Analysis

In this module, the function get_dataset retrieves a synthetic (fake) dataset. The class Results offers a few functions to summarize and merge the data, as well as assess preparedness for retirement.

CPR.analysis.get_dataset()

Function that returns a dataframe of synthetic data.

Returns

Dataframe of synthetic data.

Return type

pandas.core.frame.DataFrame

class CPR.analysis.Results(input_data, output, common, prices, extra_params)

This class prepares the results.

Parameters

• input_data (pandas.core.frame.DataFrame) – dataframe of inputs

• output (pandas.core.frame.DataFrame) – dataframe of outputs

• common (Common) – instance of the class Common

• prices (Prices) – instance of the class Prices

• extra_params (dict) – dictionary of extra parameters

summarize()

Function to summarize the simulation.

merge(add_index=True)

Function to merge input and output variables to create a database.

Parameters

add_index (bool, optional) – add index to database, True by default

check_preparedness(factor_couple=2, cons_floor=100, d_cutoffs={20: 80, 100: 65})

Function that introduces a consumption floor, computes RRI (NaN if consumption before and after retirement below cons_floor), and checks preparedness for retirement.

Parameters

• factor_couple (int, optional) – factor to normalize income for couple, by default 2

• cons_floor (int, optional) – consumption floor, by default 100

• d_cutoffs (dict, optional) – cutoffs, by default {20: 80, 100: 65}