diff --git a/plot.ipynb b/plot.ipynb
index 4fe6f4d..b59620c 100644
--- a/plot.ipynb
+++ b/plot.ipynb
@@ -1231,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -1298,12 +1298,16 @@
        "4                materials      1"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "df = pd.DataFrame({\n",
     "    'category': [\n",
     "        'emissions',\n",
@@ -1326,12 +1330,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -1346,9 +1350,11 @@
     "\n",
     "#create bar chart\n",
     "fig = plt.figure(figsize=(6,6))\n",
-    "ax = sns.barplot(data=df, x='category', y='count', palette=colors)\n",
-    "ax.set(xlabel=None)\n",
-    "ax.set(ylabel='count (n)')\n",
+    "g = sns.barplot(data=df, x='category', y='count', palette=colors)\n",
+    "g.set(xlabel=None)\n",
+    "g.set(ylabel='count (n)')\n",
+    "sns.despine(left=True)\n",
+    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
     "plt.tight_layout()\n",
     "plt.savefig('plots/content_analysis_topics.png')\n",
     "plt.show()"
diff --git a/plots/content_analysis_topics.png b/plots/content_analysis_topics.png
index c938070..ff54f5b 100644
Binary files a/plots/content_analysis_topics.png and b/plots/content_analysis_topics.png differ