BUG: rolling_count() and expanding_*() with zero-length args; rolling/expanding_apply with min_periods=0

doc/source/v0.15.0.txt

@@ -104,9 +104,9 @@ API changes
   :func:`rolling_std`, :func:`rolling_var`, :func:`rolling_skew`, :func:`rolling_kurt`, and :func:`rolling_quantile`,
   :func:`rolling_cov`, :func:`rolling_corr`, :func:`rolling_corr_pairwise`,
   :func:`rolling_window`, and :func:`rolling_apply` with ``center=True`` previously would return a result of the same
-  structure as the input ``arg`` with ``NaN``s in the final ``(window-1)/2`` entries.
+  structure as the input ``arg`` with ``NaN`` in the final ``(window-1)/2`` entries.
   Now the final ``(window-1)/2`` entries of the result are calculated as if the input ``arg`` were followed
-  by ``(window-1)/2`` ``NaN``s. (:issue:`7925`)
+  by ``(window-1)/2`` ``NaN`` values. (:issue:`7925`)
 
   Prior behavior (note final value is ``NaN``):
 
@@ -556,8 +556,8 @@ Bug Fixes
   returning results with columns sorted by name and producing an error for non-unique columns;
   now handles non-unique columns and returns columns in original order
   (except for the case of two DataFrames with ``pairwise=False``, where behavior is unchanged) (:issue:`7542`)
-
-
+- Bug in :func:`rolling_count` and ``expanding_*`` functions unnecessarily producing error message for zero-length data (:issue:`8056`)
+- Bug in :func:`rolling_apply` and :func:`expanding_apply`` interpreting ``min_periods=0`` as ``min_periods=1 (:issue:`8080`)
 - Bug in ``DataFrame.plot`` and ``Series.plot`` may ignore ``rot`` and ``fontsize`` keywords (:issue:`7844`)
 
 

pandas/algos.pyx

@@ -712,17 +712,15 @@ def rank_2d_generic(object in_arr, axis=0, ties_method='average',
 #
 # -
 
-def _check_minp(win, minp, N):
+def _check_minp(win, minp, N, floor=1):
     if minp > win:
         raise ValueError('min_periods (%d) must be <= window (%d)'
                         % (minp, win))
     elif minp > N:
         minp = N + 1
-    elif minp == 0:
-        minp = 1
     elif minp < 0:
         raise ValueError('min_periods must be >= 0')
-    return minp
+    return max(minp, floor)
 
 # original C implementation by N. Devillard.
 # This code in public domain.
@@ -1766,7 +1764,7 @@ def roll_generic(ndarray[float64_t, cast=True] input, int win,
     if n == 0:
         return input
 
-    minp = _check_minp(win, minp, n)
+    minp = _check_minp(win, minp, n, floor=0)
     output = np.empty(n, dtype=float)
     counts = roll_sum(np.isfinite(input).astype(float), win, minp)
 

pandas/stats/moments.py

@@ -206,7 +206,7 @@ def rolling_count(arg, window, freq=None, center=False, how=None):
     return_hook, values = _process_data_structure(arg, kill_inf=False)
 
     converted = np.isfinite(values).astype(float)
-    result = rolling_sum(converted, window, min_periods=1,
+    result = rolling_sum(converted, window, min_periods=0,
                          center=center)  # already converted
 
     # putmask here?
@@ -280,7 +280,8 @@ def _flex_binary_moment(arg1, arg2, f, pairwise=False):
     elif isinstance(arg1, DataFrame):
         def dataframe_from_int_dict(data, frame_template):
             result = DataFrame(data, index=frame_template.index)
-            result.columns = frame_template.columns[result.columns]
+            if len(result.columns) > 0:
+                result.columns = frame_template.columns[result.columns]
             return result
 
         results = {}
@@ -314,8 +315,10 @@ def dataframe_from_int_dict(data, frame_template):
                         else:
                             results[i][j] = f(*_prep_binary(arg1.iloc[:, i], arg2.iloc[:, j]))
                 p = Panel.from_dict(results).swapaxes('items', 'major')
-                p.major_axis = arg1.columns[p.major_axis]
-                p.minor_axis = arg2.columns[p.minor_axis]
+                if len(p.major_axis) > 0:
+                    p.major_axis = arg1.columns[p.major_axis]
+                if len(p.minor_axis) > 0:
+                    p.minor_axis = arg2.columns[p.minor_axis]
                 return p
             else:
                 raise ValueError("'pairwise' is not True/False")
@@ -372,17 +375,22 @@ def _rolling_moment(arg, window, func, minp, axis=0, freq=None, center=False,
     y : type of input
     """
     arg = _conv_timerule(arg, freq, how)
-    offset = int((window - 1) / 2.) if center else 0
-    additional_nans = np.array([np.NaN] * offset)
-    calc = lambda x: func(np.concatenate((x, additional_nans)) if center else x,
-                          window, minp=minp, args=args, kwargs=kwargs,
-                          **kwds)
+
     return_hook, values = _process_data_structure(arg)
-    # actually calculate the moment. Faster way to do this?
-    if values.ndim > 1:
-        result = np.apply_along_axis(calc, axis, values)
+
+    if values.size == 0:
+        result = values.copy()
     else:
-        result = calc(values)
+        # actually calculate the moment. Faster way to do this?
+        offset = int((window - 1) / 2.) if center else 0
+        additional_nans = np.array([np.NaN] * offset)
+        calc = lambda x: func(np.concatenate((x, additional_nans)) if center else x,
+                              window, minp=minp, args=args, kwargs=kwargs,
+                              **kwds)
+        if values.ndim > 1:
+            result = np.apply_along_axis(calc, axis, values)
+        else:
+            result = calc(values)
 
     if center:
         result = _center_window(result, window, axis)
@@ -817,11 +825,14 @@ def rolling_window(arg, window=None, win_type=None, min_periods=None,
     arg = _conv_timerule(arg, freq, how)
     return_hook, values = _process_data_structure(arg)
 
-    offset = int((len(window) - 1) / 2.) if center else 0
-    additional_nans = np.array([np.NaN] * offset)
-    f = lambda x: algos.roll_window(np.concatenate((x, additional_nans)) if center else x,
-                                    window, minp, avg=mean)
-    result = np.apply_along_axis(f, axis, values)
+    if values.size == 0:
+        result = values.copy()
+    else:
+        offset = int((len(window) - 1) / 2.) if center else 0
+        additional_nans = np.array([np.NaN] * offset)
+        f = lambda x: algos.roll_window(np.concatenate((x, additional_nans)) if center else x,
+                                        window, minp, avg=mean)
+        result = np.apply_along_axis(f, axis, values)
 
     if center:
         result = _center_window(result, len(window), axis)
@@ -856,7 +867,7 @@ def _expanding_func(func, desc, check_minp=_use_window):
     @Appender(_doc_template)
     @wraps(func)
     def f(arg, min_periods=1, freq=None, **kwargs):
-        window = len(arg)
+        window = max(len(arg), min_periods) if min_periods else len(arg)
 
         def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
             minp = check_minp(minp, window)

pandas/stats/tests/test_moments.py

@@ -244,6 +244,12 @@ def roll_mean(x, window, min_periods=None, freq=None, center=False):
                                      center=center)
         self._check_moment_func(roll_mean, np.mean)
 
+        # GH 8080
+        s = Series([None, None, None])
+        result = mom.rolling_apply(s, 2, lambda x: len(x), min_periods=0)
+        expected = Series([1., 2., 2.])
+        assert_series_equal(result, expected)
+
     def test_rolling_apply_out_of_bounds(self):
         # #1850
         arr = np.arange(4)
@@ -814,6 +820,12 @@ def expanding_mean(x, min_periods=1, freq=None):
                                        freq=freq)
         self._check_expanding(expanding_mean, np.mean)
 
+        # GH 8080
+        s = Series([None, None, None])
+        result = mom.expanding_apply(s, lambda x: len(x), min_periods=0)
+        expected = Series([1., 2., 3.])
+        assert_series_equal(result, expected)
+
     def test_expanding_apply_args_kwargs(self):
         def mean_w_arg(x, const):
             return np.mean(x) + const
@@ -989,6 +1001,77 @@ def test_rolling_functions_window_non_shrinkage(self):
             df_result_panel = f(df)
             assert_panel_equal(df_result_panel, df_expected_panel)
 
+    def test_moment_functions_zero_length(self):
+        # GH 8056
+        s = Series()
+        s_expected = s
+        df1 = DataFrame()
+        df1_expected = df1
+        df1_expected_panel = Panel(items=df1.index, major_axis=df1.columns, minor_axis=df1.columns)
+        df2 = DataFrame(columns=['a'])
+        df2_expected = df2
+        df2_expected_panel = Panel(items=df2.index, major_axis=df2.columns, minor_axis=df2.columns)
+
+        functions = [lambda x: mom.expanding_count(x),
+                     lambda x: mom.expanding_cov(x, x, pairwise=False, min_periods=5),
+                     lambda x: mom.expanding_corr(x, x, pairwise=False, min_periods=5),
+                     lambda x: mom.expanding_max(x, min_periods=5),
+                     lambda x: mom.expanding_min(x, min_periods=5),
+                     lambda x: mom.expanding_sum(x, min_periods=5),
+                     lambda x: mom.expanding_mean(x, min_periods=5),
+                     lambda x: mom.expanding_std(x, min_periods=5),
+                     lambda x: mom.expanding_var(x, min_periods=5),
+                     lambda x: mom.expanding_skew(x, min_periods=5),
+                     lambda x: mom.expanding_kurt(x, min_periods=5),
+                     lambda x: mom.expanding_quantile(x, quantile=0.5, min_periods=5),
+                     lambda x: mom.expanding_median(x, min_periods=5),
+                     lambda x: mom.expanding_apply(x, func=sum, min_periods=5),
+                     lambda x: mom.rolling_count(x, window=10),
+                     lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
+                     lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
+                     lambda x: mom.rolling_max(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_min(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_sum(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_mean(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_std(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_var(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_skew(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
+                     lambda x: mom.rolling_median(x, window=10, min_periods=5),
+                     lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
+                     lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
+                    ]
+        for f in functions:
+            try:
+                s_result = f(s)
+                assert_series_equal(s_result, s_expected)
+
+                df1_result = f(df1)
+                assert_frame_equal(df1_result, df1_expected)
+
+                df2_result = f(df2)
+                assert_frame_equal(df2_result, df2_expected)
+            except (ImportError):
+
+                # scipy needed for rolling_window
+                continue
+
+        functions = [lambda x: mom.expanding_cov(x, x, pairwise=True, min_periods=5),
+                     lambda x: mom.expanding_corr(x, x, pairwise=True, min_periods=5),
+                     lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
+                     lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
+                     # rolling_corr_pairwise is depracated, so the following line should be deleted
+                     # when rolling_corr_pairwise is removed.
+                     lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
+                    ]
+        for f in functions:
+            df1_result_panel = f(df1)
+            assert_panel_equal(df1_result_panel, df1_expected_panel)
+
+            df2_result_panel = f(df2)
+            assert_panel_equal(df2_result_panel, df2_expected_panel)
+
     def test_expanding_cov_pairwise_diff_length(self):
         # GH 7512
         df1 = DataFrame([[1,5], [3, 2], [3,9]], columns=['A','B'])