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https://github.com/datasqlsantosh/global-energy-consumption-renewable-generation-python-data-analysis-portfolio
This project focuses on analyzing global energy consumption patterns and trends in renewable energy generation using Python data analysis libraries such as Seaborn and NumPy. The analysis aims to explore energy consumption data from various regions worldwide and examine the contribution of renewable energy sources over time
https://github.com/datasqlsantosh/global-energy-consumption-renewable-generation-python-data-analysis-portfolio
data data-analysis data-visualization pandas seaborn
Last synced: 3 days ago
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This project focuses on analyzing global energy consumption patterns and trends in renewable energy generation using Python data analysis libraries such as Seaborn and NumPy. The analysis aims to explore energy consumption data from various regions worldwide and examine the contribution of renewable energy sources over time
- Host: GitHub
- URL: https://github.com/datasqlsantosh/global-energy-consumption-renewable-generation-python-data-analysis-portfolio
- Owner: datasqlsantosh
- Created: 2024-03-03T12:02:49.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-03-17T06:59:37.000Z (8 months ago)
- Last Synced: 2024-03-17T08:22:01.048Z (8 months ago)
- Topics: data, data-analysis, data-visualization, pandas, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Certainly! Below is a template for a README.md file for a project titled "Global Energy Consumption and Renewable Generation Data Analysis" using Python libraries like Seaborn and NumPy:
---
# Global Energy Consumption and Renewable Generation Data Analysis
## Overview
This project focuses on analyzing global energy consumption patterns and trends in renewable energy generation using Python data analysis libraries such as Seaborn and NumPy. The analysis aims to explore energy consumption data from various regions worldwide and examine the contribution of renewable energy sources over time.
## Technologies Used
- Python
- Pandas
- NumPy
- Seaborn
## Project Structure
- **dataset/**: Directory containing data files
- *energy_consumption.csv*: Energy consumption data file
- *renewable_generation.csv*: Renewable generation data file
- **notebooks/**: Directory for Jupyter notebooks
- *Global_Energy_Analysis.ipynb*: Jupyter notebook for data analysis
- **README.md**: Project README file
- **requirements.txt**: File listing required Python libraries
## How to Run the Project
1. Clone the repository to your local machine:
```
git clone <[repository_url](https://github.com/datasqlsantosh/Global-Energy-Consumption-Renewable-Generation-python-data-analysis-portfolio)>
```
2. Navigate to the project directory:
```
cd Global-Energy-Consumption-Renewable-Generation
```
3. Install the required Python libraries using pip:
```
pip install -r requirements.txt
```
4. Run the main Python script to execute the data analysis and generate visualizations:
```
python src/data_analysis.py
```
5. View the generated visualizations and analysis insights.
## Directory Structure
```
Global-Energy-Consumption-Renewable-Generation/
│
├── data/
│ ├── energy_consumption.csv
│ └── renewable_generation.csv
│
├── notebooks/
│ └── Global_Energy_Analysis.ipynb
│
├── README.md
└── requirements.txt
```
## Additional Notes
- Include any additional information or considerations relevant to the project.
- Provide instructions for running the code and reproducing the analysis results.
---
Feel free to customize the README.md file according to your project's specific details and requirements. This template provides a structured approach to documenting your data analysis project using Python libraries like Seaborn and NumPy.