import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import FactorAnalysis

from sklearn.impute import SimpleImputer

from factor$\_$analyzer import Rotator

# Load the data

df $=$ pd$.$read$\_$csv$(\text{'}$C:$\backslash \backslash$csv$\text{'},$ encoding$=$'latin$1\text{'})$

# Selecting only numerical columns for factor analysis

numerical$\_$columns $=$ df$.$select$\_$dtypes$($include$=[$np$.$number$]).$columns.tolist$()$

# Dropping the $\text{'}$mrt$\_$flow' column if it exists in the numerical columns

if $\text{'}$mrt$\_$flow' in numerical$\_$columns:

numerical$\_$columns.remove$(\text{'}$mrt$\_$flow'$)$

# Update DataFrame to use only the selected numerical columns

df$\_$numerical $=$ df$[$numerical$\_$columns$]$

# Imputing missing values

imputer $=$ SimpleImputer$($strategy$=$'median'$)$

df$\_$numerical$\_$imputed $=$ imputer.fit$\_$transform$($df$\_$numerical$)$

# Standardizing the data

scaler $=$ StandardScaler$()$

df$\_$numerical$\_$scaled $=$ scaler.fit$\_$transform$($df$\_$numerical$\_$imputed$)$

# Performing factor analysis

fa $=$ FactorAnalysis$($n$\_$components$=5,$ random$\_$state$=0)$

fa$\_$components $=$ fa$.$fit$\_$transform$($df$\_$numerical$\_$scaled$)$

# Creating DataFrame for factor analysis results

fa$\_$df $=$ pd$.$DataFrame$($fa$\_$components, columns$=[$f$\text{'}$Factor$\{$i$+1\}\text{'}$ for i in range$($fa$.$n$\_$components$)])$

# Factor Analysis before rotation

fa $=$ FactorAnalysis$($n$\_$components$=5,$ random$\_$state$=0)$

fa$.$fit$($df$\_$numerical$\_$scaled$)$

# Factor loadings

loadings $=$ fa$.$components$\_$

# Plotting heatmap for factor loadings

plt$.$figure$($figsize$=(20,7))$

sns$.$heatmap$($pd$.$DataFrame$($fa$.$components$\_,$ columns$=$numerical$\_$columns$),$ annot$=$True, cmap$=$'coolwarm',

yticklabels$=[$f$\text{'}$Factor$\{$i$+1\}\text{'}$ for i in range$($fa$.$n$\_$components$)],$

xticklabels$=$numerical$\_$columns$)$

plt$.$title$(\text{'}$Factor Loadings Before Rotation'$)$

plt$.$xlabel$(\text{'}$Variables$\text{'})$

plt$.$ylabel$(\text{'}$Factors$\text{'})$

plt$.$xticks$($rotation$=45,$ ha$=$"right"$)$

plt$.$yticks$($rotation$=0)$

plt$.$tight$\_$layout$()$

plt$.$show$()$

columns $=$ df$\_$numerical.columns

# Applying Varimax rotation

rotator $=$ Rotator$($method$=$'varimax'$)$

loadings$\_$rotated $=$ rotator.fit$\_$transform$($loadings$)$

# Plotting the heatmap for rotated factor loadings

plt$.$figure$($figsize$=(20,7))$

sns$.$heatmap$($pd$.$DataFrame$($loadings$\_$rotated, columns$=$columns$),$

annot$=$True, cmap$=$'coolwarm',

yticklabels$=[$f$\text{'}$Factor $\{$i$+1\}\text{'}$ for i in range$($loadings$\_$rotated.shape$[0])],$

xticklabels$=$columns$)$

plt$.$title$(\text{'}$Factor Loadings After Varimax Rotation'$)$

plt$.$xlabel$(\text{'}$Variables$\text{'})$

plt$.$ylabel$(\text{'}$Rotated Factors'$)$

plt$.$xticks$($rotation$=45,$ ha$=$"right"$)$

plt$.$tight$\_$layout$()$

plt$.$show$()$

# Plotting bar plots for factor loadings

# Before rotation

loadings$\_$before$\_$rotation $=$ pd$.$DataFrame$($fa$.$components$\_,$ columns$=$numerical$\_$columns$)$

plt$.$figure$($figsize$=(12,6))$

for i in range$($loadings$\_$before$\_$rotation.shape$[0])$:

plt$.$bar$($numerical$\_$columns, loadings$\_$before$\_$rotation.iloc$[$i$],$ label$=$f'Factor$\{$i$+1\}\text{'})$

plt$.$title$(\text{'}$Factor Loadings Before Rotation'$)$

plt$.$xlabel$(\text{'}$Variables$\text{'})$

plt$.$ylabel$(\text{'}$Loadings$\text{'})$

plt$.$xticks$($rotation$=45,$ ha$=$"right"$)$

plt$.$legend$()$

plt$.$tight$\_$layout$()$

plt$.$show$()$

# After rotation

from sklearn.decomposition import PCA

pca $=$ PCA$($n$\_$components$=5)$

df$\_$numerical$\_$rotated $=$ pca.fit$\_$transform$($df$\_$numerical$\_$scaled$)$

loadings$\_$after$\_$rotation $=$ pd$.$DataFrame$($pca$.$components$\_,$ columns$=$numerical$\_$columns$)$

plt$.$figure$($figsize$=(12,6))$

for i in range$($loadings$\_$after$\_$rotation.shape$[0])$:

plt$.$bar$($numerical$\_$columns, loadings$\_$after$\_$rotation.iloc$[$i$],$ label$=$f'Factor$\{$i$+1\}\text{'})$

plt$.$title$(\text{'}$Factor Loadings After Rotation'$)$

plt$.$xlabel$(\text{'}$Variables$\text{'})$

plt$.$ylabel$(\text{'}$Loadings$\text{'})$

plt$.$xticks$($rotation$=45,$ ha$=$"right"$)$

plt$.$legend$()$

plt$.$tight$\_$layout$()$

plt$.$show$()$

please correct the code$($the code of factor loading after rotate is not correct$),$ and plot the bar charts of the factor loading$(1$ chart for unrotate $1$ chart for rotated$).$ The bar charts are ploted by the factor loading! please give me the complete codes. Thank you!$($Please do not do it if you are not familiar with factor analysis$)$