import pandas as pd

import numpy as np

# Load the data

file$\_$path $=\text{'}$C:$/$Users$/$ASUS$/$Desktop$/$PART $1/$mugs$-$preference$-$parameters$-$full.csv$\text{'}$

data $=$ pd$.$read$\_$csv$($file$\_$path$)$

# Define the product attributes for the proposed candidate

candidate $=\{$

'price': $30,$

'time$\_$insulated': $1,$

'capacity': $20,$

'cleanability': 'Easy',

'containment': 'Leak Resistant', # This corresponds to pCnLk

'brand': $\text{'}$C$\text{'}$

$\}$

# Define the cost structure

cost$\_$structure $=\{$

'time$\_$insulated': $\{0\mathrm{.}5$: $0\mathrm{.}5,1$: $1,3$: $3\},$

'capacity': $\{12$: $1\mathrm{.}00,20$: $2\mathrm{.}6,32$: $2\mathrm{.}8\},$

'cleanability': $\{\text{'}$Difficult$\text{'}$: $1,$ 'Fair': $2\mathrm{.}2,$ 'Easy': $3\mathrm{.}0\},$

'containment': $\{\text{'}$Slosh Resistant': $0\mathrm{.}5,$ 'Spill Resistant': $0\mathrm{.}8,$ 'Leak Resistant': $1\}$

$\}$

# Calculate the cost of the proposed candidate

candidate$\_$cost $=($cost$\_$structure$[\text{'}$time$\_$insulated'$][$candidate$[\text{'}$time$\_$insulated'$]]+$

cost$\_$structure$[\text{'}$capacity$\text{'}][$candidate$[\text{'}$capacity$\text{'}]]+$

cost$\_$structure$[\text{'}$cleanability$\text{'}][$candidate$[\text{'}$cleanability$\text{'}]]+$

cost$\_$structure$[\text{'}$containment$\text{'}][$candidate$[\text{'}$containment$\text{'}]])$

# Define competitors

competitor$\_1=\{$

'price': $30,$

'time$\_$insulated': $3,$

'capacity': $20,$

'cleanability': 'Easy',

'containment': 'Leak Resistant', # This corresponds to pCnLk

'brand': $\text{'}$A$\text{'}$

$\}$

competitor$\_2=\{$

'price': $10,$

'time$\_$insulated': $1,$

'capacity': $20,$

'cleanability': 'Fair',

'containment': 'Spill Resistant', # This corresponds to pCnSp

'brand': $\text{'}$B$\text{'}$

$\}$

# Define utility calculation function

def calculate$\_$utility$($row$,$ product$)$:

utility $=0$

# Check if columns exist before accessing them

price$\_$key $=$ f$\text{'}$pPr$\{$product$["$price$"]\}\text{'}$

if price$\_$key in row:

utility $+=$ row$[$price$\_$key$]*$ row.get$(\text{'}$IPr$\text{'},0)$

time$\_$key $=$ f'pIn$\{$product$["$time$\_$insulated"$]\}\text{'}$

if time$\_$key in row:

utility $+=$ row$[$time$\_$key$]*$ row.get$(\text{'}$Iin$\text{'},0)$

capacity$\_$key $=$ f$\text{'}$pCp$\{$product$["$capacity$"]\}\text{'}$

if capacity$\_$key in row:

utility $+=$ row$[$capacity$\_$key$]*$ row.get$(\text{'}$ICp$\text{'},0)$

cleanability$\_$key $=$ f$\text{'}$pCl$\{$product$["$cleanability$"][0]\}\text{'}$

if cleanability$\_$key in row:

utility $+=$ row$[$cleanability$\_$key$]*$ row.get$(\text{'}$ICl$\text{'},0)$

containment$\_$key $=$ product$[\text{'}$containment$\text{'}].$replace$("","")$

containment$\_$key $=$ f$\text{'}$pCn$\{$containment$\_$key$\}\text{'}$ # Ensure it matches the column name

if containment$\_$key in row:

utility $+=$ row$[$containment$\_$key$]*$ row.get$(\text{'}$Icn$\text{'},0)$

brand$\_$key $=$ f$\text{'}$pBr$\{$product$["$brand$"]\}\text{'}$

if brand$\_$key in row:

utility $+=$ row$[$brand$\_$key$]*$ row.get$(\text{'}$IBr$\text{'},0)$

return utility

# Calculate utilities

data$[\text{'}$utility$\_$candidate'$]=$ data.apply$($calculate$\_$utility, product$=$candidate, axis$=1)$

data$[\text{'}$utility$\_$competitor$\_1\text{'}]=$ data.apply$($calculate$\_$utility, product$=$competitor$\_1,$ axis$=1)$

data$[\text{'}$utility$\_$competitor$\_2\text{'}]=$ data.apply$($calculate$\_$utility, product$=$competitor$\_2,$ axis$=1)$

# Calculate logit$-$adjusted probabilities

data$[\text{'}$exp$\_$utility$\_$candidate'$]=$ np$.$exp$($data$[\text{'}$utility$\_$candidate'$])$

data$[\text{'}$exp$\_$utility$\_$competitor$\_1\text{'}]=$ np$.$exp$($data$[\text{'}$utility$\_$competitor$\_1\text{'}])$

data$[\text{'}$exp$\_$utility$\_$competitor$\_2\text{'}]=$ np$.$exp$($data$[\text{'}$utility$\_$competitor$\_2\text{'}])$

data$[\text{'}$prob$\_$candidate'$]=$ data$[\text{'}$exp$\_$utility$\_$candidate'$]/($

data$[\text{'}$exp$\_$utility$\_$candidate'$]+$ data$[\text{'}$exp$\_$utility$\_$competitor$\_1\text{'}]+$ data$[\text{'}$exp$\_$utility$\_$competitor$\_2\text{'}]$

$)$

# Calculate the average market share

market$\_$share$\_$candidate $=$ data$[\text{'}$prob$\_$candidate'$].$mean$()$

# Calculate margin and expected profit per person

price$\_$candidate $=$ candidate$[\text{'}$price$\text{'}]$

margin$\_$candidate $=$ price$\_$candidate $-$ candidate$\_$cost

expected$\_$profit$\_$per$\_$person $=$ market$\_$share$\_$candidate $*$ margin$\_$candidate

# Output the results

print$($f$"$Market Share of Candidate: $\{$market$\_$share$\_$candidate$\}")$

print$($f$"$Margin of Candidate: $\{$margin$\_$candidate$\}")$

print$($f$"$Expected Profit per Person: $\{$expected$\_$profit$\_$per$\_$person$\}")$

# Check if expected profit per person is between $4$ and $4\mathrm{.}5$

if $4<=$ expected$\_$profit$\_$per$\_$person $<=4\mathrm{.}5$:

print$("$The expected profit per person is between $4$ and $4\mathrm{.}5.")$

else:

print$("$The expected profit per person is not between $4$ and $4\mathrm{.}5.")$

Please check the code and calculation. I need the expected profit per person is to be $4$