Add asv benchmarks for essential functions
I've jury-rigged `asv` with `coverage` to enable automatic identification of what parts of the code base are lacking `asv` benchmark coverage and found a number of essential functions on my first pass:
 - `pd.DataFrame.rename()`
 - `pd.cut()` / `pd.qcut()`
 - `.dot()`
 - Any `.plot()` method other than `line`
 - Lots of `.str` methods
 - `.corr()` and `.cov()` (we're covering the rolling version, which is a different path than full-sample)
 - `TimedeltaIndex`

There's still a lot left, but the above represents about a 20% increase in benchmark coverage compared to baseline. (This is a little bit of a hand-wavy metric, as `import`s/`def`s/etc that are normally counted towards coverage are excluded unless explicitly run inside a benchmark, leading to very low benchmark coverage metrics in the neighborhood of 10%).

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

Hello @qwhelan! Thanks for updating the PR.

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/binary_ops.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/binary_ops.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/frame_methods.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/frame_methods.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/plotting.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/plotting.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/reshape.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/reshape.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/rolling.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/rolling.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/stat_ops.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/stat_ops.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/strings.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/strings.py) !

 - There are no PEP8 issues in the file [`asv_bench/benchmarks/timedelta.py`](https://github.com/qwhelan/pandas/blob/be0b0cdedb7d7063f29eeebeb084048612eda60d/asv_bench/benchmarks/timedelta.py) !



##### Comment last updated on November 26, 2018 at 23:13 Hours UTC Could you show the output of `asv dev` for these added benchmarks? @mroeschke Sure, `asv run` is still doing the baseline run but here's the `asv dev` output with some of the irrelevant stuff excised. Times are very noisy due to a bug with asv on windows:
```
$ asv dev -b dot -b plot -b rename -b Cut -b Expanding -b EWM -b Correlation -b Covariance -b Methods -b Timedelta
· Discovering benchmarks
· Running 89 total benchmarks (1 commits * 1 environments * 89 benchmarks)
[  0.00%] ·· Benchmarking existing-py_home_chris_anaconda3_bin_python
[  0.56%] ··· binary_ops.Ops2.time_frame_dot                                                                                                                         125±0ms
[  1.12%] ··· binary_ops.Ops2.time_frame_series_dot                                                                                                                    0±0ns
[  1.69%] ··· binary_ops.Ops2.time_series_dot                                                                                                                          0±0ns
[  2.25%] ··· frame_methods.Rename.time_dict_rename_both_axes                                                                                                        734±0ms
[  2.81%] ··· frame_methods.Rename.time_rename_axis0                                                                                                                 531±0ms
[  3.37%] ··· frame_methods.Rename.time_rename_axis1                                                                                                                 500±0ms
[  3.93%] ··· frame_methods.Rename.time_rename_both_axes                                                                                                             719±0ms
[  4.49%] ··· frame_methods.Rename.time_rename_single                                                                                                                531±0ms
[ 10.11%] ··· plotting.FramePlotting.time_frame_plot                                                                                                                      ok
[ 10.11%] ··· ========= =========
                 kind
              --------- ---------
                 line    594±0ms
                 bar     609±0ms
                 area    1.11±0s
                 barh    625±0ms
                 hist    828±0ms
                 kde     2.03±0s
                 pie     547±0ms
               scatter   406±0ms
                hexbin   422±0ms
              ========= =========

[ 10.67%] ··· plotting.Misc.time_plot_andrews_curves                                                                                                                 1.64±0s
[ 11.24%] ··· plotting.SeriesPlotting.time_series_plot                                                                                                                    ok
[ 11.24%] ··· ====== =========
               kind
              ------ ---------
               line   734±0ms
               bar    625±0ms
               area   1.09±0s
               barh   609±0ms
               hist   750±0ms
               kde    1.98±0s
               pie    500±0ms
              ====== =========

[ 11.80%] ··· plotting.TimeseriesPlotting.time_plot_irregular                                                                                                        406±0ms
[ 12.36%] ··· plotting.TimeseriesPlotting.time_plot_regular                                                                                                          438±0ms
[ 12.92%] ··· plotting.TimeseriesPlotting.time_plot_regular_compat                                                                                                   406±0ms
[ 13.48%] ··· replace.Convert.time_replace                                                                                                                                ok
[ 13.48%] ··· ============= =========== ===========
              --                  replace_data
              ------------- -----------------------
               constructor   Timestamp   Timedelta
              ============= =========== ===========
                DataFrame       n/a       547±0ms
                  Series        n/a       547±0ms
              ============= =========== ===========

[ 14.04%] ··· reshape.Cut.time_cut_float                                                                                                                                  ok
[ 14.04%] ··· ====== ==========
               bins
              ------ ----------
                4     78.1±0ms
                10    78.1±0ms
               1000   203±0ms
              ====== ==========

[ 14.61%] ··· reshape.Cut.time_cut_int                                                                                                                                    ok
[ 14.61%] ··· ====== ==========
               bins
              ------ ----------
                4     93.8±0ms
                10    78.1±0ms
               1000   141±0ms
              ====== ==========

[ 15.17%] ··· reshape.Cut.time_qcut_float                                                                                                                                 ok
[ 15.17%] ··· ====== =========
               bins
              ------ ---------
                4     141±0ms
                10    141±0ms
               1000   266±0ms
              ====== =========

[ 15.73%] ··· reshape.Cut.time_qcut_int                                                                                                                                   ok
[ 15.73%] ··· ====== ==========
               bins
              ------ ----------
                4     109±0ms
                10    93.8±0ms
               1000   172±0ms
              ====== ==========

[ 16.29%] ··· rolling.EWMMethods.time_ewm                                                                                                                                 ok
[ 16.29%] ··· ============ ======== ============ =========== ============== =============
              --                                        dtype / method
              --------------------- -----------------------------------------------------
               contructor   window   int / mean   int / std   float / mean   float / std
              ============ ======== ============ =========== ============== =============
               DataFrame      10      31.2±0ms     46.9±0ms     31.2±0ms       31.2±0ms
               DataFrame     1000     31.2±0ms     46.9±0ms     31.2±0ms       31.2±0ms
                 Series       10      31.2±0ms     31.2±0ms     15.6±0ms       31.2±0ms
                 Series      1000     46.9±0ms     46.9±0ms     15.6±0ms       46.9±0ms
              ============ ======== ============ =========== ============== =============

[ 16.85%] ··· rolling.ExpandingMethods.time_expanding                                                                                                                     ok
[ 16.85%] ··· ============ ======== ========= ========== ========== ========== ========== ========== ========== ========== ==========
              --                                                                  dtype
              --------------------- -------------------------------------------------------------------------------------------------
               contructor   window    median     mean       max        min        std       count       skew       kurt       sum
              ============ ======== ========= ========== ========== ========== ========== ========== ========== ========== ==========
               DataFrame     int     266±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   46.9±0ms   15.6±0ms   15.6±0ms   15.6±0ms
               DataFrame    float    281±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   15.6±0ms
                 Series      int     234±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   15.6±0ms
                 Series     float    281±0ms   15.6±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   15.6±0ms
              ============ ======== ========= ========== ========== ========== ========== ========== ========== ========== ==========

[ 17.42%] ··· rolling.Methods.time_rolling                                                                                                                                ok
[ 17.42%] ··· ============ ======== ======= ========== ========== ========== ========== ========== ========== ========== ========== ==========
              --                                                                          method
              ----------------------------- --------------------------------------------------------------------------------------------------
               contructor   window   dtype    median      mean       max        min        std       count       skew       kurt       sum
              ============ ======== ======= ========== ========== ========== ========== ========== ========== ========== ========== ==========
               DataFrame      10      int    78.1±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   31.2±0ms   15.6±0ms   31.2±0ms   15.6±0ms
               DataFrame      10     float   78.1±0ms   15.6±0ms   15.6±0ms   15.6±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   15.6±0ms
               DataFrame     1000     int    141±0ms    15.6±0ms   15.6±0ms   15.6±0ms   15.6±0ms   15.6±0ms   15.6±0ms   15.6±0ms   15.6±0ms
               DataFrame     1000    float   188±0ms    31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms   15.6±0ms   15.6±0ms   15.6±0ms
                 Series       10      int    109±0ms    15.6±0ms   31.2±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   15.6±0ms   15.6±0ms
                 Series       10     float   93.8±0ms   15.6±0ms   31.2±0ms   15.6±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   15.6±0ms
                 Series      1000     int    172±0ms    31.2±0ms   31.2±0ms   31.2±0ms   46.9±0ms   31.2±0ms   31.2±0ms   15.6±0ms   31.2±0ms
                 Series      1000    float   156±0ms    15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms
              ============ ======== ======= ========== ========== ========== ========== ========== ========== ========== ========== ==========

[ 17.98%] ··· rolling.VariableWindowMethods.time_rolling                                                                                                                  ok
[ 17.98%] ··· ============ ======== ======= ========== ========== ========== ========== ========== ========== ========== ========== ==========
              --                                                                          method
              ----------------------------- --------------------------------------------------------------------------------------------------
               contructor   window   dtype    median      mean       max        min        std       count       skew       kurt       sum
              ============ ======== ======= ========== ========== ========== ========== ========== ========== ========== ========== ==========
               DataFrame     50s      int    78.1±0ms   15.6±0ms   62.5±0ms   31.2±0ms   31.2±0ms   31.2±0ms   46.9±0ms   31.2±0ms   15.6±0ms
               DataFrame     50s     float   78.1±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms    0±0ns     46.9±0ms   15.6±0ms
               DataFrame      1h      int    156±0ms    15.6±0ms   46.9±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms
               DataFrame      1h     float   141±0ms    15.6±0ms   15.6±0ms   31.2±0ms   31.2±0ms   46.9±0ms   31.2±0ms   15.6±0ms   15.6±0ms
               DataFrame      1d      int    250±0ms    31.2±0ms   31.2±0ms   46.9±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   31.2±0ms
               DataFrame      1d     float   234±0ms    15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms
                 Series      50s      int    93.8±0ms   15.6±0ms   31.2±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   15.6±0ms   15.6±0ms
                 Series      50s     float   78.1±0ms   15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms
                 Series       1h      int    156±0ms    15.6±0ms   15.6±0ms   15.6±0ms   31.2±0ms   15.6±0ms   15.6±0ms   15.6±0ms   31.2±0ms
                 Series       1h     float   156±0ms    15.6±0ms   46.9±0ms   31.2±0ms   31.2±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms
                 Series       1d      int    188±0ms    31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms
                 Series       1d     float   266±0ms    15.6±0ms   31.2±0ms   31.2±0ms   31.2±0ms   31.2±0ms   46.9±0ms   46.9±0ms   31.2±0ms
              ============ ======== ======= ========== ========== ========== ========== ========== ========== ========== ========== ==========

[ 18.54%] ··· stat_ops.Correlation.time_corr                                                                                                                              ok
[ 18.54%] ··· ========== ========== ==========
              --             use_bottleneck
              ---------- ---------------------
                method      True      False
              ========== ========== ==========
               spearman   203±0ms    203±0ms
               kendall    922±0ms    938±0ms
               pearson    15.6±0ms   15.6±0ms
              ========== ========== ==========

[ 19.10%] ··· stat_ops.Correlation.time_corr_series                                                                                                                       ok
[ 19.10%] ··· ========== ========== ==========
              --             use_bottleneck
              ---------- ---------------------
                method      True      False
              ========== ========== ==========
               spearman   484±0ms    516±0ms
               kendall    484±0ms    500±0ms
               pearson    15.6±0ms   15.6±0ms
              ========== ========== ==========

[ 19.66%] ··· stat_ops.Covariance.time_cov_series                                                                                                                         ok
[ 19.66%] ··· ================ ==========
               use_bottleneck
              ---------------- ----------
                    True        15.6±0ms
                   False        46.9±0ms
              ================ ==========

[ 20.22%] ··· strings.Methods.time_center                                                                                                                            125±0ms
[ 20.79%] ··· strings.Methods.time_count                                                                                                                             109±0ms
[ 21.35%] ··· strings.Methods.time_endswith                                                                                                                         46.9±0ms
[ 21.91%] ··· strings.Methods.time_extract                                                                                                                           391±0ms
[ 22.47%] ··· strings.Methods.time_find                                                                                                                             93.8±0ms
[ 23.03%] ··· strings.Methods.time_findall                                                                                                                           219±0ms
[ 23.60%] ··· strings.Methods.time_get                                                                                                                              78.1±0ms
[ 24.72%] ··· strings.Methods.time_join                                                                                                                              125±0ms
[ 25.28%] ··· strings.Methods.time_len                                                                                                                              78.1±0ms
[ 25.84%] ··· strings.Methods.time_lower                                                                                                                            62.5±0ms
[ 26.40%] ··· strings.Methods.time_lstrip                                                                                                                           46.9±0ms
[ 26.97%] ··· strings.Methods.time_match                                                                                                                             109±0ms
[ 27.53%] ··· strings.Methods.time_normalize                                                                                                                        78.1±0ms
[ 28.09%] ··· strings.Methods.time_pad                                                                                                                               125±0ms
[ 28.65%] ··· strings.Methods.time_partition                                                                                                                         344±0ms
[ 29.21%] ··· strings.Methods.time_replace                                                                                                                          62.5±0ms
[ 29.78%] ··· strings.Methods.time_rfind                                                                                                                             125±0ms
[ 30.90%] ··· strings.Methods.time_rpartition                                                                                                                        344±0ms
[ 31.46%] ··· strings.Methods.time_rstrip                                                                                                                           46.9±0ms
[ 32.02%] ··· strings.Methods.time_slice                                                                                                                            62.5±0ms
[ 32.58%] ··· strings.Methods.time_startswith                                                                                                                       62.5±0ms
[ 33.15%] ··· strings.Methods.time_strip                                                                                                                            46.9±0ms
[ 33.71%] ··· strings.Methods.time_title                                                                                                                            78.1±0ms
[ 34.27%] ··· strings.Methods.time_translate                                                                                                                         172±0ms
[ 34.83%] ··· strings.Methods.time_upper                                                                                                                            62.5±0ms
[ 35.39%] ··· strings.Methods.time_wrap                                                                                                                              1.06±0s
[ 35.96%] ··· strings.Methods.time_zfill                                                                                                                            46.9±0ms
[ 36.52%] ··· timedelta.TimedeltaConstructor.time_from_components                                                                                                   15.6±0ms
[ 37.08%] ··· timedelta.TimedeltaConstructor.time_from_datetime_timedelta                                                                                              0±0ns
[ 37.64%] ··· timedelta.TimedeltaConstructor.time_from_int                                                                                                             0±0ns
[ 38.20%] ··· timedelta.TimedeltaConstructor.time_from_iso_format                                                                                                      0±0ns
[ 38.76%] ··· timedelta.TimedeltaConstructor.time_from_missing                                                                                                         0±0ns
[ 39.33%] ··· timedelta.TimedeltaConstructor.time_from_np_timedelta                                                                                                    0±0ns
[ 39.89%] ··· timedelta.TimedeltaConstructor.time_from_string                                                                                                          0±0ns
[ 40.45%] ··· timedelta.TimedeltaConstructor.time_from_unit                                                                                                            0±0ns
[ 41.01%] ··· timedelta.TimedeltaIndexing.time_align                                                                                                                15.6±0ms
[ 41.57%] ··· timedelta.TimedeltaIndexing.time_get_loc                                                                                                                 0±0ns
[ 42.13%] ··· timedelta.TimedeltaIndexing.time_intersection                                                                                                         15.6±0ms
[ 42.70%] ··· timedelta.TimedeltaIndexing.time_series_loc                                                                                                           15.6±0ms
[ 43.26%] ··· timedelta.TimedeltaIndexing.time_shallow_copy                                                                                                            0±0ns
[ 43.82%] ··· timedelta.TimedeltaIndexing.time_shape                                                                                                                   0±0ns
[ 44.38%] ··· timedelta.TimedeltaIndexing.time_union                                                                                                                   0±0ns
[ 44.94%] ··· timedelta.TimedeltaIndexing.time_unique                                                                                                                  0±0ns
[ 45.51%] ··· timedelta.TimedeltaOps.time_add_td_ts                                                                                                                 46.9±0ms
[ 46.07%] ··· timedelta.ToTimedelta.time_convert_int                                                                                                                   0±0ns
[ 46.63%] ··· timedelta.ToTimedelta.time_convert_string_days                                                                                                        31.2±0ms
[ 47.19%] ··· timedelta.ToTimedelta.time_convert_string_seconds                                                                                                     31.2±0ms
[ 47.75%] ··· timedelta.ToTimedeltaErrors.time_convert                                                                                                                    ok
[ 47.75%] ··· ======== ==========
               errors
              -------- ----------
               coerce   109±0ms
               ignore   93.8±0ms
              ======== ==========

[ 48.31%] ··· Setting up timedelta.py:82                                                                                                                                  ok
[ 48.31%] ··· timedelta.TimedeltaProperties.time_timedelta_days                                                                                                        0±0ns
[ 48.88%] ··· timedelta.TimedeltaProperties.time_timedelta_microseconds                                                                                                0±0ns
[ 49.44%] ··· timedelta.TimedeltaProperties.time_timedelta_nanoseconds                                                                                                 0±0ns
[ 50.00%] ··· timedelta.TimedeltaProperties.time_timedelta_seconds                                                                                                     0±0ns
``` # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/23935?src=pr&el=h1) Report
> Merging [#23935](https://codecov.io/gh/pandas-dev/pandas/pull/23935?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/ee283faee006abf5b879d37fe17c2db4e0fa51fd?src=pr&el=desc) will **not change** coverage.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/23935/graphs/tree.svg?width=650&token=eZ4WkYLtcO&height=150&src=pr)](https://codecov.io/gh/pandas-dev/pandas/pull/23935?src=pr&el=tree)

```diff
@@           Coverage Diff           @@
##           master   #23935   +/-   ##
=======================================
  Coverage   92.31%   92.31%           
=======================================
  Files         161      161           
  Lines       51471    51471           
=======================================
  Hits        47515    47515           
  Misses       3956     3956
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.7% <ø> (ø)` | :arrow_up: |
| #single | `42.43% <ø> (ø)` | :arrow_up: |


------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/23935?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/23935?src=pr&el=footer). Last update [ee283fa...9b1a80f](https://codecov.io/gh/pandas-dev/pandas/pull/23935?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 @mroeschke Nothing too shocking, but a reasonable fraction are demonstrating small slowdowns compared to `v0.20.0`:
```
       before           after         ratio
     [f6ee9acd]       [b7294dd3]
-      6.69±0.4ms      5.77±0.05ms     0.86  rolling.ExpandingMethods.time_expanding('Series', 'int', 'min')
-      6.78±0.3ms      5.79±0.02ms     0.85  rolling.ExpandingMethods.time_expanding('Series', 'int', 'max')
-      14.8±0.2ms       12.1±0.4ms     0.81  rolling.ExpandingMethods.time_expanding('Series', 'int', 'count')
-     1.60±0.02μs          915±2ns     0.57  timedelta.TimedeltaIndexing.time_shape
       before           after         ratio
     [f6ee9acd]       [b7294dd3]
+     10.3±0.03μs      20.8±0.05μs     2.02  timedelta.TimedeltaIndexing.time_shallow_copy
+       183±0.7μs          342±1μs     1.87  timedelta.TimedeltaIndexing.time_unique
+     17.1±0.02ms      30.2±0.05ms     1.76  rolling.EWMMethods.time_ewm('DataFrame', 1000, 'float', 'std')
+     17.1±0.04ms      30.2±0.05ms     1.76  rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'std')
+      17.3±0.1ms       30.4±0.2ms     1.75  rolling.EWMMethods.time_ewm('DataFrame', 1000, 'int', 'std')
+     17.4±0.08ms       30.3±0.2ms     1.75  rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'std')
+      21.4±0.1ms       33.3±0.2ms     1.56  rolling.EWMMethods.time_ewm('Series', 10, 'float', 'std')
+     21.5±0.08ms       33.2±0.1ms     1.55  rolling.EWMMethods.time_ewm('Series', 10, 'int', 'std')
+      21.4±0.1ms       33.0±0.2ms     1.55  rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'std')
+      21.7±0.2ms       33.4±0.2ms     1.54  rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'std')
+         257±6ms          368±3ms     1.43  reshape.Cut.time_cut_float(4)
+        382±20ms         532±20ms     1.39  reshape.Cut.time_qcut_int(4)
+      58.1±0.3ms       80.3±0.3ms     1.38  strings.Methods.time_get
+        394±30ms         536±30ms     1.36  reshape.Cut.time_qcut_int(10)
+     16.3±0.03μs      22.2±0.08μs     1.36  timedelta.TimedeltaIndexing.time_get_loc
+         574±1μs          751±2μs     1.31  timedelta.TimedeltaIndexing.time_union
+      1.08±0.01s       1.41±0.09s     1.30  plotting.SeriesPlotting.time_series_plot('line')
+         247±1μs          319±2μs     1.29  timedelta.TimedeltaIndexing.time_intersection
+         333±1ms          423±1ms     1.27  strings.Methods.time_rpartition
+         338±2ms          425±1ms     1.26  strings.Methods.time_partition
+       246±0.6μs        302±0.5μs     1.22  timedelta.ToTimedelta.time_convert_int
+        589±20ms         711±30ms     1.21  reshape.Cut.time_qcut_float(4)
+      93.1±0.3μs        110±0.5μs     1.18  binary_ops.Ops2.time_series_dot
+         369±1μs        428±0.8μs     1.16  stat_ops.Correlation.time_corr_series('pearson', True)
+         370±1μs        428±0.7μs     1.16  stat_ops.Correlation.time_corr_series('pearson', False)
+     1.59±0.01ms      1.82±0.01ms     1.15  timedelta.TimedeltaIndexing.time_align
+       153±0.3ms        175±0.2ms     1.14  stat_ops.Correlation.time_corr('spearman', True)
+       153±0.1ms        175±0.1ms     1.14  stat_ops.Correlation.time_corr('spearman', False)
+     2.15±0.02ms      2.43±0.02ms     1.13  rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'sum')
+      1.30±0.02s       1.44±0.03s     1.11  reshape.Cut.time_qcut_int(1000)
``` look ok to me. @topper-123 if you have any comments.

@mroeschke feel free to merge when satisfied. @mroeschke And the `asv dev` output for the timedelta/datetime cut benchmarks:
```
$ asv dev -b cut_timedelta -b cut_datetime
· Discovering benchmarks
· Running 4 total benchmarks (1 commits * 1 environments * 4 benchmarks)
[  0.00%] ·· Benchmarking existing-py_home_chris_anaconda3_bin_python
[ 12.50%] ··· reshape.Cut.time_cut_datetime                                                                                                                               ok
[ 12.50%] ··· ====== ==========
               bins
              ------ ----------
                4     15.6±0ms
                10     0±0ns
               1000   46.9±0ms
              ====== ==========

[ 25.00%] ··· reshape.Cut.time_cut_timedelta                                                                                                                              ok
[ 25.00%] ··· ====== ==========
               bins
              ------ ----------
                4     15.6±0ms
                10    15.6±0ms
               1000   46.9±0ms
              ====== ==========

[ 37.50%] ··· reshape.Cut.time_qcut_datetime                                                                                                                              ok
[ 37.50%] ··· ====== ==========
               bins
              ------ ----------
                4     31.2±0ms
                10    15.6±0ms
               1000   46.9±0ms
              ====== ==========

[ 50.00%] ··· reshape.Cut.time_qcut_timedelta                                                                                                                             ok
[ 50.00%] ··· ====== ==========
               bins
              ------ ----------
                4      0±0ns
                10    15.6±0ms
               1000   62.5±0ms
              ====== ==========
``` Great thanks @qwhelan! More benchmarks are always appreciated.  Look good. @qwhelan , you mention 

> Times are very noisy due to a bug with asv on windows:

I'm working on windows and experience very uneven results when running ASVs and I actually have more trust in doing timeit manually, which is a bit sad. Got any pointers on this bug? Can't find anything on the ASV Github issue list. @topper-123 It's pretty simple - `asv` uses `time.time()`, which has a resolution of `15.6 ms` on Windows (with the upside of being process time instead of wall time). The default target runtime is `10 ms`, which causes some fun quantization noise as it is less than the clock resolution. Either target runtime needs to be increased by a couple orders of magnitude, or `asv` needs to use wall time for faster tests.

I've been punting on opening a PR on this as I'm waiting for comments on the one I already have open, but I have a small notebook demonstrating the quantization I threw together before I went on vacation. I'll try to post it later today. Opened https://github.com/airspeed-velocity/asv/issues/775 for anyone interested in the `asv` on Windows issue I referenced