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COMPAT: make astype to datetime/timedelta types more robust #19176

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Merged
merged 1 commit into from
Jan 11, 2018
+15 −2
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COMPAT: make astype to datetime/timedelta types more robust #19176

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15 changes: 14 additions & 1 deletion pandas/core/dtypes/cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -690,7 +690,7 @@ def astype_nansafe(arr, dtype, copy=True):
raise ValueError('Cannot convert non-finite values (NA or inf) to '
'integer')

elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
elif is_object_dtype(arr.dtype) and np.issubdtype(dtype.type, np.integer):
# work around NumPy brokenness, #1987
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)

Expand All @@ -703,6 +703,19 @@ def astype_nansafe(arr, dtype, copy=True):
dtype = np.dtype(dtype.name + "[ns]")

if copy:

if arr.dtype == dtype:
return arr.copy()

# we handle datetimelikes with pandas machinery
# to be robust to the input type
elif is_datetime64_dtype(dtype):
from pandas import to_datetime
return to_datetime(arr).values
elif is_timedelta64_dtype(dtype):
from pandas import to_timedelta
return to_timedelta(arr).values

return arr.astype(dtype)
return arr.view(dtype)

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2 changes: 1 addition & 1 deletion pandas/tests/io/json/test_json_table_schema.py
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Expand Up @@ -499,7 +499,7 @@ def test_read_json_table_orient(self, index_nm, vals):
tm.assert_frame_equal(df, result)

@pytest.mark.parametrize("index_nm", [
None, "idx", pytest.param("index", marks=pytest.mark.xfail)])
None, "idx", "index"])
@pytest.mark.parametrize("vals", [
{'timedeltas': pd.timedelta_range('1H', periods=4, freq='T')},
{'timezones': pd.date_range('2016-01-01', freq='d', periods=4,
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