rename infer to expand

Author: jreback

pandas/core/apply.py

@@ -358,8 +358,8 @@ def wrap_results_for_axis(self):
         """ return the results for the columns """
         results = self.results
 
-        # we have requested inference
-        if self.result_type == 'infer':
+        # we have requested to expand
+        if self.result_type == 'expand':
             result = self.infer_to_same_shape()
 
         # we have a non-series and don't want inference

pandas/core/frame.py

@@ -4871,9 +4871,9 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
             reduce is True a Series will always be returned, and if False a
             DataFrame will always be returned.
 
-        result_type : {'infer', 'broadcast, None}
+        result_type : {'expand', 'broadcast, None}
             These only act when axis=1 {columns}
-            * 'infer' : list-like results will be turned into columns
+            * 'expand' : list-like results will be turned into columns
             * 'broadcast' : scalar results will be broadcast to all columns
             * None : list-like results will be returned as a list
               in a single column. However if the apply function
@@ -4948,10 +4948,10 @@ def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None,
         4    [1, 2]
         5    [1, 2]
 
-        Passing result_type='infer' will expand list-like results
+        Passing result_type='expand' will expand list-like results
         to columns of a Dataframe
 
-        >>> df.apply(lambda x: [1, 2], axis=1, result_type='infer')
+        >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
            0  1
         0  1  2
         1  1  2

pandas/core/sparse/frame.py

@@ -853,9 +853,9 @@ def apply(self, func, axis=0, broadcast=None, reduce=False,
                This argument will be removed in a future version, replaced
                by result_type='broadcast'.
 
-        result_type : {'infer', 'broadcast, None}
+        result_type : {'expand', 'broadcast, None}
             These only act when axis=1 {columns}
-            * 'infer' : list-like results will be turned into columns
+            * 'expand' : list-like results will be turned into columns
             * 'broadcast' : scalar results will be broadcast to all columns
             * None : list-like results will be returned as a list
               in a single column. However if the apply function

pandas/tests/frame/test_apply.py

@@ -576,14 +576,14 @@ def test_with_dictlike_columns_with_infer(self):
         # gh 17602
         df = DataFrame([[1, 2], [1, 2]], columns=['a', 'b'])
         result = df.apply(lambda x: {'s': x['a'] + x['b']},
-                          axis=1, result_type='infer')
+                          axis=1, result_type='expand')
         expected = DataFrame({'s': [3, 3]})
         assert_frame_equal(result, expected)
 
         df['tm'] = [pd.Timestamp('2017-05-01 00:00:00'),
                     pd.Timestamp('2017-05-02 00:00:00')]
         result = df.apply(lambda x: {'s': x['a'] + x['b']},
-                          axis=1, result_type='infer')
+                          axis=1, result_type='expand')
         assert_frame_equal(result, expected)
 
     def test_with_listlike_columns(self):
@@ -705,12 +705,12 @@ def test_result_type(self):
             np.tile(np.arange(3, dtype='int64'), 6).reshape(6, -1) + 1,
             columns=['A', 'B', 'C'])
 
-        result = df.apply(lambda x: [1, 2, 3], axis=1, result_type='infer')
+        result = df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')
         expected = df.copy()
         expected.columns = [0, 1, 2]
         assert_frame_equal(result, expected)
 
-        result = df.apply(lambda x: [1, 2], axis=1, result_type='infer')
+        result = df.apply(lambda x: [1, 2], axis=1, result_type='expand')
         expected = df[['A', 'B']].copy()
         expected.columns = [0, 1]
         assert_frame_equal(result, expected)
@@ -760,7 +760,7 @@ def test_consistency_for_boxed(self, box):
         expected = Series([box([1, 2]) for t in df.itertuples()])
         assert_series_equal(result, expected)
 
-        result = df.apply(lambda x: box([1, 2]), axis=1, result_type='infer')
+        result = df.apply(lambda x: box([1, 2]), axis=1, result_type='expand')
         expected = DataFrame(
             np.tile(np.arange(2, dtype='int64'), 6).reshape(6, -1) + 1)
         assert_frame_equal(result, expected)