Package: precon
What is it?preconis 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!InstallationpipinstallpreconUseimportpreconAPIMany functions in thepreconpackage are designed to work withpandasDataFrames 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 thatpandascan 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 powerfulpandas/numpyvectorised 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.FeaturesCalculate 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.DependenciespandasNumPyContributing to preconSee CONTRIBUTING.rstDocumentationThe full documentation is athttp://precon.rtfd.org.History0.6.2   (2020-10-30)Bug fix: fixed an issue with theround_and_adjustfunction.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 forbase_price_imputationfunction acceptingtheto_imputeargument.Aggregation function now works with mean or geometric mean dependingonmethodargument.The functioncalculate_indexintroduced offering variousdifferent index methods.Theindex_calculatorpipeline function offers an end-to-endpipeline 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 fix0.3.4   (2020-05-22)Introduced improvements to round_and_adjust_weights to work with Seriesand 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 withquarterly 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 tomake 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.