Price Indices Package

Overview

This package provides functions to calculate various price indices, including both unweighted and weighted methods. The package includes native Python implementations as well as pandas DataFrame-based implementations for flexible data handling.

Features

Unweighted Methods:

• Jevons Index
• Dutot Index
• Carli Index
• Balk-Mehrhoff-Walsh (BMW) Index

Weighted Methods:

• Laspeyres Index
• Paasche Index
• Fisher Index
• Törnqvist Index
• Walsh Index
• Sato-Vartia Index

Installation

tbd...

Documentation

The full documentation is available here.

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Usage Examples

Here's a quick example of how to use the package:

import pandas as pd
from price_indices import jevons_index, jevons_index_from_df, PD_DEFAULT_PRICE_COL, PD_DEFAULT_PRODUCT_ID_COL, PD_DEFAULT_TIME_PERIOD_COL, DEFAULT_NORMALIZATION_VAL

# Example data
data = {
    PD_DEFAULT_PRODUCT_ID_COL: ['product_1', 'product_2', 'product_3'] * 2,
    PD_DEFAULT_PRICE_COL: [100, 200, 300, 110, 190, 310],
    PD_DEFAULT_TIME_PERIOD_COL: [0, 0, 0, 1, 1, 1]
}
df = pd.DataFrame(data)

# Calculate Jevons index using native Python function
prices_0 = {'product_1': 100, 'product_2': 200, 'product_3': 300}
prices_t = {'product_1': 110, 'product_2': 190, 'product_3': 310}
jevons_index_value = jevons_index(prices_0, prices_t, DEFAULT_NORMALIZATION_VAL)

# Calculate Jevons index using pandas DataFrame function
jevons_index_df_value = jevons_index_from_df(df, base_period=0, compared_period=1)

print("Jevons Index (native):", jevons_index_value)
print("Jevons Index (DataFrame):", jevons_index_df_value)