🏆 World Cup 2018 Squads Data Cleaner
This project contains a Python function to load, clean, and prepare the 2018 FIFA World Cup squads data for use in analysis tools like Tableau.
📄 File: wrangle_world_cup_squads.py Function: wrangle_world_cup_squads(file_path) Purpose
Preprocess the 2018 World Cup squads dataset to ensure it is clean, standardized, and ready for visualization or analysis.
✅ Features
📥 Loads data from an Excel file
🧹 Cleans and standardizes string fields (e.g. team names, player names)
📆 Parses dates and calculates player ages
🔢 Ensures numeric fields like caps and goals are properly typed
🧮 Creates age group categories for better grouping in visual tools
📤 Exports cleaned data to CSV: world_cup_2018_squads_cleaned.csv
📊 Output
The output CSV will include the following cleaned and enriched fields:
Type
Team
Group
Position
Name
Country and Club
DOB
Age
Caps
Goals
Age_Group (<20, 20-24, 25-29, 30+)
🧪 Example Usage from wrangle_world_cup_squads import wrangle_world_cup_squads
df_cleaned = wrangle_world_cup_squads("world_cup_2018_squads.xlsx")
Rows with missing or invalid DOB, Caps, or Goals will be removed.
Age is calculated based on the current year, which may slightly differ from the exact tournament age.
Make sure the Excel file contains columns named exactly as expected: Type, Team, Group, Position, Name, Country and Club, DOB, Caps, Goals.
🗂️ Files File Description wrangle_world_cup_squads.py Python script for cleaning the dataset world_cup_2018_squads.xlsx Input Excel file with raw data (not included) world_cup_2018_squads_cleaned.csv Output CSV file with cleaned data 📈 Suggested Use in Tableau
Once you have the world_cup_2018_squads_cleaned.csv, you can:
Import it into Tableau
Use Team, Group, Position, and Age_Group for filtering/grouping
Visualize age distribution, goals per age group, caps per team, etc.
📌 Requirements
Python 3.7+
pandas
openpyxl (required for reading .xlsx files)
Install dependencies:
pip install pandas openpyxl