ENH: Clear index cache after reindex
Add flag to clear index cache after reindex.

By default, reindex causes index to cache values which potentially increases the memory consumption significantly in the case of multiindexes. 

```
In [2]: idx = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(1000)], names=['date', 'id'])
In [3]: idx2 = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(500)], names=['date', 'id'])
In [4]: df =  pd.DataFrame({'a': 1}, index=idx)
In [5]: df.memory_usage(deep=True, index=True) # Original Memory Usage
Out[5]:
Index     7453600
a        14616000
dtype: int64
In [6]: df.reindex(idx2)  # df is still the same as original. 
In [7]: df.memory_usage(deep=True, index=True)  # Memory usage after reindex
Out[7]:
Index    91339680
a        14616000
dtype: int64
```

With  clear_cache=True

```
In [20]: idx = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(1000)], names=['date', 'id'])
In [21]: idx2 = pd.MultiIndex.from_product([pd.date_range('2010-01-01', '2015-01-01'), range(500)], names=['date', 'id'])
In [22]: df =  pd.DataFrame({'a': 1}, index=idx)
In [23]: df.memory_usage(deep=True, index=True) # Original Memory Usage
Out[23]:
Index     7453600
a        14616000
dtype: int64
In [24]: df.reindex(idx2)  # df is still the same as original.
In [25]: df.memory_usage(deep=True, index=True)  # Memory usage after reindex
Out[25]:
Index     7453600
a        14616000
dtype: int64

```

Hello @BaiBaiHi! Thanks for updating this PR. We checked the lines you've touched for [PEP 8](https://www.python.org/dev/peps/pep-0008) issues, and found:



There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: 

##### Comment last updated at 2020-04-14 04:22:14 UTC @jreback I was trying to be cautious and preserve the default behavior, but after digging into it a bit more, it doesn't seem like keeping the cache there actually provides any benefits. 
I've updated the PR so it automatically clears the mapping.  @BaiBaiHi closing this PR as stale. maybe raise an issue for discussion.