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Python_project

Built comprehensive Python Data Analytics projects utilizing CSV integration, NumPy, and Pandas for advanced data cleaning, transformation, statistical calculations, relational table joins, and pattern-character-based data search—delivering structured insights through practical problem-solving and analytical modeling.

🐍 Data Cleaning & Analysis (Python Project)

📌 Project Overview

This project involves data cleaning and transformation using Python. The source dataset was in CSV format. The script performs table joining, missing value handling (zero replacement), and average calculation to prepare the data for analysis.

🛠 Tools & Libraries Used

  • Python
  • Pandas
  • NumPy

🔧 Key Steps Performed

  • Imported CSV file and explored the dataset
  • Replaced all 0s with a fixed value where appropriate
  • Joined multiple tables using pandas.merge() based on a common key
  • Calculated average of selected columns post-cleaning
  • Exported the final clean dataset

💡 Learning Outcome

Demonstrated data preprocessing and transformation techniques using Python. Built confidence in working with raw CSV files and preparing clean datasets ready for business analysis.

📎 Files Included

  • cleaning_script.py – Python logic
  • sales_data.csv – Sample raw data
  • cleaned_data.csv – Clean output (optional)

📈 Output

Cleaned, joined dataset with accurate averages and no missing or misleading values.