Skip to content

BUG: #31464 Fix error when parsing JSON list of bool into Series #33373

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 5 commits into from
Apr 8, 2020
Merged

BUG: #31464 Fix error when parsing JSON list of bool into Series #33373

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
395a7fc
BUG: #31464 Fix error when parsing JSON list of bool into Series
jessefarnham Apr 7, 2020
96e2a0b
Code review feedback: don't even try to convert bool to date
jessefarnham Apr 7, 2020
b90ad78
update comment
jessefarnham Apr 7, 2020
0665127
After discussion, go back to original approach
jessefarnham Apr 7, 2020
19097a7
Move test
jessefarnham Apr 8, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.1.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -429,6 +429,7 @@ I/O
- Bug in :meth:`read_sas` was raising an ``AttributeError`` when reading files from Google Cloud Storage (issue:`33069`)
- Bug in :meth:`DataFrame.to_sql` where an ``AttributeError`` was raised when saving an out of bounds date (:issue:`26761`)
- Bug in :meth:`read_excel` did not correctly handle multiple embedded spaces in OpenDocument text cells. (:issue:`32207`)
- Bug in :meth:`read_json` was raising ``TypeError`` when reading a list of booleans into a Series. (:issue:`31464`)

Plotting
^^^^^^^^
Expand Down
2 changes: 1 addition & 1 deletion pandas/io/json/_json.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -982,7 +982,7 @@ def _try_convert_to_date(self, data):
for date_unit in date_units:
try:
new_data = to_datetime(new_data, errors="raise", unit=date_unit)
except (ValueError, OverflowError):
except (ValueError, OverflowError, TypeError):
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@jbrockmendel any thoughts here?

Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

the usual: if we can handle this case before the try/except that would be preferable

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@jbrockmendel I’ll give that approach a try.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@jbrockmendel Pushed an alternate approach that escapes from the _try_convert_to_date() method if the dtype is bool, avoiding the try/except but adding a conditional to the method (which I don't love, but at least the method already has dtype-based conditionals).

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

yes this is ok. an alternative approach is to infer_dtype on this and then dispatch. If someone wants to try that (later PR) would be ok.

continue
return new_data, True
return data, False
Expand Down
6 changes: 6 additions & 0 deletions pandas/tests/io/json/test_pandas.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1659,3 +1659,9 @@ def test_json_pandas_nulls(self, nulls_fixture):
# GH 31615
result = pd.DataFrame([[nulls_fixture]]).to_json()
assert result == '{"0":{"0":null}}'

def test_readjson_bool_series(self):
# GH31464
result = read_json("[true, true, false]", typ="series")
expected = pd.Series([True, True, False])
tm.assert_series_equal(result, expected)