The Relationship Between Happiness and Development of a Country: The Case of Lithuania

1. Questions and Hypothesis

1.1. Key Questions to be Answered:

1. Is Lithuania becoming a happier state as its human development increases?
2. How does Lithuania's happiness level compare to its global, continental and regional average?
3. How does Lithuania's human development compare to its global, continental and regional average?
4. What is the relationship between development and happiness?
5. What factors could made Lithuania happier in the future? Can we predict its future level of happiness?

1.2. Core Hypothesis

The development in Lithuania is dependent and explained by its level of Human Development Index (HDI).

2. Background Information

2. 1. Human Development Index (HDI)

The level of development is often mistakenly measured by pure income attribute, such as GDP or GDP per capita (PPP). The United Nations Human Development Index is more applicable measure to human development as it incorporates not only the level GDP, but also other metrics, such as the level of education achieved and human life expectency. The Human Developments Index (HDI) reflects the overall development level of the society as a whole, rather than its pure income.

In this analysis, the United Nations Human Development Index (HDI) is chosen as a key indicator of the development of the Republic of Lithuania.

2.2. World Hapiness Report

Since 2012, the United Nations also compile the official World Happiness Report. This report reveals the level of happiness achieved by any country in the world. The level of hapiness is based on based on respondent ratings of their own lives (i.e. by a survey), which the report also correlates with various quality of life factors.

Looking at the top10 of the happiest countries in the world in 2020, the gut feeling kicks in that the richest countries tend to be also the happiest countries. However, is it always the case if we go deep down?

If we take OECD country ranking of their happiness levels in 2019, one can spot that Lithuania ranks higher than Estonia, South Korea or even Japan, which are locally perceived as "the richer countries". Thus, guessing by the gut feeling, one may state that the level of happiness does not necessarily correspond to how rich or developed the country is.

This analysis looks into whether the path of the country's development corresponds to its level of happiness. As a focus of this analysis, we specifically look into the case of the Republic of Lithuania.

3. Data

In this research, I extracted datasets from Kaggle. For both World Health Report and Human Development Index, I will extract data from MySQL databases, which concern the years from 2015 to 2019. This decision is limited by the fact that no earlier data for World Health Report could be recorded earlier than 2015 and no later data could be recorded for Human Development Index later than 2019. Each dataset has up to 200 rows, which correspondes to the number of countries in any particular year.

import pandas as pd

pip install mysql-connector-python

Requirement already satisfied: mysql-connector-python in ./opt/anaconda3/lib/python3.9/site-packages (8.0.27)
Requirement already satisfied: protobuf>=3.0.0 in ./opt/anaconda3/lib/python3.9/site-packages (from mysql-connector-python) (3.19.1)
Note: you may need to restart the kernel to use updated packages.

import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3306",
    user="root",
    password="qB2*JX1ndV#5",
)
cursor = mydb.cursor()
cursor.execute('USE WHR')
WHR_2015 = pd.read_sql('SELECT * FROM Year_2015', con=mydb)
WHR_2015

	Country	Region	Happiness Rank	Happiness Score	Standard Error	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
0	Switzerland	Western Europe	1	7.587	0.03411	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	2.51738
1	Iceland	Western Europe	2	7.561	0.04884	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	2.70201
2	Denmark	Western Europe	3	7.527	0.03328	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	2.49204
3	Norway	Western Europe	4	7.522	0.03880	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	2.46531
4	Canada	North America	5	7.427	0.03553	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	2.45176
...	...	...	...	...	...	...	...	...	...	...	...	...
153	Rwanda	Sub-Saharan Africa	154	3.465	0.03464	0.22208	0.77370	0.42864	0.59201	0.55191	0.22628	0.67042
154	Benin	Sub-Saharan Africa	155	3.340	0.03656	0.28665	0.35386	0.31910	0.48450	0.08010	0.18260	1.63328
155	Syria	Middle East and Northern Africa	156	3.006	0.05015	0.66320	0.47489	0.72193	0.15684	0.18906	0.47179	0.32858
156	Burundi	Sub-Saharan Africa	157	2.905	0.08658	0.01530	0.41587	0.22396	0.11850	0.10062	0.19727	1.83302
157	Togo	Sub-Saharan Africa	158	2.839	0.06727	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681	1.56726

158 rows × 12 columns

WHR_2015['Year'] = 2015
WHR_2015 = WHR_2015[['Year', 'Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)', 'Generosity']]
WHR_2015

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity
0	2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678
1	2015	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630
2	2015	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139
3	2015	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699
4	2015	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811
...	...	...	...	...	...	...	...	...	...	...	...
153	2015	Rwanda	Sub-Saharan Africa	154	3.465	0.22208	0.77370	0.42864	0.59201	0.55191	0.22628
154	2015	Benin	Sub-Saharan Africa	155	3.340	0.28665	0.35386	0.31910	0.48450	0.08010	0.18260
155	2015	Syria	Middle East and Northern Africa	156	3.006	0.66320	0.47489	0.72193	0.15684	0.18906	0.47179
156	2015	Burundi	Sub-Saharan Africa	157	2.905	0.01530	0.41587	0.22396	0.11850	0.10062	0.19727
157	2015	Togo	Sub-Saharan Africa	158	2.839	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681

158 rows × 11 columns

WHR_2015 = WHR_2015.rename(columns={'Family': 'Social Support', 'Freedom': 'Freedom to Make Life Choices', 'Trust (Government Corruption)': 'Government Corruption'})
WHR_2015

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678
1	2015	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630
2	2015	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139
3	2015	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699
4	2015	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811
...	...	...	...	...	...	...	...	...	...	...	...
153	2015	Rwanda	Sub-Saharan Africa	154	3.465	0.22208	0.77370	0.42864	0.59201	0.55191	0.22628
154	2015	Benin	Sub-Saharan Africa	155	3.340	0.28665	0.35386	0.31910	0.48450	0.08010	0.18260
155	2015	Syria	Middle East and Northern Africa	156	3.006	0.66320	0.47489	0.72193	0.15684	0.18906	0.47179
156	2015	Burundi	Sub-Saharan Africa	157	2.905	0.01530	0.41587	0.22396	0.11850	0.10062	0.19727
157	2015	Togo	Sub-Saharan Africa	158	2.839	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681

158 rows × 11 columns

import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3306",
    user="root",
    password="qB2*JX1ndV#5",
)
cursor = mydb.cursor()
cursor.execute('USE WHR')
WHR_2016 = pd.read_sql('SELECT * FROM Year_2016', con=mydb)
WHR_2016

	Country	Region	Happiness Rank	Happiness Score	Lower Confidence Interval	Upper Confidence Interval	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity	Dystopia Residual
0	Denmark	Western Europe	1	7.526	7.460	7.592	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171	2.73939
1	Switzerland	Western Europe	2	7.509	7.428	7.590	1.52733	1.14524	0.86303	0.58557	0.41203	0.28083	2.69463
2	Iceland	Western Europe	3	7.501	7.333	7.669	1.42666	1.18326	0.86733	0.56624	0.14975	0.47678	2.83137
3	Norway	Western Europe	4	7.498	7.421	7.575	1.57744	1.12690	0.79579	0.59609	0.35776	0.37895	2.66465
4	Finland	Western Europe	5	7.413	7.351	7.475	1.40598	1.13464	0.81091	0.57104	0.41004	0.25492	2.82596
...	...	...	...	...	...	...	...	...	...	...	...	...	...
152	Benin	Sub-Saharan Africa	153	3.484	3.404	3.564	0.39499	0.10419	0.21028	0.39747	0.06681	0.20180	2.10812
153	Afghanistan	Southern Asia	154	3.360	3.288	3.432	0.38227	0.11037	0.17344	0.16430	0.07112	0.31268	2.14558
154	Togo	Sub-Saharan Africa	155	3.303	3.192	3.414	0.28123	0.00000	0.24811	0.34678	0.11587	0.17517	2.13540
155	Syria	Middle East and Northern Africa	156	3.069	2.936	3.202	0.74719	0.14866	0.62994	0.06912	0.17233	0.48397	0.81789
156	Burundi	Sub-Saharan Africa	157	2.905	2.732	3.078	0.06831	0.23442	0.15747	0.04320	0.09419	0.20290	2.10404

157 rows × 13 columns

WHR_2016['Year'] = 2016
WHR_2016 = WHR_2016[['Year', 'Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)', 'Generosity']]
WHR_2016

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Family	Health (Life Expectancy)	Freedom	Trust (Government Corruption)	Generosity
0	2016	Denmark	Western Europe	1	7.526	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171
1	2016	Switzerland	Western Europe	2	7.509	1.52733	1.14524	0.86303	0.58557	0.41203	0.28083
2	2016	Iceland	Western Europe	3	7.501	1.42666	1.18326	0.86733	0.56624	0.14975	0.47678
3	2016	Norway	Western Europe	4	7.498	1.57744	1.12690	0.79579	0.59609	0.35776	0.37895
4	2016	Finland	Western Europe	5	7.413	1.40598	1.13464	0.81091	0.57104	0.41004	0.25492
...	...	...	...	...	...	...	...	...	...	...	...
152	2016	Benin	Sub-Saharan Africa	153	3.484	0.39499	0.10419	0.21028	0.39747	0.06681	0.20180
153	2016	Afghanistan	Southern Asia	154	3.360	0.38227	0.11037	0.17344	0.16430	0.07112	0.31268
154	2016	Togo	Sub-Saharan Africa	155	3.303	0.28123	0.00000	0.24811	0.34678	0.11587	0.17517
155	2016	Syria	Middle East and Northern Africa	156	3.069	0.74719	0.14866	0.62994	0.06912	0.17233	0.48397
156	2016	Burundi	Sub-Saharan Africa	157	2.905	0.06831	0.23442	0.15747	0.04320	0.09419	0.20290

157 rows × 11 columns

WHR_2016 = WHR_2016.rename(columns={'Family': 'Social Support', 'Freedom': 'Freedom to Make Life Choices', 'Trust (Government Corruption)': 'Government Corruption'})
WHR_2016

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2016	Denmark	Western Europe	1	7.526	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171
1	2016	Switzerland	Western Europe	2	7.509	1.52733	1.14524	0.86303	0.58557	0.41203	0.28083
2	2016	Iceland	Western Europe	3	7.501	1.42666	1.18326	0.86733	0.56624	0.14975	0.47678
3	2016	Norway	Western Europe	4	7.498	1.57744	1.12690	0.79579	0.59609	0.35776	0.37895
4	2016	Finland	Western Europe	5	7.413	1.40598	1.13464	0.81091	0.57104	0.41004	0.25492
...	...	...	...	...	...	...	...	...	...	...	...
152	2016	Benin	Sub-Saharan Africa	153	3.484	0.39499	0.10419	0.21028	0.39747	0.06681	0.20180
153	2016	Afghanistan	Southern Asia	154	3.360	0.38227	0.11037	0.17344	0.16430	0.07112	0.31268
154	2016	Togo	Sub-Saharan Africa	155	3.303	0.28123	0.00000	0.24811	0.34678	0.11587	0.17517
155	2016	Syria	Middle East and Northern Africa	156	3.069	0.74719	0.14866	0.62994	0.06912	0.17233	0.48397
156	2016	Burundi	Sub-Saharan Africa	157	2.905	0.06831	0.23442	0.15747	0.04320	0.09419	0.20290

157 rows × 11 columns

import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3306",
    user="root",
    password="qB2*JX1ndV#5",
)
cursor = mydb.cursor()
cursor.execute('USE WHR')
WHR_2017 = pd.read_sql('SELECT * FROM Year_2017', con=mydb)
WHR_2017

	Country	Happiness.Rank	Happiness.Score	Whisker.high	Whisker.low	Economy..GDP.per.Capita.	Family	Health..Life.Expectancy.	Freedom	Generosity	Trust..Government.Corruption.	Dystopia.Residual
0	Norway	1	7.537	7.594445	7.479556	1.616463	1.533524	0.796667	0.635423	0.362012	0.315964	2.277027
1	Denmark	2	7.522	7.581728	7.462272	1.482383	1.551122	0.792566	0.626007	0.355280	0.400770	2.313707
2	Iceland	3	7.504	7.622030	7.385970	1.480633	1.610574	0.833552	0.627163	0.475540	0.153527	2.322715
3	Switzerland	4	7.494	7.561772	7.426227	1.564980	1.516912	0.858131	0.620071	0.290549	0.367007	2.276716
4	Finland	5	7.469	7.527542	7.410458	1.443572	1.540247	0.809158	0.617951	0.245483	0.382612	2.430182
...	...	...	...	...	...	...	...	...	...	...	...	...
150	Rwanda	151	3.471	3.543030	3.398970	0.368746	0.945707	0.326425	0.581844	0.252756	0.455220	0.540061
151	Syria	152	3.462	3.663669	3.260331	0.777153	0.396103	0.500533	0.081539	0.493664	0.151347	1.061574
152	Tanzania	153	3.349	3.461430	3.236570	0.511136	1.041990	0.364509	0.390018	0.354256	0.066035	0.621130
153	Burundi	154	2.905	3.074690	2.735310	0.091623	0.629794	0.151611	0.059901	0.204435	0.084148	1.683024
154	Central African Republic	155	2.693	2.864884	2.521116	0.000000	0.000000	0.018773	0.270842	0.280876	0.056565	2.066005

155 rows × 12 columns

WHR_2017['Year'] = 2017
WHR_2017 = WHR_2017[['Year', 'Country', 'Happiness.Rank', 'Happiness.Score', 'Economy..GDP.per.Capita.', 'Family', 'Health..Life.Expectancy.', 'Freedom', 'Trust..Government.Corruption.', 'Generosity']]
WHR_2017

	Year	Country	Happiness.Rank	Happiness.Score	Economy..GDP.per.Capita.	Family	Health..Life.Expectancy.	Freedom	Trust..Government.Corruption.	Generosity
0	2017	Norway	1	7.537	1.616463	1.533524	0.796667	0.635423	0.315964	0.362012
1	2017	Denmark	2	7.522	1.482383	1.551122	0.792566	0.626007	0.400770	0.355280
2	2017	Iceland	3	7.504	1.480633	1.610574	0.833552	0.627163	0.153527	0.475540
3	2017	Switzerland	4	7.494	1.564980	1.516912	0.858131	0.620071	0.367007	0.290549
4	2017	Finland	5	7.469	1.443572	1.540247	0.809158	0.617951	0.382612	0.245483
...	...	...	...	...	...	...	...	...	...	...
150	2017	Rwanda	151	3.471	0.368746	0.945707	0.326425	0.581844	0.455220	0.252756
151	2017	Syria	152	3.462	0.777153	0.396103	0.500533	0.081539	0.151347	0.493664
152	2017	Tanzania	153	3.349	0.511136	1.041990	0.364509	0.390018	0.066035	0.354256
153	2017	Burundi	154	2.905	0.091623	0.629794	0.151611	0.059901	0.084148	0.204435
154	2017	Central African Republic	155	2.693	0.000000	0.000000	0.018773	0.270842	0.056565	0.280876

155 rows × 10 columns

WHR_2017 = WHR_2017.rename(columns={'Happiness.Rank': 'Happiness Rank', 'Happiness.Score': 'Happiness Score', 'Economy..GDP.per.Capita.': 'Economy (GDP per Capita)', 'Family': 'Social Support', 'Health..Life.Expectancy.': 'Health (Life Expectancy)', 'Freedom': 'Freedom to Make Life Choices', 'Trust..Government.Corruption.': 'Government Corruption'})
WHR_2017

	Year	Country	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2017	Norway	1	7.537	1.616463	1.533524	0.796667	0.635423	0.315964	0.362012
1	2017	Denmark	2	7.522	1.482383	1.551122	0.792566	0.626007	0.400770	0.355280
2	2017	Iceland	3	7.504	1.480633	1.610574	0.833552	0.627163	0.153527	0.475540
3	2017	Switzerland	4	7.494	1.564980	1.516912	0.858131	0.620071	0.367007	0.290549
4	2017	Finland	5	7.469	1.443572	1.540247	0.809158	0.617951	0.382612	0.245483
...	...	...	...	...	...	...	...	...	...	...
150	2017	Rwanda	151	3.471	0.368746	0.945707	0.326425	0.581844	0.455220	0.252756
151	2017	Syria	152	3.462	0.777153	0.396103	0.500533	0.081539	0.151347	0.493664
152	2017	Tanzania	153	3.349	0.511136	1.041990	0.364509	0.390018	0.066035	0.354256
153	2017	Burundi	154	2.905	0.091623	0.629794	0.151611	0.059901	0.084148	0.204435
154	2017	Central African Republic	155	2.693	0.000000	0.000000	0.018773	0.270842	0.056565	0.280876

155 rows × 10 columns

country_and_region = WHR_2016[['Country', 'Region']]
country_and_region

	Country	Region
0	Denmark	Western Europe
1	Switzerland	Western Europe
2	Iceland	Western Europe
3	Norway	Western Europe
4	Finland	Western Europe
...	...	...
152	Benin	Sub-Saharan Africa
153	Afghanistan	Southern Asia
154	Togo	Sub-Saharan Africa
155	Syria	Middle East and Northern Africa
156	Burundi	Sub-Saharan Africa

157 rows × 2 columns

WHR_2017 = pd.merge(WHR_2017, country_and_region, on=['Country'])
WHR_2017

	Year	Country	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	Region
0	2017	Norway	1	7.537	1.616463	1.533524	0.796667	0.635423	0.315964	0.362012	Western Europe
1	2017	Denmark	2	7.522	1.482383	1.551122	0.792566	0.626007	0.400770	0.355280	Western Europe
2	2017	Iceland	3	7.504	1.480633	1.610574	0.833552	0.627163	0.153527	0.475540	Western Europe
3	2017	Switzerland	4	7.494	1.564980	1.516912	0.858131	0.620071	0.367007	0.290549	Western Europe
4	2017	Finland	5	7.469	1.443572	1.540247	0.809158	0.617951	0.382612	0.245483	Western Europe
...	...	...	...	...	...	...	...	...	...	...	...
145	2017	Togo	150	3.495	0.305445	0.431883	0.247106	0.380426	0.095665	0.196896	Sub-Saharan Africa
146	2017	Rwanda	151	3.471	0.368746	0.945707	0.326425	0.581844	0.455220	0.252756	Sub-Saharan Africa
147	2017	Syria	152	3.462	0.777153	0.396103	0.500533	0.081539	0.151347	0.493664	Middle East and Northern Africa
148	2017	Tanzania	153	3.349	0.511136	1.041990	0.364509	0.390018	0.066035	0.354256	Sub-Saharan Africa
149	2017	Burundi	154	2.905	0.091623	0.629794	0.151611	0.059901	0.084148	0.204435	Sub-Saharan Africa

150 rows × 11 columns

WHR_2017 = WHR_2017[['Year', 'Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Social Support', 'Health (Life Expectancy)', 'Freedom to Make Life Choices', 'Government Corruption', 'Generosity']]
WHR_2017

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2017	Norway	Western Europe	1	7.537	1.616463	1.533524	0.796667	0.635423	0.315964	0.362012
1	2017	Denmark	Western Europe	2	7.522	1.482383	1.551122	0.792566	0.626007	0.400770	0.355280
2	2017	Iceland	Western Europe	3	7.504	1.480633	1.610574	0.833552	0.627163	0.153527	0.475540
3	2017	Switzerland	Western Europe	4	7.494	1.564980	1.516912	0.858131	0.620071	0.367007	0.290549
4	2017	Finland	Western Europe	5	7.469	1.443572	1.540247	0.809158	0.617951	0.382612	0.245483
...	...	...	...	...	...	...	...	...	...	...	...
145	2017	Togo	Sub-Saharan Africa	150	3.495	0.305445	0.431883	0.247106	0.380426	0.095665	0.196896
146	2017	Rwanda	Sub-Saharan Africa	151	3.471	0.368746	0.945707	0.326425	0.581844	0.455220	0.252756
147	2017	Syria	Middle East and Northern Africa	152	3.462	0.777153	0.396103	0.500533	0.081539	0.151347	0.493664
148	2017	Tanzania	Sub-Saharan Africa	153	3.349	0.511136	1.041990	0.364509	0.390018	0.066035	0.354256
149	2017	Burundi	Sub-Saharan Africa	154	2.905	0.091623	0.629794	0.151611	0.059901	0.084148	0.204435

150 rows × 11 columns

import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3306",
    user="root",
    password="qB2*JX1ndV#5",
)
cursor = mydb.cursor()
cursor.execute('USE WHR')
WHR_2018 = pd.read_sql('SELECT * FROM Year_2018', con=mydb)
WHR_2018

	Overall rank	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption
0	1	Finland	7.632	1.305	1.592	0.874	0.681	0.202	0.393
1	2	Norway	7.594	1.456	1.582	0.861	0.686	0.286	0.340
2	3	Denmark	7.555	1.351	1.590	0.868	0.683	0.284	0.408
3	4	Iceland	7.495	1.343	1.644	0.914	0.677	0.353	0.138
4	5	Switzerland	7.487	1.420	1.549	0.927	0.660	0.256	0.357
...	...	...	...	...	...	...	...	...	...
150	152	Yemen	3.355	0.442	1.073	0.343	0.244	0.083	0.064
151	153	Tanzania	3.303	0.455	0.991	0.381	0.481	0.270	0.097
152	154	South Sudan	3.254	0.337	0.608	0.177	0.112	0.224	0.106
153	155	Central African Republic	3.083	0.024	0.000	0.010	0.305	0.218	0.038
154	156	Burundi	2.905	0.091	0.627	0.145	0.065	0.149	0.076

155 rows × 9 columns

WHR_2018['Year'] = 2018
WHR_2018 = WHR_2018[['Year', 'Country or region', 'Overall rank', 'Score', 'GDP per capita', 'Social support', 'Healthy life expectancy', 'Freedom to make life choices', 'Perceptions of corruption', 'Generosity']]
WHR_2018

	Year	Country or region	Overall rank	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Perceptions of corruption	Generosity
0	2018	Finland	1	7.632	1.305	1.592	0.874	0.681	0.393	0.202
1	2018	Norway	2	7.594	1.456	1.582	0.861	0.686	0.340	0.286
2	2018	Denmark	3	7.555	1.351	1.590	0.868	0.683	0.408	0.284
3	2018	Iceland	4	7.495	1.343	1.644	0.914	0.677	0.138	0.353
4	2018	Switzerland	5	7.487	1.420	1.549	0.927	0.660	0.357	0.256
...	...	...	...	...	...	...	...	...	...	...
150	2018	Yemen	152	3.355	0.442	1.073	0.343	0.244	0.064	0.083
151	2018	Tanzania	153	3.303	0.455	0.991	0.381	0.481	0.097	0.270
152	2018	South Sudan	154	3.254	0.337	0.608	0.177	0.112	0.106	0.224
153	2018	Central African Republic	155	3.083	0.024	0.000	0.010	0.305	0.038	0.218
154	2018	Burundi	156	2.905	0.091	0.627	0.145	0.065	0.076	0.149

155 rows × 10 columns

WHR_2018 = WHR_2018.rename(columns={'Country or region': 'Country', 'Overall rank': 'Happiness Rank', 'Score': 'Happiness Score', 'GDP per capita': 'Economy (GDP per Capita)', 'Social support': 'Social Support', 'Healthy life expectancy': 'Health (Life Expectancy)', 'Freedom to make life choices': 'Freedom to Make Life Choices', 'Perceptions of corruption': 'Government Corruption'})
WHR_2018

	Year	Country	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2018	Finland	1	7.632	1.305	1.592	0.874	0.681	0.393	0.202
1	2018	Norway	2	7.594	1.456	1.582	0.861	0.686	0.340	0.286
2	2018	Denmark	3	7.555	1.351	1.590	0.868	0.683	0.408	0.284
3	2018	Iceland	4	7.495	1.343	1.644	0.914	0.677	0.138	0.353
4	2018	Switzerland	5	7.487	1.420	1.549	0.927	0.660	0.357	0.256
...	...	...	...	...	...	...	...	...	...	...
150	2018	Yemen	152	3.355	0.442	1.073	0.343	0.244	0.064	0.083
151	2018	Tanzania	153	3.303	0.455	0.991	0.381	0.481	0.097	0.270
152	2018	South Sudan	154	3.254	0.337	0.608	0.177	0.112	0.106	0.224
153	2018	Central African Republic	155	3.083	0.024	0.000	0.010	0.305	0.038	0.218
154	2018	Burundi	156	2.905	0.091	0.627	0.145	0.065	0.076	0.149

155 rows × 10 columns

WHR_2018 = pd.merge(WHR_2018, country_and_region, on=['Country'])
WHR_2018

	Year	Country	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	Region
0	2018	Finland	1	7.632	1.305	1.592	0.874	0.681	0.393	0.202	Western Europe
1	2018	Norway	2	7.594	1.456	1.582	0.861	0.686	0.340	0.286	Western Europe
2	2018	Denmark	3	7.555	1.351	1.590	0.868	0.683	0.408	0.284	Western Europe
3	2018	Iceland	4	7.495	1.343	1.644	0.914	0.677	0.138	0.353	Western Europe
4	2018	Switzerland	5	7.487	1.420	1.549	0.927	0.660	0.357	0.256	Western Europe
...	...	...	...	...	...	...	...	...	...	...	...
145	2018	Rwanda	151	3.408	0.332	0.896	0.400	0.636	0.444	0.200	Sub-Saharan Africa
146	2018	Yemen	152	3.355	0.442	1.073	0.343	0.244	0.064	0.083	Middle East and Northern Africa
147	2018	Tanzania	153	3.303	0.455	0.991	0.381	0.481	0.097	0.270	Sub-Saharan Africa
148	2018	South Sudan	154	3.254	0.337	0.608	0.177	0.112	0.106	0.224	Sub-Saharan Africa
149	2018	Burundi	156	2.905	0.091	0.627	0.145	0.065	0.076	0.149	Sub-Saharan Africa

150 rows × 11 columns

WHR_2018 = WHR_2018[['Year', 'Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Social Support', 'Health (Life Expectancy)', 'Freedom to Make Life Choices', 'Government Corruption', 'Generosity']]
WHR_2018

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2018	Finland	Western Europe	1	7.632	1.305	1.592	0.874	0.681	0.393	0.202
1	2018	Norway	Western Europe	2	7.594	1.456	1.582	0.861	0.686	0.340	0.286
2	2018	Denmark	Western Europe	3	7.555	1.351	1.590	0.868	0.683	0.408	0.284
3	2018	Iceland	Western Europe	4	7.495	1.343	1.644	0.914	0.677	0.138	0.353
4	2018	Switzerland	Western Europe	5	7.487	1.420	1.549	0.927	0.660	0.357	0.256
...	...	...	...	...	...	...	...	...	...	...	...
145	2018	Rwanda	Sub-Saharan Africa	151	3.408	0.332	0.896	0.400	0.636	0.444	0.200
146	2018	Yemen	Middle East and Northern Africa	152	3.355	0.442	1.073	0.343	0.244	0.064	0.083
147	2018	Tanzania	Sub-Saharan Africa	153	3.303	0.455	0.991	0.381	0.481	0.097	0.270
148	2018	South Sudan	Sub-Saharan Africa	154	3.254	0.337	0.608	0.177	0.112	0.106	0.224
149	2018	Burundi	Sub-Saharan Africa	156	2.905	0.091	0.627	0.145	0.065	0.076	0.149

150 rows × 11 columns

import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3306",
    user="root",
    password="qB2*JX1ndV#5",
)
cursor = mydb.cursor()
cursor.execute('USE WHR')
WHR_2019 = pd.read_sql('SELECT * FROM Year_2019', con=mydb)
WHR_2019

	Overall rank	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption
0	1	Finland	7.769	1.340	1.587	0.986	0.596	0.153	0.393
1	2	Denmark	7.600	1.383	1.573	0.996	0.592	0.252	0.410
2	3	Norway	7.554	1.488	1.582	1.028	0.603	0.271	0.341
3	4	Iceland	7.494	1.380	1.624	1.026	0.591	0.354	0.118
4	5	Netherlands	7.488	1.396	1.522	0.999	0.557	0.322	0.298
...	...	...	...	...	...	...	...	...	...
151	152	Rwanda	3.334	0.359	0.711	0.614	0.555	0.217	0.411
152	153	Tanzania	3.231	0.476	0.885	0.499	0.417	0.276	0.147
153	154	Afghanistan	3.203	0.350	0.517	0.361	0.000	0.158	0.025
154	155	Central African Republic	3.083	0.026	0.000	0.105	0.225	0.235	0.035
155	156	South Sudan	2.853	0.306	0.575	0.295	0.010	0.202	0.091

156 rows × 9 columns

WHR_2019['Year'] = 2019
WHR_2019 = WHR_2019[['Year', 'Country or region', 'Overall rank', 'Score', 'GDP per capita', 'Social support', 'Healthy life expectancy', 'Freedom to make life choices', 'Perceptions of corruption', 'Generosity']]
WHR_2019

	Year	Country or region	Overall rank	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Perceptions of corruption	Generosity
0	2019	Finland	1	7.769	1.340	1.587	0.986	0.596	0.393	0.153
1	2019	Denmark	2	7.600	1.383	1.573	0.996	0.592	0.410	0.252
2	2019	Norway	3	7.554	1.488	1.582	1.028	0.603	0.341	0.271
3	2019	Iceland	4	7.494	1.380	1.624	1.026	0.591	0.118	0.354
4	2019	Netherlands	5	7.488	1.396	1.522	0.999	0.557	0.298	0.322
...	...	...	...	...	...	...	...	...	...	...
151	2019	Rwanda	152	3.334	0.359	0.711	0.614	0.555	0.411	0.217
152	2019	Tanzania	153	3.231	0.476	0.885	0.499	0.417	0.147	0.276
153	2019	Afghanistan	154	3.203	0.350	0.517	0.361	0.000	0.025	0.158
154	2019	Central African Republic	155	3.083	0.026	0.000	0.105	0.225	0.035	0.235
155	2019	South Sudan	156	2.853	0.306	0.575	0.295	0.010	0.091	0.202

156 rows × 10 columns

WHR_2019 = WHR_2019.rename(columns={'Country or region': 'Country', 'Overall rank': 'Happiness Rank', 'Score': 'Happiness Score', 'GDP per capita': 'Economy (GDP per Capita)', 'Social support': 'Social Support', 'Healthy life expectancy': 'Health (Life Expectancy)', 'Freedom to make life choices': 'Freedom to Make Life Choices', 'Perceptions of corruption': 'Government Corruption'})
WHR_2019

	Year	Country	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2019	Finland	1	7.769	1.340	1.587	0.986	0.596	0.393	0.153
1	2019	Denmark	2	7.600	1.383	1.573	0.996	0.592	0.410	0.252
2	2019	Norway	3	7.554	1.488	1.582	1.028	0.603	0.341	0.271
3	2019	Iceland	4	7.494	1.380	1.624	1.026	0.591	0.118	0.354
4	2019	Netherlands	5	7.488	1.396	1.522	0.999	0.557	0.298	0.322
...	...	...	...	...	...	...	...	...	...	...
151	2019	Rwanda	152	3.334	0.359	0.711	0.614	0.555	0.411	0.217
152	2019	Tanzania	153	3.231	0.476	0.885	0.499	0.417	0.147	0.276
153	2019	Afghanistan	154	3.203	0.350	0.517	0.361	0.000	0.025	0.158
154	2019	Central African Republic	155	3.083	0.026	0.000	0.105	0.225	0.035	0.235
155	2019	South Sudan	156	2.853	0.306	0.575	0.295	0.010	0.091	0.202

156 rows × 10 columns

WHR_2019 = pd.merge(WHR_2019, country_and_region, on=['Country'])
WHR_2019

	Year	Country	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	Region
0	2019	Finland	1	7.769	1.340	1.587	0.986	0.596	0.393	0.153	Western Europe
1	2019	Denmark	2	7.600	1.383	1.573	0.996	0.592	0.410	0.252	Western Europe
2	2019	Norway	3	7.554	1.488	1.582	1.028	0.603	0.341	0.271	Western Europe
3	2019	Iceland	4	7.494	1.380	1.624	1.026	0.591	0.118	0.354	Western Europe
4	2019	Netherlands	5	7.488	1.396	1.522	0.999	0.557	0.298	0.322	Western Europe
...	...	...	...	...	...	...	...	...	...	...	...
143	2019	Yemen	151	3.380	0.287	1.163	0.463	0.143	0.077	0.108	Middle East and Northern Africa
144	2019	Rwanda	152	3.334	0.359	0.711	0.614	0.555	0.411	0.217	Sub-Saharan Africa
145	2019	Tanzania	153	3.231	0.476	0.885	0.499	0.417	0.147	0.276	Sub-Saharan Africa
146	2019	Afghanistan	154	3.203	0.350	0.517	0.361	0.000	0.025	0.158	Southern Asia
147	2019	South Sudan	156	2.853	0.306	0.575	0.295	0.010	0.091	0.202	Sub-Saharan Africa

148 rows × 11 columns

WHR_2019 = WHR_2019[['Year', 'Country', 'Region', 'Happiness Rank', 'Happiness Score', 'Economy (GDP per Capita)', 'Social Support', 'Health (Life Expectancy)', 'Freedom to Make Life Choices', 'Government Corruption', 'Generosity']]
WHR_2019

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2019	Finland	Western Europe	1	7.769	1.340	1.587	0.986	0.596	0.393	0.153
1	2019	Denmark	Western Europe	2	7.600	1.383	1.573	0.996	0.592	0.410	0.252
2	2019	Norway	Western Europe	3	7.554	1.488	1.582	1.028	0.603	0.341	0.271
3	2019	Iceland	Western Europe	4	7.494	1.380	1.624	1.026	0.591	0.118	0.354
4	2019	Netherlands	Western Europe	5	7.488	1.396	1.522	0.999	0.557	0.298	0.322
...	...	...	...	...	...	...	...	...	...	...	...
143	2019	Yemen	Middle East and Northern Africa	151	3.380	0.287	1.163	0.463	0.143	0.077	0.108
144	2019	Rwanda	Sub-Saharan Africa	152	3.334	0.359	0.711	0.614	0.555	0.411	0.217
145	2019	Tanzania	Sub-Saharan Africa	153	3.231	0.476	0.885	0.499	0.417	0.147	0.276
146	2019	Afghanistan	Southern Asia	154	3.203	0.350	0.517	0.361	0.000	0.025	0.158
147	2019	South Sudan	Sub-Saharan Africa	156	2.853	0.306	0.575	0.295	0.010	0.091	0.202

148 rows × 11 columns

WHR = pd.concat([WHR_2015, WHR_2016, WHR_2017, WHR_2018, WHR_2019])
WHR

	Year	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
0	2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678
1	2015	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630
2	2015	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139
3	2015	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699
4	2015	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811
...	...	...	...	...	...	...	...	...	...	...	...
143	2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800
144	2019	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700
145	2019	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600
146	2019	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800
147	2019	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200

763 rows × 11 columns

import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
    host="localhost",
    port="3306",
    user="root",
    password="qB2*JX1ndV#5",
)
cursor = mydb.cursor()
cursor.execute('USE HDI')
HDI = pd.read_sql('SELECT * FROM HDI', con=mydb)
HDI

	Country	ISO_Code	Level	GDLCODE	Region	2015	2016	2017	2018	2019
0	Country	ISO_Code	Level	GDLCODE	Region	2015.000	2016.000	2017.000	2018.000	2019.000
1	Afghanistan	AFG	National	AFGt	Total	0.499	0.502	0.506	0.509	0.511
2	Albania	ALB	National	ALBt	Total	0.787	0.787	0.790	0.793	0.794
3	Algeria	DZA	National	DZAt	Total	0.739	0.742	0.746	0.747	0.748
4	Andorra	AND	National	ANDt	Total	0.862	0.866	0.863	0.867	0.868
...	...	...	...	...	...	...	...	...	...	...
181	Venezuela	VEN	National	VENt	Total	0.770	0.759	0.742	0.732	0.711
182	Vietnam	VNM	National	VNMt	Total	0.689	0.694	0.696	0.700	0.704
183	Yemen	YEM	National	YEMt	Total	0.484	0.474	0.467	0.468	0.470
184	Zambia	ZMB	National	ZMBt	Total	0.569	0.572	0.578	0.582	0.585
185	Zimbabwe	ZWE	National	ZWEt	Total	0.552	0.558	0.563	0.570	0.571

186 rows × 10 columns

HDI_2015 = HDI.loc[1:187, ['Country', '2015']]
HDI_2015 = HDI_2015.rename(columns = {'2015': 'HDI Score'})
HDI_2015['Year'] = 2015
HDI_2015

	Country	HDI Score	Year
1	Afghanistan	0.499	2015
2	Albania	0.787	2015
3	Algeria	0.739	2015
4	Andorra	0.862	2015
5	Angola	0.572	2015
...	...	...	...
181	Venezuela	0.770	2015
182	Vietnam	0.689	2015
183	Yemen	0.484	2015
184	Zambia	0.569	2015
185	Zimbabwe	0.552	2015

185 rows × 3 columns

WHR = WHR.set_index(['Year', 'Country'])
WHR

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811
...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800
	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700
	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600
	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800
	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200

763 rows × 9 columns

HDI_2015_Index = HDI_2015.set_index(['Year', 'Country'])
HDI_2015_Index

		HDI Score
Year	Country
2015	Afghanistan	0.499
	Albania	0.787
	Algeria	0.739
	Andorra	0.862
	Angola	0.572
	...	...
	Venezuela	0.770
	Vietnam	0.689
	Yemen	0.484
	Zambia	0.569
	Zimbabwe	0.552

185 rows × 1 columns

WHR_HDI = WHR.join(HDI_2015_Index)
WHR_HDI

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	0.948
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	0.934
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	0.933
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	0.947
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	0.920
...	...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	NaN
	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	NaN
	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	NaN
	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	NaN
	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	NaN

763 rows × 10 columns

WHR_HDI_2015 = WHR_HDI.dropna()
WHR_HDI_2015

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	0.948
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	0.934
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	0.933
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	0.947
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	0.920
	...	...	...	...	...	...	...	...	...	...	...
	Afghanistan	Southern Asia	153	3.575	0.31982	0.30285	0.30335	0.23414	0.09719	0.36510	0.499
	Rwanda	Sub-Saharan Africa	154	3.465	0.22208	0.77370	0.42864	0.59201	0.55191	0.22628	0.526
	Benin	Sub-Saharan Africa	155	3.340	0.28665	0.35386	0.31910	0.48450	0.08010	0.18260	0.532
	Burundi	Sub-Saharan Africa	157	2.905	0.01530	0.41587	0.22396	0.11850	0.10062	0.19727	0.437
	Togo	Sub-Saharan Africa	158	2.839	0.20868	0.13995	0.28443	0.36453	0.10731	0.16681	0.500

140 rows × 10 columns

HDI_2016 = HDI.loc[1:187, ['Country', '2016']]
HDI_2016 = HDI_2016.rename(columns = {'2016': 'HDI Score'})
HDI_2016['Year'] = 2016
HDI_2016

	Country	HDI Score	Year
1	Afghanistan	0.502	2016
2	Albania	0.787	2016
3	Algeria	0.742	2016
4	Andorra	0.866	2016
5	Angola	0.578	2016
...	...	...	...
181	Venezuela	0.759	2016
182	Vietnam	0.694	2016
183	Yemen	0.474	2016
184	Zambia	0.572	2016
185	Zimbabwe	0.558	2016

185 rows × 3 columns

HDI_2016_Index = HDI_2016.set_index(['Year', 'Country'])
HDI_2016_Index

		HDI Score
Year	Country
2016	Afghanistan	0.502
	Albania	0.787
	Algeria	0.742
	Andorra	0.866
	Angola	0.578
	...	...
	Venezuela	0.759
	Vietnam	0.694
	Yemen	0.474
	Zambia	0.572
	Zimbabwe	0.558

185 rows × 1 columns

WHR_HDI = WHR.join(HDI_2016_Index)
WHR_HDI

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	NaN
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	NaN
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	NaN
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	NaN
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	NaN
	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	NaN
	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	NaN
	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	NaN
	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	NaN

763 rows × 10 columns

WHR_HDI_2016 = WHR_HDI.dropna()
WHR_HDI_2016

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2016	Denmark	Western Europe	1	7.526	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171	0.935
	Switzerland	Western Europe	2	7.509	1.52733	1.14524	0.86303	0.58557	0.41203	0.28083	0.947
	Iceland	Western Europe	3	7.501	1.42666	1.18326	0.86733	0.56624	0.14975	0.47678	0.941
	Norway	Western Europe	4	7.498	1.57744	1.12690	0.79579	0.59609	0.35776	0.37895	0.950
	Finland	Western Europe	5	7.413	1.40598	1.13464	0.81091	0.57104	0.41004	0.25492	0.932
	...	...	...	...	...	...	...	...	...	...	...
	Rwanda	Sub-Saharan Africa	152	3.515	0.32846	0.61586	0.31865	0.54320	0.50521	0.23552	0.527
	Benin	Sub-Saharan Africa	153	3.484	0.39499	0.10419	0.21028	0.39747	0.06681	0.20180	0.532
	Afghanistan	Southern Asia	154	3.360	0.38227	0.11037	0.17344	0.16430	0.07112	0.31268	0.502
	Togo	Sub-Saharan Africa	155	3.303	0.28123	0.00000	0.24811	0.34678	0.11587	0.17517	0.501
	Burundi	Sub-Saharan Africa	157	2.905	0.06831	0.23442	0.15747	0.04320	0.09419	0.20290	0.438

140 rows × 10 columns

HDI_2017 = HDI.loc[1:187, ['Country', '2017']]
HDI_2017 = HDI_2017.rename(columns = {'2017': 'HDI Score'})
HDI_2017['Year'] = 2017
HDI_2017

	Country	HDI Score	Year
1	Afghanistan	0.506	2017
2	Albania	0.790	2017
3	Algeria	0.746	2017
4	Andorra	0.863	2017
5	Angola	0.582	2017
...	...	...	...
181	Venezuela	0.742	2017
182	Vietnam	0.696	2017
183	Yemen	0.467	2017
184	Zambia	0.578	2017
185	Zimbabwe	0.563	2017

185 rows × 3 columns

HDI_2017_Index = HDI_2017.set_index(['Year', 'Country'])
HDI_2017_Index

		HDI Score
Year	Country
2017	Afghanistan	0.506
	Albania	0.790
	Algeria	0.746
	Andorra	0.863
	Angola	0.582
	...	...
	Venezuela	0.742
	Vietnam	0.696
	Yemen	0.467
	Zambia	0.578
	Zimbabwe	0.563

185 rows × 1 columns

WHR_HDI = WHR.join(HDI_2017_Index)
WHR_HDI

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	NaN
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	NaN
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	NaN
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	NaN
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	NaN
	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	NaN
	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	NaN
	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	NaN
	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	NaN

763 rows × 10 columns

WHR_HDI_2017 = WHR_HDI.dropna()
WHR_HDI_2017

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2017	Norway	Western Europe	1	7.537	1.616463	1.533524	0.796667	0.635423	0.315964	0.362012	0.954
	Denmark	Western Europe	2	7.522	1.482383	1.551122	0.792566	0.626007	0.400770	0.355280	0.937
	Iceland	Western Europe	3	7.504	1.480633	1.610574	0.833552	0.627163	0.153527	0.475540	0.943
	Switzerland	Western Europe	4	7.494	1.564980	1.516912	0.858131	0.620071	0.367007	0.290549	0.948
	Finland	Western Europe	5	7.469	1.443572	1.540247	0.809158	0.617951	0.382612	0.245483	0.935
	...	...	...	...	...	...	...	...	...	...	...
	Guinea	Sub-Saharan Africa	149	3.507	0.244550	0.791245	0.194129	0.348588	0.110938	0.264815	0.472
	Togo	Sub-Saharan Africa	150	3.495	0.305445	0.431883	0.247106	0.380426	0.095665	0.196896	0.506
	Rwanda	Sub-Saharan Africa	151	3.471	0.368746	0.945707	0.326425	0.581844	0.455220	0.252756	0.535
	Tanzania	Sub-Saharan Africa	153	3.349	0.511136	1.041990	0.364509	0.390018	0.066035	0.354256	0.523
	Burundi	Sub-Saharan Africa	154	2.905	0.091623	0.629794	0.151611	0.059901	0.084148	0.204435	0.434

138 rows × 10 columns

HDI_2018 = HDI.loc[1:187, ['Country', '2018']]
HDI_2018 = HDI_2018.rename(columns = {'2018': 'HDI Score'})
HDI_2018['Year'] = 2018
HDI_2018

	Country	HDI Score	Year
1	Afghanistan	0.509	2018
2	Albania	0.793	2018
3	Algeria	0.747	2018
4	Andorra	0.867	2018
5	Angola	0.582	2018
...	...	...	...
181	Venezuela	0.732	2018
182	Vietnam	0.700	2018
183	Yemen	0.468	2018
184	Zambia	0.582	2018
185	Zimbabwe	0.570	2018

185 rows × 3 columns

HDI_2018_Index = HDI_2018.set_index(['Year', 'Country'])
HDI_2018_Index

		HDI Score
Year	Country
2018	Afghanistan	0.509
	Albania	0.793
	Algeria	0.747
	Andorra	0.867
	Angola	0.582
	...	...
	Venezuela	0.732
	Vietnam	0.700
	Yemen	0.468
	Zambia	0.582
	Zimbabwe	0.570

185 rows × 1 columns

WHR_HDI = WHR.join(HDI_2018_Index)
WHR_HDI

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	NaN
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	NaN
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	NaN
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	NaN
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	NaN
	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	NaN
	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	NaN
	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	NaN
	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	NaN

763 rows × 10 columns

WHR_HDI_2018 = WHR_HDI.dropna()
WHR_HDI_2018

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2018	Finland	Western Europe	1	7.632	1.305	1.592	0.874	0.681	0.393	0.202	0.936
	Norway	Western Europe	2	7.594	1.456	1.582	0.861	0.686	0.340	0.286	0.955
	Denmark	Western Europe	3	7.555	1.351	1.590	0.868	0.683	0.408	0.284	0.938
	Iceland	Western Europe	4	7.495	1.343	1.644	0.914	0.677	0.138	0.353	0.946
	Switzerland	Western Europe	5	7.487	1.420	1.549	0.927	0.660	0.357	0.256	0.954
	...	...	...	...	...	...	...	...	...	...	...
	Rwanda	Sub-Saharan Africa	151	3.408	0.332	0.896	0.400	0.636	0.444	0.200	0.539
	Yemen	Middle East and Northern Africa	152	3.355	0.442	1.073	0.343	0.244	0.064	0.083	0.468
	Tanzania	Sub-Saharan Africa	153	3.303	0.455	0.991	0.381	0.481	0.097	0.270	0.524
	South Sudan	Sub-Saharan Africa	154	3.254	0.337	0.608	0.177	0.112	0.106	0.224	0.428
	Burundi	Sub-Saharan Africa	156	2.905	0.091	0.627	0.145	0.065	0.076	0.149	0.431

137 rows × 10 columns

HDI_2019 = HDI.loc[1:187, ['Country', '2019']]
HDI_2019 = HDI_2019.rename(columns = {'2019': 'HDI Score'})
HDI_2019['Year'] = 2019
HDI_2019

	Country	HDI Score	Year
1	Afghanistan	0.511	2019
2	Albania	0.794	2019
3	Algeria	0.748	2019
4	Andorra	0.868	2019
5	Angola	0.582	2019
...	...	...	...
181	Venezuela	0.711	2019
182	Vietnam	0.704	2019
183	Yemen	0.470	2019
184	Zambia	0.585	2019
185	Zimbabwe	0.571	2019

185 rows × 3 columns

HDI_2019_Index = HDI_2019.set_index(['Year', 'Country'])
HDI_2019_Index

		HDI Score
Year	Country
2019	Afghanistan	0.511
	Albania	0.794
	Algeria	0.748
	Andorra	0.868
	Angola	0.582
	...	...
	Venezuela	0.711
	Vietnam	0.704
	Yemen	0.470
	Zambia	0.585
	Zimbabwe	0.571

185 rows × 1 columns

WHR_HDI = WHR.join(HDI_2019_Index)
WHR_HDI

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	NaN
	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	NaN
	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	NaN
	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	NaN
	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	0.470
	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	0.543
	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	0.528
	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	0.511
	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	0.433

763 rows × 10 columns

WHR_HDI_2019 = WHR_HDI.dropna()
WHR_HDI_2019

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2019	Finland	Western Europe	1	7.769	1.340	1.587	0.986	0.596	0.393	0.153	0.938
	Denmark	Western Europe	2	7.600	1.383	1.573	0.996	0.592	0.410	0.252	0.940
	Norway	Western Europe	3	7.554	1.488	1.582	1.028	0.603	0.341	0.271	0.957
	Iceland	Western Europe	4	7.494	1.380	1.624	1.026	0.591	0.118	0.354	0.949
	Netherlands	Western Europe	5	7.488	1.396	1.522	0.999	0.557	0.298	0.322	0.944
	...	...	...	...	...	...	...	...	...	...	...
	Yemen	Middle East and Northern Africa	151	3.380	0.287	1.163	0.463	0.143	0.077	0.108	0.470
	Rwanda	Sub-Saharan Africa	152	3.334	0.359	0.711	0.614	0.555	0.411	0.217	0.543
	Tanzania	Sub-Saharan Africa	153	3.231	0.476	0.885	0.499	0.417	0.147	0.276	0.528
	Afghanistan	Southern Asia	154	3.203	0.350	0.517	0.361	0.000	0.025	0.158	0.511
	South Sudan	Sub-Saharan Africa	156	2.853	0.306	0.575	0.295	0.010	0.091	0.202	0.433

136 rows × 10 columns

Final = pd.concat([WHR_HDI_2015, WHR_HDI_2016, WHR_HDI_2017, WHR_HDI_2018, WHR_HDI_2019])
Final = Final.reset_index()
Final = Final.set_index(['Year'])
Final

	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year
2015	Switzerland	Western Europe	1	7.587	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	0.948
2015	Iceland	Western Europe	2	7.561	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	0.934
2015	Denmark	Western Europe	3	7.527	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	0.933
2015	Norway	Western Europe	4	7.522	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	0.947
2015	Canada	North America	5	7.427	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	0.920
...	...	...	...	...	...	...	...	...	...	...	...
2019	Yemen	Middle East and Northern Africa	151	3.380	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	0.470
2019	Rwanda	Sub-Saharan Africa	152	3.334	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	0.543
2019	Tanzania	Sub-Saharan Africa	153	3.231	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	0.528
2019	Afghanistan	Southern Asia	154	3.203	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	0.511
2019	South Sudan	Sub-Saharan Africa	156	2.853	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	0.433

691 rows × 11 columns

Final.to_csv('Final Data(WHR_HDI).csv')

3. Findings

3.1. Is Lithuania becoming a happier state as its human development increases?

It appears that, as Lithuania developed it also showed a positive relationship with its level of happiness:

Final_HS_DS_LT = Final[Final['Country'] == 'Lithuania']
Final_HS_DS_LT = Final_HS_DS_LT[['Happiness Score', 'HDI Score']]
Final_HS_DS_LT

	Happiness Score	HDI Score
Year
2015	5.833	0.863
2016	5.813	0.868
2017	5.902	0.873
2018	5.952	0.877
2019	6.149	0.882

Final_HS_DS_LT.T

Year	2015	2016	2017	2018	2019
Happiness Score	5.833	5.813	5.902	5.952	6.149
HDI Score	0.863	0.868	0.873	0.877	0.882

Final_HS_DS_LT.plot(x='HDI Score', y='Happiness Score', title='Lithuania\'s Development and Happiness Between 2015 and 2019')

<AxesSubplot:title={'center':"Lithuania's Development and Happiness Between 2015 and 2019"}, xlabel='HDI Score'>

3.2. How does Lithuania's happiness level compare to its global, continental and regional average?

3.2.1. Average Happiness Score of Lithuania Between 2015 and 2019

On Average, between 2015 and 2019 Lithuania scored 5.93 on happiness level and took 52 position in the world:

Final_HS_LT = Final_HS_DS_LT['Happiness Score'].mean()
Final_HS_LT

5.929799990081788

Final_HR_LT = Final[Final['Country'] == 'Lithuania']
Final_HR_LT = Final_HR_LT['Happiness Rank'].mean()
Final_HR_LT

52.0

3.2.2. Average Happiness Score of the World Between 2015 and 2019

The average happiness level in Lithuania was 9% higher than the average happiness level of the world:

Final_HS_WD = Final['Happiness Score'].mean()
Final_HS_WD

5.414447177646814

Final_HS_LT / Final_HS_WD * 100 - 100

9.518105829207713

3.2.3. Average Happiness Score by Continent / Region Between 2015 and 2019

The average happiness in Lithuania was higher than its continental / regional average of 5.42. That is, Lithuania is happier than an averge Cental and Eastern European state, but not quite there to be as happy as an average Western European state:

Final.groupby('Region')['Happiness Score'].mean()

Region
Australia and New Zealand          7.294600
Central and Eastern Europe         5.429362
Eastern Asia                       5.514850
Latin America and Caribbean        5.977690
Middle East and Northern Africa    5.475929
North America                      7.174700
Southeastern Asia                  5.389475
Southern Asia                      4.580657
Sub-Saharan Africa                 4.187880
Western Europe                     6.802768
Name: Happiness Score, dtype: float64

3.2.4. Average Happiness Score in Central and Eastern Europe in 2019

In 2019, Lithuania ranked 42nd by in happiness rank and was almost on par with Uzbekistan, Poland and Slovakia. Only Czech Republic really stands out in the continent/region. With its current happiness level, Czech Republic qualifies to be considered a Western European state:

Final_HS_CEE = WHR_HDI_2019[WHR_HDI_2019['Region'] == 'Central and Eastern Europe'].sort_values(by='Happiness Rank')
Final_HS_CEE

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2019	Czech Republic	Central and Eastern Europe	20	6.852	1.269	1.487	0.920	0.457	0.036	0.046	0.900
	Slovakia	Central and Eastern Europe	38	6.198	1.246	1.504	0.881	0.334	0.014	0.121	0.860
	Poland	Central and Eastern Europe	40	6.182	1.206	1.438	0.884	0.483	0.050	0.117	0.881
	Uzbekistan	Central and Eastern Europe	41	6.174	0.745	1.529	0.756	0.631	0.240	0.322	0.721
	Lithuania	Central and Eastern Europe	42	6.149	1.238	1.515	0.818	0.291	0.042	0.043	0.882
	Slovenia	Central and Eastern Europe	44	6.118	1.258	1.523	0.953	0.564	0.057	0.144	0.917
	Kosovo	Central and Eastern Europe	46	6.100	0.882	1.232	0.758	0.489	0.006	0.262	0.750
	Romania	Central and Eastern Europe	48	6.070	1.162	1.232	0.825	0.462	0.005	0.083	0.828
	Latvia	Central and Eastern Europe	53	5.940	1.187	1.465	0.812	0.264	0.064	0.075	0.866
	Estonia	Central and Eastern Europe	55	5.893	1.237	1.528	0.874	0.495	0.161	0.103	0.892
	Kazakhstan	Central and Eastern Europe	60	5.809	1.173	1.508	0.729	0.410	0.096	0.146	0.825
	Hungary	Central and Eastern Europe	62	5.758	1.201	1.410	0.828	0.199	0.020	0.081	0.855
	Serbia	Central and Eastern Europe	70	5.603	1.004	1.383	0.854	0.282	0.039	0.137	0.806
	Moldova	Central and Eastern Europe	71	5.529	0.685	1.328	0.739	0.245	0.000	0.181	0.749
	Tajikistan	Central and Eastern Europe	74	5.467	0.493	1.098	0.718	0.389	0.144	0.230	0.668
	Croatia	Central and Eastern Europe	75	5.432	1.155	1.266	0.914	0.296	0.022	0.119	0.850
	Bosnia and Herzegovina	Central and Eastern Europe	78	5.386	0.945	1.212	0.845	0.212	0.006	0.263	0.780
	Belarus	Central and Eastern Europe	81	5.323	1.067	1.465	0.789	0.235	0.142	0.094	0.823
	Kyrgyzstan	Central and Eastern Europe	86	5.261	0.551	1.438	0.723	0.508	0.023	0.300	0.698
	Turkmenistan	Central and Eastern Europe	87	5.247	1.052	1.538	0.657	0.394	0.028	0.244	0.716
	Azerbaijan	Central and Eastern Europe	90	5.208	1.043	1.147	0.769	0.351	0.182	0.035	0.756
	Bulgaria	Central and Eastern Europe	97	5.011	1.092	1.513	0.815	0.311	0.004	0.081	0.816
	Albania	Central and Eastern Europe	107	4.719	0.947	0.848	0.874	0.383	0.027	0.178	0.794
	Armenia	Central and Eastern Europe	116	4.559	0.850	1.055	0.815	0.283	0.064	0.095	0.776
	Georgia	Central and Eastern Europe	119	4.519	0.886	0.666	0.752	0.346	0.164	0.043	0.812
	Ukraine	Central and Eastern Europe	133	4.332	0.820	1.390	0.739	0.178	0.010	0.187	0.779

3.2.5. Average Happiness in the Baltic Region Between 2015 and 2019

In the Baltic region, Poland was the happiest state between 2015 and 2019 and Lithuania came in second, leaving behind Latvia and Estonia:

Final_HS_PL = Final[Final['Country'] == 'Poland']
Final_HS_PL['Happiness Score'].mean()

5.980800009918212

Final_HS_LT = Final[Final['Country'] == 'Lithuania']
Final_HS_LT['Happiness Score'].mean()

5.929799990081788

Final_HS_LV = Final[Final['Country'] == 'Latvia']
Final_HS_LV['Happiness Score'].mean()

5.676199980926514

Final_HS_EE = Final[Final['Country'] == 'Estonia']
Final_HS_EE['Happiness Score'].mean()

5.637800012207032

3.3. How does Lithuania's human development compare to its global, continental and regional average?

3.3.1. Average Human Development Score of Lithuania Between 2015 and 2019

Between 2015 and 2019 Lithuania averaged 0.87 score on human development level:

Final_DS_LT = Final_HS_DS_LT['HDI Score'].mean()
Final_DS_LT

0.8725999999999999

3.3.2. Average Human Development Score of the World Between 2015 and 2019

The average human development level in Lithuania was 20% higher than the average human development of the world:

Final_DS_WD = Final['HDI Score'].mean()
Final_DS_WD

0.7269522431259037

Final_DS_LT / Final_DS_WD * 100 - 100

20.035395481800705

3.3.3. Average Human Development Score by Continent / Region Between 2015 and 2019

The average human development in Lithuania was higher than its continental / regional average of 0.80. That is, Lithuania is more development than an averge Cental and Eastern European state, but not quite there to be as developed as an average Western European state:

Final.groupby('Region')['HDI Score'].mean()

Region
Australia and New Zealand          0.933600
Central and Eastern Europe         0.800508
Eastern Asia                       0.827200
Latin America and Caribbean        0.729290
Middle East and Northern Africa    0.758482
North America                      0.924500
Southeastern Asia                  0.720900
Southern Asia                      0.616486
Sub-Saharan Africa                 0.529873
Western Europe                     0.919589
Name: HDI Score, dtype: float64

3.3.4. Average HDI Score in Central and Eastern Europe in 2019

In 2019, Lithuania was among the most developed countries in CEE, left behind only by Slovenia, Czech Republic and Estonia.

Final_DS_CEE = WHR_HDI_2019[WHR_HDI_2019['Region'] == 'Central and Eastern Europe'].sort_values(by='HDI Score', ascending=False)
Final_DS_CEE

		Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year	Country
2019	Slovenia	Central and Eastern Europe	44	6.118	1.258	1.523	0.953	0.564	0.057	0.144	0.917
	Czech Republic	Central and Eastern Europe	20	6.852	1.269	1.487	0.920	0.457	0.036	0.046	0.900
	Estonia	Central and Eastern Europe	55	5.893	1.237	1.528	0.874	0.495	0.161	0.103	0.892
	Lithuania	Central and Eastern Europe	42	6.149	1.238	1.515	0.818	0.291	0.042	0.043	0.882
	Poland	Central and Eastern Europe	40	6.182	1.206	1.438	0.884	0.483	0.050	0.117	0.881
	Latvia	Central and Eastern Europe	53	5.940	1.187	1.465	0.812	0.264	0.064	0.075	0.866
	Slovakia	Central and Eastern Europe	38	6.198	1.246	1.504	0.881	0.334	0.014	0.121	0.860
	Hungary	Central and Eastern Europe	62	5.758	1.201	1.410	0.828	0.199	0.020	0.081	0.855
	Croatia	Central and Eastern Europe	75	5.432	1.155	1.266	0.914	0.296	0.022	0.119	0.850
	Romania	Central and Eastern Europe	48	6.070	1.162	1.232	0.825	0.462	0.005	0.083	0.828
	Kazakhstan	Central and Eastern Europe	60	5.809	1.173	1.508	0.729	0.410	0.096	0.146	0.825
	Belarus	Central and Eastern Europe	81	5.323	1.067	1.465	0.789	0.235	0.142	0.094	0.823
	Bulgaria	Central and Eastern Europe	97	5.011	1.092	1.513	0.815	0.311	0.004	0.081	0.816
	Georgia	Central and Eastern Europe	119	4.519	0.886	0.666	0.752	0.346	0.164	0.043	0.812
	Serbia	Central and Eastern Europe	70	5.603	1.004	1.383	0.854	0.282	0.039	0.137	0.806
	Albania	Central and Eastern Europe	107	4.719	0.947	0.848	0.874	0.383	0.027	0.178	0.794
	Bosnia and Herzegovina	Central and Eastern Europe	78	5.386	0.945	1.212	0.845	0.212	0.006	0.263	0.780
	Ukraine	Central and Eastern Europe	133	4.332	0.820	1.390	0.739	0.178	0.010	0.187	0.779
	Armenia	Central and Eastern Europe	116	4.559	0.850	1.055	0.815	0.283	0.064	0.095	0.776
	Azerbaijan	Central and Eastern Europe	90	5.208	1.043	1.147	0.769	0.351	0.182	0.035	0.756
	Kosovo	Central and Eastern Europe	46	6.100	0.882	1.232	0.758	0.489	0.006	0.262	0.750
	Moldova	Central and Eastern Europe	71	5.529	0.685	1.328	0.739	0.245	0.000	0.181	0.749
	Uzbekistan	Central and Eastern Europe	41	6.174	0.745	1.529	0.756	0.631	0.240	0.322	0.721
	Turkmenistan	Central and Eastern Europe	87	5.247	1.052	1.538	0.657	0.394	0.028	0.244	0.716
	Kyrgyzstan	Central and Eastern Europe	86	5.261	0.551	1.438	0.723	0.508	0.023	0.300	0.698
	Tajikistan	Central and Eastern Europe	74	5.467	0.493	1.098	0.718	0.389	0.144	0.230	0.668

3.3.5. Average Human Development in the Baltic Region Between 2015 and 2019

In the Baltic region, Estonia was the most development state between 2015 and 2019 and Lithuania came in second, leaving behind Poland and Latvia:

Final_DS_EE = Final[Final['Country'] == 'Estonia']
Final_DS_EE['HDI Score'].mean()

0.8852

Final_DS_LT = Final[Final['Country'] == 'Lithuania']
Final_DS_LT['HDI Score'].mean()

0.8725999999999999

Final_DS_PL = Final[Final['Country'] == 'Poland']
Final_DS_PL['HDI Score'].mean()

0.8724000000000001

Final_DS_LV = Final[Final['Country'] == 'Latvia']
Final_DS_LV['HDI Score'].mean()

0.8583999999999999

On the Baltic Region level, this means that Estonia is the most developed and the most unhappy state, even though Estonia's level of happiness also showed positive relationship to its human development level:

Final_HS_DS_EE = Final[Final['Country'] == 'Estonia']
Final_HS_DS_EE = Final_HS_DS_EE[['Happiness Score', 'HDI Score']]
Final_HS_DS_EE

	Happiness Score	HDI Score
Year
2015	5.429	0.877
2016	5.517	0.882
2017	5.611	0.885
2018	5.739	0.890
2019	5.893	0.892

Final_HS_DS_EE.T

Year	2015	2016	2017	2018	2019
Happiness Score	5.429	5.517	5.611	5.739	5.893
HDI Score	0.877	0.882	0.885	0.890	0.892

Final_HS_DS_EE.plot(x='HDI Score', y='Happiness Score', title='Estonia\'s Development and Happiness Between 2015 and 2019')

<AxesSubplot:title={'center':"Estonia's Development and Happiness Between 2015 and 2019"}, xlabel='HDI Score'>

3.4. What is the relationship between development and happiness?

On an aggreggate level, cross-sectional time-series data tells us that there is a strong positive relationship between the country's development level of its level of happiness:

Final.plot(x='HDI Score', y='Happiness Score', title='The Relationship Between Development and Happiness', kind='scatter')

<AxesSubplot:title={'center':'The Relationship Between Development and Happiness'}, xlabel='HDI Score', ylabel='Happiness Score'>

However, one cannot come to a conclusion that the level of country's happiness can be explained and predicted by its level of human development:

from sklearn.linear_model import LinearRegression

X = Final[['HDI Score']]
X

	HDI Score
Year
2015	0.948
2015	0.934
2015	0.933
2015	0.947
2015	0.920
...	...
2019	0.470
2019	0.543
2019	0.528
2019	0.511
2019	0.433

691 rows × 1 columns

y = Final['Happiness Score']
y

Year
2015    7.587
2015    7.561
2015    7.527
2015    7.522
2015    7.427
        ...  
2019    3.380
2019    3.334
2019    3.231
2019    3.203
2019    2.853
Name: Happiness Score, Length: 691, dtype: float64

model = LinearRegression()
model.fit(X, y)
model.predict([[1]])

array([7.02415971])

r_squared = model.score(X, y)
r_squared

0.656350123713956

This conclusion stems from a fact that in a direct linear regression the HDI score explains roughly 65% of cases of country's happiness level. Thus, the hypothesis that Human Development Index explains the country's Happiness Level is rejected. An alternative model must be tested:

X = Final[['Economy (GDP per Capita)', 'Social Support', 'Health (Life Expectancy)', 'Freedom to Make Life Choices', 'Government Corruption', 'Generosity', 'HDI Score']]
X

	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year
2015	1.39651	1.34951	0.94143	0.66557	0.41978	0.29678	0.948
2015	1.30232	1.40223	0.94784	0.62877	0.14145	0.43630	0.934
2015	1.32548	1.36058	0.87464	0.64938	0.48357	0.34139	0.933
2015	1.45900	1.33095	0.88521	0.66973	0.36503	0.34699	0.947
2015	1.32629	1.32261	0.90563	0.63297	0.32957	0.45811	0.920
...	...	...	...	...	...	...	...
2019	0.28700	1.16300	0.46300	0.14300	0.07700	0.10800	0.470
2019	0.35900	0.71100	0.61400	0.55500	0.41100	0.21700	0.543
2019	0.47600	0.88500	0.49900	0.41700	0.14700	0.27600	0.528
2019	0.35000	0.51700	0.36100	0.00000	0.02500	0.15800	0.511
2019	0.30600	0.57500	0.29500	0.01000	0.09100	0.20200	0.433

691 rows × 7 columns

y = Final['Happiness Score']
y

Year
2015    7.587
2015    7.561
2015    7.527
2015    7.522
2015    7.427
        ...  
2019    3.380
2019    3.334
2019    3.231
2019    3.203
2019    2.853
Name: Happiness Score, Length: 691, dtype: float64

model = LinearRegression()
model.fit(X, y)
r_squared = model.score(X, y)
r_squared

0.7713549273193673

from sklearn.model_selection import train_test_split

train_test_split(X, y, test_size=0.15)

[      Economy (GDP per Capita)  Social Support  Health (Life Expectancy)  \
 Year                                                                       
 2019                  1.124000        1.465000                  0.891000   
 2015                  0.864020        0.999030                  0.790750   
 2019                  0.570000        1.167000                  0.489000   
 2016                  1.186490        0.608090                  0.705240   
 2019                  1.433000        1.457000                  0.874000   
 ...                        ...             ...                       ...   
 2015                  1.228570        1.223930                  0.913870   
 2016                  0.794220        0.837790                  0.469700   
 2017                  1.122094        1.221555                  0.341756   
 2015                  1.563910        1.219630                  0.918940   
 2017                  1.494387        1.478162                  0.830875   
 
       Freedom to Make Life Choices  Government Corruption  Generosity  \
 Year                                                                    
 2019                      0.523000               0.150000    0.127000   
 2015                      0.485740               0.180900    0.115410   
 2019                      0.066000               0.088000    0.106000   
 2016                      0.239070               0.040020    0.184340   
 2019                      0.454000               0.128000    0.280000   
 ...                            ...                    ...         ...   
 2015                      0.413190               0.077850    0.331720   
 2016                      0.509610               0.077460    0.216980   
 2017                      0.505196               0.098583    0.099348   
 2015                      0.615830               0.377980    0.280340   
 2017                      0.612924               0.384399    0.385399   
 
       HDI Score  
 Year             
 2019      0.817  
 2015      0.764  
 2019      0.546  
 2016      0.841  
 2019      0.926  
 ...         ...  
 2015      0.910  
 2016      0.702  
 2017      0.725  
 2015      0.906  
 2017      0.941  
 
 [587 rows x 7 columns],
       Economy (GDP per Capita)  Social Support  Health (Life Expectancy)  \
 Year                                                                       
 2015                  1.325480        1.360580                  0.874640   
 2016                  1.337660        0.995370                  0.849170   
 2016                  0.328460        0.615860                  0.318650   
 2015                  0.632160        0.912260                  0.746760   
 2019                  0.987000        1.224000                  0.815000   
 ...                        ...             ...                       ...   
 2017                  1.189396        1.209561                  0.638007   
 2015                  1.159910        1.139350                  0.875190   
 2015                  1.061660        1.208900                  0.811600   
 2016                  0.422140        0.631780                  0.038240   
 2019                  1.324000        1.472000                  1.045000   
 
       Freedom to Make Life Choices  Government Corruption  Generosity  \
 Year                                                                    
 2015                      0.649380               0.483570    0.341390   
 2016                      0.364320               0.087280    0.322880   
 2016                      0.543200               0.505210    0.235520   
 2015                      0.594440               0.104410    0.168600   
 2019                      0.216000               0.027000    0.166000   
 ...                            ...                    ...         ...   
 2017                      0.491247               0.042182    0.360934   
 2015                      0.514690               0.010780    0.137190   
 2015                      0.603620               0.245580    0.232400   
 2016                      0.128070               0.049520    0.186670   
 2019                      0.436000               0.183000    0.111000   
 
       HDI Score  
 Year             
 2015      0.933  
 2016      0.913  
 2016      0.527  
 2015      0.689  
 2019      0.744  
 ...         ...  
 2017      0.797  
 2015      0.855  
 2015      0.806  
 2016      0.394  
 2019      0.901  
 
 [104 rows x 7 columns],
 Year
 2019    6.293
 2015    5.975
 2019    4.490
 2016    5.488
 2019    6.892
         ...  
 2015    7.278
 2016    5.822
 2017    3.766
 2015    6.946
 2017    7.284
 Name: Happiness Score, Length: 587, dtype: float64,
 Year
 2015    7.527
 2016    7.267
 2016    3.515
 2015    5.360
 2019    5.197
         ...  
 2017    5.629
 2015    5.102
 2015    6.485
 2016    3.763
 2019    6.592
 Name: Happiness Score, Length: 104, dtype: float64]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)

model = LinearRegression()
model.fit(X_train, y_train)
y_predicted = model.predict(X_test)
model.score(X_test, y_test)

0.7905049148130956

We can come to the conclusion that any country's level of happiness cannot be explained by its pure HDI Score, but rather a broader measure of its human and social development, which includes factors, such as Social Support, Freedom to Make Life Choices, Government Corruption, Generosity, along the HDI Score.

3.5. What factors could made Lithuania happier in the future? Can we predict its future level of happiness?

As an example, one can you use the model above to provide predicted values of the inputs below and come up with a predicted level of happiness for any country in the world, including Lithuania:

◉ Economy (GDP per Capita

◉ Social Support

◉ Health (Life Expectancy)

◉ Freedom to Make Life Choices

◉ Government Corruption

◉ Generosity

◉ HDI Score

If we can predict all these variables, we can also predict the future level of happiness of Lithuania:

Final_LT = Final[Final['Country'] == 'Lithuania']
Final_LT

	Country	Region	Happiness Rank	Happiness Score	Economy (GDP per Capita)	Social Support	Health (Life Expectancy)	Freedom to Make Life Choices	Government Corruption	Generosity	HDI Score
Year
2015	Lithuania	Central and Eastern Europe	56	5.833	1.147230	1.257450	0.73128	0.213420	0.010310	0.026410	0.863
2016	Lithuania	Central and Eastern Europe	60	5.813	1.269200	1.064110	0.64674	0.189290	0.018200	0.020250	0.868
2017	Lithuania	Central and Eastern Europe	52	5.902	1.314582	1.473516	0.62895	0.234232	0.011866	0.010165	0.873
2018	Lithuania	Central and Eastern Europe	50	5.952	1.197000	1.527000	0.71600	0.350000	0.006000	0.026000	0.877
2019	Lithuania	Central and Eastern Europe	42	6.149	1.238000	1.515000	0.81800	0.291000	0.042000	0.043000	0.882

model.predict([[1.25, 1.52, 0.6, 0.3, 0.2, 0.35, 0.884]])

array([6.094927])

4. Conclusion

In this piece of research, one may come up with the following findings:

◉ Historically, countries have shown a positive relationship between their level of human development, determined by Human Development Index (HDI), and its level of happiness, assessed in World Health Report.

◉ However, the core hypothesis that the level of happiness in any country can be simply explained by its human development level is rejected.

◉ One cannot predict any country's level of happiness by pure human development level. The level of any state's happiness rather depends on broader development sense, which includes by human and social development and determining factors, such as: Social Support, Freedom to Make Life Choices, Government Corruption, Generosity, along the HDI Score.

◉ In the future, in addressing the happiness level of a society in Lithuania, the Government of Lithuania should not only facus on Economy (GDP), Health (Life Expectancy) and Education, summed up by HDI, but rather focus also on social factors, such as Social Support, Freedom to Make Life Choices, Government Corruption and Generosity.

◉ By predicting the future values of these variables, the Government of Lithuania will be able to predict the future level of happiness of Lithuania with a decent level of confidence.