
it would compress well for an archival backup, so that’s what i’d do for the ‘originals’.
if your long message chains look anything like mine, there’s far more quoted material overall than new text in most mails; and not all new text would be relevant to whatever is being saved… so it’d be quicker to do it the other way–copy and paste what you did want to new documents instead of trying to clean up these long compilations by deleting what you don’t.
Python should be able to handle this for you with 100% accuracy at removing duplicated sentences (and headers), with added complexity you could have it even make result files based on unique emails you corresponded with, and it can handle all the various file types and even nested folders if that’s involved.
Probably could do this in 50-100 lines of code depending on complexity. Runtime should be minutes at most.
Learning python to do this would probably take a couple to a few hours if you have any familiarity with coding, bit more if you don’t.
Hi @pheonixdown@sh.itjust.works, thanks for your response. Unfortunately I have no knowledge of python and my coding abilities are essentially nonexistent 🙁 . . .
I doubt you’ll find anything for this bespoke purpose built as a utility online, so if you want it you’ll need to make it (or I guess hire someone).
Python is fairly approachable to learn, the 2nd best time to plant a tree is today. Personally, I’d recommend installing Anaconda and using a Jupyter Notebook. For this process, you’d need to learn about:
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“pip” to install any libraries/packages you need.
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Importing packages
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Working with strings
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Getting a list of all files (maybe only those of certain types) in a given directory.
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For loops
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some way to read each file type (pandas can handle txt, not sure about the others but you should be able to literally just Google “Python import <filetype>” to find a package. Import them as strings and concatenate a huge list of them in a for loop.
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Pandas dataframes, Parsing strings into new columns, including into distinct lists of strings. The complexity here varies a lot based on what you specifically want to get out of the process.
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Exporting dataframe rows to files (better imho if you did it as tabs in a workbook, but it is trickier) in a for loop.
Don’t overwrite objects as you go and keep different logical chunks in their own cells. That’ll make it easier to troubleshoot. Just Google any errors you don’t immediately understand, you’ll find lots of advice online.
Don’t have whatever you make delete any of your original files.
Depending on your feelings about AI, you could probably get one to make something that’ll work after a few tries and troubleshooting by giving it error messages, but aside from the ethical problems, you’d never really know if it got it right, even if it seemed to work, so I would recommend against it.
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I have good news for you, you get to spend days learning an entire skillset to solve 1 specific problem!
In all seriousness though, there’s actually a big problem with email “quoting” as you say, that each email client will re create the entire email chain in it’s own format, leading to a complete mess.
If you still have access to that email client, it might be easier to see if there’s an export feature to export them as actual email files, which would be much easier to process than a word document.
Another option, if you are ok with “close enough” and some inaccuracies, you could ask an A.I. like Claude to process these files for you.
Otherwise you’re probably stuck with manual tedious processing. Personally I would just start again and ignore the file you have. Create an actual indexed word document with a table of contents so you can actually find anything. Add a header per email topic, with a sub header per message / reply. Manually copy paste, being careful not to include the quote this time.
Text, as far as I know (?) doesn’t take up that much space memory-wise, but on the other hand can you imagine the amount of server space that gets taken up by redundant text from people’s emails (just for starters)?
Some of my approaches to the apparently much-hated “Inbox Zero” are to be diligent about emptying the trash, not keeping copies of emails in my sent folder, and most importantly, to download messages, personal or professional ones (mostly the former) that I find personally valuable, and then delete the original email so it isn’t floating around forever in cyberspace. This leaves me with lots of txt files, which, as I said above, I then merge all together into one file and then edit. You’d think that there’d be a tool that cleans up the contents of email messages; there do appear to be some online ones, but I don’t trust them. While researching this problem, I found out about “(g)awk” and am now fiddling around with it to see if I can successfully clean up my files without completely messing them up, as in
awk '!seen[$0]++' input.txt > output.txtmaking sure that I experiment on a duplicated file and not on the original. I barely know what I’m doing here, but I’ll see what I can come up with . . .
Inbox zero doesn’t mean deleting all your emails lol. It just means getting them out of your inbox. I do this myself. For each email in my inbox I “do something” with it instead of just leaving it there. Put it in a folder, tag it, spam, delete, archive, anything. Personally I use 3 zones:
- inbox: currently to-do. Things I need to reply to or action I “flag” so they’re at the top. Everything else is moved either to
- archive: things I might need in the future. Any important correspondence. Bills. Etc. The important part is it is “out of sight”.
- trash. Auto deleted after 30 days in case I change my mind and actually need it. Most emails go to trash. Aggressively unsubscribe from newsletters.
🙂👍
