Storytelling Data Visualization on Exchange Rates

This project showcases storytelling data visualizations using Python's matplotlib library to analyze and visualize historical euro-dollar exchange rates. The visualizations provide insights into the exchange rate trends during specific periods and events.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Visualizations
• Contributing
• License

Introduction

This project aims to demonstrate the power of data visualization in conveying insights from financial data. By using matplotlib, a popular data visualization library in Python, we analyze and visualize the euro-dollar exchange rates. The project focuses on three main narratives: the impact of the 2007-2008 financial crisis, the effects of the coronavirus pandemic, and a comparison of exchange rates under different US presidents.

Dataset

The dataset used in this project contains historical euro-dollar exchange rates from 1999 to 2020. The data is sourced from European Central Bank.

Installation

To run the code locally and generate the visualizations, follow these steps:

1. Clone this repository: git clone https://github.com/vajrastra/Storytelling-Data-Visualization-on-Exchange-Rates.git
2. Navigate to the project directory: cd Storytelling-Data-Visualization-on-Exchange-Rates
3. Install the required packages using Anaconda or pip: conda install matplotlib pandas or pip install matplotlib pandas
4. Run the Jupyter Notebook or Python script to generate the visualizations.

Usage

• The main code file is data_visualization.ipynb (Jupyter Notebook).
• Alternatively, you can use the data_visualization.py script (Python script).

Visualizations

1. Financial Crisis Example: Visualizes how the euro-dollar rate changed during the 2007-2008 financial crisis, highlighting key events and the peak of the crisis.

2. Three US Presidencies Example: Compares euro-dollar exchange rates under the last three US presidents: George W. Bush, Barack Obama, and Donald Trump.

For detailed visualizations and explanations, refer to the code and comments in the Jupyter Notebook or Python script.

Contributing

Contributions to enhance and expand this project are welcome! If you'd like to contribute:

1. Fork the repository.
2. Create a new branch for your feature: git checkout -b feature-name
3. Make your changes and commit them: git commit -m 'Add new feature'
4. Push to your forked repository: git push origin feature-name
5. Create a pull request describing your changes.

License

This project is licensed under the MIT License.