import pandas as pd

import numpy as np

from scipy.stats import pearsonr

import matplotlib.pyplot as plt

# Read data files

house$\_$prices$\_$df $=$ pd$.$read$\_$excel$("$MeanHousePricesClean$-1.$xlsx$")$

crime$\_$df $=$ pd$.$read$\_$excel$("$CrimeClean$-1-1.$xlsx$")$

population$\_$df $=$ pd$.$read$\_$excel$("$PopulationClean$.$xlsx$")$

area$\_$df $=$ pd$.$read$\_$excel$("$SuburbAreas$-1.$xlsx$")$

# Step B: Clean and prepare data

def prepare$\_$data$($df$,$ columns$)$:

df $=$ df$.$dropna$($subset$=$columns$)$ # Remove rows with missing values in key columns

return df

# Rename columns for consistency

house$\_$prices$\_$df $=$ house$\_$prices$\_$df$.$rename$($columns$=\{\text{'}$Year$\text{'}$: 'year'$\})$

crime$\_$df $=$ crime$\_$df$.$rename$($columns$=\{$

'Year': 'year',

'Crime rate per $100,000$ population': 'crime$\_$rate',

'Local Government Area': 'local$\_$government$\_$area'

$\})$

population$\_$df $=$ population$\_$df$.$rename$($columns$=\{\text{'}$Year$\text{'}$: 'year'$\})$

area$\_$df $=$ area$\_$df$.$rename$($columns$=\{\text{'}$Property$\text{'}$: 'local$\_$government$\_$area'$\})$

# Clean the area DataFrame to remove non$-$relevant rows

area$\_$df $=$ area$\_$df$[$area$\_$df$[\text{'}$local$\_$government$\_$area'$]!=$ 'Area sq Km$\text{'}]$

# Step C: Analysis functions

def analyze$\_$correlation$($df$,$ col$1,$ col$2)$:

df $=$ df$.$dropna$($subset$=[$col$1,$ col$2])$

if len$($df$)<mtext\; id="EditorElement\_533950"></mtext><mn\; id="EditorElement\_533951">2</mn>$:

return np$.$nan

correlation, $\_=$ pearsonr$($df$[$col$1],$ df$[$col$2])$

return correlation

# Reshape house$\_$prices$\_$df to long format

house$\_$prices$\_$long $=$ house$\_$prices$\_$df$.$melt$($id$\_$vars$=[\text{'}$year$\text{'}],$ var$\_$name$=$'local$\_$government$\_$area', value$\_$name$=$'mean$\_$house$\_$price'$)$

# Reshape population$\_$df to long format

population$\_$long $=$ population$\_$df$.$melt$($id$\_$vars$=[\text{'}$year$\text{'}],$ var$\_$name$=$'local$\_$government$\_$area', value$\_$name$=$'population'$)$

# Reshape area$\_$df to long format

area$\_$long $=$ area$\_$df$.$melt$($id$\_$vars$=[\text{'}$local$\_$government$\_$area'$],$ var$\_$name$=$'year', value$\_$name$=$'area'$)$

# Merge the datasets on 'year' and 'local$\_$government$\_$area'

merged$\_$df $=$ pd$.$merge$($crime$\_$df$,$ house$\_$prices$\_$long, on$=[\text{'}$year$\text{'},$ 'local$\_$government$\_$area'$],$ how$=$'inner'$)$

merged$\_$df $=$ pd$.$merge$($merged$\_$df$,$ population$\_$long, on$=[\text{'}$year$\text{'},$ 'local$\_$government$\_$area'$],$ how$=$'inner'$)$

merged$\_$df $=$ pd$.$merge$($merged$\_$df$,$ area$\_$long, on$=$'local$\_$government$\_$area', how$=$'inner'$)$

# Calculate population density

merged$\_$df$[\text{'}$population$\_$density'$]=$ merged$\_$df$[\text{'}$population$\text{'}]/$ merged$\_$df$[\text{'}$area$\text{'}]$

# Step D: Prepare the data by cleaning

merged$\_$df $=$ prepare$\_$data$($merged$\_$df$,[\text{'}$mean$\_$house$\_$price', 'crime$\_$rate', 'population$\_$density'$])$

# Step E: Perform correlation analysis

house$\_$price$\_$population$\_$corr $=$ analyze$\_$correlation$($merged$\_$df$,$ 'mean$\_$house$\_$price', 'population$\_$density'$)$

crime$\_$house$\_$price$\_$corr $=$ analyze$\_$correlation$($merged$\_$df$,$ 'crime$\_$rate', 'mean$\_$house$\_$price'$)$

crime$\_$population$\_$density$\_$corr $=$ analyze$\_$correlation$($merged$\_$df$,$ 'crime$\_$rate', 'population$\_$density'$)$

# Step F: Print the results

print$($f$"$Correlation between house prices and population density: $\{$house$\_$price$\_$population$\_$corr$\}")$

print$($f$"$Correlation between crime rate and house prices: $\{$crime$\_$house$\_$price$\_$corr$\}")$

print$($f$"$Correlation between crime rate and population density: $\{$crime$\_$population$\_$density$\_$corr$\}")$

# Plotting for visual analysis

plt$.$figure$($figsize$=(10,6))$

plt$.$scatter$($merged$\_$df$[\text{'}$population$\_$density'$],$ merged$\_$df$[\text{'}$mean$\_$house$\_$price'$])$

plt$.$title$(\text{'}$House Price vs Population Density'$)$

plt$.$xlabel$(\text{'}$Population Density $($people per square km$)\text{'})$

plt$.$ylabel$(\text{'}$Mean House Price'$)$

plt$.$grid$($True$)$

plt$.$show$()$

plt$.$figure$($figsize$=(10,6))$

plt$.$scatter$($merged$\_$df$[\text{'}$mean$\_$house$\_$price'$],$ merged$\_$df$[\text{'}$crime$\_$rate'$])$

plt$.$title$(\text{'}$Crime Rate vs House Price'$)$

plt$.$xlabel$(\text{'}$Mean House Price'$)$

plt$.$ylabel$(\text{'}$Crime Rate $($per $100,000$ population$)\text{'})$

plt$.$grid$($True$)$

plt$.$show$()$

plt$.$figure$($figsize$=(10,6))$

plt$.$scatter$($merged$\_$df$[\text{'}$population$\_$density'$],$ merged$\_$df$[\text{'}$crime$\_$rate'$])$

plt$.$title$(\text{'}$Crime Rate vs Population Density'$)$

plt$.$xlabel$(\text{'}$Population Density $($people per square km$)\text{'})$

plt$.$ylabel$(\text{'}$Crime Rate $($per $100,000$ population$)\text{'})$

plt$.$grid$($True$)$

plt$.$show$()$

The above python code is run in google colab and it produces nil output as nan $.$ modify and correct the code and add some more correlation if it helps to achieve my output. chegg expert gave me the code above, it is running free of errors but my plots are nil and the corelation is shown as Nan. help me$.$ i have access to all the $4$ excel sheets of data as attached. something is wrong since it keeps on producing Nan