BUG: Fix exceptions when Series.interpolate's order parameter is missing or invalid

doc/source/whatsnew/v0.25.0.rst

@@ -139,7 +139,7 @@ Indexing
 Missing
 ^^^^^^^
 
--
+- Fixed misleading exception message in :meth:`Series.missing` if argument ``order`` is required, but omitted (:issue:`10633`, :issue:`24014`).
 -
 -
 

pandas/core/internals/blocks.py

@@ -1115,24 +1115,18 @@ def check_int_bool(self, inplace):
                                                fill_value=fill_value,
                                                coerce=coerce,
                                                downcast=downcast)
-        # try an interp method
-        try:
-            m = missing.clean_interp_method(method, **kwargs)
-        except ValueError:
-            m = None
-
-        if m is not None:
-            r = check_int_bool(self, inplace)
-            if r is not None:
-                return r
-            return self._interpolate(method=m, index=index, values=values,
-                                     axis=axis, limit=limit,
-                                     limit_direction=limit_direction,
-                                     limit_area=limit_area,
-                                     fill_value=fill_value, inplace=inplace,
-                                     downcast=downcast, **kwargs)
-
-        raise ValueError("invalid method '{0}' to interpolate.".format(method))
+        # validate the interp method
+        m = missing.clean_interp_method(method, **kwargs)
+
+        r = check_int_bool(self, inplace)
+        if r is not None:
+            return r
+        return self._interpolate(method=m, index=index, values=values,
+                                 axis=axis, limit=limit,
+                                 limit_direction=limit_direction,
+                                 limit_area=limit_area,
+                                 fill_value=fill_value, inplace=inplace,
+                                 downcast=downcast, **kwargs)
 
     def _interpolate_with_fill(self, method='pad', axis=0, inplace=False,
                                limit=None, fill_value=None, coerce=False,

pandas/core/missing.py

@@ -293,9 +293,10 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
                                     bounds_error=bounds_error)
         new_y = terp(new_x)
     elif method == 'spline':
-        # GH #10633
-        if not order:
-            raise ValueError("order needs to be specified and greater than 0")
+        # GH #10633, #24014
+        if isna(order) or (order <= 0):
+            raise ValueError("order needs to be specified and greater than 0; "
+                             "got order: {}".format(order))
         terp = interpolate.UnivariateSpline(x, y, k=order, **kwargs)
         new_y = terp(new_x)
     else:

pandas/tests/series/test_missing.py

@@ -855,6 +855,13 @@ def test_series_pad_backfill_limit(self):
 
 
 class TestSeriesInterpolateData():
+    # This list does not include method 'time'.  That method requires a Series
+    # with a DatetimeIndex, and is tested separately below.
+    NONTEMPORAL_INTERPOLATE_METHODS = [
+        'linear', 'index', 'values', 'nearest', 'zero', 'slinear',
+        'quadratic', 'cubic', 'barycentric', 'krogh', 'polynomial', 'spline',
+        'piecewise_polynomial', 'from_derivatives', 'pchip', 'akima',
+    ]
 
     def test_interpolate(self, datetime_series, string_series):
         ts = Series(np.arange(len(datetime_series), dtype=float),
@@ -875,12 +882,12 @@ def test_interpolate(self, datetime_series, string_series):
         time_interp = ord_ts_copy.interpolate(method='time')
         tm.assert_series_equal(time_interp, ord_ts)
 
-        # try time interpolation on a non-TimeSeries
-        # Only raises ValueError if there are NaNs.
-        non_ts = string_series.copy()
-        non_ts[0] = np.NaN
-        msg = ("time-weighted interpolation only works on Series or DataFrames"
-               " with a DatetimeIndex")
+    def test_interpolate_time_raises_for_non_timeseries(self):
+        # When method='time' is used on a non-TimeSeries that contains a null
+        # value, a ValueError should be raised.
+        non_ts = Series([0, 1, 2, np.NaN])
+        msg = ("time-weighted interpolation only works on Series.* "
+               "with a DatetimeIndex")
         with pytest.raises(ValueError, match=msg):
             non_ts.interpolate(method='time')
 
@@ -1061,21 +1068,36 @@ def test_interp_limit(self):
         result = s.interpolate(method='linear', limit=2)
         assert_series_equal(result, expected)
 
-        # GH 9217, make sure limit is an int and greater than 0
-        methods = ['linear', 'time', 'index', 'values', 'nearest', 'zero',
-                   'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
-                   'polynomial', 'spline', 'piecewise_polynomial', None,
-                   'from_derivatives', 'pchip', 'akima']
+    @pytest.mark.parametrize("method", NONTEMPORAL_INTERPOLATE_METHODS)
+    @pytest.mark.parametrize("limit", [-1, 0])
+    def test_interpolate_invalid_nonpositive_limit(self, method, limit):
+        # GH 9217: make sure limit is greater than zero
         s = pd.Series([1, 2, np.nan, np.nan, 5])
-        msg = (r"Limit must be greater than 0|"
-               "time-weighted interpolation only works on Series or"
-               r" DataFrames with a DatetimeIndex|"
-               r"invalid method '(polynomial|spline|None)' to interpolate|"
-               "Limit must be an integer")
-        for limit in [-1, 0, 1., 2.]:
-            for method in methods:
-                with pytest.raises(ValueError, match=msg):
-                    s.interpolate(limit=limit, method=method)
+        with pytest.raises(ValueError, match="Limit must be greater than 0"):
+            # Pass order=1 for 'spline' and 'polynomial'.
+            s.interpolate(limit=limit, method=method, order=1)
+
+    @pytest.mark.parametrize("method", NONTEMPORAL_INTERPOLATE_METHODS)
+    def test_interpolate_invalid_float_limit(self, method):
+        # GH 9217: make sure limit is an integer.
+        s = pd.Series([1, 2, np.nan, np.nan, 5])
+        limit = 2.0
+        with pytest.raises(ValueError, match="Limit must be an integer"):
+            # Pass order=1 for 'spline' and 'polynomial'.
+            s.interpolate(limit=limit, method=method, order=1)
+
+    @pytest.mark.parametrize("invalid_method", [None, 'nonexistent_method'])
+    def test_interp_invalid_method(self, invalid_method):
+        s = Series([1, 3, np.nan, 12, np.nan, 25])
+
+        msg = "method must be one of.* Got '{}' instead".format(invalid_method)
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=invalid_method)
+
+        # When an invalid method and invalid limit (such as -1) are
+        # provided, the error message reflects the invalid method.
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method=invalid_method, limit=-1)
 
     def test_interp_limit_forward(self):
         s = Series([1, 3, np.nan, np.nan, np.nan, 11])
@@ -1276,11 +1298,20 @@ def test_interp_limit_no_nans(self):
     @td.skip_if_no_scipy
     @pytest.mark.parametrize("method", ['polynomial', 'spline'])
     def test_no_order(self, method):
+        # see GH-10633, GH-24014
         s = Series([0, 1, np.nan, 3])
-        msg = "invalid method '{}' to interpolate".format(method)
+        msg = "You must specify the order of the spline or polynomial"
         with pytest.raises(ValueError, match=msg):
             s.interpolate(method=method)
 
+    @td.skip_if_no_scipy
+    @pytest.mark.parametrize('order', [-1, -1.0, 0, 0.0, np.nan])
+    def test_interpolate_spline_invalid_order(self, order):
+        s = Series([0, 1, np.nan, 3])
+        msg = "order needs to be specified and greater than 0"
+        with pytest.raises(ValueError, match=msg):
+            s.interpolate(method='spline', order=order)
+
     @td.skip_if_no_scipy
     def test_spline(self):
         s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
@@ -1313,19 +1344,6 @@ def test_spline_interpolation(self):
         expected1 = s.interpolate(method='spline', order=1)
         assert_series_equal(result1, expected1)
 
-    @td.skip_if_no_scipy
-    def test_spline_error(self):
-        # see gh-10633
-        s = pd.Series(np.arange(10) ** 2)
-        s[np.random.randint(0, 9, 3)] = np.nan
-        msg = "invalid method 'spline' to interpolate"
-        with pytest.raises(ValueError, match=msg):
-            s.interpolate(method='spline')
-
-        msg = "order needs to be specified and greater than 0"
-        with pytest.raises(ValueError, match=msg):
-            s.interpolate(method='spline', order=0)
-
     def test_interp_timedelta64(self):
         # GH 6424
         df = Series([1, np.nan, 3],