EDA on global power plants data
Update: Github no longer supports Plotly graphs in jupyter notebooks. To see all graphs, please clone the repo and re-run the notebook locally.
Some interesting facts:
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Good news: Globally, here are almost 2.5 times of renewable source power plants than non-renewable power plants.
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Bad news: Because most renewable fuel power plants (except for nuclear) has only a fraction of the capacity of non_renewable power plants, the leading power source globally is still coal(1st) and gas(2nd) (with hydro in 3rd and nuclear 4th). More worryingly, between 2013 and 2017 the power generated from non_renewable sources has increased faster than that of renewable.
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Dirties countries 1: US (1st place) has more than double the number of non-renewable source power plants than China (2nd place), and almost the total numbers in China, Brazil and Russia combined.
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Dirties counties 2: China has the most capacity in non_renewable source power plants. Closely 2nd place is US, which is almost 4 times the capacity of the 3rd place India.
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South America and Europe utilise clean energy much more in percentage to their total capacity.
Stack: plotly & seawborn for visualisation; pandas for data cleaning/wrangling