Project: 2019 Happiness Report with Animated Bubble Plot¶

--by Lu Tang

Table of Contents¶

Introduction
Data Cleaning
Data Visualization

Introduction¶

About the project:

This project focuses on visualizing the changes on happiness over the last ten years on country-level. We will find where are the happy countries and how their happiness changed over time. At the end of the notebook, we will see an animated bubble plot to display the changes.

Dataset:

This data set contains happiness data about 150 countries collected from World Happiness Report and from Gapminder

I did research on finding the data, combining different dataset and then cleaning it before the final visualization.

Brief Introduction about the World Happiness Report and the indicator they chose:¶

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Inspiration

What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness? What is Dystopia?

Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia. What are the residuals?

The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.

What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?

The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.

If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.

Data Cleaning¶

To make the final bubble plot, there is a long process on data cleaning. This Jupyter Notebook shows all the steps and methods on the cleaning process, but for people who are more interested to see the final result, you can skip this part and directly go to the last part Data Visualization.

In [1]:

# import library
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

# to make better chart
sns.set_style('whitegrid')
plt.figure(figsize = (10,6))
sns.despine(left=True, bottom=True)

# to avoid warnings
import warnings
warnings.filterwarnings('ignore')

# to avoid truncated output 
pd.options.display.max_columns = 150

<Figure size 720x432 with 0 Axes>

In [2]:

happy_2019 = pd.read_excel('Chapter2OnlineData.xls')
happy_2019.head()

Out[2]:

	Country name	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Positive affect	Negative affect	Confidence in national government	Democratic Quality	Delivery Quality	Standard deviation of ladder by country-year	Standard deviation/Mean of ladder by country-year	GINI index (World Bank estimate)	GINI index (World Bank estimate), average 2000-16	gini of household income reported in Gallup, by wp5-year	Most people can be trusted, Gallup	Most people can be trusted, WVS round 1981-1984	Most people can be trusted, WVS round 1989-1993	Most people can be trusted, WVS round 1994-1998	Most people can be trusted, WVS round 1999-2004	Most people can be trusted, WVS round 2005-2009	Most people can be trusted, WVS round 2010-2014
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	0.517637	0.258195	0.612072	-1.929690	-1.655084	1.774662	0.476600	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	0.583926	0.237092	0.611545	-2.044093	-1.635025	1.722688	0.391362	NaN	NaN	0.441906	0.286315	NaN	NaN	NaN	NaN	NaN	NaN
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	0.618265	0.275324	0.299357	-1.991810	-1.617176	1.878622	0.394803	NaN	NaN	0.327318	0.275833	NaN	NaN	NaN	NaN	NaN	NaN
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	0.611387	0.267175	0.307386	-1.919018	-1.616221	1.785360	0.465942	NaN	NaN	0.336764	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	0.710385	0.267919	0.435440	-1.842996	-1.404078	1.798283	0.475367	NaN	NaN	0.344540	NaN	NaN	NaN	NaN	NaN	NaN	NaN

In [3]:

happy_2019['Country name'].nunique()

Out[3]:

In [4]:

happy_2019.rename({'Country name':'Country'}, axis=1, inplace=True)

In [5]:

happy_2019.columns

Out[5]:

Index(['Country', 'Year', 'Life Ladder', 'Log GDP per capita',
       'Social support', 'Healthy life expectancy at birth',
       'Freedom to make life choices', 'Generosity',
       'Perceptions of corruption', 'Positive affect', 'Negative affect',
       'Confidence in national government', 'Democratic Quality',
       'Delivery Quality', 'Standard deviation of ladder by country-year',
       'Standard deviation/Mean of ladder by country-year',
       'GINI index (World Bank estimate)',
       'GINI index (World Bank estimate), average 2000-16',
       'gini of household income reported in Gallup, by wp5-year',
       'Most people can be trusted, Gallup',
       'Most people can be trusted, WVS round 1981-1984',
       'Most people can be trusted, WVS round 1989-1993',
       'Most people can be trusted, WVS round 1994-1998',
       'Most people can be trusted, WVS round 1999-2004',
       'Most people can be trusted, WVS round 2005-2009',
       'Most people can be trusted, WVS round 2010-2014'],
      dtype='object')

In [6]:

drop_columns=['Positive affect', 'Negative affect',
       'Confidence in national government', 'Democratic Quality',
       'Delivery Quality', 'Standard deviation of ladder by country-year',
       'Standard deviation/Mean of ladder by country-year',
       'GINI index (World Bank estimate)',
       'GINI index (World Bank estimate), average 2000-16',
       'gini of household income reported in Gallup, by wp5-year',
       'Most people can be trusted, Gallup',
       'Most people can be trusted, WVS round 1981-1984',
       'Most people can be trusted, WVS round 1989-1993',
       'Most people can be trusted, WVS round 1994-1998',
       'Most people can be trusted, WVS round 1999-2004',
       'Most people can be trusted, WVS round 2005-2009',
       'Most people can be trusted, WVS round 2010-2014']

In [7]:

happy_2019_clean = happy_2019.drop(columns = drop_columns, axis=1)

happy_2019_clean.head()

Out[7]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620

In [8]:

happy_2019_clean.groupby('Year')['Country'].count()

Out[8]:

Year
2005     27
2006     89
2007    102
2008    110
2009    114
2010    124
2011    146
2012    142
2013    137
2014    145
2015    143
2016    142
2017    147
2018    136
Name: Country, dtype: int64

In [9]:

happy_2019_clean['Country'].nunique()

Out[9]:

In [10]:

happy_2019_clean.head()

Out[10]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620

Add Region¶

In [11]:

table_2015 = pd.read_excel('2015-2017.xlsx', sheet_name=1)

table_2015.head(1)

Out[11]:

	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	7.46	7.592	1.44178	1.16374	0.79504	0.57941	0.44453	0.36171	2.73939

In [12]:

Country_Region = table_2015[['Country','Region']]
print(Country_Region['Country'].nunique())
Country_Region.head()

Out[12]:

	Country	Region
0	Denmark	Western Europe
1	Switzerland	Western Europe
2	Iceland	Western Europe
3	Norway	Western Europe
4	Finland	Western Europe

In [13]:

Country_Region['Region'].value_counts()

Out[13]:

Sub-Saharan Africa                 38
Central and Eastern Europe         29
Latin America and Caribbean        24
Western Europe                     21
Middle East and Northern Africa    19
Southeastern Asia                   9
Southern Asia                       7
Eastern Asia                        6
North America                       2
Australia and New Zealand           2
Name: Region, dtype: int64

In [14]:

Country_Region.loc[Country_Region['Region'].str.contains('Asia'), 'Region']='Asia'

In [15]:

Country_Region['Region'].value_counts()

Out[15]:

Sub-Saharan Africa                 38
Central and Eastern Europe         29
Latin America and Caribbean        24
Asia                               22
Western Europe                     21
Middle East and Northern Africa    19
North America                       2
Australia and New Zealand           2
Name: Region, dtype: int64

In [16]:

Country_Region['Region'].replace({'Australia and New Zealand':'North America'}, inplace=True)

In [17]:

Country_Region['Region'].value_counts()

Out[17]:

Sub-Saharan Africa                 38
Central and Eastern Europe         29
Latin America and Caribbean        24
Asia                               22
Western Europe                     21
Middle East and Northern Africa    19
North America                       4
Name: Region, dtype: int64

In [18]:

Country_Region.head()

Out[18]:

	Country	Region
0	Denmark	Western Europe
1	Switzerland	Western Europe
2	Iceland	Western Europe
3	Norway	Western Europe
4	Finland	Western Europe

In [19]:

happy_2019_clean.shape

Out[19]:

(1704, 9)

In [20]:

happy_2019_clean_region = happy_2019_clean.merge(Country_Region, on='Country',how='outer')

print(happy_2019_clean_region.shape)

happy_2019_clean_region.head()

(1708, 10)

Out[20]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region
0	Afghanistan	2008.0	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	Asia
1	Afghanistan	2009.0	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia
2	Afghanistan	2010.0	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia
3	Afghanistan	2011.0	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia
4	Afghanistan	2012.0	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia

In [21]:

happy_2019_clean_region.isna().sum()

Out[21]:

Country                               0
Year                                  4
Life Ladder                           4
Log GDP per capita                   32
Social support                       17
Healthy life expectancy at birth     32
Freedom to make life choices         33
Generosity                           86
Perceptions of corruption           100
Region                               50
dtype: int64

In [22]:

happy_2019_clean_region.to_csv('happy_2019_clean_region.csv',index=False)

In [23]:

happy_report=pd.read_excel('happy_2019_clean_region.xlsx')
happy_report.isna().sum()

Out[23]:

Country                              0
Year                                 0
Life Ladder                          0
Log GDP per capita                  28
Social support                      12
Healthy life expectancy at birth    28
Freedom to make life choices        29
Generosity                          82
Perceptions of corruption           96
Region                               0
dtype: int64

In [24]:

happy_report['Country'].nunique()

Out[24]:

In [25]:

happy_report.groupby('Year')['Country'].count()

Out[25]:

Year
2005     27
2006     89
2007    102
2008    110
2009    114
2010    124
2011    146
2012    142
2013    137
2014    145
2015    143
2016    142
2017    147
2018    136
Name: Country, dtype: int64

Add Population¶

In [26]:

population = pd.read_csv('population_total.csv')

population.head(1)

Out[26]:

	country	1800	1801	1802	1803	1804	1805	1806	1807	1808	1809	1810	1811	1812	1813	1814	1815	1816	1817	1818	1819	1820	1821	1822	1823	1824	1825	1826	1827	1828	1829	1830	1831	1832	1833	1834	1835	1836	1837	1838	1839	1840	1841	1842	1843	1844	1845	1846	1847	1848	1849	1850	1851	1852	1853	1854	1855	1856	1857	1858	1859	1860	1861	1862	1863	1864	1865	1866	1867	1868	1869	1870	1871	1872	1873	...	2026	2027	2028	2029	2030	2031	2032	2033	2034	2035	2036	2037	2038	2039	2040	2041	2042	2043	2044	2045	2046	2047	2048	2049	2050	2051	2052	2053	2054	2055	2056	2057	2058	2059	2060	2061	2062	2063	2064	2065	2066	2067	2068	2069	2070	2071	2072	2073	2074	2075	2076	2077	2078	2079	2080	2081	2082	2083	2084	2085	2086	2087	2088	2089	2090	2091	2092	2093	2094	2095	2096	2097	2098	2099	2100
0	Afghanistan	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3280000	3290000	3290000	3300000	3300000	3310000	3320000	3320000	3330000	3340000	3350000	3360000	3380000	3390000	3400000	3420000	3430000	3450000	3470000	3480000	3500000	3520000	3540000	3550000	3570000	3590000	3610000	3630000	3640000	3660000	3680000	3700000	3720000	3730000	3750000	3770000	3790000	3810000	3830000	3840000	3860000	3870000	3890000	3910000	3920000	3940000	3960000	3970000	3990000	4010000	4030000	4050000	4070000	4080000	4110000	4130000	4150000	4170000	4190000	4220000	4240000	...	43300000	44100000	45000000	45800000	46700000	47600000	48400000	49200000	50100000	50900000	51700000	52500000	53300000	54100000	54900000	55700000	56400000	57200000	57900000	58600000	59300000	60000000	60700000	61300000	61900000	62500000	63100000	63700000	64300000	64800000	65400000	65900000	66400000	66800000	67300000	67700000	68200000	68600000	68900000	69300000	69700000	70000000	70300000	70600000	70800000	71100000	71300000	71500000	71700000	71800000	72000000	72100000	72200000	72300000	72300000	72400000	72400000	72400000	72400000	72400000	72300000	72300000	72200000	72100000	72000000	71900000	71800000	71600000	71500000	71300000	71200000	71000000	70800000	70600000	70400000

1 rows × 302 columns

In [27]:

population_country = population.loc[:,['country']]

population_year = population.loc[:,'2005':'2018']

population_new = population_country.merge(population_year, on = population_year.index)

population_new.head(1)

Out[27]:

	key_0	country	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
0	0	Afghanistan	25100000	25900000	26600000	27300000	28000000	28800000	29700000	30700000	31700000	32800000	33700000	34700000	35500000	36400000

In [28]:

population_new = population_new.drop('key_0',axis = 1)

In [29]:

population_new.head()

Out[29]:

	country	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
0	Afghanistan	25100000	25900000	26600000	27300000	28000000	28800000	29700000	30700000	31700000	32800000	33700000	34700000	35500000	36400000
1	Albania	3080000	3050000	3020000	2990000	2960000	2940000	2930000	2920000	2920000	2920000	2920000	2930000	2930000	2930000
2	Algeria	33300000	33800000	34300000	34900000	35500000	36100000	36800000	37600000	38300000	39100000	39900000	40600000	41300000	42000000
3	Andorra	78900	81000	82700	83900	84500	84400	83800	82400	80800	79200	78000	77300	77000	77000
4	Angola	19600000	20300000	21000000	21800000	22500000	23400000	24200000	25100000	26000000	26900000	27900000	28800000	29800000	30800000

In [30]:

population_new_converted = pd.melt(population_new, id_vars=['country'], 
                                   value_vars =['2005','2006','2007','2008','2009','2010','2011','2012','2013',
                                                 '2014','2015','2016','2017','2018'])
population_new_converted.head()

Out[30]:

	country	variable	value
0	Afghanistan	2005	25100000
1	Albania	2005	3080000
2	Algeria	2005	33300000
3	Andorra	2005	78900
4	Angola	2005	19600000

In [31]:

population_new_converted.rename({'country':'Country','variable':'Year', 'value':'Population'}, axis=1, inplace=True)

population_new_converted.head()

Out[31]:

	Country	Year	Population
0	Afghanistan	2005	25100000
1	Albania	2005	3080000
2	Algeria	2005	33300000
3	Andorra	2005	78900
4	Angola	2005	19600000

In [32]:

population_new = population_new_converted.sort_values(by = ['Country','Year'])
population_new.head(20)

Out[32]:

	Country	Year	Population
0	Afghanistan	2005	25100000
195	Afghanistan	2006	25900000
390	Afghanistan	2007	26600000
585	Afghanistan	2008	27300000
780	Afghanistan	2009	28000000
975	Afghanistan	2010	28800000
1170	Afghanistan	2011	29700000
1365	Afghanistan	2012	30700000
1560	Afghanistan	2013	31700000
1755	Afghanistan	2014	32800000
1950	Afghanistan	2015	33700000
2145	Afghanistan	2016	34700000
2340	Afghanistan	2017	35500000
2535	Afghanistan	2018	36400000
1	Albania	2005	3080000
196	Albania	2006	3050000
391	Albania	2007	3020000
586	Albania	2008	2990000
781	Albania	2009	2960000
976	Albania	2010	2940000

In [33]:

population_new.to_csv('population_new.csv')

In [34]:

population = pd.read_csv('population_new.csv')
population.head()

Out[34]:

	Unnamed: 0	Country	Year	Population
0	0	Afghanistan	2005	25100000
1	195	Afghanistan	2006	25900000
2	390	Afghanistan	2007	26600000
3	585	Afghanistan	2008	27300000
4	780	Afghanistan	2009	28000000

In [35]:

population.drop('Unnamed: 0',axis=1, inplace=True)

In [36]:

population.head()

Out[36]:

	Country	Year	Population
0	Afghanistan	2005	25100000
1	Afghanistan	2006	25900000
2	Afghanistan	2007	26600000
3	Afghanistan	2008	27300000
4	Afghanistan	2009	28000000

In [37]:

happy_report.head()

Out[37]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	Asia
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia

In [38]:

happy_report_population=happy_report.merge(population, on=['Country','Year'])
happy_report_population.head()

Out[38]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region	Population
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	Asia	27300000
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia	28000000
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia	29700000
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia	30700000

In [39]:

happy_report_population.to_csv('happy_report.csv',index=False)

In [40]:

happy_report_population.head()

Out[40]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region	Population
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	Asia	27300000
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia	28000000
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia	29700000
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia	30700000

Make bubble df¶

In [41]:

happy_report_population.groupby('Year')['Country'].count()

Out[41]:

Year
2005     27
2006     83
2007     97
2008    103
2009    106
2010    116
2011    135
2012    130
2013    127
2014    134
2015    133
2016    131
2017    136
2018    126
Name: Country, dtype: int64

In [42]:

df = happy_report_population

In [43]:

happy2009 = df[df['Year']==2009]
happy2010 = df[df['Year']==2010]
happy2011 = df[df['Year']==2011]
happy2012 = df[df['Year']==2012]
happy2013 = df[df['Year']==2013]
happy2014 = df[df['Year']==2014]
happy2015 = df[df['Year']==2015]
happy2016 = df[df['Year']==2016]
happy2017 = df[df['Year']==2017]
happy2018 = df[df['Year']==2018]

In [44]:

df = happy2010.merge(happy2009,on='Country')
df.shape[0]

Out[44]:

In [45]:

df = df.merge(happy2010,on='Country')
df.shape[0]

Out[45]:

In [46]:

df = df.merge(happy2011,on='Country')
df.shape[0]

Out[46]:

In [47]:

df = df.merge(happy2012,on='Country')
df.shape[0]

Out[47]:

In [48]:

df = df.merge(happy2013,on='Country')
df.shape[0]

Out[48]:

In [49]:

df = df.merge(happy2014,on='Country')
df.shape[0]

Out[49]:

In [50]:

df = df.merge(happy2015,on='Country')
df.shape[0]

Out[50]:

In [51]:

df = df.merge(happy2016,on='Country')
df.shape[0]

Out[51]:

In [52]:

df = df.merge(happy2017,on='Country')
df.shape[0]

Out[52]:

In [53]:

df = df.merge(happy2018,on='Country')
df.shape[0]

Out[53]:

In [54]:

df.head()

Out[54]:

	Country	Year_x	Life Ladder_x	Log GDP per capita_x	Social support_x	Healthy life expectancy at birth_x	Freedom to make life choices_x	Generosity_x	Perceptions of corruption_x	Region_x	Population_x	Year_y	Life Ladder_y	Log GDP per capita_y	Social support_y	Healthy life expectancy at birth_y	Freedom to make life choices_y	Generosity_y	Perceptions of corruption_y	Region_y	Population_y	Year_x	Life Ladder_x	Log GDP per capita_x	Social support_x	Healthy life expectancy at birth_x	Freedom to make life choices_x	Generosity_x	Perceptions of corruption_x	Region_x	Population_x	Year_y	Life Ladder_y	Log GDP per capita_y	Social support_y	Healthy life expectancy at birth_y	Freedom to make life choices_y	Generosity_y	Perceptions of corruption_y	Region_y	Population_y	Year_x	Life Ladder_x	Log GDP per capita_x	Social support_x	Healthy life expectancy at birth_x	Freedom to make life choices_x	Generosity_x	Perceptions of corruption_x	Region_x	Population_x	Year_y	Life Ladder_y	Log GDP per capita_y	Social support_y	Healthy life expectancy at birth_y	Freedom to make life choices_y	Generosity_y	Perceptions of corruption_y	Region_y	Population_y	Year_x	Life Ladder_x	Log GDP per capita_x	Social support_x	Healthy life expectancy at birth_x	Freedom to make life choices_x	Generosity_x	Perceptions of corruption_x	Region_x	Population_x	Year_y	Life Ladder_y	Log GDP per capita_y	Social support_y	Healthy life expectancy at birth_y	Freedom to make life choices_y	Generosity_y	Perceptions of corruption_y	Region_y	Population_y	Year_x	Life Ladder_x	Log GDP per capita_x	Social support_x	Healthy life expectancy at birth_x	Freedom to make life choices_x	Generosity_x	Perceptions of corruption_x	Region_x	Population_x	Year_y	Life Ladder_y	Log GDP per capita_y	Social support_y	Healthy life expectancy at birth_y	Freedom to make life choices_y	Generosity_y	Perceptions of corruption_y	Region_y	Population_y	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region	Population
0	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia	28000000	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia	29700000	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia	30700000	2013	3.572100	7.522238	0.483552	52.560001	0.577955	0.070403	0.823204	Asia	31700000	2014	3.130896	7.516955	0.525568	52.880001	0.508514	0.113184	0.871242	Asia	32800000	2015	3.982855	7.500539	0.528597	53.200001	0.388928	0.089091	0.880638	Asia	33700000	2016	4.220169	7.497038	0.559072	53.000000	0.522566	0.051365	0.793246	Asia	34700000	2017	2.661718	7.497755	0.490880	52.799999	0.427011	-0.112198	0.954393	Asia	35500000	2018	2.694303	7.494588	0.507516	52.599998	0.373536	-0.084888	0.927606	Asia	36400000
1	Albania	2010	5.268937	9.203032	0.733152	66.400002	0.568958	-0.175367	0.726262	Central and Eastern Europe	2940000	2009	5.485470	9.161638	0.833047	66.199997	0.525223	-0.160855	0.863665	Central and Eastern Europe	2960000	2010	5.268937	9.203032	0.733152	66.400002	0.568958	-0.175367	0.726262	Central and Eastern Europe	2940000	2011	5.867422	9.230904	0.759434	66.680000	0.487496	-0.207943	0.877003	Central and Eastern Europe	2930000	2012	5.510124	9.246655	0.784502	66.959999	0.601512	-0.172262	0.847675	Central and Eastern Europe	2920000	2013	4.550648	9.258445	0.759477	67.239998	0.631830	-0.130645	0.862905	Central and Eastern Europe	2920000	2014	4.813763	9.278104	0.625587	67.519997	0.734648	-0.028162	0.882704	Central and Eastern Europe	2920000	2015	4.606651	9.302960	0.639356	67.800003	0.703851	-0.084411	0.884793	Central and Eastern Europe	2920000	2016	4.511101	9.337532	0.638411	68.099998	0.729819	-0.020687	0.901071	Central and Eastern Europe	2930000	2017	4.639548	9.376145	0.637698	68.400002	0.749611	-0.032643	0.876135	Central and Eastern Europe	2930000	2018	5.004403	9.412399	0.683592	68.699997	0.824212	0.005385	0.899129	Central and Eastern Europe	2930000
2	Argentina	2010	6.441067	9.836924	0.926799	67.300003	0.730258	-0.121725	0.854695	Latin America and Caribbean	41200000	2009	6.424133	9.750825	0.918693	67.180000	0.636646	-0.125714	0.884742	Latin America and Caribbean	40800000	2010	6.441067	9.836924	0.926799	67.300003	0.730258	-0.121725	0.854695	Latin America and Caribbean	41200000	2011	6.775805	9.884781	0.889073	67.480003	0.815802	-0.170262	0.754646	Latin America and Caribbean	41700000	2012	6.468387	9.863960	0.901776	67.660004	0.747498	-0.143875	0.816546	Latin America and Caribbean	42100000	2013	6.582260	9.877256	0.909874	67.839996	0.737250	-0.126476	0.822900	Latin America and Caribbean	42500000	2014	6.671114	9.841482	0.917870	68.019997	0.745058	-0.160449	0.854192	Latin America and Caribbean	43000000	2015	6.697131	9.858329	0.926492	68.199997	0.881224	-0.170244	0.850906	Latin America and Caribbean	43400000	2016	6.427221	9.830088	0.882819	68.400002	0.847702	-0.188304	0.850924	Latin America and Caribbean	43800000	2017	6.039330	9.848709	0.906699	68.599998	0.831966	-0.182600	0.841052	Latin America and Caribbean	44300000	2018	5.792797	9.809972	0.899912	68.800003	0.845895	-0.206937	0.855255	Latin America and Caribbean	44700000
3	Armenia	2010	4.367811	8.810287	0.660342	65.199997	0.459257	-0.162075	0.890629	Central and Eastern Europe	2880000	2009	4.177582	8.784616	0.680007	65.099998	0.441413	-0.199945	0.881887	Central and Eastern Europe	2890000	2010	4.367811	8.810287	0.660342	65.199997	0.459257	-0.162075	0.890629	Central and Eastern Europe	2880000	2011	4.260491	8.856818	0.705108	65.360001	0.464525	-0.211577	0.874601	Central and Eastern Europe	2880000	2012	4.319712	8.924142	0.676446	65.519997	0.501864	-0.201539	0.892544	Central and Eastern Europe	2880000	2013	4.277191	8.952597	0.723260	65.680000	0.504082	-0.181963	0.899797	Central and Eastern Europe	2890000	2014	4.453083	8.983580	0.738764	65.839996	0.506487	-0.205098	0.920390	Central and Eastern Europe	2910000	2015	4.348320	9.011394	0.722551	66.000000	0.551027	-0.189388	0.901462	Central and Eastern Europe	2920000	2016	4.325472	9.010698	0.709218	66.300003	0.610987	-0.157249	0.921421	Central and Eastern Europe	2920000	2017	4.287736	9.081095	0.697925	66.599998	0.613697	-0.133958	0.864683	Central and Eastern Europe	2930000	2018	5.062449	9.119424	0.814449	66.900002	0.807644	-0.149109	0.676826	Central and Eastern Europe	2930000
4	Azerbaijan	2010	4.218611	9.677230	0.687001	63.400002	0.501071	-0.142826	0.858347	Central and Eastern Europe	9030000	2009	4.573725	9.641726	0.735970	63.020000	0.498138	-0.106351	0.753850	Central and Eastern Europe	8920000	2010	4.218611	9.677230	0.687001	63.400002	0.501071	-0.142826	0.858347	Central and Eastern Europe	9030000	2011	4.680470	9.664859	0.725194	63.639999	0.537484	-0.125277	0.795119	Central and Eastern Europe	9150000	2012	4.910772	9.673333	0.761873	63.880001	0.598859	-0.160711	0.763155	Central and Eastern Europe	9260000	2013	5.481178	9.716747	0.769690	64.120003	0.671957	-0.188644	0.698820	Central and Eastern Europe	9390000	2014	5.251530	9.724068	0.799433	64.360001	0.732773	-0.228512	0.653845	Central and Eastern Europe	9500000	2015	5.146775	9.723096	0.785703	64.599998	0.764289	-0.218102	0.615553	Central and Eastern Europe	9620000	2016	5.303895	9.680427	0.777271	64.900002	0.712573	-0.224378	0.606771	Central and Eastern Europe	9730000	2017	5.152279	9.670762	0.787039	65.199997	0.731030	-0.245216	0.652539	Central and Eastern Europe	9830000	2018	5.167995	9.678014	0.781230	65.500000	0.772449	-0.251795	0.561206	Central and Eastern Europe	9920000

In [55]:

country_81=df[['Country']]

country_81.head()

Out[55]:

	Country
0	Afghanistan
1	Albania
2	Argentina
3	Armenia
4	Azerbaijan

In [56]:

happy_report_population.head()

Out[56]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region	Population
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	Asia	27300000
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia	28000000
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia	29700000
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia	30700000

In [57]:

happy_bubble=happy_report_population.copy()

In [58]:

happy_bubble = happy_bubble.merge(country_81, on='Country')
happy_bubble.head()

Out[58]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region	Population
0	Afghanistan	2008	3.723590	7.168690	0.450662	50.799999	0.718114	0.177889	0.881686	Asia	27300000
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia	28000000
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia	29700000
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia	30700000

In [59]:

happy_bubble['Year'].value_counts()

Out[59]:

2018    81
2017    81
2016    81
2015    81
2014    81
2013    81
2012    81
2011    81
2010    81
2009    81
2007    68
2008    67
2006    53
2005    20
Name: Year, dtype: int64

In [60]:

happy_bubble = happy_bubble.drop(index=happy_bubble[happy_bubble['Year']==2005].index)

In [61]:

happy_bubble = happy_bubble.drop(index=happy_bubble[happy_bubble['Year']==2006].index)

In [62]:

happy_bubble = happy_bubble.drop(index=happy_bubble[happy_bubble['Year']==2007].index)

In [63]:

happy_bubble = happy_bubble.drop(index=happy_bubble[happy_bubble['Year']==2008].index)

In [64]:

happy_bubble['Year'].value_counts()

Out[64]:

2018    81
2017    81
2016    81
2015    81
2014    81
2013    81
2012    81
2011    81
2010    81
2009    81
Name: Year, dtype: int64

In [65]:

happy_bubble.head()

Out[65]:

	Country	Year	Life Ladder	Log GDP per capita	Social support	Healthy life expectancy at birth	Freedom to make life choices	Generosity	Perceptions of corruption	Region	Population
1	Afghanistan	2009	4.401778	7.333790	0.552308	51.200001	0.678896	0.200178	0.850035	Asia	28000000
2	Afghanistan	2010	4.758381	7.386629	0.539075	51.599998	0.600127	0.134353	0.706766	Asia	28800000
3	Afghanistan	2011	3.831719	7.415019	0.521104	51.919998	0.495901	0.172137	0.731109	Asia	29700000
4	Afghanistan	2012	3.782938	7.517126	0.520637	52.240002	0.530935	0.244273	0.775620	Asia	30700000
5	Afghanistan	2013	3.572100	7.522238	0.483552	52.560001	0.577955	0.070403	0.823204	Asia	31700000

Data Visualization¶

As shown in the cleaning process, I added region to classify each country, so we can have clear comparison on different region (Note that Australia and Newsland are included in'North America' because they are smaller countries regarding to population and more similar in culture and economics). I also added population to each country, and in this way we can find where the majority of people's happiness levels are (the large two blue bubbles are China and India)

After cleaning, there are 81 countries left with all clean and complete data that are included in the plot.
Year range is from 2009 to 2018
Each bubble represent a country, and the size of the bubble is the population size in that country.
Y-axis is the total happiness score for the each country
X-axis is the log form of GDP per capita.
Colors represent different Region.

The chart is fullyy interactive.

In [66]:

from __future__ import division
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode()
from bubbly.bubbly import bubbleplot

figure = bubbleplot(dataset = happy_bubble, x_column='Log GDP per capita', y_column='Life Ladder',
  bubble_column='Country', color_column='Region', time_column='Year', size_column='Population',
  x_title="GDP per Capita", y_title="Happiness Score for each country", title='Happy Report', scale_bubble=3, height=650)

iplot(figure, config={'scrollzoom': True})

Conclusion:¶

In general, the higher the GDP per capita is, the higher the happiness score is, but for each country it's not always the case, as we can see for with the same GDP per Capita, different countries have different happiness score.
North America and Western Europe (the purple and brown on the right top) have higher GDP per Capita and higher happiness score, this is not so surprising, but if we compare the changes from 2009 to 2018, we can see the happiness score in North America is decreasing despite very high GDP per capita.
Many countries in Latin America and Caribbean (the orange bubble) have very high happiness score, with Costa Rice as the happiest and its happiness and economic score does not change too much, very stable. Other countries in Latin America and Caribbean have slight lower happiness score compared ten years ago, although they are still generally happier than many people in Aisan.
Some countries like Israel and United Arab Emirates (light purple) are in the similar position as North America and Western Europe and their hapiness score are also slightly decreased.
Sub-Saharan African countries (the red bubble) are the worst in general with lower GDP per capita and lower happiness score, with the exception of South Africa. However, we are happy to see the improvement over time for these countries.
Many Asian countries (the blue bubble) also moved to the upper right side, but some countries like India, dispite the increase in GDP per capita, the happiness score dropped, while countries like China are becoming richer and happier.
Lastly, if we see closely, in 2009, counrties in each region are more concentrated and for the world as a whole, we can see a clearer positive relation between GDP per capita and Happiness Score. However, over time, although GDP per capita is generally higher, the spread of each bubble are becoming larger. This means in the same region, the differences in happiness score is becoming larger. There are other factors that explain the overall happiness score, not only GDP per capita.
In our data, we have 6 factors to measure the happiness score, but this analysis can show us how much GDP per capita can contribute to people's happiness in different countries. e.g. For China and many Sub-Saharan African countries, as GDP goes up, happiness score goes up too, but this is not the same pattern for India, so maybe there are other problems with India, and this gives us indication that we can find more data for India to see why people in Indian are becoming less happy.