ENH: Add 'is_' method to Index for identity checks
Closes #4859.

Adds a method that can quickly tell whether something is a view or not
(in the way that actually matters). Also changes checks in `equals()` to
use `is_` rather than `is`.

worth it to push these into one of the mixin classes so that could be used on a series/frame? I guess in that case easier just to do `if df1 is df2`  (as the only way they will be equal if they are actually the same object with 2 diff references)
 @jreback it's really easy to do with index, because they always go through `__new__` or `__array_finalize__` and they aren't mutable, so it's very simple to get working. Series/Frame are mutable and don't make sense for this...too likely to have an edge case. _Could_ do it with blockmanager (maybe) but I don't think it's worth it for speed boost. 
 yep....as I was writing I was like...naw!
 One upside to this: makes `equals()` and `identical()` much faster when you're comparing and it's the same object or a view.
 so we need `identical` then? which is equiv to `equals & is_`?
 I guess though could be `identical` but not a view...duh.....hmm
 vbench results:

```
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
groupby_series_simple_cython                 |   4.3203 |   4.3933 |   0.9834 |
groupby_multi_cython                         |  14.0613 |  14.2470 |   0.9870 |
groupby_multi_different_functions            |  11.1340 |  11.2560 |   0.9892 |
groupby_frame_singlekey_integer              |   1.5600 |   1.5720 |   0.9924 |
groupby_multi_size                           |  24.0187 |  24.1320 |   0.9953 |
groupby_multi_different_numpy_functions      |   9.9383 |   9.9711 |   0.9967 |
groupby_multi_python                         | 115.3820 | 115.5843 |   0.9983 |
groupby_simple_compress_timing               |  20.6167 |  20.5903 |   1.0013 |
groupby_indices                              |   5.1444 |   5.1357 |   1.0017 |
groupby_last_float32                         |   2.7974 |   2.7874 |   1.0036 |
groupby_frame_median                         |   6.6326 |   6.5880 |   1.0068 |
groupby_multi_series_op                      |  12.7397 |  12.6499 |   1.0071 |
groupby_first_float32                        |   2.6823 |   2.6610 |   1.0080 |
groupby_last                                 |   2.8450 |   2.8173 |   1.0098 |
groupby_sum_booleans                         |   0.8566 |   0.8457 |   1.0130 |
groupby_first                                |   2.7236 |   2.6850 |   1.0144 |
groupby_pivot_table                          |  15.4137 |  15.1213 |   1.0193 |
groupby_transform                            | 127.4860 | 123.4310 |   1.0329 |
groupby_apply_dict_return                    |  29.0103 |  27.1187 |   1.0698 |
groupbym_frame_apply                         |  55.3214 |  45.1977 |   1.2240 |
groupby_frame_apply_overhead                 |  10.6590 |   8.5543 |   1.2460 |
groupby_frame_cython_many_columns            |   3.5886 |   2.7813 |   1.2903 |
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
```
 run with `./test_perf.sh -b master -t HEAD --repeats 5 --hrepeats 10 --burnin 10 -r 'groupby'`
 So mostly okay? I can't really tell. We can squeeze a bit more performance out by removing the function call, so the line would be:

```
if self.index._id is res.index._id
```

that said, could be extra overhead from view creation too. What do you think?
 @jreback also, for most intents and purposes, it really doesn't matter if two indices have the same names or not, we just want to know if their labels are the same. And that's pretty much the entire function of `is_`, to allow for more views everywhere without totally sacrificing performance/equality testing.
 @jtratner yep...this all looks fine...perf is about the same (you might need a very specific test to pick up if you actually changing something)
 i'll run vbench one more time just to see if anything changes drastically ... biggest hit is 10 ms...
 orm aybe not if @jreback thinks its okay....honestly i feel unsure whenever vbench is not less than about 1.15 ish...i have no idea of the variability of the timings
 end of vbench:

```
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
timeseries_large_lookup_value                |   0.0246 |   0.0220 |   1.1191 |
join_dataframe_index_single_key_small        |   4.8723 |   4.3330 |   1.1245 |
timeseries_timestamp_tzinfo_cons             |   0.0126 |   0.0110 |   1.1439 |
append_frame_single_homogenous               |   0.2307 |   0.2000 |   1.1538 |
mask_bools                                   |  13.8144 |  11.9720 |   1.1539 |
frame_xs_col                                 |   0.0290 |   0.0250 |   1.1624 |
dti_reset_index                              |   0.1973 |   0.1686 |   1.1701 |
reindex_multiindex                           |   1.0610 |   0.9023 |   1.1758 |
reshape_stack_simple                         |   1.7027 |   1.4450 |   1.1784 |
groupbym_frame_apply                         |  55.8174 |  44.3370 |   1.2589 |
groupby_frame_apply_overhead                 |  10.6933 |   8.4824 |   1.2607 |
frame_reindex_both_axes                      |  17.5817 |  13.7600 |   1.2777 |
frame_fillna_many_columns_pad                |  10.9213 |   6.3963 |   1.7074 |
-------------------------------------------------------------------------------
Test name                                    | head[ms] | base[ms] |  ratio   |
-------------------------------------------------------------------------------
```
 that's a weird vbench...the frame_fillna_many_columns_pad. 
 this works, but if others could take a look (@jreback @cpcloud) and see whether this is useful. Without this, not sure how to do #4202 effectively.
 looks fine to  me
