diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 6a8a4e7089ba3..ec002fae3b4b9 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -788,6 +788,7 @@ Removal of prior version deprecations/changes
 - Removal of the ``read_frame`` and ``frame_query`` (both aliases for ``pd.read_sql``)
   and ``write_frame`` (alias of ``to_sql``) functions in the ``pd.io.sql`` namespace,
   deprecated since 0.14.0 (:issue:`6292`).
+- Removal of the ``order`` keyword from ``.factorize()`` (:issue:`6930`)
 
 .. _whatsnew_0180.performance:
 
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index d516471ededb6..2c70da413bb8c 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -163,7 +163,6 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
         Sequence
     sort : boolean, default False
         Sort by values
-    order : deprecated
     na_sentinel : int, default -1
         Value to mark "not found"
     size_hint : hint to the hashtable sizer
@@ -178,11 +177,6 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
     note: an array of Periods will ignore sort as it returns an always sorted
     PeriodIndex
     """
-    if order is not None:
-        msg = "order is deprecated. See " \
-              "https://github.com/pydata/pandas/issues/6926"
-        warn(msg, FutureWarning, stacklevel=2)
-
     from pandas import Index, Series, DatetimeIndex
 
     vals = np.asarray(values)
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index 13f1c4280ba38..957a9f1f808f1 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -53,12 +53,6 @@ def test_strings(self):
 class TestFactorize(tm.TestCase):
     _multiprocess_can_split_ = True
 
-    def test_warn(self):
-
-        s = Series([1, 2, 3])
-        with tm.assert_produces_warning(FutureWarning):
-            algos.factorize(s, order='A')
-
     def test_basic(self):
 
         labels, uniques = algos.factorize(['a', 'b', 'b', 'a', 'a', 'c', 'c',