PERF: Make DateOffset (partially) immutable, cache _params for __eq__ speedup
Any time two `pd.Period` objects are compared, it starts off by checking `self.freq != other.freq`.  These `freq` attributes are `DateOffset` objects defined in `tseries.offsets`.  `DateOffset.__ne__` calls `self._params()`, which is the bottleneck in a lot of Period-based work.

```
import pandas as pd
import cProfile, pstats
nobs = 2000
dr = pd.date_range('1901-01-01', freq='M', periods=nobs)
ser = dr.to_series().apply(pd.Period, freq='M')
pr = cProfile.Profile()
pr.enable()
fr = ser.to_frame().set_index(0, append=True)
pr.disable()
p = pstats.Stats(pr)
p.sort_stats('cumulative')
p.print_stats(20)
```

Results under the status quo:
```
         340859 function calls (340834 primitive calls) in 1.564 seconds

   Ordered by: cumulative time
   List reduced from 279 to 28 due to restriction <0.1>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    1.562    1.562 /usr/local/lib/python2.7/site-packages/pandas/core/frame.py:2761(set_index)
        1    0.000    0.000    1.555    1.555 /usr/local/lib/python2.7/site-packages/pandas/core/indexes/multi.py:1059(from_arrays)
        1    0.000    0.000    1.555    1.555 /usr/local/lib/python2.7/site-packages/pandas/core/categorical.py:2171(_factorize_from_iterables)
        2    0.000    0.000    1.555    0.777 /usr/local/lib/python2.7/site-packages/pandas/core/categorical.py:2134(_factorize_from_iterable)
        2    0.000    0.000    1.555    0.777 /usr/local/lib/python2.7/site-packages/pandas/core/categorical.py:251(__init__)
        2    0.001    0.001    1.543    0.772 /usr/local/lib/python2.7/site-packages/pandas/core/algorithms.py:527(factorize)
    20013    0.037    0.000    1.453    0.000 /usr/local/lib/python2.7/site-packages/pandas/tseries/offsets.py:374(__ne__)
    20013    0.143    0.000    1.417    0.000 /usr/local/lib/python2.7/site-packages/pandas/tseries/offsets.py:360(__eq__)
        2    0.000    0.000    1.381    0.691 /usr/local/lib/python2.7/site-packages/pandas/core/algorithms.py:429(safe_sort)
        2    0.053    0.026    1.381    0.690 {method 'argsort' of 'numpy.ndarray' objects}
    40026    1.029    0.000    1.240    0.000 /usr/local/lib/python2.7/site-packages/pandas/tseries/offsets.py:311(_params)
        1    0.016    0.016    0.157    0.157 {method 'get_labels' of 'pandas._libs.hashtable.PyObjectHashTable' objects}
    40026    0.102    0.000    0.102    0.000 {sorted}
   120079    0.074    0.000    0.074    0.000 {method 'items' of 'dict' objects}
    40026    0.036    0.000    0.036    0.000 {vars}
    40431    0.034    0.000    0.034    0.000 {isinstance}
     9578    0.017    0.000    0.022    0.000 /usr/local/lib/python2.7/site-packages/pandas/tseries/offsets.py:495(freqstr)
        2    0.000    0.000    0.011    0.005 /usr/local/lib/python2.7/site-packages/pandas/core/categorical.py:543(_validate_categories)
        1    0.000    0.000    0.010    0.010 /usr/local/lib/python2.7/site-packages/pandas/core/indexes/base.py:152(__new__)
        1    0.000    0.000    0.009    0.009 /usr/local/lib/python2.7/site-packages/pandas/core/indexes/period.py:202(__new__)
```

Results under this PR:
```
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.141    0.141 pandas/core/frame.py:2862(set_index)
        1    0.000    0.000    0.139    0.139 pandas/core/indexes/multi.py:1054(from_arrays)
        1    0.000    0.000    0.139    0.139 pandas/core/categorical.py:2146(_factorize_from_iterables)
        2    0.000    0.000    0.139    0.069 pandas/core/categorical.py:2109(_factorize_from_iterable)
        2    0.000    0.000    0.139    0.069 pandas/core/categorical.py:251(__init__)
        2    0.000    0.000    0.132    0.066 pandas/core/algorithms.py:441(factorize)
    20013    0.012    0.000    0.098    0.000 pandas/tseries/offsets.py:383(__ne__)
    20013    0.036    0.000    0.086    0.000 pandas/tseries/offsets.py:369(__eq__)
        1    0.008    0.008    0.068    0.068 {method 'get_labels' of 'pandas._libs.hashtable.PyObjectHashTable' objects}
        2    0.000    0.000    0.063    0.031 pandas/core/sorting.py:383(safe_sort)
        2    0.016    0.008    0.063    0.031 {method 'argsort' of 'numpy.ndarray' objects}
     2000    0.030    0.000    0.035    0.000 pandas/tseries/offsets.py:319(_params)
     9578    0.009    0.000    0.012    0.000 pandas/tseries/offsets.py:504(freqstr)
    40456    0.011    0.000    0.011    0.000 {isinstance}
        2    0.000    0.000    0.006    0.003 pandas/core/categorical.py:544(_validate_categories)
        1    0.000    0.000    0.006    0.006 pandas/core/indexes/base.py:154(__new__)
        1    0.000    0.000    0.006    0.006 pandas/core/indexes/period.py:194(__new__)
        1    0.002    0.002    0.005    0.005 {pandas._libs.period.extract_ordinals}
     2000    0.004    0.000    0.004    0.000 pandas/tseries/offsets.py:193(__setattr__)
     9578    0.003    0.000    0.003    0.000 pandas/tseries/offsets.py:500(rule_code)
```

Note that in this example the periods were _already_ ordered, so the number of comparisons (20013) is pretty much best-case.  After shuffling them (sample size=1) there were 35181 comparisons.

There are some things I'd like to touch up to move towards actual-immutability, but the caching here is a big win.
# [Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=h1) Report
> Merging [#17137](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=desc) into [master](https://codecov.io/gh/pandas-dev/pandas/commit/f2b0bdc9bc4e57e101e306db7555eb7db28172e9?src=pr&el=desc) will **decrease** coverage by `0.07%`.
> The diff coverage is `80%`.

[![Impacted file tree graph](https://codecov.io/gh/pandas-dev/pandas/pull/17137/graphs/tree.svg?width=650&height=150&src=pr&token=eZ4WkYLtcO)](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master   #17137      +/-   ##
==========================================
- Coverage   91.03%   90.95%   -0.08%     
==========================================
  Files         161      161              
  Lines       49405    49411       +6     
==========================================
- Hits        44974    44943      -31     
- Misses       4431     4468      +37
```

| Flag | Coverage Δ | |
|---|---|---|
| #multiple | `88.74% <80%> (-0.05%)` | :arrow_down: |
| #single | `40.2% <70%> (-0.13%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [pandas/tseries/offsets.py](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree#diff-cGFuZGFzL3RzZXJpZXMvb2Zmc2V0cy5weQ==) | `97.07% <80%> (-0.06%)` | :arrow_down: |
| [pandas/io/feather\_format.py](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree#diff-cGFuZGFzL2lvL2ZlYXRoZXJfZm9ybWF0LnB5) | `20% <0%> (-65.72%)` | :arrow_down: |
| [pandas/io/gbq.py](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree#diff-cGFuZGFzL2lvL2dicS5weQ==) | `25% <0%> (-58.34%)` | :arrow_down: |
| [pandas/core/frame.py](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZnJhbWUucHk=) | `97.56% <0%> (-0.2%)` | :arrow_down: |
| [pandas/core/series.py](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvc2VyaWVzLnB5) | `94.85% <0%> (-0.1%)` | :arrow_down: |
| [pandas/core/generic.py](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=tree#diff-cGFuZGFzL2NvcmUvZ2VuZXJpYy5weQ==) | `91.96% <0%> (-0.06%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/pandas-dev/pandas/pull/17137?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/17137?src=pr&el=footer). Last update [f2b0bdc...2e3e639](https://codecov.io/gh/pandas-dev/pandas/pull/17137?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
 see if any asv's are needed for this (I don't think so actually), and show a representative speedup (not the profile), just a before / after timeit.

if it looks good, pls add a whatsnew in perf. Will do.  There are some cases I want to take a closer look at before calling this ready; parking some timeit results here in the meantime:

```
import random
import timeit

import pandas as pd

nobs = 1000

months = pd.date_range('1901-01-01', freq='M', periods=nobs)
days = pd.date_range('1923-04-05', freq='D', periods=nobs)

dser = days.to_series().apply(pd.Period, freq='D')
mser = months.to_series().apply(pd.Period, freq='M')

dlist = list(dser)
mlist = list(mser)

random.shuffle(dlist)
random.shuffle(mlist)

mser2 = pd.Series(mlist)
dser2 = pd.Series(dlist)

timeit.timeit(mser.sort_values, number=100)
timeit.timeit(dser.sort_values, number=100)
timeit.timeit(mser2.sort_values, number=100)
timeit.timeit(dser2.sort_values, number=100)
```

Old Results
```
>>> timeit.timeit(mser.sort_values, number=100)
29.13339614868164
>>> timeit.timeit(dser.sort_values, number=100)
46.98428416252136
>>> timeit.timeit(mser2.sort_values, number=100)
107.7828049659729
>>> timeit.timeit(dser2.sort_values, number=100)
113.00968098640442
```

New Results
```
>>> timeit.timeit(mser.sort_values, number=100)
2.2172720432281494
>>> timeit.timeit(dser.sort_values, number=100)
1.7678871154785156
>>> timeit.timeit(mser2.sort_values, number=100)
3.523206949234009
>>> timeit.timeit(dser2.sort_values, number=100)
2.4215428829193115
```
 There may be a problem.  A lot of cross-design appears to have built up over the years including at least two approaches to caching.

It looks like the original author had caching in mind, but it got dropped [here](https://github.com/jbrockmendel/pandas/commit/9c1bae402d4c302a033da7159096d9df288506b0).  There is a `_cacheable` attribute that is `False` everywhere except one unused mixin class, and a `_should_cache` that is referenced over in `core.indexes.datetimes` but is apparently always `False`.

AFAICT the idea in `tseries.frequencies.get_offset` is that the user calls e.g. `get_offset(B)`, gets a copy of the cached `<BusinessDay>` offset, and then can modify if desired.  My approach of making `DateOffset` immutable would break that.

The motivation is that `__eq__` is currently very slow.  It is slow because it re-evaluates `self._params()` on every call.  I'm advocating caching `self._params()`.  Doing this requires ensuring that cache remains valid, so freezing all the relevant attributes.  But those attributes are blacklist-based instead of whitelist-based:

```
def _params(self):
        all_paras = dict(list(vars(self).items()) + list(self.kwds.items()))
        if 'holidays' in all_paras and not all_paras['holidays']:
            all_paras.pop('holidays')
        exclude = ['kwds', 'name', 'normalize', 'calendar']
        attrs = [(k, v) for k, v in all_paras.items()
                 if (k not in exclude) and (k[0] != '_')]
        attrs = sorted(set(attrs))
        params = tuple([str(self.__class__)] + attrs)
        return params
```

(There is a TODO note further down suggesting this should be changed:
```
    # TODO: Combine this with DateOffset by defining a whitelisted set of
    # attributes on each object rather than the existing behavior of iterating
    # over internal ``__dict__``
```
)

Making `_params` cacheable for the general case would require basically deprecating the `self.kwds` attribute, which is I'm reticent to do as users may be using it in creative ways.  My inclination is to have `_params` defer to a `_cached_params` iff `len(self.kwds) == 0`.  Then `__setattr__` can refuse to change a handful of attributes, but it doesn't need to be an exhaustive list.

I'm still not sure if this approach would play nicely with the `frequencies.get_offset` cache. closing in favor of #17181 