BUG: fix to_datetime to handle int16 and int8 #13464
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tests! |
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@jreback Hi, I am a bit confused about which file should the tests be written in? There is no separate file that contains tests for pandas.tseries.tools . |
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https://github.com/pydata/pandas/blob/master/pandas/tseries/tests/test_timeseries.py#L2453 or you can make another function in the same class |
jreback
added
Bug
Datetime
| @@ -2563,6 +2563,26 @@ def test_dataframe(self): | |||
| with self.assertRaises(ValueError): | |||
| to_datetime(df2) | |||
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| def test_dataframe_dtypes(self): | |||
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add the issue number here as a comment
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lgtm (minor comment), pls add a whatsnew entry. ping when green. |
| @@ -510,6 +510,9 @@ def coerce(values): | |||
| # we allow coercion to if errors allows | |||
| return to_numeric(values, errors=errors) | |||
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| # prevent overflow in case of int8 or int16 | |||
| arg = arg.astype('int64', copy=False) | |||
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This should cause another problem. On current master:
df = pd.DataFrame({'year': [2000, 2001], 'month': [1.5, 1], 'day': [1, 1]})
pd.to_datetime(df)
# ValueError: cannot assemble the datetimes: 'float' object is unsliceable
pd.to_datetime(df.astype(np.int64))
# 0 2000-01-01
# 1 2001-01-01
# dtype: datetime64[ns]
Better to raise if input contains non-integer dtype.
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good point. @ravinimmi can you add checking that the passes dtypes are all integer.
use is_integer_dtype, and if not raise (need tests as well)
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@jreback What should be the output in case of string type ? Or should it raise ValueError?
df = pd.DataFrame({'year': [2000, 2001], 'month': [1, 1], 'day': [1, 1]})
pd.to_datetime(df.astype(str))There was a problem hiding this comment.
strings are already tested / work.
internally pd.to_numeric is used which coerces non-numeric to numeric (or it raises if it fails). Thus you are left with float/int.
Current coverage is 84.32%@@ master #13464 diff @@
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Files 138 138
Lines 51069 51072 +3
Methods 0 0
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+ Hits 43066 43069 +3
Misses 8003 8003
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thanks! |
git diff upstream/master | flake8 --diffFixes #13451