diff --git a/stackoverflow-survey.ipynb b/stackoverflow-survey.ipynb
index b9d3d4d..83869f6 100644
--- a/stackoverflow-survey.ipynb
+++ b/stackoverflow-survey.ipynb
@@ -122,11 +122,14 @@
     "# https://seaborn.pydata.org/examples/part_whole_bars.html\n",
     "\n",
     "# investigate extrinsic vs intrinsic motivation\n",
-    "def get_difference(dict1, dict2):\n",
+    "def get_difference(dict1, dict2, proportion=False):\n",
     "    keys = dict1.keys()\n",
     "    result = dict()\n",
     "    for key in keys:\n",
-    "        result[key] = dict1[key] - dict2[key]\n",
+    "        if proportion:\n",
+    "            result[key] = round((dict1[key] - dict2[key])/dict2[key],2)\n",
+    "        else:\n",
+    "            result[key] = dict1[key] - dict2[key]\n",
     "    return result\n",
     "\n",
     "def visualize_diff(diff_dict, color=\"lightblue\", saveto=None):\n",
@@ -137,7 +140,6 @@
     "    df    = pd.DataFrame(diff_sorted.items(), columns=['Languages', 'Value'])\n",
     "    plt.figure(figsize=(15,20)) \n",
     "    sb.barplot(x=KEY, y='Languages', data=df, color=color)\n",
-    "    \n",
     "    DELTA =  '\\u0394'\n",
     "    for index, value in enumerate(df[KEY]):\n",
     "    # chatgpt annotates my chart\n",
@@ -149,24 +151,33 @@
     "             # Adjust the x position for negative values\n",
     "            plt.text(value, index,  DELTA+str(value), va='center',  ha='right') \n",
     "    lowest = 0\n",
-    "    offset = 0.5\n",
+    "    offset = 0\n",
     "    positive_values = df[df[KEY] > 0][KEY]\n",
     "    if not positive_values.empty:\n",
     "        lowest = positive_values.min()\n",
+    "        offset = list(positive_values).count(lowest) \n",
     "    if len(positive_values) < len(df):\n",
-    "        # don't draw the line if every value is greater than 0\n",
-    "        plt.axhline(y=df[KEY].tolist().index(lowest) + offset, color='red', linestyle='--')\n",
+    "        # don't draw the line if every value is greater than 0_\n",
+    "        plt.axhline(y=df[KEY].tolist().index(lowest) + (offset-0.5), \n",
+    "                    color='red', linestyle='--', zorder=-1)\n",
     "    if saveto is not None:\n",
     "        plt.savefig(saveto, bbox_inches='tight')\n",
     "    \n",
-    "motiv_diff = get_difference(l2, l1)\n",
+    "motiv_diff = get_difference(l2, l1, proportion=True)\n",
     "# print(motiv_diff)\n",
     "visualize_diff(motiv_diff, saveto=\"images/delta.png\")\n",
+    "motiv_diff = get_difference(l2, l1)\n",
+    "visualize_diff(motiv_diff, saveto=\"images/delta-b.png\")\n",
+    "\n",
+    "# no clear description of what \"admired\" is\n",
+    "# in the schema\n",
+    "# but generally people want to use the languages\n",
+    "# they admire\n",
     "\n",
     "# determine level of hype\n",
-    "hype = get_difference(l4, l3)\n",
+    "# hype = get_difference(l4, l3)\n",
     "# print(hype)\n",
-    "visualize_diff(hype, color=\"red\")"
+    "# visualize_diff(hype, color=\"red\")"
    ]
   },
   {
@@ -263,6 +274,14 @@
     "# earn less than the mean compensation\n",
     "# (what titles have high standard deviations in earnings)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acd193c3-eb73-498c-a8d4-c59c0eb5dcdb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {