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BUG: DataFrame.to_json OverflowError with np.long* dtypes #55495

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merged 3 commits into from
Oct 16, 2023
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BUG: DataFrame.to_json OverflowError with np.long* dtypes #55495

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3e3c713
BUG: DataFrame.to_json OverflowError with np.long* dtypes
gupta-paras Oct 12, 2023
7a29022
BUG: DataFrame.to_json OverflowError with np.long* dtypes #Comment-1
gupta-paras Oct 13, 2023
774519f
BUG: DataFrame.to_json OverflowError with np.long* dtypes #Comment-2
gupta-paras Oct 14, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.2.0.rst
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Expand Up @@ -348,6 +348,7 @@ I/O
- Bug in :func:`read_csv` with ``engine="pyarrow"`` where ``usecols`` wasn't working with a csv with no headers (:issue:`54459`)
- Bug in :func:`read_excel`, with ``engine="xlrd"`` (``xls`` files) erroring when file contains NaNs/Infs (:issue:`54564`)
- Bug in :func:`to_excel`, with ``OdsWriter`` (``ods`` files) writing boolean/string value (:issue:`54994`)
- Bug in :meth:`DataFrame.to_json` OverflowError with np.long* dtypes (:issue:`55403`)
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- Bug in :meth:`pandas.read_excel` with an ODS file without cached formatted cell for float values (:issue:`55219`)

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5 changes: 5 additions & 0 deletions pandas/_libs/src/vendored/ujson/python/objToJSON.c
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Expand Up @@ -1610,6 +1610,11 @@ void Object_beginTypeContext(JSOBJ _obj, JSONTypeContext *tc) {
PyArray_DescrFromType(NPY_DOUBLE));
tc->type = JT_DOUBLE;
return;
} else if (PyArray_IsScalar(obj, LongDouble)) {
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This works, but I think the lines directly following it are supposed to be a catchall for unsupported types. Do you know why that isn't being hit? I would rather we keep this generic instead of having to specify an error message for every type that we don't serialize

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There is a code section at the end of this function:

    pc->iterBegin = Dir_iterBegin;
    pc->iterEnd = Dir_iterEnd;
    pc->iterNext = Dir_iterNext;
    pc->iterGetValue = Dir_iterGetValue;
    pc->iterGetName = Dir_iterGetName;
    return;

By default everything falls to this section and this causes infinite loop.

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Right but I'm asking about the next branch after what you've added.

} else if (PyArray_Check(obj) && PyArray_CheckScalar(obj)) {

Do you know what hits that currently? From reading the function I think the intent of that was to generically catch the issue you've described, but its possible the invariant is incorrect. Would something pass both PyArray_Check and PyArray_CheckScalar? Maybe the PyArray_Check call is incorrect and removing that alone would fix your issue?

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@gupta-paras gupta-paras Oct 13, 2023 •
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Any value like: np.array(1) [0d array] evaluates true for both of them, so that check was intended for handling this case only. Also for any numpy scalar type, second will be true. So, simply removing pyArray_check handles both cases. Only thing we need to make sure, that all numpy scalar types are handled before this if block. I will add similar comment in code as well.
Thanks

PyErr_Format(PyExc_TypeError,
"%R (np.longdouble) is not JSON serializable at the moment",
obj);
goto INVALID;
} else if (PyArray_Check(obj) && PyArray_CheckScalar(obj)) {
PyErr_Format(PyExc_TypeError,
"%R (0d array) is not JSON serializable at the moment",
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7 changes: 7 additions & 0 deletions pandas/tests/io/json/test_ujson.py
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Expand Up @@ -818,6 +818,13 @@ def test_0d_array(self):
with pytest.raises(TypeError, match=msg):
ujson.ujson_dumps(np.array(1))

def test_array_long_double(self):
msg = re.compile(
"1234.5.*\\(np.longdouble\\) is not JSON serializable at the moment"
)
with pytest.raises(TypeError, match=msg):
ujson.ujson_dumps(np.longdouble(1234.5))


class TestPandasJSONTests:
def test_dataframe(self, orient):
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