Meaningful insights from Org tasks

With a more complex Python script than shown in the post about my organizing system I got now a way better insight! Won't share the full code because it's messy, but take a look on the results:

- [2025-12-01 Mon]
  Allocated hours by task type:
  - =review= :: 7.7%
  - =feature= :: 45.4%
  - =bug= :: was 18.4%
  - =task= :: 6.8%
  - =social= :: 0.0%
  - =plan= :: 0.8%
  - =investigate= :: 2.9%
  - =polish= :: 14.8%

  Cumulated total percentage 96.8% (100% means that all tasks was considered)
- [2026-01-01 Thu]
  Allocated hours by task type:
  - =review= :: 26.8%
  - =feature= :: 18.1%
  - =bug= :: 7.3%
  - =task= :: 14.3%
  - =social= :: 0.7%
  - =plan= :: 6.3%
  - =investigate= :: 4.6%
  - =polish= :: 18.2%

  Cumulated total percentage 96.3% (100% means that all tasks was considered)
- [2026-02-01 Sun]
  Allocated hours by task type:
  - =review= :: 22.8%
  - =feature= :: 6.4%
  - =bug= :: 28.0%
  - =task= :: 30.7%
  - =social= :: 1.6%
  - =plan= :: 1.7%
  - =investigate= :: 4.0%
  - =polish= :: 1.0%

  Cumulated total percentage 96.2% (100% means that all tasks was considered)

The absolute numbers was manually removed :>

In summary, I created a lot of clocktable reports, using :match property to include and exclude the task tags, and with the data, I got the general clocktable and compared the values of each small clocktable against it.