Notebooks/Encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"/home/gabriel/Downloads/traffic-collision-data-from-2010-to-present.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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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>DR Number</th>\n",
       "      <th>Date Reported</th>\n",
       "      <th>Date Occurred</th>\n",
       "      <th>Time Occurred</th>\n",
       "      <th>Area ID</th>\n",
       "      <th>Area Name</th>\n",
       "      <th>Reporting District</th>\n",
       "      <th>Crime Code</th>\n",
       "      <th>Crime Code Description</th>\n",
       "      <th>MO Codes</th>\n",
       "      <th>...</th>\n",
       "      <th>Premise Description</th>\n",
       "      <th>Address</th>\n",
       "      <th>Cross Street</th>\n",
       "      <th>Location</th>\n",
       "      <th>Zip Codes</th>\n",
       "      <th>Census Tracts</th>\n",
       "      <th>Precinct Boundaries</th>\n",
       "      <th>LA Specific Plans</th>\n",
       "      <th>Council Districts</th>\n",
       "      <th>Neighborhood Councils (Certified)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191323054</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>130</td>\n",
       "      <td>13</td>\n",
       "      <td>Newton</td>\n",
       "      <td>1385</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>STREET</td>\n",
       "      <td>SAN PEDRO                    ST</td>\n",
       "      <td>SLAUSON                      AV</td>\n",
       "      <td>{'longitude': '-118.2696', 'human_address': '{...</td>\n",
       "      <td>22352.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>192020666</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>Olympic</td>\n",
       "      <td>2054</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>STREET</td>\n",
       "      <td>OLYMPIC                      BL</td>\n",
       "      <td>KINGSLEY                     AV</td>\n",
       "      <td>{'longitude': '-118.3031', 'human_address': '{...</td>\n",
       "      <td>22723.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191616992</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>230</td>\n",
       "      <td>16</td>\n",
       "      <td>Foothill</td>\n",
       "      <td>1669</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>STREET</td>\n",
       "      <td>TUJUNGA CANYON               BL</td>\n",
       "      <td>LA TUNA CANYON               RD</td>\n",
       "      <td>{'longitude': '-118.2704', 'human_address': '{...</td>\n",
       "      <td>3222.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191824082</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>730</td>\n",
       "      <td>18</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1802</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>0605</td>\n",
       "      <td>...</td>\n",
       "      <td>STREET</td>\n",
       "      <td>88TH                         ST</td>\n",
       "      <td>MAIN                         ST</td>\n",
       "      <td>{'longitude': '-118.2739', 'human_address': '{...</td>\n",
       "      <td>22352.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>191616980</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>720</td>\n",
       "      <td>16</td>\n",
       "      <td>Foothill</td>\n",
       "      <td>1689</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>STREET</td>\n",
       "      <td>CROCKETT</td>\n",
       "      <td>SUNLAND</td>\n",
       "      <td>{'longitude': '-118.3703', 'human_address': '{...</td>\n",
       "      <td>19335.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DR Number            Date Reported            Date Occurred  Time Occurred  \\\n",
       "0  191323054  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            130   \n",
       "1  192020666  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000             15   \n",
       "2  191616992  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            230   \n",
       "3  191824082  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            730   \n",
       "4  191616980  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            720   \n",
       "\n",
       "   Area ID  Area Name  Reporting District  Crime Code Crime Code Description  \\\n",
       "0       13     Newton                1385         997      TRAFFIC COLLISION   \n",
       "1       20    Olympic                2054         997      TRAFFIC COLLISION   \n",
       "2       16   Foothill                1669         997      TRAFFIC COLLISION   \n",
       "3       18  Southeast                1802         997      TRAFFIC COLLISION   \n",
       "4       16   Foothill                1689         997      TRAFFIC COLLISION   \n",
       "\n",
       "  MO Codes  ...  Premise Description                          Address  \\\n",
       "0      NaN  ...               STREET  SAN PEDRO                    ST   \n",
       "1      NaN  ...               STREET  OLYMPIC                      BL   \n",
       "2      NaN  ...               STREET  TUJUNGA CANYON               BL   \n",
       "3     0605  ...               STREET  88TH                         ST   \n",
       "4      NaN  ...               STREET                         CROCKETT   \n",
       "\n",
       "                      Cross Street  \\\n",
       "0  SLAUSON                      AV   \n",
       "1  KINGSLEY                     AV   \n",
       "2  LA TUNA CANYON               RD   \n",
       "3  MAIN                         ST   \n",
       "4                          SUNLAND   \n",
       "\n",
       "                                            Location Zip Codes Census Tracts  \\\n",
       "0  {'longitude': '-118.2696', 'human_address': '{...   22352.0         786.0   \n",
       "1  {'longitude': '-118.3031', 'human_address': '{...   22723.0         617.0   \n",
       "2  {'longitude': '-118.2704', 'human_address': '{...    3222.0           5.0   \n",
       "3  {'longitude': '-118.2739', 'human_address': '{...   22352.0         800.0   \n",
       "4  {'longitude': '-118.3703', 'human_address': '{...   19335.0         166.0   \n",
       "\n",
       "  Precinct Boundaries LA Specific Plans  Council Districts  \\\n",
       "0               858.0               7.0               13.0   \n",
       "1               646.0               NaN               12.0   \n",
       "2               227.0               NaN                1.0   \n",
       "3               358.0               7.0               14.0   \n",
       "4               272.0               NaN                3.0   \n",
       "\n",
       "   Neighborhood Councils (Certified)  \n",
       "0                               46.0  \n",
       "1                               89.0  \n",
       "2                                7.0  \n",
       "3                               45.0  \n",
       "4                               21.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DR Number                                 0\n",
      "Date Reported                             0\n",
      "Date Occurred                             0\n",
      "Time Occurred                             0\n",
      "Area ID                                   0\n",
      "Area Name                                 0\n",
      "Reporting District                        0\n",
      "Crime Code                                0\n",
      "Crime Code Description                    0\n",
      "MO Codes                              86261\n",
      "Victim Age                            78714\n",
      "Victim Sex                             7615\n",
      "Victim Descent                         8407\n",
      "Premise Code                             26\n",
      "Premise Description                      26\n",
      "Address                                   0\n",
      "Cross Street                          22962\n",
      "Location                                  0\n",
      "Zip Codes                               397\n",
      "Census Tracts                          6795\n",
      "Precinct Boundaries                    3156\n",
      "LA Specific Plans                    317452\n",
      "Council Districts                      5874\n",
      "Neighborhood Councils (Certified)     25062\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing = df.isnull().sum()\n",
    "print(missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplication of the Hot Encoding \n",
    "\n",
    "This function below transform all the data in \"Area Name\" for example in separated columns.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_encode = pd.get_dummies(df['Area Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>77th Street</th>\n",
       "      <th>Central</th>\n",
       "      <th>Devonshire</th>\n",
       "      <th>Foothill</th>\n",
       "      <th>Harbor</th>\n",
       "      <th>Hollenbeck</th>\n",
       "      <th>Hollywood</th>\n",
       "      <th>Mission</th>\n",
       "      <th>N Hollywood</th>\n",
       "      <th>Newton</th>\n",
       "      <th>...</th>\n",
       "      <th>Olympic</th>\n",
       "      <th>Pacific</th>\n",
       "      <th>Rampart</th>\n",
       "      <th>Southeast</th>\n",
       "      <th>Southwest</th>\n",
       "      <th>Topanga</th>\n",
       "      <th>Van Nuys</th>\n",
       "      <th>West LA</th>\n",
       "      <th>West Valley</th>\n",
       "      <th>Wilshire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   77th Street  Central  Devonshire  Foothill  Harbor  Hollenbeck  Hollywood  \\\n",
       "0            0        0           0         0       0           0          0   \n",
       "1            0        0           0         0       0           0          0   \n",
       "2            0        0           0         1       0           0          0   \n",
       "3            0        0           0         0       0           0          0   \n",
       "4            0        0           0         1       0           0          0   \n",
       "\n",
       "   Mission  N Hollywood  Newton  ...  Olympic  Pacific  Rampart  Southeast  \\\n",
       "0        0            0       1  ...        0        0        0          0   \n",
       "1        0            0       0  ...        1        0        0          0   \n",
       "2        0            0       0  ...        0        0        0          0   \n",
       "3        0            0       0  ...        0        0        0          1   \n",
       "4        0            0       0  ...        0        0        0          0   \n",
       "\n",
       "   Southwest  Topanga  Van Nuys  West LA  West Valley  Wilshire  \n",
       "0          0        0         0        0            0         0  \n",
       "1          0        0         0        0            0         0  \n",
       "2          0        0         0        0            0         0  \n",
       "3          0        0         0        0            0         0  \n",
       "4          0        0         0        0            0         0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_encode.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to concat the `area_encode` on the df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "concatenado = pd.concat([df, area_encode], axis=1)\n",
    "concatenado.drop('Area Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>DR Number</th>\n",
       "      <th>Date Reported</th>\n",
       "      <th>Date Occurred</th>\n",
       "      <th>Time Occurred</th>\n",
       "      <th>Area ID</th>\n",
       "      <th>Reporting District</th>\n",
       "      <th>Crime Code</th>\n",
       "      <th>Crime Code Description</th>\n",
       "      <th>MO Codes</th>\n",
       "      <th>Victim Age</th>\n",
       "      <th>...</th>\n",
       "      <th>Olympic</th>\n",
       "      <th>Pacific</th>\n",
       "      <th>Rampart</th>\n",
       "      <th>Southeast</th>\n",
       "      <th>Southwest</th>\n",
       "      <th>Topanga</th>\n",
       "      <th>Van Nuys</th>\n",
       "      <th>West LA</th>\n",
       "      <th>West Valley</th>\n",
       "      <th>Wilshire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191323054</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>130</td>\n",
       "      <td>13</td>\n",
       "      <td>1385</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>192020666</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>2054</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191616992</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>230</td>\n",
       "      <td>16</td>\n",
       "      <td>1669</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191824082</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>730</td>\n",
       "      <td>18</td>\n",
       "      <td>1802</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>0605</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>191616980</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>2019-11-30T00:00:00.000</td>\n",
       "      <td>720</td>\n",
       "      <td>16</td>\n",
       "      <td>1689</td>\n",
       "      <td>997</td>\n",
       "      <td>TRAFFIC COLLISION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DR Number            Date Reported            Date Occurred  Time Occurred  \\\n",
       "0  191323054  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            130   \n",
       "1  192020666  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000             15   \n",
       "2  191616992  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            230   \n",
       "3  191824082  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            730   \n",
       "4  191616980  2019-11-30T00:00:00.000  2019-11-30T00:00:00.000            720   \n",
       "\n",
       "   Area ID  Reporting District  Crime Code Crime Code Description MO Codes  \\\n",
       "0       13                1385         997      TRAFFIC COLLISION      NaN   \n",
       "1       20                2054         997      TRAFFIC COLLISION      NaN   \n",
       "2       16                1669         997      TRAFFIC COLLISION      NaN   \n",
       "3       18                1802         997      TRAFFIC COLLISION     0605   \n",
       "4       16                1689         997      TRAFFIC COLLISION      NaN   \n",
       "\n",
       "   Victim Age  ... Olympic Pacific  Rampart Southeast Southwest Topanga  \\\n",
       "0         NaN  ...       0       0        0         0         0       0   \n",
       "1        40.0  ...       1       0        0         0         0       0   \n",
       "2        18.0  ...       0       0        0         0         0       0   \n",
       "3        23.0  ...       0       0        0         1         0       0   \n",
       "4         NaN  ...       0       0        0         0         0       0   \n",
       "\n",
       "  Van Nuys  West LA  West Valley  Wilshire  \n",
       "0        0        0            0         0  \n",
       "1        0        0            0         0  \n",
       "2        0        0            0         0  \n",
       "3        0        0            0         0  \n",
       "4        0        0            0         0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concatenado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "concatenado.boxplot(column='Census Tracts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com o boxplot criado nós podemos analisar se os dados acima de 1500 considerados outliers pelo gráfico podem ser excluidos do dados.\n",
    "\n",
    "Para árvore de decisão os outliers não interferem tanto no resultado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}