Sales Data Analysis: 10000 Records

This project performs data analysis on a mock global sales dataset using the Pandas library in a Python Jupyter environment.

The analysis focuses on key business metrics such as total profit, units sold, and revenue trends over time, providing clear, formatted insights and visualizations.

Key Concepts Demonstrated

• Data Loading & Cleaning: Reading data from a CSV file (SalesRecords.csv) and converting the 'Order Date' column to the proper datetime format for time-series analysis.
• Data Aggregation: Using the powerful groupby() and sum() functions to aggregate data by categories like Item Type and Region.
• Multi-Metric Analysis: Grouping by one category (e.g., Region) to simultaneously analyze two related metrics (Units Sold and Total Profit).
• Data Formatting: Applying display options to clean up large numeric figures, converting scientific notation to readable currency, and using .to_string() for tidy console output.
• Data Visualization: Generating clear bar charts and line plots using matplotlib to visualize trends.

Deliverables

• sales_analysis.ipynb: The main Jupyter notebook containing all the Python code, analysis, and charts.
• SalesRecords.csv: The dataset used for this analysis.

Setup and Running the Analysis

This project requires Python and the following libraries:

Pandas

Numpy

matplotlib

Prerequisites

You can install the required libraries using pip:

pip install pandas matplotlib

Execution

1. Upload Files: Upload both the DataAnalysis.ipynb notebook and the SalesRecords.csv data file to your Jupyter or Google Colab workspace.
2. Open Notebook: Open DataAnalysis.ipynb.
3. Run Cells: Execute the notebook cells sequentially from top to bottom.

Key Analysis Sections

The notebook provides the following business reports:

Grouping Field	Metrics Displayed	Visualization
Item Type	Total Profit	Bar Chart
Region	Units Sold, Total Profit	N/A
Order Month	Total Revenue	Line Plot