Finding the price based on industry and number of transactions

Question

I am trying to determine the price for my input table which has the specific industry type and the average monthly transactions based on the reference table 1 which has the price for different industries (these represent the A tier prices) and the reference table 2 which classifies the price tier based on the average monthly transactions..Reference table 1 has the price for pricing tier A for all the industries .. The price for tier B is calculated as 90% of price of Tier A price; the price for tier C is calculated as 90% of price of Tier B price and so on

Input table

Industry	Avg Monthly Transactions
Automotive	1129
Financial Services	7219
Retail	11795
Financial Services	10092
Retail	9445

Reference table 1

Industry	price
Automotive	35
Financial Services	40
Retail	30

Reference table 2

Pricing Tier	Minimum Average Monthly Transactions	Maximum Average Monthly Transactions
A	1	100
B	101	1000
C	1001	2500
D	2501	5000
E	5001	10000
F	10001

Output table

Industry	Avg Monthly Transactions	Price
Automotive	1129	28.35
Financial Services	7219	26.24
Retail	11795	17.71
Financial Services	10092	23.62
Retail	9445	19.68

Python Code Tried

    import pandas as pd
    
    df1=pd.read_csv("input.csv")
    df2=pd.read_csv("reference1.csv")
    df3=pd.read_csv("reference2.csv")

industry =df1[industry]
avgmonthlytransaction=df1[Avg Monthly Transactions]
price=df1.where(df1[avg Monthly Transactions]>=df3[min average] & <=df3[maximum average],pricingtier)
&& df1.where(df1[industry]=df2[Industry],df2[Price]

Asked By: user3369545

||

Source

Answer 1

df3['factor'] = [0.9**i for i in range(6)]
df3

Use cut() to create bins, find the corresponding Pricing Tier,
and we can map its factor via df3.

tier = pd.cut(
    df1['Avg Monthly Transactions'], 
    bins=(df3.iloc[:,1].values.tolist() + [np.inf]),
    labels=df3['Pricing Tier'].values.tolist())

tier = tier.to_frame(name='Pricing Tier')

output = df1.merge(df2, on='Industry', how='left')
output['price'] = output['price']*tier.merge(df3[['Pricing Tier','factor']], on='Pricing Tier', how='left')['factor']

###
             Industry  Avg Monthly Transactions    price
0          Automotive                      1129  28.3500
1  Financial Services                      7219  26.2440
2              Retail                     11795  17.7147
3  Financial Services                     10092  23.6196
4              Retail                      9445  19.6830

Answered By: Baron Legendre

Finding the price based on industry and number of transactions

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