Converting a simple pomodoro timelog into an aggregated, importable CSV

pandas groupby aggregate

Useful tutorials and documentation:

pandas arithmetic on column

pandas convert column to integer

pandas tail where pandas filter

https://pythonspot.com/pandas-filter/

timelog[timelog[“tmp_multiplier”].isnull()]

                         started                         recorded description  time        date day_of_week project tmp_multiplier

86 2021-01-15 18:09:08.898848-05:00 2021-01-15 18:34:12.255947-05:00 NaN 30 2021-01-15 Friday NaN NaN 125 2021-01-18 17:22:37.486157-05:00 2021-01-18 17:47:38.451988-05:00 NaN 30 2021-01-18 Monday NaN NaN 225 2021-01-26 10:51:24.283985-05:00 2021-01-26 11:16:27.573177-05:00 NaN 30 2021-01-26 Tuesday NaN NaN 269 2021-01-28 12:34:49.473402-05:00 2021-01-28 12:59:50.827158-05:00 NaN 30 2021-01-28 Thursday NaN NaN 273 2021-01-28 15:31:24.476471-05:00 2021-01-28 15:56:27.917432-05:00 NaN 30 2021-01-28 Thursday NaN NaN 384 2021-02-04 11:58:29.011097-05:00 2021-02-04 12:23:34.282550-05:00 NaN 30 2021-02-04 Thursday NaN NaN 548 2021-02-17 15:34:14.029445-05:00 2021-02-17 16:00:14.674852-05:00 NaN 30 2021-02-17 Wednesday NaN NaN 725 2021-03-02 20:54:26.944317-05:00 2021-03-02 21:19:28.899199-05:00 NaN 30 2021-03-02 Tuesday NaN NaN 731 2021-03-03 13:18:02.524778-05:00 2021-03-03 13:43:07.139172-05:00 NaN 30 2021-03-03 Wednesday NaN NaN 795 2021-03-09 13:22:56.466696-05:00 2021-03-09 13:52:37.393716-05:00 NaN 30 2021-03-09 Tuesday NaN NaN

pandas drop rows pandas drop rows NaN

https://www.statology.org/drop-na-pandas/

pandas choose columns to preview

python run script from interactive shell

exec(open(‘pomodoro_to_harvest.py’).read())

pandas drop column

https://appdividend.com/2020/08/05/pandas-drop-column-example/

df.drop(columns=[‘Season’], inplace=True)

pandas filter for text values pandas filter for column string containing

tl[tl["project"].str.contains("DESPITE")]