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Data analysis of residentual electricity consumption in different regions in the world. Main focus is on relationship between electricity consumption and few technology indicatores, (phones or PCs per 100 people., for example)

HTML 63.26% Jupyter Notebook 36.74%
electricity-consumption data data-analysis pandas jupyter-notebook gapminder

electricity_consumption_in_different_regions_of_the_world's Introduction

Electricity consumption in different regions - analysis of data from Gapminder project

Gapminder is website that gathers wide range of demographic, economic and social data from countries all over the world. I tried to find out how technology indicators (PC or phone ownership per 100 people, for example) differ in countries with high electricity consumption and low electricity consumption. Electricity consumption here is residentual electricity use per person in kilowatt/hours. I analyzed data from years 2000 and 2005 in 116 countries.

This was study project for online course Data Analyst Nanodegree from Udacity.

Source:Pixabay

Instalation

  • Jupyter Notebook
  • Python 3.6
  • Anaconda

Methods used

  • data wrangling with Pandas
  • exploratory data analysis with Matplotlib

Conclusion and main findings

There is huge gap between countries with high and low electricity consumption in all tech indicators (with exception of urbanization) as well as in income. Even though differences in phone and computer ownerships and number of internet users are getting smaller over time, gap in income and urbanized population remains barely unchanged. In 2005 countries with electricity consumption had on average almost 3 times more personal phones and almost 8 times more personal computers than in countries with low electricity consumption. Income was 5,5 times higher in countries with high electricity consumption.

Authors

  • Kamila Hamalcikova
  • Udacity

License

This project is licensed under the MIT License - see the license file for details

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