Energy and GDP - Data Manipulation and Cleaning with Pandas
In this notebook we will use:
- The energy data from the file
Energy Indicators.xls, which is a list of indicators of energy supply and renewable electricity production from the United Nations for the year 2013. - The GDP data from the file
world_bank.csv, which is a csv containing countries' GDP from 1960 to 2015 from World Bank. - The Sciamgo Journal and Country Rank data for Energy Engineering and Power Technology from the file
scimagojr-3.xlsx, which ranks countries based on their journal contributions in the aforementioned area.
To answer import questions in the context of only the top 15 countries by Scimagojr Rank over the last 10 years for each country as:
- What is the
average GDPfor each country? - What is the mean of
Energy Supply per Capita? - What country has the maximum
% Renewableand what is the percentage? - What is the ratio of
Self-Citations to Total Citationsfor each country? and what country has the highest ratio? - Which countries has
% Renewableat or above the median for all countries in the top 15 countries?
And to know more about the population in each Continent
Steps:
1- Load, Validate and Clean the data.
2- Join the three datasets.
3- Answering questions in the context of only the top 15 countries by Scimagojr Rank over the last 10 years for each country.
4- Group the Countries by Continent, then create a dateframe that displays population summary statistics.