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