API: to_datetime(ints, unit) give requested unit #63347
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jorisvandenbossche
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Non-Nano
| errors=errors, | ||
| unit_for_numerics=unit, | ||
| creso=cast(int, NpyDatetimeUnit.NPY_FR_ns.value), | ||
| # creso=cast(int, NpyDatetimeUnit.NPY_FR_ns.value), |
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| >>> pd.to_datetime([1, 2, 3], unit="D", origin=pd.Timestamp("1960-01-01")) | ||
| DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], | ||
| dtype='datetime64[ns]', freq=None) | ||
| dtype='datetime64[s]', freq=None) |
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This makes me wonder about a potential corner case: what if the original actually has values for a lower resolution, like actual microseconds or nanoseconds?
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| # Without this as_unit cast, we would fail to overflow | ||
| # and get much-too-large dates | ||
| return to_datetime(new_data, errors="raise", unit=date_unit).dt.as_unit( | ||
| "ns" |
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I am not directly understanding that comment. new_data are integers here? Why does the return unit of this function need to be nanoseconds? (to preserve current functionality?) Why would this give (wrong?) too large dates?
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| result = read_json(StringIO(json), typ="series") | ||
| expected = ts.copy() | ||
| expected = ts.copy().dt.as_unit("ns") |
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Not for this PR, but so this is another case where we currently return ns unit but could change to use us by default?
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| index = pd.MultiIndex( | ||
| levels=[[1, 2, 3], [pd.to_datetime("2000-01-01", unit="ns")]], | ||
| levels=[[1, 2, 3], [pd.to_datetime("2000-01-01", unit="ns").as_unit("ns")]], |
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Did this PR change that? (that this no longer returns nanoseconds)
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to_datetime analogue of #63303.