Is there a way to load a sql query in a pandas >= 1.0.0 dataframe using Int64 instead of float?
Have you tried using 
select isnull(col_name,0) from table_name. This converts all null values to 0.
Integers are automatically cast to float values just as boolean values are cast to objects when some values are n/a.
When loading the output of query into a DataFrame using pandas, the standard behavior was to convert integer fields containing NULLs to float so that NULLs would became NaN. 
Starting with pandas 1.0.0, they included a new type called pandas.NA to deal with integer columns having NULLs. However, when using pandas.read_sql(), the integer columns are still being transformed in float instead of integer when NULLs are present. Added to that, the read_sql() method doesn't support the dtype parameter to coerce fields, like read_csv().
Is there a way to load integer columns from a query directly into a Int64 dtype instead of first coercing it first to float and then having to manually covert it to Int64?