cum_transform_feature Docstrings

featuretools/primitives/standard/cum_transform_feature.py

@@ -5,8 +5,20 @@
 
 
 class CumSum(TransformPrimitive):
-    """Returns the cumulative sum after grouping"""
-
+    """Calculates the cumulative sum.
+
+    Description:
+        Given a list of values, return the cumulative sum
+        (or running total). There is no set window, so the
+        sum at each point is calculated over all prior values.
+        `NaN` values will return `NaN`, but in the window of a
+        cumulative caluclation, they're ignored.
+
+    Examples:
+        >>> cum_sum = CumSum()
+        >>> cum_sum([1, 2, 3, 4, None, 5]).tolist()
+        [1.0, 3.0, 6.0, 10.0, nan, 15.0]
+    """
     name = "cum_sum"
     input_types = [Numeric]
     return_type = Numeric
@@ -20,8 +32,19 @@ def cum_sum(values):
 
 
 class CumCount(TransformPrimitive):
-    """Returns the cumulative count after grouping"""
-
+    """Calculates the cumulative count.
+
+    Description:
+        Given a list of values, return the cumulative count
+        (or running count). There is no set window, so the
+        count at each point is calculated over all prior
+        values. `NaN` values are counted.
+
+    Examples:
+        >>> cum_count = CumCount()
+        >>> cum_count([1, 2, 3, 4, None, 5]).tolist()
+        [1, 2, 3, 4, 5, 6]
+    """
     name = "cum_count"
     input_types = [[Id], [Discrete]]
     return_type = Numeric
@@ -35,8 +58,20 @@ def cum_count(values):
 
 
 class CumMean(TransformPrimitive):
-    """Returns the cumulative mean after grouping"""
-
+    """Calculates the cumulative mean.
+
+    Description:
+        Given a list of values, return the cumulative mean
+        (or running mean). There is no set window, so the
+        mean at each point is calculated over all prior values.
+        `NaN` values will return `NaN`, but in the window of a
+        cumulative caluclation, they're treated as 0.
+
+    Examples:
+        >>> cum_mean = CumMean()
+        >>> cum_mean([1, 2, 3, 4, None, 5]).tolist()
+        [1.0, 1.5, 2.0, 2.5, nan, 2.5]
+    """
     name = "cum_mean"
     input_types = [Numeric]
     return_type = Numeric
@@ -50,8 +85,20 @@ def cum_mean(values):
 
 
 class CumMin(TransformPrimitive):
-    """Returns the cumulative min after grouping"""
-
+    """Calculates the cumulative minimum.
+
+    Description:
+        Given a list of values, return the cumulative min
+        (or running min). There is no set window, so the min
+        at each point is calculated over all prior values.
+        `NaN` values will return `NaN`, but in the window of a
+        cumulative caluclation, they're ignored.
+
+    Examples:
+        >>> cum_min = CumMin()
+        >>> cum_min([1, 2, -3, 4, None, 5]).tolist()
+        [1.0, 1.0, -3.0, -3.0, nan, -3.0]
+    """
     name = "cum_min"
     input_types = [Numeric]
     return_type = Numeric
@@ -65,8 +112,20 @@ def cum_min(values):
 
 
 class CumMax(TransformPrimitive):
-    """Returns the cumulative max after grouping"""
-
+    """Calculates the cumulative maximum.
+
+    Description:
+        Given a list of values, return the cumulative max
+        (or running max). There is no set window, so the max
+        at each point is calculated over all prior values.
+        `NaN` values will return `NaN`, but in the window of a
+        cumulative caluclation, they're ignored.
+
+    Examples:
+        >>> cum_max = CumMax()
+        >>> cum_max([1, 2, 3, 4, None, 5]).tolist()
+        [1.0, 2.0, 3.0, 4.0, nan, 5.0]
+    """
     name = "cum_max"
     input_types = [Numeric]
     return_type = Numeric