Performance regressions since 0.8.0
Should investigate some of these ones at the bottom. The pivot benchmark is related to refactoring I made in the unstack code, and is not so simple to fix:

```

Results:
                                           t_head t_baseline  ratio
name                                                               
indexing_panel_subset                      3.2588  1997.1172 0.0016
groupby_frame_median                      10.2211   181.5090 0.0563
datetimeindex_add_offset                   0.3107     2.2951 0.1354
read_csv_thou_vb                          41.6154   205.6429 0.2024
frame_constructor_ndarray                  0.0409     0.0888 0.4604
read_csv_vb                               23.5146    50.1700 0.4687
read_csv_standard                         14.4659    29.6830 0.4873
reindex_multiindex                         1.4252     2.7399 0.5202
groupby_frame_singlekey_integer            3.8736     6.6471 0.5828
groupby_first                              7.2411    12.2543 0.5909
groupby_last                               7.6421    12.7714 0.5984
panel_from_dict_same_index                30.3418    49.3051 0.6154
panel_from_dict_equiv_indexes             30.5921    49.1175 0.6228
groupby_simple_compress_timing            53.7012    85.8531 0.6255
reindex_frame_level_reindex                1.8341     2.7030 0.6786
append_frame_single_homogenous             0.3622     0.4860 0.7453
timeseries_add_irregular                  22.9419    30.2414 0.7586
write_csv_standard                       359.9060   458.5490 0.7849
stat_ops_level_series_sum                  3.7688     4.6419 0.8119
groupby_series_simple_cython               6.5816     7.9662 0.8262
dataframe_reindex_columns                  0.3087     0.3718 0.8303
groupby_multi_size                        39.3789    45.9909 0.8562
frame_get_numeric_data                     0.0842     0.0981 0.8589
reshape_stack_simple                       2.5648     2.9615 0.8660
frame_reindex_axis1                        3.3879     3.8442 0.8813
timeseries_asof_nan                        8.5555     9.6283 0.8886
timeseries_asof                            9.2042    10.3367 0.8904
stat_ops_level_frame_sum                   4.6151     5.0289 0.9177
frame_reindex_both_axes_ix                 0.7154     0.7768 0.9209
indexing_dataframe_boolean_rows            0.2306     0.2500 0.9225
stats_rank_average_int                    24.2966    26.2871 0.9243
frame_to_csv                             474.3190   511.4419 0.9274
sort_level_zero                            6.2975     6.7836 0.9283
reindex_frame_level_align                  1.8621     1.9765 0.9421
dataframe_reindex_daterange                0.3935     0.4141 0.9503
frame_ctor_nested_dict_int64             135.1471   142.0300 0.9515
join_dataframe_index_single_key_bigger     8.2120     8.6290 0.9517
timeseries_large_lookup_value              0.0248     0.0260 0.9526
join_dataframe_index_single_key_small      7.7202     8.0596 0.9579
indexing_dataframe_boolean_rows_object     0.4611     0.4768 0.9670
timeseries_sort_index                     21.2128    21.9074 0.9683
frame_boolean_row_select                   0.2998     0.3096 0.9683
read_table_multiple_date                2367.1391  2435.5052 0.9719
read_table_multiple_date_baseline       1086.0040  1116.9188 0.9723
series_align_left_monotonic               17.5190    17.9150 0.9779
groupby_multi_series_op                   19.7853    20.2238 0.9783
series_align_int64_index                  37.7741    38.5680 0.9794
panel_from_dict_two_different_indexes     99.2441   101.2650 0.9800
frame_fillna_many_columns_pad             16.8927    17.2210 0.9809
reindex_daterange_backfill                 0.1794     0.1813 0.9894
groupby_multi_cython                      22.6835    22.9242 0.9895
reindex_daterange_pad                      0.1835     0.1853 0.9901
reindex_fillna_pad                         0.1220     0.1228 0.9940
frame_fancy_lookup                         2.1757     2.1861 0.9953
index_int64_intersection                  38.0053    38.1207 0.9970
index_int64_union                         88.7940    89.0169 0.9975
groupby_multi_python                      67.7776    67.8983 0.9982
frame_ctor_nested_dict                    96.4491    96.5813 0.9986
stat_ops_series_std                        0.2840     0.2827 1.0044
series_value_counts_int64                  2.6949     2.6773 1.0066
timeseries_timestamp_downsample_mean       5.9724     5.9310 1.0070
sort_level_one                             6.2288     6.1651 1.0103
groupby_apply_dict_return                 40.3767    39.8300 1.0137
stats_rank2d_axis0_average                26.6424    26.1975 1.0170
concat_series_axis1                       73.4706    72.2370 1.0171
groupby_frame_cython_many_columns          5.5423     5.4274 1.0212
stats_rank_average                        35.6590    34.8816 1.0223
join_dataframe_index_multi                28.8276    28.1839 1.0228
reindex_fillna_backfill                    0.1190     0.1160 1.0259
frame_fillna_inplace                      19.4959    18.9305 1.0299
timeseries_asof_single                     0.0586     0.0567 1.0333
stats_rank2d_axis1_average                18.5875    17.8737 1.0399
join_dataframe_index_single_key_bigger    21.5043    20.6628 1.0407
groupby_pivot_table                       24.8929    23.6659 1.0518
timeseries_slice_minutely                  0.0583     0.0553 1.0549
timeseries_timestamp_tzinfo_cons           0.0177     0.0168 1.0590
groupby_multi_different_functions         19.3980    18.1736 1.0674
read_csv_comment_vb                      106.1771    99.0222 1.0723
match_strings                              0.4124     0.3835 1.0753
series_constructor_ndarray                 0.0110     0.0103 1.0773
groupby_indices                           11.7223    10.7914 1.0863
timeseries_1min_5min_ohlc                  0.6142     0.5541 1.1086
append_frame_single_mixed                  1.7186     1.5501 1.1087
reshape_unstack_simple                     3.3168     2.9830 1.1119
frame_ctor_list_of_dict                  109.4160    98.0361 1.1161
groupby_multi_different_numpy_functions   20.6145    18.4240 1.1189
timeseries_1min_5min_mean                  0.6310     0.5584 1.1300
frame_reindex_axis0                        3.0153     2.5576 1.1790
frame_reindex_both_axes                    0.7285     0.6088 1.1967
series_ctor_from_dict                      3.7974     3.1420 1.2086
stat_ops_level_frame_sum_multiple         12.9655    10.3905 1.2478
sparse_series_to_frame                   208.4851   146.2591 1.4255
stat_ops_level_series_sum_multiple        12.6471     8.4403 1.4984
panel_from_dict_all_different_indexes    181.9479   104.8920 1.7346
reshape_pivot_time_series                318.2459   148.5360 2.1426
frame_fancy_lookup_all                   125.4880    36.3095 3.4561

Columns: test_name | head_time [ms] | baseline_time [ms] | ratio

- a Ratio of 1.30 means HEAD is 30% slower then the Baseline.

Head [5af2f95] : ENH: accelerate label compression. accept >= 10 micros in test_perf
Baseline [a901af2] : RLS: version 0.8.0
```
