Hash a Categorical by hashing its categories, and then mapping the codes
    to the hashes

    Parameters
    ----------
    c : Categorical
    encoding : string, default 'utf8'
    hash_key : string key to encode, default to _default_hash_key

    Returns
    -------
    ndarray of hashed values array, same size as len(c)

def _hash_categorical(c, encoding, hash_key):
    """
    Hash a Categorical by hashing its categories, and then mapping the codes
    to the hashes

    Parameters
    ----------
    c : Categorical
    encoding : string, default 'utf8'
    hash_key : string key to encode, default to _default_hash_key

    Returns
    -------
    ndarray of hashed values array, same size as len(c)
    """
    # Convert ExtensionArrays to ndarrays
    values = np.asarray(c.categories.values)
    hashed = hash_array(values, encoding, hash_key,
                        categorize=False)

    # we have uint64, as we don't directly support missing values
    # we don't want to use take_nd which will coerce to float
    # instead, directly construct the result with a
    # max(np.uint64) as the missing value indicator
    #
    # TODO: GH 15362

    mask = c.isna()
    if len(hashed):
        result = hashed.take(c.codes)
    else:
        result = np.zeros(len(mask), dtype='uint64')

    if mask.any():
        result[mask] = np.iinfo(np.uint64).max

    return result