To go one step further, I assume you want to do something with these dtypes.
df.dtypes.to_dict() comes in handy.
my_type = 'float64' #<---
dtypes = dataframe.dtypes.to_dict()
for col_nam, typ in dtypes.items():
if (typ != my_type): #<---
raise ValueError(f"Yikes - `dataframe['{col_name}'].dtype == {typ}` not {my_type}")
You'll find that Pandas did a really good job comparing NumPy classes and user-provided strings. For example: even things like 'double' == dataframe['col_name'].dtype will succeed when .dtype==np.float64.