<h1 align="center">
<a href="https://prompts.chat">
A script for converting basic timelogs to the formats for timetracking import.
Sign in to like and favorite skills
A script for converting basic timelogs to the formats for timetracking import.
Initially for Pomodoro Prompt to Harvest and Business Tracker
Pmodoro Prompt is extremely simplistic, and only has a description field and automatically saves the date. The time unit for each entry is half an hour.
sudo apt install python3-venv python3-pip
This will also install python3 if it isn't already.
We don't use venv in these instructions but you can if you want to sort of sandbox this project.
Pip is needed.
sudo su update-alternatives --install /usr/bin/python python /usr/bin/python3 1 exit
If you don't do the above, substitute
python3 for python in the following.
mkdir -p ~/Projects/agaric/python git clone [email protected]:agaric/python/parse-timelogs-for-upload.git cd parse-timelogs-for-upload python -m pip install --user -r requirements.txt
In a
.env file, put your Harvest account ID and access token, both of which you can get at https://id.getharvest.com/
HARVEST_ACCESS_TOKEN=12345.pt.6W7wKRJEsG73NaNwBWBhv_5rQz1YkiC7_0U-OuYNnYZlMh4xP-HvmloBlrFcpJ5ZbT666HJOhNo3tXispFz4wk HARVEST_ACCOUNT_ID=123456
python pomodoro_to_harvest.py
To import into a timetracking system of any sophistication, we need to parse our description and
Harvest allows CSV import, with a bunch of annoying fields.
If project doesn't exist it will create a new project.
Rather than having to type out all 40 plus lines of data processing, you can also run the whole script in the interactive shell and play with it:
After typing
python to get the interactive Python shell in this directory, you can do this line:
exec(open('pomodoro_to_harvest.py').read())
And now you can interact with the resulting timelog DataFrame:
timelog.query("time>30").loc[:100,["description","time","orig_desc"]].tail(50)
Or the slightly more processed tl DataFrame, for example to get the hours worked per project:
tl.groupby("project").agg({"time": "sum"})["time"]/60
And yeah you can just sort of tack on the column you want to mess with and do an operation like that!
tl[tl.project == ""]
pd.unique(harvest.Task)