wf-rappi 0.2.0

No project description provided

Rappi WFM Data Analysis and Forecasting

This Python package provides functionality for data analysis and forecasting for Rappi's Workforce Management (WFM) system.

Installation

You can install the package using pip:

pip install rappi-wfm


# #The package includes functions for processing and preparing data for analysis.

# from data_processing import *

# # Example usage
# date_range = ['2024-04-01', '2024-04-10']
# df_ordered = read_and_sort_orders(date_range)
# special_days = preprocess_special_dates('Calendario Rappi - BD_Feriados.csv')
# df_filtered = filter_special_dates(df_ordered, special_days)
# orders = pivot_orders(df_filtered)

# Modeling
# The package provides tools for building and training models for forecasting.
# from modeling import *

# # Example usage
# train, test = split_train_test_data(orders, '2024-01-01')
# train = create_features(train)
# test = create_features(test)
# X_train, y_train = extract_features_target(train)
# model = train_xgboost_model(X_train, y_train)

# Order Distribution
# The order_distribution module helps distribute financial orders based on predicted values and average daily weights.

# from order_distribution import *

# # Example usage
# ordenes_financieras = create_ordenes_financieras(df_ordered, abril)
# curva_ordenes = create_curva_ordenes(df_ordered)
# df3 = distribute_orders(ordenes_financieras, curva_ordenes)

# License
# This project is licensed under the MIT License - see the LICENSE file for details