ENH: Add support for Classifiers.

Classifiers are computations that represent grouping keys. They can be
used in conjuction with normalization functions like ``zscore`` or
``demean`` to perform normalizations over subsets of a dataset.

Notable changes:

- Added ``demean()`` and ``zscore()`` methods to ``Factor``.

- Added a classifier versions of ``Latest`` and ``CustomTermMixin``.
  The .latest attribute of int64 dataset columns no produces a
  classifier by default.

- Added ``Everything``, a classifier that maps all data to the same
  value.

- Added ``zipline.lib.normalize``, which implements a naive, pure-Python
  grouped normalize function.  This will likely be moved to Cython in a
  subsequent PR.

tests/test_doctests.py

@@ -4,7 +4,7 @@ import doctest
 from unittest import TestCase
 
 from zipline import testing
-from zipline.lib import adjustment
+from zipline.lib import adjustment, normalize
 from zipline.pipeline import (
     engine,
     expression,
@@ -86,3 +86,6 @@ class DoctestTestCase(TestCase):
 
     def test_functional_docs(self):
         self._check_docs(functional)
+
+    def test_normalize_docs(self):
+        self._check_docs(normalize)