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.
```
Thanks for the PR. So we don't necessarily want to remove these as much as see if moving them somewhere else in the module saves a lot of time running the benchmarks. Do you know how long it took before / after to run this module? I did run the tests before making the changes, if that's what you mean by before / after.
The output is as follows:

```
· Discovering benchmarks
·· Uninstalling from conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Building 165d5ee4 <fix-kendall-issues> for conda-py3.6-Cython-matplotlib-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...............................................
·· Installing 165d5ee4 <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 <fix-kendall-issues> (round 1/2):
[  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--)...
[ 17.86%] ··· Running (stat_ops.Correlation.time_corr_wide_nans--).
[ 21.43%] ··· Running (stat_ops.Correlation.time_corrwith_cols--)..
[ 25.00%] · For pandas commit 165d5ee4 <fix-kendall-issues> (round 1/2):
[ 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--)...
[ 42.86%] ··· Running (stat_ops.Correlation.time_corr_wide_nans--)^B^[[B^B^[[B.
[ 46.43%] ··· Running (stat_ops.Correlation.time_corrwith_cols--)..
[ 50.00%] · For pandas commit 165d5ee4 <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    107M 
               kendall    129M    129M 
               pearson    105M    105M 
              ========== ====== =======

[ 57.14%] ··· stat_ops.Correlation.time_corr                                                       ok
[ 57.14%] ··· ========== ============ =============
              --               use_bottleneck      
              ---------- --------------------------
                method       True         False    
              ========== ============ =============
               spearman   7.36±0.3ms     7.99±2ms  
               kendall     187±3ms       188±2ms   
               pearson    2.26±0.2ms   2.30±0.09ms 
              ========== ============ =============

[ 60.71%] ··· stat_ops.Correlation.time_corr_series                                                ok
[ 60.71%] ··· ========== ============= ==========
              --              use_bottleneck     
              ---------- ------------------------
                method        True       False   
              ========== ============= ==========
               spearman   1.05±0.08ms   932±80μs 
               kendall      608±20μs    671±50μs 
               pearson      226±20μs    220±9μs  
              ========== ============= ==========

[ 64.29%] ··· stat_ops.Correlation.time_corr_wide                                                  ok
[ 64.29%] ··· ========== ============ ===========
              --              use_bottleneck     
              ---------- ------------------------
                method       True        False   
              ========== ============ ===========
               spearman    247±7ms      254±10ms 
               kendall    8.47±0.06s   8.85±0.2s 
               pearson     155±4ms      190±30ms 
              ========== ============ ===========

[ 67.86%] ··· stat_ops.Correlation.time_corr_wide_nans                                             ok
[ 67.86%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman   3.31±0.04s   3.28±0.04s 
               kendall    9.11±0.2s    9.85±0.5s  
               pearson     246±30ms     212±20ms  
              ========== ============ ============

[ 71.43%] ··· stat_ops.Correlation.time_corrwith_cols                                              ok
[ 71.43%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman    29.4±4ms    30.3±0.4ms 
               kendall     17.6±1ms    16.4±0.5ms 
               pearson    35.6±0.8ms    36.1±1ms  
              ========== ============ ============

[ 75.00%] ··· stat_ops.Correlation.time_corrwith_rows                                              ok
[ 75.00%] ··· ========== ========== ==========
              --             use_bottleneck   
              ---------- ---------------------
                method      True      False   
              ========== ========== ==========
               spearman   553±20ms   558±20ms 
               kendall    972±10ms   957±10ms 
               pearson    896±90ms   901±20ms 
              ========== ========== ==========

[ 75.00%] · For pandas commit 165d5ee4 <fix-kendall-issues> (round 2/2):
[ 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 
               kendall    129M    129M 
               pearson    105M    105M 
              ========== ====== =======

[ 82.14%] ··· stat_ops.Correlation.time_corr                                                       ok
[ 82.14%] ··· ========== ============= ============
              --               use_bottleneck      
              ---------- --------------------------
                method        True        False    
              ========== ============= ============
               spearman    7.28±0.2ms    7.23±1ms  
               kendall      185±3ms      188±2ms   
               pearson    2.22±0.09ms   2.19±0.1ms 
              ========== ============= ============

[ 85.71%] ··· stat_ops.Correlation.time_corr_series                                                ok
[ 85.71%] ··· ========== ============= ==========
              --              use_bottleneck     
              ---------- ------------------------
                method        True       False   
              ========== ============= ==========
               spearman   1.05±0.09ms   912±50μs 
               kendall      607±30μs    651±50μs 
               pearson      226±20μs    228±9μs  
              ========== ============= ==========

[ 89.29%] ··· stat_ops.Correlation.time_corr_wide                                                  ok
[ 89.29%] ··· ========== ============ ===========
              --              use_bottleneck     
              ---------- ------------------------
                method       True        False   
              ========== ============ ===========
               spearman    252±8ms      248±10ms 
               kendall    8.56±0.06s   8.61±0.2s 
               pearson     156±4ms      199±20ms 
              ========== ============ ===========

[ 92.86%] ··· stat_ops.Correlation.time_corr_wide_nans                                                                                                                                                    ok
[ 92.86%] ··· ========== ============ ============
              --               use_bottleneck     
              ---------- -------------------------
                method       True        False    
              ========== ============ ============
               spearman   3.32±0.04s   3.26±0.04s 
               kendall    8.72±0.3s    8.89±0.6s  
               pearson     213±30ms     210±10ms  
              ========== ============ ============

[ 96.43%] ··· stat_ops.Correlation.time_corrwith_cols                                                                                                                                                     ok
[ 96.43%] ··· ========== ============ ==========
              --              use_bottleneck    
              ---------- -----------------------
                method       True       False   
              ========== ============ ==========
               spearman    28.0±3ms    30.6±5ms 
               kendall     19.0±1ms    16.9±1ms 
               pearson    35.6±0.6ms   37.7±3ms 
              ========== ============ ==========

[100.00%] ··· stat_ops.Correlation.time_corrwith_rows                                                                                                                                                     ok
[100.00%] ··· ========== ========== ==========
              --             use_bottleneck   
              ---------- ---------------------
                method      True      False   
              ========== ========== ==========
               spearman   556±4ms    558±10ms 
               kendall    971±10ms   946±10ms 
               pearson    894±40ms   890±10ms 
              ========== ========== ==========


BENCHMARKS NOT SIGNIFICANTLY CHANGED.
``` No not the benchmark timings themselves as those shouldn't change. Just how long it took to run from start to finish Ah! I am afraid I didn't time them. 
 Before: 10m24s
After: 4m19s By halving the dimensions and removing the bottleneck flags, one could get a runtime of about **5m14s**.  @deepandas11 this looks close to ready.  can you see why the CI is failing and update? > @deepandas11 this looks close to ready. can you see why the CI is failing and update?

Sorry for not being able to resolve this sooner. Was a bit swamped.  hmm this looks ok, I think just merge master and good to go. Thanks @deepandas11 !