Profitability Graph Analyser - Python

Contents

1. Brief Summary
2. Aims and Motivation
3. Technologies, Requirements and Software Tools
4. Design
5. Graph Screenshots

Brief Summary

• Personal Group Project which employs Python to generate visually intuitive graphs from stock sales data retrieved from a database through Comma-Separated Values (CSV) file handling.
• Agile Methodology followed with Pair Programming alongside 3 other developers.
• The project is now fully operational and being utilised at a local convenience store.
• The project was planned collaboratively and Pair Programmed with the involvement of 3 other developers, emphasising teamwork in software design and development.

🎯Aims and Motivation

• The primary objective of this project was to enable users to effortlessly visualise their sales data from the database through graphical representation, aiding in better comprehension and analysis.
• Motivated by an unrelenting passion for knowledge and personal growth, I took on the responsibility of utilising back-end technologies to assist others as well as expanding my expertise in back-end technologies.

⚙️Technologies, Requirements and Software Tools

Programming & Scriping Languages

• Python3
• MySQL

Pip & Python Package Requirements

• Matplotlib

Other Software Tools

• Comma-Separated Values (CSV) files were utilised in conjunction with file handling operations as part of the project's implementation.
• Git was used as a Version Control System (VCS) to maintain a history of the software project.
• GitHub was used to host and maintain history of the project.

✏️Design

Back-End Technology Stack

• As a back-end project, Python3 was employed to develop profitability graphs using the matplotlib package which enables users to conveniently monitor and track the performance of products, identifying both successful and underperforming items.
• Upon converting database information into a CSV file, a sophisticated data structure was created which was used to store the information retrieved from the database using CSV File Handling. In this case, a dictionary data structure was implemented, where the month number served as the key, and the corresponding values were arrays that stored the sale numbers per month.
• A function named "filter_by_characteristic" was created, which accepts the data structure and the item by which the user wishes to filter, facilitating the convenient plotting and viewing of sale data.
• The function named "aggregate_by_characteristic" achieves a similar result.
• The "plot_total_profit" function takes the data structure as input and generates a visually appealing line graph that illustrates the months and total profits. The line graph presents the sales information in a modern and aesthetically pleasing manner.
• The "plot_histogram_total_units" function achieves a similar outcome as the "plot_total_profit" function, but instead of a line graph, it generates a histogram that showcases the frequencies and distribution of total units sold. This graphical representation offers an alternative visual perspective for analysing sales data.
• The "plot_toothpaste_sales" function displays the overall sales of a specific product, in this case toothpaste, along with the total sales for each month. This function provides a graphical representation that highlights the sales performance of the product over time.

🚀 Graph Screenshots

Monthly Profit Line Graph

Frequency Histogram

Toothpaste Montly Sales Bar Graph