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BUG: fix to_datetime to handle int16 and int8 #13464

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BUG: fix to_datetime to handle int16 and int8 #13464

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2 changes: 2 additions & 0 deletions doc/source/whatsnew/v0.18.2.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -517,3 +517,5 @@ Bug Fixes


- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`)

- Bug in ``DataFrame.to_datetime()`` raises ValueError in case of dtype ``int8`` and ``int16`` (:issue:`13451`)
27 changes: 27 additions & 0 deletions pandas/tseries/tests/test_timeseries.py
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Expand Up @@ -2563,6 +2563,33 @@ def test_dataframe(self):
with self.assertRaises(ValueError):
to_datetime(df2)

def test_dataframe_dtypes(self):
# #13451
df = DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5]})

# int16
result = to_datetime(df.astype('int16'))
expected = Series([Timestamp('20150204 00:00:00'),
Timestamp('20160305 00:00:00')])
assert_series_equal(result, expected)

# mixed dtypes
df['month'] = df['month'].astype('int8')
df['day'] = df['day'].astype('int8')
result = to_datetime(df)
expected = Series([Timestamp('20150204 00:00:00'),
Timestamp('20160305 00:00:00')])
assert_series_equal(result, expected)

# float
df = DataFrame({'year': [2000, 2001],
'month': [1.5, 1],
'day': [1, 1]})
with self.assertRaises(ValueError):
to_datetime(df)


class TestDatetimeIndex(tm.TestCase):
_multiprocess_can_split_ = True
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6 changes: 5 additions & 1 deletion pandas/tseries/tools.py
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Expand Up @@ -508,7 +508,11 @@ def f(value):

def coerce(values):
# we allow coercion to if errors allows
return to_numeric(values, errors=errors)
values = to_numeric(values, errors=errors)
# prevent overflow in case of int8 or int16
if com.is_integer_dtype(values):
values = values.astype('int64', copy=False)
return values

values = (coerce(arg[unit_rev['year']]) * 10000 +
coerce(arg[unit_rev['month']]) * 100 +
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