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BUG: read_json not handling string dtype when converting to dates #56195

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Merged
merged 1 commit into from
Nov 27, 2023
Merged

BUG: read_json not handling string dtype when converting to dates #56195

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.2.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -530,6 +530,7 @@ I/O
- Bug in :func:`read_csv` where ``on_bad_lines="warn"`` would write to ``stderr`` instead of raise a Python warning. This now yields a :class:`.errors.ParserWarning` (:issue:`54296`)
- 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:`read_json` not handling dtype conversion properly if ``infer_string`` is set (:issue:`56195`)
- Bug in :func:`to_excel`, with ``OdsWriter`` (``ods`` files) writing boolean/string value (:issue:`54994`)
- Bug in :meth:`DataFrame.to_hdf` and :func:`read_hdf` with ``datetime64`` dtypes with non-nanosecond resolution failing to round-trip correctly (:issue:`55622`)
- Bug in :meth:`pandas.read_excel` with ``engine="odf"`` (``ods`` files) when string contains annotation (:issue:`55200`)
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11 changes: 9 additions & 2 deletions pandas/io/json/_json.py
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Expand Up @@ -32,7 +32,10 @@
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import check_dtype_backend

from pandas.core.dtypes.common import ensure_str
from pandas.core.dtypes.common import (
ensure_str,
is_string_dtype,
)
from pandas.core.dtypes.dtypes import PeriodDtype
from pandas.core.dtypes.generic import ABCIndex

Expand Down Expand Up @@ -1249,7 +1252,7 @@ def _try_convert_data(
if self.dtype_backend is not lib.no_default and not isinstance(data, ABCIndex):
# Fall through for conversion later on
return data, True
elif data.dtype == "object":
elif is_string_dtype(data.dtype):
# try float
try:
data = data.astype("float64")
Expand Down Expand Up @@ -1301,6 +1304,10 @@ def _try_convert_to_date(self, data):
return data, False

new_data = data

if new_data.dtype == "string":
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Should this include ArrowDtype[string]?

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@phofl phofl Nov 27, 2023 •
edited
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No that shouldn't be necessary, you can't end up with arrow strings in columns that could be dates, that can only happen with the automatic conversion when the infer string option is set

new_data = new_data.astype(object)

if new_data.dtype == "object":
try:
new_data = data.astype("int64")
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30 changes: 17 additions & 13 deletions pandas/tests/io/json/test_compression.py
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Expand Up @@ -93,27 +93,31 @@ def test_read_unsupported_compression_type():
pd.read_json(path, compression="unsupported")


@pytest.mark.parametrize(
"infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
)
@pytest.mark.parametrize("to_infer", [True, False])
@pytest.mark.parametrize("read_infer", [True, False])
def test_to_json_compression(
compression_only, read_infer, to_infer, compression_to_extension
compression_only, read_infer, to_infer, compression_to_extension, infer_string
):
# see gh-15008
compression = compression_only
with pd.option_context("future.infer_string", infer_string):
# see gh-15008
compression = compression_only

# We'll complete file extension subsequently.
filename = "test."
filename += compression_to_extension[compression]
# We'll complete file extension subsequently.
filename = "test."
filename += compression_to_extension[compression]

df = pd.DataFrame({"A": [1]})
df = pd.DataFrame({"A": [1]})

to_compression = "infer" if to_infer else compression
read_compression = "infer" if read_infer else compression
to_compression = "infer" if to_infer else compression
read_compression = "infer" if read_infer else compression

with tm.ensure_clean(filename) as path:
df.to_json(path, compression=to_compression)
result = pd.read_json(path, compression=read_compression)
tm.assert_frame_equal(result, df)
with tm.ensure_clean(filename) as path:
df.to_json(path, compression=to_compression)
result = pd.read_json(path, compression=read_compression)
tm.assert_frame_equal(result, df)


def test_to_json_compression_mode(compression):
Expand Down