pypricetrend 0.1.0

The `pypricetrend` package provides tools to analyze and forecast product demand based on historical sales data

pypricetrend

Introduction

The pypricetrend package provides tools to analyze and forecast product demand based on historical sales data. It includes the DemandCurve class for generating demand curves and the Forecast class for predicting future demand.

Classes

DemandCurve

The DemandCurve class generates demand curves for a given product.

Initialization

To initialize the DemandCurve class, you need to provide a DataFrame containing the columns price, quantity, and date, as well as the landed_cost and fulfillment_cost of the product.

from pypricetrend import DemandCurve
import pandas as pd

# Sample data
data = {
    'price': [10, 20, 15, 25, 30],
    'quantity': [100, 80, 90, 70, 60],
    'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'])
}
orders_df = pd.DataFrame(data)

# Initialize the DemandCurve class
landed_cost = 5.0
fulfillment_cost = 2.0
demand_curve = DemandCurve(orders_df, landed_cost, fulfillment_cost)

Predicting Demand

You can predict the demand for a given price using the predict_demand method.

price = 22.5
predicted_demand = demand_curve.predict_demand(price)
print(f"Predicted demand for price {price}: {predicted_demand}")

Plotting the Demand Curve

You can plot the demand curve for the given product using the plot_demand_curve method.

demand_curve.plot_demand_curve()

Saving and Loading the Model

You can save the demand curve model to a file and load it later.

# Save the model
demand_curve.save_model('demand_curve_model.npy')

# Load the model
demand_curve.load_model('demand_curve_model.npy')

Forecast

The Forecast class predicts future demand based on historical sales data.

Initialization

To initialize the Forecast class, you need to provide a DataFrame containing the columns price, quantity, and date

from pypricetrend import Forecast
import pandas as pd

# Sample data
data = {
    'price': [10, 20, 15, 25, 30],
    'quantity': [100, 80, 90, 70, 60],
    'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'])
}
orders_df = pd.DataFrame(data)

# Initialize the Forecast class
forecast = Forecast(orders_df)

Forecasting Demand

You can forecast the demand for a given price and sale date using the forcast_demand method.

Forecasting Demand
You can forecast the demand for a given price and sale date using the forcast_demand method.

Forecasting for the Next 30 Days

You can forecast the demand for the next 30 days using the forecast_thrity_days method.

Installation

pip install pypricetrend