Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kirolos00daniel/retails_sales_analyzer
https://github.com/kirolos00daniel/retails_sales_analyzer
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/kirolos00daniel/retails_sales_analyzer
- Owner: Kirolos00Daniel
- Created: 2024-05-10T08:06:06.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-08-17T11:29:25.000Z (5 months ago)
- Last Synced: 2024-08-17T12:40:45.429Z (5 months ago)
- Language: Jupyter Notebook
- Size: 45.9 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Security: SECURITY.md
Awesome Lists containing this project
README
# Retail Sales Analyzer
This Python script provides a simple and effective way to analyze retail sales data using the Pandas and Matplotlib libraries. The script reads sales data from a CSV file, processes it, and provides various insights and visualizations.
## Features
- **Data Cleaning**: The script removes any missing values from the dataset.
- **Total Sales Per Product**: Calculates the total sales for each product.
- **Best Selling Product**: Identifies the best-selling product based on total sales.
- **Average Daily Sales**: Computes the average daily sales across the dataset.
- **Sales Trend Visualization**: Plots the sales trend over time.
- **Sales Per Product Visualization**: Plots the total sales per product in a bar chart.
## Requirements
- Python 3.x
- Pandas
- Matplotlib
You can install the required packages using pip:
```bash
pip install pandas matplotlib
```
## Usage
1. **Data Preparation**: Ensure you have a CSV file named `retail_sales.csv` with the following columns:
- `Date`: The date of the sales (in YYYY-MM-DD format).
- `Product`: The name of the product.
- `Sales`: The sales amount.
2. **Running the Script**: Simply run the script to get the analysis results. The script will print out the total sales per product, the best-selling product, and the average daily sales. It will also display two plots: one for the sales trend over time and another for sales per product.
```python
analyzer = RetailSalesAnalyzer()
print('Total Sales per Product: \n', analyzer.total_sales_per_product())
print('Best Selling Product: ', analyzer.best_selling_product())
print('Average Daily Sales: ', analyzer.average_daily_Sales())
analyzer.plot_sales_per_product()
analyzer.plot_sales_trend()
```
## Outcomes
### Textual Output:
- **Total Sales per Product:** Shows the total sales for each product.
- **Best-Selling Product:** Identifies the product with the highest sales.
- **Average Daily Sales:** Displays the average daily sales across all dates.
### Visual Output:
- **Sales Per Product Bar Chart:** Visualizes the sales distribution among products.
- **Sales Trend Line Chart:** Plots the sales trend over time.
## Conclusion
This script provides a comprehensive way to analyze retail sales data, from cleaning and processing the data to performing key analyses and visualizations. The class-based structure makes