shift-counter 1.1.1

Library for processing attendance plan data into time-segmented DataFrames

Shift Counter

A Python library for processing attendance plan data into time-segmented DataFrames. This library takes JSON attendance data and converts it into a structured pandas DataFrame with 30-minute time segments, making it easy to analyze working hours by different shift types.

Installation

pip install shift-counter

Features

• Processes attendance data into 30-minute segments
• Classifies shifts into different types (Normal, VD, Red, Vacation)
• Handles timezone-aware timestamps
• Aggregates overlapping shifts
• Compatible with Tria HR API output format

Usage

Basic Usage

from shift_counter import ShiftCounter, ShiftType

# Initialize counter
counter = ShiftCounter()

# Add individual shifts
counter.add_shift(
    from_time="2025-01-08T08:00:00+01:00",
    to_time="2025-01-08T16:00:00+01:00",
    unit_id=120,
    shift_type=ShiftType.NORMAL
)

# Get resulting DataFrame
df = counter.get_dataframe()

Processing Attendance Plan

# Process complete attendance plan (e.g., from Tria HR API)
attendance_plan = {
    "data": [{
        "shifts": [{
            "date_time_from": "2025-01-08T08:00:00+01:00",
            "date_time_to": "2025-01-08T16:00:00+01:00",
            "name": "VD"
        }],
        "absences": [{
            "date_time_from": "2025-01-09T08:00:00+01:00",
            "date_time_to": "2025-01-09T16:00:00+01:00"
        }]
    }]
}

counter = ShiftCounter()
counter.count_attendance_plan(attendance_plan)

Output Format

The resulting DataFrame contains the following columns:

• department_id: Integer identifier for the organizational unit
• detail_level: Granularity of the data (always 'hour')
• time_segment: Start time of the 30-minute segment
• extraction_date: When the data was processed
• hours_normal: Regular shift hours
• hours_vd: Variable day shift hours
• hours_red: Red shift hours
• hours_vacation: Vacation/leave hours

Each row represents a 30-minute segment, with hours columns showing 0.5 for each shift type present in that segment.

Integration with Tria HR API

While this library can be used independently, it's designed to work seamlessly with data from the Tria HR API:

from triahr import TriaHRAPI
from shift_counter import ShiftCounter

# Get attendance data
api = TriaHRAPI.from_config()
attendance = api.attendance_plan(
    date_from="2025-01-01",
    date_to="2025-01-31",
    unit_id=120
)

# Process into DataFrame
counter = ShiftCounter()
counter.count_attendance_plan(attendance)
df = counter.get_dataframe()

Error Handling

The library automatically handles:

• Timezone conversions
• Overlapping shifts
• Missing or invalid shift types
• Data type consistency

Requirements

• Python ≥ 3.7
• pandas ≥ 1.0.0

License

MIT License