the code and the output is attached as below. Give me some paragraphs on discussion. here is the discussion format you should give me in$.$ it should be detailed and in standard format such as for a research report.

DIscussion:

Analysis and Discussion

The analysis will include:

Detailed discussion of the correlation coefficients and scatter plots.

Comparison of findings with existing research and theoretical expectations.

Discussion of anomalies and outliers, providing plausible explanations for any deviations from expected trends.

Code:

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# 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$",$ header$=$None$)$

# Transform area$\_$df to long format

area$\_$df$.$columns $=$ area$\_$df$.$iloc$[0]$ # Set the first row as the header

area$\_$df $=$ area$\_$df$[1$:$]$ # Remove the first row from the dataframe

area$\_$df $=$ area$\_$df$.$set$\_$index$(\text{'}$Property$\text{'}).$transpose$().$reset$\_$index$()$

area$\_$df$.$columns $=[\text{'}$local$\_$government$\_$area', 'area$\_$sq$\_$km$\text{'}]$

# Convert 'area$\_$sq$\_$km$\text{'}$ to numeric

area$\_$df$[\text{'}$area$\_$sq$\_$km$\text{'}]=$ pd$.$to$\_$numeric$($area$\_$df$[\text{'}$area$\_$sq$\_$km$\text{'}],$ errors$=$'coerce'$)$

# Rename columns in house$\_$prices$\_$df and crime$\_$df to ensure consistent naming

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

crime$\_$df $=$ crime$\_$df$.$rename$($columns$=\{\text{'}$Year$\text{'}$: 'year', 'Local Government Area': 'local$\_$government$\_$area',

'Incidents recorded': 'incidents$\_$recorded',

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

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

# Function to normalize LGA names

def normalize$\_$lga$\_$names$($df$,$ lga$\_$column$)$:

if lga$\_$column in df$.$columns:

df$[$lga$\_$column$]=$ df$[$lga$\_$column$].$astype$($str$).$str$.$strip$().$str$.$replace$(\text{'}$Shire$\text{'},\text{'}\text{'}).$str$.$replace$(\text{'}$City$\text{'},\text{'}\text{'}).$str$.$strip$()$

return df

# Normalize LGA names in all relevant DataFrames

house$\_$prices$\_$df $=$ normalize$\_$lga$\_$names$($house$\_$prices$\_$df$,$ 'local$\_$government$\_$area'$)$

crime$\_$df $=$ normalize$\_$lga$\_$names$($crime$\_$df$,$ 'local$\_$government$\_$area'$)$

for col in population$\_$df$.$columns$[1$:$]$:

population$\_$df $=$ normalize$\_$lga$\_$names$($population$\_$df$,$ col$)$

area$\_$df $=$ normalize$\_$lga$\_$names$($area$\_$df$,$ 'local$\_$government$\_$area'$)$

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

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

# Normalize 'local$\_$government$\_$area' column in house$\_$prices$\_$long$\_$df

house$\_$prices$\_$long$\_$df $=$ normalize$\_$lga$\_$names$($house$\_$prices$\_$long$\_$df$,$ 'local$\_$government$\_$area'$)$

# Transform population$\_$df to long format

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

# Normalize 'local$\_$government$\_$area' column in population$\_$long$\_$df

population$\_$long$\_$df $=$ normalize$\_$lga$\_$names$($population$\_$long$\_$df$,$ 'local$\_$government$\_$area'$)$

# Merge DataFrames

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

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

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

# Convert 'population' to numeric

merged$\_$df$[\text{'}$population$\text{'}]=$ pd$.$to$\_$numeric$($merged$\_$df$[\text{'}$population$\text{'}],$ errors$=$'coerce'$)$

# Calculate population density

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

# Calculate correlations

correlation$\_$house$\_$prices$\_$population$\_$density $=$ merged$\_$df$[\text{'}$house$\_$price'$].$corr$($merged$\_$df$[\text{'}$population$\_$density'$])$

correlation$\_$crime$\_$rate$\_$house$\_$prices $=$ merged$\_$df$[\text{'}$crime$\_$rate'$].$corr$($merged$\_$df$[\text{'}$house$\_$price'$])$

correlation$\_$crime$\_$rate$\_$population$\_$density $=$ merged$\_$df$[\text{'}$crime$\_$rate'$].$corr$($merged$\_$df$[\text{'}$population$\_$density'$])$

# Print correlations

print$("$Correlation between house prices and population density:", correlation$\_$house$\_$prices$\_$population$\_$density$)$

print$("$Correlation between crime rate and house prices:", correlation$\_$crime$\_$rate$\_$house$\_$prices$)$

print$("$Correlation between crime rate and population density:",

the output is in the image. add intext citation while using other research paper where this hypothesis was discussed and provide apa reference at the end. dont answer if all the above criteria are not met.