Initial. (#3188)

docs/source/release_notes.rst

@@ -1,6 +1,18 @@
 Release Notes
 -------------
 **Future Releases**
+    * Enhancements
+    * Fixes
+    * Changes
+    * Documentation Changes
+    * Testing Changes
+
+.. warning::
+
+    **Breaking Changes**
+
+
+**v0.41.0 Jan. 06, 2022**
     * Enhancements
         * Added string support for DataCheckActionCode :pr:`3167`
         * Added ``DataCheckActionOption`` class :pr:`3134`
@@ -10,6 +22,7 @@ Release Notes
         * Fixed bug in nightly linux tests :pr:`3189`
     * Changes
         * Removed usage of scikit-learn's ``LabelEncoder`` in favor of ours :pr:`3161`
+        * Removed nullable types checking from ``infer_feature_types`` :pr:`3156`
         * Fixed ``mean_cv_data`` and ``validation_score`` values in AutoMLSearch.rankings to reflect cv score or ``NaN`` when appropriate :pr:`3162`
     * Documentation Changes
     * Testing Changes
@@ -27,7 +40,6 @@ Release Notes
     * Changes
         * ``TimeSeriesParametersDataCheck`` was added to ``DefaultDataChecks`` for time series problems :pr:`3139`
         * Renamed ``date_index`` to ``time_index`` in ``problem_configuration`` for time series problems :pr:`3137`
-        * Removed nullable types checking from ``infer_feature_types`` :pr:`3156`
         * Updated ``nlp-primitives`` minimum version to 2.1.0 :pr:`3166`
         * Updated minimum version of ``woodwork`` to v0.11.0 :pr:`3171`
         * Revert `3160` until uninferrable frequency can be addressed earlier in the process :pr:`3198`
@@ -36,8 +48,6 @@ Release Notes
     * Testing Changes
         * Parameterized tests in ``test_datasets.py`` :pr:`3145`
 
-
-
 .. warning::
 
     **Breaking Changes**

evalml/__init__.py

@@ -23,4 +23,4 @@
 warnings.filterwarnings("ignore", category=FutureWarning)
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
-__version__ = "0.40.0"
+__version__ = "0.41.0"

setup.py

@@ -11,7 +11,7 @@
 
 setup(
     name='evalml',
-    version='0.40.0',
+    version='0.41.0',
     author='Alteryx, Inc.',
     author_email='[email protected]',
     description='EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.',