Boolean diff is slower than float, int diff
#### Code Sample

This code in Google Colab (Jupyter Notebook/Lab):
```python
import numpy as np
import pandas as pd


size = int(1e7)
value = np.sin(np.linspace(0, 100, size)) < 0.5

print("Numpy Boolean")
%timeit np.diff(value)

value = value.astype(int)
print("Numpy Integer")
%timeit np.diff(value)
df = pd.Series(np.sin(np.linspace(0, 100, size))) < 0.5

print("Pandas Boolean")
%timeit df.diff()

print("Pandas Integer")
%timeit df.astype(int).diff()

print("Pandas Float")
%timeit df.astype(float).diff()
```
Creates the following output:
```
Numpy Boolean
100 loops, best of 3: 11.8 ms per loop
Numpy Integer
100 loops, best of 3: 19 ms per loop
Pandas Boolean
1 loop, best of 3: 208 ms per loop
Pandas Integer
10 loops, best of 3: 51.7 ms per loop
Pandas Float
10 loops, best of 3: 43.8 ms per loop
```
#### Problem description

The diff function on a boolean series is about four times slower than on an integer or float series. This behavior is not reproducable in numpy, where diff on a boolean series is the fastest. Right now it is even faster to convert the boolean series to integer, do the diff and then convert back to boolean.

#### Expected Output

Boolean diff should be the fastest.

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

Google Colab with pandas 1.0.1 installed.

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.14.137+
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.17.5
pytz             : 2018.9
dateutil         : 2.6.1
pip              : 19.3.1
setuptools       : 45.1.0
Cython           : 0.29.15
pytest           : 3.6.4
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : 0.4.0
xlsxwriter       : None
lxml.etree       : 4.2.6
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2           : 2.11.1
IPython          : 5.5.0
pandas_datareader: None
bs4              : 4.6.3
bottleneck       : 1.3.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.2.6
matplotlib       : 3.1.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 2.5.9
pandas_gbq       : 0.11.0
pyarrow          : 0.14.1
pytables         : None
pytest           : 3.6.4
pyxlsb           : None
s3fs             : 0.4.0
scipy            : 1.4.1
sqlalchemy       : 1.3.13
tables           : 3.4.4
tabulate         : 0.8.6
xarray           : 0.14.1
xlrd             : 1.1.0
xlwt             : 1.3.0
xlsxwriter       : None
numba            : 0.47.0

</details>

#### Next steps
- Find out why it is slower
- Quick fix: `df.astype(int).diff().dropna().astype(bool)` which benchmarks to `10 loops, best of 3: 114 ms per loop`