I am working with a pandas DataFrame that contains data structured in a certain way. I need to transform it to a new structure with the following columns:

my desired output

my_current_file

I have started to write a function to transform the DataFrame, but I need help to adjust it so that it matches the desired output format.

def transform_table(df):
    # Check if the DataFrame is valid or has the expected structure
    if df.empty or df.shape[1] < 10:
        print("DataFrame is empty or does not have enough columns.")
        return pd.DataFrame()  # Return an empty DataFrame if input is invalid

    # Extract the company name and other data
    company_name = df.iloc[0, 2]  # Get the company name from the first row, third column

    # Create a list to store the output data
    out_data = []
    out_data.append(['indicator name', 'channels', 'gender', 'region', 'age', 'date', 'value', 'company'])

    # Loop through the rows (starting from row 6 in the original array, which is index 5 in 0-based indexing)
    for idx, row in df.iloc[5:0].iterrows():
        for v in range(0, 3):  # Loop through the 3 quarters
            out_data.append([
                "Number of Vehicles", row[5], row[4], row[2], row[1], df.iloc[3, 6 + v * 4], df.iloc[idx, 6 + v * 4], company_name
            ])
            out_data.append([
                "Total Premium excluding commission", row[5], row[4], row[2], row[1],df.iloc[3, 6 + v * 4], df.iloc[idx, 7 + v * 4], company_name
            ])
            out_data.append([
                "Commission amount", row[5], row[4], row[2], row[1], df.iloc[3, 6 + v * 4], df.iloc[idx, 8 + v * 4], company_name
            ])
            out_data.append([
                "Total Premium including commission", row[5], row[4], row[2], row[1], df.iloc[3, 6 + v * 4], df.iloc[idx, 9 + v * 4], company_name
            ])

    return pd.DataFrame(out_data) 

the ouput from the code above

Question:
How can I adjust my existing code to produce the desired output format? Any suggestions to help achieve this transformation effectively would be greatly appreciated!