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Reproducible Example

from sqlalchemy import types
dtypezzz = { 
 'date':types.Date(), 
 'bus_plates':types.Text(), 
 'driver_names':types.Text(), 
 'behavior':types.Text()
 }
df_bus.to_sql('mytable', 
 con=engine, schema='myschema', 
 if_exists='append', 
 index=False, 
 dtype=dtypezzz)
# df_bus have columns of bus_id and bus_plates

Issue Description

I spend time reading articles on how to ensure the datatype of dataframe is followed by the table datatype by using method .to_sql(). Most of it said that we can use the dtype argument in the method and put in a dict of column name and data type using sqlAlchemy from sqlalchemy import type.

However, I have tested this, to see if the method return an error by passing a dataframe that has totally different column name and datatype. My expectation is it should return an error, but instead of returning error due to wrong column name or data type, it allowed the dataframe to be inserted within the database table. This is very bad as I cannot ensure the data integrity within the table and because of the nature of this method that replace and alter the datatype, it could lead to many issue like wrongly inserted dataframe in the table and wrong datatype inserted without returning a useful error.

Expected Behavior

Expected behavior should be the to_sql() with dtype argument used will return error if dataframe has different column name/datatype to ensure the data integrity within the database table (this is possible in SQL as of we declare the datatype during the table creation)

Installed Versions