[FEA] Series and DataFrame mean absolute deviation

For API compatibility and supporting exploratory analysis, we should support Series and DataFrame mean absolute deviation. See the pandas [mean absolute deviation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.mad.html) for more information.

This can be implemented for Python Series and DataFrame as a stopgap as `Series.mad` and then leverage `_apply_support_method` for `DataFrame.mad`. It's not 10x faster, but it gets the job done well.

```python
import cudf
import numpy as np
​
def mad(self):
# mad formula
n = len(self)
m = self.mean()
mad = ((self - m).abs() / n).sum()
return mad
​
​# 1 million rows
s = cudf.Series(np.random.normal(10,5,1_000_000))
ps = s.to_pandas()
​
%time mp = ps.mad()
%time mg = mad(s)
print(mp)
print(mg)
CPU times: user 31.9 ms, sys: 0 ns, total: 31.9 ms
Wall time: 32 ms
CPU times: user 0 ns, sys: 7.24 ms, total: 7.24 ms
Wall time: 23.2 ms
3.990811998439671
3.990811998439673
```