101 lines
2.7 KiB
Markdown
101 lines
2.7 KiB
Markdown
# Parse timelogs for upload
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A script for converting basic timelogs to the formats for timetracking import.
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Initially for [Pomodoro Prompt](https://gitlab.com/agaric/python/pomodoroprompt) to Harvest and [Business Tracker](https://gitlab.com/novawebdevelopment/business-tracker)
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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.
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## Installation
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### Prerequisites
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#### Install venv and pip- and python, too!
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```
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sudo apt install python3-venv python3-pip
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```
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This will also install python3 if it isn't already.
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We don't use venv in these instructions but you can if you want to sort of sandbox this project.
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Pip is needed.
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#### Make Python 3 the default
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```
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sudo su
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update-alternatives --install /usr/bin/python python /usr/bin/python3 1
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exit
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```
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If you don't do the above, substitute `python3` for `python` in the following.
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### Install
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```
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mkdir -p ~/Projects/agaric/python
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git clone git@gitlab.com:agaric/python/parse-timelogs-for-upload.git
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cd parse-timelogs-for-upload
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python -m pip install --user -r requirements.txt
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```
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### Create local environments file
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In a `.env` file, put your Harvest account ID and access token, both of which you can get at https://id.getharvest.com/
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```
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HARVEST_ACCESS_TOKEN=12345.pt.6W7wKRJEsG73NaNwBWBhv_5rQz1YkiC7_0U-OuYNnYZlMh4xP-HvmloBlrFcpJ5ZbT666HJOhNo3tXispFz4wk
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HARVEST_ACCOUNT_ID=123456
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```
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## Usage
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```bash
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python pomodoro_to_harvest.py
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```
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## Background notes
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To import into a timetracking system of any sophistication, we need to parse our description and
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Harvest allows CSV import, with a bunch of annoying fields.
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If project doesn't exist it will create a new project.
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# Learning from this script and continuing development
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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:
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After typing `python` to get the interactive Python shell in this directory, you can do this line:
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```python
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exec(open('pomodoro_to_harvest.py').read())
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```
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And now you can interact with the resulting timelog DataFrame:
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```python
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timelog.query("time>30").loc[:100,["description","time","orig_desc"]].tail(50)
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```
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Or the slightly more processed tl DataFrame, for example to get the hours worked per project:
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```python
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tl.groupby("project").agg({"time": "sum"})["time"]/60
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```
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And yeah you can just sort of tack on the column you want to mess with and do an operation like that!
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##### List tasks that had no properly defined project
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```python
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tl[tl.project == ""]
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```
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#### See which tasks we ended up with
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```python
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pd.unique(harvest.Task)
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```
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