Mismatched behavior of sorting functions for `DataFrame` vs. `Series`
The behavior of these methods differs in confusing ways:

DF:
- [`DataFrame.sort_index`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) takes a `by` parameter, in which case the index is resorted by a specific column's values
- [`DataFrame.sort`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort.html) defaults to `inplace=False`
- `DataFrame.order` does not exist

Series:
- [`Series.sort`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort.html) defaults to `inplace=True`
- [`Series.order`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.order.html) defaults to `inplace=False`, but is otherwise identical to `Series.sort`
- [`Series.sort_index`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) does not take a `by` parameter

This makes it very hard to remember which one to use, especially after an aggregating function. For example:

```
df.max(axis=1).order(ascending=False)      # max returns a Series in this case, use Series methods
df.max(level='Foo').sort_index(by='Bar')      # max returns a DF in this case, use DF methods
```

Any reason not to make the `inplace` behavior identical for `DataFrame.sort` and `Series.sort`? Also, `Series.sort_index` could take `by=0` to replicate the behavior of `DataFrame`.
