This is an illustrative repo containing dummy data that I'm using to troubleshoot my scripts
I have data in Markdown files, one for each work day. These are basically work journals that I use to document my work.
You can see what the data looks like in the dummy_data.md file.
My goal is to use a tool like Pandas to read each file, and extract data into a dataframe or CSV file (whichever is faster) in the following format:
| Date | Entry | ticket | chat | other_tag | project_sales | narrative |
|---|---|---|---|---|---|---|
| Date string | String (full entry without the line number) | Boolean | Boolean | Boolean | Boolean | String |
The Booleans should just be True or False where a row contains a regex pattern match corresponding with the ticket, chat, other_tag, and project_sales column titles (also entry types).
My broad goal is to extract data from these Markdown files into something like a dataframe or even a CSV file (whichever is more efficient) so I can answer questions like the following:
- Which tickets or chats did I respond to (I work in a support role)
- Were the interactions tickets or chats?
- Did the interactions fall into specific categories that are designated by hashtags that I add inline? Here I think I just really need a
TrueorFalseresponse. - What are the entries that are not interactions (for example, just notes about something I reviewed on the day)?
The purpose of this is to help me automate a weekly report on my work for the previous week that includes the following information:
- How many chats and tickets I completed?
- How many interactions from each category (such as
#project_salesor#other_tag)? - Which interactions are
#project_salesand tickets/chats (so I can calculate the ratio of both)? - What else did I do in the week that wasn't a direct support interaction?