Running Present Value Example Ep.3

This notebook runs present_value_ep3.py and outputs results in tables. The result tables replicate figures shown in a video on Systemortph’s YouTube channel, Systemorph #Techucation for IFRS17: Adapt the Systemorph Notebook to your needs.

To run this notebook, the ifrs17 and present_value_ep3 modules in your copy of the ifrs17a library need to be importable. To ensure they are importable, make sure the current directory is set to your ifrs17a copy. Typically, the current directory is set to the location where you started this notebook, so it should run fine unless you have moved the notebook elsewhere.

To check the current directory from Python, import os, and execute os.getcwd(). To change the current directory in Python, use the os.chdir function.

Click the badge below to run this notebook online on Google Colab. You need a Google account and need to be logged in to it to run this notebook on Google Colab.

The next code cell below is relevant only when you run this notebook on Google Colab. It installs lifelib and creates a copy of the library for this notebook.

import sys, os

if 'google.colab' in sys.modules:
    lib = 'ifrs17a'; lib_dir = '/content/'+ lib
    if not os.path.exists(lib_dir):
        !pip install lifelib
        import lifelib; lifelib.create(lib, lib_dir)

    %cd $lib_dir

Running present_value_ep3.py

present_value_ep3.py is executed when it’s imported by the import statement. ifrsvars defined in the template module refers to a DataFrame that contains all the IFRS variables as rows.

import pandas as pd
from present_value_ep3 import ifrsvars as vars

The video shows present values by repoting nodes in different tables. The table below replicates the figures for the reporting node G for the period ending at the end of March 2021, which are shown around the 1:50 mark in the video.

filter = ((vars['EconomicBasis']=='L') & (vars['EstimateType']=='BE')
          & (vars['ReportingNode']=='G') & (vars['Year']==2021) & (vars['Month']==3))

df = vars[filter].set_index(['Novelty', 'AocType', 'AmountType'])['Value'].groupby(level=[0, 1, 2]).sum().unstack(level=2)
df

	AmountType	CL	PR
Novelty	AocType
C	EOP	11459.723798	-14323.349204
N	AU	-770.405525	-1068.926528
	BOP	11364.693439	-17128.546413
	CF	-240.320160	2210.550000
	EV	916.777904	1913.390722
	IA	188.978140	-249.816985

The table below replicates the figures for the reporting node DE for the period ending at the end of December 2021, which are shown around the 7:50 mark in the video.

filter = ((vars['EconomicBasis']=='L') & (vars['EstimateType']=='BE')
          & (vars['ReportingNode']=='DE') & (vars['Year']==2021) & (vars['Month']==12))

df = vars[filter].set_index(['Novelty', 'AocType', 'AmountType'])['Value'].groupby(level=[0, 1, 2]).sum().unstack(level=2)
df

	AmountType	CL	PR
Novelty	AocType
C	EOP	7051.271474	-6587.673594
N	AU	-470.146752	-667.863724
	BOP	7102.933399	-11801.034216
	CF	-614.620787	5237.597000
	EV	564.101718	1219.303073
	IA	469.003896	-575.675728