206530b235
+# On some systems, using np.where worked but others failed. Why it worked is +# unknown but why it failed is because numpy where evaluates all parts, even +# the parts that will never get used because the where clause does not apply! +# This caused the chained strings to fail because— no string. This worked fine on the System76 and didn't on Bridget's computer, even after updating the version of python, but anyway, .apply() is better for what i am trying to do here. TODO convert other np.where uses to .apply See #4 in https://datatofish.com/if-condition-in-pandas-dataframe/ |
||
|---|---|---|
| .gitignore | ||
| pomodoro_to_harvest.py | ||
| README.md | ||
| requirements.txt | ||
| settings.py | ||
Parse timelogs for upload
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.
Installation
mkdir -p ~/Projects/agaric/python
git clone git@gitlab.com:agaric/python/parse-timelogs-for-upload.git
cd parse-timelogs-for-upload
python3 -m pip install --user -r requirements.txt
Usage
python3 pomodoro_to_harvest.py
Background notes
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.
Learning from this script and continuing development
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 python3 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!