PERF: pd.to_datetime, unit='s' much slower for float64 than for int64
- [x] closes #20445
- [x] tests passed
- [x] new benchmarks added
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

As per discussion in #20445 this PR addresses performance of `to_datetime` on a uniform type array of floats. The aim is to get a speed-up by implementing `astype`-ing, and avoid looping, for floats.
As a quick measure of the efficacy of this PR, I ran the checks from #20445 on current branch and master. 

The set-up is:
```
timestamp_seconds_int = pd.Series(np.random.randint(1521685107 - 604800, 1521685107, 1000000, dtype='int64'))
timestamp_seconds_float = timestamp_seconds_int.astype('float64')
```
For ints 
```
%%timeit -r 3 
pd.to_datetime(timestamp_seconds_int, unit='s')
```
I get
```
current branch: 34.8 ms ± 934 µs per loop (mean ± std. dev. of 3 runs, 10 loops each)
master: 35.1 ms ± 694 µs per loop (mean ± std. dev. of 3 runs, 10 loops each)
```
and for floats
```
%%timeit -r 3 
pd.to_datetime(timestamp_seconds_float, unit='s')
```
I get
```
current branch: 30.9 ms ± 228 µs per loop (mean ± std. dev. of 3 runs, 10 loops each)
master: 15 s ± 448 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)
``` @WillAyd @jbrockmendel I have something that works on some Linux environments but not on others and not on Mac/Windows. Not sure what the problem(s) are and the best way to go about debugging ping @WillAyd @jbrockmendel  Update on this PR: I've rewritten the cast in a more reasonable way. There are still ~30 test failures (platform-independent) to do with a mixture of float precision, datetime overflow and missing value handling. 

I'll keep working on this but I'll take any suggestions if someone has time to have a look @arw2019 is this still active? @WillAyd yes. I feel like I'm most of the way there but stuck on the outstanding test failures. Keen to make it work but I would need some pointers. I'll rebase and post representative examples if you'd be happy to take a look? I'm down to 7 failing tests. I believe these are due to precision errors. For example, in 
```
FAILED pandas/tests/tools/test_to_datetime.py::TestOrigin::test_epoch[us-str_1960]
```
the issue is
```
(...)
        expected = Series(
            [pd.Timedelta(x, unit=units) + epoch_1960 for x in units_from_epochs]
        )
(...)
        result = Series(pd.to_datetime(units_from_epochs, unit=units, origin=epochs))
>       tm.assert_series_equal(result, expected)
(...)
E           numpy array values are different (80.0 %)
E           [index]: [0, 1, 2, 3, 4]
E           [left]:  [-315619200000000000, -315619199999998976, -315619199999998016, -315619199999996992, -315619199999996032]
E           [right]: [-315619200000000000, -315619199999999000, -315619199999998000, -315619199999997000, -315619199999996000]
``` I'm down to one test failure. These look to me like floating point errors rather than a fault with my rounding...?
```____________________________________________________________________________________ TestToDatetimeUnit.test_to_datetime_unit ____________________________________________________________________________________

    def test_to_datetime_unit(self):
    
        epoch = 1370745748
        # GH13834
        s = Series([epoch + t for t in np.arange(0, 2, 0.25)] + [iNaT]).astype(float)
        result = to_datetime(s, unit="s")
        expected = Series(
            [
                Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t)
                for t in np.arange(0, 2, 0.25)
            ]
            + [NaT]
        )
>       tm.assert_series_equal(result, expected)

E           AssertionError: numpy array are different
E           
E           numpy array values are different (44.44444 %)
E           [index]: [0, 1, 2, 3, 4, 5, 6, 7, 8]
E           [left]:  [1370745748000000000, 1370745748249999872, 1370745748500000000, 1370745748750000128, 1370745749000000000, 1370745749249999872, 1370745749500000000, 1370745749750000128, -9223372036854775808]
E           [right]: [1370745748000000000, 1370745748250000000, 1370745748500000000, 1370745748750000000, 1370745749000000000, 1370745749250000000, 1370745749500000000, 1370745749750000000, -9223372036854775808]

pandas/_testing.py:1104: AssertionError
============================================================================================ short test summary info =============================================================================================
FAILED pandas/tests/tools/test_to_datetime.py::TestToDatetimeUnit::test_to_datetime_unit - AssertionError: numpy array are different
```
 Benchmark results are below. Would appreciate any comments on how to triage the areas which take a hit
```
asv continuous -f 1.1 upstream/master HEAD -b tslibs

       before           after         ratio
     [fd20f7d3]       [b308ba71]
     <pd-todatetime-unit_s-float-vs-int~1^2>       <pd-todatetime-unit_s-float-vs-int>
+     2.36±0.04μs       2.97±0.2μs     1.26  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'end', 'year', 'QS', 3)
+      8.51±0.2μs       10.6±0.7μs     1.25  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthBegin: day_of_month=15>)
+     2.39±0.04μs       2.98±0.2μs     1.25  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'end', 'year', 'QS', 12)
+     2.33±0.02μs       2.89±0.3μs     1.24  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'year', None, 12)
+      11.2±0.6μs       13.8±0.3μs     1.23  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterEnd: startingMonth=3>)
+      5.82±0.2μs       7.13±0.4μs     1.23  tslibs.normalize.Normalize.time_normalize_i8_timestamps(0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+     2.48±0.03μs       2.95±0.2μs     1.19  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', 'QS', 3)
+      5.77±0.1μs       6.74±0.2μs     1.17  tslibs.normalize.Normalize.time_normalize_i8_timestamps(1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+     2.47±0.01μs      2.87±0.06μs     1.16  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'year', 'QS', 3)
+     13.0±0.07μs       15.0±0.8μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<DateOffset: days=2, months=2>)
+     2.47±0.01μs      2.85±0.04μs     1.15  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'year', 'QS', 12)
+      8.14±0.2μs       9.29±0.2μs     1.14  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(100, 'start', 'quarter', 'QS', 12)
+         566±1μs          641±8μs     1.13  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'end', 'quarter', 'QS', 5)
+         295±3ns          333±6ns     1.13  tslibs.period.PeriodProperties.time_property('min', 'quarter')
+       538±0.9μs         607±10μs     1.13  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'end', 'year', 'B', 5)
+     1.72±0.04μs       1.94±0.3μs     1.13  tslibs.fields.TimeGetDateField.time_get_date_field(0, 'q')
+         350±8ns         395±20ns     1.13  tslibs.period.PeriodProperties.time_property('min', 'day')
+     2.35±0.03μs       2.64±0.2μs     1.13  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'quarter', None, 3)
+     7.20±0.06μs       8.10±0.5μs     1.12  tslibs.fields.TimeGetDateField.time_get_date_field(100, 'h')
+         558±2μs          628±7μs     1.12  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'end', 'quarter', 'QS', 3)
+     2.23±0.01μs      2.51±0.06μs     1.12  tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, datetime.timezone.utc)
+     2.34±0.01μs      2.63±0.07μs     1.12  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', None, 5)
+     2.48±0.01μs       2.78±0.2μs     1.12  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', 'B', 12)
+         617±4μs         691±40μs     1.12  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'start', 'month', 'B', 12)
+         549±1μs         613±30μs     1.12  tslibs.fields.TimeGetDateField.time_get_date_field(10000, 'm')
+     2.39±0.02μs      2.67±0.04μs     1.11  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', None, 3)
+     8.28±0.07μs       9.22±0.8μs     1.11  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthEnd>)
+         303±1ns          336±6ns     1.11  tslibs.period.PeriodProperties.time_property('min', 'qyear')
+     2.45±0.04μs       2.71±0.1μs     1.11  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'year', 'QS', 5)
+         386±4ns         427±10ns     1.11  tslibs.period.PeriodProperties.time_property('min', 'is_leap_year')
+         541±3μs          598±9μs     1.11  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'end', 'year', None, 12)
+         538±2μs         594±10μs     1.11  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'start', 'year', 'B', 5)
+     2.48±0.02μs       2.74±0.3μs     1.10  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'month', 'B', 3)
+         557±1μs         614±10μs     1.10  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'start', 'year', None, 12)
+         550±8μs         606±10μs     1.10  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(10000, 'end', 'year', None, 5)
+     1.72±0.05μs       1.90±0.1μs     1.10  tslibs.fields.TimeGetDateField.time_get_date_field(0, 'woy')
+     2.37±0.01μs       2.61±0.3μs     1.10  tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'year', None, 5)
-         573±9ms         520±10ms     0.91  tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1000000, datetime.timezone.utc)
-      7.30±0.3μs      6.61±0.04μs     0.91  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, datetime.timezone(datetime.timedelta(seconds=3600)))
-      6.37±0.3μs       5.78±0.2μs     0.91  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        707±20μs          640±8μs     0.91  tslibs.resolution.TimeResolution.time_get_resolution('m', 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      11.7±0.2μs      10.5±0.08μs     0.90  tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 4006)
-     2.29±0.05μs      2.06±0.02μs     0.90  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, None)
-     2.31±0.07μs      2.08±0.02μs     0.90  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 9000, tzlocal())
-      8.12±0.8μs      7.31±0.05μs     0.90  tslibs.timedelta.TimedeltaConstructor.time_from_iso_format
-     2.27±0.09μs      2.04±0.03μs     0.90  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011, tzlocal())
-      8.03±0.2μs      7.21±0.04μs     0.90  tslibs.resolution.TimeResolution.time_get_resolution('us', 100, None)
-     2.15±0.07μs      1.93±0.01μs     0.90  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, datetime.timezone.utc)
-     2.27±0.03μs      2.04±0.02μs     0.90  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, tzlocal())
-     2.30±0.08μs      2.05±0.01μs     0.89  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, None)
-      7.45±0.3μs      6.63±0.04μs     0.89  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011, datetime.timezone(datetime.timedelta(seconds=3600)))
-      2.33±0.2μs      2.06±0.05μs     0.89  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, tzlocal())
-         125±4ms          111±2ms     0.88  tslibs.resolution.TimeResolution.time_get_resolution('us', 10000, tzlocal())
-      6.12±0.1μs       5.39±0.2μs     0.88  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-      2.34±0.1μs      2.05±0.01μs     0.88  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000, tzlocal())
-     2.35±0.04μs      2.06±0.01μs     0.87  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000, tzlocal())
-      2.38±0.2μs      2.06±0.02μs     0.86  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000, None)
-      2.45±0.3μs      2.09±0.06μs     0.85  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000, None)
-     2.33±0.07μs      1.99±0.04μs     0.85  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000, datetime.timezone.utc)
-     2.25±0.07μs      1.91±0.05μs     0.85  tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(0, 1000)
-      6.77±0.3μs       5.74±0.1μs     0.85  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 8000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      2.46±0.2μs      2.08±0.02μs     0.85  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011, None)
-      2.46±0.2μs      2.06±0.02μs     0.84  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000, None)
-        7.08±1μs      5.91±0.09μs     0.83  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-      2.50±0.8μs      2.04±0.02μs     0.82  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, tzlocal())
-      6.64±0.1μs       5.41±0.3μs     0.81  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
-     1.62±0.08μs      1.29±0.02μs     0.80  tslibs.resolution.TimeResolution.time_get_resolution('h', 1, None)
-      2.40±0.4μs      1.91±0.02μs     0.79  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, datetime.timezone.utc)
-      2.59±0.1μs      2.03±0.03μs     0.79  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000, None)
-        7.80±2μs      5.83±0.02μs     0.75  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
-        8.13±2μs       5.46±0.2μs     0.67  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
```

```
 asv continuous -f 1.1 upstream/master HEAD -b timeseries

       before           after         ratio
     [fd20f7d3]       [a3f42df7]
     <pd-todatetime-unit_s-float-vs-int~3^2>       <pd-todatetime-unit_s-float-vs-int>
+        559±10μs        799±100μs     1.43  timeseries.DatetimeIndex.time_unique('repeated')
+      2.64±0.2ms       3.68±0.1ms     1.39  timeseries.Lookup.time_lookup_and_cleanup
+     1.94±0.08ms       2.49±0.3ms     1.28  timeseries.ResampleDataFrame.time_method('max')
+         304±8μs         380±60μs     1.25  timeseries.ToDatetimeCacheSmallCount.time_unique_date_strings(True, 500)
+      2.15±0.1ms       2.54±0.1ms     1.18  timeseries.DatetimeIndex.time_unique('tz_local')
+     2.88±0.07ms       3.30±0.2ms     1.15  timeseries.ToDatetimeNONISO8601.time_same_offset
+      3.45±0.1ms       3.89±0.2ms     1.13  timeseries.ToDatetimeCache.time_unique_seconds_and_unit(False)
+      3.05±0.1ms      3.40±0.09ms     1.12  timeseries.ToDatetimeISO8601.time_iso8601_nosep
+      1.23±0.04s       1.37±0.07s     1.11  timeseries.DatetimeIndex.time_to_time('tz_local')
+      5.36±0.1ms       5.94±0.2ms     1.11  timeseries.AsOf.time_asof('DataFrame')
+      15.5±0.1ms       17.1±0.4ms     1.10  timeseries.ToDatetimeFormat.time_no_exact
+      3.64±0.1ms       4.01±0.2ms     1.10  timeseries.ToDatetimeCache.time_dup_seconds_and_unit(False)
+      4.76±0.2ms       5.24±0.1ms     1.10  timeseries.ToDatetimeYYYYMMDD.time_format_YYYYMMDD
``` @jreback with your suggestion (bb8c35b) benchmarks are now (largely) unaffected:
``` 
asv continuous -f 1.1 upstream/master HEAD -b tslibs
       before           after         ratio
     [80f0a74c]       [bb8c35bf]
     <GH35612^2>       <pd-todatetime-unit_s-float-vs-int>
+     1.72±0.02ms      1.93±0.02ms     1.13  tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
+      23.1±0.1μs       25.7±0.2μs     1.12  tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, datetime.timezone(datetime.timedelta(seconds=3600)))
-      2.89±0.3μs      2.63±0.06μs     0.91  tslibs.fields.TimeGetTimedeltaField.time_get_timedelta_field(100, 'ns')
-      25.4±0.1μs      23.0±0.07μs     0.91  tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, None)
-      2.89±0.2μs      2.62±0.09μs     0.91  tslibs.fields.TimeGetTimedeltaField.time_get_timedelta_field(100, 'us')
-      25.3±0.2μs      22.9±0.09μs     0.90  tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, datetime.timezone.utc)
-      1.87±0.2μs      1.69±0.03μs     0.90  tslibs.fields.TimeGetTimedeltaField.time_get_timedelta_field(0, 's')
-      2.14±0.2μs      1.91±0.07μs     0.89  tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 0, datetime.timezone(datetime.timedelta(seconds=3600)))

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
``` thanks @arw2019 very nice! thanks @jreback @WillAyd @jbrockmendel for reviewing!!!