A package for ABC classification
Project description
ABC classification library
ABC classification is an inventory categorisation technique. A typical example of ABC classification is the segmentation of products (entity) based on sales (value). The best-selling products that contribute to up to 70% of the total sales belong to cluster A. The products making up the next 20% of sales are in cluster B, whereas the products representing the last 10% of sales, belong to class C. Hence, the pattern is named after the three clusters (ABC).
Example
Installation
pip install abc-classification
Import
from abc_classification.abc_classifier import ABCClassifier
Let's say we have dataframe
product | total sold |
---|---|
fade cream | 27000 |
powders | 24000 |
shadows | 18000 |
mascara | 16000 |
lipstick | 6000 |
concealer | 5000 |
sculptors | 4000 |
You can create ABCClasifier object, pass your dataframe to it and call classify method.
abc_clf = ABCClassifier(df)
abc_df = abc_clf.classify('product', 'total sold')
This way you'll get new dataframe with classified products.
product | total sold | class |
---|---|---|
fade cream | 27000 | A |
powders | 24000 | A |
shadows | 18000 | A |
mascara | 16000 | B |
lipstick | 6000 | B |
concealer | 5000 | C |
sculptors | 4000 | C |
You also can use brief_abc method to get aggregated information
abc_clf.brief_abc(abc_df)
class | total sold |
---|---|
A | 69000 |
B | 16000 |
C | 15000 |
You can plot pareto chart.
from abc_classification.abc_visualiser import pareto_chart
pareto_chart(abc_df, 'total_sold', 'product')
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