Draft for skew implementation

src/pykx/pandas_api/pandas_meta.py

@@ -219,17 +219,6 @@ def any(self, axis=0, bool_only=False, skipna=True):
         res, cols = preparse_computations(self, axis, skipna, bool_only=bool_only)
         return (q('{"b"$x}', [any(x) for x in res]), cols)
 
-    @convert_result
-    def cumsum(self, axis=0, skipna=True):
-        res, cols = preparse_computations(self, axis, skipna)
-        return (q(
-            '{[row]' 
-            '{$[11h=type x;' 
-            '{[x1; y1] `$string[x1], string y1} scan x;' 
-            'sums x]} each row}', 
-            res
-        ), cols)
-
     @convert_result
     def max(self, axis=0, skipna=True, numeric_only=False):
         res, cols = preparse_computations(self, axis, skipna, numeric_only)
@@ -255,6 +244,17 @@ def prod(self, axis=0, skipna=True, numeric_only=False, min_count=0):
             min_count
         ), cols)
 
+    @convert_result
+    def skew(self, axis=0, skipna=True, numeric_only=False):
+        res, cols = preparse_computations(self, axis, skipna, numeric_only)
+        return (q(
+            '{[row]'
+            'm:{(sum(y-avg y)xexp x)%count y};'
+            'u:{sqrt[n*n-1]%neg[2]+n:count x};'
+            '{[u;m;x](u[x]*m[3][x]%(m[2][x]xexp 3%2))}[u;m]each row}',
+            res
+        ), cols)
+
     @convert_result
     def sum(self, axis=0, skipna=True, numeric_only=False, min_count=0):
         res, cols = preparse_computations(self, axis, skipna, numeric_only)

tests/test_pandas_api.py

@@ -1900,6 +1900,16 @@ def test_pandas_sum(q):
     for i in range(10):
         assert qsum[i] == q('0N')
         assert str(psum[i]) == 'nan'
+
+
+def test_pandas_skew(q):
+    tab = q('([] price: 250.0f - 100?500.0f; ints: 100 - 100?200)')
+    df = tab.pd()
+
+    qskew = tab.skew().py()
+    pskew = df.skew()
+    assert round(float(qskew['price']), 6) == round(float(pskew['price']), 6)
+    assert round(float(qskew['ints']), 6) == round(float(pskew['ints']), 6)
 
 
 def test_pandas_groupby_errors(kx, q):