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Fix read_json category dtype #30728

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Fix read_json category dtype #30728

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8de37e9
Fix read_json category dtype
taoufik07 Jan 6, 2020
3f99f04
Use pandas_dtype instead of np.dtype
taoufik07 Jan 6, 2020
e92b4cb
Add tests
taoufik07 Jan 6, 2020
de21602
Sort imports
taoufik07 Jan 6, 2020
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5 changes: 3 additions & 2 deletions pandas/io/json/_json.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
from pandas._typing import JSONSerializable
from pandas.errors import AbstractMethodError

from pandas.core.dtypes.common import ensure_str, is_period_dtype
from pandas.core.dtypes.common import ensure_str, is_period_dtype, is_categorical_dtype

from pandas import DataFrame, MultiIndex, Series, isna, to_datetime
from pandas.core.construction import create_series_with_explicit_dtype
Expand Down Expand Up @@ -892,7 +892,8 @@ def _try_convert_data(self, name, data, use_dtypes=True, convert_dates=True):
)
if dtype is not None:
try:
dtype = np.dtype(dtype)
if not is_categorical_dtype(dtype):
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use pandas_dtype here

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done

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I don't think you need both the is_categorical_dtype and pandas_dtype here; just the latter should be OK

dtype = np.dtype(dtype)
return data.astype(dtype), True
except (TypeError, ValueError):
return data, False
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12 changes: 12 additions & 0 deletions pandas/tests/io/json/test_pandas.py
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Expand Up @@ -1197,6 +1197,18 @@ def test_read_local_jsonl(self):
expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
tm.assert_frame_equal(result, expected)

def test_read_json_category_dtype(self):
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this will need to test all extension dtypes that we support, pls parametrize this over interval, period, categorical, datetime w/tz

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Hey, according to you, what's the best way to deal/pass different input dfs and expected dfs ?

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@taoufik07 have a go using https://docs.pytest.org/en/latest/parametrize.html, there's lots of examples of it being used it other parts of the test suite

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I know about parametrize, but I find having multiple lines (type, input, expected) unpleasant to the eye and I tought that there was some magic function that handles the data generations for all these types, maybe I will just go with the naive way

json = (
'{"a": 0, "b": "A"}\n'
'{"a": 1, "b": "B"}\n'
'{"a": 2, "b": "B"}\n'
'{"a": 3, "b": "B"}\n'
)
json = StringIO(json)
result = read_json(json, lines=True, dtype={"a": "category"})
expected = DataFrame([[0, "foo"], [1, "bar"], [2, "foo"], [3, "bar"]])
tm.assert_frame_equal(result, expected)

def test_read_jsonl_unicode_chars(self):
# GH15132: non-ascii unicode characters
# \u201d == RIGHT DOUBLE QUOTATION MARK
Expand Down