diff --git a/flight_analysis.ipynb b/flight_analysis.ipynb
index abbf029..a02daf5 100644
--- a/flight_analysis.ipynb
+++ b/flight_analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,17 +11,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "flights = Scrape(\"CTA\", \"PMO\", \"2023-10-28\")\n",
+    "flights = Scrape(\"Frosinone\", \"Sydney\", \"2023-10-25\")\n",
     "flights.run_scrape()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -60,310 +60,278 @@
        "      <th>access_date</th>\n",
        "      <th>one_way</th>\n",
        "      <th>has_train</th>\n",
+       "      <th>days_advance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2023-10-28 07:50:00</td>\n",
-       "      <td>2023-10-28 11:25:00</td>\n",
-       "      <td>[easyJet]</td>\n",
-       "      <td>0 days 03:35:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
+       "      <td>2023-10-25 21:10:00</td>\n",
+       "      <td>2023-10-27 13:35:00</td>\n",
+       "      <td>[China Eastern]</td>\n",
+       "      <td>1 days 07:25:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
        "      <td>1</td>\n",
-       "      <td>0 days 01:30:00</td>\n",
-       "      <td>[NAP]</td>\n",
-       "      <td>58</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150904</td>\n",
+       "      <td>0 days 09:25:00</td>\n",
+       "      <td>[PVG]</td>\n",
+       "      <td>694</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469385</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2023-10-28 14:00:00</td>\n",
-       "      <td>2023-10-28 18:20:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 04:20:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
+       "      <td>2023-10-25 21:10:00</td>\n",
+       "      <td>2023-10-27 10:00:00</td>\n",
+       "      <td>[China Eastern]</td>\n",
+       "      <td>1 days 03:50:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
        "      <td>1</td>\n",
-       "      <td>0 days 01:50:00</td>\n",
-       "      <td>[FCO]</td>\n",
-       "      <td>222</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150920</td>\n",
+       "      <td>0 days 05:55:00</td>\n",
+       "      <td>[PVG]</td>\n",
+       "      <td>924</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469406</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2023-10-28 19:10:00</td>\n",
-       "      <td>2023-10-28 22:55:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 03:45:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0 days 01:10:00</td>\n",
-       "      <td>[FCO]</td>\n",
-       "      <td>222</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150922</td>\n",
+       "      <td>2023-10-25 08:40:00</td>\n",
+       "      <td>2023-10-26 21:30:00</td>\n",
+       "      <td>[ITA, THAI]</td>\n",
+       "      <td>1 days 03:50:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>[FRA, BKK]</td>\n",
+       "      <td>961</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469420</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2023-10-28 12:50:00</td>\n",
-       "      <td>2023-10-28 22:55:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 10:05:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>[LIN, NAP, FCO]</td>\n",
-       "      <td>191</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150924</td>\n",
+       "      <td>2023-10-25 15:30:00</td>\n",
+       "      <td>2023-10-26 23:05:00</td>\n",
+       "      <td>[Emirates, Qantas]</td>\n",
+       "      <td>0 days 22:35:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0 days 03:05:00</td>\n",
+       "      <td>[DXB]</td>\n",
+       "      <td>1312</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469423</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2023-10-28 15:25:00</td>\n",
-       "      <td>2023-10-28 22:10:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 06:45:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
+       "      <td>2023-10-25 10:20:00</td>\n",
+       "      <td>2023-10-26 19:10:00</td>\n",
+       "      <td>[Etihad]</td>\n",
+       "      <td>0 days 23:50:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
        "      <td>1</td>\n",
-       "      <td>0 days 04:20:00</td>\n",
-       "      <td>[FCO]</td>\n",
-       "      <td>222</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150926</td>\n",
+       "      <td>0 days 04:05:00</td>\n",
+       "      <td>[AUH]</td>\n",
+       "      <td>1322</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469426</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>2023-10-28 11:05:00</td>\n",
-       "      <td>2023-10-28 16:20:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 05:15:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0 days 01:50:00</td>\n",
-       "      <td>[LIN]</td>\n",
-       "      <td>228</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150927</td>\n",
+       "      <td>2023-10-25 08:40:00</td>\n",
+       "      <td>2023-10-27 09:15:00</td>\n",
+       "      <td>[ITA, Vietnam Airlines]</td>\n",
+       "      <td>1 days 15:35:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>[CDG, SGN]</td>\n",
+       "      <td>877</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469428</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>2023-10-28 06:00:00</td>\n",
-       "      <td>2023-10-28 09:25:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 03:25:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0 days 00:55:00</td>\n",
-       "      <td>[FCO]</td>\n",
-       "      <td>254</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150929</td>\n",
+       "      <td>2023-10-25 08:40:00</td>\n",
+       "      <td>2023-10-27 09:15:00</td>\n",
+       "      <td>[ITA, Vietnam Airlines]</td>\n",
+       "      <td>1 days 15:35:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>[FRA, SGN]</td>\n",
+       "      <td>885</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469430</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>2023-10-28 10:15:00</td>\n",
-       "      <td>2023-10-28 14:25:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 04:10:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0 days 01:35:00</td>\n",
-       "      <td>[FCO]</td>\n",
-       "      <td>265</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150931</td>\n",
+       "      <td>2023-10-25 15:30:00</td>\n",
+       "      <td>2023-10-27 11:00:00</td>\n",
+       "      <td>[Turkish Airlines, Malaysia Airlines]</td>\n",
+       "      <td>1 days 10:30:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>[IST, KUL]</td>\n",
+       "      <td>923</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469433</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>2023-10-28 07:00:00</td>\n",
-       "      <td>2023-10-28 11:55:00</td>\n",
-       "      <td>[ITA]</td>\n",
-       "      <td>0 days 04:55:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0 days 02:20:00</td>\n",
-       "      <td>[FCO]</td>\n",
-       "      <td>280</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150933</td>\n",
+       "      <td>2023-10-25 10:45:00</td>\n",
+       "      <td>2023-10-26 21:30:00</td>\n",
+       "      <td>[ITA, THAI]</td>\n",
+       "      <td>1 days 01:45:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>[ZRH, BKK]</td>\n",
+       "      <td>1004</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469435</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>2023-10-28 06:00:00</td>\n",
-       "      <td>2023-10-28 11:05:00</td>\n",
-       "      <td>[Lufthansa]</td>\n",
-       "      <td>0 days 05:05:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
+       "      <td>2023-10-25 10:20:00</td>\n",
+       "      <td>2023-10-27 07:20:00</td>\n",
+       "      <td>[Etihad]</td>\n",
+       "      <td>1 days 12:00:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
        "      <td>1</td>\n",
-       "      <td>0 days 01:00:00</td>\n",
-       "      <td>[MUC]</td>\n",
-       "      <td>408</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150934</td>\n",
+       "      <td>0 days 16:20:00</td>\n",
+       "      <td>[AUH]</td>\n",
+       "      <td>1077</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469438</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>2023-10-28 21:35:00</td>\n",
-       "      <td>2023-10-29 11:10:00</td>\n",
-       "      <td>[British Airways]</td>\n",
-       "      <td>0 days 14:35:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
+       "      <td>2023-10-25 21:25:00</td>\n",
+       "      <td>2023-10-27 07:15:00</td>\n",
+       "      <td>[Korean Air]</td>\n",
+       "      <td>1 days 00:50:00</td>\n",
+       "      <td>FCO</td>\n",
+       "      <td>SYD</td>\n",
        "      <td>1</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>None</td>\n",
-       "      <td>457</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150936</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2023-10-28 19:25:00</td>\n",
-       "      <td>2023-10-29 17:40:00</td>\n",
-       "      <td>[Turkish Airlines]</td>\n",
-       "      <td>0 days 23:15:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0 days 18:20:00</td>\n",
-       "      <td>[IST]</td>\n",
-       "      <td>504</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150938</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2023-10-28 07:00:00</td>\n",
-       "      <td>2023-10-28 13:55:00</td>\n",
-       "      <td>[ITA, SWISS]</td>\n",
-       "      <td>0 days 06:55:00</td>\n",
-       "      <td>CTA</td>\n",
-       "      <td>PMO</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>[FCO, ZRH]</td>\n",
-       "      <td>617</td>\n",
-       "      <td>typical</td>\n",
-       "      <td>None</td>\n",
-       "      <td>2023-08-07 20:18:11.150939</td>\n",
+       "      <td>0 days 03:15:00</td>\n",
+       "      <td>[ICN]</td>\n",
+       "      <td>1249</td>\n",
+       "      <td>low</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2023-08-08 18:23:03.469440</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>78</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    departure_datetime    arrival_datetime            airlines  \\\n",
-       "0  2023-10-28 07:50:00 2023-10-28 11:25:00           [easyJet]   \n",
-       "1  2023-10-28 14:00:00 2023-10-28 18:20:00               [ITA]   \n",
-       "2  2023-10-28 19:10:00 2023-10-28 22:55:00               [ITA]   \n",
-       "3  2023-10-28 12:50:00 2023-10-28 22:55:00               [ITA]   \n",
-       "4  2023-10-28 15:25:00 2023-10-28 22:10:00               [ITA]   \n",
-       "5  2023-10-28 11:05:00 2023-10-28 16:20:00               [ITA]   \n",
-       "6  2023-10-28 06:00:00 2023-10-28 09:25:00               [ITA]   \n",
-       "7  2023-10-28 10:15:00 2023-10-28 14:25:00               [ITA]   \n",
-       "8  2023-10-28 07:00:00 2023-10-28 11:55:00               [ITA]   \n",
-       "9  2023-10-28 06:00:00 2023-10-28 11:05:00         [Lufthansa]   \n",
-       "10 2023-10-28 21:35:00 2023-10-29 11:10:00   [British Airways]   \n",
-       "11 2023-10-28 19:25:00 2023-10-29 17:40:00  [Turkish Airlines]   \n",
-       "12 2023-10-28 07:00:00 2023-10-28 13:55:00        [ITA, SWISS]   \n",
+       "    departure_datetime    arrival_datetime  \\\n",
+       "0  2023-10-25 21:10:00 2023-10-27 13:35:00   \n",
+       "1  2023-10-25 21:10:00 2023-10-27 10:00:00   \n",
+       "2  2023-10-25 08:40:00 2023-10-26 21:30:00   \n",
+       "3  2023-10-25 15:30:00 2023-10-26 23:05:00   \n",
+       "4  2023-10-25 10:20:00 2023-10-26 19:10:00   \n",
+       "5  2023-10-25 08:40:00 2023-10-27 09:15:00   \n",
+       "6  2023-10-25 08:40:00 2023-10-27 09:15:00   \n",
+       "7  2023-10-25 15:30:00 2023-10-27 11:00:00   \n",
+       "8  2023-10-25 10:45:00 2023-10-26 21:30:00   \n",
+       "9  2023-10-25 10:20:00 2023-10-27 07:20:00   \n",
+       "10 2023-10-25 21:25:00 2023-10-27 07:15:00   \n",
        "\n",
-       "       travel_time origin destination  layover_n    layover_time  \\\n",
-       "0  0 days 03:35:00    CTA         PMO          1 0 days 01:30:00   \n",
-       "1  0 days 04:20:00    CTA         PMO          1 0 days 01:50:00   \n",
-       "2  0 days 03:45:00    CTA         PMO          1 0 days 01:10:00   \n",
-       "3  0 days 10:05:00    CTA         PMO          3             NaT   \n",
-       "4  0 days 06:45:00    CTA         PMO          1 0 days 04:20:00   \n",
-       "5  0 days 05:15:00    CTA         PMO          1 0 days 01:50:00   \n",
-       "6  0 days 03:25:00    CTA         PMO          1 0 days 00:55:00   \n",
-       "7  0 days 04:10:00    CTA         PMO          1 0 days 01:35:00   \n",
-       "8  0 days 04:55:00    CTA         PMO          1 0 days 02:20:00   \n",
-       "9  0 days 05:05:00    CTA         PMO          1 0 days 01:00:00   \n",
-       "10 0 days 14:35:00    CTA         PMO          1             NaT   \n",
-       "11 0 days 23:15:00    CTA         PMO          1 0 days 18:20:00   \n",
-       "12 0 days 06:55:00    CTA         PMO          2             NaT   \n",
+       "                                 airlines     travel_time origin destination  \\\n",
+       "0                         [China Eastern] 1 days 07:25:00    FCO         SYD   \n",
+       "1                         [China Eastern] 1 days 03:50:00    FCO         SYD   \n",
+       "2                             [ITA, THAI] 1 days 03:50:00    FCO         SYD   \n",
+       "3                      [Emirates, Qantas] 0 days 22:35:00    FCO         SYD   \n",
+       "4                                [Etihad] 0 days 23:50:00    FCO         SYD   \n",
+       "5                 [ITA, Vietnam Airlines] 1 days 15:35:00    FCO         SYD   \n",
+       "6                 [ITA, Vietnam Airlines] 1 days 15:35:00    FCO         SYD   \n",
+       "7   [Turkish Airlines, Malaysia Airlines] 1 days 10:30:00    FCO         SYD   \n",
+       "8                             [ITA, THAI] 1 days 01:45:00    FCO         SYD   \n",
+       "9                                [Etihad] 1 days 12:00:00    FCO         SYD   \n",
+       "10                           [Korean Air] 1 days 00:50:00    FCO         SYD   \n",
        "\n",
-       "   layover_location  price_eur price_trend price_value  \\\n",
-       "0             [NAP]         58     typical        None   \n",
-       "1             [FCO]        222     typical        None   \n",
-       "2             [FCO]        222     typical        None   \n",
-       "3   [LIN, NAP, FCO]        191     typical        None   \n",
-       "4             [FCO]        222     typical        None   \n",
-       "5             [LIN]        228     typical        None   \n",
-       "6             [FCO]        254     typical        None   \n",
-       "7             [FCO]        265     typical        None   \n",
-       "8             [FCO]        280     typical        None   \n",
-       "9             [MUC]        408     typical        None   \n",
-       "10             None        457     typical        None   \n",
-       "11            [IST]        504     typical        None   \n",
-       "12       [FCO, ZRH]        617     typical        None   \n",
+       "    layover_n    layover_time layover_location  price_eur price_trend  \\\n",
+       "0           1 0 days 09:25:00            [PVG]        694         low   \n",
+       "1           1 0 days 05:55:00            [PVG]        924         low   \n",
+       "2           2             NaT       [FRA, BKK]        961         low   \n",
+       "3           1 0 days 03:05:00            [DXB]       1312         low   \n",
+       "4           1 0 days 04:05:00            [AUH]       1322         low   \n",
+       "5           2             NaT       [CDG, SGN]        877         low   \n",
+       "6           2             NaT       [FRA, SGN]        885         low   \n",
+       "7           2             NaT       [IST, KUL]        923         low   \n",
+       "8           2             NaT       [ZRH, BKK]       1004         low   \n",
+       "9           1 0 days 16:20:00            [AUH]       1077         low   \n",
+       "10          1 0 days 03:15:00            [ICN]       1249         low   \n",
        "\n",
-       "                  access_date  one_way  has_train  \n",
-       "0  2023-08-07 20:18:11.150904     True      False  \n",
-       "1  2023-08-07 20:18:11.150920     True      False  \n",
-       "2  2023-08-07 20:18:11.150922     True      False  \n",
-       "3  2023-08-07 20:18:11.150924     True      False  \n",
-       "4  2023-08-07 20:18:11.150926     True      False  \n",
-       "5  2023-08-07 20:18:11.150927     True      False  \n",
-       "6  2023-08-07 20:18:11.150929     True      False  \n",
-       "7  2023-08-07 20:18:11.150931     True      False  \n",
-       "8  2023-08-07 20:18:11.150933     True      False  \n",
-       "9  2023-08-07 20:18:11.150934     True      False  \n",
-       "10 2023-08-07 20:18:11.150936     True      False  \n",
-       "11 2023-08-07 20:18:11.150938     True      False  \n",
-       "12 2023-08-07 20:18:11.150939     True      False  "
+       "    price_value                access_date  one_way  has_train  days_advance  \n",
+       "0           194 2023-08-08 18:23:03.469385     True      False            78  \n",
+       "1           194 2023-08-08 18:23:03.469406     True      False            78  \n",
+       "2           194 2023-08-08 18:23:03.469420     True      False            77  \n",
+       "3           194 2023-08-08 18:23:03.469423     True      False            77  \n",
+       "4           194 2023-08-08 18:23:03.469426     True      False            77  \n",
+       "5           194 2023-08-08 18:23:03.469428     True      False            77  \n",
+       "6           194 2023-08-08 18:23:03.469430     True      False            77  \n",
+       "7           194 2023-08-08 18:23:03.469433     True      False            77  \n",
+       "8           194 2023-08-08 18:23:03.469435     True      False            77  \n",
+       "9           194 2023-08-08 18:23:03.469438     True      False            77  \n",
+       "10          194 2023-08-08 18:23:03.469440     True      False            78  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -374,16 +342,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'https://www.google.com/travel/flights?q=Flights%20to%20PMO%20from%20CTA%20on%202023-10-28%20oneway&curr=EUR&gl=IT'"
+       "'https://www.google.com/travel/flights?q=Flights%20to%20Sydney%20from%20Frosinone%20on%202023-10-25%20oneway&curr=EUR&gl=IT'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +372,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m/Users/emanuelesalonico/Library/CloudStorage/GoogleDrive-esalonico@gmail.com/My Drive/SYNC/Dev/flight-analysis/flight_analysis.ipynb Cell 5\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/emanuelesalonico/Library/CloudStorage/GoogleDrive-esalonico%40gmail.com/My%20Drive/SYNC/Dev/flight-analysis/flight_analysis.ipynb#X31sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m()\n",
+      "\u001b[1;32m/Users/emanuelesalonico/Library/CloudStorage/GoogleDrive-esalonico@gmail.com/My Drive/SYNC/Dev/flight-analysis/flight_analysis.ipynb Cell 5\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/emanuelesalonico/Library/CloudStorage/GoogleDrive-esalonico%40gmail.com/My%20Drive/SYNC/Dev/flight-analysis/flight_analysis.ipynb#W4sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m()\n",
       "\u001b[0;31mNotImplementedError\u001b[0m: "
      ]
     }
@@ -422,6 +390,7 @@
     "import pandas as pd\n",
     "from src.flight_analysis.database import Database\n",
     "import private.private as private\n",
+    "import numpy as np\n",
     "\n",
     "db = Database(\n",
     "    db_host=private.DB_HOST,\n",
@@ -441,9 +410,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "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></th>\n",
+       "      <th>flight_uuid</th>\n",
+       "      <th>layover_location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>bc9de66a-e11b-4ded-a230-0eccbc7f3838</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>37e1aaeb-80d9-4cd2-b24f-1d197a93671f</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>f6956f34-ddc4-4c57-80ef-706049058c34</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8b0795c7-2acb-427e-81aa-4181827a483e</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>f7aa01e7-6fac-48ab-bb37-22f6b39c8213</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111415</th>\n",
+       "      <td>17893f94-0096-42d0-b71c-c5fedd3be13b</td>\n",
+       "      <td>BCN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111416</th>\n",
+       "      <td>a2dc98dd-6268-44ea-af71-598f6a59fad3</td>\n",
+       "      <td>BCN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111417</th>\n",
+       "      <td>dd722f79-10c7-4f1d-97fa-fc31b608c182</td>\n",
+       "      <td>LHR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111418</th>\n",
+       "      <td>aa93dd96-6ffd-4a69-ac9b-e464b7e24b43</td>\n",
+       "      <td>AMS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111419</th>\n",
+       "      <td>d05452e6-e69c-45a1-aa86-6048cc3327c4</td>\n",
+       "      <td>ATH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>111420 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 flight_uuid layover_location\n",
+       "0       bc9de66a-e11b-4ded-a230-0eccbc7f3838             None\n",
+       "1       37e1aaeb-80d9-4cd2-b24f-1d197a93671f             None\n",
+       "2       f6956f34-ddc4-4c57-80ef-706049058c34             None\n",
+       "3       8b0795c7-2acb-427e-81aa-4181827a483e             None\n",
+       "4       f7aa01e7-6fac-48ab-bb37-22f6b39c8213             None\n",
+       "...                                      ...              ...\n",
+       "111415  17893f94-0096-42d0-b71c-c5fedd3be13b              BCN\n",
+       "111416  a2dc98dd-6268-44ea-af71-598f6a59fad3              BCN\n",
+       "111417  dd722f79-10c7-4f1d-97fa-fc31b608c182              LHR\n",
+       "111418  aa93dd96-6ffd-4a69-ac9b-e464b7e24b43              AMS\n",
+       "111419  d05452e6-e69c-45a1-aa86-6048cc3327c4              ATH\n",
+       "\n",
+       "[111420 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = pd.DataFrame(res, columns=[\"flight_uuid\", \"layover_location\"])\n",
     "df"
@@ -451,9 +528,125 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/_6/c56rsvy15k9_gv99mkdll8pc0000gn/T/ipykernel_94061/3424689233.py:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
+      "  df.lauover_location = df.layover_location.str.strip()\n"
+     ]
+    },
+    {
+     "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>flight_uuid</th>\n",
+       "      <th>layover_location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>811962d6-e250-4480-bb8a-2d2ae511afe4</td>\n",
+       "      <td>ATH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>bea04f18-6fc3-4f39-8a57-caeaa25bf35b</td>\n",
+       "      <td>LGW</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>941c0948-ceda-4a4f-bb60-f9fc5a322b95</td>\n",
+       "      <td>BCN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>4a86fa79-dc3d-4e92-9c03-412bdfbf3810</td>\n",
+       "      <td>DUS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>6a341fc6-9761-4cdc-bced-cf3fa636c2a9</td>\n",
+       "      <td>BCN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111415</th>\n",
+       "      <td>17893f94-0096-42d0-b71c-c5fedd3be13b</td>\n",
+       "      <td>BCN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111416</th>\n",
+       "      <td>a2dc98dd-6268-44ea-af71-598f6a59fad3</td>\n",
+       "      <td>BCN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111417</th>\n",
+       "      <td>dd722f79-10c7-4f1d-97fa-fc31b608c182</td>\n",
+       "      <td>LHR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111418</th>\n",
+       "      <td>aa93dd96-6ffd-4a69-ac9b-e464b7e24b43</td>\n",
+       "      <td>AMS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111419</th>\n",
+       "      <td>d05452e6-e69c-45a1-aa86-6048cc3327c4</td>\n",
+       "      <td>ATH</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50651 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 flight_uuid layover_location\n",
+       "5       811962d6-e250-4480-bb8a-2d2ae511afe4              ATH\n",
+       "8       bea04f18-6fc3-4f39-8a57-caeaa25bf35b              LGW\n",
+       "9       941c0948-ceda-4a4f-bb60-f9fc5a322b95              BCN\n",
+       "19      4a86fa79-dc3d-4e92-9c03-412bdfbf3810              DUS\n",
+       "22      6a341fc6-9761-4cdc-bced-cf3fa636c2a9              BCN\n",
+       "...                                      ...              ...\n",
+       "111415  17893f94-0096-42d0-b71c-c5fedd3be13b              BCN\n",
+       "111416  a2dc98dd-6268-44ea-af71-598f6a59fad3              BCN\n",
+       "111417  dd722f79-10c7-4f1d-97fa-fc31b608c182              LHR\n",
+       "111418  aa93dd96-6ffd-4a69-ac9b-e464b7e24b43              AMS\n",
+       "111419  d05452e6-e69c-45a1-aa86-6048cc3327c4              ATH\n",
+       "\n",
+       "[50651 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# get rid of \"min\"\n",
     "df.lauover_location = df.layover_location.str.strip()\n",
@@ -467,9 +660,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "db.create_scraped_layovers_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 / 11\n",
+      "2 / 11\n",
+      "3 / 11\n",
+      "4 / 11\n",
+      "5 / 11\n",
+      "6 / 11\n",
+      "7 / 11\n",
+      "8 / 11\n",
+      "9 / 11\n",
+      "10 / 11\n",
+      "11 / 11\n"
+     ]
+    }
+   ],
    "source": [
     "def split_dataframe(df, chunk_size=5000):\n",
     "    chunks = list()\n",
@@ -656,21 +876,167 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import re\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>iata</th>\n",
+       "      <th>name</th>\n",
+       "      <th>country</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>region</th>\n",
+       "      <th>municipality</th>\n",
+       "      <th>continent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40729</th>\n",
+       "      <td>FCO</td>\n",
+       "      <td>Rome–Fiumicino Leonardo da Vinci International...</td>\n",
+       "      <td>IT</td>\n",
+       "      <td>41.804532</td>\n",
+       "      <td>12.251998</td>\n",
+       "      <td>IT-62</td>\n",
+       "      <td>Rome</td>\n",
+       "      <td>EU</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      iata                                               name country  \\\n",
+       "40729  FCO  Rome–Fiumicino Leonardo da Vinci International...      IT   \n",
+       "\n",
+       "             lat        lon region municipality continent  \n",
+       "40729  41.804532  12.251998  IT-62         Rome        EU  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AIRPORTS_DF_URL = \"https://raw.githubusercontent.com/datasets/airport-codes/master/data/airport-codes.csv\"\n",
+    "df = pd.read_csv(AIRPORTS_DF_URL, encoding=\"utf-8\")\n",
+    "\n",
+    "# filter original data\n",
+    "# take only aiprort that have a IATA code\n",
+    "df = df.loc[df.iata_code.notnull()]\n",
+    "\n",
+    "# exclude closed, seaplane_base and heliport\n",
+    "df = df.loc[df.type.isin([\"large_airport\", \"medium_airport\", \"small_airport\"])]\n",
+    "\n",
+    "# split coordinates in latitude and longitude\n",
+    "df[\"lat\"] = df.coordinates.apply(lambda x: x.split(\",\")[0]).astype(float)\n",
+    "df[\"lon\"] = df.coordinates.apply(lambda x: x.split(\",\")[1]).astype(float)\n",
+    "\n",
+    "# take only useful columns\n",
+    "cols = [\n",
+    "    \"iata_code\",\n",
+    "    \"name\",\n",
+    "    \"iso_country\",\n",
+    "    \"lat\",\n",
+    "    \"lon\",\n",
+    "    \"iso_region\",\n",
+    "    \"municipality\",\n",
+    "    \"continent\",\n",
+    "]\n",
+    "df = df[cols]\n",
+    "\n",
+    "# rename columns\n",
+    "df = df.rename(\n",
+    "    columns={\n",
+    "        \"iata_code\": \"iata\",\n",
+    "        \"iso_country\": \"country\",\n",
+    "        \"iso_region\": \"region\",\n",
+    "    }\n",
+    ")\n",
     "\n",
-    "not bool(re.search(\"ITA, \", \"ITA, Singapore\"))"
+    "# replace nan with None\n",
+    "df = df.replace({pd.NA: None, np.nan: None})\n",
+    "\n",
+    "df.loc[df.iata == \"FCO\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import pandas as pd\n",
+    "from src.flight_analysis.database import Database\n",
+    "import private.private as private\n",
+    "\n",
+    "db = Database(\n",
+    "    db_host=private.DB_HOST,\n",
+    "    db_name=private.DB_NAME,\n",
+    "    db_user=private.DB_USER,\n",
+    "    db_pw=private.DB_PW,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['scraped', 'scraped_layovers', 'scraped_airlines']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.list_all_tables()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db.create_data_airports_table()"
+   ]
   }
  ],
  "metadata": {
diff --git a/flight_analysis.py b/flight_analysis.py
index cd31d79..f8d438d 100644
--- a/flight_analysis.py
+++ b/flight_analysis.py
@@ -108,10 +108,12 @@ def get_routes_df(routes: list):
         final_df = pd.concat(all_results).reset_index(drop=True)
 
         # clean and transform the dataframe
-        final_df["layover_time"] = final_df["layover_time"].fillna(-1)
-        final_df["layover_location"] = (
-            final_df["layover_location"].fillna(np.nan).replace([np.nan], [None])
-        )
+        # convert layover time and travel time to minutes
+        final_df["layover_time"] = final_df["layover_time"].dt.total_seconds() / 60
+        final_df["layover_time"] = final_df["layover_time"].fillna(0).astype(int)
+        final_df["travel_time"] = final_df["travel_time"].dt.total_seconds() / 60
+        final_df["travel_time"] = final_df["travel_time"].fillna(0).astype(int)
+
         final_df["price_value"] = (
             final_df["price_value"].fillna(np.nan).replace([np.nan], [None])
         )
@@ -122,7 +124,7 @@ def get_routes_df(routes: list):
     return final_df
 
 
-def generate_airline_df_from_flights(flights_df):
+def generate_airlines_df_from_flights(flights_df):
     """
     From a flights dataframe, generate an airline dataframe.
     Goal: respect good database conditions.
@@ -145,6 +147,32 @@ def generate_airline_df_from_flights(flights_df):
     return airlines_df
 
 
+def generate_layovers_df_from_flights(flights_df):
+    """
+    From a flights dataframe, generate a layovers dataframe.
+    Goal: respect good database conditions.
+    """
+    # check if all indices are unique
+    if not flights_df.index.is_unique:
+        flights_df = flights_df.reset_index(drop=True)
+
+    # create a dataframe with all the layovers, referencing the index
+    layovers_df = flights_df.explode("layover_location")[
+        ["layover_location"]
+    ].reset_index()
+
+    # get rid of rows with no layover
+    layovers_df = layovers_df.dropna().reset_index(drop=True)
+
+    # rename column
+    layovers_df = layovers_df.rename(columns={"uuid": "flight_uuid"})
+
+    # rename index to uuid
+    layovers_df.index.names = ["uuid"]
+
+    return layovers_df
+
+
 if __name__ == "__main__":
     SKIP_SAVE_TO_DB = len(sys.argv) > 1 and sys.argv[1] == "nodb"
 
@@ -154,11 +182,12 @@ def generate_airline_df_from_flights(flights_df):
     # scrape routes into a dataframe
     scraped_flights = get_routes_df(routes)
 
-    # generate an airline dataframe
-    scraped_airlines = generate_airline_df_from_flights(scraped_flights)
+    # generate airline and layovers dataframe
+    scraped_airlines = generate_airlines_df_from_flights(scraped_flights)
+    scraped_layovers = generate_layovers_df_from_flights(scraped_flights)
 
-    # drop airlines from flights dataframe
-    scraped_flights = scraped_flights.drop(columns=["airlines"])
+    # drop airlines and layovers from flights dataframe
+    scraped_flights = scraped_flights.drop(columns=["airlines", "layover_location"])
 
     # connect to database
     db = Database(
@@ -175,6 +204,7 @@ def generate_airline_df_from_flights(flights_df):
     if not SKIP_SAVE_TO_DB:
         db.add_pandas_df_to_db(scraped_flights, table_name=db.table_scraped)
         db.add_pandas_df_to_db(scraped_airlines, table_name=db.table_scraped_airlines)
+        db.add_pandas_df_to_db(scraped_layovers, table_name=db.table_scraped_layovers)
 
     # if it's a monday, backup the database
     if datetime.today().weekday() == 0:
diff --git a/src/flight_analysis/database.py b/src/flight_analysis/database.py
index 4b0ab6c..8ddfcbf 100644
--- a/src/flight_analysis/database.py
+++ b/src/flight_analysis/database.py
@@ -6,6 +6,8 @@
 import logging
 from datetime import datetime
 import subprocess
+import pandas as pd
+import numpy as np
 
 # logging
 logger_name = os.path.basename(__file__)
@@ -96,7 +98,6 @@ def create_scraped_table(self):
                 destination character(3) COLLATE pg_catalog."default" NOT NULL,
                 layover_n smallint NOT NULL,
                 layover_time smallint,
-                layover_location text COLLATE pg_catalog."default",
                 price_eur smallint NOT NULL,
                 price_trend text COLLATE pg_catalog."default",
                 price_value text COLLATE pg_catalog."default",
@@ -110,6 +111,11 @@ def create_scraped_table(self):
             TABLESPACE pg_default;
 
             ALTER TABLE IF EXISTS public.{self.table_scraped} OWNER to postgres;
+            
+            CREATE INDEX idx_access_date ON public.{self.table_scraped} USING btree (access_date);
+            CREATE INDEX idx_origin ON public.{self.table_scraped} USING btree (origin);
+            CREATE INDEX idx_destination ON public.{self.table_scraped} USING btree (destination);
+            CREATE INDEX idx_origin_destination ON public.{self.table_scraped} USING btree (origin, destination);
             """
 
         cursor = self.conn.cursor()
@@ -139,6 +145,8 @@ def create_scraped_airlines_table(self):
                 ON public.{self.table_scraped_airlines} USING btree
                 (flight_uuid ASC NULLS LAST)
                 TABLESPACE pg_default;
+                
+            CREATE INDEX idx_airline ON public.{self.table_scraped_airlines} USING btree (airline ASC NULLS LAST);
             """
 
         cursor = self.conn.cursor()
@@ -168,13 +176,98 @@ def create_scraped_layovers_table(self):
                 ON public.{self.table_scraped_layovers} USING btree
                 (flight_uuid ASC NULLS LAST)
                 TABLESPACE pg_default;
+                   
+            CREATE INDEX idx_layover_location ON public.{self.table_scraped_layovers} USING btree (layover_location ASC NULLS LAST);
             """
 
         cursor = self.conn.cursor()
         cursor.execute(query)
         cursor.close()
 
-    
+    def create_data_airports_table(self):
+        # create empty table
+        query = """
+        CREATE TABLE IF NOT EXISTS public.data_airports
+            (
+                iata character(3) NOT NULL,
+                name text,
+                country character(2),
+                lat numeric,
+                lon numeric,
+                region text,
+                municipality text,
+                continent character(2),
+                PRIMARY KEY (iata)
+            )
+
+        TABLESPACE pg_default;
+
+        ALTER TABLE IF EXISTS public.data_airports
+            OWNER to postgres;
+            
+        CREATE INDEX idx_iata
+            ON public.data_airports USING hash
+            (iata)
+        """
+
+        cursor = self.conn.cursor()
+        cursor.execute(query)
+        cursor.close()
+        logger.info("Table [data_airports] created.")
+
+        # download airports data
+        airports_df = self.download_data_airports()
+
+        # add airports data to table
+        logger.info("Adding airports data to table [data_airports]...")
+        self.add_pandas_df_to_db(airports_df, table_name="data_airports")
+
+    def download_data_airports(self):
+        AIRPORTS_DF_URL = "https://raw.githubusercontent.com/datasets/airport-codes/master/data/airport-codes.csv"
+        df = pd.read_csv(AIRPORTS_DF_URL, encoding="utf-8")
+
+        # filter original data
+        # take only aiprort that have a IATA code
+        df = df.loc[df.iata_code.notnull()]
+
+        # exclude closed, seaplane_base and heliport
+        df = df.loc[df.type.isin(["large_airport", "medium_airport", "small_airport"])]
+
+        # split coordinates in latitude and longitude
+        df["lat"] = (
+            df.coordinates.apply(lambda x: x.split(",")[0]).astype(float).round(4)
+        )
+        df["lon"] = (
+            df.coordinates.apply(lambda x: x.split(",")[1]).astype(float).round(4)
+        )
+
+        # take only useful columns
+        cols = [
+            "iata_code",
+            "name",
+            "iso_country",
+            "lat",
+            "lon",
+            "iso_region",
+            "municipality",
+            "continent",
+        ]
+        df = df[cols]
+
+        # rename columns
+        df = df.rename(
+            columns={
+                "iata_code": "iata",
+                "iso_country": "country",
+                "iso_region": "region",
+            }
+        )
+
+        # replace nan with None
+        df = df.replace({pd.NA: None, np.nan: None})
+
+        return df.reset_index(drop=True)
+
     def prepare_db_and_tables(self):
         """
         Creates the database and the table if they don't exist.
@@ -183,6 +276,10 @@ def prepare_db_and_tables(self):
         if self.db_name not in self.list_all_databases():
             self.create_db()
 
+        # create data_airports table
+        if "data_airports" not in self.list_all_tables():
+            self.create_data_airports_table()
+
         # create scraped table
         if self.table_scraped not in self.list_all_tables():
             self.create_scraped_table()
@@ -191,13 +288,19 @@ def prepare_db_and_tables(self):
         if self.table_scraped_airlines not in self.list_all_tables():
             self.create_scraped_airlines_table()
 
+        # create scraped_layovers table
+        if self.table_scraped_layovers not in self.list_all_tables():
+            self.create_scraped_layovers_table()
+
     def add_pandas_df_to_db(self, df, table_name):
         extras.register_uuid()
 
         # Create a list of tuples from the dataframe values
         if table_name == self.table_scraped:
-            df = df.reset_index() # otherwise the index (uuid) is not added to the table
-        
+            df = (
+                df.reset_index()
+            )  # otherwise the index (uuid) is not added to the table
+
         tuples = [tuple(x) for x in df.to_numpy()]
 
         # Comma-separated dataframe columns
@@ -216,20 +319,19 @@ def add_pandas_df_to_db(self, df, table_name):
 
         cursor.close()
 
-        # fix layover time
-        # TODO: improve this
+        # fix layover time manually
         if table_name == self.table_scraped:
-            cursor = self.conn.cursor()
-            query = f"""
-                UPDATE {self.table_scraped}
-                SET layover_time = CASE
-                WHEN layover_time = -1 THEN null ELSE layover_time END;
-
-                ALTER TABLE public.{self.table_scraped}
-                ALTER COLUMN layover_time TYPE smallint;
-            """
-            cursor.execute(query)
-            cursor.close()
+            try:
+                query = f"""
+                    UPDATE public.{self.table_scraped}
+                    SET layover_time = NULL
+                    WHERE layover_time = 0;"""
+                cursor = self.conn.cursor()
+                cursor.execute(query)
+                cursor.close()
+            except Exception as e:
+                logger.error(f"Error while updating layover_time: {e}")
+                self.conn.rollback()
 
     def dump_database_to_file(self):
         """
diff --git a/src/flight_analysis/flight.py b/src/flight_analysis/flight.py
index 552cd11..a7f388a 100644
--- a/src/flight_analysis/flight.py
+++ b/src/flight_analysis/flight.py
@@ -117,6 +117,13 @@ def _is_arg_orig_dest(self, arg):
         # example: MUCFCO, BCNMAD
         if len(arg) == 6 and arg.isupper():
             return True
+
+        return False
+
+    def _is_arg_train_service(self, arg):
+        train_keywords = ["Train service", "Flight + Train"]
+        if arg in train_keywords:
+            return True
         return False
 
     # ---------------------------------------------------------------
@@ -197,14 +204,14 @@ def _parse_price(self, arg):
         return int(arg.replace(",", ""))
 
     def _parse_orig_dest(self, arg):
-        # special case: "Flight + Train"
-        if "Flight + Train" in arg:
-            self._has_train = True
-            return (self._queried_orig, self._queried_dest)
-
         # regular case: like MUCFCO, LAXJFK
         return (arg[:3], arg[3:])
 
+    def _parse_train_service(self, arg):
+        if "Train service" in arg:
+            self._has_train = True
+            return (self._queried_orig, self._queried_dest)
+
     # ---------------------------------------------------------------
 
     def _classify_arg(self, arg: str):
@@ -212,8 +219,6 @@ def _classify_arg(self, arg: str):
         Classifies a string (arg) into the correct attribute for a flight,
         such as price, numer of layover stops, arrival time...
         """
-        parsed = False
-
         # define cases for which to return early
         arg_empty = arg is None or arg == "" or len(arg) == 0
         arg_useless = arg in ["Change of airport", "round trip", "Climate friendly"]
@@ -238,6 +243,10 @@ def _classify_arg(self, arg: str):
             self._airline = arg.split(", ")
             return
 
+        # train service
+        if self._is_arg_train_service(arg):
+            self._origin, self._dest = self._parse_train_service(arg)
+
         # departure and arrival times
         if self._is_arg_departure_arrival_times(arg):
             (
@@ -330,7 +339,7 @@ def make_dataframe(flights):
             "access_date": [],
             "one_way": [],
             "has_train": [],
-            # "days_advance": [],
+            "days_advance": [],
         }
 
         # populate the dictionary
@@ -351,9 +360,9 @@ def make_dataframe(flights):
                 data["access_date"] += [datetime.today()]
                 data["one_way"] += [(False if flight._roundtrip else True)]
                 data["has_train"] += [flight._has_train]
-                # data["days_advance"] += [
-                #     (flight._time_departure - datetime.today()).days
-                # ]
+                data["days_advance"] += [
+                    (flight._time_departure - datetime.today()).days
+                ]
             except Exception as e:
                 print("Error with flight", flight, flight._price)
                 print(e)