Skip to content

TST: Assign back multiple column to datetime #46982

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
jreback merged 4 commits into from
Jun 5, 2022
Merged

TST: Assign back multiple column to datetime #46982

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
0e6a22a
TST: Additional to iloc setitem
parthi-siva May 10, 2022
6928918
used ArrayManager for type evualation
parthi-siva May 11, 2022
f7e7902
Removed test
parthi-siva May 12, 2022
cae8b05
Merge branch 'main' into TST_for_#20511
mroeschke Jun 3, 2022
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
19 changes: 19 additions & 0 deletions pandas/tests/indexing/test_iloc.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,11 +21,13 @@
Interval,
NaT,
Series,
Timestamp,
array,
concat,
date_range,
interval_range,
isna,
to_datetime,
)
import pandas._testing as tm
from pandas.api.types import is_scalar
Expand Down Expand Up @@ -1196,6 +1198,23 @@ def test_iloc_getitem_int_single_ea_block_view(self):
arr[2] = arr[-1]
assert ser[0] == arr[-1]

def test_iloc_setitem_multicolumn_to_datetime(self, using_array_manager):

# GH#20511
df = DataFrame({"A": ["2022-01-01", "2022-01-02"], "B": ["2021", "2022"]})

df.iloc[:, [0]] = DataFrame({"A": to_datetime(["2021", "2022"])})
expected = DataFrame(
{
"A": [
Timestamp("2021-01-01 00:00:00"),
Timestamp("2022-01-01 00:00:00"),
],
"B": ["2021", "2022"],
}
)
tm.assert_frame_equal(df, expected, check_dtype=using_array_manager)


class TestILocErrors:
# NB: this test should work for _any_ Series we can pass as
Expand Down