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FIFA 21 Data Wrangling & Tableau Preparation

Project Overview

This project cleans and prepares the FIFA 21 raw dataset for analysis and visualization in Tableau. The goal is to transform messy raw data into a clean, structured format suitable for exploring player performance, market value, and club/nationality insights.

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Features

• Cleans categorical and numeric columns.
• Converts monetary values (Value, Wage) into numeric format.
• Handles missing and unrealistic values.
• Adds calculated fields like Value_per_OVA, Wage_per_OVA, and Age_Group.
• Produces a clean CSV ready for Tableau dashboards.

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Dataset

• File: fifa21 raw data v2.csv

• Size: 18,979 entries, 77 columns

• Key Columns Used:

  • Player Info: Name, LongName, Nationality, Club, Positions, Preferred Foot, Best Position
  • Ratings: OVA, POT, PHY, PAC, SHO, PAS, DRI, DEF
  • Financials: Value, Wage
  • Others: Age

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How It Works

Steps Performed in wrangle_fifa_data:

1. Load CSV

   • Load the raw FIFA 21 dataset into a Pandas DataFrame.
   • Rename special columns (like ↓OVA) for easier reference.

2. Clean Categorical Columns

   • Strip spaces and standardize capitalization.
   • Fill missing values with 'Unknown'.

3. Clean Numeric Columns

   • Convert ratings, age, and skill stats to numeric.
   • Invalid entries are converted to NaN.

4. Convert Monetary Columns

   • Remove symbols and letters (€, M, K).
   • Convert to float and scale according to millions or thousands.

5. Handle Missing and Unrealistic Data

   • Drop rows missing OVA, POT, or Age.
   • Remove rows with impossible values (e.g., Age <16 or >49, OVA/POT ≤ 0).

6. Add Calculated Fields

   • Value_per_OVA → market value efficiency
   • Wage_per_OVA → wage efficiency
   • Age_Group → categorized player age groups for analysis

7. Save Cleaned CSV

   • Saves as fifa21_cleaned.csv for Tableau or other visualization tools.

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Usage

from wrangle_fifa import wrangle_fifa_data
# Clean FIFA 21 dataset
df_cleaned = wrangle_fifa_data("fifa21 raw data v2.csv")

• The function returns a cleaned DataFrame.
• A new CSV fifa21_cleaned.csv is created for Tableau dashboards.

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Insights for Tableau

You can use the cleaned dataset to build dashboards on:

1. Player Ratings & Potential
   • Scatter plots of OVA vs POT
   • Histograms of OVA distribution
   • Boxplots per position
2. Value & Wage Analysis
   • Total value per club
   • Average value by nationality
   • Value_per_OVA and Wage_per_OVA comparisons
3. Age & Demographics
   • Age distribution histograms
   • Age group performance per position
   • Preferred foot distribution
4. Position & Skill Stats
   • Compare skill stats per position using radar or heatmap charts
   • Correlation of OVA with individual skill attributes
5. Club & Nationality Insights
   • Club strength comparison (Avg OVA/POT)
   • Tree map of nationality vs player count & avg rating
   • ROI of players by club using Value_per_OVA

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Requirements

• Python 3.x
• Pandas
• Jupyter Notebook (optional, recommended for visualization prep)

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Notes

• This project is designed for data cleaning and preparation. Tableau or Python visualizations can be built on the cleaned dataset.
• Ensure the raw CSV file fifa21 raw data v2.csv is in the same directory as the notebook or script.

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