org-analyze 0.1.5

Collect data from org-mode/org-roam pages and do some simple analyzing it.

OrgAnalyze

Collect data from org-mode/org-roam pages and do some simple analyzing it

Items parsed:

• Lines starting with "CLOCK:" or "#+CLK:" as OrgClock
• Headers starting with "*", "**", etc. as OrgHeader
• Tables starting with "|", as OrgTable

read_org_clocks_2

This function parses all *.org files in a given directory. It extracts all clocking information and associates it with its parent header (Feature) and sub-header (Task).

The function returns a tuple containing a list of column names and a list of rows. This structure is ideal for creating a pandas DataFrame.

Example Usage

Let's say you have an org file tasks.org in a directory called my_orgs with the following content:

* Feature A
** Task 1
CLOCK: [2025-10-25 Sat 10:00]--[2025-10-25 Sat 11:30] =>  1:30
** Task 2
CLOCK: [2025-10-25 Sat 12:00]--[2025-10-25 Sat 13:00] =>  1:00

* Feature B
** Task 3
CLOCK: [2025-10-25 Sat 14:00]--[2025-10-25 Sat 14:30] =>  0:30

You can parse this file and analyze the data with pandas like this:

import pandas as pd
from org_analyze.clocks import read_org_clocks_2

# 1. Parse the org files in the directory
columns, rows = read_org_clocks_2('my_orgs')

# 2. Create a pandas DataFrame
df = pd.DataFrame(rows, columns=columns)

# 3. Analyze the data: Group by feature (head1) and sum the duration
feature_hours = df.groupby('head1')['duration'].sum()

print("Total hours per feature:")
print(feature_hours)

Output:

Total hours per feature:
head1
Feature A    2.5
Feature B    0.5
Name: duration, dtype: float64