DOC: Updated aggregate docstring
Link to current `Series.agg`: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.agg.html

Original question asked in gitter:
Does `pd.Series.agg` with `func` parameter set to `'median'` uses `np.nanmedian`(Not only `median` including `mean`, `mode`)?
```python
s= pd.Series([np.nan, np.nan,1,1,1])
s.agg('median') 
# 1
s.agg(np.median)
# 1
np.median(s.to_numpy())
# nan
np.nanmedian(s.to_numpy())
# 1
```
Whenever `NaN` is present does it fallback to using `np.nanmedian`?

---

Reply from @MarcoGorelli 
if you look at `pandas/core/base.py` you'll see
```
    np.median: "median",
    np.nanmedian: "median",
```
in `_cython_table`. So, both resolve to the same internal cython function.

---

IMO it's better to mention this in the docs under `Note:` section.

Under note section saying:
*Some NumPy functions such as `np.mean`, `np.nanmean`, `np.median` etc. resolve to their corresponding internal cython function.*


#### Output of `python validate_docstrings.py pandas.Series.agg`
<details>

```
################################################################################
######################## Docstring (pandas.Series.agg)  ########################
################################################################################

Aggregate using one or more operations over the specified axis.

.. versionadded:: 0.20.0

Parameters
----------
func : function, str, list or dict
    Function to use for aggregating the data. If a function, must either
    work when passed a Series or when passed to Series.apply.

    Accepted combinations are:

    - function
    - string function name
    - list of functions and/or function names, e.g. ``[np.sum, 'mean']``
    - dict of axis labels -> functions, function names or list of such.
axis : {0 or 'index'}
        Parameter needed for compatibility with DataFrame.
*args
    Positional arguments to pass to `func`.
**kwargs
    Keyword arguments to pass to `func`.

Returns
-------
scalar, Series or DataFrame

    The return can be:

    * scalar : when Series.agg is called with single function
    * Series : when DataFrame.agg is called with a single function
    * DataFrame : when DataFrame.agg is called with several functions

    Return scalar, Series or DataFrame.

See Also
--------
Series.apply : Invoke function on a Series.
Series.transform : Transform function producing a Series with like indexes.

Notes
-----
`agg` is an alias for `aggregate`. Use the alias.
Some NumPy functions such as `np.mean`, `np.nanmean`, `np.median` etc.
resolve to their corresponding internal cython function.

A passed user-defined-function will be passed a Series for evaluation.

Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.agg('min')
1

>>> s.agg(['min', 'max'])
min   1
max   4
dtype: int64

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.Series.agg" correct. :)
```
</details>