removing kendall tests
closes #29270 

Following is the output after removing "kendall"

```
· Creating environments
· Discovering benchmarks
·· Uninstalling from conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Building cd59acf5 <fix-kendall-issues> for conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt................................................
·· Installing cd59acf5 <fix-kendall-issues> into conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
· Running 14 total benchmarks (2 commits * 1 environments * 7 benchmarks)
[  0.00%] · For pandas commit 165d5ee4 <master> (round 1/2):
[  0.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[  0.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[  7.14%] ··· Running (stat_ops.Correlation.time_corr--)....
[ 21.43%] ··· Running (stat_ops.Correlation.time_corrwith_cols--)..
[ 25.00%] · For pandas commit cd59acf5 <fix-kendall-issues> (round 1/2):
[ 25.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 25.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 32.14%] ··· Running (stat_ops.Correlation.time_corr--)....
[ 46.43%] ··· Running (stat_ops.Correlation.time_corrwith_cols--)..
[ 50.00%] · For pandas commit cd59acf5 <fix-kendall-issues> (round 2/2):
[ 50.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 53.57%] ··· stat_ops.Correlation.peakmem_corr_wide                                                                                                                                                      ok
[ 53.57%] ··· ========== ====== =======
              --         use_bottleneck
              ---------- --------------
                method    True   False 
              ========== ====== =======
               spearman   108M    108M 
               pearson    105M    105M 
              ========== ====== =======

[ 57.14%] ··· stat_ops.Correlation.time_corr                                                                                                                                                              ok
[ 57.14%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman    8.74±1ms     8.71±2ms  
               pearson    2.88±0.6ms   2.84±0.4ms 
              ========== ============ ============

[ 60.71%] ··· stat_ops.Correlation.time_corr_series                                                                                                                                                       ok
[ 60.71%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman   1.34±0.2ms   1.35±0.3ms 
               pearson     317±60μs     319±70μs  
              ========== ============ ============

[ 64.29%] ··· stat_ops.Correlation.time_corr_wide                                                                                                                                                         ok
[ 64.29%] ··· ========== ========== ==========
              --             use_bottleneck   
              ---------- ---------------------
                method      True      False   
              ========== ========== ==========
               spearman   286±40ms   273±10ms 
               pearson    176±10ms   178±10ms 
              ========== ========== ==========

[ 67.86%] ··· stat_ops.Correlation.time_corr_wide_nans                                                                                                                                                    ok
[ 67.86%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman   3.23±0.02s   3.20±0.01s 
               pearson     196±7ms      192±10ms  
              ========== ============ ============

[ 71.43%] ··· stat_ops.Correlation.time_corrwith_cols                                                                                                                                                     ok
[ 71.43%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman    24.2±1ms     25.5±1ms  
               pearson    36.1±0.3ms   36.2±0.4ms 
              ========== ============ ============

[ 75.00%] ··· stat_ops.Correlation.time_corrwith_rows                                                                                                                                                     ok
[ 75.00%] ··· ========== ========= ==========
              --            use_bottleneck   
              ---------- --------------------
                method      True     False   
              ========== ========= ==========
               spearman   549±4ms   551±3ms  
               pearson    884±7ms   890±20ms 
              ========== ========= ==========

[ 75.00%] · For pandas commit 165d5ee4 <master> (round 2/2):
[ 75.00%] ·· Building for conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 75.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 78.57%] ··· stat_ops.Correlation.peakmem_corr_wide                                                                                                                                                      ok
[ 78.57%] ··· ========== ====== =======
              --         use_bottleneck
              ---------- --------------
                method    True   False 
              ========== ====== =======
               spearman   108M    108M 
               pearson    106M    106M 
              ========== ====== =======

[ 82.14%] ··· stat_ops.Correlation.time_corr                                                                                                                                                              ok
[ 82.14%] ··· ========== ============ =============
              --               use_bottleneck      
              ---------- --------------------------
                method       True         False    
              ========== ============ =============
               spearman   10.2±0.2ms    10.1±0.2ms 
               pearson    3.51±0.2ms   3.54±0.09ms 
              ========== ============ =============

[ 85.71%] ··· stat_ops.Correlation.time_corr_series                                                                                                                                                       ok
[ 85.71%] ··· ========== ============= =============
              --                use_bottleneck      
              ---------- ---------------------------
                method        True         False    
              ========== ============= =============
               spearman   1.52±0.03ms   1.26±0.01ms 
               pearson      316±10μs     389±0.6μs  
              ========== ============= =============

[ 89.29%] ··· stat_ops.Correlation.time_corr_wide                                                                                                                                                         ok
[ 89.29%] ··· ========== ========== ==========
              --             use_bottleneck   
              ---------- ---------------------
                method      True      False   
              ========== ========== ==========
               spearman   359±20ms   288±10ms 
               pearson    186±6ms    184±7ms  
              ========== ========== ==========

[ 92.86%] ··· stat_ops.Correlation.time_corr_wide_nans                                                                                                                                                    ok
[ 92.86%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman   3.29±0.01s   3.26±0.03s 
               pearson     195±6ms      203±5ms   
              ========== ============ ============

[ 96.43%] ··· stat_ops.Correlation.time_corrwith_cols                                                                                                                                                     ok
[ 96.43%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman    24.4±2ms    24.8±0.9ms 
               pearson    35.8±0.5ms   36.1±0.5ms 
              ========== ============ ============

[100.00%] ··· stat_ops.Correlation.time_corrwith_rows                                                                                                                                                     ok
[100.00%] ··· ========== ========== ==========
              --             use_bottleneck   
              ---------- ---------------------
                method      True      False   
              ========== ========== ==========
               spearman   551±6ms    555±7ms  
               pearson    894±10ms   899±10ms 
              ========== ========== ==========

       before           after         ratio
     [165d5ee4]       [cd59acf5]
     <master>         <fix-kendall-issues>
-     3.54±0.09ms       2.84±0.4ms     0.80  stat_ops.Correlation.time_corr('pearson', False)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```