Data Analysis: World Happiness Report (Fun)

World Happiness

Some really interesting features are contained in these data sets. It would be interesting to see how these features relate to our day to day lives.

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn # data visualization
from matplotlib import pyplot as plt
#import plotly.plotly as py
#import plotly.tools as tls


sn.set(color_codes = True, style="white")
import matplotlib.pyplot as ml # data visualisation as well
import warnings
warnings.filterwarnings("ignore")
stats17 = pd.read_csv("2017.csv", sep=",", header=0)
stats16 = pd.read_csv("2016.csv", sep=",", header=0)
stats15 = pd.read_csv("2015.csv", sep=",", header=0)

Exploration

stats16.describe()

	Happiness.Rank	Happiness.Score	Whisker.high	Whisker.low	Economy..GDP.per.Capita.	Family	Health..Life.Expectancy.	Freedom	Generosity	Trust..Government.Corruption.	Dystopia.Residual
count	155.000000	155.000000	155.000000	155.000000	155.000000	155.000000	155.000000	155.000000	155.000000	155.000000	155.000000
mean	78.000000	5.354019	5.452326	5.255713	0.984718	1.188898	0.551341	0.408786	0.246883	0.123120	1.850238
std	44.888751	1.131230	1.118542	1.145030	0.420793	0.287263	0.237073	0.149997	0.134780	0.101661	0.500028
min	1.000000	2.693000	2.864884	2.521116	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.377914
25%	39.500000	4.505500	4.608172	4.374955	0.663371	1.042635	0.369866	0.303677	0.154106	0.057271	1.591291
50%	78.000000	5.279000	5.370032	5.193152	1.064578	1.253918	0.606042	0.437454	0.231538	0.089848	1.832910
75%	116.500000	6.101500	6.194600	6.006527	1.318027	1.414316	0.723008	0.516561	0.323762	0.153296	2.144654
max	155.000000	7.537000	7.622030	7.479556	1.870766	1.610574	0.949492	0.658249	0.838075	0.464308	3.117485

stats16.head()

Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',
       'Standard Error', 'Economy (GDP per Capita)', 'Family',
       'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)',
       'Generosity', 'Dystopia Residual'],
      dtype='object')

stats16.isna().sum()

Country                          0
Region                           0
Happiness Rank                   0
Happiness Score                  0
Lower Confidence Interval        0
Upper Confidence Interval        0
Economy (GDP per Capita)         0
Family                           0
Health (Life Expectancy)         0
Freedom                          0
Trust (Government Corruption)    0
Generosity                       0
Dystopia Residual                0
dtype: int64

We see what our data set looks like. We also conducted a summary to get a rough idea if we are missing anything, which we can see that we dont.

After having a look at the head we can see the top countries are in Europe. we need to see if this hunch carries.

Region

Lets see how many countries we have in each region

stats16.groupby('Region').agg('count')[['Happiness Rank']].plot(kind='bar', figsize=(25, 7), stacked=True, color=['b', 'tab:pink']);

We can see that Africa and Central and Eastern Europe have the highest numder of countries.

ml.figure(figsize=(15,10)) 
sn.stripplot(x="Region", y="Happiness Rank", data=stats16, jitter=True)
ml.xticks(rotation=-45)

(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), <a list of 10 Text xticklabel objects>)

We can see that Africa has the highest mean, with Australia/New Zealand and North America having the lowest. We must remember that Australia/New Zealand and North America have 2 countries each and that with these 2 regions the means do not really mean much.

We need to see if our data set has any features that correlate to each other.

ml.figure(figsize=(20,10)) 
sn.heatmap(stats16.corr(), annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0xdc35a58>

There is a high correlation between Economy and Happiness score. Life Expectancy and Economy. There is also a moderate correlation between Freedom and government trust.

sn.pairplot(stats16[['Happiness Score','Economy (GDP per Capita)','Family','Health (Life Expectancy)']])

<seaborn.axisgrid.PairGrid at 0xdc66dd8>