{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import statistics\n",
    "import polars as pl\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import polars.selectors as cs\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KPI\n",
    "\n",
    "Vamos a utilizar el **MAE**. Para ello necesitamos obtener las variables:\n",
    "-  `y_pred`: la predicción de la hora que llega el autobús que ofrece la API cuando se realiza la llamada\n",
    "-  `y_true`: para cada autobús,linea, parada y destino concreto, la predicción que ofrece la API más fiable (entendiendose por fiable aquella que es inferior a 60 segundos).\n",
    "\n",
    "Para obtener `y_true`, cogemos el valor de `y_pred` para los valores en los que `estimateArrive` sea inferior a 60 segundos de cada autobús, linea, parada y destino concretos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_02 = pl.scan_csv('/home/mlia/proyectos/data-generation/data/train/emt/2024/03/03/emt_20240303.csv')\n",
    "sample_data_aux_02 = pl.scan_csv('/home/mlia/proyectos/data-generation/data/train/emt/2024/03/03/emt_20240303_aux.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "</style>\n",
       "<small>shape: (5, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th></th><th>PK</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th></tr><tr><td>i64</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>f64</td><td>i64</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>0</td><td>&quot;2024-03-03 22:…</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 22:…</td><td>2331</td><td>&quot;127&quot;</td><td>1617</td><td>-3.71898</td><td>40.465098</td><td>3645</td><td>&quot;CUATRO CAMINOS…</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>818</td></tr><tr><td>1</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 10:…</td><td>2133</td><td>&quot;107&quot;</td><td>497</td><td>-3.688693</td><td>40.467328</td><td>2778</td><td>&quot;HORTALEZA&quot;</td><td>15.0</td><td>0</td><td>&quot;FE&quot;</td><td>555</td></tr><tr><td>2</td><td>&quot;2024-03-03 20:…</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 20:…</td><td>541</td><td>&quot;70&quot;</td><td>232</td><td>-3.675485</td><td>40.467472</td><td>6703</td><td>&quot;ALSACIA&quot;</td><td>12.0</td><td>0</td><td>&quot;FE&quot;</td><td>1369</td></tr><tr><td>3</td><td>&quot;2024-03-03 13:…</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 13:…</td><td>2268</td><td>&quot;175&quot;</td><td>4508</td><td>-3.696627</td><td>40.486987</td><td>13206</td><td>&quot;PLAZA CASTILLA…</td><td>20.0</td><td>1</td><td>&quot;FE&quot;</td><td>2318</td></tr><tr><td>4</td><td>&quot;2024-03-03 15:…</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 15:…</td><td>3273</td><td>&quot;11&quot;</td><td>220</td><td>-3.669398</td><td>40.458809</td><td>8153</td><td>&quot;BARRIO BLANCO&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>2399</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 15)\n",
       "┌─────┬──────────────┬────────────┬─────────────┬───┬─────────────┬────────┬─────────┬─────────────┐\n",
       "│     ┆ PK           ┆ date       ┆ datetime    ┆ … ┆ MinimunFreq ┆ isHead ┆ dayType ┆ estimateArr │\n",
       "│ --- ┆ ---          ┆ ---        ┆ ---         ┆   ┆ uency       ┆ ---    ┆ ---     ┆ ive         │\n",
       "│ i64 ┆ str          ┆ str        ┆ str         ┆   ┆ ---         ┆ i64    ┆ str     ┆ ---         │\n",
       "│     ┆              ┆            ┆             ┆   ┆ f64         ┆        ┆         ┆ i64         │\n",
       "╞═════╪══════════════╪════════════╪═════════════╪═══╪═════════════╪════════╪═════════╪═════════════╡\n",
       "│ 0   ┆ 2024-03-03   ┆ 2024-03-03 ┆ 2024-03-03  ┆ … ┆ null        ┆ 0      ┆ FE      ┆ 818         │\n",
       "│     ┆ 22:40:05.502 ┆            ┆ 22:40:05.50 ┆   ┆             ┆        ┆         ┆             │\n",
       "│     ┆ 889_B2331…   ┆            ┆ 2889        ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 1   ┆ 2024-03-03   ┆ 2024-03-03 ┆ 2024-03-03  ┆ … ┆ 15.0        ┆ 0      ┆ FE      ┆ 555         │\n",
       "│     ┆ 10:56:04.279 ┆            ┆ 10:56:04.27 ┆   ┆             ┆        ┆         ┆             │\n",
       "│     ┆ 680_B2133…   ┆            ┆ 9680        ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2   ┆ 2024-03-03   ┆ 2024-03-03 ┆ 2024-03-03  ┆ … ┆ 12.0        ┆ 0      ┆ FE      ┆ 1369        │\n",
       "│     ┆ 20:48:03.810 ┆            ┆ 20:48:03.81 ┆   ┆             ┆        ┆         ┆             │\n",
       "│     ┆ 984_B541_…   ┆            ┆ 0984        ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 3   ┆ 2024-03-03   ┆ 2024-03-03 ┆ 2024-03-03  ┆ … ┆ 20.0        ┆ 1      ┆ FE      ┆ 2318        │\n",
       "│     ┆ 13:49:21.341 ┆            ┆ 13:49:21.34 ┆   ┆             ┆        ┆         ┆             │\n",
       "│     ┆ 701_B2268…   ┆            ┆ 1701        ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 4   ┆ 2024-03-03   ┆ 2024-03-03 ┆ 2024-03-03  ┆ … ┆ null        ┆ 0      ┆ FE      ┆ 2399        │\n",
       "│     ┆ 15:32:19.394 ┆            ┆ 15:32:19.39 ┆   ┆             ┆        ┆         ┆             │\n",
       "│     ┆ 222_B3273…   ┆            ┆ 4222        ┆   ┆             ┆        ┆         ┆             │\n",
       "└─────┴──────────────┴────────────┴─────────────┴───┴─────────────┴────────┴─────────┴─────────────┘"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data_02.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th></th><th>PK</th><th>reliable_arrival_date</th><th>predict_arrival_date</th><th>interval_time</th><th>estimateArrive</th></tr><tr><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>0</td><td>&quot;2024-03-03 13:…</td><td>&quot;2024-03-03 13:…</td><td>&quot;2024-03-03 14:…</td><td>&quot;[12 14]&quot;</td><td>2318</td></tr><tr><td>1</td><td>&quot;2024-03-03 15:…</td><td>&quot;2024-03-03 15:…</td><td>&quot;2024-03-03 16:…</td><td>&quot;[14 16]&quot;</td><td>2399</td></tr><tr><td>2</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 13:…</td><td>&quot;[11 13]&quot;</td><td>2053</td></tr><tr><td>3</td><td>&quot;2024-03-03 13:…</td><td>&quot;2024-03-03 13:…</td><td>&quot;2024-03-03 13:…</td><td>&quot;[12 14]&quot;</td><td>56</td></tr><tr><td>4</td><td>&quot;2024-03-03 19:…</td><td>&quot;2024-03-03 19:…</td><td>&quot;2024-03-03 19:…</td><td>&quot;[18 20]&quot;</td><td>201</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 6)\n",
       "┌─────┬───────────────────┬───────────────────┬───────────────────┬───────────────┬────────────────┐\n",
       "│     ┆ PK                ┆ reliable_arrival_ ┆ predict_arrival_d ┆ interval_time ┆ estimateArrive │\n",
       "│ --- ┆ ---               ┆ date              ┆ ate               ┆ ---           ┆ ---            │\n",
       "│ i64 ┆ str               ┆ ---               ┆ ---               ┆ str           ┆ i64            │\n",
       "│     ┆                   ┆ str               ┆ str               ┆               ┆                │\n",
       "╞═════╪═══════════════════╪═══════════════════╪═══════════════════╪═══════════════╪════════════════╡\n",
       "│ 0   ┆ 2024-03-03 13:49: ┆ 2024-03-03        ┆ 2024-03-03        ┆ [12 14]       ┆ 2318           │\n",
       "│     ┆ 21.341701_B2268…  ┆ 13:34:04.533532   ┆ 14:27:59.341701   ┆               ┆                │\n",
       "│ 1   ┆ 2024-03-03 15:32: ┆ 2024-03-03        ┆ 2024-03-03        ┆ [14 16]       ┆ 2399           │\n",
       "│     ┆ 19.394222_B3273…  ┆ 15:02:52.864953   ┆ 16:12:18.394222   ┆               ┆                │\n",
       "│ 2   ┆ 2024-03-03 12:48: ┆ 2024-03-03        ┆ 2024-03-03        ┆ [11 13]       ┆ 2053           │\n",
       "│     ┆ 05.546055_B8830…  ┆ 12:34:04.041122   ┆ 13:22:18.546055   ┆               ┆                │\n",
       "│ 3   ┆ 2024-03-03 13:08: ┆ 2024-03-03        ┆ 2024-03-03        ┆ [12 14]       ┆ 56             │\n",
       "│     ┆ 03.566043_B2134…  ┆ 13:08:45.337469   ┆ 13:08:59.566043   ┆               ┆                │\n",
       "│ 4   ┆ 2024-03-03 19:07: ┆ 2024-03-03        ┆ 2024-03-03        ┆ [18 20]       ┆ 201            │\n",
       "│     ┆ 19.641947_B8804…  ┆ 19:10:04.418367   ┆ 19:10:40.641947   ┆               ┆                │\n",
       "└─────┴───────────────────┴───────────────────┴───────────────────┴───────────────┴────────────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data_aux_02.head().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def KPI_fun(date_true,date_pred):\n",
    "    date_true = datetime.strptime(date_true, '%Y-%m-%d %H:%M:%S.%f')\n",
    "    date_pred = datetime.strptime(date_pred, '%Y-%m-%d %H:%M:%S.%f')\n",
    "    \n",
    "    dif = max(date_true,date_pred) - min(date_true,date_pred)\n",
    "    return dif.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "KPI_data = sample_data_aux_02.with_columns(pl.struct(pl.col('reliable_arrival_date'),pl.col('predict_arrival_date')).alias('struct').map_elements(lambda x: KPI_fun(x['reliable_arrival_date'], x['predict_arrival_date'])).alias('KPI_value')).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_data = KPI_data.sort('estimateArrive').select('estimateArrive','KPI_value').to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_data2 = KPI_data.filter(pl.col('KPI_value')<500).sort('estimateArrive').select('estimateArrive','KPI_value').to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_KPI(dict):\n",
    "    new_dict = {}\n",
    "    MAE = []\n",
    "    for eta, kpi in zip(dict['estimateArrive'], dict['KPI_value']):\n",
    "        # Verificar si el entero ya está en el diccionario\n",
    "        if eta in new_dict:\n",
    "            # Si el entero ya está en el diccionario, agregar el valor numérico a la lista existente\n",
    "            new_dict[eta].append(kpi)\n",
    "        else:\n",
    "            # Si el entero no está en el diccionario, crear una nueva lista con el valor numérico\n",
    "            new_dict[eta] = [kpi]\n",
    "            \n",
    "    for key,val in zip(new_dict.keys(),new_dict.values()):\n",
    "        MAE.append((key/60,statistics.mean(val)/60))\n",
    "        \n",
    "\n",
    "    plt.plot([data[0] for data in MAE], [data[1] for data in MAE])\n",
    "    plt.gca().invert_xaxis()\n",
    "    plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "    plt.title('CRTM API estimation error')\n",
    "    plt.xlabel('Remaining time (minutes)')\n",
    "    plt.ylabel('MAE (minutes)')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_KPI(dict_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: una posibilidad es filtrar por los que el KPI sea <500 segundos (aprox 8 minutos) para asi quitar los valores erroneos que hayan salido con la creacion del intervalo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_KPI(dict_data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtramos\n",
    "* Bus 51\n",
    "* Line BR1\n",
    "* Stop 4366\n",
    "* Destination VALDEBEBAS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_02 = sample_data_02.filter(pl.col('bus')==51, pl.col('line')==\"BR1\",pl.col('stop')==4366, pl.col('destination')==\"VALDEBEBAS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sample_data_aux_02.join(sample_data_02,on='PK',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (116, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th></th><th>PK</th><th>reliable_arrival_date</th><th>predict_arrival_date</th><th>interval_time</th><th>estimateArrive</th><th>_right</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive_right</th></tr><tr><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>f64</td><td>i64</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>13333</td><td>&quot;2024-03-03 11:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;[10 12]&quot;</td><td>864</td><td>17814</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 11:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.698413</td><td>40.490519</td><td>8045</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>864</td></tr><tr><td>20709</td><td>&quot;2024-03-03 11:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;[10 12]&quot;</td><td>540</td><td>27536</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 11:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.661998</td><td>40.498973</td><td>3287</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>540</td></tr><tr><td>32386</td><td>&quot;2024-03-03 11:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;[10 12]&quot;</td><td>1250</td><td>43012</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 11:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.692432</td><td>40.486629</td><td>10340</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>1250</td></tr><tr><td>35941</td><td>&quot;2024-03-03 08:…</td><td>&quot;2024-03-03 08:…</td><td>&quot;2024-03-03 08:…</td><td>&quot;[7 9]&quot;</td><td>2044</td><td>47768</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 08:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.641676</td><td>40.484641</td><td>18705</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>2044</td></tr><tr><td>40658</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;[ 9 11]&quot;</td><td>1410</td><td>54070</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 10:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.67561</td><td>40.484079</td><td>12528</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>1410</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>780200</td><td>&quot;2024-03-03 08:…</td><td>&quot;2024-03-03 08:…</td><td>&quot;2024-03-03 08:…</td><td>&quot;[7 9]&quot;</td><td>628</td><td>1037349</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 08:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.676432</td><td>40.483876</td><td>6105</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>628</td></tr><tr><td>786139</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03 10:…</td><td>&quot;[ 9 11]&quot;</td><td>168</td><td>1045276</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 10:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.653508</td><td>40.487681</td><td>868</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>168</td></tr><tr><td>797676</td><td>&quot;2024-03-03 08:…</td><td>&quot;2024-03-03 08:…</td><td>&quot;2024-03-03 08:…</td><td>&quot;[7 9]&quot;</td><td>0</td><td>1060661</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 08:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.642333</td><td>40.484241</td><td>0</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>0</td></tr><tr><td>807589</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03 10:…</td><td>&quot;2024-03-03 11:…</td><td>&quot;[ 9 11]&quot;</td><td>1239</td><td>1073832</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 10:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.696335</td><td>40.487389</td><td>10513</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>1239</td></tr><tr><td>831602</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;[11 13]&quot;</td><td>343</td><td>1106062</td><td>&quot;2024-03-03&quot;</td><td>&quot;2024-03-03 12:…</td><td>51</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.658238</td><td>40.493502</td><td>2131</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;FE&quot;</td><td>343</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (116, 20)\n",
       "┌────────┬─────────────┬─────────────┬────────────┬───┬────────────┬────────┬─────────┬────────────┐\n",
       "│        ┆ PK          ┆ reliable_ar ┆ predict_ar ┆ … ┆ MinimunFre ┆ isHead ┆ dayType ┆ estimateAr │\n",
       "│ ---    ┆ ---         ┆ rival_date  ┆ rival_date ┆   ┆ quency     ┆ ---    ┆ ---     ┆ rive_right │\n",
       "│ i64    ┆ str         ┆ ---         ┆ ---        ┆   ┆ ---        ┆ i64    ┆ str     ┆ ---        │\n",
       "│        ┆             ┆ str         ┆ str        ┆   ┆ f64        ┆        ┆         ┆ i64        │\n",
       "╞════════╪═════════════╪═════════════╪════════════╪═══╪════════════╪════════╪═════════╪════════════╡\n",
       "│ 13333  ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 864        │\n",
       "│        ┆ 11:02:06.65 ┆ 11:17:08.10 ┆ 11:16:30.6 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 1109_B51_L… ┆ 9869        ┆ 51109      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 20709  ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 540        │\n",
       "│        ┆ 11:08:05.04 ┆ 11:17:08.10 ┆ 11:17:05.0 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 0397_B51_L… ┆ 9869        ┆ 40397      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 32386  ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 1250       │\n",
       "│        ┆ 11:00:06.06 ┆ 11:17:08.10 ┆ 11:20:56.0 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 6596_B51_L… ┆ 9869        ┆ 66596      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 35941  ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 2044       │\n",
       "│        ┆ 08:11:05.46 ┆ 08:45:38.99 ┆ 08:45:09.4 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 0142_B51_L… ┆ 5529        ┆ 60142      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 40658  ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 1410       │\n",
       "│        ┆ 10:54:04.24 ┆ 10:04:27.20 ┆ 11:17:34.2 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 3333_B51_L… ┆ 4599        ┆ 43333      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ …      ┆ …           ┆ …           ┆ …          ┆ … ┆ …          ┆ …      ┆ …       ┆ …          │\n",
       "│ 780200 ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 628        │\n",
       "│        ┆ 08:31:08.15 ┆ 08:45:38.99 ┆ 08:41:36.1 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 0538_B51_L… ┆ 5529        ┆ 50538      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 786139 ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 168        │\n",
       "│        ┆ 10:02:04.50 ┆ 10:04:27.20 ┆ 10:04:52.5 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 0658_B51_L… ┆ 4599        ┆ 00658      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 797676 ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 0          │\n",
       "│        ┆ 08:46:05.34 ┆ 08:45:38.99 ┆ 08:46:05.3 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 6794_B51_L… ┆ 5529        ┆ 46794      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 807589 ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 1239       │\n",
       "│        ┆ 10:56:05.03 ┆ 10:04:27.20 ┆ 11:16:44.0 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 9063_B51_L… ┆ 4599        ┆ 39063      ┆   ┆            ┆        ┆         ┆            │\n",
       "│ 831602 ┆ 2024-03-03  ┆ 2024-03-03  ┆ 2024-03-03 ┆ … ┆ null       ┆ 0      ┆ FE      ┆ 343        │\n",
       "│        ┆ 12:28:06.54 ┆ 12:34:01.47 ┆ 12:33:49.5 ┆   ┆            ┆        ┆         ┆            │\n",
       "│        ┆ 9569_B51_L… ┆ 1140        ┆ 49569      ┆   ┆            ┆        ┆         ┆            │\n",
       "└────────┴─────────────┴─────────────┴────────────┴───┴────────────┴────────┴─────────┴────────────┘"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_aux_02_filter = data.filter(pl.col('interval_time')==\"[11 13]\").select(pl.col('datetime'),pl.col('reliable_arrival_date'),pl.col('predict_arrival_date'),pl.col('estimateArrive')).sort('datetime','estimateArrive',descending=True).collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "KPI_data = sample_data_aux_02_filter.with_columns(pl.struct(reliable_arrival_date = pl.col('reliable_arrival_date'), predict_arrival_date = pl.col('predict_arrival_date').alias('struct')).map_elements(lambda x: KPI_fun(x['reliable_arrival_date'], x['predict_arrival_date'])).alias('KPI_value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (30, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>reliable_arrival_date</th><th>predict_arrival_date</th><th>estimateArrive</th><th>KPI_value</th></tr><tr><td>str</td><td>str</td><td>str</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1958</td><td>341.567779</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1716</td><td>341.148263</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1837</td><td>341.135457</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1898</td><td>340.938425</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>2061</td><td>340.932139</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>175</td><td>0.396977</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1180</td><td>0.389483</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1196</td><td>0.331104</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>1075</td><td>0.265574</td></tr><tr><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>&quot;2024-03-03 12:…</td><td>56</td><td>0.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (30, 5)\n",
       "┌─────────────────┬─────────────────────────┬────────────────────────┬────────────────┬────────────┐\n",
       "│ datetime        ┆ reliable_arrival_date   ┆ predict_arrival_date   ┆ estimateArrive ┆ KPI_value  │\n",
       "│ ---             ┆ ---                     ┆ ---                    ┆ ---            ┆ ---        │\n",
       "│ str             ┆ str                     ┆ str                    ┆ i64            ┆ f64        │\n",
       "╞═════════════════╪═════════════════════════╪════════════════════════╪════════════════╪════════════╡\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1958           ┆ 341.567779 │\n",
       "│ 12:07:05.038919 ┆ 12:34:01.471140         ┆ 12:39:43.038919        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1716           ┆ 341.148263 │\n",
       "│ 12:11:06.619403 ┆ 12:34:01.471140         ┆ 12:39:42.619403        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1837           ┆ 341.135457 │\n",
       "│ 12:09:05.606597 ┆ 12:34:01.471140         ┆ 12:39:42.606597        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1898           ┆ 340.938425 │\n",
       "│ 12:08:04.409565 ┆ 12:34:01.471140         ┆ 12:39:42.409565        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 2061           ┆ 340.932139 │\n",
       "│ 12:05:21.403279 ┆ 12:34:01.471140         ┆ 12:39:42.403279        ┆                ┆            │\n",
       "│ …               ┆ …                       ┆ …                      ┆ …              ┆ …          │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 175            ┆ 0.396977   │\n",
       "│ 12:31:06.074163 ┆ 12:34:01.471140         ┆ 12:34:01.074163        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1180           ┆ 0.389483   │\n",
       "│ 12:14:21.081657 ┆ 12:34:01.471140         ┆ 12:34:01.081657        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1196           ┆ 0.331104   │\n",
       "│ 12:14:05.140036 ┆ 12:34:01.471140         ┆ 12:34:01.140036        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 1075           ┆ 0.265574   │\n",
       "│ 12:16:06.205566 ┆ 12:34:01.471140         ┆ 12:34:01.205566        ┆                ┆            │\n",
       "│ 2024-03-03      ┆ 2024-03-03              ┆ 2024-03-03             ┆ 56             ┆ 0.0        │\n",
       "│ 12:33:05.471140 ┆ 12:34:01.471140         ┆ 12:34:01.471140        ┆                ┆            │\n",
       "└─────────────────┴─────────────────────────┴────────────────────────┴────────────────┴────────────┘"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KPI_data.sort('KPI_value',descending=True) #TODO: tabla para la ppt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict = KPI_data.filter(pl.col('KPI_value')<500).sort('estimateArrive').select('estimateArrive','KPI_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_KPI(dict) #TODO: gráfica para la ppt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PRUEBA DEFINITIVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BUS,LINEA,PARADA DESTINO FIJOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import statistics\n",
    "import random\n",
    "import polars as pl\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import polars.selectors as cs\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import warnings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(os.getcwd()))))\n",
    "DATA_PATH = os.path.join(ROOT_PATH, \"data\", \"processed\")\n",
    "EMT_DATA_PATH = os.path.join(DATA_PATH, \"emt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pl.scan_csv(os.path.join(EMT_DATA_PATH, \"2024\", \"03\", f\"emt_202403.csv\"))\n",
    "list_day = ['02','03','04','05','06','07','08','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30','31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    }
   ],
   "source": [
    "random.seed(1234)  \n",
    "day = random.randint(2, 31)\n",
    "\n",
    "print(day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = pl.scan_csv(os.path.join(EMT_DATA_PATH, \"2024\", \"03\",str(day), f\"emt_202403{str(day)}.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_final_dataset(sample_data):\n",
    "    sample_data = sample_data.with_columns((pl.col('datetime').cast(pl.String)+\"_B\"+pl.col('bus').cast(pl.String)+\"_L\"+ pl.col('line').cast(pl.String)+\"_S\"+pl.col('stop').cast(pl.String)).alias('PK'))\n",
    "    \n",
    "    # ETA <2400\n",
    "    sample_data = sample_data.filter(pl.col('estimateArrive')<888888)\n",
    "    sample_data = sample_data.group_by('PK').min()\n",
    "    \n",
    "    sample_data = sample_data.with_columns(pl.col(\"date\").cast(pl.Date),pl.col('bus').cast(pl.String),pl.col('line').cast(pl.String),pl.col('isHead').cast(pl.UInt8))\n",
    "    \n",
    "    sample_data = sample_data.with_columns(pl.col('datetime').map_elements(lambda x: datetime.strptime(x, \"%Y-%m-%d %H:%M:%S.%f\")))\n",
    "    \n",
    "    # Rellenamos valores nulos de dayType\n",
    "    sample_data = sample_data.with_columns(pl.when(pl.col('dayType').is_null()).then(pl.col('date').apply(get_type_day)).otherwise(pl.col('dayType')).alias('dayType'))\n",
    "    \n",
    "    # Eliminamos variables\n",
    "    sample_data = sample_data.drop('positionTypeBus','deviation','MaximumFrequency','StartTime','StopTime','strike')\n",
    "    \n",
    "    return sample_data.collect()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_type_day(date):\n",
    "    \n",
    "    day = date.strftime(\"%A\")\n",
    "    \n",
    "    if day in ['Monday','Tuesday','Wednesday','Thursday','Friday']:\n",
    "        \n",
    "        type = 'LA'\n",
    "    elif day == 'Saturday':\n",
    "        type = 'SA'\n",
    "    else:\n",
    "        type = 'FE'\n",
    "        \n",
    "    return type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = create_final_dataset(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1_136_086, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PK</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th></tr><tr><td>str</td><td>date</td><td>datetime[μs]</td><td>str</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>i64</td><td>u8</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;2024-03-26 21:…</td><td>2024-03-26</td><td>2024-03-26 21:40:09.739783</td><td>&quot;2536&quot;</td><td>&quot;170&quot;</td><td>5399</td><td>-3.684673</td><td>40.511029</td><td>5665</td><td>&quot;SANCHINARRO&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>967</td></tr><tr><td>&quot;2024-03-26 08:…</td><td>2024-03-26</td><td>2024-03-26 08:10:09.881904</td><td>&quot;9109&quot;</td><td>&quot;177&quot;</td><td>5803</td><td>-3.688479</td><td>40.467095</td><td>1383</td><td>&quot;MARQUES DE VIA…</td><td>9</td><td>0</td><td>&quot;LA&quot;</td><td>300</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:23:08.488793</td><td>&quot;2477&quot;</td><td>&quot;173&quot;</td><td>5518</td><td>-3.664287</td><td>40.493674</td><td>1672</td><td>&quot;PLAZA CASTILLA…</td><td>7</td><td>1</td><td>&quot;LA&quot;</td><td>590</td></tr><tr><td>&quot;2024-03-26 16:…</td><td>2024-03-26</td><td>2024-03-26 16:50:07.442667</td><td>&quot;2497&quot;</td><td>&quot;170&quot;</td><td>2970</td><td>-3.701602</td><td>40.504168</td><td>9687</td><td>&quot;SANCHINARRO&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>2178</td></tr><tr><td>&quot;2024-03-26 20:…</td><td>2024-03-26</td><td>2024-03-26 20:30:06.307634</td><td>&quot;3279&quot;</td><td>&quot;11&quot;</td><td>222</td><td>-3.693661</td><td>40.459219</td><td>3559</td><td>&quot;BARRIO BLANCO&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1062</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-26 21:…</td><td>2024-03-26</td><td>2024-03-26 21:42:07.452901</td><td>&quot;4708&quot;</td><td>&quot;82&quot;</td><td>1629</td><td>-3.727311</td><td>40.445077</td><td>6819</td><td>&quot;PITIS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1068</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:13:08.297954</td><td>&quot;2477&quot;</td><td>&quot;173&quot;</td><td>3603</td><td>-3.689128</td><td>40.468215</td><td>4298</td><td>&quot;SANCHINARRO&quot;</td><td>7</td><td>0</td><td>&quot;LA&quot;</td><td>499</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:46:11.429187</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>5899</td><td>-3.63154</td><td>40.48568</td><td>505</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>94</td></tr><tr><td>&quot;2024-03-26 16:…</td><td>2024-03-26</td><td>2024-03-26 16:42:05.914865</td><td>&quot;2468&quot;</td><td>&quot;134&quot;</td><td>1022</td><td>-3.714127</td><td>40.48642</td><td>4337</td><td>&quot;MONTECARMELO&quot;</td><td>9</td><td>0</td><td>&quot;LA&quot;</td><td>893</td></tr><tr><td>&quot;2024-03-26 15:…</td><td>2024-03-26</td><td>2024-03-26 15:44:08.527764</td><td>&quot;2514&quot;</td><td>&quot;175&quot;</td><td>4508</td><td>-3.6655</td><td>40.504171</td><td>0</td><td>&quot;PLAZA CASTILLA…</td><td>9</td><td>1</td><td>&quot;LA&quot;</td><td>231</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1_136_086, 14)\n",
       "┌─────────────┬────────────┬─────────────┬──────┬───┬─────────────┬────────┬─────────┬─────────────┐\n",
       "│ PK          ┆ date       ┆ datetime    ┆ bus  ┆ … ┆ MinimunFreq ┆ isHead ┆ dayType ┆ estimateArr │\n",
       "│ ---         ┆ ---        ┆ ---         ┆ ---  ┆   ┆ uency       ┆ ---    ┆ ---     ┆ ive         │\n",
       "│ str         ┆ date       ┆ datetime[μs ┆ str  ┆   ┆ ---         ┆ u8     ┆ str     ┆ ---         │\n",
       "│             ┆            ┆ ]           ┆      ┆   ┆ i64         ┆        ┆         ┆ i64         │\n",
       "╞═════════════╪════════════╪═════════════╪══════╪═══╪═════════════╪════════╪═════════╪═════════════╡\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2536 ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 967         │\n",
       "│ 21:40:09.73 ┆            ┆ 21:40:09.73 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 9783_B2536… ┆            ┆ 9783        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 9109 ┆ … ┆ 9           ┆ 0      ┆ LA      ┆ 300         │\n",
       "│ 08:10:09.88 ┆            ┆ 08:10:09.88 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 1904_B9109… ┆            ┆ 1904        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2477 ┆ … ┆ 7           ┆ 1      ┆ LA      ┆ 590         │\n",
       "│ 22:23:08.48 ┆            ┆ 22:23:08.48 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 8793_B2477… ┆            ┆ 8793        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2497 ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 2178        │\n",
       "│ 16:50:07.44 ┆            ┆ 16:50:07.44 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2667_B2497… ┆            ┆ 2667        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 3279 ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1062        │\n",
       "│ 20:30:06.30 ┆            ┆ 20:30:06.30 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 7634_B3279… ┆            ┆ 7634        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ …           ┆ …          ┆ …           ┆ …    ┆ … ┆ …           ┆ …      ┆ …       ┆ …           │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 4708 ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1068        │\n",
       "│ 21:42:07.45 ┆            ┆ 21:42:07.45 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2901_B4708… ┆            ┆ 2901        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2477 ┆ … ┆ 7           ┆ 0      ┆ LA      ┆ 499         │\n",
       "│ 22:13:08.29 ┆            ┆ 22:13:08.29 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 7954_B2477… ┆            ┆ 7954        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51   ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 94          │\n",
       "│ 22:46:11.42 ┆            ┆ 22:46:11.42 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 9187_B51_L… ┆            ┆ 9187        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2468 ┆ … ┆ 9           ┆ 0      ┆ LA      ┆ 893         │\n",
       "│ 16:42:05.91 ┆            ┆ 16:42:05.91 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 4865_B2468… ┆            ┆ 4865        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2514 ┆ … ┆ 9           ┆ 1      ┆ LA      ┆ 231         │\n",
       "│ 15:44:08.52 ┆            ┆ 15:44:08.52 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 7764_B2514… ┆            ┆ 7764        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "└─────────────┴────────────┴─────────────┴──────┴───┴─────────────┴────────┴─────────┴─────────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = sample_data.filter(pl.col('bus')=='51', pl.col('line')==\"BR1\",pl.col('stop')==4366, pl.col('destination')==\"VALDEBEBAS\").sort('datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (222, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PK</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th></tr><tr><td>str</td><td>date</td><td>datetime[μs]</td><td>str</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>i64</td><td>u8</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26</td><td>2024-03-26 09:07:08.486404</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>9195</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1536</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26</td><td>2024-03-26 09:08:08.881899</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>8836</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1476</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26</td><td>2024-03-26 09:09:08.755131</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>9226</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1381</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26</td><td>2024-03-26 09:10:08.875354</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>8825</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1321</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26</td><td>2024-03-26 09:11:10.677695</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.696617</td><td>40.48821</td><td>9028</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1239</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:39:07.409588</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.658057</td><td>40.493668</td><td>1920</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>339</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:40:08.462574</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.659287</td><td>40.490522</td><td>1455</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>263</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:41:08.942691</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.654665</td><td>40.488051</td><td>1123</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>203</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:42:10.443984</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.650573</td><td>40.486745</td><td>760</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>198</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26</td><td>2024-03-26 22:43:10.564491</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (222, 14)\n",
       "┌──────────────┬────────────┬─────────────┬─────┬───┬─────────────┬────────┬─────────┬─────────────┐\n",
       "│ PK           ┆ date       ┆ datetime    ┆ bus ┆ … ┆ MinimunFreq ┆ isHead ┆ dayType ┆ estimateArr │\n",
       "│ ---          ┆ ---        ┆ ---         ┆ --- ┆   ┆ uency       ┆ ---    ┆ ---     ┆ ive         │\n",
       "│ str          ┆ date       ┆ datetime[μs ┆ str ┆   ┆ ---         ┆ u8     ┆ str     ┆ ---         │\n",
       "│              ┆            ┆ ]           ┆     ┆   ┆ i64         ┆        ┆         ┆ i64         │\n",
       "╞══════════════╪════════════╪═════════════╪═════╪═══╪═════════════╪════════╪═════════╪═════════════╡\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1536        │\n",
       "│ 09:07:08.486 ┆            ┆ 09:07:08.48 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 404_B51_L…   ┆            ┆ 6404        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1476        │\n",
       "│ 09:08:08.881 ┆            ┆ 09:08:08.88 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 899_B51_L…   ┆            ┆ 1899        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1381        │\n",
       "│ 09:09:08.755 ┆            ┆ 09:09:08.75 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 131_B51_L…   ┆            ┆ 5131        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1321        │\n",
       "│ 09:10:08.875 ┆            ┆ 09:10:08.87 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 354_B51_L…   ┆            ┆ 5354        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1239        │\n",
       "│ 09:11:10.677 ┆            ┆ 09:11:10.67 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 695_B51_L…   ┆            ┆ 7695        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ …            ┆ …          ┆ …           ┆ …   ┆ … ┆ …           ┆ …      ┆ …       ┆ …           │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 339         │\n",
       "│ 22:39:07.409 ┆            ┆ 22:39:07.40 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 588_B51_L…   ┆            ┆ 9588        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 263         │\n",
       "│ 22:40:08.462 ┆            ┆ 22:40:08.46 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 574_B51_L…   ┆            ┆ 2574        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 203         │\n",
       "│ 22:41:08.942 ┆            ┆ 22:41:08.94 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 691_B51_L…   ┆            ┆ 2691        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 198         │\n",
       "│ 22:42:10.443 ┆            ┆ 22:42:10.44 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 984_B51_L…   ┆            ┆ 3984        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26   ┆ 2024-03-26 ┆ 2024-03-26  ┆ 51  ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 39          │\n",
       "│ 22:43:10.564 ┆            ┆ 22:43:10.56 ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 491_B51_L…   ┆            ┆ 4491        ┆     ┆   ┆             ┆        ┆         ┆             │\n",
       "└──────────────┴────────────┴─────────────┴─────┴───┴─────────────┴────────┴─────────┴─────────────┘"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_predict_arrival_date(date_datetime,second):\n",
    "    new_date_datetime = date_datetime + timedelta(seconds=second)\n",
    "    \n",
    "    return new_date_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = sample_data.with_columns(pl.struct(datetime = pl.col('datetime'), estimateArrive = pl.col('estimateArrive').alias('struct')).map_elements(lambda x: calculate_predict_arrival_date(x['datetime'], x['estimateArrive'])).alias('predict_arrival_date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_pd = sample_data.to_pandas()\n",
    "sample_data_pd['bloque_id'] = None\n",
    "\n",
    "bloque_actual = 1\n",
    "for i in range(0, len(sample_data_pd)-1):\n",
    "    if (sample_data_pd['datetime'][i + 1] - sample_data_pd['datetime'][i]) > timedelta(minutes=5) and (sample_data_pd['estimateArrive'][i] < sample_data_pd['estimateArrive'][i + 1]):\n",
    "        sample_data_pd['bloque_id'][i] = bloque_actual\n",
    "        bloque_actual += 1\n",
    "        \n",
    "    else:\n",
    "        sample_data_pd['bloque_id'][i] = bloque_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_pd.at[sample_data_pd.index[-1], 'bloque_id'] = bloque_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_pd.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_pl = pl.from_pandas(sample_data_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
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       "</style>\n",
       "<small>shape: (222, 16)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PK</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th><th>predict_arrival_date</th><th>bloque_id</th></tr><tr><td>str</td><td>datetime[ms]</td><td>datetime[μs]</td><td>str</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>f64</td><td>u8</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>i64</td></tr></thead><tbody><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:05:08.146916</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.625174</td><td>40.482623</td><td>19754</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>2515</td><td>2024-03-26 22:47:03.146916</td><td>9</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:06:08.223862</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.635248</td><td>40.486303</td><td>19275</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>2454</td><td>2024-03-26 22:47:02.223862</td><td>9</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:07:07.950631</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.643053</td><td>40.484473</td><td>18804</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>2394</td><td>2024-03-26 22:47:01.950631</td><td>9</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:08:08.530312</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.643053</td><td>40.484473</td><td>18286</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>2328</td><td>2024-03-26 22:46:56.530312</td><td>9</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:09:08.167593</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.643053</td><td>40.484473</td><td>17862</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>2274</td><td>2024-03-26 22:47:02.167593</td><td>9</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:23:08.256559</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.659662</td><td>40.4922</td><td>1746</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>321</td><td>2024-03-26 09:28:29.256559</td><td>1</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:24:08.643877</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.658428</td><td>40.489981</td><td>1276</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>232</td><td>2024-03-26 09:28:00.643877</td><td>1</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:25:09.036527</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.655349</td><td>40.487956</td><td>847</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>159</td><td>2024-03-26 09:27:48.036527</td><td>1</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:26:08.257810</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.650483</td><td>40.486716</td><td>533</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>100</td><td>2024-03-26 09:27:48.257810</td><td>1</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:27:08.674862</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>1</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (222, 16)\n",
       "┌─────────────┬─────────────┬────────────┬─────┬───┬─────────┬────────────┬────────────┬───────────┐\n",
       "│ PK          ┆ date        ┆ datetime   ┆ bus ┆ … ┆ dayType ┆ estimateAr ┆ predict_ar ┆ bloque_id │\n",
       "│ ---         ┆ ---         ┆ ---        ┆ --- ┆   ┆ ---     ┆ rive       ┆ rival_date ┆ ---       │\n",
       "│ str         ┆ datetime[ms ┆ datetime[μ ┆ str ┆   ┆ str     ┆ ---        ┆ ---        ┆ i64       │\n",
       "│             ┆ ]           ┆ s]         ┆     ┆   ┆         ┆ i64        ┆ datetime[μ ┆           │\n",
       "│             ┆             ┆            ┆     ┆   ┆         ┆            ┆ s]         ┆           │\n",
       "╞═════════════╪═════════════╪════════════╪═════╪═══╪═════════╪════════════╪════════════╪═══════════╡\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 2515       ┆ 2024-03-26 ┆ 9         │\n",
       "│ 22:05:08.14 ┆ 00:00:00    ┆ 22:05:08.1 ┆     ┆   ┆         ┆            ┆ 22:47:03.1 ┆           │\n",
       "│ 6916_B51_L… ┆             ┆ 46916      ┆     ┆   ┆         ┆            ┆ 46916      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 2454       ┆ 2024-03-26 ┆ 9         │\n",
       "│ 22:06:08.22 ┆ 00:00:00    ┆ 22:06:08.2 ┆     ┆   ┆         ┆            ┆ 22:47:02.2 ┆           │\n",
       "│ 3862_B51_L… ┆             ┆ 23862      ┆     ┆   ┆         ┆            ┆ 23862      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 2394       ┆ 2024-03-26 ┆ 9         │\n",
       "│ 22:07:07.95 ┆ 00:00:00    ┆ 22:07:07.9 ┆     ┆   ┆         ┆            ┆ 22:47:01.9 ┆           │\n",
       "│ 0631_B51_L… ┆             ┆ 50631      ┆     ┆   ┆         ┆            ┆ 50631      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 2328       ┆ 2024-03-26 ┆ 9         │\n",
       "│ 22:08:08.53 ┆ 00:00:00    ┆ 22:08:08.5 ┆     ┆   ┆         ┆            ┆ 22:46:56.5 ┆           │\n",
       "│ 0312_B51_L… ┆             ┆ 30312      ┆     ┆   ┆         ┆            ┆ 30312      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 2274       ┆ 2024-03-26 ┆ 9         │\n",
       "│ 22:09:08.16 ┆ 00:00:00    ┆ 22:09:08.1 ┆     ┆   ┆         ┆            ┆ 22:47:02.1 ┆           │\n",
       "│ 7593_B51_L… ┆             ┆ 67593      ┆     ┆   ┆         ┆            ┆ 67593      ┆           │\n",
       "│ …           ┆ …           ┆ …          ┆ …   ┆ … ┆ …       ┆ …          ┆ …          ┆ …         │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 321        ┆ 2024-03-26 ┆ 1         │\n",
       "│ 09:23:08.25 ┆ 00:00:00    ┆ 09:23:08.2 ┆     ┆   ┆         ┆            ┆ 09:28:29.2 ┆           │\n",
       "│ 6559_B51_L… ┆             ┆ 56559      ┆     ┆   ┆         ┆            ┆ 56559      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 232        ┆ 2024-03-26 ┆ 1         │\n",
       "│ 09:24:08.64 ┆ 00:00:00    ┆ 09:24:08.6 ┆     ┆   ┆         ┆            ┆ 09:28:00.6 ┆           │\n",
       "│ 3877_B51_L… ┆             ┆ 43877      ┆     ┆   ┆         ┆            ┆ 43877      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 159        ┆ 2024-03-26 ┆ 1         │\n",
       "│ 09:25:09.03 ┆ 00:00:00    ┆ 09:25:09.0 ┆     ┆   ┆         ┆            ┆ 09:27:48.0 ┆           │\n",
       "│ 6527_B51_L… ┆             ┆ 36527      ┆     ┆   ┆         ┆            ┆ 36527      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 100        ┆ 2024-03-26 ┆ 1         │\n",
       "│ 09:26:08.25 ┆ 00:00:00    ┆ 09:26:08.2 ┆     ┆   ┆         ┆            ┆ 09:27:48.2 ┆           │\n",
       "│ 7810_B51_L… ┆             ┆ 57810      ┆     ┆   ┆         ┆            ┆ 57810      ┆           │\n",
       "│ 2024-03-26  ┆ 2024-03-26  ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA      ┆ 0          ┆ 2024-03-26 ┆ 1         │\n",
       "│ 09:27:08.67 ┆ 00:00:00    ┆ 09:27:08.6 ┆     ┆   ┆         ┆            ┆ 09:27:08.6 ┆           │\n",
       "│ 4862_B51_L… ┆             ┆ 74862      ┆     ┆   ┆         ┆            ┆ 74862      ┆           │\n",
       "└─────────────┴─────────────┴────────────┴─────┴───┴─────────┴────────────┴────────────┴───────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data_pl.sort('bloque_id','estimateArrive',descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (8, 17)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bus</th><th>line</th><th>stop</th><th>destination</th><th>date</th><th>bloque_id</th><th>PK</th><th>datetime</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th><th>predict_arrival_date</th><th>reliable_arrival_date</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>str</td><td>datetime[ms]</td><td>i64</td><td>str</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td><td>u8</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>datetime[μs]</td></tr></thead><tbody><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>4</td><td>&quot;2024-03-26 13:…</td><td>2024-03-26 13:42:09.058943</td><td>-3.649841</td><td>40.486513</td><td>202</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>28</td><td>2024-03-26 13:42:37.058943</td><td>2024-03-26 13:42:37.058943</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>3</td><td>&quot;2024-03-26 12:…</td><td>2024-03-26 12:15:09.310898</td><td>-3.646471</td><td>40.485319</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 12:15:09.310898</td><td>2024-03-26 12:15:09.310898</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>7</td><td>&quot;2024-03-26 20:…</td><td>2024-03-26 20:00:08.897971</td><td>-3.647037</td><td>40.485411</td><td>214</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>33</td><td>2024-03-26 20:00:41.897971</td><td>2024-03-26 20:00:41.897971</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>8</td><td>&quot;2024-03-26 21:…</td><td>2024-03-26 21:22:09.062779</td><td>-3.647071</td><td>40.484471</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 21:22:55.062779</td><td>2024-03-26 21:22:55.062779</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>6</td><td>&quot;2024-03-26 18:…</td><td>2024-03-26 18:31:10.487315</td><td>-3.643471</td><td>40.484603</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 18:31:10.487315</td><td>2024-03-26 18:31:10.487315</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>1</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>2</td><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:49:07.411681</td><td>-3.644106</td><td>40.484726</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:49:23.411681</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>9</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (8, 17)\n",
       "┌─────┬──────┬──────┬─────────────┬───┬─────────┬────────────────┬────────────────┬────────────────┐\n",
       "│ bus ┆ line ┆ stop ┆ destination ┆ … ┆ dayType ┆ estimateArrive ┆ predict_arriva ┆ reliable_arriv │\n",
       "│ --- ┆ ---  ┆ ---  ┆ ---         ┆   ┆ ---     ┆ ---            ┆ l_date         ┆ al_date        │\n",
       "│ str ┆ str  ┆ i64  ┆ str         ┆   ┆ str     ┆ i64            ┆ ---            ┆ ---            │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ datetime[μs]   ┆ datetime[μs]   │\n",
       "╞═════╪══════╪══════╪═════════════╪═══╪═════════╪════════════════╪════════════════╪════════════════╡\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 28             ┆ 2024-03-26 13: ┆ 2024-03-26 13: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 42:37.058943   ┆ 42:37.058943   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 12: ┆ 2024-03-26 12: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 15:09.310898   ┆ 15:09.310898   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 33             ┆ 2024-03-26 20: ┆ 2024-03-26 20: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 00:41.897971   ┆ 00:41.897971   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 21: ┆ 2024-03-26 21: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 22:55.062779   ┆ 22:55.062779   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 18: ┆ 2024-03-26 18: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 31:10.487315   ┆ 31:10.487315   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 09: ┆ 2024-03-26 09: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 27:08.674862   ┆ 27:08.674862   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 10: ┆ 2024-03-26 10: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 49:23.411681   ┆ 49:23.411681   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 39             ┆ 2024-03-26 22: ┆ 2024-03-26 22: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 43:49.564491   ┆ 43:49.564491   │\n",
       "└─────┴──────┴──────┴─────────────┴───┴─────────┴────────────────┴────────────────┴────────────────┘"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_sample_data = sample_data_pl.filter(pl.col('estimateArrive')<=60).group_by(pl.col('bus'),pl.col('line'),pl.col('stop'),pl.col('destination'),pl.col('date'),pl.col('bloque_id')).min().with_columns(pl.col('predict_arrival_date').alias('reliable_arrival_date'))\n",
    "small_sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_sample_data = small_sample_data.filter(pl.col('bloque_id').is_not_null())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (8, 17)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bus</th><th>line</th><th>stop</th><th>destination</th><th>date</th><th>bloque_id</th><th>PK</th><th>datetime</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th><th>predict_arrival_date</th><th>reliable_arrival_date</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>str</td><td>datetime[ms]</td><td>i64</td><td>str</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td><td>u8</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>datetime[μs]</td></tr></thead><tbody><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>4</td><td>&quot;2024-03-26 13:…</td><td>2024-03-26 13:42:09.058943</td><td>-3.649841</td><td>40.486513</td><td>202</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>28</td><td>2024-03-26 13:42:37.058943</td><td>2024-03-26 13:42:37.058943</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>3</td><td>&quot;2024-03-26 12:…</td><td>2024-03-26 12:15:09.310898</td><td>-3.646471</td><td>40.485319</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 12:15:09.310898</td><td>2024-03-26 12:15:09.310898</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>7</td><td>&quot;2024-03-26 20:…</td><td>2024-03-26 20:00:08.897971</td><td>-3.647037</td><td>40.485411</td><td>214</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>33</td><td>2024-03-26 20:00:41.897971</td><td>2024-03-26 20:00:41.897971</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>8</td><td>&quot;2024-03-26 21:…</td><td>2024-03-26 21:22:09.062779</td><td>-3.647071</td><td>40.484471</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 21:22:55.062779</td><td>2024-03-26 21:22:55.062779</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>6</td><td>&quot;2024-03-26 18:…</td><td>2024-03-26 18:31:10.487315</td><td>-3.643471</td><td>40.484603</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 18:31:10.487315</td><td>2024-03-26 18:31:10.487315</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>1</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>2</td><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:49:07.411681</td><td>-3.644106</td><td>40.484726</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:49:23.411681</td></tr><tr><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>9</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (8, 17)\n",
       "┌─────┬──────┬──────┬─────────────┬───┬─────────┬────────────────┬────────────────┬────────────────┐\n",
       "│ bus ┆ line ┆ stop ┆ destination ┆ … ┆ dayType ┆ estimateArrive ┆ predict_arriva ┆ reliable_arriv │\n",
       "│ --- ┆ ---  ┆ ---  ┆ ---         ┆   ┆ ---     ┆ ---            ┆ l_date         ┆ al_date        │\n",
       "│ str ┆ str  ┆ i64  ┆ str         ┆   ┆ str     ┆ i64            ┆ ---            ┆ ---            │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ datetime[μs]   ┆ datetime[μs]   │\n",
       "╞═════╪══════╪══════╪═════════════╪═══╪═════════╪════════════════╪════════════════╪════════════════╡\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 28             ┆ 2024-03-26 13: ┆ 2024-03-26 13: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 42:37.058943   ┆ 42:37.058943   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 12: ┆ 2024-03-26 12: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 15:09.310898   ┆ 15:09.310898   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 33             ┆ 2024-03-26 20: ┆ 2024-03-26 20: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 00:41.897971   ┆ 00:41.897971   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 21: ┆ 2024-03-26 21: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 22:55.062779   ┆ 22:55.062779   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 18: ┆ 2024-03-26 18: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 31:10.487315   ┆ 31:10.487315   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 09: ┆ 2024-03-26 09: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 27:08.674862   ┆ 27:08.674862   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 0              ┆ 2024-03-26 10: ┆ 2024-03-26 10: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 49:23.411681   ┆ 49:23.411681   │\n",
       "│ 51  ┆ BR1  ┆ 4366 ┆ VALDEBEBAS  ┆ … ┆ LA      ┆ 39             ┆ 2024-03-26 22: ┆ 2024-03-26 22: │\n",
       "│     ┆      ┆      ┆             ┆   ┆         ┆                ┆ 43:49.564491   ┆ 43:49.564491   │\n",
       "└─────┴──────┴──────┴─────────────┴───┴─────────┴────────────────┴────────────────┴────────────────┘"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_sample_data = sample_data_pl.join(small_sample_data,on=pl.col('bloque_id'),how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (222, 32)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PK</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th><th>predict_arrival_date</th><th>bloque_id</th><th>bus_right</th><th>line_right</th><th>stop_right</th><th>destination_right</th><th>date_right</th><th>PK_right</th><th>datetime_right</th><th>positionBusLon_right</th><th>positionBusLat_right</th><th>DistanceBus_right</th><th>MinimunFrequency_right</th><th>isHead_right</th><th>dayType_right</th><th>estimateArrive_right</th><th>predict_arrival_date_right</th><th>reliable_arrival_date</th></tr><tr><td>str</td><td>datetime[ms]</td><td>datetime[μs]</td><td>str</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>f64</td><td>u8</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>i64</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>datetime[ms]</td><td>str</td><td>datetime[μs]</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td><td>u8</td><td>str</td><td>i64</td><td>datetime[μs]</td><td>datetime[μs]</td></tr></thead><tbody><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:07:08.486404</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>9195</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1536</td><td>2024-03-26 09:32:44.486404</td><td>1</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:08:08.881899</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>8836</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1476</td><td>2024-03-26 09:32:44.881899</td><td>1</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:09:08.755131</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>9226</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1381</td><td>2024-03-26 09:32:09.755131</td><td>1</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:10:08.875354</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.695309</td><td>40.486747</td><td>8825</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1321</td><td>2024-03-26 09:32:09.875354</td><td>1</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 09:11:10.677695</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.696617</td><td>40.48821</td><td>9028</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1239</td><td>2024-03-26 09:31:49.677695</td><td>1</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:27:08.674862</td><td>-3.643678</td><td>40.48467</td><td>0</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>0</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:27:08.674862</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:39:07.409588</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.658057</td><td>40.493668</td><td>1920</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>339</td><td>2024-03-26 22:44:46.409588</td><td>9</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:40:08.462574</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.659287</td><td>40.490522</td><td>1455</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>263</td><td>2024-03-26 22:44:31.462574</td><td>9</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:41:08.942691</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.654665</td><td>40.488051</td><td>1123</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>203</td><td>2024-03-26 22:44:31.942691</td><td>9</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:42:10.443984</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.650573</td><td>40.486745</td><td>760</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>198</td><td>2024-03-26 22:45:28.443984</td><td>9</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr><tr><td>&quot;2024-03-26 22:…</td><td>2024-03-26 00:00:00</td><td>2024-03-26 22:43:10.564491</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>9</td><td>&quot;51&quot;</td><td>&quot;BR1&quot;</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>2024-03-26 00:00:00</td><td>&quot;2024-03-26 22:…</td><td>2024-03-26 22:43:10.564491</td><td>-3.645521</td><td>40.485224</td><td>236</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>39</td><td>2024-03-26 22:43:49.564491</td><td>2024-03-26 22:43:49.564491</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (222, 32)\n",
       "┌────────────┬────────────┬────────────┬─────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
       "│ PK         ┆ date       ┆ datetime   ┆ bus ┆ … ┆ dayType_ri ┆ estimateAr ┆ predict_a ┆ reliable_ │\n",
       "│ ---        ┆ ---        ┆ ---        ┆ --- ┆   ┆ ght        ┆ rive_right ┆ rrival_da ┆ arrival_d │\n",
       "│ str        ┆ datetime[m ┆ datetime[μ ┆ str ┆   ┆ ---        ┆ ---        ┆ te_right  ┆ ate       │\n",
       "│            ┆ s]         ┆ s]         ┆     ┆   ┆ str        ┆ i64        ┆ ---       ┆ ---       │\n",
       "│            ┆            ┆            ┆     ┆   ┆            ┆            ┆ datetime[ ┆ datetime[ │\n",
       "│            ┆            ┆            ┆     ┆   ┆            ┆            ┆ μs]       ┆ μs]       │\n",
       "╞════════════╪════════════╪════════════╪═════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 0          ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 09:07:08.4 ┆ 00:00:00   ┆ 09:07:08.4 ┆     ┆   ┆            ┆            ┆ 6 09:27:0 ┆ 6 09:27:0 │\n",
       "│ 86404_B51_ ┆            ┆ 86404      ┆     ┆   ┆            ┆            ┆ 8.674862  ┆ 8.674862  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 0          ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 09:08:08.8 ┆ 00:00:00   ┆ 09:08:08.8 ┆     ┆   ┆            ┆            ┆ 6 09:27:0 ┆ 6 09:27:0 │\n",
       "│ 81899_B51_ ┆            ┆ 81899      ┆     ┆   ┆            ┆            ┆ 8.674862  ┆ 8.674862  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 0          ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 09:09:08.7 ┆ 00:00:00   ┆ 09:09:08.7 ┆     ┆   ┆            ┆            ┆ 6 09:27:0 ┆ 6 09:27:0 │\n",
       "│ 55131_B51_ ┆            ┆ 55131      ┆     ┆   ┆            ┆            ┆ 8.674862  ┆ 8.674862  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 0          ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 09:10:08.8 ┆ 00:00:00   ┆ 09:10:08.8 ┆     ┆   ┆            ┆            ┆ 6 09:27:0 ┆ 6 09:27:0 │\n",
       "│ 75354_B51_ ┆            ┆ 75354      ┆     ┆   ┆            ┆            ┆ 8.674862  ┆ 8.674862  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 0          ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 09:11:10.6 ┆ 00:00:00   ┆ 09:11:10.6 ┆     ┆   ┆            ┆            ┆ 6 09:27:0 ┆ 6 09:27:0 │\n",
       "│ 77695_B51_ ┆            ┆ 77695      ┆     ┆   ┆            ┆            ┆ 8.674862  ┆ 8.674862  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ …          ┆ …          ┆ …          ┆ …   ┆ … ┆ …          ┆ …          ┆ …         ┆ …         │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 39         ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 22:39:07.4 ┆ 00:00:00   ┆ 22:39:07.4 ┆     ┆   ┆            ┆            ┆ 6 22:43:4 ┆ 6 22:43:4 │\n",
       "│ 09588_B51_ ┆            ┆ 09588      ┆     ┆   ┆            ┆            ┆ 9.564491  ┆ 9.564491  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 39         ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 22:40:08.4 ┆ 00:00:00   ┆ 22:40:08.4 ┆     ┆   ┆            ┆            ┆ 6 22:43:4 ┆ 6 22:43:4 │\n",
       "│ 62574_B51_ ┆            ┆ 62574      ┆     ┆   ┆            ┆            ┆ 9.564491  ┆ 9.564491  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 39         ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 22:41:08.9 ┆ 00:00:00   ┆ 22:41:08.9 ┆     ┆   ┆            ┆            ┆ 6 22:43:4 ┆ 6 22:43:4 │\n",
       "│ 42691_B51_ ┆            ┆ 42691      ┆     ┆   ┆            ┆            ┆ 9.564491  ┆ 9.564491  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 39         ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 22:42:10.4 ┆ 00:00:00   ┆ 22:42:10.4 ┆     ┆   ┆            ┆            ┆ 6 22:43:4 ┆ 6 22:43:4 │\n",
       "│ 43984_B51_ ┆            ┆ 43984      ┆     ┆   ┆            ┆            ┆ 9.564491  ┆ 9.564491  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "│ 2024-03-26 ┆ 2024-03-26 ┆ 2024-03-26 ┆ 51  ┆ … ┆ LA         ┆ 39         ┆ 2024-03-2 ┆ 2024-03-2 │\n",
       "│ 22:43:10.5 ┆ 00:00:00   ┆ 22:43:10.5 ┆     ┆   ┆            ┆            ┆ 6 22:43:4 ┆ 6 22:43:4 │\n",
       "│ 64491_B51_ ┆            ┆ 64491      ┆     ┆   ┆            ┆            ┆ 9.564491  ┆ 9.564491  │\n",
       "│ L…         ┆            ┆            ┆     ┆   ┆            ┆            ┆           ┆           │\n",
       "└────────────┴────────────┴────────────┴─────┴───┴────────────┴────────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_sample_data = final_sample_data.select(pl.col('PK'),pl.col('datetime'),pl.col('reliable_arrival_date'),pl.col('predict_arrival_date'),pl.col('bloque_id'),pl.col('estimateArrive'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_sample_data = final_sample_data.filter(pl.col('reliable_arrival_date').is_not_null())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>PK</th>\n",
       "      <th>datetime</th>\n",
       "      <th>reliable_arrival_date</th>\n",
       "      <th>predict_arrival_date</th>\n",
       "      <th>bloque_id</th>\n",
       "      <th>estimateArrive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-03-26 09:07:08.486404_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:07:08.486404</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.486404</td>\n",
       "      <td>1</td>\n",
       "      <td>1536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-03-26 09:08:08.881899_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:08:08.881899</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.881899</td>\n",
       "      <td>1</td>\n",
       "      <td>1476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-03-26 09:09:08.755131_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:09:08.755131</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.755131</td>\n",
       "      <td>1</td>\n",
       "      <td>1381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-03-26 09:10:08.875354_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:10:08.875354</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.875354</td>\n",
       "      <td>1</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-03-26 09:11:10.677695_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:11:10.677695</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:49.677695</td>\n",
       "      <td>1</td>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-03-26 09:12:08.822076_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:12:08.822076</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:53.822076</td>\n",
       "      <td>1</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-03-26 09:13:09.160954_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:13:09.160954</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:28.160954</td>\n",
       "      <td>1</td>\n",
       "      <td>1039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-03-26 09:14:08.414499_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:14:08.414499</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:00.414499</td>\n",
       "      <td>1</td>\n",
       "      <td>952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-03-26 09:15:09.979541_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:15:09.979541</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:29:15.979541</td>\n",
       "      <td>1</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-03-26 09:17:07.807332_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:17:07.807332</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.807332</td>\n",
       "      <td>1</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-03-26 09:18:09.292909_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:18:09.292909</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.292909</td>\n",
       "      <td>1</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-03-26 09:19:09.264869_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:19:09.264869</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.264869</td>\n",
       "      <td>1</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024-03-26 09:20:08.308902_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:20:08.308902</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:28.308902</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024-03-26 09:22:08.748819_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:22:08.748819</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.748819</td>\n",
       "      <td>1</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024-03-26 09:23:08.256559_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:23:08.256559</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.256559</td>\n",
       "      <td>1</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024-03-26 09:24:08.643877_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:24:08.643877</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:00.643877</td>\n",
       "      <td>1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2024-03-26 09:25:09.036527_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:25:09.036527</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.036527</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2024-03-26 09:26:08.257810_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:26:08.257810</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.257810</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2024-03-26 09:27:08.674862_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2024-03-26 10:28:06.759858_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:28:06.759858</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:06:15.759858</td>\n",
       "      <td>2</td>\n",
       "      <td>2289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2024-03-26 10:29:08.164543_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:29:08.164543</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:07:21.164543</td>\n",
       "      <td>2</td>\n",
       "      <td>2293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2024-03-26 10:30:08.351999_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:30:08.351999</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:08:30.351999</td>\n",
       "      <td>2</td>\n",
       "      <td>2302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2024-03-26 10:31:08.228017_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:31:08.228017</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:08:25.228017</td>\n",
       "      <td>2</td>\n",
       "      <td>2237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2024-03-26 10:32:08.593895_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:32:08.593895</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:09:56.593895</td>\n",
       "      <td>2</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024-03-26 10:34:08.147077_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:34:08.147077</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:03:07.147077</td>\n",
       "      <td>2</td>\n",
       "      <td>1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2024-03-26 10:35:06.721532_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:35:06.721532</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 11:03:06.721532</td>\n",
       "      <td>2</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2024-03-26 10:36:08.939174_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:36:08.939174</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:55:26.939174</td>\n",
       "      <td>2</td>\n",
       "      <td>1158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2024-03-26 10:38:07.059568_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:38:07.059568</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:54:28.059568</td>\n",
       "      <td>2</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2024-03-26 10:39:07.192176_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:39:07.192176</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:54:09.192176</td>\n",
       "      <td>2</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2024-03-26 10:40:08.062428_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:40:08.062428</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:52:33.062428</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2024-03-26 10:41:08.947750_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:41:08.947750</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:52:36.947750</td>\n",
       "      <td>2</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2024-03-26 10:43:08.450481_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:43:08.450481</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:51:55.450481</td>\n",
       "      <td>2</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2024-03-26 10:44:08.198504_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:44:08.198504</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:51:56.198504</td>\n",
       "      <td>2</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2024-03-26 10:45:08.323245_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:45:08.323245</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:51:55.323245</td>\n",
       "      <td>2</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2024-03-26 10:46:08.687412_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:46:08.687412</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:51:23.687412</td>\n",
       "      <td>2</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2024-03-26 10:47:09.993700_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:47:09.993700</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:50:42.993700</td>\n",
       "      <td>2</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2024-03-26 10:48:08.170464_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:48:08.170464</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:50:27.170464</td>\n",
       "      <td>2</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2024-03-26 10:49:07.411681_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:49:07.411681</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2024-03-26 10:50:08.062525_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 10:50:08.062525</td>\n",
       "      <td>2024-03-26 10:49:23.411681</td>\n",
       "      <td>2024-03-26 10:50:08.062525</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2024-03-26 11:51:09.085827_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:51:09.085827</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:18:49.085827</td>\n",
       "      <td>3</td>\n",
       "      <td>1660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2024-03-26 11:52:09.190452_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:52:09.190452</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:19:54.190452</td>\n",
       "      <td>3</td>\n",
       "      <td>1665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2024-03-26 11:53:08.698945_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:53:08.698945</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:23:53.698945</td>\n",
       "      <td>3</td>\n",
       "      <td>1845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2024-03-26 11:54:08.804854_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:54:08.804854</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:26:07.804854</td>\n",
       "      <td>3</td>\n",
       "      <td>1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2024-03-26 11:55:08.948127_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:55:08.948127</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:26:06.948127</td>\n",
       "      <td>3</td>\n",
       "      <td>1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2024-03-26 11:56:10.271055_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:56:10.271055</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:27:19.271055</td>\n",
       "      <td>3</td>\n",
       "      <td>1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2024-03-26 11:57:08.365116_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:57:08.365116</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:28:25.365116</td>\n",
       "      <td>3</td>\n",
       "      <td>1877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2024-03-26 11:58:08.667291_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 11:58:08.667291</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:29:35.667291</td>\n",
       "      <td>3</td>\n",
       "      <td>1887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2024-03-26 12:00:08.855249_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 12:00:08.855249</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:21:28.855249</td>\n",
       "      <td>3</td>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2024-03-26 12:01:08.169960_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 12:01:08.169960</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:21:28.169960</td>\n",
       "      <td>3</td>\n",
       "      <td>1220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2024-03-26 12:02:08.420660_B51_LBR1_S4366</td>\n",
       "      <td>2024-03-26 12:02:08.420660</td>\n",
       "      <td>2024-03-26 12:15:09.310898</td>\n",
       "      <td>2024-03-26 12:20:54.420660</td>\n",
       "      <td>3</td>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           PK                   datetime  \\\n",
       "0   2024-03-26 09:07:08.486404_B51_LBR1_S4366 2024-03-26 09:07:08.486404   \n",
       "1   2024-03-26 09:08:08.881899_B51_LBR1_S4366 2024-03-26 09:08:08.881899   \n",
       "2   2024-03-26 09:09:08.755131_B51_LBR1_S4366 2024-03-26 09:09:08.755131   \n",
       "3   2024-03-26 09:10:08.875354_B51_LBR1_S4366 2024-03-26 09:10:08.875354   \n",
       "4   2024-03-26 09:11:10.677695_B51_LBR1_S4366 2024-03-26 09:11:10.677695   \n",
       "5   2024-03-26 09:12:08.822076_B51_LBR1_S4366 2024-03-26 09:12:08.822076   \n",
       "6   2024-03-26 09:13:09.160954_B51_LBR1_S4366 2024-03-26 09:13:09.160954   \n",
       "7   2024-03-26 09:14:08.414499_B51_LBR1_S4366 2024-03-26 09:14:08.414499   \n",
       "8   2024-03-26 09:15:09.979541_B51_LBR1_S4366 2024-03-26 09:15:09.979541   \n",
       "9   2024-03-26 09:17:07.807332_B51_LBR1_S4366 2024-03-26 09:17:07.807332   \n",
       "10  2024-03-26 09:18:09.292909_B51_LBR1_S4366 2024-03-26 09:18:09.292909   \n",
       "11  2024-03-26 09:19:09.264869_B51_LBR1_S4366 2024-03-26 09:19:09.264869   \n",
       "12  2024-03-26 09:20:08.308902_B51_LBR1_S4366 2024-03-26 09:20:08.308902   \n",
       "13  2024-03-26 09:22:08.748819_B51_LBR1_S4366 2024-03-26 09:22:08.748819   \n",
       "14  2024-03-26 09:23:08.256559_B51_LBR1_S4366 2024-03-26 09:23:08.256559   \n",
       "15  2024-03-26 09:24:08.643877_B51_LBR1_S4366 2024-03-26 09:24:08.643877   \n",
       "16  2024-03-26 09:25:09.036527_B51_LBR1_S4366 2024-03-26 09:25:09.036527   \n",
       "17  2024-03-26 09:26:08.257810_B51_LBR1_S4366 2024-03-26 09:26:08.257810   \n",
       "18  2024-03-26 09:27:08.674862_B51_LBR1_S4366 2024-03-26 09:27:08.674862   \n",
       "19  2024-03-26 10:28:06.759858_B51_LBR1_S4366 2024-03-26 10:28:06.759858   \n",
       "20  2024-03-26 10:29:08.164543_B51_LBR1_S4366 2024-03-26 10:29:08.164543   \n",
       "21  2024-03-26 10:30:08.351999_B51_LBR1_S4366 2024-03-26 10:30:08.351999   \n",
       "22  2024-03-26 10:31:08.228017_B51_LBR1_S4366 2024-03-26 10:31:08.228017   \n",
       "23  2024-03-26 10:32:08.593895_B51_LBR1_S4366 2024-03-26 10:32:08.593895   \n",
       "24  2024-03-26 10:34:08.147077_B51_LBR1_S4366 2024-03-26 10:34:08.147077   \n",
       "25  2024-03-26 10:35:06.721532_B51_LBR1_S4366 2024-03-26 10:35:06.721532   \n",
       "26  2024-03-26 10:36:08.939174_B51_LBR1_S4366 2024-03-26 10:36:08.939174   \n",
       "27  2024-03-26 10:38:07.059568_B51_LBR1_S4366 2024-03-26 10:38:07.059568   \n",
       "28  2024-03-26 10:39:07.192176_B51_LBR1_S4366 2024-03-26 10:39:07.192176   \n",
       "29  2024-03-26 10:40:08.062428_B51_LBR1_S4366 2024-03-26 10:40:08.062428   \n",
       "30  2024-03-26 10:41:08.947750_B51_LBR1_S4366 2024-03-26 10:41:08.947750   \n",
       "31  2024-03-26 10:43:08.450481_B51_LBR1_S4366 2024-03-26 10:43:08.450481   \n",
       "32  2024-03-26 10:44:08.198504_B51_LBR1_S4366 2024-03-26 10:44:08.198504   \n",
       "33  2024-03-26 10:45:08.323245_B51_LBR1_S4366 2024-03-26 10:45:08.323245   \n",
       "34  2024-03-26 10:46:08.687412_B51_LBR1_S4366 2024-03-26 10:46:08.687412   \n",
       "35  2024-03-26 10:47:09.993700_B51_LBR1_S4366 2024-03-26 10:47:09.993700   \n",
       "36  2024-03-26 10:48:08.170464_B51_LBR1_S4366 2024-03-26 10:48:08.170464   \n",
       "37  2024-03-26 10:49:07.411681_B51_LBR1_S4366 2024-03-26 10:49:07.411681   \n",
       "38  2024-03-26 10:50:08.062525_B51_LBR1_S4366 2024-03-26 10:50:08.062525   \n",
       "39  2024-03-26 11:51:09.085827_B51_LBR1_S4366 2024-03-26 11:51:09.085827   \n",
       "40  2024-03-26 11:52:09.190452_B51_LBR1_S4366 2024-03-26 11:52:09.190452   \n",
       "41  2024-03-26 11:53:08.698945_B51_LBR1_S4366 2024-03-26 11:53:08.698945   \n",
       "42  2024-03-26 11:54:08.804854_B51_LBR1_S4366 2024-03-26 11:54:08.804854   \n",
       "43  2024-03-26 11:55:08.948127_B51_LBR1_S4366 2024-03-26 11:55:08.948127   \n",
       "44  2024-03-26 11:56:10.271055_B51_LBR1_S4366 2024-03-26 11:56:10.271055   \n",
       "45  2024-03-26 11:57:08.365116_B51_LBR1_S4366 2024-03-26 11:57:08.365116   \n",
       "46  2024-03-26 11:58:08.667291_B51_LBR1_S4366 2024-03-26 11:58:08.667291   \n",
       "47  2024-03-26 12:00:08.855249_B51_LBR1_S4366 2024-03-26 12:00:08.855249   \n",
       "48  2024-03-26 12:01:08.169960_B51_LBR1_S4366 2024-03-26 12:01:08.169960   \n",
       "49  2024-03-26 12:02:08.420660_B51_LBR1_S4366 2024-03-26 12:02:08.420660   \n",
       "\n",
       "        reliable_arrival_date       predict_arrival_date  bloque_id  \\\n",
       "0  2024-03-26 09:27:08.674862 2024-03-26 09:32:44.486404          1   \n",
       "1  2024-03-26 09:27:08.674862 2024-03-26 09:32:44.881899          1   \n",
       "2  2024-03-26 09:27:08.674862 2024-03-26 09:32:09.755131          1   \n",
       "3  2024-03-26 09:27:08.674862 2024-03-26 09:32:09.875354          1   \n",
       "4  2024-03-26 09:27:08.674862 2024-03-26 09:31:49.677695          1   \n",
       "5  2024-03-26 09:27:08.674862 2024-03-26 09:31:53.822076          1   \n",
       "6  2024-03-26 09:27:08.674862 2024-03-26 09:30:28.160954          1   \n",
       "7  2024-03-26 09:27:08.674862 2024-03-26 09:30:00.414499          1   \n",
       "8  2024-03-26 09:27:08.674862 2024-03-26 09:29:15.979541          1   \n",
       "9  2024-03-26 09:27:08.674862 2024-03-26 09:28:29.807332          1   \n",
       "10 2024-03-26 09:27:08.674862 2024-03-26 09:28:29.292909          1   \n",
       "11 2024-03-26 09:27:08.674862 2024-03-26 09:28:29.264869          1   \n",
       "12 2024-03-26 09:27:08.674862 2024-03-26 09:28:28.308902          1   \n",
       "13 2024-03-26 09:27:08.674862 2024-03-26 09:28:29.748819          1   \n",
       "14 2024-03-26 09:27:08.674862 2024-03-26 09:28:29.256559          1   \n",
       "15 2024-03-26 09:27:08.674862 2024-03-26 09:28:00.643877          1   \n",
       "16 2024-03-26 09:27:08.674862 2024-03-26 09:27:48.036527          1   \n",
       "17 2024-03-26 09:27:08.674862 2024-03-26 09:27:48.257810          1   \n",
       "18 2024-03-26 09:27:08.674862 2024-03-26 09:27:08.674862          1   \n",
       "19 2024-03-26 10:49:23.411681 2024-03-26 11:06:15.759858          2   \n",
       "20 2024-03-26 10:49:23.411681 2024-03-26 11:07:21.164543          2   \n",
       "21 2024-03-26 10:49:23.411681 2024-03-26 11:08:30.351999          2   \n",
       "22 2024-03-26 10:49:23.411681 2024-03-26 11:08:25.228017          2   \n",
       "23 2024-03-26 10:49:23.411681 2024-03-26 11:09:56.593895          2   \n",
       "24 2024-03-26 10:49:23.411681 2024-03-26 11:03:07.147077          2   \n",
       "25 2024-03-26 10:49:23.411681 2024-03-26 11:03:06.721532          2   \n",
       "26 2024-03-26 10:49:23.411681 2024-03-26 10:55:26.939174          2   \n",
       "27 2024-03-26 10:49:23.411681 2024-03-26 10:54:28.059568          2   \n",
       "28 2024-03-26 10:49:23.411681 2024-03-26 10:54:09.192176          2   \n",
       "29 2024-03-26 10:49:23.411681 2024-03-26 10:52:33.062428          2   \n",
       "30 2024-03-26 10:49:23.411681 2024-03-26 10:52:36.947750          2   \n",
       "31 2024-03-26 10:49:23.411681 2024-03-26 10:51:55.450481          2   \n",
       "32 2024-03-26 10:49:23.411681 2024-03-26 10:51:56.198504          2   \n",
       "33 2024-03-26 10:49:23.411681 2024-03-26 10:51:55.323245          2   \n",
       "34 2024-03-26 10:49:23.411681 2024-03-26 10:51:23.687412          2   \n",
       "35 2024-03-26 10:49:23.411681 2024-03-26 10:50:42.993700          2   \n",
       "36 2024-03-26 10:49:23.411681 2024-03-26 10:50:27.170464          2   \n",
       "37 2024-03-26 10:49:23.411681 2024-03-26 10:49:23.411681          2   \n",
       "38 2024-03-26 10:49:23.411681 2024-03-26 10:50:08.062525          2   \n",
       "39 2024-03-26 12:15:09.310898 2024-03-26 12:18:49.085827          3   \n",
       "40 2024-03-26 12:15:09.310898 2024-03-26 12:19:54.190452          3   \n",
       "41 2024-03-26 12:15:09.310898 2024-03-26 12:23:53.698945          3   \n",
       "42 2024-03-26 12:15:09.310898 2024-03-26 12:26:07.804854          3   \n",
       "43 2024-03-26 12:15:09.310898 2024-03-26 12:26:06.948127          3   \n",
       "44 2024-03-26 12:15:09.310898 2024-03-26 12:27:19.271055          3   \n",
       "45 2024-03-26 12:15:09.310898 2024-03-26 12:28:25.365116          3   \n",
       "46 2024-03-26 12:15:09.310898 2024-03-26 12:29:35.667291          3   \n",
       "47 2024-03-26 12:15:09.310898 2024-03-26 12:21:28.855249          3   \n",
       "48 2024-03-26 12:15:09.310898 2024-03-26 12:21:28.169960          3   \n",
       "49 2024-03-26 12:15:09.310898 2024-03-26 12:20:54.420660          3   \n",
       "\n",
       "    estimateArrive  \n",
       "0             1536  \n",
       "1             1476  \n",
       "2             1381  \n",
       "3             1321  \n",
       "4             1239  \n",
       "5             1185  \n",
       "6             1039  \n",
       "7              952  \n",
       "8              846  \n",
       "9              682  \n",
       "10             620  \n",
       "11             560  \n",
       "12             500  \n",
       "13             381  \n",
       "14             321  \n",
       "15             232  \n",
       "16             159  \n",
       "17             100  \n",
       "18               0  \n",
       "19            2289  \n",
       "20            2293  \n",
       "21            2302  \n",
       "22            2237  \n",
       "23            2268  \n",
       "24            1739  \n",
       "25            1680  \n",
       "26            1158  \n",
       "27             981  \n",
       "28             902  \n",
       "29             745  \n",
       "30             688  \n",
       "31             527  \n",
       "32             468  \n",
       "33             407  \n",
       "34             315  \n",
       "35             213  \n",
       "36             139  \n",
       "37              16  \n",
       "38               0  \n",
       "39            1660  \n",
       "40            1665  \n",
       "41            1845  \n",
       "42            1919  \n",
       "43            1858  \n",
       "44            1869  \n",
       "45            1877  \n",
       "46            1887  \n",
       "47            1280  \n",
       "48            1220  \n",
       "49            1126  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample_data.to_pandas().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def KPI_fun(date_true,date_pred):\n",
    "    #date_true = datetime.strptime(date_true, '%Y-%m-%d %H:%M:%S.%f')\n",
    "    #date_pred = datetime.strptime(date_pred, '%Y-%m-%d %H:%M:%S.%f')\n",
    "    \n",
    "    dif = max(date_true,date_pred) - min(date_true,date_pred)\n",
    "    return dif.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "KPI_data = final_sample_data.with_columns(pl.struct(pl.col('reliable_arrival_date'),pl.col('predict_arrival_date')).alias('struct').map_elements(lambda x: KPI_fun(x['reliable_arrival_date'], x['predict_arrival_date'])).alias('KPI_value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
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       "</style>\n",
       "<small>shape: (35, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PK</th><th>datetime</th><th>reliable_arrival_date</th><th>predict_arrival_date</th><th>bloque_id</th><th>estimateArrive</th><th>KPI_value</th></tr><tr><td>str</td><td>datetime[μs]</td><td>datetime[μs]</td><td>datetime[μs]</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:07:08.486404</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:32:44.486404</td><td>1</td><td>1536</td><td>335.811542</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:08:08.881899</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:32:44.881899</td><td>1</td><td>1476</td><td>336.207037</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:09:08.755131</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:32:09.755131</td><td>1</td><td>1381</td><td>301.080269</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:10:08.875354</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:32:09.875354</td><td>1</td><td>1321</td><td>301.200492</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26 09:11:10.677695</td><td>2024-03-26 09:27:08.674862</td><td>2024-03-26 09:31:49.677695</td><td>1</td><td>1239</td><td>281.002833</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:41:08.947750</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:52:36.947750</td><td>2</td><td>688</td><td>193.536069</td></tr><tr><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:43:08.450481</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:51:55.450481</td><td>2</td><td>527</td><td>152.0388</td></tr><tr><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:44:08.198504</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:51:56.198504</td><td>2</td><td>468</td><td>152.786823</td></tr><tr><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:45:08.323245</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:51:55.323245</td><td>2</td><td>407</td><td>151.911564</td></tr><tr><td>&quot;2024-03-26 10:…</td><td>2024-03-26 10:46:08.687412</td><td>2024-03-26 10:49:23.411681</td><td>2024-03-26 10:51:23.687412</td><td>2</td><td>315</td><td>120.275731</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (35, 7)\n",
       "┌──────────────┬──────────────┬──────────────┬──────────────┬───────────┬─────────────┬────────────┐\n",
       "│ PK           ┆ datetime     ┆ reliable_arr ┆ predict_arri ┆ bloque_id ┆ estimateArr ┆ KPI_value  │\n",
       "│ ---          ┆ ---          ┆ ival_date    ┆ val_date     ┆ ---       ┆ ive         ┆ ---        │\n",
       "│ str          ┆ datetime[μs] ┆ ---          ┆ ---          ┆ i64       ┆ ---         ┆ f64        │\n",
       "│              ┆              ┆ datetime[μs] ┆ datetime[μs] ┆           ┆ i64         ┆            │\n",
       "╞══════════════╪══════════════╪══════════════╪══════════════╪═══════════╪═════════════╪════════════╡\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 1         ┆ 1536        ┆ 335.811542 │\n",
       "│ 09:07:08.486 ┆ 09:07:08.486 ┆ 09:27:08.674 ┆ 09:32:44.486 ┆           ┆             ┆            │\n",
       "│ 404_B51_L…   ┆ 404          ┆ 862          ┆ 404          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 1         ┆ 1476        ┆ 336.207037 │\n",
       "│ 09:08:08.881 ┆ 09:08:08.881 ┆ 09:27:08.674 ┆ 09:32:44.881 ┆           ┆             ┆            │\n",
       "│ 899_B51_L…   ┆ 899          ┆ 862          ┆ 899          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 1         ┆ 1381        ┆ 301.080269 │\n",
       "│ 09:09:08.755 ┆ 09:09:08.755 ┆ 09:27:08.674 ┆ 09:32:09.755 ┆           ┆             ┆            │\n",
       "│ 131_B51_L…   ┆ 131          ┆ 862          ┆ 131          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 1         ┆ 1321        ┆ 301.200492 │\n",
       "│ 09:10:08.875 ┆ 09:10:08.875 ┆ 09:27:08.674 ┆ 09:32:09.875 ┆           ┆             ┆            │\n",
       "│ 354_B51_L…   ┆ 354          ┆ 862          ┆ 354          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 1         ┆ 1239        ┆ 281.002833 │\n",
       "│ 09:11:10.677 ┆ 09:11:10.677 ┆ 09:27:08.674 ┆ 09:31:49.677 ┆           ┆             ┆            │\n",
       "│ 695_B51_L…   ┆ 695          ┆ 862          ┆ 695          ┆           ┆             ┆            │\n",
       "│ …            ┆ …            ┆ …            ┆ …            ┆ …         ┆ …           ┆ …          │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2         ┆ 688         ┆ 193.536069 │\n",
       "│ 10:41:08.947 ┆ 10:41:08.947 ┆ 10:49:23.411 ┆ 10:52:36.947 ┆           ┆             ┆            │\n",
       "│ 750_B51_L…   ┆ 750          ┆ 681          ┆ 750          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2         ┆ 527         ┆ 152.0388   │\n",
       "│ 10:43:08.450 ┆ 10:43:08.450 ┆ 10:49:23.411 ┆ 10:51:55.450 ┆           ┆             ┆            │\n",
       "│ 481_B51_L…   ┆ 481          ┆ 681          ┆ 481          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2         ┆ 468         ┆ 152.786823 │\n",
       "│ 10:44:08.198 ┆ 10:44:08.198 ┆ 10:49:23.411 ┆ 10:51:56.198 ┆           ┆             ┆            │\n",
       "│ 504_B51_L…   ┆ 504          ┆ 681          ┆ 504          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2         ┆ 407         ┆ 151.911564 │\n",
       "│ 10:45:08.323 ┆ 10:45:08.323 ┆ 10:49:23.411 ┆ 10:51:55.323 ┆           ┆             ┆            │\n",
       "│ 245_B51_L…   ┆ 245          ┆ 681          ┆ 245          ┆           ┆             ┆            │\n",
       "│ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2024-03-26   ┆ 2         ┆ 315         ┆ 120.275731 │\n",
       "│ 10:46:08.687 ┆ 10:46:08.687 ┆ 10:49:23.411 ┆ 10:51:23.687 ┆           ┆             ┆            │\n",
       "│ 412_B51_L…   ┆ 412          ┆ 681          ┆ 412          ┆           ┆             ┆            │\n",
       "└──────────────┴──────────────┴──────────────┴──────────────┴───────────┴─────────────┴────────────┘"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KPI_data.head(35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = KPI_data.to_pandas()[['estimateArrive','KPI_value','reliable_arrival_date','predict_arrival_date','datetime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = KPI_data.to_pandas().iloc[0:19][['estimateArrive','KPI_value','reliable_arrival_date','predict_arrival_date','datetime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['reliable_estimateArrive'] = a['reliable_arrival_date'] - a['datetime']\n",
    "a['reliable_estimateArrive'] = abs(pd.to_timedelta(a['reliable_estimateArrive']).dt.total_seconds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estimateArrive</th>\n",
       "      <th>KPI_value</th>\n",
       "      <th>reliable_arrival_date</th>\n",
       "      <th>predict_arrival_date</th>\n",
       "      <th>datetime</th>\n",
       "      <th>reliable_estimateArrive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1536</td>\n",
       "      <td>335.811542</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.486404</td>\n",
       "      <td>2024-03-26 09:07:08.486404</td>\n",
       "      <td>1200.188458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1476</td>\n",
       "      <td>336.207037</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.881899</td>\n",
       "      <td>2024-03-26 09:08:08.881899</td>\n",
       "      <td>1139.792963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1381</td>\n",
       "      <td>301.080269</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.755131</td>\n",
       "      <td>2024-03-26 09:09:08.755131</td>\n",
       "      <td>1079.919731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1321</td>\n",
       "      <td>301.200492</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.875354</td>\n",
       "      <td>2024-03-26 09:10:08.875354</td>\n",
       "      <td>1019.799508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1239</td>\n",
       "      <td>281.002833</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:49.677695</td>\n",
       "      <td>2024-03-26 09:11:10.677695</td>\n",
       "      <td>957.997167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1185</td>\n",
       "      <td>285.147214</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:53.822076</td>\n",
       "      <td>2024-03-26 09:12:08.822076</td>\n",
       "      <td>899.852786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1039</td>\n",
       "      <td>199.486092</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:28.160954</td>\n",
       "      <td>2024-03-26 09:13:09.160954</td>\n",
       "      <td>839.513908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>952</td>\n",
       "      <td>171.739637</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:00.414499</td>\n",
       "      <td>2024-03-26 09:14:08.414499</td>\n",
       "      <td>780.260363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>846</td>\n",
       "      <td>127.304679</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:29:15.979541</td>\n",
       "      <td>2024-03-26 09:15:09.979541</td>\n",
       "      <td>718.695321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>682</td>\n",
       "      <td>81.132470</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.807332</td>\n",
       "      <td>2024-03-26 09:17:07.807332</td>\n",
       "      <td>600.867530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>620</td>\n",
       "      <td>80.618047</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.292909</td>\n",
       "      <td>2024-03-26 09:18:09.292909</td>\n",
       "      <td>539.381953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>560</td>\n",
       "      <td>80.590007</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.264869</td>\n",
       "      <td>2024-03-26 09:19:09.264869</td>\n",
       "      <td>479.409993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>500</td>\n",
       "      <td>79.634040</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:28.308902</td>\n",
       "      <td>2024-03-26 09:20:08.308902</td>\n",
       "      <td>420.365960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>381</td>\n",
       "      <td>81.073957</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.748819</td>\n",
       "      <td>2024-03-26 09:22:08.748819</td>\n",
       "      <td>299.926043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>321</td>\n",
       "      <td>80.581697</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.256559</td>\n",
       "      <td>2024-03-26 09:23:08.256559</td>\n",
       "      <td>240.418303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>232</td>\n",
       "      <td>51.969015</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:00.643877</td>\n",
       "      <td>2024-03-26 09:24:08.643877</td>\n",
       "      <td>180.030985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>159</td>\n",
       "      <td>39.361665</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.036527</td>\n",
       "      <td>2024-03-26 09:25:09.036527</td>\n",
       "      <td>119.638335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>39.582948</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.257810</td>\n",
       "      <td>2024-03-26 09:26:08.257810</td>\n",
       "      <td>60.417052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    estimateArrive   KPI_value      reliable_arrival_date  \\\n",
       "0             1536  335.811542 2024-03-26 09:27:08.674862   \n",
       "1             1476  336.207037 2024-03-26 09:27:08.674862   \n",
       "2             1381  301.080269 2024-03-26 09:27:08.674862   \n",
       "3             1321  301.200492 2024-03-26 09:27:08.674862   \n",
       "4             1239  281.002833 2024-03-26 09:27:08.674862   \n",
       "5             1185  285.147214 2024-03-26 09:27:08.674862   \n",
       "6             1039  199.486092 2024-03-26 09:27:08.674862   \n",
       "7              952  171.739637 2024-03-26 09:27:08.674862   \n",
       "8              846  127.304679 2024-03-26 09:27:08.674862   \n",
       "9              682   81.132470 2024-03-26 09:27:08.674862   \n",
       "10             620   80.618047 2024-03-26 09:27:08.674862   \n",
       "11             560   80.590007 2024-03-26 09:27:08.674862   \n",
       "12             500   79.634040 2024-03-26 09:27:08.674862   \n",
       "13             381   81.073957 2024-03-26 09:27:08.674862   \n",
       "14             321   80.581697 2024-03-26 09:27:08.674862   \n",
       "15             232   51.969015 2024-03-26 09:27:08.674862   \n",
       "16             159   39.361665 2024-03-26 09:27:08.674862   \n",
       "17             100   39.582948 2024-03-26 09:27:08.674862   \n",
       "18               0    0.000000 2024-03-26 09:27:08.674862   \n",
       "\n",
       "         predict_arrival_date                   datetime  \\\n",
       "0  2024-03-26 09:32:44.486404 2024-03-26 09:07:08.486404   \n",
       "1  2024-03-26 09:32:44.881899 2024-03-26 09:08:08.881899   \n",
       "2  2024-03-26 09:32:09.755131 2024-03-26 09:09:08.755131   \n",
       "3  2024-03-26 09:32:09.875354 2024-03-26 09:10:08.875354   \n",
       "4  2024-03-26 09:31:49.677695 2024-03-26 09:11:10.677695   \n",
       "5  2024-03-26 09:31:53.822076 2024-03-26 09:12:08.822076   \n",
       "6  2024-03-26 09:30:28.160954 2024-03-26 09:13:09.160954   \n",
       "7  2024-03-26 09:30:00.414499 2024-03-26 09:14:08.414499   \n",
       "8  2024-03-26 09:29:15.979541 2024-03-26 09:15:09.979541   \n",
       "9  2024-03-26 09:28:29.807332 2024-03-26 09:17:07.807332   \n",
       "10 2024-03-26 09:28:29.292909 2024-03-26 09:18:09.292909   \n",
       "11 2024-03-26 09:28:29.264869 2024-03-26 09:19:09.264869   \n",
       "12 2024-03-26 09:28:28.308902 2024-03-26 09:20:08.308902   \n",
       "13 2024-03-26 09:28:29.748819 2024-03-26 09:22:08.748819   \n",
       "14 2024-03-26 09:28:29.256559 2024-03-26 09:23:08.256559   \n",
       "15 2024-03-26 09:28:00.643877 2024-03-26 09:24:08.643877   \n",
       "16 2024-03-26 09:27:48.036527 2024-03-26 09:25:09.036527   \n",
       "17 2024-03-26 09:27:48.257810 2024-03-26 09:26:08.257810   \n",
       "18 2024-03-26 09:27:08.674862 2024-03-26 09:27:08.674862   \n",
       "\n",
       "    reliable_estimateArrive  \n",
       "0               1200.188458  \n",
       "1               1139.792963  \n",
       "2               1079.919731  \n",
       "3               1019.799508  \n",
       "4                957.997167  \n",
       "5                899.852786  \n",
       "6                839.513908  \n",
       "7                780.260363  \n",
       "8                718.695321  \n",
       "9                600.867530  \n",
       "10               539.381953  \n",
       "11               479.409993  \n",
       "12               420.365960  \n",
       "13               299.926043  \n",
       "14               240.418303  \n",
       "15               180.030985  \n",
       "16               119.638335  \n",
       "17                60.417052  \n",
       "18                 0.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>estimateArrive</th>\n",
       "      <th>KPI_value</th>\n",
       "      <th>reliable_arrival_date</th>\n",
       "      <th>predict_arrival_date</th>\n",
       "      <th>datetime</th>\n",
       "      <th>reliable_estimateArrive</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1536</td>\n",
       "      <td>335.811542</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.486404</td>\n",
       "      <td>2024-03-26 09:07:08.486404</td>\n",
       "      <td>1200.188458</td>\n",
       "      <td>27.979901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1476</td>\n",
       "      <td>336.207037</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.881899</td>\n",
       "      <td>2024-03-26 09:08:08.881899</td>\n",
       "      <td>1139.792963</td>\n",
       "      <td>29.497202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1381</td>\n",
       "      <td>301.080269</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.755131</td>\n",
       "      <td>2024-03-26 09:09:08.755131</td>\n",
       "      <td>1079.919731</td>\n",
       "      <td>27.879875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1321</td>\n",
       "      <td>301.200492</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.875354</td>\n",
       "      <td>2024-03-26 09:10:08.875354</td>\n",
       "      <td>1019.799508</td>\n",
       "      <td>29.535265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1239</td>\n",
       "      <td>281.002833</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:49.677695</td>\n",
       "      <td>2024-03-26 09:11:10.677695</td>\n",
       "      <td>957.997167</td>\n",
       "      <td>29.332324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1185</td>\n",
       "      <td>285.147214</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:53.822076</td>\n",
       "      <td>2024-03-26 09:12:08.822076</td>\n",
       "      <td>899.852786</td>\n",
       "      <td>31.688207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1039</td>\n",
       "      <td>199.486092</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:28.160954</td>\n",
       "      <td>2024-03-26 09:13:09.160954</td>\n",
       "      <td>839.513908</td>\n",
       "      <td>23.762095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>952</td>\n",
       "      <td>171.739637</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:00.414499</td>\n",
       "      <td>2024-03-26 09:14:08.414499</td>\n",
       "      <td>780.260363</td>\n",
       "      <td>22.010555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>846</td>\n",
       "      <td>127.304679</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:29:15.979541</td>\n",
       "      <td>2024-03-26 09:15:09.979541</td>\n",
       "      <td>718.695321</td>\n",
       "      <td>17.713303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>682</td>\n",
       "      <td>81.132470</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.807332</td>\n",
       "      <td>2024-03-26 09:17:07.807332</td>\n",
       "      <td>600.867530</td>\n",
       "      <td>13.502555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>620</td>\n",
       "      <td>80.618047</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.292909</td>\n",
       "      <td>2024-03-26 09:18:09.292909</td>\n",
       "      <td>539.381953</td>\n",
       "      <td>14.946375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>560</td>\n",
       "      <td>80.590007</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.264869</td>\n",
       "      <td>2024-03-26 09:19:09.264869</td>\n",
       "      <td>479.409993</td>\n",
       "      <td>16.810248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>500</td>\n",
       "      <td>79.634040</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:28.308902</td>\n",
       "      <td>2024-03-26 09:20:08.308902</td>\n",
       "      <td>420.365960</td>\n",
       "      <td>18.943979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>381</td>\n",
       "      <td>81.073957</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.748819</td>\n",
       "      <td>2024-03-26 09:22:08.748819</td>\n",
       "      <td>299.926043</td>\n",
       "      <td>27.031316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>321</td>\n",
       "      <td>80.581697</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.256559</td>\n",
       "      <td>2024-03-26 09:23:08.256559</td>\n",
       "      <td>240.418303</td>\n",
       "      <td>33.517289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>232</td>\n",
       "      <td>51.969015</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:00.643877</td>\n",
       "      <td>2024-03-26 09:24:08.643877</td>\n",
       "      <td>180.030985</td>\n",
       "      <td>28.866706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>159</td>\n",
       "      <td>39.361665</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.036527</td>\n",
       "      <td>2024-03-26 09:25:09.036527</td>\n",
       "      <td>119.638335</td>\n",
       "      <td>32.900546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>39.582948</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.257810</td>\n",
       "      <td>2024-03-26 09:26:08.257810</td>\n",
       "      <td>60.417052</td>\n",
       "      <td>65.516186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    estimateArrive   KPI_value      reliable_arrival_date  \\\n",
       "0             1536  335.811542 2024-03-26 09:27:08.674862   \n",
       "1             1476  336.207037 2024-03-26 09:27:08.674862   \n",
       "2             1381  301.080269 2024-03-26 09:27:08.674862   \n",
       "3             1321  301.200492 2024-03-26 09:27:08.674862   \n",
       "4             1239  281.002833 2024-03-26 09:27:08.674862   \n",
       "5             1185  285.147214 2024-03-26 09:27:08.674862   \n",
       "6             1039  199.486092 2024-03-26 09:27:08.674862   \n",
       "7              952  171.739637 2024-03-26 09:27:08.674862   \n",
       "8              846  127.304679 2024-03-26 09:27:08.674862   \n",
       "9              682   81.132470 2024-03-26 09:27:08.674862   \n",
       "10             620   80.618047 2024-03-26 09:27:08.674862   \n",
       "11             560   80.590007 2024-03-26 09:27:08.674862   \n",
       "12             500   79.634040 2024-03-26 09:27:08.674862   \n",
       "13             381   81.073957 2024-03-26 09:27:08.674862   \n",
       "14             321   80.581697 2024-03-26 09:27:08.674862   \n",
       "15             232   51.969015 2024-03-26 09:27:08.674862   \n",
       "16             159   39.361665 2024-03-26 09:27:08.674862   \n",
       "17             100   39.582948 2024-03-26 09:27:08.674862   \n",
       "18               0    0.000000 2024-03-26 09:27:08.674862   \n",
       "\n",
       "         predict_arrival_date                   datetime  \\\n",
       "0  2024-03-26 09:32:44.486404 2024-03-26 09:07:08.486404   \n",
       "1  2024-03-26 09:32:44.881899 2024-03-26 09:08:08.881899   \n",
       "2  2024-03-26 09:32:09.755131 2024-03-26 09:09:08.755131   \n",
       "3  2024-03-26 09:32:09.875354 2024-03-26 09:10:08.875354   \n",
       "4  2024-03-26 09:31:49.677695 2024-03-26 09:11:10.677695   \n",
       "5  2024-03-26 09:31:53.822076 2024-03-26 09:12:08.822076   \n",
       "6  2024-03-26 09:30:28.160954 2024-03-26 09:13:09.160954   \n",
       "7  2024-03-26 09:30:00.414499 2024-03-26 09:14:08.414499   \n",
       "8  2024-03-26 09:29:15.979541 2024-03-26 09:15:09.979541   \n",
       "9  2024-03-26 09:28:29.807332 2024-03-26 09:17:07.807332   \n",
       "10 2024-03-26 09:28:29.292909 2024-03-26 09:18:09.292909   \n",
       "11 2024-03-26 09:28:29.264869 2024-03-26 09:19:09.264869   \n",
       "12 2024-03-26 09:28:28.308902 2024-03-26 09:20:08.308902   \n",
       "13 2024-03-26 09:28:29.748819 2024-03-26 09:22:08.748819   \n",
       "14 2024-03-26 09:28:29.256559 2024-03-26 09:23:08.256559   \n",
       "15 2024-03-26 09:28:00.643877 2024-03-26 09:24:08.643877   \n",
       "16 2024-03-26 09:27:48.036527 2024-03-26 09:25:09.036527   \n",
       "17 2024-03-26 09:27:48.257810 2024-03-26 09:26:08.257810   \n",
       "18 2024-03-26 09:27:08.674862 2024-03-26 09:27:08.674862   \n",
       "\n",
       "    reliable_estimateArrive       MAPE  \n",
       "0               1200.188458  27.979901  \n",
       "1               1139.792963  29.497202  \n",
       "2               1079.919731  27.879875  \n",
       "3               1019.799508  29.535265  \n",
       "4                957.997167  29.332324  \n",
       "5                899.852786  31.688207  \n",
       "6                839.513908  23.762095  \n",
       "7                780.260363  22.010555  \n",
       "8                718.695321  17.713303  \n",
       "9                600.867530  13.502555  \n",
       "10               539.381953  14.946375  \n",
       "11               479.409993  16.810248  \n",
       "12               420.365960  18.943979  \n",
       "13               299.926043  27.031316  \n",
       "14               240.418303  33.517289  \n",
       "15               180.030985  28.866706  \n",
       "16               119.638335  32.900546  \n",
       "17                60.417052  65.516186  \n",
       "18                 0.000000        NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['MAPE'] = 100*abs((a['estimateArrive'] - a['reliable_estimateArrive'])/a['reliable_estimateArrive'])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['MAPE2'] = 100*abs((a['estimateArrive'] - a['reliable_estimateArrive'])/a[['reliable_estimateArrive','estimateArrive']].max(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>estimateArrive</th>\n",
       "      <th>KPI_value</th>\n",
       "      <th>reliable_arrival_date</th>\n",
       "      <th>predict_arrival_date</th>\n",
       "      <th>datetime</th>\n",
       "      <th>reliable_estimateArrive</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>MAPE2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1536</td>\n",
       "      <td>335.811542</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.486404</td>\n",
       "      <td>2024-03-26 09:07:08.486404</td>\n",
       "      <td>1200.188458</td>\n",
       "      <td>27.979901</td>\n",
       "      <td>21.862731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1476</td>\n",
       "      <td>336.207037</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:44.881899</td>\n",
       "      <td>2024-03-26 09:08:08.881899</td>\n",
       "      <td>1139.792963</td>\n",
       "      <td>29.497202</td>\n",
       "      <td>22.778255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1381</td>\n",
       "      <td>301.080269</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.755131</td>\n",
       "      <td>2024-03-26 09:09:08.755131</td>\n",
       "      <td>1079.919731</td>\n",
       "      <td>27.879875</td>\n",
       "      <td>21.801613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1321</td>\n",
       "      <td>301.200492</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:32:09.875354</td>\n",
       "      <td>2024-03-26 09:10:08.875354</td>\n",
       "      <td>1019.799508</td>\n",
       "      <td>29.535265</td>\n",
       "      <td>22.800946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1239</td>\n",
       "      <td>281.002833</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:49.677695</td>\n",
       "      <td>2024-03-26 09:11:10.677695</td>\n",
       "      <td>957.997167</td>\n",
       "      <td>29.332324</td>\n",
       "      <td>22.679809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1185</td>\n",
       "      <td>285.147214</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:31:53.822076</td>\n",
       "      <td>2024-03-26 09:12:08.822076</td>\n",
       "      <td>899.852786</td>\n",
       "      <td>31.688207</td>\n",
       "      <td>24.063056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1039</td>\n",
       "      <td>199.486092</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:28.160954</td>\n",
       "      <td>2024-03-26 09:13:09.160954</td>\n",
       "      <td>839.513908</td>\n",
       "      <td>23.762095</td>\n",
       "      <td>19.199816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>952</td>\n",
       "      <td>171.739637</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:30:00.414499</td>\n",
       "      <td>2024-03-26 09:14:08.414499</td>\n",
       "      <td>780.260363</td>\n",
       "      <td>22.010555</td>\n",
       "      <td>18.039878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>846</td>\n",
       "      <td>127.304679</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:29:15.979541</td>\n",
       "      <td>2024-03-26 09:15:09.979541</td>\n",
       "      <td>718.695321</td>\n",
       "      <td>17.713303</td>\n",
       "      <td>15.047834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>682</td>\n",
       "      <td>81.132470</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.807332</td>\n",
       "      <td>2024-03-26 09:17:07.807332</td>\n",
       "      <td>600.867530</td>\n",
       "      <td>13.502555</td>\n",
       "      <td>11.896257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>620</td>\n",
       "      <td>80.618047</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.292909</td>\n",
       "      <td>2024-03-26 09:18:09.292909</td>\n",
       "      <td>539.381953</td>\n",
       "      <td>14.946375</td>\n",
       "      <td>13.002911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>560</td>\n",
       "      <td>80.590007</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.264869</td>\n",
       "      <td>2024-03-26 09:19:09.264869</td>\n",
       "      <td>479.409993</td>\n",
       "      <td>16.810248</td>\n",
       "      <td>14.391073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>500</td>\n",
       "      <td>79.634040</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:28.308902</td>\n",
       "      <td>2024-03-26 09:20:08.308902</td>\n",
       "      <td>420.365960</td>\n",
       "      <td>18.943979</td>\n",
       "      <td>15.926808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>381</td>\n",
       "      <td>81.073957</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.748819</td>\n",
       "      <td>2024-03-26 09:22:08.748819</td>\n",
       "      <td>299.926043</td>\n",
       "      <td>27.031316</td>\n",
       "      <td>21.279254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>321</td>\n",
       "      <td>80.581697</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:29.256559</td>\n",
       "      <td>2024-03-26 09:23:08.256559</td>\n",
       "      <td>240.418303</td>\n",
       "      <td>33.517289</td>\n",
       "      <td>25.103332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>232</td>\n",
       "      <td>51.969015</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:28:00.643877</td>\n",
       "      <td>2024-03-26 09:24:08.643877</td>\n",
       "      <td>180.030985</td>\n",
       "      <td>28.866706</td>\n",
       "      <td>22.400438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>159</td>\n",
       "      <td>39.361665</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.036527</td>\n",
       "      <td>2024-03-26 09:25:09.036527</td>\n",
       "      <td>119.638335</td>\n",
       "      <td>32.900546</td>\n",
       "      <td>24.755764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100</td>\n",
       "      <td>39.582948</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:48.257810</td>\n",
       "      <td>2024-03-26 09:26:08.257810</td>\n",
       "      <td>60.417052</td>\n",
       "      <td>65.516186</td>\n",
       "      <td>39.582948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>2024-03-26 09:27:08.674862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    estimateArrive   KPI_value      reliable_arrival_date  \\\n",
       "0             1536  335.811542 2024-03-26 09:27:08.674862   \n",
       "1             1476  336.207037 2024-03-26 09:27:08.674862   \n",
       "2             1381  301.080269 2024-03-26 09:27:08.674862   \n",
       "3             1321  301.200492 2024-03-26 09:27:08.674862   \n",
       "4             1239  281.002833 2024-03-26 09:27:08.674862   \n",
       "5             1185  285.147214 2024-03-26 09:27:08.674862   \n",
       "6             1039  199.486092 2024-03-26 09:27:08.674862   \n",
       "7              952  171.739637 2024-03-26 09:27:08.674862   \n",
       "8              846  127.304679 2024-03-26 09:27:08.674862   \n",
       "9              682   81.132470 2024-03-26 09:27:08.674862   \n",
       "10             620   80.618047 2024-03-26 09:27:08.674862   \n",
       "11             560   80.590007 2024-03-26 09:27:08.674862   \n",
       "12             500   79.634040 2024-03-26 09:27:08.674862   \n",
       "13             381   81.073957 2024-03-26 09:27:08.674862   \n",
       "14             321   80.581697 2024-03-26 09:27:08.674862   \n",
       "15             232   51.969015 2024-03-26 09:27:08.674862   \n",
       "16             159   39.361665 2024-03-26 09:27:08.674862   \n",
       "17             100   39.582948 2024-03-26 09:27:08.674862   \n",
       "18               0    0.000000 2024-03-26 09:27:08.674862   \n",
       "\n",
       "         predict_arrival_date                   datetime  \\\n",
       "0  2024-03-26 09:32:44.486404 2024-03-26 09:07:08.486404   \n",
       "1  2024-03-26 09:32:44.881899 2024-03-26 09:08:08.881899   \n",
       "2  2024-03-26 09:32:09.755131 2024-03-26 09:09:08.755131   \n",
       "3  2024-03-26 09:32:09.875354 2024-03-26 09:10:08.875354   \n",
       "4  2024-03-26 09:31:49.677695 2024-03-26 09:11:10.677695   \n",
       "5  2024-03-26 09:31:53.822076 2024-03-26 09:12:08.822076   \n",
       "6  2024-03-26 09:30:28.160954 2024-03-26 09:13:09.160954   \n",
       "7  2024-03-26 09:30:00.414499 2024-03-26 09:14:08.414499   \n",
       "8  2024-03-26 09:29:15.979541 2024-03-26 09:15:09.979541   \n",
       "9  2024-03-26 09:28:29.807332 2024-03-26 09:17:07.807332   \n",
       "10 2024-03-26 09:28:29.292909 2024-03-26 09:18:09.292909   \n",
       "11 2024-03-26 09:28:29.264869 2024-03-26 09:19:09.264869   \n",
       "12 2024-03-26 09:28:28.308902 2024-03-26 09:20:08.308902   \n",
       "13 2024-03-26 09:28:29.748819 2024-03-26 09:22:08.748819   \n",
       "14 2024-03-26 09:28:29.256559 2024-03-26 09:23:08.256559   \n",
       "15 2024-03-26 09:28:00.643877 2024-03-26 09:24:08.643877   \n",
       "16 2024-03-26 09:27:48.036527 2024-03-26 09:25:09.036527   \n",
       "17 2024-03-26 09:27:48.257810 2024-03-26 09:26:08.257810   \n",
       "18 2024-03-26 09:27:08.674862 2024-03-26 09:27:08.674862   \n",
       "\n",
       "    reliable_estimateArrive       MAPE      MAPE2  \n",
       "0               1200.188458  27.979901  21.862731  \n",
       "1               1139.792963  29.497202  22.778255  \n",
       "2               1079.919731  27.879875  21.801613  \n",
       "3               1019.799508  29.535265  22.800946  \n",
       "4                957.997167  29.332324  22.679809  \n",
       "5                899.852786  31.688207  24.063056  \n",
       "6                839.513908  23.762095  19.199816  \n",
       "7                780.260363  22.010555  18.039878  \n",
       "8                718.695321  17.713303  15.047834  \n",
       "9                600.867530  13.502555  11.896257  \n",
       "10               539.381953  14.946375  13.002911  \n",
       "11               479.409993  16.810248  14.391073  \n",
       "12               420.365960  18.943979  15.926808  \n",
       "13               299.926043  27.031316  21.279254  \n",
       "14               240.418303  33.517289  25.103332  \n",
       "15               180.030985  28.866706  22.400438  \n",
       "16               119.638335  32.900546  24.755764  \n",
       "17                60.417052  65.516186  39.582948  \n",
       "18                 0.000000        NaN        NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_MAE = a.groupby('estimateArrive')['KPI_value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultados_MAPE = a.groupby('estimateArrive')['MAPE2'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: nan,\n",
       " 100: 39.582948,\n",
       " 159: 24.755764150943396,\n",
       " 232: 22.400437500000006,\n",
       " 321: 25.103332398753892,\n",
       " 381: 21.27925380577428,\n",
       " 500: 15.926808000000007,\n",
       " 560: 14.391072678571431,\n",
       " 620: 13.00291080645162,\n",
       " 682: 11.89625659824047,\n",
       " 846: 15.047834397163115,\n",
       " 952: 18.039877836134455,\n",
       " 1039: 19.199816361886427,\n",
       " 1185: 24.06305603375527,\n",
       " 1239: 22.679808958837775,\n",
       " 1321: 22.800945647236944,\n",
       " 1381: 21.80161252715424,\n",
       " 1476: 22.778254539295386,\n",
       " 1536: 21.862730598958326}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dict_MAPE = resultados_MAPE.to_dict()\n",
    "new_dict_MAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.0,\n",
       " 100: 39.582948,\n",
       " 159: 39.361665,\n",
       " 232: 51.969015,\n",
       " 321: 80.581697,\n",
       " 381: 81.073957,\n",
       " 500: 79.63404,\n",
       " 560: 80.590007,\n",
       " 620: 80.618047,\n",
       " 682: 81.13247,\n",
       " 846: 127.304679,\n",
       " 952: 171.739637,\n",
       " 1039: 199.486092,\n",
       " 1185: 285.147214,\n",
       " 1239: 281.002833,\n",
       " 1321: 301.200492,\n",
       " 1381: 301.080269,\n",
       " 1476: 336.207037,\n",
       " 1536: 335.811542}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dict_MAE = resultados_MAE.to_dict()\n",
    "new_dict_MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 0.0),\n",
       " (1.6666666666666667, 0.6597158000000001),\n",
       " (2.65, 0.65602775),\n",
       " (3.8666666666666667, 0.86615025),\n",
       " (5.35, 1.3430282833333334),\n",
       " (6.35, 1.3512326166666666),\n",
       " (8.333333333333334, 1.327234),\n",
       " (9.333333333333334, 1.3431667833333334),\n",
       " (10.333333333333334, 1.3436341166666668),\n",
       " (11.366666666666667, 1.3522078333333334),\n",
       " (14.1, 2.1217446499999997),\n",
       " (15.866666666666667, 2.8623272833333333),\n",
       " (17.316666666666666, 3.3247682000000003),\n",
       " (19.75, 4.752453566666667),\n",
       " (20.65, 4.68338055),\n",
       " (22.016666666666666, 5.0200082),\n",
       " (23.016666666666666, 5.018004483333333),\n",
       " (24.6, 5.603450616666667),\n",
       " (25.6, 5.596859033333333)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE = []\n",
    "for key in new_dict_MAE.keys():\n",
    "    MAE.append((key/60,new_dict_MAE[key]/60))\n",
    "\n",
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, nan),\n",
       " (1.6666666666666667, 39.582948),\n",
       " (2.65, 24.755764150943396),\n",
       " (3.8666666666666667, 22.400437500000006),\n",
       " (5.35, 25.103332398753892),\n",
       " (6.35, 21.27925380577428),\n",
       " (8.333333333333334, 15.926808000000007),\n",
       " (9.333333333333334, 14.391072678571431),\n",
       " (10.333333333333334, 13.00291080645162),\n",
       " (11.366666666666667, 11.89625659824047),\n",
       " (14.1, 15.047834397163115),\n",
       " (15.866666666666667, 18.039877836134455),\n",
       " (17.316666666666666, 19.199816361886427),\n",
       " (19.75, 24.06305603375527),\n",
       " (20.65, 22.679808958837775),\n",
       " (22.016666666666666, 22.800945647236944),\n",
       " (23.016666666666666, 21.80161252715424),\n",
       " (24.6, 22.778254539295386),\n",
       " (25.6, 21.862730598958326)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAPE = []\n",
    "for key in new_dict_MAPE.keys():\n",
    "    MAPE.append((key/60,new_dict_MAPE[key]))\n",
    "\n",
    "MAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Dibujar el primer gráfico con eje y a la izquierda\n",
    "ax1.plot([data[0] for data in MAE], [data[1] for data in MAE])\n",
    "ax1.set_xlabel('Remaining time (minutes)')\n",
    "ax1.set_ylabel('MAE (minutes)')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot([data[0] for data in MAPE], [data[1] for data in MAPE], 'orange')\n",
    "ax2.set_ylabel('MAPE (%)')\n",
    "\n",
    "plt.title('CRTM API estimation error')\n",
    "plt.gca().invert_xaxis()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([data[0] for data in MAPE], [data[1] for data in MAPE])\n",
    "plt.ylim(0,40)\n",
    "plt.xlim(-1,27)\n",
    "plt.gca().invert_xaxis()\n",
    "plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "plt.title('CRTM API estimation error')\n",
    "plt.xlabel('Remaining time (minutes)')\n",
    "plt.ylabel('MAPE (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([data[0] for data in MAE], [data[1] for data in MAE])\n",
    "plt.gca().invert_xaxis()\n",
    "plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "plt.title('CRTM API estimation error')\n",
    "plt.xlabel('Remaining time (minutes)')\n",
    "plt.ylabel('MAE (minutes)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict = KPI_data.sort('estimateArrive').select('estimateArrive','KPI_value').to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_KPI(dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Para un día generalizando lo anterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1_136_086, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PK</th><th>date</th><th>datetime</th><th>bus</th><th>line</th><th>stop</th><th>positionBusLon</th><th>positionBusLat</th><th>DistanceBus</th><th>destination</th><th>MinimunFrequency</th><th>isHead</th><th>dayType</th><th>estimateArrive</th></tr><tr><td>str</td><td>date</td><td>datetime[μs]</td><td>str</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>str</td><td>i64</td><td>u8</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;2024-03-26 19:…</td><td>2024-03-26</td><td>2024-03-26 19:04:07.830005</td><td>&quot;2592&quot;</td><td>&quot;178&quot;</td><td>1487</td><td>-3.697154</td><td>40.487528</td><td>2377</td><td>&quot;MONTECARMELO&quot;</td><td>6</td><td>0</td><td>&quot;LA&quot;</td><td>729</td></tr><tr><td>&quot;2024-03-26 11:…</td><td>2024-03-26</td><td>2024-03-26 11:13:06.952057</td><td>&quot;8828&quot;</td><td>&quot;9&quot;</td><td>218</td><td>-3.649361</td><td>40.472321</td><td>1724</td><td>&quot;SOL SEVILLA&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>496</td></tr><tr><td>&quot;2024-03-26 13:…</td><td>2024-03-26</td><td>2024-03-26 13:39:08.711052</td><td>&quot;2515&quot;</td><td>&quot;175&quot;</td><td>3769</td><td>-3.696384</td><td>40.487048</td><td>6472</td><td>&quot;LAS TABLAS NOR…</td><td>9</td><td>0</td><td>&quot;LA&quot;</td><td>918</td></tr><tr><td>&quot;2024-03-26 13:…</td><td>2024-03-26</td><td>2024-03-26 13:41:09.490335</td><td>&quot;8831&quot;</td><td>&quot;171&quot;</td><td>5911</td><td>-3.631982</td><td>40.468928</td><td>4366</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1159</td></tr><tr><td>&quot;2024-03-26 16:…</td><td>2024-03-26</td><td>2024-03-26 16:30:07.126711</td><td>&quot;8857&quot;</td><td>&quot;176&quot;</td><td>3261</td><td>-3.687647</td><td>40.470278</td><td>3590</td><td>&quot;LAS TABLAS SUR…</td><td>6</td><td>0</td><td>&quot;LA&quot;</td><td>375</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;2024-03-26 19:…</td><td>2024-03-26</td><td>2024-03-26 19:44:09.474926</td><td>&quot;4737&quot;</td><td>&quot;49&quot;</td><td>5329</td><td>-3.704893</td><td>40.47915</td><td>281</td><td>&quot;PITIS&quot;</td><td>4</td><td>0</td><td>&quot;LA&quot;</td><td>137</td></tr><tr><td>&quot;2024-03-26 14:…</td><td>2024-03-26</td><td>2024-03-26 14:26:08.037914</td><td>&quot;2078&quot;</td><td>&quot;174&quot;</td><td>3256</td><td>-3.685166</td><td>40.466831</td><td>4128</td><td>&quot;VALDEBEBAS&quot;</td><td>7</td><td>0</td><td>&quot;LA&quot;</td><td>922</td></tr><tr><td>&quot;2024-03-26 09:…</td><td>2024-03-26</td><td>2024-03-26 09:05:08.090547</td><td>&quot;8856&quot;</td><td>&quot;176&quot;</td><td>2653</td><td>-3.688789</td><td>40.467426</td><td>1094</td><td>&quot;LAS TABLAS SUR…</td><td>6</td><td>0</td><td>&quot;LA&quot;</td><td>553</td></tr><tr><td>&quot;2024-03-26 13:…</td><td>2024-03-26</td><td>2024-03-26 13:47:09.444763</td><td>&quot;53&quot;</td><td>&quot;BR1&quot;</td><td>5397</td><td>-3.694223</td><td>40.487813</td><td>8534</td><td>&quot;VALDEBEBAS&quot;</td><td>null</td><td>0</td><td>&quot;LA&quot;</td><td>1073</td></tr><tr><td>&quot;2024-03-26 08:…</td><td>2024-03-26</td><td>2024-03-26 08:16:07.762153</td><td>&quot;5563&quot;</td><td>&quot;67&quot;</td><td>1604</td><td>-3.686624</td><td>40.476524</td><td>3230</td><td>&quot;BARRIO PEÑAGRA…</td><td>9</td><td>0</td><td>&quot;LA&quot;</td><td>1322</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1_136_086, 14)\n",
       "┌─────────────┬────────────┬─────────────┬──────┬───┬─────────────┬────────┬─────────┬─────────────┐\n",
       "│ PK          ┆ date       ┆ datetime    ┆ bus  ┆ … ┆ MinimunFreq ┆ isHead ┆ dayType ┆ estimateArr │\n",
       "│ ---         ┆ ---        ┆ ---         ┆ ---  ┆   ┆ uency       ┆ ---    ┆ ---     ┆ ive         │\n",
       "│ str         ┆ date       ┆ datetime[μs ┆ str  ┆   ┆ ---         ┆ u8     ┆ str     ┆ ---         │\n",
       "│             ┆            ┆ ]           ┆      ┆   ┆ i64         ┆        ┆         ┆ i64         │\n",
       "╞═════════════╪════════════╪═════════════╪══════╪═══╪═════════════╪════════╪═════════╪═════════════╡\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2592 ┆ … ┆ 6           ┆ 0      ┆ LA      ┆ 729         │\n",
       "│ 19:04:07.83 ┆            ┆ 19:04:07.83 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 0005_B2592… ┆            ┆ 0005        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 8828 ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 496         │\n",
       "│ 11:13:06.95 ┆            ┆ 11:13:06.95 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2057_B8828… ┆            ┆ 2057        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2515 ┆ … ┆ 9           ┆ 0      ┆ LA      ┆ 918         │\n",
       "│ 13:39:08.71 ┆            ┆ 13:39:08.71 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 1052_B2515… ┆            ┆ 1052        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 8831 ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1159        │\n",
       "│ 13:41:09.49 ┆            ┆ 13:41:09.49 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 0335_B8831… ┆            ┆ 0335        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 8857 ┆ … ┆ 6           ┆ 0      ┆ LA      ┆ 375         │\n",
       "│ 16:30:07.12 ┆            ┆ 16:30:07.12 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 6711_B8857… ┆            ┆ 6711        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ …           ┆ …          ┆ …           ┆ …    ┆ … ┆ …           ┆ …      ┆ …       ┆ …           │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 4737 ┆ … ┆ 4           ┆ 0      ┆ LA      ┆ 137         │\n",
       "│ 19:44:09.47 ┆            ┆ 19:44:09.47 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 4926_B4737… ┆            ┆ 4926        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 2078 ┆ … ┆ 7           ┆ 0      ┆ LA      ┆ 922         │\n",
       "│ 14:26:08.03 ┆            ┆ 14:26:08.03 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 7914_B2078… ┆            ┆ 7914        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 8856 ┆ … ┆ 6           ┆ 0      ┆ LA      ┆ 553         │\n",
       "│ 09:05:08.09 ┆            ┆ 09:05:08.09 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 0547_B8856… ┆            ┆ 0547        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 53   ┆ … ┆ null        ┆ 0      ┆ LA      ┆ 1073        │\n",
       "│ 13:47:09.44 ┆            ┆ 13:47:09.44 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 4763_B53_L… ┆            ┆ 4763        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2024-03-26  ┆ 2024-03-26 ┆ 2024-03-26  ┆ 5563 ┆ … ┆ 9           ┆ 0      ┆ LA      ┆ 1322        │\n",
       "│ 08:16:07.76 ┆            ┆ 08:16:07.76 ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "│ 2153_B5563… ┆            ┆ 2153        ┆      ┆   ┆             ┆        ┆         ┆             │\n",
       "└─────────────┴────────────┴─────────────┴──────┴───┴─────────────┴────────┴─────────┴─────────────┘"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_02 = sample_data.group_by(pl.col('bus'),pl.col('line'),pl.col('stop'),pl.col('destination')).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (7_913, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>bus</th><th>line</th><th>stop</th><th>destination</th><th>count</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;5726&quot;</td><td>&quot;147&quot;</td><td>29</td><td>&quot;BARRIO DEL PIL…</td><td>114</td></tr><tr><td>&quot;8835&quot;</td><td>&quot;171&quot;</td><td>5892</td><td>&quot;VALDEBEBAS&quot;</td><td>29</td></tr><tr><td>&quot;2550&quot;</td><td>&quot;150&quot;</td><td>60</td><td>&quot;VIRGEN CORTIJO…</td><td>123</td></tr><tr><td>&quot;2298&quot;</td><td>&quot;29&quot;</td><td>214</td><td>&quot;MANOTERAS&quot;</td><td>171</td></tr><tr><td>&quot;4796&quot;</td><td>&quot;45&quot;</td><td>66</td><td>&quot;REINA VICTORIA…</td><td>207</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;577&quot;</td><td>&quot;70&quot;</td><td>5603</td><td>&quot;ALSACIA&quot;</td><td>119</td></tr><tr><td>&quot;4794&quot;</td><td>&quot;45&quot;</td><td>60</td><td>&quot;REINA VICTORIA…</td><td>65</td></tr><tr><td>&quot;4836&quot;</td><td>&quot;132&quot;</td><td>1364</td><td>&quot;HOSPITAL LA PA…</td><td>108</td></tr><tr><td>&quot;539&quot;</td><td>&quot;70&quot;</td><td>212</td><td>&quot;ALSACIA&quot;</td><td>185</td></tr><tr><td>&quot;54&quot;</td><td>&quot;BR1&quot;</td><td>5917</td><td>&quot;VALDEBEBAS&quot;</td><td>29</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (7_913, 5)\n",
       "┌──────┬──────┬──────┬──────────────────┬───────┐\n",
       "│ bus  ┆ line ┆ stop ┆ destination      ┆ count │\n",
       "│ ---  ┆ ---  ┆ ---  ┆ ---              ┆ ---   │\n",
       "│ str  ┆ str  ┆ i64  ┆ str              ┆ u32   │\n",
       "╞══════╪══════╪══════╪══════════════════╪═══════╡\n",
       "│ 5726 ┆ 147  ┆ 29   ┆ BARRIO DEL PILAR ┆ 114   │\n",
       "│ 8835 ┆ 171  ┆ 5892 ┆ VALDEBEBAS       ┆ 29    │\n",
       "│ 2550 ┆ 150  ┆ 60   ┆ VIRGEN CORTIJO   ┆ 123   │\n",
       "│ 2298 ┆ 29   ┆ 214  ┆ MANOTERAS        ┆ 171   │\n",
       "│ 4796 ┆ 45   ┆ 66   ┆ REINA VICTORIA   ┆ 207   │\n",
       "│ …    ┆ …    ┆ …    ┆ …                ┆ …     │\n",
       "│ 577  ┆ 70   ┆ 5603 ┆ ALSACIA          ┆ 119   │\n",
       "│ 4794 ┆ 45   ┆ 60   ┆ REINA VICTORIA   ┆ 65    │\n",
       "│ 4836 ┆ 132  ┆ 1364 ┆ HOSPITAL LA PAZ  ┆ 108   │\n",
       "│ 539  ┆ 70   ┆ 212  ┆ ALSACIA          ┆ 185   │\n",
       "│ 54   ┆ BR1  ┆ 5917 ┆ VALDEBEBAS       ┆ 29    │\n",
       "└──────┴──────┴──────┴──────────────────┴───────┘"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data_02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data_pd = sample_data_02.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>bus</th>\n",
       "      <th>line</th>\n",
       "      <th>stop</th>\n",
       "      <th>destination</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5726</td>\n",
       "      <td>147</td>\n",
       "      <td>29</td>\n",
       "      <td>BARRIO DEL PILAR</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8835</td>\n",
       "      <td>171</td>\n",
       "      <td>5892</td>\n",
       "      <td>VALDEBEBAS</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2550</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>VIRGEN CORTIJO</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2298</td>\n",
       "      <td>29</td>\n",
       "      <td>214</td>\n",
       "      <td>MANOTERAS</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4796</td>\n",
       "      <td>45</td>\n",
       "      <td>66</td>\n",
       "      <td>REINA VICTORIA</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7908</th>\n",
       "      <td>577</td>\n",
       "      <td>70</td>\n",
       "      <td>5603</td>\n",
       "      <td>ALSACIA</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7909</th>\n",
       "      <td>4794</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>REINA VICTORIA</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7910</th>\n",
       "      <td>4836</td>\n",
       "      <td>132</td>\n",
       "      <td>1364</td>\n",
       "      <td>HOSPITAL LA PAZ</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7911</th>\n",
       "      <td>539</td>\n",
       "      <td>70</td>\n",
       "      <td>212</td>\n",
       "      <td>ALSACIA</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7912</th>\n",
       "      <td>54</td>\n",
       "      <td>BR1</td>\n",
       "      <td>5917</td>\n",
       "      <td>VALDEBEBAS</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7913 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bus line  stop       destination  count\n",
       "0     5726  147    29  BARRIO DEL PILAR    114\n",
       "1     8835  171  5892        VALDEBEBAS     29\n",
       "2     2550  150    60    VIRGEN CORTIJO    123\n",
       "3     2298   29   214         MANOTERAS    171\n",
       "4     4796   45    66    REINA VICTORIA    207\n",
       "...    ...  ...   ...               ...    ...\n",
       "7908   577   70  5603           ALSACIA    119\n",
       "7909  4794   45    60    REINA VICTORIA     65\n",
       "7910  4836  132  1364   HOSPITAL LA PAZ    108\n",
       "7911   539   70   212           ALSACIA    185\n",
       "7912    54  BR1  5917        VALDEBEBAS     29\n",
       "\n",
       "[7913 rows x 5 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in sample_data_pd.iterrows():\n",
    "    print(type(str(row[1])))\n",
    "    dataset_aux =  sample_data.filter(pl.col('bus')==str(row[0]), pl.col('line')==str(row[1]),pl.col('stop')==int(row[2]), pl.col('destination')==str(row[3]))\n",
    "    print(dataset_aux.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FUNCION \n",
    "Dado un bus, linea, parada y destino fijos, obtenemos el dataset con la PK, HORA PREDICHA, HORA EXACTA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(os.getcwd()))))\n",
    "DATA_PATH = os.path.join(ROOT_PATH, \"data\", \"processed\")\n",
    "EMT_DATA_PATH = os.path.join(DATA_PATH, \"emt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pl.scan_csv(os.path.join(EMT_DATA_PATH, \"2024\", \"03\", f\"emt_202403.csv\"))\n",
    "list_day = ['02','03','04','05','06','07','08','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30','31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(1234)  \n",
    "day = random.randint(2, 31)\n",
    "\n",
    "print(day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = pl.scan_csv(os.path.join(EMT_DATA_PATH, \"2024\", \"03\",str(day), f\"emt_202403{str(day)}.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_final_dataset(sample_data):\n",
    "    sample_data = sample_data.with_columns((pl.col('datetime').cast(pl.String)+\"_B\"+pl.col('bus').cast(pl.String)+\"_L\"+ pl.col('line').cast(pl.String)+\"_S\"+pl.col('stop').cast(pl.String)).alias('PK'))\n",
    "    \n",
    "    # ETA <2400\n",
    "    sample_data = sample_data.filter(pl.col('estimateArrive')<888888)\n",
    "    sample_data = sample_data.group_by('PK').min()\n",
    "    \n",
    "    sample_data = sample_data.with_columns(pl.col(\"date\").cast(pl.Date),pl.col('line').cast(pl.String),pl.col('isHead').cast(pl.UInt8))\n",
    "    \n",
    "    sample_data = sample_data.with_columns(pl.col('datetime').map_elements(lambda x: datetime.strptime(x, \"%Y-%m-%d %H:%M:%S.%f\")))\n",
    "    \n",
    "    # Rellenamos valores nulos de dayType\n",
    "    sample_data = sample_data.with_columns(pl.when(pl.col('dayType').is_null()).then(pl.col('date').apply(get_type_day)).otherwise(pl.col('dayType')).alias('dayType'))\n",
    "    \n",
    "    # Eliminamos variables\n",
    "    sample_data = sample_data.drop('positionTypeBus','deviation','MaximumFrequency','StartTime','StopTime','strike')\n",
    "    \n",
    "    return sample_data.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_type_day(date):\n",
    "    \n",
    "    day = date.strftime(\"%A\")\n",
    "    \n",
    "    if day in ['Monday','Tuesday','Wednesday','Thursday','Friday']:\n",
    "        \n",
    "        type = 'LA'\n",
    "    elif day == 'Saturday':\n",
    "        type = 'SA'\n",
    "    else:\n",
    "        type = 'FE'\n",
    "        \n",
    "    return type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = create_final_dataset(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_predict_arrival_date(date_datetime,second):\n",
    "    new_date_datetime = date_datetime + timedelta(seconds=second)\n",
    "    \n",
    "    return new_date_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_auxiliar_dataset(filter_data):\n",
    "    # Creamos la hora predicha\n",
    "    filter_data = filter_data.with_columns(pl.struct(datetime = pl.col('datetime'), estimateArrive = pl.col('estimateArrive').alias('struct')).map_elements(lambda x: calculate_predict_arrival_date(x['datetime'], x['estimateArrive'])).alias('predict_arrival_date'))\n",
    "    \n",
    "    # Creamos los bloques\n",
    "    sample_data_pd = filter_data.to_pandas()\n",
    "    sample_data_pd['bloque_id'] = None\n",
    "\n",
    "    bloque_actual = 1\n",
    "    for i in range(0, len(sample_data_pd)-1):\n",
    "        if (sample_data_pd['datetime'][i + 1] - sample_data_pd['datetime'][i]) > timedelta(minutes=5) and (sample_data_pd['estimateArrive'][i] < sample_data_pd['estimateArrive'][i + 1]):\n",
    "            sample_data_pd['bloque_id'][i] = bloque_actual\n",
    "            bloque_actual += 1\n",
    "            \n",
    "        else: \n",
    "            sample_data_pd['bloque_id'][i] = bloque_actual\n",
    "            \n",
    "    sample_data_pd.at[sample_data_pd.index[-1], 'bloque_id'] = bloque_actual\n",
    "    \n",
    "    # Cremos el dataset exacto\n",
    "    sample_data_pl = pl.from_pandas(sample_data_pd)\n",
    "    small_sample_data = sample_data_pl.filter(pl.col('estimateArrive')<=60).group_by(pl.col('bus'),pl.col('line'),pl.col('stop'),pl.col('destination'),pl.col('date'),pl.col('bloque_id')).min().with_columns(pl.col('predict_arrival_date').alias('reliable_arrival_date'))   \n",
    "    \n",
    "    final_sample_data = sample_data_pl.join(small_sample_data,on=pl.col('bloque_id'),how = 'left') \n",
    "    final_sample_data = final_sample_data.select(pl.col('PK'),pl.col('reliable_arrival_date'),pl.col('predict_arrival_date'),pl.col('bloque_id'),pl.col('estimateArrive'))\n",
    "    \n",
    "    return final_sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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