This Jupyter Notebook contains Python code for analyzing sales data from a fictional company called "ABC Corporation". The data is in a CSV file format and covers the period from January 2019 to December 2022, with information about the sales revenue, units sold, and discounts for various products and regions.
The analysis focuses on answering several questions about the sales performance of ABC Corporation, such as:
- What was the total revenue, units sold, and average price per unit for each year and quarter?
- Which product categories and regions generated the most and least revenue, units sold, and discounts?
- What was the trend in sales revenue and units sold over time, and how did it vary by region and product category?
- What was the correlation between sales revenue, units sold, and discounts, and how did it differ by region and product category?
To answer these questions, the code uses various Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn, to load, clean, transform, and visualize the data. The code also includes comments and explanations to help understand the data analysis process and the rationale behind the code.
To run this code on your machine, you need to have Jupyter Notebook installed, along with the required Python libraries. You can install them by running the following commands in your terminal or Anaconda prompt:
- pip install jupyter pandas numpy matplotlib seaborn
After installing the required dependencies, you can download the "Sales data analysis.ipynb" file from this repository and open it in Jupyter Notebook. Make sure that the "sales_data.csv" file is in the same directory as the notebook.
You can then run the code cells one by one, or all at once using the "Run all" command. The code will generate various charts and tables that illustrate the sales data analysis results.
This code is created as a personal project to practice data analysis skills. The sales data is fictional and provided for educational purposes only.