ENH: Add Timedelta Support to JSON Reader with orient=table (#21140)

Author: fjdiod

pandas/io/json/table_schema.py

@@ -6,7 +6,7 @@
 import warnings
 
 import pandas._libs.json as json
-from pandas import DataFrame
+from pandas import DataFrame, Timedelta
 from pandas.api.types import CategoricalDtype
 import pandas.core.common as com
 from pandas.core.dtypes.common import (
@@ -163,7 +163,7 @@ def convert_json_field_to_pandas_type(field):
     elif typ == 'boolean':
         return 'bool'
     elif typ == 'duration':
-        return 'timedelta64'
+        return 'timedelta64[ns]'
     elif typ == 'datetime':
         if field.get('tz'):
             return 'datetime64[ns, {tz}]'.format(tz=field['tz'])
@@ -306,10 +306,9 @@ def parse_table_schema(json, precise_float):
         raise NotImplementedError('table="orient" can not yet read timezone '
                                   'data')
 
-    # No ISO constructor for Timedelta as of yet, so need to raise
-    if 'timedelta64' in dtypes.values():
-        raise NotImplementedError('table="orient" can not yet read '
-                                  'ISO-formatted Timedelta data')
+    for col, dtype in dtypes.items():
+        if dtype == 'timedelta64[ns]':
+            df[col] = df[col].apply(Timedelta)
 
     df = df.astype(dtypes)
 

pandas/tests/io/json/test_json_table_schema.py

@@ -390,7 +390,7 @@ def test_convert_pandas_type_to_json_field_categorical(self, kind,
         ({'type': 'integer'}, 'int64'),
         ({'type': 'number'}, 'float64'),
         ({'type': 'boolean'}, 'bool'),
-        ({'type': 'duration'}, 'timedelta64'),
+        ({'type': 'duration'}, 'timedelta64[ns]'),
         ({'type': 'datetime'}, 'datetime64[ns]'),
         ({'type': 'datetime', 'tz': 'US/Hawaii'}, 'datetime64[ns, US/Hawaii]'),
         ({'type': 'any'}, 'object'),
@@ -499,6 +499,7 @@ class TestTableOrientReader(object):
         'level_0'])
     @pytest.mark.parametrize("vals", [
         {'ints': [1, 2, 3, 4]},
+        {'timedeltas': pd.timedelta_range('1H', periods=4, freq='T')},
         {'objects': ['a', 'b', 'c', 'd']},
         {'date_ranges': pd.date_range('2016-01-01', freq='d', periods=4)},
         {'categoricals': pd.Series(pd.Categorical(['a', 'b', 'c', 'c']))},
@@ -516,7 +517,6 @@ def test_read_json_table_orient(self, index_nm, vals, recwarn):
     @pytest.mark.parametrize("index_nm", [
         None, "idx", "index"])
     @pytest.mark.parametrize("vals", [
-        {'timedeltas': pd.timedelta_range('1H', periods=4, freq='T')},
         {'timezones': pd.date_range('2016-01-01', freq='d', periods=4,
                                     tz='US/Central')}])
     def test_read_json_table_orient_raises(self, index_nm, vals, recwarn):
@@ -530,7 +530,7 @@ def test_comprehensive(self):
             {'A': [1, 2, 3, 4],
              'B': ['a', 'b', 'c', 'c'],
              'C': pd.date_range('2016-01-01', freq='d', periods=4),
-             # 'D': pd.timedelta_range('1H', periods=4, freq='T'),
+             'D': pd.timedelta_range('1H', periods=4, freq='T'),
              'E': pd.Series(pd.Categorical(['a', 'b', 'c', 'c'])),
              'F': pd.Series(pd.Categorical(['a', 'b', 'c', 'c'],
                                            ordered=True)),