⚡ Global EV Market & Infrastructure Analysis

📌 About the Project

This project is a comprehensive Exploratory Data Analysis (EDA) of the global electric vehicle market based on historical data from the International Energy Agency (IEA). The main goal of the project is to investigate the adoption rates of EV technologies and assess the readiness of charging infrastructure in key regions (the "Big Three": China, Europe, USA).

🛠 Tech Stack

• Python 3
• Pandas: Complex filtering, aggregation (groupby), table merging, MultiIndex operations.
• Seaborn & Matplotlib: Plotting linear trends, scatter plots, custom styling, and color coding.

📊 Key Findings

1. Technological Shift: The analysis showed an unconditional victory of pure Battery Electric Vehicles (BEV) over Plug-in Hybrid Electric Vehicles (PHEV) on a global scale.
2. Data Sanity: During the analysis, "double counting" was identified and eliminated (World vs Regions, Historical vs Projections), which allowed for obtaining real market metrics (about 14 million sales in 2023).
3. Infrastructure Load Index:
   • China leads in infrastructure comfort (~8 cars per 1 charging point in 2023).
   • USA experiences an acute shortage of public chargers (~26 cars per 1 charging point), which forms a high barrier to the development of intercity travel.
4. Correlation Analysis: The Pearson coefficient between the car fleet and the number of chargers was calculated. Global correlation was 0.95+, with the strongest connection found in China (0.995).

🚀 What Was Done:

• Wrote a universal data cleaning function (DRY principle) protecting against hidden aggregation bugs.
• Implemented the merging of two independent metrics (EV Stock and EV Charging Points) to calculate a derived business indicator (Driver's "Pain Index").

📁 Project Structure

Global-EV-Market-Analysis/
├── data/
│   ├── processed/          # Cleaned and processed data
│   │   └── merged_ev_data.csv
│   └── raw/                # Original data downloaded from IEA
│       └── IEA_Global_EV_Data_2024.csv
├── images/                 # Generated plots and visualizations
│   ├── car_charger_corr_matrix.png
│   ├── charger_growth_tempo.png
│   ├── global_ev_region_sales.png
│   ├── global_ev_sales.png
│   ├── global_ev_type_sales.png
│   └── region_chargers_load.png
├── notebooks/              # Jupyter notebooks for analysis
│   └── data_exploration_01.ipynb
├── .gitignore
├── LICENSE
├── README.md               # Project description
└── requirements.txt        # Python dependencies

⚙️ Getting Started (How to Run)

Follow these steps to run the project on your local machine after downloading it from GitHub:

1. Clone the repository:

   git clone https://github.com/atomicDataDev/Global-EV-Market-Analysis.git
   cd Global-EV-Market-Analysis

2. Create a virtual environment (recommended):

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required dependencies:

   pip install -r requirements.txt

5. Run the Jupyter Notebook:

   jupyter notebook

   Navigate to the notebooks/ folder in the opened web interface and launch data_exploration_01.ipynb.

📄 License

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