This data project explores the adoption of Electric Vehicles (EVs) using two datasets obtained from the International Energy Agency (IEA). The datasets consist of car sales and EV sales data from 2010 to 2022. The main objective of the project is to analyze the current state of EV adoption and answer several questions related to the trend, stage, leading countries, and rate of adoption.
The project addresses the following questions:
- What trend (linear, quadratic, exponential, etc.) is EV adoption currently following?
- What is the current stage of EV adoption? In what stage of adoption are we on?
- Which countries are leading the adoption of EVs?
- Which countries are adopting EVs faster (rate of adoption)?
- Estimate the trend and speed of the transition using the Diffusion of Innovations Theory as a basis
- Python: The main programming language used for data processing, analysis, and visualization.
- Jupyter Notebook: Used for data cleaning, merging datasets, estimating the adoption curve, and addressing the project questions.
- Streamlit: A Python library used to create a dashboard displaying the project results.
The project structure is as follows:
notebooks/main.py: The Jupyter Notebook containing the data processing, analysis, and visualization code.data/: A folder containing the dataset files obtained from the IEA.app/: A folder containing the application code for creating the project dashboard.requirements.txt: A file specifying the dependencies required to run the project.
To access the project dashboard created with Streamlit, please visit the following link: EV Adoption Tracker
This data project provides insights into the adoption of Electric Vehicles (EVs) based on the analysis of car sales and EV sales datasets from 2010 to 2022. By exploring the trend, adoption stage, leading countries, and adoption rate, it offers a comprehensive understanding of the current state of EV adoption. The Streamlit app provides an interactive and user-friendly way to explore the project results and gain valuable insights.