update

Author: dnth

examples/heatmaps.ipynb

@@ -16,16 +16,16 @@
     "# Use fastdup to cluster heatmaps of user GPS movements\n",
     "\n",
     "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/heatmaps.ipynb)\n",
-    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/heatmaps.ipynb)"
+    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/heatmaps.ipynb)\n",
+    "\n",
+    "In this tutorial we learn how to cluster heatmaps, look at their labels and find anomalies in heatmaps. As an example heatmaps are generate by GPS coordinates of taxi data in NY. However there are multiple other use cases which produce heatmaps where fastdup can be used.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "99516834-fbfe-4f82-a929-11653ae0ca86",
    "metadata": {},
-   "source": [
-    "### In this tutorial we learn how to cluster heatmaps, look at their labels and find anomalies in heatmaps. As an example heatmaps are generate by GPS coordinates of taxi data in NY. However there are multiple other use cases which produce heatmaps where fastdup can be used.\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -73,18 +73,15 @@
    "source": [
     "import folium\n",
     "from folium.plugins import HeatMap\n",
-    "import random\n",
-    "\n"
+    "import random"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 31,
+   "cell_type": "markdown",
    "id": "4afe829e-9656-42d1-b7fa-4fc2e9538764",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Download Kaggle New York's taxi fare prediction test.csv dataset from https://www.kaggle.com/competitions/new-york-city-taxi-fare-prediction/data\n"
+    "Download Kaggle New York's taxi fare prediction test.csv dataset from https://www.kaggle.com/competitions/new-york-city-taxi-fare-prediction/data"
    ]
   },
   {
@@ -94,7 +91,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "import pandas as pd\n",
     "df = pd.read_csv('test.csv')"
    ]