precon 0.7.0a2

A set of functions to calculate Prices Economics statistics.

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

Documentation

The full documentation is at http://precon.rtfd.org.

History

0.7.0 (2020-11-05)

Added new aggregation functionality:

• Added the aggregate_level function to aggregate by a grouping.

• Added the aggregate_up_hierarchy function to aggregate up a

  hierarchy given by MultiIndex levels.

0.6.2 (2020-10-30)

• Bug fix: fixed an issue with the round_and_adjust function.

0.6.1 (2020-10-15)

• Bug fix: fixed broken API definition.

• Updated README to reflect new installation instructions.

0.6.0 (2020-10-14)

• Added functionality for base_price_imputation function accepting

  the to_impute argument.
• Aggregation function now works with mean or geometric mean depending

  on method argument.
• The function calculate_index introduced offering various

  different index methods.
• The index_calculator pipeline function offers an end-to-end

  pipeline for calculating indices with optional base price imputation.

0.5.1 (2020-06-09)

• Bug fix in uprate function occuring in Q4 periods.

0.5.0 (2020-06-09)

• Removed the prorate function.

0.4.0 (2020-06-05)

• Introduced new function uprate and get_uprating_factors for price uprating.

0.3.5 (2020-05-22)

• Bug fix

0.3.4 (2020-05-22)

• Introduced improvements to round_and_adjust_weights to work with Series

  and on any axis of a DataFrame with the axis option.

0.3.3 (2020-05-15)

• Rolled back set_first_period in chaining as it introduced a bug.

0.3.2 (2020-05-15)

• Bug fix: included flip_axis function in helpers.

0.3.1 (2020-05-15)

• Modified aggregation function to work with weight Series and different axes.

• Changed set_jans in chaining to set_first_period_to_100 to work with

  quarterly series.

0.3.0 (2020-05-14)

• Added round_and_adjust_weights function in rounding.py.

• Add set_jans function and improved time series validation in chaining to

  make functions more robust.

0.2.0 (2020-03-31)

• Added create_special_aggregation function.

0.1.1 (2020-03-31)

• Fixed bug in importing functions in get_stats module.

0.1.0 (2020-01-27)

• First installable version.