Someone will give you a better answe than this possibly, but one thing I tend to do is if all my numeric data are int64 or float64 objects, then you can create a dict of the column data types and then use the values to create your list of columns.
So for example, in a dataframe where I have columns of type float64, int64 and object firstly you can look at the data types as so:
DF.dtypes
and if they conform to the standard whereby the non-numeric columns of data are all object types (as they are in my dataframes), then you can do the following to get a list of the numeric columns:
[key for key in dict(DF.dtypes) if dict(DF.dtypes)[key] in ['float64', 'int64']]
Its just a simple list comprehension. Nothing fancy. Again, though whether this works for you will depend upon how you set up you dataframe…