GDP Analysis using Python (Pandas, Plotly, Numpy)

Overview

Gross Domestic Product (GDP) is a crucial economic indicator representing the total value of goods and services produced within a country over a specific period, usually a year. This project aims to analyze GDP data to gain insights into economic performance, growth rates, and comparisons between countries using Python libraries such as Pandas, Plotly, and Numpy.

Key Features

1. Dataset Walkthrough:

   • Understand the structure and content of the dataset.
   • Use df.head() to display the first few rows of the dataset for an overview.

2. GDP Growth Calculation:

   • GDP growth rate is calculated using the formula:

     GDP growth rate = (GDP in current period - GDP in previous period) / GDP in previous period * 100

   • For example, if GDP was 100 in 2020 and 105 in 2021, the growth rate is:

     GDP growth rate = (105 - 100) / 100 * 100 = 5%
     

3. Visualization Using Plotly:

   • Create interactive and dynamic graphs for better data visualization.
   • Utilize plotly.express and plotly.graph_objects for plotting.

4. World GDP Growth Rate Analysis:

   • Visualize GDP growth trends across all countries in the dataset.

5. Country-wise GDP Comparison:

   • Compare GDP across countries to understand relative economic performance.

   • Focused comparison of GDP growth between India and China:

     c1 = df[df['Country Name'] == 'China']
     c2 = df[df['Country Name'] == 'India']
     df_pr = pd.concat([c1, c2], axis=0)
     fig = px.line(df_pr, x='Year', y='Value', title='GDP Comparison | India & China', color='Country Name')
     pyo.plot(fig, filename='IND|CHN.html')

Libraries Required

1. Pandas: For data manipulation and analysis.
2. Plotly: For creating interactive and visually appealing plots.
3. Numpy: For numerical operations and calculations.

Installation

1. Install the required libraries:

   pip install pandas plotly numpy

2. Set up Plotly account (if required) at plotly.com.

Instructions

1. Load the dataset using Pandas and explore its structure with df.head().
2. Calculate GDP growth rates for the dataset using the formula provided.
3. Use Plotly to create:
   • Global GDP growth visualizations.
   • Comparisons across countries.
   • Focused comparisons between India and China.
4. Save the interactive plots locally (e.g., IND|CHN.html).

Insights

• Visualization of GDP trends highlights differences in economic growth across countries.
• Comparing India and China’s GDP illustrates how China’s GDP has grown significantly faster than India’s over the analyzed period.

Outputs

1. Interactive plots showcasing:
   • Global GDP growth rates.
   • Country-wise GDP comparisons.
   • India vs. China GDP trends.

Example Code Snippets

GDP Growth Calculation

# Example calculation of GDP growth rate
gdp_growth_rate = (current_gdp - previous_gdp) / previous_gdp * 100

Plotly Visualization

import plotly.express as px
import pandas as pd

c1 = df[df['Country Name'] == 'China']
c2 = df[df['Country Name'] == 'India']
df_pr = pd.concat([c1, c2], axis=0)
fig = px.line(df_pr, x='Year', y='Value', title='GDP Comparison | India & China', color='Country Name')
fig.show()

Conclusion

This project leverages Python's data analysis and visualization libraries to analyze GDP data, providing valuable insights into economic growth and inter-country comparisons. The interactive plots enable users to explore data dynamically and make informed conclusions about global and country-specific economic trends.