PERF: O(n) speedup in any/all by re-enabling short-circuiting for bool case
At present, the `.all()` and `.any()` functions scale as `O(n)`, more or less regardless of their exit condition being fulfilled:
```
$ asv run -b Any -b All upstream/master
[  0.01%] ··· series_methods.All.time_all                                                                                                                                                                 ok
[  0.01%] ··· ========= ============ ============
              --                   case          
              --------- -------------------------
                  N         fast         slow    
              ========= ============ ============
                 1000     115±3μs      123±5μs   
               1000000   11.1±0.2ms   13.6±0.1ms 
              ========= ============ ============

[  0.01%] ··· series_methods.Any.time_any                                                                                                                                                                 ok
[  0.01%] ··· ========= ============ ============
              --                   case          
              --------- -------------------------
                  N         fast         slow    
              ========= ============ ============
                 1000     118±2μs      117±3μs   
               1000000   14.0±0.2ms   11.6±0.4ms 
              ========= ============ ============

```
The root cause of this was identified by `asv find` to be https://github.com/pandas-dev/pandas/pull/8550, which was released as part of `v0.15.2`. Specifically, we call `isna()` on the entire input prior to dispatching to `np.all`/`np.any`; the latter does short circuit but is insignificant compared to `isna()`.

My proposal here is to exempt `np.bool` from the `isna()` call on the basis that it is not capable of storing `NaN` values; I'm hesitant to make this case much broader than necessary to accommodate calls like `pd.isnull(s).all()`. The `hasattr()` check is necessary due to a `Panel` test case directly invoking `nanall(Panel)`.

The good news is that this approach is very effective:
```
$ asv run -b Any -b All HEAD
[  0.01%] ··· series_methods.All.time_all                                                                                                                                                                 ok
[  0.01%] ··· ========= ============ ============
              --                   case          
              --------- -------------------------
                  N         fast         slow    
              ========= ============ ============
                 1000     59.8±1μs    60.9±0.7μs 
               1000000   73.7±0.4μs    138±4μs   
              ========= ============ ============

[  0.01%] ··· series_methods.Any.time_any                                                                                                                                                                 ok
[  0.01%] ··· ========= ========== ============
              --                  case         
              --------- -----------------------
                  N        fast        slow    
              ========= ========== ============
                 1000    59.7±1μs   59.7±0.8μs 
               1000000   75.7±1μs    133±3μs   
              ========= ========== ============
```
Additionally, this call is not too widely used within the `pandas` codebase as most invoke the `numpy` equivalent. That being said, we do see some broad speedups when running the entire suite:
```
$ asv compare ea013a25 37c2e7a2 -s --sort ratio --only-changed
       before           after         ratio
     [ea013a25]       [37c2e7a2]
     <master~1>       <master>  
-      10.4±0.2ms       9.41±0.1ms     0.90  timeseries.AsOf.time_asof('DataFrame')
-     1.54±0.03ms      1.39±0.01ms     0.90  groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'transformation')
-      38.3±0.8ms       34.3±0.6ms     0.90  stat_ops.FrameOps.time_op('median', 'float', 1, True)
-     1.18±0.03ms      1.05±0.01ms     0.89  groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'direct')
-      23.2±0.3ms       20.8±0.2ms     0.89  groupby.MultiColumn.time_cython_sum
-     1.19±0.05ms      1.06±0.01ms     0.89  groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct')
-         128±1ms          114±2ms     0.89  indexing.CategoricalIndexIndexing.time_get_indexer_list('non_monotonic')
-     4.12±0.08ms      3.64±0.08ms     0.88  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     1.23±0.02ms      1.09±0.01ms     0.88  groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation')
-     1.20±0.04ms      1.06±0.02ms     0.88  groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct')
-     1.20±0.02ms      1.06±0.01ms     0.88  groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'direct')
-     10.5±0.08ms       9.12±0.3ms     0.87  timeseries.AsOf.time_asof_nan('DataFrame')
-      6.30±0.3ms       5.46±0.2ms     0.87  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     1.55±0.04ms      1.34±0.02ms     0.86  groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'transformation')
-         130±1ms        112±0.8ms     0.86  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr')
-      28.1±0.7ms       22.9±0.3ms     0.82  reshape.Cut.time_cut_float(10)
-      26.4±0.4ms         21.0±1ms     0.80  reshape.Cut.time_cut_float(4)
-        12.7±2ms       9.06±0.2ms     0.71  inference.DateInferOps.time_subtract_datetimes
-     4.33±0.05ms      3.03±0.04ms     0.70  timeseries.AsOf.time_asof_single('DataFrame')
-      1.04±0.2ms          681±7μs     0.66  groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'transformation')
-     4.51±0.05ms      2.69±0.05ms     0.60  timeseries.AsOf.time_asof_nan_single('DataFrame')
-         112±2μs       59.5±0.8μs     0.53  series_methods.All.time_all(1000, 'fast')
-         115±2μs         60.7±1μs     0.53  series_methods.Any.time_any(1000, 'fast')
-         117±7μs         61.3±2μs     0.53  series_methods.All.time_all(1000, 'slow')
-         115±1μs         59.8±1μs     0.52  series_methods.Any.time_any(1000, 'slow')
-      2.39±0.6ms      1.20±0.01ms     0.50  stat_ops.SeriesOps.time_op('kurt', 'int', False)
-      2.45±0.6ms      1.21±0.01ms     0.49  stat_ops.SeriesOps.time_op('skew', 'int', False)
-      2.43±0.6ms      1.20±0.01ms     0.49  stat_ops.SeriesOps.time_op('kurt', 'int', True)
-      11.1±0.2ms        132±0.5μs     0.01  series_methods.Any.time_any(1000000, 'slow')
-      13.5±0.2ms          133±2μs     0.01  series_methods.All.time_all(1000000, 'slow')
-      10.9±0.3ms         74.9±1μs     0.01  series_methods.All.time_all(1000000, 'fast')
-      13.4±0.3ms         75.3±2μs     0.01  series_methods.Any.time_any(1000000, 'fast')
```
For the largest `n` tested, we see a speedup of `~170x` in the fast case.

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

Hello @qwhelan! Thanks for updating this PR. We checked the lines you've touched for [PEP 8](https://www.python.org/dev/peps/pep-0008) issues, and found:





There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: 

##### Comment last updated at 2019-04-29 05:29:16 UTC # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=h1) Report
> Merging [#25070](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/ea013a254847fbc5551e3ec91a468370c7118589?src=pr&el=desc) will **decrease** coverage by `<.01%`.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/25070/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #25070      +/-   ##
==========================================
- Coverage   92.37%   92.36%   -0.01%     
==========================================
  Files         166      166              
  Lines       52403    52409       +6     
==========================================
+ Hits        48405    48410       +5     
- Misses       3998     3999       +1
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.79% <100%> (ø)` | :arrow_up: |
| #single | `42.88% <100%> (+0.01%)` | :arrow_up: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/nanops.py](https://codecov.io/gh/pandas-dev/pandas/pull/25070/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvbmFub3BzLnB5) | `93.9% <100%> (+0.06%)` | :arrow_up: |
| [pandas/util/testing.py](https://codecov.io/gh/pandas-dev/pandas/pull/25070/diff?src=pr&el=tree#diff-cGFuZGFzL3V0aWwvdGVzdGluZy5weQ==) | `88.04% <0%> (-0.1%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/25070?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/25070?src=pr&el=footer). Last update [ea013a2...3acc6b6](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=h1) Report
> Merging [#25070](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/9feb3ad92cc0397a04b665803a49299ee7aa1037?src=pr&el=desc) will **decrease** coverage by `<.01%`.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/25070/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #25070      +/-   ##
==========================================
- Coverage   91.97%   91.96%   -0.01%     
==========================================
  Files         175      175              
  Lines       52368    52378      +10     
==========================================
+ Hits        48164    48171       +7     
- Misses       4204     4207       +3
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.52% <100%> (ø)` | :arrow_up: |
| #single | `40.69% <40.62%> (-0.15%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/nanops.py](https://codecov.io/gh/pandas-dev/pandas/pull/25070/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvbmFub3BzLnB5) | `94.11% <100%> (+0.28%)` | :arrow_up: |
| [pandas/io/gbq.py](https://codecov.io/gh/pandas-dev/pandas/pull/25070/diff?src=pr&el=tree#diff-cGFuZGFzL2lvL2dicS5weQ==) | `78.94% <0%> (-10.53%)` | :arrow_down: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/25070/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `96.9% <0%> (-0.12%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/25070?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/25070?src=pr&el=footer). Last update [9feb3ad...0387154](https://codecov.io/gh/pandas-dev/pandas/pull/25070?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 Does the implementation become any simpler after Panel is removed? can you merge master Apologies for the delay - interviewing processes have gotten in the way here.

@jbrockmendel Not especially.

@jreback Rebased on master. I've kept my additions in response to your comments as a separate commit for now - will squash if it seems they're going in the right general direction.

My PR to fix the underlying performance bug is here https://github.com/numpy/numpy/pull/12988 and pending merge; hopefully it will make it into numpy 1.17 and to a large extent obviate the need for this change. It may make sense to expand this PR to include all integer `dtypes`, as they also cannot store `NaN`.

Additionally, `np.any()` and `np.all()` attempt to short-circuit but actually exhibit `O(n)` behavior due to processing of reduction ufuncs in `NPY_BUFSIZE` (ie, 8192 byte) chunks. I'm investigating ways to remove or increase the buffer size there, which should make `pd.all()` and `pd.any()` considerably faster. It's worth noting that there's no attempt to short-circuit for any non-`bool` dtypes. can you merge master and update to comments I'm going to see if I can simplify further - I was able to remove the "copy=" parameter from `_get_values()`.

That being said, allowing `mask=None` requires some more involved modifications that also allow for some decent performance gains. The most recent commit extends the speedups across all `nanops` and to all `integer` dtypes. In particular, we're now getting a `7-8x` speedup in `nansum()` and `nanmean()` for `int64` data. The `nanprod()`, `nanvar()`, and `nanstd()` functions see a `~3x` speedup:
```
$ asv compare upstream/master HEAD -s --sort ratio --only-changed
       before           after         ratio
     [68b1da7f]       [d96b086c]
     <any_all_fix~3>       <any_all_fix>
-     2.06±0.01ms         1.85±0ms     0.89  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-      52.3±0.5ms       46.5±0.7ms     0.89  reshape.Cut.time_cut_int(1000)
-         162±3ms        141±0.8ms     0.87  groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'transformation')
-         238±2ms          208±2ms     0.87  groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'direct')
-         240±3ms          208±2ms     0.87  groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'transformation')
-         374±2ms          324±3ms     0.87  groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation')
-         373±3ms          321±2ms     0.86  groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct')
-      24.6±0.4ms       21.1±0.2ms     0.86  groupby.Nth.time_groupby_nth_all('float64')
-     4.67±0.07ms      3.98±0.08ms     0.85  reshape.SimpleReshape.time_stack
-      25.2±0.4ms       21.4±0.4ms     0.85  groupby.Nth.time_groupby_nth_all('datetime')
-        971±10μs         826±10μs     0.85  stat_ops.SeriesOps.time_op('median', 'int', False)
-         511±7μs         433±10μs     0.85  groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'transformation')
-         166±4ms          140±2ms     0.85  groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'direct')
-        983±10μs         821±30μs     0.84  stat_ops.SeriesOps.time_op('median', 'int', True)
-     6.33±0.02ms      5.27±0.04ms     0.83  timeseries.AsOf.time_asof('DataFrame')
-     16.9±0.07ms       14.1±0.1ms     0.83  indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-         115±7μs         91.2±6μs     0.80  series_methods.NanOps.time_std(1000, 'int64')
-      4.23±0.1ms       3.36±0.1ms     0.79  indexing.NumericSeriesIndexing.time_ix_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     6.42±0.06ms      5.03±0.03ms     0.78  timeseries.AsOf.time_asof_nan('DataFrame')
-        218±10μs          170±4μs     0.78  series_methods.NanOps.time_sem(1000, 'int64')
-      18.5±0.4ms       14.0±0.3ms     0.76  algorithms.Hashing.time_frame
-        70.4±2μs         53.2±4μs     0.76  series_methods.NanOps.time_sum(1000, 'int64')
-       39.3±10ms       29.7±0.5ms     0.75  series_methods.NanOps.time_sem(1000000, 'int64')
-     2.79±0.05ms      2.05±0.05ms     0.73  timeseries.AsOf.time_asof_single('DataFrame')
-      26.1±0.2ms         19.1±2ms     0.73  stat_ops.FrameOps.time_op('sem', 'int', 1, False)
-      26.2±0.2ms         19.2±2ms     0.73  stat_ops.FrameOps.time_op('sem', 'int', 1, True)
-        21.7±2ms         15.6±2ms     0.72  stat_ops.FrameOps.time_op('sem', 'int', 0, False)
-        21.8±2ms         15.6±2ms     0.72  stat_ops.FrameOps.time_op('sem', 'int', 0, True)
-         258±4μs          184±4μs     0.71  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), 'monotonic_incr')
-         248±3μs          176±2μs     0.71  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), 'monotonic_incr')
-        91.8±3μs         64.7±6μs     0.71  series_methods.NanOps.time_var(1000, 'int64')
-         129±3ms       90.5±0.4ms     0.70  frame_methods.Describe.time_dataframe_describe
-         312±3μs          217±1μs     0.69  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.UInt32Engine'>, <class 'numpy.uint32'>), 'monotonic_incr')
-        78.1±4μs         53.8±3μs     0.69  series_methods.NanOps.time_mean(1000, 'int64')
-      41.0±0.2ms       28.2±0.2ms     0.69  frame_methods.Describe.time_series_describe
-        89.0±6μs         61.0±4μs     0.69  series_methods.NanOps.time_skew(1000, 'int64')
-        92.7±4μs         62.0±2μs     0.67  series_methods.NanOps.time_kurt(1000, 'int64')
-        35.9±4ms         23.6±4ms     0.66  series_methods.NanOps.time_skew(1000000, 'int64')
-     2.92±0.02ms      1.91±0.03ms     0.66  timeseries.AsOf.time_asof_nan_single('DataFrame')
-      27.3±0.8ms       17.8±0.3ms     0.65  stat_ops.FrameOps.time_op('skew', 'int', 1, True)
-        59.2±2μs         38.6±3μs     0.65  series_methods.NanOps.time_argmax(1000, 'int64')
-     1.55±0.01ms      1.01±0.06ms     0.65  stat_ops.SeriesOps.time_op('sem', 'int', False)
-      9.85±0.3ms       6.36±0.1ms     0.65  groupby.TransformBools.time_transform_mean
-        36.1±4ms         23.2±4ms     0.64  series_methods.NanOps.time_kurt(1000000, 'int64')
-        51.2±1μs         32.7±3μs     0.64  series_methods.NanOps.time_prod(1000, 'int64')
-     1.58±0.04ms         999±60μs     0.63  stat_ops.SeriesOps.time_op('sem', 'int', True)
-         208±1ms        128±0.6ms     0.62  frame_methods.Dropna.time_dropna('all', 1)
-         207±1ms        125±0.6ms     0.60  frame_methods.Dropna.time_dropna('all', 0)
-        6.96±4ms      4.18±0.02ms     0.60  algorithms.Duplicated.time_duplicated(False, 'int')
-      24.9±0.1ms       14.9±0.2ms     0.60  stat_ops.FrameOps.time_op('kurt', 'int', 1, False)
-         253±2ms          151±4ms     0.60  frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 1)
-        53.3±2μs         31.6±3μs     0.59  series_methods.All.time_all(1000, 'slow')
-      25.2±0.9ms       14.9±0.2ms     0.59  stat_ops.FrameOps.time_op('kurt', 'int', 1, True)
-        62.9±3μs         36.4±3μs     0.58  series_methods.NanOps.time_max(1000, 'int64')
-     5.05±0.04ms      2.93±0.01ms     0.58  stat_ops.FrameOps.time_op('median', 'int', 0, False)
-     5.07±0.03ms      2.93±0.03ms     0.58  stat_ops.FrameOps.time_op('median', 'int', 0, True)
-      16.2±0.1ms         9.29±2ms     0.57  stat_ops.FrameOps.time_op('skew', 'int', 0, False)
-      16.1±0.6ms         9.22±1ms     0.57  stat_ops.FrameOps.time_op('kurt', 'int', 0, True)
-      16.1±0.2ms         9.21±2ms     0.57  stat_ops.FrameOps.time_op('kurt', 'int', 0, False)
-        16.3±1ms         9.28±2ms     0.57  stat_ops.FrameOps.time_op('skew', 'int', 0, True)
-        63.9±4μs         35.4±1μs     0.55  series_methods.NanOps.time_min(1000, 'int64')
-        54.6±3μs         30.0±2μs     0.55  series_methods.All.time_all(1000, 'fast')
-        54.7±3μs         28.7±2μs     0.53  series_methods.Any.time_any(1000, 'fast')
-         264±1ms        136±0.3ms     0.51  frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 0)
-        56.8±4μs         28.3±1μs     0.50  series_methods.Any.time_any(1000, 'slow')
-        852±20μs         388±10μs     0.46  stat_ops.SeriesOps.time_op('skew', 'int', False)
-        852±10μs          376±6μs     0.44  stat_ops.SeriesOps.time_op('skew', 'int', True)
-         852±9μs          376±6μs     0.44  stat_ops.SeriesOps.time_op('kurt', 'int', True)
-         292±3μs        123±0.9μs     0.42  stat_ops.SeriesOps.time_op('prod', 'int', True)
-         288±5μs          120±3μs     0.42  stat_ops.SeriesOps.time_op('prod', 'int', False)
-     1.23±0.01ms          490±4μs     0.40  stat_ops.FrameOps.time_op('prod', 'int', 0, True)
-     1.24±0.02ms          490±5μs     0.40  stat_ops.FrameOps.time_op('prod', 'int', 0, False)
-       931±100μs          369±6μs     0.40  stat_ops.SeriesOps.time_op('kurt', 'int', False)
-       158±0.6ms       62.5±0.4ms     0.40  frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 1)
-         322±7μs          123±4μs     0.38  stat_ops.SeriesOps.time_op('mean', 'int', True)
-        708±10μs          261±8μs     0.37  stat_ops.SeriesOps.time_op('std', 'int', False)
-         328±3μs          120±2μs     0.37  stat_ops.SeriesOps.time_op('mean', 'int', False)
-         681±9μs         249±10μs     0.37  stat_ops.SeriesOps.time_op('var', 'int', False)
-      3.03±0.9ms      1.10±0.01ms     0.36  series_methods.NanOps.time_argmax(1000000, 'int64')
-        701±10μs          251±4μs     0.36  stat_ops.SeriesOps.time_op('std', 'int', True)
-     2.70±0.01ms          966±9μs     0.36  series_methods.NanOps.time_prod(1000000, 'int64')
-         690±6μs          243±3μs     0.35  stat_ops.SeriesOps.time_op('var', 'int', True)
-         305±7μs          103±3μs     0.34  stat_ops.SeriesOps.time_op('sum', 'int', False)
-        311±10μs         99.1±3μs     0.32  stat_ops.SeriesOps.time_op('sum', 'int', True)
-         121±1ms         38.4±1ms     0.32  frame_methods.Dropna.time_dropna('any', 0)
-       114±0.3ms       35.5±0.5ms     0.31  frame_methods.Dropna.time_dropna('any', 1)
-      8.80±0.2ms      2.64±0.07ms     0.30  stat_ops.FrameOps.time_op('std', 'int', 1, True)
-        17.5±4ms         5.20±4ms     0.30  series_methods.NanOps.time_std(1000000, 'int64')
-      8.83±0.2ms       2.61±0.2ms     0.30  stat_ops.FrameOps.time_op('std', 'int', 1, False)
-        8.61±1ms       2.41±0.3ms     0.28  stat_ops.FrameOps.time_op('var', 'int', 1, False)
-     1.37±0.01ms         382±10μs     0.28  stat_ops.FrameOps.time_op('prod', 'int', 1, True)
-     8.59±0.06ms      2.38±0.08ms     0.28  stat_ops.FrameOps.time_op('var', 'int', 1, True)
-     1.40±0.03ms         385±10μs     0.27  stat_ops.FrameOps.time_op('prod', 'int', 1, False)
-        17.8±4ms         4.75±4ms     0.27  series_methods.NanOps.time_var(1000000, 'int64')
-        3.70±1ms         963±10μs     0.26  series_methods.NanOps.time_mean(1000000, 'int64')
-      7.49±0.1ms      1.92±0.07ms     0.26  stat_ops.FrameOps.time_op('var', 'int', 0, True)
-      7.48±0.1ms      1.89±0.04ms     0.25  stat_ops.FrameOps.time_op('std', 'int', 0, False)
-      7.42±0.1ms      1.86±0.06ms     0.25  stat_ops.FrameOps.time_op('var', 'int', 0, False)
-      7.47±0.1ms      1.85±0.09ms     0.25  stat_ops.FrameOps.time_op('std', 'int', 0, True)
-         169±1ms       41.0±0.4ms     0.24  frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 0)
-        3.39±1ms         723±10μs     0.21  series_methods.NanOps.time_min(1000000, 'int64')
-        3.67±1ms         722±10μs     0.20  series_methods.NanOps.time_max(1000000, 'int64')
-        3.40±1ms          649±9μs     0.19  series_methods.NanOps.time_sum(1000000, 'int64')
-     3.93±0.04ms          646±9μs     0.16  stat_ops.FrameOps.time_op('mean', 'int', 1, True)
-     3.91±0.03ms         641±10μs     0.16  stat_ops.FrameOps.time_op('mean', 'int', 1, False)
-     3.28±0.03ms         493±10μs     0.15  stat_ops.FrameOps.time_op('mean', 'int', 0, False)
-     3.27±0.03ms          488±8μs     0.15  stat_ops.FrameOps.time_op('mean', 'int', 0, True)
-     2.69±0.04ms         381±10μs     0.14  stat_ops.FrameOps.time_op('sum', 'int', 0, False)
-     2.70±0.02ms          375±5μs     0.14  stat_ops.FrameOps.time_op('sum', 'int', 0, True)
-     2.95±0.01ms         400±10μs     0.14  stat_ops.FrameOps.time_op('sum', 'int', 1, True)
-     2.94±0.02ms          383±8μs     0.13  stat_ops.FrameOps.time_op('sum', 'int', 1, False)
-      6.29±0.1ms       70.3±0.8μs     0.01  series_methods.Any.time_any(1000000, 'slow')
-      6.48±0.2ms         68.0±4μs     0.01  series_methods.All.time_all(1000000, 'slow')
-      6.50±0.2ms       37.2±0.9μs     0.01  series_methods.Any.time_any(1000000, 'fast')
-     6.37±0.07ms         35.5±1μs     0.01  series_methods.All.time_all(1000000, 'fast')
``` Updated to reflect comments - the single failing test appears to be unrelated. ping thanks @qwhelan very nice!