Use more, cheaper dtype checking utilities in cudf Python

[mroeschke]

Description

Avoids using potentially more expensive dtype checking utilities referenced in #12494

is_string_dtype -> == CUDF_STRING_DTYPE
is_decimal_dtype -> isinstance
is_numeric_dtype -> (new) is_dtype_obj_numeric

In [1]: import numpy as np

In [2]: from cudf.api.types import is_numeric_dtype

In [3]: from cudf.utils.dtypes import is_dtype_obj_numeric

In [4]: dtype = np.dtype(np.int64)

In [5]: %timeit is_dtype_obj_numeric(dtype)
211 ns ± 2.26 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

In [6]: %timeit is_numeric_dtype(dtype)
1.14 μs ± 2.61 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

Also standardizes some imports from cudf.api.types

Checklist

• I am familiar with the Contributing Guidelines.
• New or existing tests cover these changes.
• The documentation is up to date with these changes.

[mroeschke]

/merge

[vyasr]

/merge