diff --git a/Assignment1/Assignment1.ipynb b/Assignment1/Assignment1.ipynb
index 349e1c3..15f0ea6 100644
--- a/Assignment1/Assignment1.ipynb
+++ b/Assignment1/Assignment1.ipynb
@@ -22,12 +22,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "id": "fcf3beb9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#%pip install pandas seaborn matplotlib bokeh ftfy\n",
+    "#%pip install pandas seaborn matplotlib bokeh ftfy geopandas jupyter_bokeh\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -35,7 +35,11 @@
     "import matplotlib.pyplot as plt\n",
     "import bokeh\n",
     "import ftfy\n",
-    "import matplotlib as mpl"
+    "import matplotlib as mpl\n",
+    "import geopandas as gpd\n",
+    "from bokeh.plotting import figure, show, output_notebook\n",
+    "from bokeh.models import GeoJSONDataSource, ColumnDataSource, Legend, BoxSelectTool, HoverTool, TapTool, CustomJS\n",
+    "from bokeh.layouts import gridplot, column, row"
    ]
   },
   {
@@ -297,16 +301,16 @@
        " 'offerType': ['str'],\n",
        " 'price': ['int64'],\n",
        " 'abtest': ['str'],\n",
-       " 'vehicleType': ['nan', 'str'],\n",
+       " 'vehicleType': ['str', 'nan'],\n",
        " 'yearOfRegistration': ['int64'],\n",
-       " 'gearbox': ['nan', 'str'],\n",
+       " 'gearbox': ['str', 'nan'],\n",
        " 'powerPS': ['int64'],\n",
-       " 'model': ['nan', 'str'],\n",
+       " 'model': ['str', 'nan'],\n",
        " 'kilometer': ['int64'],\n",
        " 'monthOfRegistration': ['int64'],\n",
-       " 'fuelType': ['nan', 'str'],\n",
+       " 'fuelType': ['str', 'nan'],\n",
        " 'brand': ['str'],\n",
-       " 'notRepairedDamage': ['nan', 'str'],\n",
+       " 'notRepairedDamage': ['str', 'nan'],\n",
        " 'dateCreated': ['str'],\n",
        " 'nrOfPictures': ['int64'],\n",
        " 'postalCode': ['int64'],\n",
@@ -643,7 +647,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "6a3f2455",
    "metadata": {},
@@ -801,7 +804,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "47a3929f",
    "metadata": {},
@@ -818,7 +820,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e2ae928d",
    "metadata": {},
@@ -842,7 +843,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 900x600 with 1 Axes>"
       ]
@@ -882,7 +883,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "7cc33371",
    "metadata": {},
@@ -904,7 +904,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 1800x800 with 1 Axes>"
       ]
@@ -963,7 +963,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "139bb76f",
    "metadata": {},
@@ -1020,7 +1019,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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/VkREhI4fP64cOXJo1qxZypgxowYNGqSuXbvqwIEDCgsLU1BQkCSpSpUqmj17ti5evKhevXqpb9++cnd319y5c+Xv7//IGAEAgHPgutO+150AHJOb8V/fWgwAAAAAAAAADo47SAEAAAAAAAC4LAqkAAAAAAAAAFwWBVIAAAAAAAAALosCKQAAAAAAAACXRYEUAAAAAAAAgMuiQAoAAAAAAADAZVEgBQAAAAAAAOCyKJACAAAAAAAAcFkUSAEAAAAAAAC4LAqkAAAAAAAAAFwWBVIAAAAAAAAALuv/AUFC2rDe7p92AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 1500x800 with 2 Axes>"
       ]
@@ -1054,7 +1053,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "06e7f954",
    "metadata": {},
@@ -1084,7 +1082,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_air = pd.read_csv(\"./datasets/airports.csv\", index_col='ID', na_values=['\\\\N'])\n",
     "df_del = pd.read_csv(\"./datasets/airports-delays.csv\", index_col='ID', sep=\";\", na_values=['\\\\N']) \\\n",
     "    .dropna(subset=['tz_database_timezone'])"
    ]
@@ -1448,7 +1445,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x800 with 1 Axes>"
       ]
@@ -1477,7 +1474,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "04aa4de5",
    "metadata": {},
@@ -1512,16 +1508,634 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "386875c8",
+   "execution_count": 122,
+   "id": "5d1fad2a",
    "metadata": {},
-   "outputs": [],
+   "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>country</th>\n",
+       "      <th>Albania</th>\n",
+       "      <th>Andorra</th>\n",
+       "      <th>Austria</th>\n",
+       "      <th>Belarus</th>\n",
+       "      <th>Belgium</th>\n",
+       "      <th>Bosnia and Herzegovina</th>\n",
+       "      <th>Bulgaria</th>\n",
+       "      <th>Croatia</th>\n",
+       "      <th>Cyprus</th>\n",
+       "      <th>Czech Republic</th>\n",
+       "      <th>...</th>\n",
+       "      <th>San Marino</th>\n",
+       "      <th>Serbia</th>\n",
+       "      <th>Slovakia</th>\n",
+       "      <th>Slovenia</th>\n",
+       "      <th>Spain</th>\n",
+       "      <th>Sweden</th>\n",
+       "      <th>Switzerland</th>\n",
+       "      <th>Ukraine</th>\n",
+       "      <th>United Kingdom</th>\n",
+       "      <th>Vatican City</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>country_dest</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Albania</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Andorra</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Austria</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Belarus</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Belgium</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>60</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "country       Albania  Andorra  Austria  Belarus  Belgium  \\\n",
+       "country_dest                                                \n",
+       "Albania             0        0        1        0        0   \n",
+       "Andorra             0        0        0        0        0   \n",
+       "Austria             1        0       15        2        2   \n",
+       "Belarus             0        0        2        0        0   \n",
+       "Belgium             0        0        2        0        1   \n",
+       "\n",
+       "country       Bosnia and Herzegovina  Bulgaria  Croatia  Cyprus  \\\n",
+       "country_dest                                                      \n",
+       "Albania                            0         0        0       0   \n",
+       "Andorra                            0         0        0       0   \n",
+       "Austria                            1         3        6       3   \n",
+       "Belarus                            0         0        0       1   \n",
+       "Belgium                            0         4        5       2   \n",
+       "\n",
+       "country       Czech Republic  ...  San Marino  Serbia  Slovakia  Slovenia  \\\n",
+       "country_dest                  ...                                           \n",
+       "Albania                    0  ...           0       0         0         1   \n",
+       "Andorra                    0  ...           0       0         0         0   \n",
+       "Austria                    1  ...           0       3         1         2   \n",
+       "Belarus                    2  ...           0       0         0         0   \n",
+       "Belgium                    4  ...           0       2         1         3   \n",
+       "\n",
+       "country       Spain  Sweden  Switzerland  Ukraine  United Kingdom  \\\n",
+       "country_dest                                                        \n",
+       "Albania           0       0            0        0               1   \n",
+       "Andorra           0       0            0        0               0   \n",
+       "Austria          40       4           11       10              11   \n",
+       "Belarus           1       1            1        2               1   \n",
+       "Belgium          60       6            6        2              17   \n",
+       "\n",
+       "country       Vatican City  \n",
+       "country_dest                \n",
+       "Albania                  0  \n",
+       "Andorra                  0  \n",
+       "Austria                  0  \n",
+       "Belarus                  0  \n",
+       "Belgium                  0  \n",
+       "\n",
+       "[5 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# TODO: 5"
+    "df_air = pd.read_csv(\"./datasets/airports.csv\", index_col='ID', na_values=['\\\\N'], dtype={'ID': pd.Int64Dtype()}) \\\n",
+    "    .drop(columns=['latitude', 'longitude'])\n",
+    "\n",
+    "df_routes = pd.read_csv(\"./datasets/routes.csv\", na_values=['\\\\N'], sep=\";\") \\\n",
+    "    .rename(lambda x: x.strip(), axis=1)\n",
+    "\n",
+    "df_countries = pd.read_csv(\"./datasets/countries.csv\") \\\n",
+    "    .rename(columns={'name': 'country'}).drop(columns=['continent'])\n",
+    "\n",
+    "df_countries.loc[df_countries.country == 'Faroe Is.', 'country'] = 'Faroe Islands'\n",
+    "\n",
+    "df_id_country = df_air.join(df_countries.set_index('country'), on='country', how='right', lsuffix='_air') \\\n",
+    "    .reset_index(drop=True) \\\n",
+    "    .loc[:, ['IATA', 'country']] \\\n",
+    "    .set_index('IATA')\n",
+    "\n",
+    "# Right join twice with source airport country and destination airport country\n",
+    "# A right join assures we include all countries in the final dataframe\n",
+    "df_routes_count = df_routes \\\n",
+    "    .loc[:, ['source_airport', 'destination_airport']] \\\n",
+    "    .join(df_id_country, how='right', on='source_airport') \\\n",
+    "    .join(df_id_country, how='right', on='destination_airport', rsuffix='_dest')\n",
+    "\n",
+    "# Count only a pair of notna source and destination airport as a valid route\n",
+    "# When this is not a case the row is an artifact of the right join. We assign 0\n",
+    "# as a value so that in the final sum the value will still appear to include \n",
+    "# no-flight countries, albeit with a total number of routes to 0\n",
+    "df_routes_count['# routes'] = 0\n",
+    "df_routes_count.loc[df_routes_count.source_airport.notna() & \\\n",
+    "                    df_routes_count.destination_airport.notna(), '# routes'] = 1\n",
+    "\n",
+    "# destination as rows, source as columns\n",
+    "df_routes_count = df_routes_count \\\n",
+    "    .groupby(['country_dest', 'country']).agg({'# routes': 'sum'}) \\\n",
+    "    .rename(columns={0: '# routes'}) \\\n",
+    "    .unstack() \\\n",
+    "    .fillna(0) \\\n",
+    "    .sort_values('country_dest')\n",
+    "\n",
+    "# Change type of cells and remove column level for geopandas compatibility\n",
+    "df_routes_count = df_routes_count[df_routes_count.columns].astype(int)\n",
+    "df_routes_count.columns = df_routes_count.columns.droplevel(0)\n",
+    "df_routes_count.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "f3c0739b",
+   "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>NAME</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>Albania</th>\n",
+       "      <th>Andorra</th>\n",
+       "      <th>Austria</th>\n",
+       "      <th>Belarus</th>\n",
+       "      <th>Belgium</th>\n",
+       "      <th>Bosnia and Herzegovina</th>\n",
+       "      <th>Bulgaria</th>\n",
+       "      <th>Croatia</th>\n",
+       "      <th>...</th>\n",
+       "      <th>San Marino</th>\n",
+       "      <th>Serbia</th>\n",
+       "      <th>Slovakia</th>\n",
+       "      <th>Slovenia</th>\n",
+       "      <th>Spain</th>\n",
+       "      <th>Sweden</th>\n",
+       "      <th>Switzerland</th>\n",
+       "      <th>Ukraine</th>\n",
+       "      <th>United Kingdom</th>\n",
+       "      <th>Vatican City</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Azerbaijan</td>\n",
+       "      <td>MULTIPOLYGON (((45.08332 39.76804, 45.26639 39...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>POLYGON ((19.43621 41.02107, 19.45055 41.06000...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Armenia</td>\n",
+       "      <td>MULTIPOLYGON (((45.57305 40.63249, 45.52888 40...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Bosnia and Herzegovina</td>\n",
+       "      <td>POLYGON ((17.64984 42.88908, 17.57853 42.94382...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bulgaria</td>\n",
+       "      <td>POLYGON ((27.87917 42.84110, 27.89500 42.80250...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     NAME                                           geometry  \\\n",
+       "0              Azerbaijan  MULTIPOLYGON (((45.08332 39.76804, 45.26639 39...   \n",
+       "1                 Albania  POLYGON ((19.43621 41.02107, 19.45055 41.06000...   \n",
+       "2                 Armenia  MULTIPOLYGON (((45.57305 40.63249, 45.52888 40...   \n",
+       "3  Bosnia and Herzegovina  POLYGON ((17.64984 42.88908, 17.57853 42.94382...   \n",
+       "4                Bulgaria  POLYGON ((27.87917 42.84110, 27.89500 42.80250...   \n",
+       "\n",
+       "   Albania  Andorra  Austria  Belarus  Belgium  Bosnia and Herzegovina  \\\n",
+       "0      NaN      NaN      NaN      NaN      NaN                     NaN   \n",
+       "1      0.0      0.0      1.0      0.0      0.0                     0.0   \n",
+       "2      NaN      NaN      NaN      NaN      NaN                     NaN   \n",
+       "3      0.0      0.0      1.0      0.0      0.0                     2.0   \n",
+       "4      0.0      0.0      3.0      0.0      4.0                     0.0   \n",
+       "\n",
+       "   Bulgaria  Croatia  ...  San Marino  Serbia  Slovakia  Slovenia  Spain  \\\n",
+       "0       NaN      NaN  ...         NaN     NaN       NaN       NaN    NaN   \n",
+       "1       0.0      0.0  ...         0.0     0.0       0.0       1.0    0.0   \n",
+       "2       NaN      NaN  ...         NaN     NaN       NaN       NaN    NaN   \n",
+       "3       0.0      1.0  ...         0.0     3.0       0.0       1.0    0.0   \n",
+       "4       6.0      0.0  ...         0.0     2.0       0.0       0.0    8.0   \n",
+       "\n",
+       "   Sweden  Switzerland  Ukraine  United Kingdom  Vatican City  \n",
+       "0     NaN          NaN      NaN             NaN           NaN  \n",
+       "1     0.0          0.0      0.0             1.0           0.0  \n",
+       "2     NaN          NaN      NaN             NaN           NaN  \n",
+       "3     3.0          1.0      0.0             0.0           0.0  \n",
+       "4     0.0          1.0      0.0            10.0           0.0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Some countries have column values = 'NaN'. These countries are not in Europe according to the dataset,\n",
+    "# but I choose to include them in the map as 'no data' (i.e. in grey)\n",
+    "yurop = gpd.read_file(\"./datasets/europe.geojson\") \\\n",
+    "    .loc[:, ['NAME', 'geometry']] \\\n",
+    "    .set_index('NAME') \\\n",
+    "    .join(df_routes_count, how='left') \\\n",
+    "    .reset_index()\n",
+    "\n",
+    "yurop.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "11612845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.bokehjs_exec.v0+json": "",
+      "text/html": [
+       "<script id=\"p2994\">\n",
+       "  (function() {\n",
+       "    const xhr = new XMLHttpRequest()\n",
+       "    xhr.responseType = 'blob';\n",
+       "    xhr.open('GET', \"http://localhost:50568/autoload.js?bokeh-autoload-element=p2994&bokeh-absolute-url=http://localhost:50568&resources=none\", true);\n",
+       "    xhr.onload = function (event) {\n",
+       "      const script = document.createElement('script');\n",
+       "      const src = URL.createObjectURL(event.target.response);\n",
+       "      script.src = src;\n",
+       "      document.body.appendChild(script);\n",
+       "    };\n",
+       "    xhr.send();\n",
+       "  })();\n",
+       "</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.bokehjs_exec.v0+json": {
+       "server_id": "41ac9a694474477fa4aff5c18022d7c8"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aaaaaaa\n",
+      "aaaaaaa\n",
+      "aaaaaaa\n",
+      "aaaaaaa\n",
+      "aaaaaaa\n"
+     ]
+    }
+   ],
+   "source": [
+    "from bokeh.events import Tap\n",
+    "from bokeh.models.widgets import MultiSelect, Slider, DateRangeSlider\n",
+    "from bokeh.layouts import row\n",
+    "from bokeh.application import Application\n",
+    "from bokeh.application.handlers import FunctionHandler\n",
+    "from bokeh.models.ranges import Range1d\n",
+    "from bokeh.palettes import Reds\n",
+    "from bokeh.models import LinearColorMapper, LogColorMapper, ColorBar\n",
+    "\n",
+    "yurop_json = yurop.to_json()\n",
+    "\n",
+    "def figure_flights(doc):\n",
+    "    palette = Reds[6]\n",
+    "    palette = palette[::-1]\n",
+    "\n",
+    "    color_mapper = LinearColorMapper(palette = palette, low = 0, high = 600)\n",
+    "\n",
+    "    color_bar = ColorBar(color_mapper = color_mapper, \n",
+    "                         width = 20, height = 600,\n",
+    "                         label_standoff = 8,\n",
+    "                         location = (0,0))\n",
+    "\n",
+    "    \n",
+    "    p = figure(title = 'Number of flights to each country', \n",
+    "           frame_height = 600,\n",
+    "           frame_width = 800, \n",
+    "           toolbar_location = 'below',\n",
+    "           tools = \"pan, wheel_zoom, box_zoom, reset\")\n",
+    "\n",
+    "    geo_ds = GeoJSONDataSource(geojson=yurop_json)\n",
+    "    plotted_districts = p.patches('xs','ys', source = geo_ds,\n",
+    "                    line_color = 'black', \n",
+    "                    line_width = 0.25)\n",
+    "\n",
+    "    p.xgrid.grid_line_color = None\n",
+    "    p.ygrid.grid_line_color = None\n",
+    "    p.axis.visible = False\n",
+    "\n",
+    "    p.patches(\"xs\",\"ys\", source = geo_ds,\n",
+    "                   fill_color = {\"field\" : \"TotaleAbitantiCurr\",\n",
+    "                                 \"transform\" : color_mapper},\n",
+    "                   line_color = \"gray\", \n",
+    "                   line_width = 0.25, \n",
+    "                   fill_alpha = 1)\n",
+    "    \n",
+    "    p.add_tools(HoverTool(renderers = [plotted_districts],\n",
+    "                        tooltips = [(\"Country\",\"@NAME\")]))\n",
+    "\n",
+    "    def event():\n",
+    "        print(\"aaaaaaa\")\n",
+    "\n",
+    "    tap = p.add_tools(TapTool())\n",
+    "    p.on_event(Tap, event)\n",
+    "    \n",
+    "    p.add_layout(color_bar, \"right\")\n",
+    "    doc.add_root(p)\n",
+    "\n",
+    "handler = FunctionHandler(figure_flights)\n",
+    "app = Application(handler)\n",
+    "\n",
+    "show(app)"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "9b9c5983",
    "metadata": {},
@@ -1630,7 +2244,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1644,7 +2258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,