https://github.com/didierrlopes/jobanalysis
This repository aims to extract an insight from your job statistics. For that purpose it uses Jupyter notebook to process data gathered in an Excel data sheet.
https://github.com/didierrlopes/jobanalysis
diary job-analysis performance
Last synced: about 1 year ago
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This repository aims to extract an insight from your job statistics. For that purpose it uses Jupyter notebook to process data gathered in an Excel data sheet.
- Host: GitHub
- URL: https://github.com/didierrlopes/jobanalysis
- Owner: DidierRLopes
- Created: 2019-02-08T23:04:20.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-02-09T00:28:49.000Z (over 7 years ago)
- Last Synced: 2025-04-10T03:56:48.893Z (about 1 year ago)
- Topics: diary, job-analysis, performance
- Language: Jupyter Notebook
- Homepage:
- Size: 194 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# JobAnalysis
This repository aims to extract an insight of your job statistics.
The idea is to have an easily readable Excel where you can update your work days, and use a Jupyter notebook (with common Python libraries for data science) to process this data and extract further insights.
The Excel is pretty straightforward. Each table corresponds to a week work where you can add:
* Entry and Exit hours, which will automatically calculate your working hours and update the total weekly hours.
This is particularly convenient when you have a flexible schedule as long as you respect the working hours.
* Status, which contains the project you are working on and your location, with the format: Project (location).
Notice that you should write "DAY OFF" or "BANK HOLIDAYS" on the Status column when you did not work on that day.
* Comment, allowing you to remember that particular day (e.g. Discussed with XXX about ZZZ product).
Here are some examples of the type of insights that you can extract from the data.
