ABC-Analyse

Welcome to my very b̲e̲g̲i̲n̲n̲e̲r̲-level project. The first one I've shared on GitHub. I was scared hahaha. This project actually just automates a school assignment, so if you're in Germany with a similar task, feel free to use this code)! But for normal people this program has no value, which I really regret and will fix this one in future projects. It's part of my learning journey, and I aim to create more meaningful projects in the future.

[⚠️] If it is your pleasure to notice any advice and you find a spare 5 minutes in your busy schedule to make a commit, I would be incredibly grateful for this.

How it works

1. Table1: Visualizes basic product data like product number, quantity, and price.

2. Table2: Introduces calculations like worth, share of total quantity, share of total worth, and rank.

3. Table3: Builds upon Table2 with cumulative calculations and categories (A, B, C) based on the share of total worth.

How it use

1. pip install tabulate
2. Run abc_analyse.py
3. You can inport your own data or take test data set:

iPhone 12 Silicone Case, USB-C Cabel, AirPods Pro 2. Gen, Spinner, Orbit Spearmint, Coca-Cola 0.5

95, 353, 2, 90, 228, 554

4.99, 2.98, 225, 3.98, 0.75, 0.99

Original Task:

Now see how this works with a stall that wanted to find out which product was bringing in the most revenue:

Table 2: The higher the Worth, the higher the rank. Top 3 products - means the top 3 best selling products by Worth.

Table 3: By analogy, let's analyze the third table: T3 is an expanded version of T2. In essence, the higher the category, the smaller the cumulative share in the total volume. Consequently, Category A products outsell Category B and C products—think of it as a hierarchy, with Category A at the top and Categories B and C following in decreasing order

16.12.2023