diff --git a/README.md b/README.md
index dd85aa8..2fee0f9 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,9 @@ data from:
 
 * Web browser
 * Loop Habits
+* * Nextcloud backup
 * Taskwarrior
+* * Run 'task export'
 * Wallabag
 * Journal
 * RSS stars
diff --git a/notebook.ipynb b/notebook.ipynb
index 458435b..69c86f2 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -9,14 +9,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
     "import json\n",
     "from tasklib import TaskWarrior\n",
     "import datetime\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -43,34 +44,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
     "with open('data/task.json', 'r') as myfile:\n",
     "    task = json.load(myfile)\n",
     "\n",
-    "tasks = pd.DataFrame.from_dict(task)"
+    "tasks = pd.DataFrame.from_dict(task)\n",
+    "tasks.set_axis(tasks['entry'], axis='index', inplace=True)\n",
+    "tasks = tasks.drop(['annotations', 'depends', 'description', 'parent', 'uuid', 'entry'], axis=1)\n",
+    "only2019(tasks).count()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([164.,   3.,  27.,   2.,  11.,   1.,   2.,   1.,   2.,   2.]),\n",
+       " array([ 0. ,  1.5,  3. ,  4.5,  6. ,  7.5,  9. , 10.5, 12. , 13.5, 15. ]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# this was dumb, using a list\n",
+    "l = only2019(tasks).tags.dropna().to_list()\n",
+    "flat_list = [item for sublist in l for item in sublist]\n",
     "\n",
-    "tagslist = []\n",
-    "for e in task:\n",
-    "    # filter based on date\n",
-    "    ###\n",
-    "    # only check tags where they exist\n",
-    "    if 'tags' in e.keys():\n",
-    "        count += 1\n",
-    "        tagslist.append(e[\"tags\"])\n",
-    "\n",
-    "print(tagslist)"
+    "plt.hist(flat_list)"
    ]
   },
   {
@@ -317,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [