Metadata-Version: 2.1
Name: retail-stats
Version: 0.0.1
Summary: A simple way to calculate retail stats
Home-page: https://github.com/insectatorious/retail-stats
Author: Sumanas Sarma
Author-email: insectatorious+pypi@gmail.com
License: UNKNOWN
Keywords: price-elasticity cross-elasticity sales-analysis
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Office/Business
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# Retail Stats

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This repository contains code to calculate various values used in retail for 
products whose sales and prices are provided.

Metrics currently available:

 1. Price Elasticity (_In Progress_)
 2. Cross Elasticity (**Complete**)

## Calculations

### Cross Elasticity
From [Wikipedia](https://en.wikipedia.org/wiki/Cross_elasticity_of_demand), 
> measures the responsiveness of the quantity demanded for a good to a change 
>in the price of another good, _ceteris paribus_.

This can be seen written using the formula:

```text
Percentage Change in Quantity Sold of Product B
-------------------------------------------------
Percentage Change in Price Charged for Product A
``` 

The implementation is a direct copy of the formula. 

#### Calculate Cross Elasticity for a single pair of products
```python
from math import isclose
from retail_stats import elasticity

original_quantity = 200
new_quantity = 400

original_price = 1000
new_price = 1050
# (200 / 300) / (50 / 1025)
expected_ced = 13.66666666666666
ced = elasticity.calculate_cross_elasticity(original_quantity, 
                                            new_quantity, 
                                            original_price, 
                                            new_price)

assert isclose(expected_ced, ced)
```

#### Calculate All Cross Elasticities

```python
from math import isclose

import numpy as np

from retail_stats.elasticity import get_all_cross_elasticities

skus = np.array(list("ABCD"))
# [original, new]
qty_a = [200, 0]
qty_b = [200, 400]
prc_a = [1000, 1050]
prc_b = [1000, 1000]

qty_c = [1000, 1050]
qty_d = [1000, 1100]
prc_c = [100, 80]
prc_d = [80, 80]

original_quantities = [qty_a[0], qty_b[0], qty_c[0], qty_d[0]]
new_quantities = [qty_a[1], qty_b[1], qty_c[1], qty_d[1]]
original_prices = [prc_a[0], prc_b[0], prc_c[0], prc_d[0]]
new_prices = [prc_a[1], prc_b[1], prc_c[1], prc_d[1]]

"""
Cross Elasticities between pairs A,B and C,D

  | A | B | C | D 
A |   |   |   |
B |   |   |   | 
C |   |   |   | 
D |   |   |   |
"""

ceds = get_all_cross_elasticities(original_quantities=original_quantities,
                                  new_quantities=new_quantities,
                                  original_prices=original_prices,
                                  new_prices=new_prices)

assert ceds.shape == (len(skus), len(skus))
assert isclose(ceds[np.argwhere(skus == "A"), np.argwhere(skus == "A")], -41)
assert isclose(ceds[np.argwhere(skus == "B"), np.argwhere(skus == "A")], 13.66666666666666)
assert isclose(ceds[np.argwhere(skus == "D"), np.argwhere(skus == "C")], -0.4285714286)
assert isclose(ceds[np.argwhere(skus == "C"), np.argwhere(skus == "A")], 1)
assert isclose(ceds[np.argwhere(skus == "A"), np.argwhere(skus == "C")], 9)

```


