Identifying-Non-EU-European-Countries-Using-Python-Pandas

Introduction

This project analyses two datasets — one listing European countries and another listing EU member countries — to determine which European countries are not part of the European Union. It demonstrates a simple but powerful use of Python, Pandas, and set operations to answer a real-world membership question.

Objectives

• Import and clean country data from two Excel sheets.
• Use Python set operations to compare European and EU country lists.
• Produce a sorted list of European countries that are not EU members.
• Practice practical data cleaning, manipulation, and comparison workflows.

Tools & Libraries

• Python 3.x
• Pandas – for reading Excel files, cleaning data, and manipulating tables.
• Python Sets – for efficient membership and difference operations.
• Excel – as the data source format.

Workflow

1. Import Data

   • Read Europe and EU sheets from Europe_and_EU.xlsx into Pandas DataFrames.

2. Clean Data

   • Strip leading and trailing whitespace from the Country column.
   • Drop any null or empty country entries.

3. Convert to Sets

   • Convert the Country columns of both DataFrames into Python sets:
     • europe_countries = all European countries.
     • eu_countries = EU member countries.

4. Compare Sets

   • Check if EU countries are a subset of European countries with issubset().
   • Use set difference europe_countries - eu_countries to find non-EU countries.

5. Output Results

   • Sort and print the resulting list of non-EU European countries (2018).

Skills Demonstrated

• Data Import: Reading multi-sheet Excel files into Pandas.
• Data Cleaning: Standardising string fields and handling missing values.
• Data Wrangling: Extracting unique values from DataFrames.
• Set Operations: Using Python’s built-in set methods (issubset, difference) to compare groups.
• Analytical Thinking: Breaking a real-world question into a reproducible, code-based workflow.
• Result Presentation: Producing a sorted, human-readable list of findings.

Conclusion

The project shows how combining Pandas for tabular data manipulation with simple Python set operations can efficiently solve a membership analysis task. This workflow is lightweight, fast, and easily adapted to similar comparison problems in data science.