Pandas to_sql with sqlAlchemy duplicate entries error in mysqldb

Not sure if you found an answer but here's a workaround that worked for me:

call the .to_sql() on a temporary table then use a query to update the main table with the temp table. Then you can drop the temp table. So for example:

df.to_sql(con=con, name='tablename_temp', if_exists='replace')
connection = con.connect()
connection.execute(text("INSERT INTO tablename SELECT * FROM tablename_temp ON DUPLICATE KEY UPDATE tablename.field_to_update=tablename_temp.field_to_update"))
connection.execute(text('DROP TABLE tablename_temp '))

Here is what I ended up doing:

    #df is a dataframe
    num_rows = len(df)
    #Iterate one row at a time
    for i in range(num_rows):
        try:
            #Try inserting the row
            df.iloc[i:i+1].to_sql(name="Table_Name",con = Engine_Name,if_exists = 'append',index=False)
        except IntegrityError:
            #Ignore duplicates
            pass

I know this post is 10 years old but it is the first in all my searches and I finally found a, I think, better solution:

from sqlalchemy.dialects.mysql import insert

def insert_ignore(table, conn, keys, data_iter):
    df = pd.DataFrame(data_iter,columns=keys)
    insert_stmt = insert(table.table).values(df)
    ignore_stmt = insert_stmt.prefix_with('IGNORE')
    conn.execute(ignore_stmt)

Then, call it with method= in df.to_sql

df.to_sql("sometable",connection,if_exists="append",index=False, 
method=insert_ignore)