CLN: json_table_schema remove unused arg, annotate (#33375)

Author: jbrockmendel

pandas/io/json/_table_schema.py

@@ -6,6 +6,7 @@
 import warnings
 
 import pandas._libs.json as json
+from pandas._typing import DtypeObj
 
 from pandas.core.dtypes.common import (
     is_bool_dtype,
@@ -26,17 +27,17 @@
 loads = json.loads
 
 
-def as_json_table_type(x):
+def as_json_table_type(x: DtypeObj) -> str:
     """
     Convert a NumPy / pandas type to its corresponding json_table.
 
     Parameters
     ----------
-    x : array or dtype
+    x : np.dtype or ExtensionDtype
 
     Returns
     -------
-    t : str
+    str
         the Table Schema data types
 
     Notes
@@ -96,8 +97,8 @@ def set_default_names(data):
     return data
 
 
-def convert_pandas_type_to_json_field(arr, dtype=None):
-    dtype = dtype or arr.dtype
+def convert_pandas_type_to_json_field(arr):
+    dtype = arr.dtype
     if arr.name is None:
         name = "values"
     else:

pandas/tests/io/json/test_json_table_schema.py

@@ -103,19 +103,23 @@ class TestTableSchemaType:
     @pytest.mark.parametrize("int_type", [np.int, np.int16, np.int32, np.int64])
     def test_as_json_table_type_int_data(self, int_type):
         int_data = [1, 2, 3]
-        assert as_json_table_type(np.array(int_data, dtype=int_type)) == "integer"
+        assert as_json_table_type(np.array(int_data, dtype=int_type).dtype) == "integer"
 
     @pytest.mark.parametrize(
         "float_type", [np.float, np.float16, np.float32, np.float64]
     )
     def test_as_json_table_type_float_data(self, float_type):
         float_data = [1.0, 2.0, 3.0]
-        assert as_json_table_type(np.array(float_data, dtype=float_type)) == "number"
+        assert (
+            as_json_table_type(np.array(float_data, dtype=float_type).dtype) == "number"
+        )
 
     @pytest.mark.parametrize("bool_type", [bool, np.bool])
     def test_as_json_table_type_bool_data(self, bool_type):
         bool_data = [True, False]
-        assert as_json_table_type(np.array(bool_data, dtype=bool_type)) == "boolean"
+        assert (
+            as_json_table_type(np.array(bool_data, dtype=bool_type).dtype) == "boolean"
+        )
 
     @pytest.mark.parametrize(
         "date_data",
@@ -128,11 +132,11 @@ def test_as_json_table_type_bool_data(self, bool_type):
         ],
     )
     def test_as_json_table_type_date_data(self, date_data):
-        assert as_json_table_type(date_data) == "datetime"
+        assert as_json_table_type(date_data.dtype) == "datetime"
 
     @pytest.mark.parametrize("str_data", [pd.Series(["a", "b"]), pd.Index(["a", "b"])])
     def test_as_json_table_type_string_data(self, str_data):
-        assert as_json_table_type(str_data) == "string"
+        assert as_json_table_type(str_data.dtype) == "string"
 
     @pytest.mark.parametrize(
         "cat_data",
@@ -145,7 +149,7 @@ def test_as_json_table_type_string_data(self, str_data):
         ],
     )
     def test_as_json_table_type_categorical_data(self, cat_data):
-        assert as_json_table_type(cat_data) == "any"
+        assert as_json_table_type(cat_data.dtype) == "any"
 
     # ------
     # dtypes
@@ -189,7 +193,7 @@ def test_as_json_table_type_categorical_dtypes(self):
         # TODO: I think before is_categorical_dtype(Categorical)
         # returned True, but now it's False. Figure out why or
         # if it matters
-        assert as_json_table_type(pd.Categorical(["a"])) == "any"
+        assert as_json_table_type(pd.Categorical(["a"]).dtype) == "any"
         assert as_json_table_type(CategoricalDtype()) == "any"