rapidsai · rapids-bot · Oct 15, 2021 · Oct 6, 2021 · Oct 6, 2021 · Oct 14, 2021
@@ -3487,6 +3487,127 @@ def sqrt(self):
         """
         return self._unaryop("sqrt")
 
+    def abs(self):
+        """
+        Return a Series/DataFrame with absolute numeric value of each element.
+
+        This function only applies to elements that are all numeric.
+
+        Returns
+        -------
+        DataFrame/Series
+            Absolute value of each element.
+
+        Examples
+        --------
+        Absolute numeric values in a Series
+
+        >>> s = cudf.Series([-1.10, 2, -3.33, 4])
+        >>> s.abs()
+        0    1.10
+        1    2.00
+        2    3.33
+        3    4.00
+        dtype: float64
+        """
+        return self._unaryop("abs")
+
+    # Rounding
+    def ceil(self):
+        """
+        Rounds each value upward to the smallest integral value not less
+        than the original.
+
+        Returns
+        -------
+        DataFrame or Series
+            Ceiling value of each element.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([1.1, 2.8, 3.5, 4.5])
+        >>> series
+        0    1.1
+        1    2.8
+        2    3.5
+        3    4.5
+        dtype: float64
+        >>> series.ceil()
+        0    2.0
+        1    3.0
+        2    4.0
+        3    5.0
+        dtype: float64
+        """
+        return self._unaryop("ceil")
+
+    def floor(self):
+        """Rounds each value downward to the largest integral value not greater
+        than the original.
+
+        Returns
+        -------
+        DataFrame or Series
+            Flooring value of each element.
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([-1.9, 2, 0.2, 1.5, 0.0, 3.0])
+        >>> series
+        0   -1.9
+        1    2.0
+        2    0.2
+        3    1.5
+        4    0.0
+        5    3.0
+        dtype: float64
+        >>> series.floor()
+        0   -2.0
+        1    2.0
+        2    0.0
+        3    1.0
+        4    0.0
+        5    3.0
+        dtype: float64
+        """
+        return self._unaryop("floor")
+
+    def scale(self):
+        """
+        Scale values to [0, 1] in float64
+
+        Returns
+        -------
+        DataFrame or Series
+            Values scaled to [0, 1].
+
+        Examples
+        --------
+        >>> import cudf
+        >>> series = cudf.Series([10, 11, 12, 0.5, 1])
+        >>> series
+        0    10.0
+        1    11.0
+        2    12.0
+        3     0.5
+        4     1.0
+        dtype: float64
+        >>> series.scale()
+        0    0.826087
+        1    0.913043
+        2    1.000000
+        3    0.000000
+        4    0.043478
+        dtype: float64
+        """
+        vmin = self.min()
+        vmax = self.max()
+        scaled = (self - vmin) / (vmax - vmin)
+        scaled._index = self._index.copy(deep=False)
+        return scaled
+
     def _merge(
         self,
         right,

@@ -3476,130 +3476,6 @@ def value_counts(
             res = res / float(res._column.sum())
         return res
 
-    def scale(self):
-        """
-        Scale values to [0, 1] in float64
-
-        Returns
-        -------
-        Series
-            A new series with values scaled to [0, 1].
-
-        Examples
-        --------
-        >>> import cudf
-        >>> series = cudf.Series([10, 11, 12, 0.5, 1])
-        >>> series
-        0    10.0
-        1    11.0
-        2    12.0
-        3     0.5
-        4     1.0
-        dtype: float64
-        >>> series.scale()
-        0    0.826087
-        1    0.913043
-        2    1.000000
-        3    0.000000
-        4    0.043478
-        dtype: float64
-        """
-        vmin = self.min()
-        vmax = self.max()
-        scaled = (self - vmin) / (vmax - vmin)
-        scaled._index = self._index.copy(deep=False)
-        return scaled
-
-    # Absolute
-    def abs(self):
-        """Absolute value of each element of the series.
-
-        Returns
-        -------
-        abs
-            Series containing the absolute value of each element.
-
-        Examples
-        --------
-        >>>
10000
 import cudf
-        >>> series = cudf.Series([-1.10, 2, -3.33, 4])
-        >>> series
-        0   -1.10
-        1    2.00
-        2   -3.33
-        3    4.00
-        dtype: float64
-        >>> series.abs()
-        0    1.10
-        1    2.00
-        2    3.33
-        3    4.00
-        dtype: float64
-        """
-        return self._unaryop("abs")
-
-    # Rounding
-    def ceil(self):
-        """
-        Rounds each value upward to the smallest integral value not less
-        than the original.
-
-        Returns
-        -------
-        res
-            Returns a new Series with ceiling value of each element.
-
-        Examples
-        --------
-        >>> import cudf
-        >>> series = cudf.Series([1.1, 2.8, 3.5, 4.5])
-        >>> series
-        0    1.1
-        1    2.8
-        2    3.5
-        3    4.5
-        dtype: float64
-        >>> series.ceil()
-        0    2.0
-        1    3.0
-        2    4.0
-        3    5.0
-        dtype: float64
-        """
-        return self._unaryop("ceil")
-
-    def floor(self):
-        """Rounds each value downward to the largest integral value not greater
-        than the original.
-
-        Returns
-        -------
-        res
-            Returns a new Series with floor of each element.
-
-        Examples
-        --------
-        >>> import cudf
-        >>> series = cudf.Series([-1.9, 2, 0.2, 1.5, 0.0, 3.0])
-        >>> series
-        0   -1.9
-        1    2.0
-        2    0.2
-        3    1.5
-        4    0.0
-        5    3.0
-        dtype: float64
-        >>> series.floor()
-        0   -2.0
-        1    2.0
-        2    0.0
-        3    1.0
-        4    0.0
-        5    3.0
-        dtype: float64
-        """
-        return self._unaryop("floor")
-
     def hash_values(self, method="murmur3"):
         """Compute the hash of values in this column.
 

@@ -2068,6 +2068,24 @@ def test_unaryops_df(pdf, gdf, unaryop):
     assert_eq(d, g)
 
 
+@pytest.mark.parametrize("unary_func", ["abs", "floor", "ceil"])
+def test_unary_func_df(pdf, unary_func):
+    np.random.seed(0)
+    disturbance = pd.Series(np.random.rand(10))
+    pdf = pdf - 5 + disturbance
+    d = pdf.apply(getattr(np, unary_func))
+    g = getattr(cudf.from_pandas(pdf), unary_func)()
+    assert_eq(d, g)
+
+
+def test_scale_df(gdf):
+    got = (gdf - 5).scale()
+    expect = cudf.DataFrame(
+        {"x": np.linspace(0.0, 1.0, 10), "y": np.linspace(0.0, 1.0, 10)}
+    )
+    assert_eq(expect, got)
+
+
 @pytest.mark.parametrize(
     "func",
     [