Merge branch 'quantile-axis' of https://github.com/TomAugspurger/pandas into TomAugspurger-quantile-axis

Conflicts:
	doc/source/v0.14.1.txt

Author: jreback

doc/source/v0.14.1.txt

@@ -169,16 +169,7 @@ Bug Fixes
 
 - Bug in ``DatetimeIndex.insert`` doesn't preserve ``name`` and ``tz`` (:issue:`7299`)
 - Bug in ``DatetimeIndex.asobject`` doesn't preserve ``name`` (:issue:`7299`)
-
-
-
-
 - Bug in multi-index slicing with datetimelike ranges (strings and Timestamps), (:issue:`7429`)
-
-
-
-
-
 - Bug in ``Index.min`` and ``max`` doesn't handle ``nan`` and ``NaT`` properly (:issue:`7261`)
 - Bug in ``resample`` where ``fill_method`` was ignored if you passed ``how`` (:issue:`7261`)
 - Bug in ``TimeGrouper`` doesn't exclude column specified by ``key`` (:issue:`7227`)
@@ -221,4 +212,18 @@ Bug Fixes
   :issue:`7409`).
 - Bug where bool objects were converted to ``nan`` in ``convert_objects``
   (:issue:`7416`).
+- Bug in ``quantile`` ignoring the axis keyword argument (:issue`7306`)
+
+
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+
 

pandas/core/frame.py

@@ -4181,6 +4181,8 @@ def f(arr, per):
                 return _quantile(values, per)
 
         data = self._get_numeric_data() if numeric_only else self
+        if axis == 1:
+            data = data.T
 
         # need to know which cols are timestamp going in so that we can
         # map timestamp over them after getting the quantile.

pandas/tests/test_frame.py

@@ -11166,6 +11166,26 @@ def test_quantile(self):
         xp = df.median()
         assert_series_equal(rs, xp)
 
+        # axis
+        df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
+        result = df.quantile(.5, axis=1)
+        expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3])
+        assert_series_equal(result, expected)
+
+        result = df.quantile([.5, .75], axis=1)
+        expected = DataFrame({1: [1.5, 1.75], 2: [2.5, 2.75],
+                              3: [3.5, 3.75]}, index=["0.5", "0.75"])
+        assert_frame_equal(result, expected)
+
+        # We may want to break API in the future to change this
+        # so that we exclude non-numeric along the same axis
+        # See GH #7312
+        df = DataFrame([[1, 2, 3],
+                        ['a', 'b', 4]])
+        result = df.quantile(.5, axis=1)
+        expected = Series([3., 4.], index=[0, 1])
+        assert_series_equal(result, expected)
+
     def test_quantile_multi(self):
         df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]],
                        columns=['a', 'b', 'c'])
@@ -11204,6 +11224,20 @@ def test_quantile_datetime(self):
                              index=[.5], columns=['a', 'b'])
         assert_frame_equal(result, expected)
 
+        # axis = 1
+        df['c'] = pd.to_datetime(['2011', '2012'])
+        result = df[['a', 'c']].quantile(.5, axis=1, numeric_only=False)
+        expected = Series([Timestamp('2010-07-02 12:00:00'),
+                           Timestamp('2011-07-02 12:00:00')],
+                          index=[0, 1])
+        assert_series_equal(result, expected)
+
+        result = df[['a', 'c']].quantile([.5], axis=1, numeric_only=False)
+        expected = DataFrame([[Timestamp('2010-07-02 12:00:00'),
+                               Timestamp('2011-07-02 12:00:00')]],
+                             index=[0.5], columns=[0, 1])
+        assert_frame_equal(result, expected)
+
     def test_cumsum(self):
         self.tsframe.ix[5:10, 0] = nan
         self.tsframe.ix[10:15, 1] = nan