add section on enumerated data to docs (#568)

* add section on enumerated data to docs

* Update src/hdmf/common/table.py

docs/gallery/dynamictable.py

@@ -132,9 +132,35 @@
     data=[True, True, False, True],  # specify data for the 4 rows in the table
 )
 
+###############################################################################
+# Enumerated Data
+# ---------------
+# :py:class:`~hdmf.common.table.EnumData` is a special type of column for storing
+# an enumerated data type. This way each unique value is stored once, and the data
+# references those values by index. Using this method is more efficient than storing
+# a single value many types, and has the advantage of communicating to downstream
+# tools that the data is categorical in nature.
+
+from hdmf.common.table import EnumData
+
+# this column has a length of 5, not 3
+enum_col = EnumData(
+    name="cell_type",
+    description="this column holds categorical variables",
+    data=[0, 1, 2, 1, 0],
+    elements=["aa", "bb", "cc"]
+)
+
+my_table = DynamicTable(
+    name='my table',
+    description='an example table',
+    columns=[enum_col],
+)
+
+
 ###############################################################################
 # Ragged array columns
-# ^^^^^^^^^^^^^^^^^^^^
+# --------------------
 # A table column with a different number of elements for each row is called a
 # ragged array. To initialize a :py:class:`~hdmf.common.table.DynamicTable`
 # with a ragged array column, pass both
@@ -598,11 +624,6 @@
 table_double_ragged_col['col6']  # returns col6_ind_ind
 table_double_ragged_col.col6  # returns col6
 
-###############################################################################
-# Referencing rows of a DynamicTable
-# ----------------------------------
-# TODO
-
 ###############################################################################
 # Creating custom DynamicTable subclasses
 # ---------------------------------------

src/hdmf/common/table.py

@@ -1180,12 +1180,12 @@ class EnumData(VectorData):
 
     __fields__ = ('elements', )
 
-    @docval({'name': 'name', 'type': str, 'doc': 'the name of this VectorData'},
+    @docval({'name': 'name', 'type': str, 'doc': 'the name of this column'},
             {'name': 'description', 'type': str, 'doc': 'a description for this column'},
             {'name': 'data', 'type': ('array_data', 'data'),
-             'doc': 'a dataset where the first dimension is a concatenation of multiple vectors', 'default': list()},
+             'doc': 'integers that index into elements for the value of each row', 'default': list()},
             {'name': 'elements', 'type': ('array_data', 'data', VectorData), 'default': list(),
-             'doc': 'the items in this elements'})
+             'doc': 'lookup values for each integer in ``data``'})
     def __init__(self, **kwargs):
         elements = popargs('elements', kwargs)
         super().__init__(**kwargs)