Metadata-Version: 2.1
Name: abc-classification
Version: 0.0.6
Summary: A package for ABC classification
Home-page: https://github.com/MelikbekyanAshot/abc-classification
Author: Ashot Melikbekyan
Author-email: melikbekyan.ashot@yandex.ru
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy

# 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
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      |


