pandas to_sql all columns as nvarchar

You can create this dict dynamically if you do not know the column names in advance:

from sqlalchemy.types import NVARCHAR
df.to_sql(...., dtype={col_name: NVARCHAR for col_name in df})

Note that you have to pass the sqlalchemy type object itself (or an instance to specify parameters like NVARCHAR(length=10)) and not a string as in your example.

To use dtype, pass a dictionary keyed to each data frame column with corresponding sqlalchemy types. Change keys to actual data frame column names:

import sqlalchemy
import pandas as pd
...

column_errors.to_sql('load_errors',push_conn, 
                      if_exists = 'append', 
                      index = False, 
                      dtype={'datefld': sqlalchemy.DateTime(), 
                             'intfld':  sqlalchemy.types.INTEGER(),
                             'strfld': sqlalchemy.types.NVARCHAR(length=255)
                             'floatfld': sqlalchemy.types.Float(precision=3, asdecimal=True)
                             'booleanfld': sqlalchemy.types.Boolean})

You may even be able to dynamically create this dtype dictionary given you do not know column names or types beforehand:

def sqlcol(dfparam):    
    
    dtypedict = {}
    for i,j in zip(dfparam.columns, dfparam.dtypes):
        if "object" in str(j):
            dtypedict.update({i: sqlalchemy.types.NVARCHAR(length=255)})
                                 
        if "datetime" in str(j):
            dtypedict.update({i: sqlalchemy.types.DateTime()})

        if "float" in str(j):
            dtypedict.update({i: sqlalchemy.types.Float(precision=3, asdecimal=True)})

        if "int" in str(j):
            dtypedict.update({i: sqlalchemy.types.INT()})

    return dtypedict

outputdict = sqlcol(df)    
column_errors.to_sql('load_errors', 
                     push_conn, 
                     if_exists = 'append', 
                     index = False, 
                     dtype = outputdict)