🌍 Data Wrangling and Visualization Project

Unlocking Insights from World Bank Data through Advanced Data Wrangling and Visualization Techniques

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📌 Overview

This project focuses on data preprocessing and visualization using financial data from the World Bank. The aim is to clean, merge, and analyze datasets to reveal key economic insights, such as the impact of income classification on inflation rates. This repository is a comprehensive guide for data enthusiasts looking to understand the nuances of data wrangling and visualization in Python.

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🎯 Key Objectives

• Efficiently clean and transform large datasets.
• Merge and enrich datasets to create a unified dataset for analysis.
• Generate exploratory visualizations to summarize key insights.
• Provide a reusable workflow for financial data analysis.

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🛠️ Features

1. Data Loading:
   • Reads data from multiple Excel files.
2. Data Cleaning:
   • Renames and standardizes column names.
   • Filters and cleans financial data.
3. Data Transformation:
   • Pivots and aggregates financial indicators for easier analysis.
   • Merges datasets with country and income classification metadata.
4. Visualization:
   • Produces bar plots and summaries of financial trends.

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Chat Screenshot

Here’s a screenshot of the chat that inspired this project:

📂 Project Structure

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├── Data_Wrangling.py       # Main Python script
├── (2.2) WDI World Bank.xlsx   # World Bank Indicators dataset
├── (2.3) WDI Income Group.xlsx # Income Group metadata
├── (2.4) WDI Country.xlsx      # Country-level metadata
├── README.md              # Documentation