leadtimeutils 0.2.0

a utility module for lead time analysis in procurement & logistic division mineral alam abadi

Lead Time Utils

a utility module for lead time analysis, specifically designed for handling business logic related to date calculations and constraints internally in procurement & logistic division mineral alam abadi.

Installation

pip install leadtimeutils

Quick Start

import pandas as pd
import numpy as np
import leadtimeutils as ltu

Available Functions

has_weekday_in_range()

Checks whether a specific weekday occurs within a date range, with a minimum number of days after the start date.

Signature:

has_weekday_in_range(
    df: pd.DataFrame,
    start_date_col: str,
    end_date_col: str,
    chosen_day: int,
    main_days: int
) -> np.ndarray

Parameters:

• df (pd.DataFrame): DataFrame containing the date columns
• start_date_col (str): Name of the start date column
• end_date_col (str): Name of the end date column
• chosen_day (int): Weekday to check for (0=Monday, 1=Tuesday, ..., 6=Sunday)
• main_days (int): Minimum number of days after start_date before checking for the weekday

Returns:

• np.ndarray: Boolean array indicating whether the condition is met for each row

Behavior:

• Returns True if the chosen weekday occurs between start_date + main_days and end_date
• Returns False if the end_date itself is the chosen weekday
• Handles invalid dates gracefully (returns False for those rows)

---

get_days_between()

Returns a list of day names between two dates for each row.

Signature:

get_days_between(
    df: pd.DataFrame,
    start_date_col: str,
    end_date_col: str
) -> np.ndarray

Parameters:

• df (pd.DataFrame): DataFrame containing the date columns
• start_date_col (str): Name of the start date column
• end_date_col (str): Name of the end date column

Returns:

• np.ndarray: Object array where each element is a list of day names (e.g., ['Monday', 'Tuesday', ...])

---

Usage Examples

Example 1: Check for Thursday After 5 Days

import pandas as pd
import leadtimeutils as ltu

df = pd.DataFrame({
    'start': ['2023-10-01', '2023-10-05'],
    'end': ['2023-10-15', '2023-10-08']
})

# Check if Thursday (day 3) occurs at least 5 days after start
results = ltu.has_weekday_in_range(df, 'start', 'end', chosen_day=3, main_days=5)
print(results)  # [True, False]

Example 2: Check for Monday After 7 Days

df = pd.DataFrame({
    'first_date': ['2023-11-01', '2023-11-10'],
    'end_date': ['2023-11-15', '2023-11-20']
})

# Check if Monday (day 0) occurs at least 7 days after order
results = ltu.has_weekday_in_range(df, 'first_date', 'end_date', chosen_day=0, main_days=7)
print(results)

Example 3: Check for Friday After 3 Days

df = pd.DataFrame({
    'first_date': ['2023-12-01'],
    'end_date': ['2023-12-10']
})

# Check if Friday (day 4) occurs at least 3 days after request
results = ltu.has_weekday_in_range(df, 'first_date', 'end_date', chosen_day=4, main_days=3)
print(results)

Example 4: Get All Days Between Dates

df = pd.DataFrame({
    'first_date': ['2023-10-01'],
    'end_date': ['2023-10-05']
})

day_lists = ltu.get_days_between(df, 'first_date', 'end_date')
print(day_lists[0])  # ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday']

---

Weekday Reference

Use these values for the chosen_day parameter:

Day	Value
Monday	0
Tuesday	1
Wednesday	2
Thursday	3
Friday	4
Saturday	5
Sunday	6

---

Migration Guide (v0.1.x → v0.2.0)

Breaking Changes

The hardcoded has_thursday_after_5_days() functions have been replaced with a flexible has_weekday_in_range() function.

Old (v0.1.x):

# Scalar version
result = ltu.has_thursday_after_5_days(start, end)

# Vectorized version
results = ltu.has_thursday_after_5_days_vectorized(df, 'start_col', 'end_col')

New (v0.2.0):

# Unified vectorized function
results = ltu.has_weekday_in_range(df, 'start_col', 'end_col', chosen_day=3, main_days=5)

Migration Steps

1. Replace has_thursday_after_5_days_vectorized() calls with has_weekday_in_range()
2. Add chosen_day=3 (for Thursday) and main_days=5 parameters
3. For scalar checks, create a single-row DataFrame first

---

License

MIT License - see LICENSE file for details.

Author

Fajar Amry (nagabonar27)
Email: [e-mail]

Repository

• Homepage: https://github.com/data-system-proclog-maa/leadtime-utils
• Issues: https://github.com/data-system-proclog-maa/leadtime-utils/issues