CSV Data Cleaning with Python/Pandas

Need help with the below scenario:

Excel data in csv source file is like:

I want to clean and rearrange it so that it looks like:

Adding the messy and cleaned dicts as requested

import pandas as pd
file_path = r"C:\Users\abcd\OneDrive\Documents"
messy_data_file_name = 'messy_data.csv'
cleaned_data_file_name = 'cleaned_Data.csv'
messy_df1 = pd.read_csv(file_path + '\\' + messy_data_file_name, skiprows=0)
messy_df1.to_dict()
{
'Company Name ': {0: 'Report Month',  1: 'Report Year',  2: nan,  3: nan,  4: nan,  5: nan,  6: nan,  7: nan,  8: nan,  9: nan}
'Toyota': {0: 'Jan ',	1: '2023',  2: nan,  3: nan,  4: nan,  5: nan,  6: nan,  7: nan,  8: nan,  9: nan},
'Unnamed: 2': {0: nan,  1: nan,  2: nan,  3: nan,  4: 'Total Inventory Cost',  5: 'Sold Inventory Cost',  6: 'Total Profit Incurred',  7: 'Total Manpower Expense',8: 'Total Infra Expense',9: 'Total Transaction Expense'},
'Unnamed: 3': {0: nan,  1: nan,  2: nan,  3: nan,  4: nan,  5: nan,  6: nan,  7: nan,  8: nan,  9: nan}, 'Unnamed: 4': {0: nan,  1: nan,  2: 'Sales in',  3: 'Ohio Showroom',  4: '344469',  5: '300690',  6: '43779',  7: '15000',  8: '500',9: '110'},
'Unnamed: 5': {0: nan,  1: nan,  2: 'Sales in',  3: 'Wincosin Showroom ',  4: '11261',  5: '9050',  6: '2211',  7: '1000',  8: '200',  9: '55'}, 'Unnamed: 6': {0: nan,  1: nan,  2: 'Service in',  3: 'Ohio Showroom',  4: '659923',  5: '612231',  6: '47692',  7: '12000',  8: '400',  9: '110'},
'Unnamed: 7': {0: nan,  1: nan,  2: 'Service in',  3: 'Wincosin Showroom ',  4: '15656',  5: '12812',  6: '2844',  7: '1200',  8: '250',  9: '45'}
}
###################################################
cleaned_df1 = pd.read_csv(file_path + '\\' + cleaned_data_file_name, skiprows=0)
cleaned_df1.to_dict()
{
'Company Name': {0: 'Toyota ',  1: 'Toyota ',  2: 'Toyota ',  3: 'Toyota ',  4: 'Toyota ',  5: 'Toyota '},
'Report Month': {0: 'January',  1: 'January',  2: 'January',  3: 'January',  4: 'January',  5: 'January'},
'Report Year ': {0: 2023, 1: 2023, 2: 2023, 3: 2023, 4: 2023, 5: 2023},
'Parameter': {0: 'Total Inventory Cost',  1: 'Sold Inventory Cost',  2: 'Total Profit Incurred',  3: 'Total Manpower Expense',  4: 'Total Infra Expense',  5: 'Total Transaction Expense'},
'Sales in Ohio Showroom': {0: 344469,  1: 300690,  2: 43779,  3: 15000,  4: 500,  5: 110}, 
'Sales in Wincosin Showroom': {0: 11261,  1: 9050,  2: 2211,  3: 1000,  4: 200,  5: 55},
'Service in Ohio Showroom': {0: 659923,  1: 612231,  2: 47692,  3: 12000,  4: 400,  5: 110},
'Service in Wincosin Showroom ': {0: 15656,  1: 12812,  2: 2844,  3: 1200,  4: 250,  5: 45}
}

you can do like this
read data from your csv file

df=pd.read_csv(r"C:\Users\Aparna\Downloads\messydata.csv",skiprows=5,header=None) 
# change location path of your file 
df1=df.dropna(axis=1)
# since your column names are not alligned correctly try to make new column names like this
df1.columns = ["Parameter", "Sales in Ohio Showroom", "Sales in Wincosin Showroom", "Service in Ohio Showroom","Service in Wincosin Showroom"]
df1["Company"]="Toyota"
df1["Report Month"]="January"
df1["Report Year"]=2023
df1 = df1.reindex(columns=["Company","Report Month","Report Year","Parameter", "Sales in Ohio Showroom", "Sales in Wincosin Showroom", "Service in Ohio Showroom","Service in Wincosin Showroom"])
# finally export dataframe in to csv file
df1.to_csv(r"C:\Users\abcd\OneDrive\Documents\file_path.csv")
output:[![enter image description here][1]][1]
[1]: https://i.stack.imgur.com/1hM5P.png
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