DOC: update the pandas.core.groupby.GroupBy.max docstring 
Added extendible dictionary to do the same for other generic numeric operations in module pandas.core.groupby.

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

- [X] PR title is "DOC: update the pandas.core.groupby.GroupBy.max docstring"
- [X] The validation script passes: `scripts/validate_docstrings.py pandas.core.groupby.GroupBy.max`
- [X] The PEP8 style check passes: `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] The html version looks good: `python doc/make.py --single pandas.core.groupby.GroupBy.max`
- [x] It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

```
################################################################################
################# Docstring (pandas.core.groupby.GroupBy.max)  #################
################################################################################

Compute the maximum of each group.

For multiple groupers, the result index will be a
:class:`~pandas.MultiIndex`.

Parameters
----------
kwargs : dict
    Optional keyword arguments to pass to `max`.

    `numeric_only` : bool
        Include only float, int, boolean columns.
    `min_count` : int
        The required number of valid values to perform the operation.
        If fewer than `min_count` non-NA values are present the result
        will be NA.

Returns
-------
Series or DataFrame

Examples
--------
>>> df = pd.DataFrame(
...     {'type': ['apple', 'apple', 'apple', 'orange', 'orange'],
...      'variety': ['gala', 'fuji', 'fuji', 'valencia', 'navel'],
...      'quantity': [2, 4, 8, 3, 1],
...      'price': [0.8, 1.25, 2.5, 1.25, 1.0],
...     },
...     columns=['type', 'variety', 'quantity', 'price']
... )
>>> df
     type   variety  quantity  price
0   apple      gala         2   0.80
1   apple      fuji         4   1.25
2   apple      fuji         8   2.50
3  orange  valencia         3   1.25
4  orange     navel         1   1.00

>>> g = df.groupby('type')
>>> g.max()
         variety  quantity  price
type
apple       gala         8   2.50
orange  valencia         3   1.25

By default, the `max` operation is performed on columns of all dtypes
(including the 'variety' columns which is of type `str`).

In order to only keep only the numeric columns ('quantity' and 'price'),
the `numeric_only` keyword argument can be used:

>>> g.max(numeric_only=True)
        quantity  price
type
apple          8   2.50
orange         3   1.25

Grouping by more than one column results in :class:`~pandas.DataFrame` with
a :class:`~pandas.MultiIndex`.

>>> g = df.groupby(['type', 'variety'])
>>> g.max()
                 quantity  price
type   variety
apple  fuji             8   2.50
       gala             2   0.80
orange navel            1   1.00
       valencia         3   1.25

See Also
    --------
    pandas.Series.max: compute max of values
    pandas.DataFrame.max: compute max of values
    pandas.Series.groupby: groupby method of Series
    pandas.DataFrame.groupby: groupby method of DataFrame
    pandas.Panel.groupby: goupby method of Panel

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

Docstring for "pandas.core.groupby.GroupBy.max" correct. :)
```

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

for groupby examples they should reference the Series (or DataFrame) method of the same name in See Also

@jorisvandenbossche @datapythonista generic point. # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=h1) Report
> Merging [#20073](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/670c2e437e90c08105b2d83df868fc8c6c668a3b?src=pr&el=desc) will **increase** coverage by `<.01%`.
> The diff coverage is `100%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/20073/graphs/tree.svg?width=650&height=150&src=pr&token=eZ4WkYLtcO)](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #20073      +/-   ##
==========================================
+ Coverage   91.79%    91.8%   +<.01%     
==========================================
  Files         152      152              
  Lines       49205    49208       +3     
==========================================
+ Hits        45169    45174       +5     
+ Misses       4036     4034       -2
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.18% <100%> (ø)` | :arrow_up: |
| #single | `41.85% <100%> (ø)` | :arrow_up: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/groupby.py](https://codecov.io/gh/pandas-dev/pandas/pull/20073/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ3JvdXBieS5weQ==) | `92.15% <100%> (+0.01%)` | :arrow_up: |
| [pandas/core/internals.py](https://codecov.io/gh/pandas-dev/pandas/pull/20073/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvaW50ZXJuYWxzLnB5) | `95.53% <0%> (ø)` | :arrow_up: |
| [pandas/util/testing.py](https://codecov.io/gh/pandas-dev/pandas/pull/20073/diff?src=pr&el=tree#diff-cGFuZGFzL3V0aWwvdGVzdGluZy5weQ==) | `83.95% <0%> (+0.2%)` | :arrow_up: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=footer). Last update [670c2e4...38059c9](https://codecov.io/gh/pandas-dev/pandas/pull/20073?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 @shivam6294 do you have time to update the PR based on the feedback? Hey @jorisvandenbossche. I'm working on this right now Thanks for the review @jorisvandenbossche :+1: I'll create a new issue for the complicated template substitution. I had a question about the Parameters - listing the parameters like this:
```
    Parameters
    ----------
    kwargs : dict
        For compatibility with other groupby methods.
    numeric_only : bool
        Include only float, int, boolean columns.
    min_count : int
        The required number of valid values to perform the operation.
        If fewer than `min_count` non-NA values are present the result
        will be NA.
```
results in errors when I run the `scripts/validate_docstrings.py` script:
```
Errors found:
        Errors in parameters section
                Unknown parameters {'numeric_only', 'min_count'}
```

Is this alright? Also, should I still leave `kwargs` in there even though all the `kwargs` have been listed? closing as stale. if you'd like to continue pls ping.