Values first approach

docs/user-guide/advanced/Pandas_API.ipynb

@@ -353,6 +353,45 @@
     "tab.size"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4023811f-0f23-4e4b-919c-c055b865d9d2",
+   "metadata": {},
+   "source": [
+    "### Table.values\n",
+    "Return a matricial representation of the DataFrame.\n",
+    "\n",
+    "Only the values in the DataFrame will be returned, the axes labels will be removed.\n",
+    "\n",
+    "**Returns:**\n",
+    "\n",
+    "| Type              | Description                                                          |\n",
+    "| :---------------: | :------------------------------------------------------------------- |\n",
+    "| List | Rows of the table, each as a list. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a7843813-43fc-4920-acd9-6fd2dd50c32e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'tab' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_77428/394326152.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'tab' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "tab.values"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2be2ece3",
@@ -1287,7 +1326,9 @@
   {
    "cell_type": "markdown",
    "id": "35062560",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "source": [
     "Remove the `z` and `w` columns from the table and return them."
    ]
@@ -1296,9 +1337,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a46189b2",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "display(tab.head())\n",

src/pykx/pandas_api/pandas_meta.py

@@ -134,6 +134,11 @@ def shape(self):
     @property
     def size(self):
         return q('{count[x] * count[cols x]}', self)
+
+    @property
+    def values(self):
+        tab = self
+        return q('value peach', tab)
 
     @api_return
     def mean(self, axis: int = 0, numeric_only: bool = False):
@@ -257,6 +262,7 @@ def sum(self, axis=0, skipna=True, numeric_only=False, min_count=0):
             min_count
         ), cols)
 
+
     def agg(self, func, axis=0, *args, **kwargs): # noqa: C901
         if 'KeyedTable' in str(type(self)):
             raise NotImplementedError("'agg' method not presently supported for KeyedTable")

tests/test_pandas_api.py

@@ -2029,3 +2029,13 @@ def test_keyed_loc_fixes(q):
         mkt[['k1', 'y']]
     with pytest.raises(KeyError):
         mkt['k1']
+
+def test_values(q):
+    col1 = q('10?100')
+    col2 = q('10?`a`b`c`d')
+    col3 = q('10?`x`y`z`1`2`3')
+    tab = q('{[price; sym; id] ([]p: price; s: sym; i: id)}', col1, col2, col3)
+    q_table = tab
+    pandas_table = tab.pd()
+    lol = np.array(q_table.values())
+    assert pandas_table.values == np.array(q_table.values().py())