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Fix JSON orient='table' issues with numeric column names #25488

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Fix JSON orient='table' issues with numeric column names #25488

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e6f7969
Tests to be fixed
Feb 28, 2019
c88affc
Fix JSON orient='table' issues for numeric column names (#19129) (#22…
Feb 28, 2019
e7abdb1
Fix tests
Feb 28, 2019
11930e5
Merge branch 'master' into fix-19129
Mar 2, 2019
953c128
Merge branch 'master' into fix-19129
Mar 3, 2019
82d486e
Change JSON schema version number
Mar 3, 2019
e183094
Fix typo table='orient' to orient='table'
Mar 3, 2019
3332fec
Parse table schema according to the version
Mar 3, 2019
36d9350
Set values as default orient for JSONTableWriter
Mar 3, 2019
3123dc6
Fix minor typos
Mar 3, 2019
3345119
Test read_json for table generated by version 0.20.0
Mar 3, 2019
1465e39
Merge branch 'master' into fix-19129
Apr 20, 2019
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7 changes: 6 additions & 1 deletion pandas/io/json/json.py
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Original file line number Diff line number Diff line change
Expand Up @@ -211,7 +211,7 @@ def __init__(self, obj, orient, date_format, double_precision,
else:
self.obj = obj.reset_index(drop=False)
self.date_format = 'iso'
self.orient = 'records'
self.orient = 'values'
self.index = index

def _write(self, obj, orient, double_precision, ensure_ascii,
Expand All @@ -221,6 +221,11 @@ def _write(self, obj, orient, double_precision, ensure_ascii,
ensure_ascii, date_unit,
iso_dates,
default_handler)
# add column names
column_names = dumps(obj.columns)
if len(data) > 2:
column_names = column_names + ','
data = data[0] + column_names + data[1:]
serialized = '{{"schema": {schema}, "data": {data}}}'.format(
schema=dumps(self.schema), data=data)
return serialized
Expand Down
5 changes: 3 additions & 2 deletions pandas/io/json/table_schema.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,8 +296,8 @@ def parse_table_schema(json, precise_float):
pandas.read_json
"""
table = loads(json, precise_float=precise_float)
col_order = [field['name'] for field in table['schema']['fields']]
df = DataFrame(table['data'], columns=col_order)[col_order]
col_order = table['data'][0]
df = DataFrame(table['data'][1:], columns=col_order)[col_order]

dtypes = {field['name']: convert_json_field_to_pandas_type(field)
for field in table['schema']['fields']}
Expand All @@ -322,5 +322,6 @@ def parse_table_schema(json, precise_float):
else:
df.index.names = [None if x.startswith('level_') else x for x in
df.index.names]
df.columns = df.columns.values.tolist()

return df
65 changes: 20 additions & 45 deletions pandas/tests/io/json/test_json_table_schema.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,8 +208,8 @@ def test_build_series(self):

expected = OrderedDict([
('schema', schema),
('data', [OrderedDict([('id', 0), ('a', 1)]),
OrderedDict([('id', 1), ('a', 2)])])])
('data', [['id', 'a'], [0, 1], [1, 2]])
])
assert result == expected

def test_to_json(self):
Expand Down Expand Up @@ -243,32 +243,15 @@ def test_to_json(self):
'fields': fields,
'primaryKey': ['idx'],
}
data = [
OrderedDict([('idx', 0), ('A', 1), ('B', 'a'),
('C', '2016-01-01T00:00:00.000Z'),
('D', 'P0DT1H0M0S'),
('E', 'a'), ('F', 'a'), ('G', 1.),
('H', '2016-01-01T06:00:00.000Z')
]),
OrderedDict([('idx', 1), ('A', 2), ('B', 'b'),
('C', '2016-01-02T00:00:00.000Z'),
('D', 'P0DT1H1M0S'),
('E', 'b'), ('F', 'b'), ('G', 2.),
('H', '2016-01-02T06:00:00.000Z')
]),
OrderedDict([('idx', 2), ('A', 3), ('B', 'c'),
('C', '2016-01-03T00:00:00.000Z'),
('D', 'P0DT1H2M0S'),
('E', 'c'), ('F', 'c'), ('G', 3.),
('H', '2016-01-03T06:00:00.000Z')
]),
OrderedDict([('idx', 3), ('A', 4), ('B', 'c'),
('C', '2016-01-04T00:00:00.000Z'),
('D', 'P0DT1H3M0S'),
('E', 'c'), ('F', 'c'), ('G', 4.),
('H', '2016-01-04T06:00:00.000Z')
]),
]
data = [['idx', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
[0, 1, 'a', '2016-01-01T00:00:00.000Z', 'P0DT1H0M0S', 'a', 'a',
1., '2016-01-01T06:00:00.000Z'],
[1, 2, 'b', '2016-01-02T00:00:00.000Z', 'P0DT1H1M0S', 'b', 'b',
2., '2016-01-02T06:00:00.000Z'],
[2, 3, 'c', '2016-01-03T00:00:00.000Z', 'P0DT1H2M0S', 'c', 'c',
3., '2016-01-03T06:00:00.000Z'],
[3, 4, 'c', '2016-01-04T00:00:00.000Z', 'P0DT1H3M0S', 'c', 'c',
4., '2016-01-04T06:00:00.000Z']]
expected = OrderedDict([('schema', schema), ('data', data)])
assert result == expected

Expand All @@ -277,16 +260,14 @@ def test_to_json_float_index(self):
result = data.to_json(orient='table', date_format='iso')
result = json.loads(result, object_pairs_hook=OrderedDict)
result['schema'].pop('pandas_version')

expected = (
OrderedDict([('schema', {
expected = (OrderedDict([
('schema', {
'fields': [{'name': 'index', 'type': 'number'},
{'name': 'values', 'type': 'integer'}],
'primaryKey': ['index']
}),
('data', [OrderedDict([('index', 1.0), ('values', 1)]),
OrderedDict([('index', 2.0), ('values', 1)])])])
)
('data', [['index', 'values'], [1.0, 1], [2.0, 1]])
]))
assert result == expected

def test_to_json_period_index(self):
Expand All @@ -300,10 +281,9 @@ def test_to_json_period_index(self):
{'name': 'values', 'type': 'integer'}]

schema = {'fields': fields, 'primaryKey': ['index']}
data = [OrderedDict([('index', '2015-11-01T00:00:00.000Z'),
('values', 1)]),
OrderedDict([('index', '2016-02-01T00:00:00.000Z'),
('values', 1)])]
data = [['index', 'values'],
['2015-11-01T00:00:00.000Z', 1],
['2016-02-01T00:00:00.000Z', 1]]
expected = OrderedDict([('schema', schema), ('data', data)])
assert result == expected

Expand All @@ -320,10 +300,7 @@ def test_to_json_categorical_index(self):
'ordered': False},
{'name': 'values', 'type': 'integer'}],
'primaryKey': ['index']}),
('data', [
OrderedDict([('index', 'a'),
('values', 1)]),
OrderedDict([('index', 'b'), ('values', 1)])])])
('data', [['index', 'values'], ['a', 1], ['b', 1]])])
)
assert result == expected

Expand Down Expand Up @@ -428,9 +405,7 @@ def test_categorical(self):
expected = OrderedDict([
('schema', {'fields': fields,
'primaryKey': ['idx']}),
('data', [OrderedDict([('idx', 0), ('values', 'a')]),
OrderedDict([('idx', 1), ('values', 'b')]),
OrderedDict([('idx', 2), ('values', 'a')])])])
('data', [['idx', 'values'], [0, 'a'], [1, 'b'], [2, 'a']])])
assert result == expected

@pytest.mark.parametrize('idx,nm,prop', [
Expand Down
14 changes: 4 additions & 10 deletions pandas/tests/io/json/test_pandas.py
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Expand Up @@ -1204,9 +1204,10 @@ def test_data_frame_size_after_to_json(self):

@pytest.mark.parametrize('index', [None, [1, 2], [1., 2.], ['a', 'b'],
['1', '2'], ['1.', '2.']])
@pytest.mark.parametrize('columns', [['a', 'b'], ['1', '2'], ['1.', '2.']])
@pytest.mark.parametrize('columns', [None, [1, 2], [1., 2.], ['a', 'b'],
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So I don't know that we want to do this. Is it valid JSON in the table spec to have column names that are non-string?

Understood you have gotten this to round trip but if it violates the Table spec for JSON then I'd rather raise as commented previously

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@WillAyd as I already commented in #19129, after having tried to start a discussion before doing the PR:

I was suggesting not just "raising a more descriptive ValueError" (sic), but changing the implementation of the JSON serialization for orient='table'.

Could you please tell me where the JSON table spec claims that column names MUST be strings?

I am going to make a longer comment to further justify my PR.

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Could you please tell me where the JSON table spec claims that column names MUST be strings?

Not specific to the table spec as much as just JSON itself. See the description of an object here:

https://json.org

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@albertvillanova albertvillanova Mar 1, 2019 •
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@WillAyd you are talking about different things:

  • JSON spec imposes that in a JSON object (composed of key-value pairs), its keys must be strings
  • but we are talking about column names, not JSON object keys

And my question is: where the JSON table spec claims that COLUMN NAMES (not JSON object keys) must be strings?

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@pwalsh can you comment on this? Is the name field in a Field Descriptor expected to be a string?

['1', '2'], ['1.', '2.']])
def test_from_json_to_json_table_index_and_columns(self, index, columns):
# GH25433 GH25435
# GH19129 GH25433 GH25435
expected = DataFrame([[1, 2], [3, 4]], index=index, columns=columns)
dfjson = expected.to_json(orient='table')
result = pd.read_json(dfjson, orient='table')
Expand Down Expand Up @@ -1272,16 +1273,9 @@ def test_index_false_to_json_split(self, data, expected):
def test_index_false_to_json_table(self, data):
# GH 17394
# Testing index=False in to_json with orient='table'

result = data.to_json(orient='table', index=False)
result = json.loads(result)

expected = {
'schema': pd.io.json.build_table_schema(data, index=False),
'data': DataFrame(data).to_dict(orient='records')
}

assert result == expected
assert 'primaryKey' not in result['schema']

@pytest.mark.parametrize('orient', [
'records', 'index', 'columns', 'values'
Expand Down