Fix bug in 'nearest' interpolation

Add whatsnew
Fix bug: catch KeyError not ValueError
Add return type to roll_quantile

Author: kornilova203

doc/source/whatsnew/v0.23.0.txt

@@ -449,6 +449,7 @@ Other Enhancements
 - Updated ``to_gbq`` and ``read_gbq`` signature and documentation to reflect changes from
   the Pandas-GBQ library version 0.4.0. Adds intersphinx mapping to Pandas-GBQ
   library. (:issue:`20564`)
+- :meth:`Rolling.quantile` and :meth:`Expanding.quantile` now accept ``interpolation`` keyword
 
 .. _whatsnew_0230.api_breaking:
 

pandas/_libs/window.pyx

@@ -1396,7 +1396,7 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win,
 
     try:
         interpolation_type = interpolation_types[interpolation]
-    except ValueError:
+    except KeyError:
         raise ValueError("Interpolation {} is not supported"
                          .format(interpolation))
 
@@ -1442,7 +1442,7 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win,
                 # Single value in skip list
                 output[i] = skiplist.get(0)
             else:
-                idx_with_fraction = quantile * <double> (nobs - 1)
+                idx_with_fraction = quantile * (nobs - 1)
                 idx = int(idx_with_fraction)
 
                 if interpolation_type == LINEAR:
@@ -1455,10 +1455,7 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win,
                 elif interpolation_type == HIGHER:
                     output[i] = skiplist.get(idx + 1)
                 elif interpolation_type == NEAREST:
-                    if idx_with_fraction - idx < 0.5:
-                        output[i] = skiplist.get(idx)
-                    else:
-                        output[i] = skiplist.get(idx + 1)
+                    output[i] = skiplist.get(round(idx_with_fraction))
                 elif interpolation_type == MIDPOINT:
                     vlow = skiplist.get(idx)
                     vhigh = skiplist.get(idx + 1)

pandas/core/window.py

@@ -1287,7 +1287,8 @@ def kurt(self, **kwargs):
               fractional part of the index surrounded by `i` and `j`.
             * lower: `i`.
             * higher: `j`.
-            * nearest: `i` or `j` whichever is nearest.
+            * nearest: `i` or `j` whichever is nearest. Implementation uses
+              round() built-in.
             * midpoint: (`i` + `j`) / 2.
 
     Returns
@@ -1305,6 +1306,7 @@ def kurt(self, **kwargs):
     2    2.0
     3    3.0
     dtype: float64
+
     >>> s.rolling(2).quantile(.4, interpolation='midpoint')
     0    NaN
     1    1.5
@@ -1316,7 +1318,7 @@ def kurt(self, **kwargs):
     --------
     pandas.Series.quantile
     pandas.DataFrame.quantile
-    
+
     """)
 
     def quantile(self, quantile, interpolation='linear', **kwargs):
@@ -1655,7 +1657,7 @@ def kurt(self, **kwargs):
     @Substitution(name='rolling')
     @Appender(_doc_template)
     @Appender(_shared_docs['quantile'])
-    def quantile(self, quantile, interpolation='linear', **kwargs):  # here
+    def quantile(self, quantile, interpolation='linear', **kwargs):
         return super(Rolling, self).quantile(quantile=quantile,
                                              interpolation=interpolation,
                                              **kwargs)
@@ -1916,8 +1918,10 @@ def kurt(self, **kwargs):
     @Substitution(name='expanding')
     @Appender(_doc_template)
     @Appender(_shared_docs['quantile'])
-    def quantile(self, quantile, **kwargs):
-        return super(Expanding, self).quantile(quantile=quantile, **kwargs)
+    def quantile(self, quantile, interpolation='linear', **kwargs,):
+        return super(Expanding, self).quantile(quantile=quantile,
+                                               interpolation=interpolation,
+                                               **kwargs)
 
     @Substitution(name='expanding')
     @Appender(_doc_template)

pandas/tests/test_window.py

@@ -1168,7 +1168,7 @@ def test_rolling_quantile_np_percentile(self):
 
     def test_rolling_quantile_series(self):
         # #16211: Tests that rolling window's quantile default behavior
-        # is analogus to Series' quantile
+        # is analogous to Series' quantile
         arr = np.arange(100)
         s = Series(arr)
         q1 = s.quantile(0.1)
@@ -1177,16 +1177,29 @@ def test_rolling_quantile_series(self):
         tm.assert_almost_equal(q1, q2)
 
     @pytest.mark.parametrize('quantile', [0.0, 0.1, 0.45, 0.5, 1])
+    @pytest.mark.parametrize('na_probability', [0.0, 0.3])
     @pytest.mark.parametrize('interpolation', ['linear', 'lower', 'higher',
                                                'nearest', 'midpoint'])
     def test_rolling_quantile_interpolation_options(self, quantile,
+                                                    na_probability,
                                                     interpolation):
-        # Tests that rolling window's quantile behavior is analogus to
+        # Tests that rolling window's quantile behavior is analogous to
         # Series' quantile for each interpolation option
         size = 100
-        s = Series(np.arange(size))
+        s = Series(np.random.rand(size))
+
+        # set NaN values
+        na_count = 0
+        na_total = int(size * na_probability)
+        while na_count < na_total:
+            index = np.random.randint(0, size)
+            if not np.isnan(s[index]):
+                s[index] = np.NaN
+                na_count += 1
+
         q1 = s.quantile(quantile, interpolation)
-        q2 = s.rolling(size).quantile(quantile, interpolation).iloc[-1]
+        q2 = s.rolling(size, min_periods=1).quantile(
+            quantile, interpolation).iloc[-1]
 
         tm.assert_almost_equal(q1, q2)