GDP Analysis and Visualization

This project analyzes a GDP dataset containing country and World Bank aggregate GDP values from 1960 to 2016. It includes a Jupyter notebook for interactive exploration, a reusable Python script for regenerating the analysis, exported plot images, and summary CSV tables.

The goal is to make the dataset easy to understand through clean exploratory analysis and visual storytelling.

Project Highlights

• Loads and validates GDP data from gdp.csv
• Separates country-level records from aggregate groups such as World, High income, and regional totals
• Identifies the latest available year in the dataset, which is 2016
• Ranks the top economies by GDP
• Compares GDP trends across major economies
• Calculates the largest GDP increases from 2000 to 2016
• Visualizes year-over-year GDP growth
• Exports all charts as PNG files and key result tables as CSV files

Dataset

The dataset contains four original columns:

Column	Description
Country Name	Country, region, or aggregate group name
Country Code	Three-letter country or aggregate code
Year	Observation year
Value	GDP in current US dollars

Additional derived columns are created during analysis:

Column	Description
GDP Trillion USD	GDP converted from dollars to trillions of current US dollars
Is Aggregate	Boolean flag used to separate aggregate groups from individual countries

Key Findings

• The dataset has 11,507 rows and 256 unique series.
• The year range is 1960-2016.
• There are no missing values in the core dataset columns.
• The top country-level GDPs in 2016 are led by the United States, China, Japan, Germany, and the United Kingdom.
• China has the largest absolute GDP increase from 2000 to 2016 among country-level records.
• Rankings exclude aggregate groups so countries are not compared against regions or income categories.

Visualizations

The generated charts are saved in the figures/ directory.

World GDP Trend

Top 15 Countries by GDP in 2016

Major Economy GDP Trends

Largest GDP Increases, 2000-2016

Year-over-Year GDP Growth

Top Country GDP Heatmap

Project Structure

GDP_Analysis/
|-- GDP_Analysis.ipynb
|-- README.md
|-- gdp.csv
|-- gdp_analysis.py
|-- figures/
|   |-- 01_world_gdp_trend.png
|   |-- 02_top_15_countries_latest_year.png
|   |-- 03_major_economy_trends.png
|   |-- 04_largest_gdp_increases_2000_2016.png
|   |-- 05_selected_country_yoy_growth.png
|   `-- 06_top_country_gdp_heatmap.png
`-- summary_tables/
    |-- latest_top_15_countries.csv
    `-- largest_gdp_increases_2000_2016.csv

How to Run

1. Install Requirements

This project uses:

• Python 3
• pandas
• matplotlib
• seaborn
• Jupyter Notebook or JupyterLab

Install the required packages:

pip install pandas matplotlib seaborn notebook

2. Open the Notebook

For an interactive walkthrough, open:

jupyter notebook GDP_Analysis.ipynb

The notebook includes data loading, quality checks, visualizations, and key takeaways.

Outputs

Figures

File	Description
01_world_gdp_trend.png	Global GDP trend over time
02_top_15_countries_latest_year.png	Top 15 country-level GDPs in 2016
03_major_economy_trends.png	GDP trends for the largest economies
04_largest_gdp_increases_2000_2016.png	Countries with the largest absolute GDP growth
05_selected_country_yoy_growth.png	Year-over-year GDP growth for selected economies
06_top_country_gdp_heatmap.png	Heatmap of GDP levels across selected years

Summary Tables

File	Description
latest_top_15_countries.csv	Top 15 countries by GDP in the latest dataset year
largest_gdp_increases_2000_2016.csv	Largest country-level GDP increases from 2000 to 2016

Notes

• GDP values are measured in current US dollars.
• Because the dataset includes aggregate groups, country-level rankings use only non-aggregate records.
• The latest year available in this dataset is 2016; the project does not claim to represent current GDP rankings.

Author

Created as a data analysis and visualization project for exploring global GDP patterns.