Python pandas to_sql populates all rows with NA

I am new to Python and want to upload a Panda’s data frame into a table on Snowflake database. The desired behavior is to replace an existing table if it already exists. Here’s my code to do so:

#Import the required modules and packages
from snowflake.connector.pandas_tools import pandas
from snowflake.connector.pandas_tools import pd_writer
from snowflake.sqlalchemy import URL
import sqlalchemy
from sqlalchemy import create_engine
import os 

#import the data from a local csv
my_data_frame= pandas.read_csv("my_data_frame.csv" , sep=',' , low_memory=False)

#Connect to Snowflake
engine = create_engine(URL(
                        account = 'my_snowflake_account',
                        user = 'my_snowflake_id',
                        password = 'my_snowflake_password',
                        database = 'my_database',
                        schema = 'my_schema',
                        warehouse = 'my_warehouse',
                        role='my_role',
                      ))
connection = engine.connect()

#Push my local data frame into a new table
my_data_frame.to_sql('new_table_name_on_Snowflake', engine, 
                    index=False, method=pd_writer, if_exists='replace')

The code runs. It creates a table and assignes the correct table names in it. However all rows for all columns are populated with NA. I suspect it has to do with data types. How may I resolve this?

Please note that I specify the method=pd_writer as suggested in Snowflakes’ documentation:
https://docs.snowflake.com/en/user-guide/python-connector-api.html#pd_writer

When inspecting the my_data_fame with my_data_frame.dtypes it returns:

column1             object
column2             int64
column3             object
column4             object
column5             object
column6             object
column7             int64
column8             int64