AsuquoAA/Energy_Consumption_Dashboard_for_African_Subregions_1980-2019
Domain:
environment and energyRecord type:
dataset
Creator:
Asu
Host:
Interactive dashboard that allows users to explore the major energy sources across various subregions in Africa and analyze them
# Energy Consumption Dashboard for African Subregions (1980–2019)
This interactive dashboard allows users to explore the major energy sources across various subregions in Africa and analyze which subregion produced the highest amount of energy for consumption from 1980 to 2019.
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## Research Question
1. What are the major energy sources in the subregions of Africa
2. Which subregion produces the highest amount of energy for consumption from 1980 to 2019?
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## Features
1. Interactive Grid: The dashboard contains a 3x3 grid layout where:
- 5 Interactive Bar Charts: These bar charts represent the energy sources for the countries in Northern, Western, Central, Eastern, and Southern Africa. The bar charts are dynamically updated based on the year selected using the slider.
- 1 Static Line Chart: A larger, static line chart shows the total energy consumption of each African subregion (North, West, Central, East, South) from 1980 to 2019. This chart provides a comprehensive view of the energy consumption trends for each subregion.
2. Year Slider: The year slider allows users to interactively choose a year between 1980 and 2019, and the bar charts are updated accordingly to reflect the energy sources for that year in each subregion.
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## Setup and Installation
To run this dashboard, the following libraries are required:
1. pandas
2. numpy
3. matplotlib
4. seaborn
5. ipywidgets
- code: pip install pandas numpy matplotlib seaborn ipywidgets
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## How to run this project
1. Clone the Repository:
- code: git clone github.com
2. Navigating to directory
- code: cd Energy_Consumption_Dashboard_for_African_Subregions_1980-2019
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## Dataset Sources
1. Energy_Consumption_Data
2. African_Countries_Information
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## Process Workflow
### Data Processing
- The energy data is cleaned by removing unnecessary columns, replacing certain country names for consistency, and convert …