implement bits of numpy_helper in cython where possible
Like with the transition to tslibs.np_datetime, this implements pieces of numpy_helper.h directly in cython in util.pxd.  The generated C should be equivalent to existing versions, but that is worth double-checking.

One dependency is removed from setup.py that was missed in #19415, should have been deleted there.
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=h1) Report
> Merging [#19450](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/4618a0918e1bbdb40a493d8a32d46ab8c94fd0b4?src=pr&el=desc) will **decrease** coverage by `0.02%`.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/19450/graphs/tree.svg?height=150&width=650&token=eZ4WkYLtcO&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #19450      +/-   ##
==========================================
- Coverage   91.62%    91.6%   -0.03%     
==========================================
  Files         150      150              
  Lines       48724    48724              
==========================================
- Hits        44644    44632      -12     
- Misses       4080     4092      +12
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `89.97% <ø> (-0.03%)` | :arrow_down: |
| #single | `41.74% <ø> (ø)` | :arrow_up: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/plotting/\_converter.py](https://codecov.io/gh/pandas-dev/pandas/pull/19450/diff?src=pr&el=tree#diff-cGFuZGFzL3Bsb3R0aW5nL19jb252ZXJ0ZXIucHk=) | `65.22% <0%> (-1.74%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=footer). Last update [4618a09...7034c8e](https://codecov.io/gh/pandas-dev/pandas/pull/19450?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 looks ok. needs a perf test, pls run with affinity!  Will do.  Might take a while.  In the interim, I'm hopeful that #19301 may be good to go, possibly also #19336. One run without affinity, three with.  Recently rebooted the machine, might explain the uncharacteristically stable results.

```
asv continuous -E virtualenv -f 1.1 master HEAD -b timeseries
[...]
    before     after       ratio
  [238499ab] [7034c8ef]
+    2.32ms     3.41ms      1.47  timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
+    2.40ms     3.28ms      1.36  timeseries.ToDatetimeCache.time_dup_seconds_and_unit(False)
-  128.73ms   116.98ms      0.91  timeseries.DatetimeIndex.time_to_time('tz_aware')
-    2.59ms     2.35ms      0.91  timeseries.ResampleSeries.time_resample('datetime', '5min', 'ohlc')
-    6.95ms     3.58ms      0.51  timeseries.ToDatetimeCache.time_dup_seconds_and_unit(True)
```

```
taskset 4 asv continuous -E virtualenv -f 1.1 master HEAD -b timeseries
[...]
before     after       ratio
  [238499ab] [7034c8ef]
+    2.38ms     2.67ms      1.12  timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
-    4.38μs     3.94μs      0.90  timeseries.DatetimeIndex.time_get('dst')
-  498.15ms   440.07ms      0.88  timeseries.ToDatetimeFormat.time_exact
-    7.55ms     6.48ms      0.86  timeseries.Factorize.time_factorize('Asia/Tokyo')
```

```
taskset 4 asv continuous -E virtualenv -f 1.1 master HEAD -b timeseries
[...]
before     after       ratio
  [238499ab] [7034c8ef]
+    2.05ms     2.31ms      1.13  timeseries.ResampleSeries.time_resample('datetime', '1D', 'ohlc')
-    1.86ms     1.68ms      0.91  timeseries.DatetimeIndex.time_add_timedelta('repeated')
```

```
taskset 4 asv continuous -E virtualenv -f 1.1 master HEAD -b timeseries
[...]
    before     after       ratio
  [238499ab] [7034c8ef]
+    2.40ms     2.66ms      1.11  timeseries.ResampleSeries.time_resample('datetime', '5min', 'ohlc')
-   17.84μs    16.14μs      0.90  timeseries.AsOf.time_asof_single('Series')
-  137.94ms   123.97ms      0.90  timeseries.DatetimeIndex.time_to_pydatetime('tz_aware')
-   11.96ms    10.71ms      0.90  timeseries.Iteration.time_iter_preexit(<function period_range at 0x7f37a14670c8>)
``` pls run on a lot more benchmarks, this change affects practically everything. I don't expect anything but, need to check. ```taskset 4 asv continuous -E virtualenv -f 1.1 master HEAD
[...]
    before     after       ratio
  [238499ab] [7034c8ef]
+    1.47ms     2.32ms      1.58  sparse.ArithmeticBlock.time_intersect(nan)
+    1.11ms     1.49ms      1.34  inference.NumericInferOps.time_subtract(<type 'numpy.int64'>)
+    2.10ms     2.77ms      1.32  gil.ParallelRolling.time_rolling('rolling_max')
+    9.46μs    12.00μs      1.27  multiindex_object.GetLoc.time_med_get_loc
+    6.33ms     7.94ms      1.25  gil.ParallelReadCSV.time_read_csv('object')
+   52.96ms    65.09ms      1.23  gil.ParallelGroupbyMethods.time_parallel(4, 'max')
+   17.04μs    20.85μs      1.22  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessYearEnd: month=12>)
+    8.98μs    10.98μs      1.22  offset.OffestDatetimeArithmetic.time_apply(<BusinessMonthBegin>)
+   24.36ms    29.60ms      1.21  gil.ParallelGroupbyMethods.time_parallel(2, 'max')
+    2.02ms     2.45ms      1.21  stat_ops.Correlation.time_corr('pearson')
+   22.94μs    27.18μs      1.18  offset.OffestDatetimeArithmetic.time_subtract_10(<Day>)
+  808.74μs   952.88μs      1.18  inference.NumericInferOps.time_subtract(<type 'numpy.int32'>)
+    9.21μs    10.81μs      1.17  offset.OffestDatetimeArithmetic.time_apply(<BusinessYearEnd: month=12>)
+   10.13μs    11.67μs      1.15  offset.OffestDatetimeArithmetic.time_apply(<SemiMonthBegin: day_of_month=15>)
+    1.76ms     2.03ms      1.15  categoricals.Concat.time_concat
+    6.81μs     7.73μs      1.14  timestamp.TimestampProperties.time_is_quarter_end(None, 'B')
+    2.06ms     2.33ms      1.13  timeseries.ResampleSeries.time_resample('datetime', '1D', 'ohlc')
+     2.41s      2.73s      1.13  groupby.FirstLast.time_groupby_last('object')
+   11.65μs    13.06μs      1.12  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessDay>)
+   17.12μs    19.16μs      1.12  period.PeriodUnaryMethods.time_asfreq('min')
+   63.06ms    70.42ms      1.12  timeseries.ToDatetimeISO8601.time_iso8601_tz_spaceformat
+   10.90μs    12.17μs      1.12  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<DateOffset: kwds={'months': 2, 'days': 2}>)
+   69.86ms    77.84ms      1.11  gil.ParallelGroupbyMethods.time_parallel(4, 'var')
+    5.59ms     6.22ms      1.11  timeseries.Factorize.time_factorize('Asia/Tokyo')
+   15.32μs    17.02μs      1.11  offset.OffestDatetimeArithmetic.time_add_10(<SemiMonthEnd: day_of_month=15>)
+  837.40ns   928.48ns      1.11  period.PeriodProperties.time_property('M', 'quarter')
+   16.62μs    18.42μs      1.11  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessDay>)
+   11.01μs    12.20μs      1.11  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthEnd>)
+   58.41ms    64.71ms      1.11  rolling.Methods.time_rolling('Series', 1000, 'float', 'median')
+    9.21ms    10.20ms      1.11  stat_ops.FrameOps.time_op('skew', 'int', 0, False)
+  125.17ms   138.37ms      1.11  gil.ParallelGroupbyMethods.time_parallel(8, 'count')
+    5.92ms     6.53ms      1.10  groupby.Float32.time_sum
+  763.14μs   840.71μs      1.10  inference.NumericInferOps.time_subtract(<type 'numpy.uint32'>)
+   21.81ms    24.00ms      1.10  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<CustomBusinessDay>)
+    4.74ms     5.22ms      1.10  stat_ops.SeriesMultiIndexOps.time_op(0, 'sem')
-    3.48μs     3.15μs      0.91  timedelta.TimedeltaConstructor.time_from_int
-   10.60μs     9.59μs      0.91  offset.OffestDatetimeArithmetic.time_apply(<QuarterBegin: startingMonth=3>)
-  119.58ms   107.69ms      0.90  gil.ParallelGroupbyMethods.time_parallel(8, 'min')
-   12.22μs    10.97μs      0.90  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<YearBegin: month=1>)
-   30.60ms    27.43ms      0.90  gil.ParallelFactorize.time_parallel(4)
-   11.00μs     9.83μs      0.89  timedelta.TimedeltaConstructor.time_from_components
-   20.33μs    18.12μs      0.89  offset.OffestDatetimeArithmetic.time_add(<CustomBusinessDay>)
-  144.10ms   128.25ms      0.89  timeseries.DatetimeIndex.time_to_pydatetime('tz_aware')
-   29.40ms    26.11ms      0.89  gil.ParallelGroupbyMethods.time_parallel(2, 'sum')
-  325.11μs   288.09μs      0.89  inference.NumericInferOps.time_multiply(<type 'numpy.uint8'>)
-   17.48μs    15.46μs      0.88  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterEnd: startingMonth=3>)
-    6.91μs     6.09μs      0.88  algorithms.Duplicated.time_duplicated_string(False)
-  103.69ms    91.25ms      0.88  timedelta.ToTimedeltaErrors.time_convert('coerce')
-   16.28μs    14.30μs      0.88  offset.OffestDatetimeArithmetic.time_subtract(<YearBegin: month=1>)
-   12.94μs    11.32μs      0.87  timestamp.TimestampProperties.time_is_month_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
-    3.12ms     2.72ms      0.87  algorithms.Duplicated.time_duplicated_int(False)
-  376.11μs   327.12μs      0.87  inference.NumericInferOps.time_multiply(<type 'numpy.uint16'>)
-  322.78μs   280.49μs      0.87  inference.NumericInferOps.time_multiply(<type 'numpy.int8'>)
-   12.12ms    10.52ms      0.87  multiindex_object.GetLoc.time_small_get_loc_warm
-  601.16ns   516.43ns      0.86  timestamp.TimestampProperties.time_weekday_name(None, 'B')
-   15.89μs    13.65μs      0.86  offset.OffestDatetimeArithmetic.time_add_10(<QuarterBegin: startingMonth=3>)
-  150.32ms   128.97ms      0.86  gil.ParallelGroupbyMethods.time_parallel(8, 'var')
-   11.73ms    10.03ms      0.85  multiindex_object.GetLoc.time_med_get_loc_warm
-   18.55ms    15.82ms      0.85  period.DataFramePeriodColumn.time_setitem_period_column
-  754.02μs   642.96μs      0.85  inference.NumericInferOps.time_add(<type 'numpy.uint32'>)
-   21.19μs    18.02μs      0.85  offset.OffestDatetimeArithmetic.time_add(<Day>)
-    7.92μs     6.73μs      0.85  timestamp.TimestampProperties.time_is_year_start(None, 'B')
-  123.69ms   101.66ms      0.82  gil.ParallelGroupbyMethods.time_parallel(8, 'max')
-   12.10μs     9.86μs      0.81  multiindex_object.GetLoc.time_string_get_loc
-   23.77μs    19.35μs      0.81  period.PeriodUnaryMethods.time_now('M')
-   77.71ms    63.17ms      0.81  groupby.MultiColumn.time_col_select_lambda_sum
-   85.82ms    69.71ms      0.81  join_merge.ConcatDataFrames.time_c_ordered(1, False)
```

```
taskset 4 asv continuous -E virtualenv -f 1.1 master HEAD
[...]
    before     after       ratio
  [238499ab] [7034c8ef]
+    2.05ms     3.27ms      1.59  gil.ParallelRolling.time_rolling('rolling_max')
+    1.16μs     1.46μs      1.25  timedelta.TimedeltaConstructor.time_from_missing
+    5.65ms     7.03ms      1.24  gil.ParallelReadCSV.time_read_csv('object')
+   65.03ms    79.15ms      1.22  gil.ParallelGroupbyMethods.time_parallel(4, 'var')
+    2.07ms     2.52ms      1.22  gil.ParallelRolling.time_rolling('rolling_min')
+    6.33μs     7.66μs      1.21  timedelta.TimedeltaConstructor.time_from_string
+    3.26μs     3.90μs      1.19  timedelta.TimedeltaConstructor.time_from_unit
+    9.94μs    11.80μs      1.19  multiindex_object.GetLoc.time_string_get_loc
+  288.30ns   336.67ns      1.17  timestamp.TimestampProperties.time_dayofweek(None, 'B')
+  720.24μs   839.99μs      1.17  inference.NumericInferOps.time_add(<type 'numpy.uint32'>)
+    5.20μs     6.06μs      1.16  timestamp.TimestampOps.time_replace_None('US/Eastern')
+  289.00ns   332.24ns      1.15  timestamp.TimestampProperties.time_is_quarter_start(None, None)
+  888.09μs     1.01ms      1.14  inference.NumericInferOps.time_multiply(<type 'numpy.int32'>)
+   15.10μs    17.12μs      1.13  offset.OffestDatetimeArithmetic.time_add_10(<BusinessDay>)
+  771.36μs   872.03μs      1.13  inference.ToNumericDowncast.time_downcast('datetime64', None)
+   11.50μs    12.98μs      1.13  timestamp.TimestampProperties.time_is_month_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+   56.30ms    63.53ms      1.13  gil.ParallelGroupbyMethods.time_parallel(4, 'sum')
+   18.32μs    20.61μs      1.12  offset.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthBegin: day_of_month=15>)
+    6.82μs     7.65μs      1.12  timestamp.TimestampProperties.time_is_quarter_end(None, 'B')
+   30.52μs    34.03μs      1.12  offset.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessDay>)
+    6.20ms     6.90ms      1.11  timeseries.Factorize.time_factorize(None)
+  985.93μs     1.10ms      1.11  inference.NumericInferOps.time_multiply(<type 'numpy.uint64'>)
+   11.13μs    12.37μs      1.11  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterEnd: startingMonth=3>)
+  273.67μs   303.79μs      1.11  inference.NumericInferOps.time_multiply(<type 'numpy.int8'>)
+   15.61μs    17.33μs      1.11  offset.OffestDatetimeArithmetic.time_subtract_10(<YearEnd: month=12>)
+  771.44μs   854.41μs      1.11  inference.NumericInferOps.time_add(<type 'numpy.float32'>)
+  152.52μs   168.34μs      1.10  join_merge.Concat.time_concat_empty_right(1)
+    5.26ms     5.79ms      1.10  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'sem')
+   11.27μs    12.41μs      1.10  timestamp.TimestampProperties.time_is_year_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+  134.58ms   148.19ms      1.10  gil.ParallelGroupbyMethods.time_parallel(8, 'var')
-   21.32μs    19.37μs      0.91  offset.OffestDatetimeArithmetic.time_add_10(<Day>)
-    1.21ms     1.09ms      0.91  inference.NumericInferOps.time_multiply(<type 'numpy.int64'>)
-   15.67μs    14.19μs      0.91  offset.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterEnd: startingMonth=3>)
-   29.13ms    26.36ms      0.90  join_merge.Concat.time_concat_small_frames(0)
-    4.09ms     3.70ms      0.90  timeseries.ToDatetimeISO8601.time_iso8601
-   17.84μs    16.13μs      0.90  timeseries.AsOf.time_asof_single('Series')
-   18.21μs    16.35μs      0.90  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessYearEnd: month=12>)
-   65.01ms    58.22ms      0.90  gil.ParallelGroupbyMethods.time_parallel(4, 'max')
-   65.68ms    58.76ms      0.89  gil.ParallelGroupbyMethods.time_parallel(4, 'mean')
-    8.40ms     7.49ms      0.89  frame_methods.MaskBool.time_frame_mask_floats
-   31.52ms    28.11ms      0.89  gil.ParallelFactorize.time_parallel(4)
-  310.70μs   274.38μs      0.88  inference.NumericInferOps.time_add(<type 'numpy.int8'>)
-   29.68ms    26.20ms      0.88  gil.ParallelGroupbyMethods.time_parallel(2, 'last')
-  128.31ms   112.87ms      0.88  gil.ParallelGroupbyMethods.time_parallel(8, 'max')
-    1.01ms   886.81μs      0.88  inference.NumericInferOps.time_subtract(<type 'numpy.int32'>)
-   52.00ms    45.52ms      0.88  plotting.TimeseriesPlotting.time_plot_regular_compat
-   14.88μs    13.03μs      0.88  offset.OffestDatetimeArithmetic.time_add_10(<MonthBegin>)
-   18.81μs    16.36μs      0.87  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessDay>)
-    3.89ms     3.37ms      0.87  algorithms.Duplicated.time_duplicated_float('first')
-    1.38ms     1.19ms      0.86  stat_ops.FrameOps.time_op('prod', 'int', 0, False)
-   12.26μs    10.60μs      0.86  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessYearEnd: month=12>)
-    3.09ms     2.62ms      0.85  inference.ToNumeric.time_from_numeric_str('coerce')
-    3.90ms     3.31ms      0.85  algorithms.Duplicated.time_duplicated_float('last')
-   17.10μs    14.35μs      0.84  offset.OffestDatetimeArithmetic.time_subtract(<BusinessMonthEnd>)
-   28.26ms    23.44ms      0.83  gil.ParallelGroupbyMethods.time_parallel(2, 'max')
-   29.33ms    23.86ms      0.81  gil.ParallelGroupbyMethods.time_parallel(2, 'prod')
-   85.89ms    69.82ms      0.81  join_merge.ConcatDataFrames.time_c_ordered(1, False)
-    2.79ms     2.14ms      0.76  stat_ops.Correlation.time_corr('pearson')
-   66.96ms    49.72ms      0.74  gil.ParallelGroupbyMethods.time_parallel(4, 'prod')
-  163.15ms   108.13ms      0.66  gil.ParallelGroupbyMethods.time_loop(8, 'sum')
``` thanks!