This project is to apply Python to analyze information from different datasets to gain the insights about energy consumption, GDP in Top 15 countries with the most advanced energy engineering and technologies.
The tasks include:
Merge 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 and 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.
Use only the last 10 years (2006-2015) of GDP data and only the top 15 countries by Scimagojr 'Rank' (Rank 1 through 15).
Include steps to summarize impoyrtant indicators/variables in the dataset. Observations are also catetorized into continent groups and binds of % Renewable Energy.
Finally, by the cleaned data, visual graphics could be conducted to investigate the data.
Exercises to get the sense of coding in Python.
People say: "Hard-work pays-off", my aspiration is turning programming to a daily exercise to improve myself. This is a sets of exercises and assignments I have worked on in my first stage of using Python in working with data.
Most of them are parts from the course "Introduction to Data Science in Python" by the University of Michigan on Coursera, and the "Python Programming", by Gert De Geyter, provided at Toulouse School of Economics.
I have received the best support and explanation from these sources, yet programming might be a great example of team-work, but also a polygon of self-discipline, self-motivated, and self-learning process. I am thankful for the inspirations and sharings available in Stack Overflow, Google, Youtube, in general the Internet that enable me to fill the gaps in my understandings.
To "Pay-it-forward" and also as my dairy of programming journey, I create this collections.