is there a way to set colum names and datatypes in a more dynamic way? So if i wanna reuse the code with a different table i dont have to change the whole code?

i already tried to set a placeholder for columns but it ran into severall errors.
i also defined in the code that it will check the metadata before and convert my hana datatypes into spark datatypes

# Update Lakehouse Table
if initialLoad:
    df = pd.DataFrame(BWDataInit, columns=columns)
    print(f"DataFrame columns: {df.columns}")  # Debug output
    if 'kennzahl' in df.columns:
        df['kennzahl'] = pd.to_numeric(df['kennzahl'], errors='coerce')  # Convert datatype of 'kennzahl' to float
    else:
        print("Error: 'kennzahl' column not found in the data.")
    if 'recordmode' in df.columns:
        df = df.drop(['recordmode'], axis=1)
    # Save initial load to SQL table
    append_records(df, DBWRITESCHEMA, DBWRITETABLE)
    # Get requests from BWData
    dfRequests = pd.DataFrame(BWDataRequest, columns=['REQTSN'])
    # Update last Request table
    update_last_request_table(dfRequests, DBWRITESCHEMA, DBWRITETABLE)
else:
    df = pd.DataFrame(BWDataChange, columns=columns)
    print(f"DataFrame columns: {df.columns}")  # Debug output
    if 'kennzahl' in df.columns:
        df['kennzahl'] = pd.to_numeric(df['kennzahl'], errors='coerce')  # Convert datatype of 'kennzahl' to float
    else:
        print("Error: 'kennzahl' column not found in the data.")
    # Get list of all new Requests in Change Log
    dfNewReq = df['reqtsn'].unique()
    dfNewReq = pd.DataFrame(dfNewReq, columns=["REQTSN"])

    # Set Key Columns for Join Condition
    colsKey = ['name']

    # Set KPI Columns for Join
    colsKPI = ['kennzahl']

    if not dfNewReq.empty:
        # Read content of SQL Table
        sdfTable = spark.read.table(f"{DBWRITESCHEMA}.{DBWRITETABLE}")
        # Ensure 'kennzahl' column in Spark DataFrame has the same datatype as in Pandas DataFrame
        sdfTable = sdfTable.withColumn('kennzahl', sdfTable['kennzahl'].cast('float'))
        # Update SQL table for each request and record mode
        for request in dfNewReq['REQTSN']:
            # Select only new records (Recordmode = N)
            dfNew = df[(df['reqtsn'] == request) & (df['recordmode'] == "N")]        
            if not dfNew.empty:
                # Drop support columns
                dfNew = dfNew.drop(['reqtsn', 'datapakid', 'record', 'recordmode'], axis=1)
                # Convert pandas df to Spark df
                sdfNew = spark.createDataFrame(dfNew)
                # Append new data to old data
                sdfTable = sdfTable.union(sdfNew)

            # Update changed records (Recordmode = "")
            dfUpdate = df[(df['reqtsn'] == request) & (df['recordmode'] == "")]
            if not dfUpdate.empty:
                # Drop unnecessary data left over from changelog structure
                dfUpdate = dfUpdate.drop(['reqtsn', 'datapakid', 'record', 'recordmode'], axis=1)
                # Convert pandas to spark df
                sdfUpdate = spark.createDataFrame(dfUpdate)
                # Inner join sdfTable with sdfUpdate
                sdfTable = sdfTable.alias('old').join(
                    sdfUpdate.alias('new'), colsKey, how='inner'
                )
                for col in colsKPI:
                    sdfTable = sdfTable.withColumn(f"old.{col}", col("new.{col}"))

            # Delete old records (Recordmode = R)
            dfDelete = df[(df['reqtsn'] == request) & (df['recordmode'] == "R")]
            if not dfDelete.empty:
                # Drop unnecessary data left over from changelog structure
                dfDelete = dfDelete.drop(['reqtsn', 'datapakid', 'record', 'recordmode'], axis=1)
                # Convert pandas to spark df
                sdfDelete = spark.createDataFrame(dfDelete)
                # Anti join sdfTable with sdfDelete to remove all records
                sdfTable = sdfTable.alias('old').join(
                    sdfDelete.alias('new'), colsKey, how='left_anti'
                )

    # Overwrite old content of SQL Table with updated Spark df
    update_last_request_table(dfNewReq, DBWRITESCHEMA, DBWRITETABLE)
    sdfTable.write.mode("overwrite").format("delta").saveAsTable(f"{DBWRITESCHEMA}.{DBWRITETABLE}")


#print result table
print("Result Table:")
spark.read.table("LHS_SAP.tbl_rbr_01").sort("Name").show()