pypricing

Bayesian log-demand pricing analytics with PyMC for long-format panels (one row per SKU–time observation): own-price elasticity, optional controls, instruments, hierarchy, trend/seasonality, and counterfactual prediction.

Install

pip install pypricing

Optional model-graph rendering (also needs the system Graphviz binaries):

pip install 'pypricing[graphviz]'

Full docs (guides + API + notebooks): pypricing.readthedocs.io

Quickstart

import numpy as np

from pypricing import LogLogDemandModel, generate_mock_data

df = generate_mock_data(
    n_periods=20,
    n_skus=5,
    n_controls=2,
    include_seasonality=False,
    random_state=0,
)

model = LogLogDemandModel()
model.fit(df, draws=1000, tune=1000, chains=4, random_seed=42)

df_scenario = df.drop(columns=["quantity"]).copy()
df_scenario["price"] = df_scenario["price"] * 1.05
print(model.predict(df_scenario, hdi_prob=0.9, random_seed=123).head())

Data contract

fit(df) expects level-scale columns:

• Required: sku, price (strictly positive), quantity (non-negative)
• Optional: control_*, iv_*, hierarchy via PanelColumns(group_columns=...), period / region (datetime period required for trend / seasonality)

Column names are configurable via PanelColumns. Internally the model uses log(price) and log(max(quantity, quantity_floor)) (default floor 1.0).

When this is not identified

If prices respond to the same shocks that move demand, a regression of quantity on price mixes the demand slope with that feedback. More regressors do not fix it. You need exogenous price variation: a randomized experiment, cost / commodity shocks, or other valid instruments (iv_*). See Identification.