DOC: Improve the docstring of DataFrame.equals()
- [X] closes #22462
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

```
################################################################################
##################### Docstring (pandas.DataFrame.equals)  #####################
################################################################################

Test whether two DataFrame objects contain the same elements.

This function allows two DataFrame objects to be compared against
each other to see if they same shape and elements. NaNs in the same
location are considered equal. The column headers do not need to have
the same type, but the elements within the columns must be
the same dtype.

Parameters
----------
other : DataFrame
    The other DataFrame to be compared with the first.

Returns
-------
bool
    True if all elements are the same in both DataFrames, False
    otherwise.

See Also
--------
pandas.Series.eq : Compare two Series objects of the same length
    and return a Series where each element is True if the element
    in each Series is equal, False otherwise.

numpy.array_equal : Return True if two arrays have the same shape
    and elements, False otherwise.

Notes
-----
This function requires that the elements have the same dtype as their
respective elements in the other DataFrame. However, the indices do
not need to have the same type, as long as they are still considered
equal.

Examples
--------
>>> a = pd.DataFrame({1:[0], 0:[1]})
>>> b = pd.DataFrame({1.0:[0], 0.0:[1]})

DataFrames a and b have the same element types and values, but have
different types for the indices, which will still return True.

>>> a.equals(b)
True

DataFrames a and c have different types for the same values for their
elements, and will return False even though the indices are the same
values and types.

>>> c = pd.DataFrame({1:[0.0], 0:[1.0]})
>>> a.equals(c)
False

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

Docstring for "pandas.DataFrame.equals" correct. :)
```
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22539?src=pr&el=h1) Report
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> The diff coverage is `n/a`.

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```diff
@@            Coverage Diff            @@
##             master   #22539   +/-   ##
=========================================
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=========================================
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  Branches          ?        0           
=========================================
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```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `90.45% <ø> (?)` | |
| #single | `42.29% <ø> (?)` | |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/22539?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/core/generic.py](https://codecov.io/gh/pandas-dev/pandas/pull/22539/diff?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ2VuZXJpYy5weQ==) | `96.44% <ø> (ø)` | |

------

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> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/22539?src=pr&el=footer). Last update [a5fe9cf...9730d0b](https://codecov.io/gh/pandas-dev/pandas/pull/22539?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 Hello @seantchan! Thanks for updating the PR.

 - There are no PEP8 issues in the file [`pandas/core/generic.py`](https://github.com/seantchan/pandas/blob/9730d0b09fad2b6738b61093eaca7a0a955e577c/pandas/core/generic.py) !

