DataFrame.corr(method="spearman") much slower than DataFrame.rank().corr(method="pearson")
```python
import numpy as np 
import pandas as pd 
 
np.random.seed(1234)                                                                                                                   

df = pd.DataFrame(np.random.random((5000, 100)))                                                                                       

%timeit df.corr(method="spearman")                                                                                                     
# 4.54 s ± 113 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit df.rank().corr(method="pearson")                                                                                               
# 105 ms ± 2.55 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

pd.testing.assert_frame_equal(df.corr(method="spearman"), df.rank().corr(method="pearson"))
# No error

pd.__version__                                                                                                                         
# '0.25.0'
```

#### Problem description

`DataFrame.corr(method="spearman")` seems to be an order of magnitude slower than `DataFrame.rank().corr(method="pearson")` even though the two are ultimately doing the same calculation.   While probably not the cleanest option, I would think that `corr(method="spearman")` could at least be made an alias for `rank().corr(method="pearson")`, or maybe a change could be made in `algos.pyx`.

#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.0
numpy            : 1.16.4
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.5.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.0
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
</details>
