Skip to content

BUG: to_timedelta fails on Int64 Series with null values #35582

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 4 commits into from
Aug 7, 2020
Merged

BUG: to_timedelta fails on Int64 Series with null values #35582

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
99c6b6d
BUG: to_timedelta fails on Int64 Series with null values
egoddard Aug 5, 2020
4fbe729
Move nullable int64 dtypes to separate test and isort
egoddard Aug 6, 2020
77389bb
Update whatsnew v1.1.1.rst
egoddard Aug 6, 2020
1ace0ec
Add GH issue number to tests
egoddard Aug 7, 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
4 changes: 3 additions & 1 deletion pandas/core/arrays/timedeltas.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@

from pandas.core import nanops
from pandas.core.algorithms import checked_add_with_arr
from pandas.core.arrays import datetimelike as dtl
from pandas.core.arrays import datetimelike as dtl, IntegerArray
from pandas.core.arrays._ranges import generate_regular_range
import pandas.core.common as com
from pandas.core.construction import extract_array
Expand Down Expand Up @@ -921,6 +921,8 @@ def sequence_to_td64ns(data, copy=False, unit=None, errors="raise"):
elif isinstance(data, (ABCTimedeltaIndex, TimedeltaArray)):
inferred_freq = data.freq
data = data._data
elif isinstance(data, IntegerArray):
data = data.to_numpy("int64", na_value=tslibs.iNaT)

# Convert whatever we have into timedelta64[ns] dtype
if is_object_dtype(data.dtype) or is_string_dtype(data.dtype):
Expand Down
12 changes: 12 additions & 0 deletions pandas/tests/tools/test_to_timedelta.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,18 @@ def test_to_timedelta(self):
result = to_timedelta(Series(["1d", "1days 00:00:01"]))
tm.assert_series_equal(result, expected)

# IntegerArray Series
expected = Series([timedelta(days=1), timedelta(days=2)])
result = to_timedelta(Series([1, 2], dtype="Int64"), unit="days")

tm.assert_series_equal(result, expected)

# IntegerArray Series with nulls
Copy link
Contributor

Choose a reason for hiding this comment

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

it looks like other tests for missing values are in

pandas/pandas/tests/tools/test_to_timedelta.py

Line 133 in 3701a9b

def test_to_timedelta_on_missing_values(self):

so might want to move this part there

Copy link
Member

Choose a reason for hiding this comment

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

Alternatively, I would maybe just put this new code in its own test (so a test specifically for passing nullable Int64 dtype data)

expected = Series([timedelta(days=1), None])
result = to_timedelta(Series([1, None], dtype="Int64"), unit="days")

tm.assert_series_equal(result, expected)

# with units
result = TimedeltaIndex(
[np.timedelta64(0, "ns"), np.timedelta64(10, "s").astype("m8[ns]")]
Expand Down