shift-counter 1.1.4

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()

Unit Organization

The library includes a UnitOrganiser class for managing and mapping organizational units:

from shift_counter import UnitOrganiser, UnitFilter
import pandas as pd

# Create DataFrame with store mappings from sales database
stores_df = pd.DataFrame({
    'tria_id': [1],
    'but_num_business_unit': [2097]
})

# Initialize organiser with store mappings
organiser = UnitOrganiser(stores_list=stores_df)

# Get organization units from API
api = TriaHRAPI.from_config()
org_units = api.organization_units(company_id=1)

# Create filter to get active departments from specific stores
unit_filter = UnitFilter()\
    .constraint(name="parent_unit_id", keep=True, value=[1, 2])\
    .constraint(name="departments", keep=True)\
    .constraint(name="deactivated", keep=False, value=[True])

# Apply filter
filtered_units = organiser.filter_organization_units(
    organization_units=org_units,
    unit_filter=unit_filter
)

# Add units to organiser and map them to sales database stores
organiser.add_department(filtered_units)

# Get resulting DataFrame with mapped units
mapped_units = organiser.get_dataframe()

Filter Examples

The UnitFilter class allows for flexible unit filtering using method chaining:

# Get only active stores
filter_active_stores = UnitFilter()\
    .constraint(name="stores", keep=True)\
    .constraint(name="deactivated", keep=False, value=[True])

# Get departments from specific stores
filter_specific_deps = UnitFilter()\
    .constraint(name="parent_unit_id", keep=True, value=[1, 2])\
    .constraint(name="departments", keep=True)

# Get both stores and departments, excluding deactivated
filter_all_active = UnitFilter()\
    .constraint(name="stores", keep=True)\
    .constraint(name="departments", keep=True)\
    .constraint(name="deactivated", keep=False, value=[True])

# Get specific units by ID
filter_specific_units = UnitFilter()\
    .constraint(name="id", keep=True, value=[1, 2, 3])

Available filter constraints:

• stores: Match store units (no parent_unit_id or parent_unit_id=0)
• departments: Match department units (has parent_unit_id)
• id: Match specific unit IDs
• parent_unit_id: Match units with specific parent unit IDs
• deactivated: Match units based on their deactivation status

The resulting DataFrame contains:

• department_id: Unit ID from Tria HR
• department_name: Full name of the unit
• extraction_date: When the data was processed
• store_department_id: Parent unit ID (null for stores)
• store_id: Mapped store ID from sales database
• deactivated: Whether the unit is deactivated

Error Handling

The library automatically handles:

• Timezone conversions
• Overlapping shifts
• Missing or invalid shift types
• Data type consistency
• Store-department mappings

Requirements

• Python ≥ 3.7
• pandas ≥ 1.0.0

License

MIT License