precon: Python functions for Price Index production

What is it?

precon is a Python package that provides a suite of speedy, vectorised functions for implementing common methods in the production of Price Indices. It aims to provide the high-level building blocks for building statistical systems at National Statistical Institutes (NSIs) and other research institutions concerned with creating indices. It has been developed in-house at the Office for National Statistics (ONS) and aims to become the standard library for price index production. This can only be achieved with help from the community, so all contributions are welcome!

Installation

pip install precon

Use

import precon

API

Many functions in the precon package are designed to work with pandas DataFrames or Series that contain only one type of value, with any categorical or descriptive metadata contained within either the index or columns axis. Each component of a statistical operation or equation will usually be within it's own DataFrame, i.e. prices in one Frame and weights in another. When dealing with time series data, the functions expect one axis to contain only the datetime index. Where a function accepts more than one input DataFrame, they will need to share the same index values so that pandas can match up the components that the programmer wants to process together. Processing values using this matrix format approach allows the functions to take advantage of powerful pandas/numpy vectorised methods.

It is not always necessary that the time series period frequencies match up if the values in one DataFrame do not change over the given period frequency in another DataFrame, as the functions will resample to the smaller period frequency and fill forward the values.

Check the docs for detailed guidance on each function and its parameters.

Features

• Calculate fixed-base price indices using common index methods.
• Combine or aggregate lower-level indices to create higher-level indices.
• Chain fixed-base indices together for a continuous time series.
• Re-reference indices to start from a different time period.
• Calculate contributions to higher-level indices from each of the component indices.
• Impute new base prices over a time series.
• Uprating values by index movements.
• Rounding weight values with adjustment to ensure the sum doesn't change.
• Stat compiler functions to quickly produce common sets of statistics.

Dependencies

• pandas
• NumPy

Contributing to precon

See CONTRIBUTING.rst