Update ASVs, docs and tests

Author: kornilova203

asv_bench/benchmarks/rolling.py

@@ -22,6 +22,7 @@ def setup(self, constructor, window, dtype, method):
     def time_rolling(self, constructor, window, dtype, method):
         getattr(self.roll, method)()
 
+
 class VariableWindowMethods(Methods):
     sample_time = 0.2
     params = (['DataFrame', 'Series'],
@@ -37,6 +38,7 @@ def setup(self, constructor, window, dtype, method):
         index = pd.date_range('2017-01-01', periods=N, freq='5s')
         self.roll = getattr(pd, constructor)(arr, index=index).rolling(window)
 
+
 class Pairwise(object):
 
     sample_time = 0.2
@@ -59,7 +61,6 @@ def time_pairwise(self, window, method, pairwise):
 
 
 class Quantile(object):
-
     sample_time = 0.2
     params = (['DataFrame', 'Series'],
               [10, 1000],
@@ -68,9 +69,21 @@ class Quantile(object):
     param_names = ['constructor', 'window', 'dtype', 'percentile']
 
     def setup(self, constructor, window, dtype, percentile):
-        N = 10**5
+        N = 10 ** 5
         arr = np.random.random(N).astype(dtype)
         self.roll = getattr(pd, constructor)(arr).rolling(window)
 
     def time_quantile(self, constructor, window, dtype, percentile):
         self.roll.quantile(percentile)
+
+    def time_quantile_nearest(self, constructor, window, dtype, percentile):
+        self.roll.quantile(percentile, interpolation='nearest')
+
+    def time_quantile_lower(self, constructor, window, dtype, percentile):
+        self.roll.quantile(percentile, interpolation='lower')
+
+    def time_quantile_higher(self, constructor, window, dtype, percentile):
+        self.roll.quantile(percentile, interpolation='higher')
+
+    def time_quantile_midpoint(self, constructor, window, dtype, percentile):
+        self.roll.quantile(percentile, interpolation='midpoint')

pandas/core/window.py

@@ -1257,15 +1257,14 @@ 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. Implementation uses
-              round() built-in.
+            * nearest: `i` or `j` whichever is nearest.
             * midpoint: (`i` + `j`) / 2.
 
     Returns
     -------
     Series or DataFrame
         Returned object type is determined by the caller of the %(name)s
-        calculation
+        calculation.
 
     Examples
     --------
@@ -1286,8 +1285,10 @@ def kurt(self, **kwargs):
 
     See Also
     --------
-    pandas.Series.quantile
-    pandas.DataFrame.quantile
+    pandas.Series.quantile : Computes value at the given quantile over all data
+        in Series.
+    pandas.DataFrame.quantile : Computes values at the given quantile over
+        requested axis in DataFrame.
 
     """)
 

pandas/tests/test_window.py

@@ -1140,14 +1140,14 @@ def test_rolling_quantile_np_percentile(self):
                                       [np.nan, np.nan, np.nan, np.nan],
                                       [np.nan, 0.1, np.nan, 0.3, 0.4, 0.5],
                                       [0.5], [np.nan, 0.7, 0.6]])
-    def test_rolling_quantile_interpolation_options(self, quantile,
-                                                    interpolation, data):
+    def test_moving_quantile_interpolation_options(self, quantile,
+                                                   interpolation, data):
         # Tests that rolling window's quantile behavior is analogous to
         # Series' quantile for each interpolation option
         s = Series(data)
 
         q1 = s.quantile(quantile, interpolation)
-        q2 = s.rolling(len(data), min_periods=1).quantile(
+        q2 = s.expanding(min_periods=1).quantile(
             quantile, interpolation).iloc[-1]
 
         if np.isnan(q1):
@@ -1164,7 +1164,7 @@ def test_invalid_quantile_value(self):
             s.rolling(len(data), min_periods=1).quantile(
                 0.5, interpolation='invalid')
 
-    def test_rolling_quantile_param(self):
+    def test_moving_quantile_param(self):
         ser = Series([0.0, .1, .5, .9, 1.0])
 
         with pytest.raises(ValueError):