API: add top-level functions as method
 - [ ] closes #12640
   - [x] pd.crosstab (DataFrame)
   - [x] pd.melt (DataFrame) (xref #14876)
   - [x] pd.pivot_table (might need a new name as would be confusing with DataFrame.pivot)
   - [x] pd.get_dummies (both)
   - [x] pd.cut/qcut (only on Series)

 - [ ] tests added / passed
 - [ ] passes ``git diff upstream/master | flake8 --diff``
 - [ ] whatsnew entry

Note: this conversation is continued from #12640.

This is a PR which aliases certain top-level functions into `DataFrame` and/or `Series` object methods, for reasons of syntax and method chaining. Still a WIP atm.

The top-level method bodies remain in the tooling libraries in which they were defined before, but the docstrings have been moved to `_shared_docs` dictionary objects in `core/frame.py` and/or `core/series.py`. `_shared_docs` is then back-ported into the tooling libraries and attached to the original method using the `Appender` decorator.

This is done so that the docstrings between the top-level and object-method differ in two respects:

1. The new object method gets a `new in 0.20.0` marker in the docs.
2. The new object method links to the old top-level function in `See also`, and vice versa.

This reuses an earlier-established convention used elsewhere throughout the library.

Have not written nor attempted to pass tests just yet.

That being said, in the conversation on the [issue](#12640) @jreback mentions tweaking the pattern by possible e.g. [using another newly created decorator](https://github.com/pandas-dev/pandas/pull/15484/commits/e75eacb2f7d165fa4b2148c2c5cb051b0bff816c). I agree that this could be a smart change, because the use of `Appender` is starting to become a little fritzy (see my notes on `pd.get_dummies` below).

In this PR: `pd.crosstab <-> pd.DataFrame.crosstab`, `pd.melt <-> pd.DataFrame.melt`, `pd.cut <-> pd.Series.cut`, `pd.qcut <-> pd.Series.qcut`.

Not in this PR: `pd.pivot_table <-> pd.DataFrame.pivot_table`, which was already done long ago but needs a refactor; and `pd.get_dummies <-> pd.DataFrame.get_dummies/pd.Series.get_dummies`, which is a bit problematic. The issue with the latter is the question as to where the docstring should go: should it be hosted in `series.py` or in `frame.py`? Whichever of those files doesn't host it would have to import it from its sibling.

Finally, `Appender` is used somewhat haphazardously right now. What I mean by that is that the docstrings are sometimes imported as e.g. `from ... import base; base._shared_docs` and sometimes directly as `from ... import _shared_docs`. I think it'd be useful to establish a convention here: `from base import _shared_docs as _base_shared_docs` and `from frame import _shared_docs as _frame_shared_docs`, for example. @ResidentMario to address a point that you made.

When we have a shared method that is needed for *both* Series & DataFrame, we actually put it in ``pandas.core.generic`` and put the shared doc-string there. So that's a good place for ``.get_dummies`` (note that there is a section near the end so that the methods are *only* for ``Series/DataFrames``, and not all inherited objects). alternatively, since some of our files (e.g. ``frame.py``) are getting very large. We could do something like:

``pandas/core/docs.py`` where *only* doc-strings exist (or maybe ``pandas/core/frame/docs`` and similar). A bit odd to separate code from the doc-strings, but it might be appropriate in this case.

@jorisvandenbossche  Re: `generic`, gotcha, can-do then. It looks like the two remaining functions can also be touched up using `Appender` alone, then.

Re: globing off the docs, I don't have a strong opinion on this. Having a file for docstrings is weird, but then again, so is having docstrings floating somewhere about their method bodies :). It would certainly make things significantly cleaner, architecturally.

I wouldn't mind implementing either/or. The latter kind of sounds like a different PR, though. @ResidentMario yep go ahead and make changes to move things to ``generic.py`` as appropriate. The generaized move to something like a ``docs.py`` would be a different PR (looking more some community thought whether this makes sense). Sorry that I didn't raise my concert earlier on the issue, and @ResidentMario I find it difficult to give this backlash given the very nice effort you are putting in this issue. 
But, I am personally not sure I find it a good idea to add all of these top-level functions as methods on a dataframe/series. I don't have much time this evening to look more into detail, but we already have a large amount of methods on DataFrame/Series, and I think adding new ones should really be worth it.
 Regarding the docstrings, that's something I also already thought about once. Our current infrastructure of shared_docs is a bit of a mess for contributors, and can use some rethinking. But that indeed sounds like another issue/PR.

In case of `melt`, you could also do it like pivot_table, which is added to DataFrame in pivot.py So long as you agree with the [method chaining](https://tomaugspurger.github.io/method-chaining.html) philosophy, the ability to use `df.melt(...)` instead of `df.pipe(lambda df: pd.melt(df, ...))` seems to absolutely be worth the maintenance burden.

The other consideration is a broader one about API design. Where does `pandas` want to try to keep its algorithms? Should they be object methods, or top-level functions? Having things available in both was once convenient (`pd.pivot_table(data)` beats `pd.DataFrame(data).pivot_table()`), but the API is so large now that having some kind of strict differential&mdash;e.g. IO tasks only in the top-level, everything else in object mehtods&mdash;seems like a necessary way forward.

Otherwise, `pandas` risks confusing newcomers by providing them too many options: to do x you can use either route, to do y you have to use an object, z is only available as a top-level...if there could be a simple and memorable rule controlling which is where, that would be really helpful for users!

It sounds like `pandas` has already been moving towards object methods for some time. Looking at the docs, here are the current top-level methods (excluding file I/O):

* `bdate_range`
* `concat`
* `crosstab`
* `cut`
* `date_range`
* `eval`
* `factorize`
* `get_dummies`
* `get_option`
* `isnull`
* `melt`
* `merge`
* `merge_asof`
* `merge_ordered`
* `notnull`
* `option_context`
* `period_range`
* `piviot`
* `pivot_table`
* `qcut`
* `reset_option`
* `set_option`
* `test`
* `to_datetime`
* `to_numeric`
* `to_timedelta`
* `wide_to_long`

Maybe the agreement to be reached here is on which of these "ought" to be where?

cc: @TomAugspurger @ResidentMario 

so *most* of the functions on the list, should certainly be top-level, and yes we should have a clear policy. 

One easy differentiator is a function which only takes a single argument, that is a Series OR a DataFrame only

- This eliminates things like concat/merge.``pd.concat`` can take multiple objects, as for example does ``pd.merge``. Now one could make the case that ``DataFrame.merge`` should exist (though we have ``.join`` which by-definition does auto alignment).

- A function like ``.cut/.qcut`` *does* fall into this category, but OTOH, it can take *multiple* types of arguments (e.g. ``ndarray/Index/Series``), so I would put about +0 for adding these (include also the ``.to_datetime/.to_numeric/.to_timedelta`` in this bucket).). 

- Something that I am +1 on converting is ``.melt``, as this completes the reshaping ops, with ``.unstack/.stack/.pivot`` already methods.

In fact I would go as far as deprecating ``pd.pivot/pd.pivot_table``, which are *only* useful as methods.

So I agree there is much confusion here on what should be where. Another example, we used to have *only* ``pd.null/isnull``. But these make *much* more sense as methods, esp for having clean-concise code.

@jorisvandenbossche is right that we have too many methods as it is, BUT, I still think we need to clean this up and have as much consistency as possible.
 # [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/15513?src=pr&el=h1) Report
> Merging [#15513](https://codecov.io/gh/pandas-dev/pandas/pull/15513?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/b3ae4c7698de4623e1279d579b46192ef79250d1?src=pr&el=desc) will **decrease** coverage by `-0.01%`.
> The diff coverage is `76.47%`.


```diff
@@            Coverage Diff             @@
##           master   #15513      +/-   ##
==========================================
- Coverage   90.36%   90.36%   -0.01%     
==========================================
  Files         136      136              
  Lines       49553    49587      +34     
==========================================
+ Hits        44780    44807      +27     
- Misses       4773     4780       +7
```


| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/15513?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/tools/pivot.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL3Rvb2xzL3Bpdm90LnB5) | `95.1% <100%> (+0.06%)` | :white_check_mark: |
| [pandas/core/reshape.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvcmVzaGFwZS5weQ==) | `99.26% <100%> (ø)` | :white_check_mark: |
| [pandas/tools/tile.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL3Rvb2xzL3RpbGUucHk=) | `96.95% <100%> (+0.09%)` | :white_check_mark: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.64% <60%> (-0.19%)` | :x: |
| [pandas/core/series.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvc2VyaWVzLnB5) | `94.64% <63.63%> (-0.33%)` | :x: |
| [pandas/core/generic.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ2VuZXJpYy5weQ==) | `96.31% <0%> (ø)` | :white_check_mark: |
| [pandas/core/common.py](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...4a64c390b04b469381c2698a3e36273f3784be2d?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvY29tbW9uLnB5) | `91.36% <0%> (+0.33%)` | :white_check_mark: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/15513?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/15513?src=pr&el=footer). Last update [b3ae4c7...643d54b](https://codecov.io/gh/pandas-dev/pandas/compare/b3ae4c7698de4623e1279d579b46192ef79250d1...643d54b76edabb5d5954d2ecd960978dbf4d8d83?el=footer&src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments). >  So long as you agree with the method chaining philosophy, the ability to use df.melt(...) instead of df.pipe(lambda df: pd.melt(df, ...)) seems to absolutely be worth the maintenance burden.

I think the alternative of `df.pipe(pd.melt, ...)` is not too bad. Oof, my bad there with the method signature. I still don't want to have to use `pipe` though, if I can help it, for a built-in. Alright, I've added commits for each of the functions. Whichever ones we decide to transfer can be cherry-picked off the pull request.

`get_dummies` was easily the most difficult of the set, since it required transferring some things between files. Hopefully I got the implementation right.

Tests still TBD, pending further discussion (?). @jreback I think you are missing a few methods on DataFrame :-) 
Both `pivot_table` and `merge` already *exist* also as DataFrame methods (one can discuss whether it is good that they are duplicated, but in any case they already are).

A bit more specific concerns:

- `crosstab`: 
  - the current signature is not suitable for adding it as a DataFrame method. As a consequence, your current implementation in this PR will also not work.
  - The result of `crosstab` can already be obtained with `DataFrame.pivot_table` using `aggfunc='count'`, so a bit less essential although a convenient shortcut. But the convenient shortcut can also be a top-level function
- `melt`:
  - I agree this one would be nice to complement the existing reshaping dataframe method `pivot`
  - But, there have also some issues been raised about the current melt API (https://github.com/pandas-dev/pandas/issues/10109, https://github.com/pandas-dev/pandas/issues/14876#issuecomment-266907956), so we should make sure that the *current* melt is what we actually want to add as a method

I would also not move the `get_dummies` implementation, but keep it here consistent with the approach for the other reshaping methods: functionality in a separate module, import from there for adding it to NDFrame. We don't have any other public functions (apart from methods) defined in generic.py

 @jorisvandenbossche you can see how much I use ``DataFrame.merge`` !

yeah I think main method that needs inclusion is ``.melt`` (though ackowledge the signature confusion), but that's the case either way.

and agree on ``.get_dummies``. I would argue that we should call this ``.dummies`` for consistency, but that's another issue.

ok @ResidentMario can you strip out just ``.melt`` for now (into a new PR) or update this one, either way. I'll open a new PR with just the `melt` changes, then.