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📊 Sales Data Analysis with NumPy and Matplotlib

This project is a beginner-to-intermediate level data analysis of sales data using pandas, NumPy, and Matplotlib. It demonstrates how to read, clean, analyze, and visualize sales information from a CSV file.

📂 Files

  • sales2_1.csv: The dataset.
  • sales2.py: Main Python script that processes and visualizes the data.
  • revenue_profit_chart.png: Output chart showing revenue per product.

🧠 Objectives

  • Convert raw sales data into useful insights.
  • Calculate total revenue per product.
  • Use NumPy for array manipulation and slicing.
  • Visualize results with a colorful, labeled bar chart.

🗂️ Dataset Description

The dataset contains the following columns:

  • Product: The name of the product.
  • Quantity: Units sold.
  • Price: Unit price in dollars.
  • Date: Date of sale.

🧮 Analysis Steps

  1. Read CSV Data using pandas.
  2. Convert Columns to Numeric types with error handling.
  3. Calculate Revenue per row (Price × Quantity).
  4. Convert DataFrame to NumPy Array for slicing and filtering.
  5. Extract Unique Products and compute:
    • Total revenue per product.
    • Percentage share of total revenue.
  6. Visualize the Results using Matplotlib:
    • Each product is assigned a unique color.
    • Products are displayed as numbered bars.
    • A dynamic legend explains which number corresponds to which product.

📈 Output Example

Bar Chart

🛠️ Technologies Used

  • Python
  • pandas
  • NumPy
  • Matplotlib

💡 What You Will Learn

  • Data cleaning with pandas
  • NumPy slicing and boolean masking
  • Revenue calculation by category
  • Building clear, colorful visualizations
  • Working with legends and layout in Matplotlib

🚀 Future Improvements

  • Group data by date and analyze revenue trends over time.
  • Add Seaborn or Plotly for interactive visualizations.
  • Build a simple dashboard using Streamlit.

📬 Contact

If you like this project or have questions, feel free to connect:

🔖 License

This project is open-source and available under the MIT License.