diff --git a/test-notebooks/image.png b/test-notebooks/image.png
new file mode 100644
index 0000000..49903f3
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diff --git a/test-notebooks/misc.ipynb b/test-notebooks/misc.ipynb
index a357f43..42e6d70 100644
--- a/test-notebooks/misc.ipynb
+++ b/test-notebooks/misc.ipynb
@@ -753,9 +753,19 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "id": "c8cba3d9-0145-4b37-9ebf-07f0e3c61815",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Py4Cytoscape"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "id": "fcd9247f-c4f9-4f73-9fd3-2ab56700073f",
    "metadata": {},
    "outputs": [
@@ -775,13 +785,13 @@
        " 'py4cytoscapeVersion': '1.9.0'}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import py4cytoscape as py4\n",
+    "import py4cytoscape as p4c\n",
     "dir(py4)\n",
     "py4.cytoscape_ping()\n",
     "py4.cytoscape_version_info()"
@@ -789,10 +799,325 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "b9290659-e33c-47fc-8d89-7aa3dd6e843a",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "nodes = pd.DataFrame(data={'id': [\"node 0\",\"node 1\",\"node 2\",\"node 3\"], 'group': [\"A\",\"A\",\"B\",\"B\"], 'score': [20,10,15,5]})\n",
+    "edges = pd.DataFrame(data={'source': [\"node 0\",\"node 0\",\"node 0\",\"node 2\"], 'target': [\"node 1\",\"node 2\",\"node 3\",\"node 3\"], 'interaction': [\"inhibits\",\"interacts\",\"activates\",\"interacts\"], 'weight': [5.1,3.0,5.2,9.9]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "979d6def-83ac-47f6-ac6f-0d20ddf48d48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>node 0</td>\n",
+       "      <td>A</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>node 1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>node 2</td>\n",
+       "      <td>B</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>node 3</td>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       id group  score\n",
+       "0  node 0     A     20\n",
+       "1  node 1     A     10\n",
+       "2  node 2     B     15\n",
+       "3  node 3     B      5"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "81702429-5735-48de-96a4-1f32c7c7d68c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>target</th>\n",
+       "      <th>interaction</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>node 0</td>\n",
+       "      <td>node 1</td>\n",
+       "      <td>inhibits</td>\n",
+       "      <td>5.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>node 0</td>\n",
+       "      <td>node 2</td>\n",
+       "      <td>interacts</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>node 0</td>\n",
+       "      <td>node 3</td>\n",
+       "      <td>activates</td>\n",
+       "      <td>5.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>node 2</td>\n",
+       "      <td>node 3</td>\n",
+       "      <td>interacts</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   source  target interaction  weight\n",
+       "0  node 0  node 1    inhibits     5.1\n",
+       "1  node 0  node 2   interacts     3.0\n",
+       "2  node 0  node 3   activates     5.2\n",
+       "3  node 2  node 3   interacts     9.9"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6b29d561-fffd-4a5b-91c1-8fb6a075ae4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Applying default style...\n",
+      "Applying preferred layout\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "344"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p4c.create_network_from_data_frames(nodes, edges, title=\"my first network\", collection=\"DataFrame Example\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2e6878db-40c0-4ae6-89d6-9b1a5e50baaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p4c.notebook_export_show_image()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "66128f17-16eb-43d3-9d63-bbac3f8f803a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'message': 'Visual Style applied.'}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p4c.set_visual_style('Marquee')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ca0cc760-74e4-4c4a-b78a-c932ab16ab06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p4c.notebook_export_show_image()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "24c29cb3-cf64-4fad-8b1a-b0962f1005b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'message': 'Visual Style applied.'}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "style_name = \"myStyle\"\n",
+    "defaults = {'NODE_SHAPE': \"diamond\", 'NODE_SIZE': 30, 'EDGE_TRANSPARENCY': 120, 'NODE_LABEL_POSITION': \"W,E,c,0.00,0.00\"}\n",
+    "nodeLabels = p4c.map_visual_property('node label', 'id', 'p') #'p' means 'passthrough' mapping\n",
+    "edgeWidth = p4c.map_visual_property('edge width', 'weight', 'p') #'p' means 'passthrough' mapping\n",
+    "p4c.create_visual_style(style_name, defaults, [nodeLabels, edgeWidth])\n",
+    "p4c.set_visual_style(style_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1dfb553a-2367-463e-8a3d-0d232c2aedd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p4c.notebook_export_show_image()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3492ec28-71e4-4366-855b-44238c12bf88",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],