Primer approach a los datos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7eca0496",
   "metadata": {},
   "source": [
    "***En este notebook se hizo un pequeño análisis preliminar de los datos recolectados de distintas fuentes, se verificó si existen nulos, los tipos de datos de las columnas, y una pequeña descripcion estadística de las columnas con tipos de datos numéricos***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43690cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from simpledbf import Dbf5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77dd9d95",
   "metadata": {},
   "source": [
    "***Primero vamos a leer cada csv y a hacer una pequeña exploracion. En cada celda voy a poner un comentario diciendo lo que encontré***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d649c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>AccelSec</th>\n",
       "      <th>TopSpeed_KmH</th>\n",
       "      <th>Range_Km</th>\n",
       "      <th>Efficiency_WhKm</th>\n",
       "      <th>FastCharge_KmH</th>\n",
       "      <th>RapidCharge</th>\n",
       "      <th>PowerTrain</th>\n",
       "      <th>PlugType</th>\n",
       "      <th>BodyStyle</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Seats</th>\n",
       "      <th>PriceEuro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>Model 3 Long Range Dual Motor</td>\n",
       "      <td>4.6</td>\n",
       "      <td>233</td>\n",
       "      <td>450</td>\n",
       "      <td>161</td>\n",
       "      <td>940</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>55480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>ID.3 Pure</td>\n",
       "      <td>10.0</td>\n",
       "      <td>160</td>\n",
       "      <td>270</td>\n",
       "      <td>167</td>\n",
       "      <td>250</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polestar</td>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>210</td>\n",
       "      <td>400</td>\n",
       "      <td>181</td>\n",
       "      <td>620</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Liftback</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>56440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>iX3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>180</td>\n",
       "      <td>360</td>\n",
       "      <td>206</td>\n",
       "      <td>560</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>68040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda</td>\n",
       "      <td>e</td>\n",
       "      <td>9.5</td>\n",
       "      <td>145</td>\n",
       "      <td>170</td>\n",
       "      <td>168</td>\n",
       "      <td>190</td>\n",
       "      <td>Yes</td>\n",
       "      <td>RWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>32997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Ariya 63kWh</td>\n",
       "      <td>7.5</td>\n",
       "      <td>160</td>\n",
       "      <td>330</td>\n",
       "      <td>191</td>\n",
       "      <td>440</td>\n",
       "      <td>Yes</td>\n",
       "      <td>FWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Audi</td>\n",
       "      <td>e-tron S Sportback 55 quattro</td>\n",
       "      <td>4.5</td>\n",
       "      <td>210</td>\n",
       "      <td>335</td>\n",
       "      <td>258</td>\n",
       "      <td>540</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>96050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Ariya e-4ORCE 63kWh</td>\n",
       "      <td>5.9</td>\n",
       "      <td>200</td>\n",
       "      <td>325</td>\n",
       "      <td>194</td>\n",
       "      <td>440</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Ariya e-4ORCE 87kWh Performance</td>\n",
       "      <td>5.1</td>\n",
       "      <td>200</td>\n",
       "      <td>375</td>\n",
       "      <td>232</td>\n",
       "      <td>450</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Byton</td>\n",
       "      <td>M-Byte 95 kWh 2WD</td>\n",
       "      <td>7.5</td>\n",
       "      <td>190</td>\n",
       "      <td>400</td>\n",
       "      <td>238</td>\n",
       "      <td>480</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AWD</td>\n",
       "      <td>Type 2 CCS</td>\n",
       "      <td>SUV</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Brand                            Model  AccelSec  TopSpeed_KmH  \\\n",
       "0         Tesla     Model 3 Long Range Dual Motor       4.6           233   \n",
       "1    Volkswagen                         ID.3 Pure      10.0           160   \n",
       "2      Polestar                                 2       4.7           210   \n",
       "3           BMW                              iX3        6.8           180   \n",
       "4         Honda                                e        9.5           145   \n",
       "..           ...                              ...       ...           ...   \n",
       "98       Nissan                       Ariya 63kWh       7.5           160   \n",
       "99         Audi     e-tron S Sportback 55 quattro       4.5           210   \n",
       "100      Nissan               Ariya e-4ORCE 63kWh       5.9           200   \n",
       "101      Nissan   Ariya e-4ORCE 87kWh Performance       5.1           200   \n",
       "102       Byton                 M-Byte 95 kWh 2WD       7.5           190   \n",
       "\n",
       "     Range_Km  Efficiency_WhKm FastCharge_KmH RapidCharge PowerTrain  \\\n",
       "0         450              161            940         Yes        AWD   \n",
       "1         270              167            250         Yes        RWD   \n",
       "2         400              181            620         Yes        AWD   \n",
       "3         360              206            560         Yes        RWD   \n",
       "4         170              168            190         Yes        RWD   \n",
       "..        ...              ...            ...         ...        ...   \n",
       "98        330              191            440         Yes        FWD   \n",
       "99        335              258            540         Yes        AWD   \n",
       "100       325              194            440         Yes        AWD   \n",
       "101       375              232            450         Yes        AWD   \n",
       "102       400              238            480         Yes        AWD   \n",
       "\n",
       "       PlugType  BodyStyle Segment  Seats  PriceEuro  \n",
       "0    Type 2 CCS      Sedan       D      5      55480  \n",
       "1    Type 2 CCS  Hatchback       C      5      30000  \n",
       "2    Type 2 CCS   Liftback       D      5      56440  \n",
       "3    Type 2 CCS        SUV       D      5      68040  \n",
       "4    Type 2 CCS  Hatchback       B      4      32997  \n",
       "..          ...        ...     ...    ...        ...  \n",
       "98   Type 2 CCS  Hatchback       C      5      45000  \n",
       "99   Type 2 CCS        SUV       E      5      96050  \n",
       "100  Type 2 CCS  Hatchback       C      5      50000  \n",
       "101  Type 2 CCS  Hatchback       C      5      65000  \n",
       "102  Type 2 CCS        SUV       E      5      62000  \n",
       "\n",
       "[103 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataClean=pd.read_csv('Datasets/ElectricCarData_Clean.csv')\n",
    "ElectricCarDataClean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "707728ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand              0\n",
       "Model              0\n",
       "AccelSec           0\n",
       "TopSpeed_KmH       0\n",
       "Range_Km           0\n",
       "Efficiency_WhKm    0\n",
       "FastCharge_KmH     0\n",
       "RapidCharge        0\n",
       "PowerTrain         0\n",
       "PlugType           0\n",
       "BodyStyle          0\n",
       "Segment            0\n",
       "Seats              0\n",
       "PriceEuro          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataClean.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "147b12ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AccelSec</th>\n",
       "      <th>TopSpeed_KmH</th>\n",
       "      <th>Range_Km</th>\n",
       "      <th>Efficiency_WhKm</th>\n",
       "      <th>Seats</th>\n",
       "      <th>PriceEuro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.396117</td>\n",
       "      <td>179.194175</td>\n",
       "      <td>338.786408</td>\n",
       "      <td>189.165049</td>\n",
       "      <td>4.883495</td>\n",
       "      <td>55811.563107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.017430</td>\n",
       "      <td>43.573030</td>\n",
       "      <td>126.014444</td>\n",
       "      <td>29.566839</td>\n",
       "      <td>0.795834</td>\n",
       "      <td>34134.665280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>34429.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>45000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>65000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.400000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>970.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>215000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AccelSec  TopSpeed_KmH    Range_Km  Efficiency_WhKm       Seats  \\\n",
       "count  103.000000    103.000000  103.000000       103.000000  103.000000   \n",
       "mean     7.396117    179.194175  338.786408       189.165049    4.883495   \n",
       "std      3.017430     43.573030  126.014444        29.566839    0.795834   \n",
       "min      2.100000    123.000000   95.000000       104.000000    2.000000   \n",
       "25%      5.100000    150.000000  250.000000       168.000000    5.000000   \n",
       "50%      7.300000    160.000000  340.000000       180.000000    5.000000   \n",
       "75%      9.000000    200.000000  400.000000       203.000000    5.000000   \n",
       "max     22.400000    410.000000  970.000000       273.000000    7.000000   \n",
       "\n",
       "           PriceEuro  \n",
       "count     103.000000  \n",
       "mean    55811.563107  \n",
       "std     34134.665280  \n",
       "min     20129.000000  \n",
       "25%     34429.500000  \n",
       "50%     45000.000000  \n",
       "75%     65000.000000  \n",
       "max    215000.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataClean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe7f559a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand               object\n",
       "Model               object\n",
       "AccelSec           float64\n",
       "TopSpeed_KmH         int64\n",
       "Range_Km             int64\n",
       "Efficiency_WhKm      int64\n",
       "FastCharge_KmH      object\n",
       "RapidCharge         object\n",
       "PowerTrain          object\n",
       "PlugType            object\n",
       "BodyStyle           object\n",
       "Segment             object\n",
       "Seats                int64\n",
       "PriceEuro            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataClean.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd8d765",
   "metadata": {},
   "source": [
    "***Todo en orden, no hay nulos, hay algunas categoricas que podemos hacerle el dummies, y hay columnas que podemos eliminar***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6444517d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ElectricCarDataNorm=pd.read_csv('Datasets/ElectricCarData_Norm.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf65818a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand          0\n",
       "Model          0\n",
       "Accel          0\n",
       "TopSpeed       0\n",
       "Range          0\n",
       "Efficiency     0\n",
       "FastCharge     0\n",
       "RapidCharge    0\n",
       "PowerTrain     0\n",
       "PlugType       0\n",
       "BodyStyle      0\n",
       "Segment        0\n",
       "Seats          0\n",
       "PriceEuro      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataNorm.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4fa29a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.883495</td>\n",
       "      <td>55811.563107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.795834</td>\n",
       "      <td>34134.665280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>20129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>34429.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>45000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>65000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>215000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Seats      PriceEuro\n",
       "count  103.000000     103.000000\n",
       "mean     4.883495   55811.563107\n",
       "std      0.795834   34134.665280\n",
       "min      2.000000   20129.000000\n",
       "25%      5.000000   34429.500000\n",
       "50%      5.000000   45000.000000\n",
       "75%      5.000000   65000.000000\n",
       "max      7.000000  215000.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataNorm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "361fa3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand          object\n",
       "Model          object\n",
       "Accel          object\n",
       "TopSpeed       object\n",
       "Range          object\n",
       "Efficiency     object\n",
       "FastCharge     object\n",
       "RapidCharge    object\n",
       "PowerTrain     object\n",
       "PlugType       object\n",
       "BodyStyle      object\n",
       "Segment        object\n",
       "Seats           int64\n",
       "PriceEuro       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElectricCarDataNorm.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8722177",
   "metadata": {},
   "source": [
    "***Todo en orden, no hay nulos, hay algunas categoricas que podemos hacerle el dummies, y hay columnas que podemos eliminar***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eda1434f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dbf = Dbf5('Datasets/taxi_zones.dbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69b135fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "taxiZones = dbf.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "daf84a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OBJECTID      0\n",
       "Shape_Leng    0\n",
       "Shape_Area    0\n",
       "zone          0\n",
       "LocationID    0\n",
       "borough       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxiZones.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f18d3eb7",
   "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>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>LocationID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.094269</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>131.984791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>76.065761</td>\n",
       "      <td>0.054594</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>76.073787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014306</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>66.500000</td>\n",
       "      <td>0.054722</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>66.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>197.500000</td>\n",
       "      <td>0.119488</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>197.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>263.000000</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         OBJECTID  Shape_Leng  Shape_Area  LocationID\n",
       "count  263.000000  263.000000  263.000000  263.000000\n",
       "mean   132.000000    0.094269    0.000402  131.984791\n",
       "std     76.065761    0.054594    0.000482   76.073787\n",
       "min      1.000000    0.014306    0.000006    1.000000\n",
       "25%     66.500000    0.054722    0.000133   66.500000\n",
       "50%    132.000000    0.084341    0.000268  132.000000\n",
       "75%    197.500000    0.119488    0.000480  197.500000\n",
       "max    263.000000    0.433470    0.004866  263.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxiZones.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f12a99db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OBJECTID        int64\n",
       "Shape_Leng    float64\n",
       "Shape_Area    float64\n",
       "zone           object\n",
       "LocationID      int64\n",
       "borough        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxiZones.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa90568b",
   "metadata": {},
   "source": [
    "***Todo en orden, no hay nulos, hay algunas categoricas que podemos hacerle el dummies, y hay columnas que podemos eliminar***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a140ad75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8264/2293964561.py:1: DtypeWarning: Columns (6,16,20,31,33,36,39,40,41,43,46,52,53,55,57,58,60,62) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  AlternativeFuelChargingStations=pd.read_csv('Datasets/AlternativeFuelChargingStations.csv')\n"
     ]
    }
   ],
   "source": [
    "AlternativeFuelChargingStations=pd.read_csv('Datasets/AlternativeFuelChargingStations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f66f341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel Type Code                     0\n",
       "Station Name                       0\n",
       "Street Address                     1\n",
       "Intersection Directions        65913\n",
       "City                               0\n",
       "                               ...  \n",
       "CNG PSI                        68809\n",
       "CNG Vehicle Class              68784\n",
       "LNG Vehicle Class              70248\n",
       "EV On-Site Renewable Source    70036\n",
       "Restricted Access              16971\n",
       "Length: 65, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlternativeFuelChargingStations.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "34e812b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>Plus4</th>\n",
       "      <th>EV Level1 EVSE Num</th>\n",
       "      <th>EV Level2 EVSE Num</th>\n",
       "      <th>EV DC Fast Count</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>ID</th>\n",
       "      <th>Federal Agency ID</th>\n",
       "      <th>CNG Dispenser Num</th>\n",
       "      <th>CNG Total Compression Capacity</th>\n",
       "      <th>CNG Storage Capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>54144.000000</td>\n",
       "      <td>8307.000000</td>\n",
       "      <td>70406.000000</td>\n",
       "      <td>70406.000000</td>\n",
       "      <td>70406.000000</td>\n",
       "      <td>955.000000</td>\n",
       "      <td>1039.000000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>357.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.461538</td>\n",
       "      <td>2.315289</td>\n",
       "      <td>3.576381</td>\n",
       "      <td>38.929309</td>\n",
       "      <td>-96.061447</td>\n",
       "      <td>148730.326066</td>\n",
       "      <td>13.905759</td>\n",
       "      <td>2.731473</td>\n",
       "      <td>873.766949</td>\n",
       "      <td>47866.787115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.548356</td>\n",
       "      <td>3.157387</td>\n",
       "      <td>4.129470</td>\n",
       "      <td>5.628458</td>\n",
       "      <td>19.545273</td>\n",
       "      <td>54261.672118</td>\n",
       "      <td>6.767439</td>\n",
       "      <td>8.333492</td>\n",
       "      <td>1032.371466</td>\n",
       "      <td>54327.698853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-159.788556</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.182981</td>\n",
       "      <td>-117.860563</td>\n",
       "      <td>113723.250000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>215.750000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>39.105989</td>\n",
       "      <td>-92.002897</td>\n",
       "      <td>165247.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>632.000000</td>\n",
       "      <td>36000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42.702185</td>\n",
       "      <td>-78.881493</td>\n",
       "      <td>190653.750000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>64.852466</td>\n",
       "      <td>40.432278</td>\n",
       "      <td>224382.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>10620.000000</td>\n",
       "      <td>593136.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Plus4  EV Level1 EVSE Num  EV Level2 EVSE Num  EV DC Fast Count  \\\n",
       "count    0.0          286.000000        54144.000000       8307.000000   \n",
       "mean     NaN            3.461538            2.315289          3.576381   \n",
       "std      NaN            6.548356            3.157387          4.129470   \n",
       "min      NaN            1.000000            1.000000          1.000000   \n",
       "25%      NaN            1.000000            2.000000          1.000000   \n",
       "50%      NaN            1.000000            2.000000          2.000000   \n",
       "75%      NaN            2.000000            2.000000          4.000000   \n",
       "max      NaN           51.000000          311.000000         56.000000   \n",
       "\n",
       "           Latitude     Longitude             ID  Federal Agency ID  \\\n",
       "count  70406.000000  70406.000000   70406.000000         955.000000   \n",
       "mean      38.929309    -96.061447  148730.326066          13.905759   \n",
       "std        5.628458     19.545273   54261.672118           6.767439   \n",
       "min        0.000000   -159.788556      17.000000           2.000000   \n",
       "25%       34.182981   -117.860563  113723.250000           8.000000   \n",
       "50%       39.105989    -92.002897  165247.500000          14.000000   \n",
       "75%       42.702185    -78.881493  190653.750000          17.000000   \n",
       "max       64.852466     40.432278  224382.000000          60.000000   \n",
       "\n",
       "       CNG Dispenser Num  CNG Total Compression Capacity  CNG Storage Capacity  \n",
       "count        1039.000000                      708.000000            357.000000  \n",
       "mean            2.731473                      873.766949          47866.787115  \n",
       "std             8.333492                     1032.371466          54327.698853  \n",
       "min             0.000000                        2.000000              0.000000  \n",
       "25%             1.000000                      215.750000          30000.000000  \n",
       "50%             2.000000                      632.000000          36000.000000  \n",
       "75%             2.000000                     1100.000000          60000.000000  \n",
       "max           202.000000                    10620.000000         593136.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlternativeFuelChargingStations.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "609ef55b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel Type Code                 object\n",
       "Station Name                   object\n",
       "Street Address                 object\n",
       "Intersection Directions        object\n",
       "City                           object\n",
       "                                ...  \n",
       "CNG PSI                        object\n",
       "CNG Vehicle Class              object\n",
       "LNG Vehicle Class              object\n",
       "EV On-Site Renewable Source    object\n",
       "Restricted Access              object\n",
       "Length: 65, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlternativeFuelChargingStations.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa928dd",
   "metadata": {},
   "source": [
    "***Aqui si tenemos que verificar los datos, estan desordenados***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91df94de",
   "metadata": {},
   "outputs": [],
   "source": [
    "AlternativeFuelVehiclesUS=pd.read_csv('Datasets/AlternativeFuelVehiclesUS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cf07a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category                                0\n",
       "Model                                   0\n",
       "Model Year                            288\n",
       "Manufacturer                            0\n",
       "Fuel                                    0\n",
       "All-Electric Range                    660\n",
       "PHEV Total Range                      839\n",
       "Alternative Fuel Economy City         713\n",
       "Alternative Fuel Economy Highway      713\n",
       "Alternative Fuel Economy Combined     620\n",
       "Conventional Fuel Economy City        438\n",
       "Conventional Fuel Economy Highway     438\n",
       "Conventional Fuel Economy Combined    438\n",
       "Transmission Type                     242\n",
       "Transmission Make                     757\n",
       "Engine Type                           293\n",
       "Engine Size                           293\n",
       "Engine Cylinder Count                 433\n",
       "Number of Passengers                  761\n",
       "Heavy-Duty Power System               637\n",
       "Notes                                 703\n",
       "Drivetrain                            553\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlternativeFuelVehiclesUS.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6761719e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Model Year</th>\n",
       "      <th>All-Electric Range</th>\n",
       "      <th>PHEV Total Range</th>\n",
       "      <th>Alternative Fuel Economy City</th>\n",
       "      <th>Alternative Fuel Economy Highway</th>\n",
       "      <th>Alternative Fuel Economy Combined</th>\n",
       "      <th>Conventional Fuel Economy City</th>\n",
       "      <th>Conventional Fuel Economy Highway</th>\n",
       "      <th>Conventional Fuel Economy Combined</th>\n",
       "      <th>Engine Cylinder Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>594.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>449.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2021.538721</td>\n",
       "      <td>165.927928</td>\n",
       "      <td>450.465116</td>\n",
       "      <td>84.585799</td>\n",
       "      <td>77.591716</td>\n",
       "      <td>75.992366</td>\n",
       "      <td>25.900901</td>\n",
       "      <td>30.085586</td>\n",
       "      <td>27.261261</td>\n",
       "      <td>5.452116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.498919</td>\n",
       "      <td>132.992955</td>\n",
       "      <td>93.042578</td>\n",
       "      <td>39.430696</td>\n",
       "      <td>32.735123</td>\n",
       "      <td>32.303279</td>\n",
       "      <td>10.860665</td>\n",
       "      <td>12.815653</td>\n",
       "      <td>9.803912</td>\n",
       "      <td>1.396121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Model Year  All-Electric Range  PHEV Total Range  \\\n",
       "count   594.000000          222.000000         43.000000   \n",
       "mean   2021.538721          165.927928        450.465116   \n",
       "std       0.498919          132.992955         93.042578   \n",
       "min    2021.000000            8.000000        290.000000   \n",
       "25%    2021.000000           23.000000        380.000000   \n",
       "50%    2022.000000          203.000000        460.000000   \n",
       "75%    2022.000000          260.000000        520.000000   \n",
       "max    2022.000000          520.000000        640.000000   \n",
       "\n",
       "       Alternative Fuel Economy City  Alternative Fuel Economy Highway  \\\n",
       "count                     169.000000                        169.000000   \n",
       "mean                       84.585799                         77.591716   \n",
       "std                        39.430696                         32.735123   \n",
       "min                        10.000000                         12.000000   \n",
       "25%                        71.000000                         66.000000   \n",
       "50%                        94.000000                         85.000000   \n",
       "75%                       115.000000                         99.000000   \n",
       "max                       150.000000                        133.000000   \n",
       "\n",
       "       Alternative Fuel Economy Combined  Conventional Fuel Economy City  \\\n",
       "count                         262.000000                      444.000000   \n",
       "mean                           75.992366                       25.900901   \n",
       "std                            32.303279                       10.860665   \n",
       "min                            11.000000                       13.000000   \n",
       "25%                            56.000000                       19.000000   \n",
       "50%                            76.000000                       22.000000   \n",
       "75%                           101.000000                       28.000000   \n",
       "max                           142.000000                       59.000000   \n",
       "\n",
       "       Conventional Fuel Economy Highway  Conventional Fuel Economy Combined  \\\n",
       "count                         444.000000                          444.000000   \n",
       "mean                           30.085586                           27.261261   \n",
       "std                            12.815653                            9.803912   \n",
       "min                            16.000000                           15.000000   \n",
       "25%                            24.000000                           21.000000   \n",
       "50%                            28.000000                           24.000000   \n",
       "75%                            33.000000                           30.000000   \n",
       "max                           231.000000                           59.000000   \n",
       "\n",
       "       Engine Cylinder Count  \n",
       "count             449.000000  \n",
       "mean                5.452116  \n",
       "std                 1.396121  \n",
       "min                 3.000000  \n",
       "25%                 4.000000  \n",
       "50%                 6.000000  \n",
       "75%                 6.000000  \n",
       "max                 8.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlternativeFuelVehiclesUS.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78c7f3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category                               object\n",
       "Model                                  object\n",
       "Model Year                            float64\n",
       "Manufacturer                           object\n",
       "Fuel                                   object\n",
       "All-Electric Range                    float64\n",
       "PHEV Total Range                      float64\n",
       "Alternative Fuel Economy City         float64\n",
       "Alternative Fuel Economy Highway      float64\n",
       "Alternative Fuel Economy Combined     float64\n",
       "Conventional Fuel Economy City        float64\n",
       "Conventional Fuel Economy Highway     float64\n",
       "Conventional Fuel Economy Combined    float64\n",
       "Transmission Type                      object\n",
       "Transmission Make                      object\n",
       "Engine Type                            object\n",
       "Engine Size                            object\n",
       "Engine Cylinder Count                 float64\n",
       "Number of Passengers                   object\n",
       "Heavy-Duty Power System                object\n",
       "Notes                                  object\n",
       "Drivetrain                             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AlternativeFuelVehiclesUS.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe83e9a",
   "metadata": {},
   "source": [
    "***Aqui hay datos nulos, hay que verificar y eliminar, hay columnas que podemos eliminar y otras que podemos hacerle dummies***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "969ca8d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "LightDutyVehicles=pd.read_csv('Datasets/LightDutyVehicles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3800ff06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle ID                               0\n",
       "Fuel ID                                  0\n",
       "Fuel Configuration ID                   35\n",
       "Manufacturer ID                          0\n",
       "Category ID                              0\n",
       "Model                                    0\n",
       "Model Year                               0\n",
       "Alternative Fuel Economy City         1413\n",
       "Alternative Fuel Economy Highway      1503\n",
       "Alternative Fuel Economy Combined     2541\n",
       "Conventional Fuel Economy City         964\n",
       "Conventional Fuel Economy Highway      962\n",
       "Conventional Fuel Economy Combined    2245\n",
       "Transmission Type                       84\n",
       "Engine Type                            797\n",
       "Engine Size                            134\n",
       "Engine Cylinder Count                  565\n",
       "Engine Description                    1037\n",
       "Manufacturer                             0\n",
       "Manufacturer URL                       164\n",
       "Category                                 0\n",
       "Fuel Code                                0\n",
       "Fuel                                     0\n",
       "Fuel Configuration Name                 35\n",
       "Electric-Only Range                   2634\n",
       "PHEV Total Range                      2965\n",
       "PHEV Type                             2832\n",
       "Notes                                 2935\n",
       "Drivetrain                            2679\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LightDutyVehicles.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61742004",
   "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>Vehicle ID</th>\n",
       "      <th>Fuel ID</th>\n",
       "      <th>Fuel Configuration ID</th>\n",
       "      <th>Manufacturer ID</th>\n",
       "      <th>Category ID</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Alternative Fuel Economy City</th>\n",
       "      <th>Alternative Fuel Economy Highway</th>\n",
       "      <th>Alternative Fuel Economy Combined</th>\n",
       "      <th>Conventional Fuel Economy City</th>\n",
       "      <th>Conventional Fuel Economy Highway</th>\n",
       "      <th>Conventional Fuel Economy Combined</th>\n",
       "      <th>Engine Cylinder Count</th>\n",
       "      <th>Electric-Only Range</th>\n",
       "      <th>PHEV Total Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3008.00000</td>\n",
       "      <td>3008.000000</td>\n",
       "      <td>2973.000000</td>\n",
       "      <td>3008.000000</td>\n",
       "      <td>3008.000000</td>\n",
       "      <td>3008.000000</td>\n",
       "      <td>1595.000000</td>\n",
       "      <td>1505.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>2044.000000</td>\n",
       "      <td>2046.000000</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>2443.000000</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9424.59242</td>\n",
       "      <td>31.003989</td>\n",
       "      <td>4.907164</td>\n",
       "      <td>255.855718</td>\n",
       "      <td>24.988697</td>\n",
       "      <td>2014.493019</td>\n",
       "      <td>38.439298</td>\n",
       "      <td>37.392651</td>\n",
       "      <td>72.126338</td>\n",
       "      <td>22.781605</td>\n",
       "      <td>27.443206</td>\n",
       "      <td>27.832241</td>\n",
       "      <td>6.085960</td>\n",
       "      <td>148.251337</td>\n",
       "      <td>450.465116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4667.98027</td>\n",
       "      <td>18.311570</td>\n",
       "      <td>3.263154</td>\n",
       "      <td>64.474477</td>\n",
       "      <td>6.021677</td>\n",
       "      <td>6.722191</td>\n",
       "      <td>47.778798</td>\n",
       "      <td>42.724296</td>\n",
       "      <td>36.776309</td>\n",
       "      <td>10.794132</td>\n",
       "      <td>10.123663</td>\n",
       "      <td>10.265666</td>\n",
       "      <td>1.762977</td>\n",
       "      <td>128.708353</td>\n",
       "      <td>93.042578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10280.75000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11560.50000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12336.25000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>254.750000</td>\n",
       "      <td>520.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13105.00000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>640.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Vehicle ID      Fuel ID  Fuel Configuration ID  Manufacturer ID  \\\n",
       "count   3008.00000  3008.000000            2973.000000      3008.000000   \n",
       "mean    9424.59242    31.003989               4.907164       255.855718   \n",
       "std     4667.98027    18.311570               3.263154        64.474477   \n",
       "min        1.00000     3.000000               1.000000       209.000000   \n",
       "25%    10280.75000    11.000000               1.000000       215.000000   \n",
       "50%    11560.50000    41.000000               5.000000       221.000000   \n",
       "75%    12336.25000    45.000000               9.000000       243.000000   \n",
       "max    13105.00000    57.000000               9.000000       470.000000   \n",
       "\n",
       "       Category ID   Model Year  Alternative Fuel Economy City  \\\n",
       "count  3008.000000  3008.000000                    1595.000000   \n",
       "mean     24.988697  2014.493019                      38.439298   \n",
       "std       6.021677     6.722191                      47.778798   \n",
       "min       3.000000  1991.000000                       0.000000   \n",
       "25%      25.000000  2012.000000                      11.000000   \n",
       "50%      27.000000  2016.000000                      14.000000   \n",
       "75%      29.000000  2020.000000                      60.000000   \n",
       "max      29.000000  2022.000000                     800.000000   \n",
       "\n",
       "       Alternative Fuel Economy Highway  Alternative Fuel Economy Combined  \\\n",
       "count                       1505.000000                         467.000000   \n",
       "mean                          37.392651                          72.126338   \n",
       "std                           42.724296                          36.776309   \n",
       "min                            0.000000                          10.000000   \n",
       "25%                           16.000000                          49.000000   \n",
       "50%                           20.000000                          74.000000   \n",
       "75%                           41.000000                         103.500000   \n",
       "max                          800.000000                         142.000000   \n",
       "\n",
       "       Conventional Fuel Economy City  Conventional Fuel Economy Highway  \\\n",
       "count                     2044.000000                        2046.000000   \n",
       "mean                        22.781605                          27.443206   \n",
       "std                         10.794132                          10.123663   \n",
       "min                          0.000000                           0.000000   \n",
       "25%                         16.000000                          21.000000   \n",
       "50%                         19.000000                          26.000000   \n",
       "75%                         26.000000                          31.000000   \n",
       "max                         66.000000                         231.000000   \n",
       "\n",
       "       Conventional Fuel Economy Combined  Engine Cylinder Count  \\\n",
       "count                          763.000000            2443.000000   \n",
       "mean                            27.832241               6.085960   \n",
       "std                             10.265666               1.762977   \n",
       "min                             15.000000               0.000000   \n",
       "25%                             21.000000               4.000000   \n",
       "50%                             24.000000               6.000000   \n",
       "75%                             31.000000               8.000000   \n",
       "max                             59.000000              12.000000   \n",
       "\n",
       "       Electric-Only Range  PHEV Total Range  \n",
       "count           374.000000         43.000000  \n",
       "mean            148.251337        450.465116  \n",
       "std             128.708353         93.042578  \n",
       "min               8.000000        290.000000  \n",
       "25%              21.000000        380.000000  \n",
       "50%             126.000000        460.000000  \n",
       "75%             254.750000        520.000000  \n",
       "max             520.000000        640.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LightDutyVehicles.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e22076e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vehicle ID                              int64\n",
       "Fuel ID                                 int64\n",
       "Fuel Configuration ID                 float64\n",
       "Manufacturer ID                         int64\n",
       "Category ID                             int64\n",
       "Model                                  object\n",
       "Model Year                              int64\n",
       "Alternative Fuel Economy City         float64\n",
       "Alternative Fuel Economy Highway      float64\n",
       "Alternative Fuel Economy Combined     float64\n",
       "Conventional Fuel Economy City        float64\n",
       "Conventional Fuel Economy Highway     float64\n",
       "Conventional Fuel Economy Combined    float64\n",
       "Transmission Type                      object\n",
       "Engine Type                            object\n",
       "Engine Size                            object\n",
       "Engine Cylinder Count                 float64\n",
       "Engine Description                     object\n",
       "Manufacturer                           object\n",
       "Manufacturer URL                       object\n",
       "Category                               object\n",
       "Fuel Code                              object\n",
       "Fuel                                   object\n",
       "Fuel Configuration Name                object\n",
       "Electric-Only Range                   float64\n",
       "PHEV Total Range                      float64\n",
       "PHEV Type                              object\n",
       "Notes                                  object\n",
       "Drivetrain                             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LightDutyVehicles.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db154d28",
   "metadata": {},
   "source": [
    "***Aqui hay datos nulos, hay que verificar y eliminar, hay columnas que podemos eliminar y otras que podemos hacerle dummies***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1f35a433",
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiZoneLookup=pd.read_csv('Datasets/taxi_zone_lookup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "334e1477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LocationID      0\n",
       "Borough         0\n",
       "Zone            1\n",
       "service_zone    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TaxiZoneLookup.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4458475d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>LocationID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>265.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>76.643112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>265.000000</td>\n",
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       "       LocationID\n",
       "count  265.000000\n",
       "mean   133.000000\n",
       "std     76.643112\n",
       "min      1.000000\n",
       "25%     67.000000\n",
       "50%    133.000000\n",
       "75%    199.000000\n",
       "max    265.000000"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "TaxiZoneLookup.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9573688f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LocationID       int64\n",
       "Borough         object\n",
       "Zone            object\n",
       "service_zone    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TaxiZoneLookup.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e2f024",
   "metadata": {},
   "source": [
    "***Aqui hay datos nulos, hay que verificar y eliminar, hay columnas que podemos eliminar y otras que podemos hacerle dummies***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d9f656fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8264/3156014710.py:1: DtypeWarning: Columns (73,75) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  VehicleFuelEconomyData=pd.read_csv('Datasets/VehicleFuelEconomyData.csv')\n"
     ]
    }
   ],
   "source": [
    "VehicleFuelEconomyData=pd.read_csv('Datasets/VehicleFuelEconomyData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4b4cf7fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                0\n",
       "Manufacturer        0\n",
       "Model              10\n",
       "barrels08          17\n",
       "barrelsA08         17\n",
       "                ...  \n",
       "modifiedOn       2016\n",
       "startStop       31829\n",
       "phevCity         2016\n",
       "phevHwy          2016\n",
       "phevComb         2016\n",
       "Length: 82, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VehicleFuelEconomyData.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "910cee73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>barrels08</th>\n",
       "      <th>barrelsA08</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityA08</th>\n",
       "      <th>cityA08U</th>\n",
       "      <th>cityCD</th>\n",
       "      <th>cityE</th>\n",
       "      <th>...</th>\n",
       "      <th>rangeHwyA</th>\n",
       "      <th>UCity</th>\n",
       "      <th>UCityA</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>UHighwayA</th>\n",
       "      <th>youSaveSpend</th>\n",
       "      <th>charge240b</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46186.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>46169.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>44192.000000</td>\n",
       "      <td>44192.000000</td>\n",
       "      <td>44192.000000</td>\n",
       "      <td>44192.000000</td>\n",
       "      <td>44192.000000</td>\n",
       "      <td>44192.000000</td>\n",
       "      <td>44170.000000</td>\n",
       "      <td>44170.000000</td>\n",
       "      <td>44170.000000</td>\n",
       "      <td>44170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2003.733079</td>\n",
       "      <td>15.306719</td>\n",
       "      <td>0.166865</td>\n",
       "      <td>0.114687</td>\n",
       "      <td>19.167493</td>\n",
       "      <td>7.965150</td>\n",
       "      <td>0.843618</td>\n",
       "      <td>0.701921</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.701618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164464</td>\n",
       "      <td>24.554556</td>\n",
       "      <td>1.107372</td>\n",
       "      <td>35.704448</td>\n",
       "      <td>0.866718</td>\n",
       "      <td>-5093.925371</td>\n",
       "      <td>0.014150</td>\n",
       "      <td>0.283858</td>\n",
       "      <td>0.286710</td>\n",
       "      <td>0.283903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.252017</td>\n",
       "      <td>4.343434</td>\n",
       "      <td>0.869974</td>\n",
       "      <td>1.012054</td>\n",
       "      <td>10.448985</td>\n",
       "      <td>14.226484</td>\n",
       "      <td>6.450475</td>\n",
       "      <td>6.316454</td>\n",
       "      <td>0.033236</td>\n",
       "      <td>5.649704</td>\n",
       "      <td>...</td>\n",
       "      <td>2.477254</td>\n",
       "      <td>15.141158</td>\n",
       "      <td>9.236931</td>\n",
       "      <td>13.782685</td>\n",
       "      <td>5.869861</td>\n",
       "      <td>4258.449576</td>\n",
       "      <td>0.296926</td>\n",
       "      <td>3.845646</td>\n",
       "      <td>3.723701</td>\n",
       "      <td>3.766720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1984.000000</td>\n",
       "      <td>0.047081</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-37250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1992.000000</td>\n",
       "      <td>12.935217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.749900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7750.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>14.875500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4750.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>17.500588</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.666700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024.000000</td>\n",
       "      <td>42.501429</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>224.800000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>187.100000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year     barrels08    barrelsA08     charge240        city08  \\\n",
       "count  46186.000000  46169.000000  46169.000000  46169.000000  46169.000000   \n",
       "mean    2003.733079     15.306719      0.166865      0.114687     19.167493   \n",
       "std       12.252017      4.343434      0.869974      1.012054     10.448985   \n",
       "min     1984.000000      0.047081      0.000000      0.000000      6.000000   \n",
       "25%     1992.000000     12.935217      0.000000      0.000000     15.000000   \n",
       "50%     2005.000000     14.875500      0.000000      0.000000     18.000000   \n",
       "75%     2015.000000     17.500588      0.000000      0.000000     21.000000   \n",
       "max     2024.000000     42.501429     16.000000     19.000000    153.000000   \n",
       "\n",
       "            city08U       cityA08      cityA08U        cityCD         cityE  \\\n",
       "count  46169.000000  46169.000000  46169.000000  46169.000000  46169.000000   \n",
       "mean       7.965150      0.843618      0.701921      0.000238      0.701618   \n",
       "std       14.226484      6.450475      6.316454      0.033236      5.649704   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       17.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max      153.000000    145.000000    145.000000      5.000000    122.000000   \n",
       "\n",
       "       ...     rangeHwyA         UCity        UCityA      UHighway  \\\n",
       "count  ...  44192.000000  44192.000000  44192.000000  44192.000000   \n",
       "mean   ...      0.164464     24.554556      1.107372     35.704448   \n",
       "std    ...      2.477254     15.141158      9.236931     13.782685   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...      0.000000     18.749900      0.000000     28.300000   \n",
       "50%    ...      0.000000     22.000000      0.000000     34.000000   \n",
       "75%    ...      0.000000     26.666700      0.000000     40.000000   \n",
       "max    ...    114.000000    224.800000    207.000000    187.100000   \n",
       "\n",
       "          UHighwayA  youSaveSpend    charge240b      phevCity       phevHwy  \\\n",
       "count  44192.000000  44192.000000  44170.000000  44170.000000  44170.000000   \n",
       "mean       0.866718  -5093.925371      0.014150      0.283858      0.286710   \n",
       "std        5.869861   4258.449576      0.296926      3.845646      3.723701   \n",
       "min        0.000000 -37250.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000  -7750.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000  -4750.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000  -2250.000000      0.000000      0.000000      0.000000   \n",
       "max      173.000000   7000.000000      9.000000     97.000000     81.000000   \n",
       "\n",
       "           phevComb  \n",
       "count  44170.000000  \n",
       "mean       0.283903  \n",
       "std        3.766720  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max       88.000000  \n",
       "\n",
       "[8 rows x 58 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VehicleFuelEconomyData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d171ce5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year              int64\n",
       "Manufacturer     object\n",
       "Model            object\n",
       "barrels08       float64\n",
       "barrelsA08      float64\n",
       "                 ...   \n",
       "modifiedOn       object\n",
       "startStop        object\n",
       "phevCity        float64\n",
       "phevHwy         float64\n",
       "phevComb        float64\n",
       "Length: 82, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VehicleFuelEconomyData.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ca52d3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Model</th>\n",
       "      <th>barrels08</th>\n",
       "      <th>barrelsA08</th>\n",
       "      <th>charge240</th>\n",
       "      <th>city08</th>\n",
       "      <th>city08U</th>\n",
       "      <th>cityA08</th>\n",
       "      <th>cityA08U</th>\n",
       "      <th>...</th>\n",
       "      <th>mfrCode</th>\n",
       "      <th>c240Dscr</th>\n",
       "      <th>charge240b</th>\n",
       "      <th>c240bDscr</th>\n",
       "      <th>createdOn</th>\n",
       "      <th>modifiedOn</th>\n",
       "      <th>startStop</th>\n",
       "      <th>phevCity</th>\n",
       "      <th>phevHwy</th>\n",
       "      <th>phevComb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>BMW</td>\n",
       "      <td>M6 Coupe</td>\n",
       "      <td>17.500588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>BMX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-04-2016</td>\n",
       "      <td>09-09-2016</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>BMW</td>\n",
       "      <td>X3 sDrive28i</td>\n",
       "      <td>12.396250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>BMX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-04-2016</td>\n",
       "      <td>22-11-2016</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>Infiniti</td>\n",
       "      <td>Q50S Red Sport</td>\n",
       "      <td>13.523182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NSX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-04-2016</td>\n",
       "      <td>26-09-2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>MINI</td>\n",
       "      <td>John Cooper Works Convertible</td>\n",
       "      <td>11.018889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>BMX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12-04-2016</td>\n",
       "      <td>16-09-2016</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>Kia</td>\n",
       "      <td>Sedona SX</td>\n",
       "      <td>14.167143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KMX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12-04-2016</td>\n",
       "      <td>02-12-2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46181</th>\n",
       "      <td>1993</td>\n",
       "      <td>Plymouth</td>\n",
       "      <td>Sundance/Duster</td>\n",
       "      <td>12.935217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46182</th>\n",
       "      <td>1993</td>\n",
       "      <td>Pontiac</td>\n",
       "      <td>Grand Am</td>\n",
       "      <td>14.167143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46183</th>\n",
       "      <td>1993</td>\n",
       "      <td>Saab</td>\n",
       "      <td>900</td>\n",
       "      <td>14.167143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46184</th>\n",
       "      <td>1993</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Legacy</td>\n",
       "      <td>13.523182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46185</th>\n",
       "      <td>1993</td>\n",
       "      <td>Subaru</td>\n",
       "      <td>Legacy AWD</td>\n",
       "      <td>14.167143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>01-01-2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46186 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year Manufacturer                          Model  barrels08  \\\n",
       "0      2017          BMW                       M6 Coupe  17.500588   \n",
       "1      2017          BMW                   X3 sDrive28i  12.396250   \n",
       "2      2016     Infiniti                 Q50S Red Sport  13.523182   \n",
       "3      2017         MINI  John Cooper Works Convertible  11.018889   \n",
       "4      2017          Kia                      Sedona SX  14.167143   \n",
       "...     ...          ...                            ...        ...   \n",
       "46181  1993     Plymouth                Sundance/Duster  12.935217   \n",
       "46182  1993      Pontiac                       Grand Am  14.167143   \n",
       "46183  1993         Saab                            900  14.167143   \n",
       "46184  1993       Subaru                         Legacy  13.523182   \n",
       "46185  1993       Subaru                     Legacy AWD  14.167143   \n",
       "\n",
       "       barrelsA08  charge240  city08  city08U  cityA08  cityA08U  ...  \\\n",
       "0             0.0        0.0    15.0     14.0      0.0       0.0  ...   \n",
       "1             0.0        0.0    21.0     21.0      0.0       0.0  ...   \n",
       "2             0.0        0.0    20.0     19.0      0.0       0.0  ...   \n",
       "3             0.0        0.0    24.0     23.0      0.0       0.0  ...   \n",
       "4             0.0        0.0    18.0     18.0      0.0       0.0  ...   \n",
       "...           ...        ...     ...      ...      ...       ...  ...   \n",
       "46181         0.0        0.0    21.0      0.0      0.0       0.0  ...   \n",
       "46182         0.0        0.0    18.0      0.0      0.0       0.0  ...   \n",
       "46183         0.0        0.0    18.0      0.0      0.0       0.0  ...   \n",
       "46184         0.0        0.0    19.0      0.0      0.0       0.0  ...   \n",
       "46185         0.0        0.0    18.0      0.0      0.0       0.0  ...   \n",
       "\n",
       "       mfrCode  c240Dscr  charge240b  c240bDscr   createdOn  modifiedOn  \\\n",
       "0          BMX       NaN         0.0        NaN  04-04-2016  09-09-2016   \n",
       "1          BMX       NaN         0.0        NaN  04-04-2016  22-11-2016   \n",
       "2          NSX       NaN         0.0        NaN  04-04-2016  26-09-2016   \n",
       "3          BMX       NaN         0.0        NaN  12-04-2016  16-09-2016   \n",
       "4          KMX       NaN         0.0        NaN  12-04-2016  02-12-2016   \n",
       "...        ...       ...         ...        ...         ...         ...   \n",
       "46181      NaN       NaN         0.0        NaN  01-01-2013  01-01-2013   \n",
       "46182      NaN       NaN         0.0        NaN  01-01-2013  01-01-2013   \n",
       "46183      NaN       NaN         NaN        NaN         NaN         NaN   \n",
       "46184      NaN       NaN         0.0        NaN  01-01-2013  01-01-2013   \n",
       "46185      NaN       NaN         0.0        NaN  01-01-2013  01-01-2013   \n",
       "\n",
       "       startStop  phevCity  phevHwy  phevComb  \n",
       "0              Y       0.0      0.0       0.0  \n",
       "1              Y       0.0      0.0       0.0  \n",
       "2              N       0.0      0.0       0.0  \n",
       "3              Y       0.0      0.0       0.0  \n",
       "4              N       0.0      0.0       0.0  \n",
       "...          ...       ...      ...       ...  \n",
       "46181        NaN       0.0      0.0       0.0  \n",
       "46182        NaN       0.0      0.0       0.0  \n",
       "46183        NaN       NaN      NaN       NaN  \n",
       "46184        NaN       0.0      0.0       0.0  \n",
       "46185        NaN       0.0      0.0       0.0  \n",
       "\n",
       "[46186 rows x 82 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VehicleFuelEconomyData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f52f8f6",
   "metadata": {},
   "source": [
    "***Aqui hay datos nulos, hay que verificar y eliminar, hay columnas que podemos eliminar y otras que podemos \n",
    "hacerle dummies. Ademas estan desordenados, hay que ordenarlos, son datos interesantes, pero no los entiendo***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1a44bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "yellowTrip2023_01=pd.read_parquet('yellow_tripdata_2023-01.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b6385a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                     0\n",
       "tpep_pickup_datetime         0\n",
       "tpep_dropoff_datetime        0\n",
       "passenger_count          71743\n",
       "trip_distance                0\n",
       "RatecodeID               71743\n",
       "store_and_fwd_flag       71743\n",
       "PULocationID                 0\n",
       "DOLocationID                 0\n",
       "payment_type                 0\n",
       "fare_amount                  0\n",
       "extra                        0\n",
       "mta_tax                      0\n",
       "tip_amount                   0\n",
       "tolls_amount                 0\n",
       "improvement_surcharge        0\n",
       "total_amount                 0\n",
       "congestion_surcharge     71743\n",
       "airport_fee              71743\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_01.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "317648c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>2.995023e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>2.995023e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>3.066766e+06</td>\n",
       "      <td>2.995023e+06</td>\n",
       "      <td>2.995023e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.730215e+00</td>\n",
       "      <td>1.362532e+00</td>\n",
       "      <td>3.847342e+00</td>\n",
       "      <td>1.497440e+00</td>\n",
       "      <td>1.663980e+02</td>\n",
       "      <td>1.643926e+02</td>\n",
       "      <td>1.194483e+00</td>\n",
       "      <td>1.836707e+01</td>\n",
       "      <td>1.537842e+00</td>\n",
       "      <td>4.882900e-01</td>\n",
       "      <td>3.367941e+00</td>\n",
       "      <td>5.184907e-01</td>\n",
       "      <td>9.820847e-01</td>\n",
       "      <td>2.702038e+01</td>\n",
       "      <td>2.274231e+00</td>\n",
       "      <td>1.074086e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.438480e-01</td>\n",
       "      <td>8.961200e-01</td>\n",
       "      <td>2.495838e+02</td>\n",
       "      <td>6.474767e+00</td>\n",
       "      <td>6.424413e+01</td>\n",
       "      <td>6.994368e+01</td>\n",
       "      <td>5.294762e-01</td>\n",
       "      <td>1.780782e+01</td>\n",
       "      <td>1.789592e+00</td>\n",
       "      <td>1.034641e-01</td>\n",
       "      <td>3.826759e+00</td>\n",
       "      <td>2.017579e+00</td>\n",
       "      <td>1.833529e-01</td>\n",
       "      <td>2.216359e+01</td>\n",
       "      <td>7.718454e-01</td>\n",
       "      <td>3.556511e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.000000e+02</td>\n",
       "      <td>-7.500000e+00</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-9.622000e+01</td>\n",
       "      <td>-6.500000e+01</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-7.510000e+02</td>\n",
       "      <td>-2.500000e+00</td>\n",
       "      <td>-1.250000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.060000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.320000e+02</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.600000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.540000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.800000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.280000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>2.720000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.016000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.330000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.050000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>4.200000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.870000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.589281e+05</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>2.650000e+02</td>\n",
       "      <td>2.650000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.160100e+03</td>\n",
       "      <td>1.250000e+01</td>\n",
       "      <td>5.316000e+01</td>\n",
       "      <td>3.808000e+02</td>\n",
       "      <td>1.969900e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.169400e+03</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>1.250000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VendorID  passenger_count  trip_distance    RatecodeID  \\\n",
       "count  3.066766e+06     2.995023e+06   3.066766e+06  2.995023e+06   \n",
       "mean   1.730215e+00     1.362532e+00   3.847342e+00  1.497440e+00   \n",
       "std    4.438480e-01     8.961200e-01   2.495838e+02  6.474767e+00   \n",
       "min    1.000000e+00     0.000000e+00   0.000000e+00  1.000000e+00   \n",
       "25%    1.000000e+00     1.000000e+00   1.060000e+00  1.000000e+00   \n",
       "50%    2.000000e+00     1.000000e+00   1.800000e+00  1.000000e+00   \n",
       "75%    2.000000e+00     1.000000e+00   3.330000e+00  1.000000e+00   \n",
       "max    2.000000e+00     9.000000e+00   2.589281e+05  9.900000e+01   \n",
       "\n",
       "       PULocationID  DOLocationID  payment_type   fare_amount         extra  \\\n",
       "count  3.066766e+06  3.066766e+06  3.066766e+06  3.066766e+06  3.066766e+06   \n",
       "mean   1.663980e+02  1.643926e+02  1.194483e+00  1.836707e+01  1.537842e+00   \n",
       "std    6.424413e+01  6.994368e+01  5.294762e-01  1.780782e+01  1.789592e+00   \n",
       "min    1.000000e+00  1.000000e+00  0.000000e+00 -9.000000e+02 -7.500000e+00   \n",
       "25%    1.320000e+02  1.140000e+02  1.000000e+00  8.600000e+00  0.000000e+00   \n",
       "50%    1.620000e+02  1.620000e+02  1.000000e+00  1.280000e+01  1.000000e+00   \n",
       "75%    2.340000e+02  2.340000e+02  1.000000e+00  2.050000e+01  2.500000e+00   \n",
       "max    2.650000e+02  2.650000e+02  4.000000e+00  1.160100e+03  1.250000e+01   \n",
       "\n",
       "            mta_tax    tip_amount  tolls_amount  improvement_surcharge  \\\n",
       "count  3.066766e+06  3.066766e+06  3.066766e+06           3.066766e+06   \n",
       "mean   4.882900e-01  3.367941e+00  5.184907e-01           9.820847e-01   \n",
       "std    1.034641e-01  3.826759e+00  2.017579e+00           1.833529e-01   \n",
       "min   -5.000000e-01 -9.622000e+01 -6.500000e+01          -1.000000e+00   \n",
       "25%    5.000000e-01  1.000000e+00  0.000000e+00           1.000000e+00   \n",
       "50%    5.000000e-01  2.720000e+00  0.000000e+00           1.000000e+00   \n",
       "75%    5.000000e-01  4.200000e+00  0.000000e+00           1.000000e+00   \n",
       "max    5.316000e+01  3.808000e+02  1.969900e+02           1.000000e+00   \n",
       "\n",
       "       total_amount  congestion_surcharge   airport_fee  \n",
       "count  3.066766e+06          2.995023e+06  2.995023e+06  \n",
       "mean   2.702038e+01          2.274231e+00  1.074086e-01  \n",
       "std    2.216359e+01          7.718454e-01  3.556511e-01  \n",
       "min   -7.510000e+02         -2.500000e+00 -1.250000e+00  \n",
       "25%    1.540000e+01          2.500000e+00  0.000000e+00  \n",
       "50%    2.016000e+01          2.500000e+00  0.000000e+00  \n",
       "75%    2.870000e+01          2.500000e+00  0.000000e+00  \n",
       "max    1.169400e+03          2.500000e+00  1.250000e+00  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_01.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4fdecaaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                          int64\n",
       "tpep_pickup_datetime     datetime64[ns]\n",
       "tpep_dropoff_datetime    datetime64[ns]\n",
       "passenger_count                 float64\n",
       "trip_distance                   float64\n",
       "RatecodeID                      float64\n",
       "store_and_fwd_flag               object\n",
       "PULocationID                      int64\n",
       "DOLocationID                      int64\n",
       "payment_type                      int64\n",
       "fare_amount                     float64\n",
       "extra                           float64\n",
       "mta_tax                         float64\n",
       "tip_amount                      float64\n",
       "tolls_amount                    float64\n",
       "improvement_surcharge           float64\n",
       "total_amount                    float64\n",
       "congestion_surcharge            float64\n",
       "airport_fee                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_01.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d08f04ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>airport_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-01-01 00:32:10</td>\n",
       "      <td>2023-01-01 00:40:36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-01-01 00:55:08</td>\n",
       "      <td>2023-01-01 01:01:27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>43</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-01-01 00:25:04</td>\n",
       "      <td>2023-01-01 00:37:49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>48</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.90</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 00:03:48</td>\n",
       "      <td>2023-01-01 00:13:25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-01-01 00:10:29</td>\n",
       "      <td>2023-01-01 00:21:19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>107</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.68</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         2  2023-01-01 00:32:10   2023-01-01 00:40:36              1.0   \n",
       "1         2  2023-01-01 00:55:08   2023-01-01 01:01:27              1.0   \n",
       "2         2  2023-01-01 00:25:04   2023-01-01 00:37:49              1.0   \n",
       "3         1  2023-01-01 00:03:48   2023-01-01 00:13:25              0.0   \n",
       "4         2  2023-01-01 00:10:29   2023-01-01 00:21:19              1.0   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0           0.97         1.0                  N           161           141   \n",
       "1           1.10         1.0                  N            43           237   \n",
       "2           2.51         1.0                  N            48           238   \n",
       "3           1.90         1.0                  N           138             7   \n",
       "4           1.43         1.0                  N           107            79   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             2          9.3   1.00      0.5        0.00           0.0   \n",
       "1             1          7.9   1.00      0.5        4.00           0.0   \n",
       "2             1         14.9   1.00      0.5       15.00           0.0   \n",
       "3             1         12.1   7.25      0.5        0.00           0.0   \n",
       "4             1         11.4   1.00      0.5        3.28           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  airport_fee  \n",
       "0                    1.0         14.30                   2.5         0.00  \n",
       "1                    1.0         16.90                   2.5         0.00  \n",
       "2                    1.0         34.90                   2.5         0.00  \n",
       "3                    1.0         20.85                   0.0         1.25  \n",
       "4                    1.0         19.68                   2.5         0.00  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_01.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ea0150",
   "metadata": {},
   "source": [
    "***Aqui hay datos nulos, hay que verificar y eliminar, hay columnas que podemos eliminar y otras que podemos hacerle dummies***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7e19056e",
   "metadata": {},
   "outputs": [],
   "source": [
    "yellowTrip2023_03=pd.read_parquet('yellow_tripdata_2023-03.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d1ed015e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                     0\n",
       "tpep_pickup_datetime         0\n",
       "tpep_dropoff_datetime        0\n",
       "passenger_count          87619\n",
       "trip_distance                0\n",
       "RatecodeID               87619\n",
       "store_and_fwd_flag       87619\n",
       "PULocationID                 0\n",
       "DOLocationID                 0\n",
       "payment_type                 0\n",
       "fare_amount                  0\n",
       "extra                        0\n",
       "mta_tax                      0\n",
       "tip_amount                   0\n",
       "tolls_amount                 0\n",
       "improvement_surcharge        0\n",
       "total_amount                 0\n",
       "congestion_surcharge     87619\n",
       "Airport_fee              87619\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_03.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69124d7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>Airport_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.316147e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.316147e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.403766e+06</td>\n",
       "      <td>3.316147e+06</td>\n",
       "      <td>3.316147e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.725320e+00</td>\n",
       "      <td>1.351417e+00</td>\n",
       "      <td>3.903871e+00</td>\n",
       "      <td>1.623961e+00</td>\n",
       "      <td>1.654540e+02</td>\n",
       "      <td>1.641003e+02</td>\n",
       "      <td>1.187310e+00</td>\n",
       "      <td>1.890845e+01</td>\n",
       "      <td>1.629128e+00</td>\n",
       "      <td>4.874393e-01</td>\n",
       "      <td>3.495237e+00</td>\n",
       "      <td>5.670059e-01</td>\n",
       "      <td>9.810640e-01</td>\n",
       "      <td>2.780343e+01</td>\n",
       "      <td>2.275176e+00</td>\n",
       "      <td>1.040236e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.588677e-01</td>\n",
       "      <td>8.861919e-01</td>\n",
       "      <td>1.916866e+02</td>\n",
       "      <td>7.358691e+00</td>\n",
       "      <td>6.373958e+01</td>\n",
       "      <td>6.967282e+01</td>\n",
       "      <td>5.334025e-01</td>\n",
       "      <td>1.825637e+01</td>\n",
       "      <td>1.842778e+00</td>\n",
       "      <td>1.020516e-01</td>\n",
       "      <td>3.996473e+00</td>\n",
       "      <td>2.121658e+00</td>\n",
       "      <td>1.892314e-01</td>\n",
       "      <td>2.286614e+01</td>\n",
       "      <td>7.745563e-01</td>\n",
       "      <td>3.503260e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.599000e+02</td>\n",
       "      <td>-7.500000e+00</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>-8.000000e+01</td>\n",
       "      <td>-7.330000e+01</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-9.829500e+02</td>\n",
       "      <td>-2.500000e+00</td>\n",
       "      <td>-1.250000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.050000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.320000e+02</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.300000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.570000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.790000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.350000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.380000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.330000e+02</td>\n",
       "      <td>2.340000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>4.340000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.976000e+01</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.169870e+05</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>2.650000e+02</td>\n",
       "      <td>2.650000e+02</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>2.100000e+03</td>\n",
       "      <td>1.375000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>9.843000e+02</td>\n",
       "      <td>1.770000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.100000e+03</td>\n",
       "      <td>2.500000e+00</td>\n",
       "      <td>1.250000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VendorID  passenger_count  trip_distance    RatecodeID  \\\n",
       "count  3.403766e+06     3.316147e+06   3.403766e+06  3.316147e+06   \n",
       "mean   1.725320e+00     1.351417e+00   3.903871e+00  1.623961e+00   \n",
       "std    4.588677e-01     8.861919e-01   1.916866e+02  7.358691e+00   \n",
       "min    1.000000e+00     0.000000e+00   0.000000e+00  1.000000e+00   \n",
       "25%    1.000000e+00     1.000000e+00   1.050000e+00  1.000000e+00   \n",
       "50%    2.000000e+00     1.000000e+00   1.790000e+00  1.000000e+00   \n",
       "75%    2.000000e+00     1.000000e+00   3.380000e+00  1.000000e+00   \n",
       "max    6.000000e+00     9.000000e+00   2.169870e+05  9.900000e+01   \n",
       "\n",
       "       PULocationID  DOLocationID  payment_type   fare_amount         extra  \\\n",
       "count  3.403766e+06  3.403766e+06  3.403766e+06  3.403766e+06  3.403766e+06   \n",
       "mean   1.654540e+02  1.641003e+02  1.187310e+00  1.890845e+01  1.629128e+00   \n",
       "std    6.373958e+01  6.967282e+01  5.334025e-01  1.825637e+01  1.842778e+00   \n",
       "min    1.000000e+00  1.000000e+00  0.000000e+00 -9.599000e+02 -7.500000e+00   \n",
       "25%    1.320000e+02  1.140000e+02  1.000000e+00  9.300000e+00  0.000000e+00   \n",
       "50%    1.620000e+02  1.620000e+02  1.000000e+00  1.350000e+01  1.000000e+00   \n",
       "75%    2.330000e+02  2.340000e+02  1.000000e+00  2.120000e+01  2.500000e+00   \n",
       "max    2.650000e+02  2.650000e+02  5.000000e+00  2.100000e+03  1.375000e+01   \n",
       "\n",
       "            mta_tax    tip_amount  tolls_amount  improvement_surcharge  \\\n",
       "count  3.403766e+06  3.403766e+06  3.403766e+06           3.403766e+06   \n",
       "mean   4.874393e-01  3.495237e+00  5.670059e-01           9.810640e-01   \n",
       "std    1.020516e-01  3.996473e+00  2.121658e+00           1.892314e-01   \n",
       "min   -5.000000e-01 -8.000000e+01 -7.330000e+01          -1.000000e+00   \n",
       "25%    5.000000e-01  1.000000e+00  0.000000e+00           1.000000e+00   \n",
       "50%    5.000000e-01  2.800000e+00  0.000000e+00           1.000000e+00   \n",
       "75%    5.000000e-01  4.340000e+00  0.000000e+00           1.000000e+00   \n",
       "max    4.000000e+00  9.843000e+02  1.770000e+02           1.000000e+00   \n",
       "\n",
       "       total_amount  congestion_surcharge   Airport_fee  \n",
       "count  3.403766e+06          3.316147e+06  3.316147e+06  \n",
       "mean   2.780343e+01          2.275176e+00  1.040236e-01  \n",
       "std    2.286614e+01          7.745563e-01  3.503260e-01  \n",
       "min   -9.829500e+02         -2.500000e+00 -1.250000e+00  \n",
       "25%    1.570000e+01          2.500000e+00  0.000000e+00  \n",
       "50%    2.060000e+01          2.500000e+00  0.000000e+00  \n",
       "75%    2.976000e+01          2.500000e+00  0.000000e+00  \n",
       "max    2.100000e+03          2.500000e+00  1.250000e+00  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_03.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "76f12275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                          int32\n",
       "tpep_pickup_datetime     datetime64[ns]\n",
       "tpep_dropoff_datetime    datetime64[ns]\n",
       "passenger_count                 float64\n",
       "trip_distance                   float64\n",
       "RatecodeID                      float64\n",
       "store_and_fwd_flag               object\n",
       "PULocationID                      int32\n",
       "DOLocationID                      int32\n",
       "payment_type                      int64\n",
       "fare_amount                     float64\n",
       "extra                           float64\n",
       "mta_tax                         float64\n",
       "tip_amount                      float64\n",
       "tolls_amount                    float64\n",
       "improvement_surcharge           float64\n",
       "total_amount                    float64\n",
       "congestion_surcharge            float64\n",
       "Airport_fee                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_03.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0de5a11b",
   "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>Airport_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-03-01 00:06:43</td>\n",
       "      <td>2023-03-01 00:16:43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>238</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-03-01 00:08:25</td>\n",
       "      <td>2023-03-01 00:39:30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>138</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>52.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.49</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-03-01 00:15:04</td>\n",
       "      <td>2023-03-01 00:29:26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>140</td>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.05</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-03-01 00:49:37</td>\n",
       "      <td>2023-03-01 01:01:05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>140</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.70</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-03-01 00:08:04</td>\n",
       "      <td>2023-03-01 00:11:06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>79</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.64</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         2  2023-03-01 00:06:43   2023-03-01 00:16:43              1.0   \n",
       "1         2  2023-03-01 00:08:25   2023-03-01 00:39:30              2.0   \n",
       "2         1  2023-03-01 00:15:04   2023-03-01 00:29:26              0.0   \n",
       "3         1  2023-03-01 00:49:37   2023-03-01 01:01:05              1.0   \n",
       "4         2  2023-03-01 00:08:04   2023-03-01 00:11:06              1.0   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0           0.00         1.0                  N           238            42   \n",
       "1          12.40         1.0                  N           138           231   \n",
       "2           3.30         1.0                  N           140           186   \n",
       "3           2.90         1.0                  N           140            43   \n",
       "4           1.23         1.0                  N            79           137   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             2          8.6    1.0      0.5        0.00           0.0   \n",
       "1             1         52.7    6.0      0.5       12.54           0.0   \n",
       "2             1         18.4    3.5      0.5        4.65           0.0   \n",
       "3             1         15.6    3.5      0.5        4.10           0.0   \n",
       "4             1          7.2    1.0      0.5        2.44           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  Airport_fee  \n",
       "0                    1.0         11.10                   0.0         0.00  \n",
       "1                    1.0         76.49                   2.5         1.25  \n",
       "2                    1.0         28.05                   2.5         0.00  \n",
       "3                    1.0         24.70                   2.5         0.00  \n",
       "4                    1.0         14.64                   2.5         0.00  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yellowTrip2023_03.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f149c656",
   "metadata": {},
   "source": [
    "***Aqui hay datos nulos, hay que verificar y eliminar, hay columnas que podemos eliminar y otras que podemos hacerle dummies***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9a5645",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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