It is a common phrase that "money can't buy happiness" (and for some a common response is that it doesn't buy sadness either). Recently, this has been a hot topic in the media. One example of a recent article is seen in CNN (https://www.cnn.com/2022/05/28/health/money-happiness-wellness/index.html) however many more studenes and resulting news articles have docused on this over the past years.
Most of these studeies focus on average income, however in this dataset, the relatinship between happiness (as described by the World Happiness Survey) is compared to the GDP per capita of ac ountry, which gives a much more broad look than just income. Furthermore, using question responses from the world happiness survey responses, this project sets out to determine the roles that changing and different societal ideals have on the impact of economic factors on the happienss of people in a particular country.
sources:
GDP | Sourced initally from multple soources. Collated and colectd for this project from https://ourworldindata.org/grapher/gdp-per-capita-maddison-2020
Happiness | The world happiness report. Year over year data was colated on Kaggle, https://www.kaggle.com/datasets/mathurinache/world-happiness-report/code?resource=download# needed to create a df
import pandas as pd
# Loads the relavent columns
gdp = pd.read_csv("gdp-per-capita-maddison-2020.csv").drop(labels=["417485-annotations","Code"],axis=1)
# Filters to the year included in the world happiness report
gdp = gdp.loc[gdp['Year'] >= 2015]
gdp.head()
| Entity | Year | GDP per capita | |
|---|---|---|---|
| 65 | Afghanistan | 2015 | 1928.0000 |
| 66 | Afghanistan | 2016 | 1929.0000 |
| 67 | Afghanistan | 2017 | 2014.7452 |
| 68 | Afghanistan | 2018 | 1934.5550 |
| 140 | Albania | 2015 | 10032.0000 |
# WoW!! There is a better way to do this but for some reason every refused to work
# REgardless, for a smaller data set, this is not the bestbut not horrible
# for each year, reads the file, adds the year and adjusts formats
happy2015 = pd.read_csv("archive/2015.csv")
happy2015['year'] = 2015
happy2015.columns = [x.lower() for x in happy2015.columns]
# fixes a difference in year over year reporting
happy2015['happiness score'] = happy2015['happiness score'] * 1000
happy2016 = pd.read_csv("archive/2016.csv")
happy2016['year'] = 2016
happy2016.columns = [x.lower() for x in happy2016.columns]
happy2017 = pd.read_csv("archive/2017.csv")
happy2017['year'] = 2017
happy2017.columns = [x.lower() for x in happy2017.columns]
happy2018 = pd.read_csv("archive/2018.csv")
happy2018['year'] = 2018
happy2018.columns = [x.lower() for x in happy2018.columns]
happy2019 = pd.read_csv("archive/2019.csv")
happy2019['year'] = 2019
happy2019.columns = [x.lower() for x in happy2019.columns]
happy2020 = pd.read_csv("archive/2020.csv")
happy2020['year'] = 2020
happy2020.columns = [x.lower() for x in happy2020.columns]
happy2021 = pd.read_csv("archive/2021.csv")
happy2021['year'] = 2021
happy2021.columns = [x.lower() for x in happy2021.columns]
happy2022 = pd.read_csv("archive/2022.csv")
happy2022['year'] = 2022
happy2022.columns = [x.lower() for x in happy2022.columns]
happy2022['happiness score']
# adds all the years into one df
years = [happy2015,happy2016,happy2017,happy2018,happy2019,happy2020,happy2021,happy2022]
happiness = pd.concat(years,ignore_index=True)
# fixes issue in year over year reporting differences
happiness["happiness rank"] = happiness["happiness rank"].fillna(0) + happiness["rank"].fillna(0)
# extracts just neccasary values
happiness = happiness[["country","happiness rank","happiness score","year"]]
happiness.head()
| country | happiness rank | happiness score | year | |
|---|---|---|---|---|
| 0 | Switzerland | 1.0 | 7587.0 | 2015 |
| 1 | Iceland | 2.0 | 7561.0 | 2015 |
| 2 | Denmark | 3.0 | 7527.0 | 2015 |
| 3 | Norway | 4.0 | 7522.0 | 2015 |
| 4 | Canada | 5.0 | 7427.0 | 2015 |
# gets set of countrues that are in both data
countries = set(gdp["Entity"].tolist()).intersection(set(happiness["country"].tolist()))
# trims data to only countries in both dfs
happiness = happiness[happiness['country'].isin(countries)]
gdp = gdp[gdp['Entity'].isin(countries)]
# standardizes data
gdp = gdp.rename(columns={"Entity": "country", "Year": "year"})
country: the name of the ranked country
happiness rank: how happy the named country is compared to every other country that year
happiness score: an arbitrary score of happienss for each country
year: the year that the data points are reffering to
gdp per capita: gdp per capita for that year of the named country
The above data will be used to determine if there is a particular link in happiness of a country and its GDP per capita. Once this simple analysis is done, some more exploration into the other data included in the world happiness data will occur to determine ways in which changing soceital ideas affect the impact GDP per capita (and by connection, the econonomic condition) has on happiness.