CasoPracticoFinal_v2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JesserEdua/Data/blob/main/CasoPracticoFinal_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# IMPORT DE LIBRERÍAS"
      ],
      "metadata": {
        "id": "SMYRG-7qZKaW"
      },
      "id": "SMYRG-7qZKaW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f6ed6823",
      "metadata": {
        "id": "f6ed6823"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04edbcf8",
      "metadata": {
        "id": "04edbcf8"
      },
      "source": [
        "### 1. Cargar los datos y realizar un análisis exploratorio y una evaluación de la calidad de los datos necesarios para el resto del caso. Específicamente, evaluar la integridad, validez y actualidad de los datos y proponer estrategias de mitigación de los posibles problemas encontrados."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c790c03",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c790c03",
        "outputId": "c8999727-a174-4611-b897-01e1838d0bd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id,name,first,last,compas_screening_date,sex,dob,age,age_cat,race,juv_fel_count,decile_score,juv_misd_count,juv_other_count,priors_count,days_b_screening_arrest,c_jail_in,c_jail_out,c_case_number,c_offense_date,c_arrest_date,c_days_from_compas,c_charge_degree,c_charge_desc,is_recid,num_r_cases,r_case_number,r_charge_degree,r_days_from_arrest,r_offense_date,r_charge_desc,r_jail_in,r_jail_out,is_violent_recid,num_vr_cases,vr_case_number,vr_charge_degree,vr_offense_date,vr_charge_desc,v_type_of_assessment,v_decile_score,v_score_text,v_screening_date,type_of_assessment,decile_score,score_text,screening_date\n",
            "\n",
            "1,miguel hernandez,miguel,hernandez,2013-08-14,Male,1947-04-18,69,Greater than 45,Other,0,1,0,0,0,-1,2013-08-13 06:03:42,2013-08-14 05:41:20,13011352CF10A,2013-08-13,,1,F,Aggravated Assault w/Firearm,0,,,O,,,,,,0,,,,,,Risk of Violence,1,Low,2013-08-14,Risk of Recidivism,1,Low,2013-08-14\n",
            "\n",
            "2,michael ryan,michael,ryan,2014-12-31,Male,1985-02-06,31,25 - 45,Caucasian,0,5,0,0,0,,,,,,,,O,,-1,,,O,,,,,,0,,,,,,Risk of Violence,2,Low,2014-12-31,Risk of Recidivism,5,Medium,2014-12-31\n",
            "\n",
            "3,kevon dixon,kevon,dixon,2013-01-27,Male,1982-01-22,34,25 - 45,African-American,0,3,0,0,0,-1,2013-01-26 03:45:27,2013-02-05 05:36:53,13001275CF10A,2013-01-26,,1,F,Felony Battery w/Prior Convict,1,,13009779CF10A,F,,2013-07-05,Felony Battery (Dom Strang),,,1,,13009779CF10A,(F3),2013-07-05,Felony Battery (Dom Strang),Risk of Violence,1,Low,2013-01-27,Risk of Recidivism,3,Low,2013-01-27\n",
            "\n",
            "4,ed philo,ed,philo,2013-04-14,Male,1991-05-14,24,Less than 25,African-American,0,4,0,1,4,-1,2013-04-13 04:58:34,2013-04-14 07:02:04,13005330CF10A,2013-04-13,,1,F,Possession of Cocaine,1,,13011511MM10A,M,0,2013-06-16,Driving Under The Influence,2013-06-16,2013-06-16,0,,,,,,Risk of Violence,3,Low,2013-04-14,Risk of Recidivism,4,Low,2013-04-14\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Revision del archivo compas-score\n",
        "with open('compas-scores.csv', encoding = 'utf8') as f:\n",
        "    for _ in range(5) : print(f.readline())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "286873a1",
      "metadata": {
        "id": "286873a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "51807a8a-3c27-486b-a467-087dc41c8446"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>name</th>\n",
              "      <th>first</th>\n",
              "      <th>last</th>\n",
              "      <th>compas_screening_date</th>\n",
              "      <th>sex</th>\n",
              "      <th>dob</th>\n",
              "      <th>age</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>race</th>\n",
              "      <th>juv_fel_count</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>juv_misd_count</th>\n",
              "      <th>juv_other_count</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>days_b_screening_arrest</th>\n",
              "      <th>c_jail_in</th>\n",
              "      <th>c_jail_out</th>\n",
              "      <th>c_case_number</th>\n",
              "      <th>c_offense_date</th>\n",
              "      <th>c_arrest_date</th>\n",
              "      <th>c_days_from_compas</th>\n",
              "      <th>c_charge_degree</th>\n",
              "      <th>c_charge_desc</th>\n",
              "      <th>is_recid</th>\n",
              "      <th>num_r_cases</th>\n",
              "      <th>r_case_number</th>\n",
              "      <th>r_charge_degree</th>\n",
              "      <th>r_days_from_arrest</th>\n",
              "      <th>r_offense_date</th>\n",
              "      <th>r_charge_desc</th>\n",
              "      <th>r_jail_in</th>\n",
              "      <th>r_jail_out</th>\n",
              "      <th>is_violent_recid</th>\n",
              "      <th>num_vr_cases</th>\n",
              "      <th>vr_case_number</th>\n",
              "      <th>vr_charge_degree</th>\n",
              "      <th>vr_offense_date</th>\n",
              "      <th>vr_charge_desc</th>\n",
              "      <th>v_type_of_assessment</th>\n",
              "      <th>v_decile_score</th>\n",
              "      <th>v_score_text</th>\n",
              "      <th>v_screening_date</th>\n",
              "      <th>type_of_assessment</th>\n",
              "      <th>decile_score.1</th>\n",
              "      <th>score_text</th>\n",
              "      <th>screening_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>miguel hernandez</td>\n",
              "      <td>miguel</td>\n",
              "      <td>hernandez</td>\n",
              "      <td>2013-08-14</td>\n",
              "      <td>Male</td>\n",
              "      <td>1947-04-18</td>\n",
              "      <td>69</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-08-13 06:03:42</td>\n",
              "      <td>2013-08-14 05:41:20</td>\n",
              "      <td>13011352CF10A</td>\n",
              "      <td>2013-08-13</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Aggravated Assault w/Firearm</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-08-14</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-08-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>michael ryan</td>\n",
              "      <td>michael</td>\n",
              "      <td>ryan</td>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>Male</td>\n",
              "      <td>1985-02-06</td>\n",
              "      <td>31</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>5</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2014-12-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kevon dixon</td>\n",
              "      <td>kevon</td>\n",
              "      <td>dixon</td>\n",
              "      <td>2013-01-27</td>\n",
              "      <td>Male</td>\n",
              "      <td>1982-01-22</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-01-26 03:45:27</td>\n",
              "      <td>2013-02-05 05:36:53</td>\n",
              "      <td>13001275CF10A</td>\n",
              "      <td>2013-01-26</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Felony Battery w/Prior Convict</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13009779CF10A</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>Felony Battery (Dom Strang)</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13009779CF10A</td>\n",
              "      <td>(F3)</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>Felony Battery (Dom Strang)</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-01-27</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-01-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>ed philo</td>\n",
              "      <td>ed</td>\n",
              "      <td>philo</td>\n",
              "      <td>2013-04-14</td>\n",
              "      <td>Male</td>\n",
              "      <td>1991-05-14</td>\n",
              "      <td>24</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-04-13 04:58:34</td>\n",
              "      <td>2013-04-14 07:02:04</td>\n",
              "      <td>13005330CF10A</td>\n",
              "      <td>2013-04-13</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Possession of Cocaine</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13011511MM10A</td>\n",
              "      <td>M</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>Driving Under The Influence</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-04-14</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-04-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>marcu brown</td>\n",
              "      <td>marcu</td>\n",
              "      <td>brown</td>\n",
              "      <td>2013-01-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>1993-01-21</td>\n",
              "      <td>23</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>13000570CF10A</td>\n",
              "      <td>2013-01-12</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Possession of Cannabis</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2013-01-13</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>8</td>\n",
              "      <td>High</td>\n",
              "      <td>2013-01-13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id              name    first  ... decile_score.1 score_text screening_date\n",
              "0   1  miguel hernandez   miguel  ...              1        Low     2013-08-14\n",
              "1   2      michael ryan  michael  ...              5     Medium     2014-12-31\n",
              "2   3       kevon dixon    kevon  ...              3        Low     2013-01-27\n",
              "3   4          ed philo       ed  ...              4        Low     2013-04-14\n",
              "4   5       marcu brown    marcu  ...              8       High     2013-01-13\n",
              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# Se crea el dataframe\n",
        "reincidencias = pd.read_csv('compas-scores.csv', encoding = 'utf8', sep = ',', parse_dates = ['compas_screening_date',\n",
        "                                                                                              'dob',\n",
        "                                                                                              'c_jail_in',\n",
        "                                                                                              'c_jail_out',\n",
        "                                                                                              'c_offense_date',\n",
        "                                                                                              'c_arrest_date',\n",
        "                                                                                              'r_offense_date',\n",
        "                                                                                              'r_jail_in',\n",
        "                                                                                              'r_jail_out',\n",
        "                                                                                              'vr_offense_date',\n",
        "                                                                                              'v_screening_date',\n",
        "                                                                                              'screening_date'\n",
        "                                                                                             ])\n",
        "reincidencias.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04167b98",
      "metadata": {
        "id": "04167b98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0b6f207-36d4-4f70-cde8-e2656ec7ae59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11757 entries, 0 to 11756\n",
            "Data columns (total 47 columns):\n",
            " #   Column                   Non-Null Count  Dtype         \n",
            "---  ------                   --------------  -----         \n",
            " 0   id                       11757 non-null  int64         \n",
            " 1   name                     11757 non-null  object        \n",
            " 2   first                    11757 non-null  object        \n",
            " 3   last                     11757 non-null  object        \n",
            " 4   compas_screening_date    11757 non-null  datetime64[ns]\n",
            " 5   sex                      11757 non-null  object        \n",
            " 6   dob                      11757 non-null  datetime64[ns]\n",
            " 7   age                      11757 non-null  int64         \n",
            " 8   age_cat                  11757 non-null  object        \n",
            " 9   race                     11757 non-null  object        \n",
            " 10  juv_fel_count            11757 non-null  int64         \n",
            " 11  decile_score             11757 non-null  int64         \n",
            " 12  juv_misd_count           11757 non-null  int64         \n",
            " 13  juv_other_count          11757 non-null  int64         \n",
            " 14  priors_count             11757 non-null  int64         \n",
            " 15  days_b_screening_arrest  10577 non-null  float64       \n",
            " 16  c_jail_in                10577 non-null  datetime64[ns]\n",
            " 17  c_jail_out               10577 non-null  datetime64[ns]\n",
            " 18  c_case_number            11015 non-null  object        \n",
            " 19  c_offense_date           9157 non-null   datetime64[ns]\n",
            " 20  c_arrest_date            1858 non-null   datetime64[ns]\n",
            " 21  c_days_from_compas       11015 non-null  float64       \n",
            " 22  c_charge_degree          11757 non-null  object        \n",
            " 23  c_charge_desc            11008 non-null  object        \n",
            " 24  is_recid                 11757 non-null  int64         \n",
            " 25  num_r_cases              0 non-null      float64       \n",
            " 26  r_case_number            3703 non-null   object        \n",
            " 27  r_charge_degree          11757 non-null  object        \n",
            " 28  r_days_from_arrest       2460 non-null   float64       \n",
            " 29  r_offense_date           3703 non-null   datetime64[ns]\n",
            " 30  r_charge_desc            3643 non-null   object        \n",
            " 31  r_jail_in                2460 non-null   datetime64[ns]\n",
            " 32  r_jail_out               2460 non-null   datetime64[ns]\n",
            " 33  is_violent_recid         11757 non-null  int64         \n",
            " 34  num_vr_cases             0 non-null      float64       \n",
            " 35  vr_case_number           882 non-null    object        \n",
            " 36  vr_charge_degree         882 non-null    object        \n",
            " 37  vr_offense_date          882 non-null    datetime64[ns]\n",
            " 38  vr_charge_desc           882 non-null    object        \n",
            " 39  v_type_of_assessment     11757 non-null  object        \n",
            " 40  v_decile_score           11757 non-null  int64         \n",
            " 41  v_score_text             11752 non-null  object        \n",
            " 42  v_screening_date         11757 non-null  datetime64[ns]\n",
            " 43  type_of_assessment       11757 non-null  object        \n",
            " 44  decile_score.1           11757 non-null  int64         \n",
            " 45  score_text               11742 non-null  object        \n",
            " 46  screening_date           11757 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](12), float64(5), int64(11), object(19)\n",
            "memory usage: 4.2+ MB\n"
          ]
        }
      ],
      "source": [
        "reincidencias.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Se crea un nuevo dataframe y se seleccionan los atributos para desarrollar el caso.\n",
        "# Se seleccionan los 7 atributos indicados por el caso: compas_screening_date, decile_score, v_decile_score, is_recid, r_offense_date, is_violent_recid, vr_offense_date\n",
        "# Se seleccionan 8 atributos adicionales que se consideran importantes para el analisis y que contibuyen a una mejor identificación\n",
        "# id : Identificador único del registro.\n",
        "# sex: Distinguir si es hombre o mujer en la evualuación de riesgo. \n",
        "# age: \n",
        "# age_cat:\n",
        "# race: Distinguir si el tipo de raza en el tipo de reincidencia.\n",
        "# priors_count:\n",
        "# v_score_text:\n",
        "# score_text:\n",
        "\n",
        "reincidencias2 = reincidencias[['id', 'sex','compas_screening_date','age', 'age_cat', 'race', 'decile_score', 'priors_count','is_recid', 'r_offense_date','is_violent_recid', 'vr_offense_date','v_decile_score',  \n",
        "'v_score_text', 'score_text']].copy()\n",
        "reincidencias2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kP646hT2Yjuc",
        "outputId": "3a80daac-204c-4be7-c02c-f534735bb3ea"
      },
      "id": "kP646hT2Yjuc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>sex</th>\n",
              "      <th>compas_screening_date</th>\n",
              "      <th>age</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>race</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>is_recid</th>\n",
              "      <th>r_offense_date</th>\n",
              "      <th>is_violent_recid</th>\n",
              "      <th>vr_offense_date</th>\n",
              "      <th>v_decile_score</th>\n",
              "      <th>v_score_text</th>\n",
              "      <th>score_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-08-14</td>\n",
              "      <td>69</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-12-31</td>\n",
              "      <td>31</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-01-27</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-04-14</td>\n",
              "      <td>24</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-01-13</td>\n",
              "      <td>23</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>High</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11752</th>\n",
              "      <td>11753</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-09-22</td>\n",
              "      <td>47</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11753</th>\n",
              "      <td>11754</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-05-17</td>\n",
              "      <td>22</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-10-11</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>5</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11754</th>\n",
              "      <td>11755</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-10-08</td>\n",
              "      <td>35</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11755</th>\n",
              "      <td>11756</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-12-03</td>\n",
              "      <td>29</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11756</th>\n",
              "      <td>11757</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-01-11</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Asian</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11757 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id   sex  ... v_score_text  score_text\n",
              "0          1  Male  ...          Low         Low\n",
              "1          2  Male  ...          Low      Medium\n",
              "2          3  Male  ...          Low         Low\n",
              "3          4  Male  ...          Low         Low\n",
              "4          5  Male  ...       Medium        High\n",
              "...      ...   ...  ...          ...         ...\n",
              "11752  11753  Male  ...          Low         Low\n",
              "11753  11754  Male  ...       Medium      Medium\n",
              "11754  11755  Male  ...          Low         Low\n",
              "11755  11756  Male  ...          Low         Low\n",
              "11756  11757  Male  ...          Low         Low\n",
              "\n",
              "[11757 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "729a00b8",
      "metadata": {
        "id": "729a00b8"
      },
      "outputs": [],
      "source": [
        "# Se crea una función para realizar un analisis exploratorio (integridad, validez y actualidad).\n",
        "\n",
        "def revisa_columna(df,col):\n",
        "    print(f'Columna: \\033[1m {col} \\033[0m')\n",
        "    print(f' \\033[1m Informacion: \\033[0m \\n{df[col].describe()} ')\n",
        "    print('*'*25)\n",
        "    print(f' \\033[1m NULOS: \\033[0m {df[col].isnull().sum()} ')\n",
        "    print('*'*25)\n",
        "    if df[col].nunique() < 50 :\n",
        "        print(f' \\033[1m VALORES UNICOS: \\033[0m \\n{df[col].unique()}')\n",
        "    else :\n",
        "        print(f' \\033[1m DATOS COLUMNA: \\033[0m \\n{df[col].head()}')\n",
        "    print('-'*25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22306886",
      "metadata": {
        "id": "22306886",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9af4dd9-d3f3-4f96-81c9-4590072c3db2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'sex', 'compas_screening_date', 'age', 'age_cat', 'race',\n",
              "       'decile_score', 'priors_count', 'is_recid', 'r_offense_date',\n",
              "       'is_violent_recid', 'vr_offense_date', 'v_decile_score', 'v_score_text',\n",
              "       'score_text'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "reincidencias2.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c33228da",
      "metadata": {
        "id": "c33228da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad428b2d-5bfc-47c5-89d6-8bae887f4b1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columna: \u001b[1m id \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean      5879.000000\n",
            "std       3394.097892\n",
            "min          1.000000\n",
            "25%       2940.000000\n",
            "50%       5879.000000\n",
            "75%       8818.000000\n",
            "max      11757.000000\n",
            "Name: id, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "4    5\n",
            "Name: id, dtype: int64\n",
            "-------------------------\n",
            "Columna: \u001b[1m sex \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11757\n",
            "unique        2\n",
            "top        Male\n",
            "freq       9336\n",
            "Name: sex, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Male' 'Female']\n",
            "-------------------------\n",
            "Columna: \u001b[1m compas_screening_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   11757\n",
            "unique                    704\n",
            "top       2013-03-20 00:00:00\n",
            "freq                       39\n",
            "first     2013-01-01 00:00:00\n",
            "last      2014-12-31 00:00:00\n",
            "Name: compas_screening_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   2013-08-14\n",
            "1   2014-12-31\n",
            "2   2013-01-27\n",
            "3   2013-04-14\n",
            "4   2013-01-13\n",
            "Name: compas_screening_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m age \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean        35.143319\n",
            "std         12.022894\n",
            "min         18.000000\n",
            "25%         25.000000\n",
            "50%         32.000000\n",
            "75%         43.000000\n",
            "max         96.000000\n",
            "Name: age, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    69\n",
            "1    31\n",
            "2    34\n",
            "3    24\n",
            "4    23\n",
            "Name: age, dtype: int64\n",
            "-------------------------\n",
            "Columna: \u001b[1m age_cat \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count       11757\n",
            "unique          3\n",
            "top       25 - 45\n",
            "freq         6649\n",
            "Name: age_cat, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Greater than 45' '25 - 45' 'Less than 25']\n",
            "-------------------------\n",
            "Columna: \u001b[1m race \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                11757\n",
            "unique                   6\n",
            "top       African-American\n",
            "freq                  5813\n",
            "Name: race, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Other' 'Caucasian' 'African-American' 'Hispanic' 'Asian'\n",
            " 'Native American']\n",
            "-------------------------\n",
            "Columna: \u001b[1m decile_score \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean         4.371268\n",
            "std          2.877598\n",
            "min         -1.000000\n",
            "25%          2.000000\n",
            "50%          4.000000\n",
            "75%          7.000000\n",
            "max         10.000000\n",
            "Name: decile_score, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 1  5  3  4  8  6 10  9  2 -1  7]\n",
            "-------------------------\n",
            "Columna: \u001b[1m priors_count \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean         3.082164\n",
            "std          4.687410\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          1.000000\n",
            "75%          4.000000\n",
            "max         43.000000\n",
            "Name: priors_count, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0  4  1  2 14  3  7  6  5 13  8  9 15 19 21 22 20 10 12 28 11 23 25 24\n",
            " 36 18 29 16 17 33 30 43 27 26 38 37 35 31 39]\n",
            "-------------------------\n",
            "Columna: \u001b[1m is_recid \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean         0.253806\n",
            "std          0.558324\n",
            "min         -1.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          1.000000\n",
            "max          1.000000\n",
            "Name: is_recid, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0 -1  1]\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_offense_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                    3703\n",
            "unique                   1090\n",
            "top       2014-12-08 00:00:00\n",
            "freq                       12\n",
            "first     2013-01-03 00:00:00\n",
            "last      2016-03-29 00:00:00\n",
            "Name: r_offense_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 8054 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0          NaT\n",
            "1          NaT\n",
            "2   2013-07-05\n",
            "3   2013-06-16\n",
            "4          NaT\n",
            "Name: r_offense_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m is_violent_recid \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean         0.075019\n",
            "std          0.263433\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.000000\n",
            "max          1.000000\n",
            "Name: is_violent_recid, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[0 1]\n",
            "-------------------------\n",
            "Columna: \u001b[1m vr_offense_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                     882\n",
            "unique                    599\n",
            "top       2015-08-15 00:00:00\n",
            "freq                        6\n",
            "first     2013-01-28 00:00:00\n",
            "last      2016-03-13 00:00:00\n",
            "Name: vr_offense_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 10875 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0          NaT\n",
            "1          NaT\n",
            "2   2013-07-05\n",
            "3          NaT\n",
            "4          NaT\n",
            "Name: vr_offense_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m v_decile_score \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11757.000000\n",
            "mean         3.571489\n",
            "std          2.500479\n",
            "min         -1.000000\n",
            "25%          1.000000\n",
            "50%          3.000000\n",
            "75%          5.000000\n",
            "max         10.000000\n",
            "Name: v_decile_score, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 1  2  3  6  9  5  4  7 10  8 -1]\n",
            "-------------------------\n",
            "Columna: \u001b[1m v_score_text \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11752\n",
            "unique        3\n",
            "top         Low\n",
            "freq       7968\n",
            "Name: v_score_text, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 5 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Low' 'Medium' 'High' nan]\n",
            "-------------------------\n",
            "Columna: \u001b[1m score_text \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11742\n",
            "unique        3\n",
            "top         Low\n",
            "freq       6607\n",
            "Name: score_text, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 15 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Low' 'Medium' 'High' nan]\n",
            "-------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n"
          ]
        }
      ],
      "source": [
        "# Se aplica la función a cada una de las columnas del dataframe\n",
        "for ncolum in reincidencias2.columns :\n",
        "    revisa_columna(reincidencias2,ncolum)\n",
        "\n",
        "# Comentarios relevantes:\n",
        "# Sex: El 79 % de los casos corresponden a hombres, y es necesario transformar a tipo de dato categorico.\n",
        "# compas_screening_date: Es necesario transformar a tipo fecha.\n",
        "# age: Es necesario transformar a tipo int.\n",
        "# age_cat : Es necesario transformar a tipo de dato categorico y verificar la validez del dato.\n",
        "# race: Es necesario transformar a tipo de dato categorico.\n",
        "# decile_score: Es necesario analizar la existencia de valores negativos.\n",
        "# prior_count: \n",
        "# ***is_recid : REVISAR VALORES NEGATIVOS !!! Se identifican a valores negativos y luego decidir la transformación a tipo categorico.\n",
        "# r_offense_date : Es necesario transformar a tipo date.\n",
        "# is_violent_recid: Es necesario transformar a tipo de dato categorico.\n",
        "# vr_offense_date: Es necesario transformar a tipo date.\n",
        "# v_decile_score: REVISAR VALORES NEGATIVOS !!! Se identifican a valores negativos y luego decidir la transformación a tipo categorico.\n",
        "# v_score_text: REVISAR VALORES NAN, y transformar a categorico.\n",
        "# score_text: REVISAR VALORES NAN, y transformar a categorico.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYKiuZUZub7R",
        "outputId": "5e2e7ad2-cbe9-4ace-9ba2-96b88cdbc4ae"
      },
      "id": "oYKiuZUZub7R",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'sex', 'compas_screening_date', 'age', 'age_cat', 'race',\n",
              "       'decile_score', 'priors_count', 'is_recid', 'r_offense_date',\n",
              "       'is_violent_recid', 'vr_offense_date', 'v_decile_score', 'v_score_text',\n",
              "       'score_text'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias2[reincidencias2['is_recid']==-1]['id'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWaDSBJhyBcL",
        "outputId": "644dbde1-22df-4911-e70c-f104d8eaa0fe"
      },
      "id": "UWaDSBJhyBcL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "719"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias3 = reincidencias2.drop(reincidencias2[reincidencias2['is_recid']==-1].index)"
      ],
      "metadata": {
        "id": "fawzXa0QxvE4"
      },
      "id": "fawzXa0QxvE4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnvTHVh_x2Ip",
        "outputId": "76e6a5c1-9f34-4aa6-faf8-b47af3155f21"
      },
      "id": "MnvTHVh_x2Ip",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11038, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "revisa_columna(reincidencias3,'decile_score')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PH1nk5ZpypZ8",
        "outputId": "853d9728-e41b-44f9-e4e8-90da6b3a5df0"
      },
      "id": "PH1nk5ZpypZ8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columna: \u001b[1m decile_score \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11038.000000\n",
            "mean         4.406324\n",
            "std          2.877409\n",
            "min         -1.000000\n",
            "25%          2.000000\n",
            "50%          4.000000\n",
            "75%          7.000000\n",
            "max         10.000000\n",
            "Name: decile_score, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 1  3  4  8  6 10  5  9  2 -1  7]\n",
            "-------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformación de tipos de datos en cada atributo\n",
        "\n",
        "reincidencias3['sex'] = reincidencias3['sex'].astype('category')\n",
        "reincidencias3['compas_screening_date'] = pd.to_datetime(reincidencias3['compas_screening_date'], format=\"%Y-%m-%d\")\n",
        "reincidencias3['age'] = reincidencias3['age'].astype('int')\n",
        "reincidencias3['age_cat'] = reincidencias3['age_cat'].astype('category')\n",
        "reincidencias3['race'] = reincidencias3['race'].astype('category')\n",
        "#reincidencias3['is_recid'] = reincidencias3['is_recid'].astype('category')\n",
        "reincidencias3['r_offense_date'] = pd.to_datetime(reincidencias3['r_offense_date'], format=\"%Y-%m-%d\")\n",
        "#reincidencias3['is_violent_recid'] = reincidencias3['is_violent_recid'].astype('category')\n",
        "reincidencias3['vr_offense_date'] = pd.to_datetime(reincidencias3['vr_offense_date'], format=\"%Y-%m-%d\")\n",
        "reincidencias3['v_decile_score'] = reincidencias3['v_decile_score'].astype('category')\n",
        "reincidencias3['v_score_text'] = reincidencias3['v_score_text'].astype('category')\n",
        "reincidencias3['score_text'] = reincidencias3['score_text'].astype('category')"
      ],
      "metadata": {
        "id": "OqolbPdAuMwe"
      },
      "id": "OqolbPdAuMwe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias3.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8b0yqQq2uMtm",
        "outputId": "ae388930-3940-4082-bf82-1b673b9a0261"
      },
      "id": "8b0yqQq2uMtm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 11038 entries, 0 to 11756\n",
            "Data columns (total 15 columns):\n",
            " #   Column                 Non-Null Count  Dtype         \n",
            "---  ------                 --------------  -----         \n",
            " 0   id                     11038 non-null  int64         \n",
            " 1   sex                    11038 non-null  category      \n",
            " 2   compas_screening_date  11038 non-null  datetime64[ns]\n",
            " 3   age                    11038 non-null  int64         \n",
            " 4   age_cat                11038 non-null  category      \n",
            " 5   race                   11038 non-null  category      \n",
            " 6   decile_score           11038 non-null  int64         \n",
            " 7   priors_count           11038 non-null  int64         \n",
            " 8   is_recid               11038 non-null  int64         \n",
            " 9   r_offense_date         3703 non-null   datetime64[ns]\n",
            " 10  is_violent_recid       11038 non-null  int64         \n",
            " 11  vr_offense_date        882 non-null    datetime64[ns]\n",
            " 12  v_decile_score         11038 non-null  category      \n",
            " 13  v_score_text           11034 non-null  category      \n",
            " 14  score_text             11027 non-null  category      \n",
            "dtypes: category(6), datetime64[ns](3), int64(6)\n",
            "memory usage: 928.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(reincidencias3['compas_screening_date'].describe())\n",
        "print(reincidencias3['r_offense_date'].describe())\n",
        "print(reincidencias3['vr_offense_date'].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXq8Qw07uMqo",
        "outputId": "37ed1282-e154-4e8d-f827-ef712d658355"
      },
      "id": "wXq8Qw07uMqo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count                   11038\n",
            "unique                    704\n",
            "top       2013-03-20 00:00:00\n",
            "freq                       36\n",
            "first     2013-01-01 00:00:00\n",
            "last      2014-12-31 00:00:00\n",
            "Name: compas_screening_date, dtype: object\n",
            "count                    3703\n",
            "unique                   1090\n",
            "top       2014-12-08 00:00:00\n",
            "freq                       12\n",
            "first     2013-01-03 00:00:00\n",
            "last      2016-03-29 00:00:00\n",
            "Name: r_offense_date, dtype: object\n",
            "count                     882\n",
            "unique                    599\n",
            "top       2015-08-15 00:00:00\n",
            "freq                        6\n",
            "first     2013-01-28 00:00:00\n",
            "last      2016-03-13 00:00:00\n",
            "Name: vr_offense_date, dtype: object\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.- ¿Son los campos “is_recid” e “is_violent_recid” en este conjunto de datos adecuados para evaluar la precisión de las estimaciones de riesgo generadas por el sistema COMPAS? Si no es así, definir y calcular una feature que sí lo sea."
      ],
      "metadata": {
        "id": "dwnDiYJA2Mf2"
      },
      "id": "dwnDiYJA2Mf2"
    },
    {
      "cell_type": "code",
      "source": [
        "#Distribución de decile score segun puntaje.\n",
        "puntaje_compass = reincidencias3.groupby('decile_score')['id'].count()\n",
        "print(puntaje_compass)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZGd2e2d2Q5x",
        "outputId": "d45562ae-f940-4467-e513-7247460c2579"
      },
      "id": "5ZGd2e2d2Q5x",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "decile_score\n",
            "-1       11\n",
            " 1     2368\n",
            " 2     1478\n",
            " 3     1167\n",
            " 4     1130\n",
            " 5      982\n",
            " 6      941\n",
            " 7      862\n",
            " 8      758\n",
            " 9      761\n",
            " 10     580\n",
            "Name: id, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "puntaje_violent = reincidencias3[reincidencias3['is_violent_recid']==1]\n",
        "puntaje_violent"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-Ym-jsLG2Q27",
        "outputId": "f810e826-19f1-46a5-f083-7fab61a3f0f8"
      },
      "id": "-Ym-jsLG2Q27",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>sex</th>\n",
              "      <th>compas_screening_date</th>\n",
              "      <th>age</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>race</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>is_recid</th>\n",
              "      <th>r_offense_date</th>\n",
              "      <th>is_violent_recid</th>\n",
              "      <th>vr_offense_date</th>\n",
              "      <th>v_decile_score</th>\n",
              "      <th>v_score_text</th>\n",
              "      <th>score_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-01-27</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-11-04</td>\n",
              "      <td>21</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>5</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-11-17</td>\n",
              "      <td>31</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-07-16</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-07-16</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>37</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-05-06</td>\n",
              "      <td>27</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-11-06</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-02-23</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>40</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-10-24</td>\n",
              "      <td>33</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-08-15</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-08-15</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>High</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11678</th>\n",
              "      <td>11679</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-03-29</td>\n",
              "      <td>26</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-12-05</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-12-05</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11680</th>\n",
              "      <td>11681</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-04-10</td>\n",
              "      <td>33</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-10-22</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-10-22</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11683</th>\n",
              "      <td>11684</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-12-10</td>\n",
              "      <td>45</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-07-12</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-12-20</td>\n",
              "      <td>9</td>\n",
              "      <td>High</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11696</th>\n",
              "      <td>11697</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-08-21</td>\n",
              "      <td>28</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-06-24</td>\n",
              "      <td>1</td>\n",
              "      <td>2015-06-24</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11737</th>\n",
              "      <td>11738</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-01-07</td>\n",
              "      <td>20</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-05-07</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-10-24</td>\n",
              "      <td>10</td>\n",
              "      <td>High</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>882 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id   sex compas_screening_date  ...  v_decile_score v_score_text score_text\n",
              "2          3  Male            2013-01-27  ...               1          Low        Low\n",
              "12        13  Male            2013-11-04  ...               5       Medium        Low\n",
              "22        23  Male            2013-11-17  ...               4          Low     Medium\n",
              "36        37  Male            2013-05-06  ...               3          Low        Low\n",
              "39        40  Male            2014-10-24  ...               6       Medium       High\n",
              "...      ...   ...                   ...  ...             ...          ...        ...\n",
              "11678  11679  Male            2014-03-29  ...               2          Low        Low\n",
              "11680  11681  Male            2013-04-10  ...               6       Medium       High\n",
              "11683  11684  Male            2013-12-10  ...               9         High       High\n",
              "11696  11697  Male            2014-08-21  ...               4          Low     Medium\n",
              "11737  11738  Male            2014-01-07  ...              10         High       High\n",
              "\n",
              "[882 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "puntaje_violent.groupby('decile_score')['id'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x84uz-VQ2Qzf",
        "outputId": "d17d6350-1f92-4373-ed8d-19a5c75376b8"
      },
      "id": "x84uz-VQ2Qzf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "decile_score\n",
              "1      82\n",
              "2      66\n",
              "3      70\n",
              "4      84\n",
              "5      80\n",
              "6      86\n",
              "7     102\n",
              "8     103\n",
              "9     107\n",
              "10    102\n",
              "Name: id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias3.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rmgi_L4n7OvG",
        "outputId": "5f167690-9db4-452c-cc3d-700dd2c698b2"
      },
      "id": "Rmgi_L4n7OvG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 11038 entries, 0 to 11756\n",
            "Data columns (total 15 columns):\n",
            " #   Column                 Non-Null Count  Dtype         \n",
            "---  ------                 --------------  -----         \n",
            " 0   id                     11038 non-null  int64         \n",
            " 1   sex                    11038 non-null  category      \n",
            " 2   compas_screening_date  11038 non-null  datetime64[ns]\n",
            " 3   age                    11038 non-null  int64         \n",
            " 4   age_cat                11038 non-null  category      \n",
            " 5   race                   11038 non-null  category      \n",
            " 6   decile_score           11038 non-null  int64         \n",
            " 7   priors_count           11038 non-null  int64         \n",
            " 8   is_recid               11038 non-null  int64         \n",
            " 9   r_offense_date         3703 non-null   datetime64[ns]\n",
            " 10  is_violent_recid       11038 non-null  int64         \n",
            " 11  vr_offense_date        882 non-null    datetime64[ns]\n",
            " 12  v_decile_score         11038 non-null  category      \n",
            " 13  v_score_text           11034 non-null  category      \n",
            " 14  score_text             11027 non-null  category      \n",
            "dtypes: category(6), datetime64[ns](3), int64(6)\n",
            "memory usage: 928.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlacion Pearson entre variables numericas.\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "correlacion = reincidencias3.corr(method ='spearman')\n",
        "f, ax = plt.subplots(figsize=(4,4))\n",
        "plt.title('Correlación de atributos', size=10)\n",
        "sns.heatmap(correlacion, cmap=\"Blues\", annot=True , linewidths=.5, center=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "a6_oDluBuMjl",
        "outputId": "8e78a4f8-47b4-490d-e067-30982049f723"
      },
      "id": "a6_oDluBuMjl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlacion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "djD20OknuMfu",
        "outputId": "cc353e54-5e4f-46da-88e9-bc15ff0824b5"
      },
      "id": "djD20OknuMfu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>id</th>\n",
              "      <th>age</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>is_recid</th>\n",
              "      <th>is_violent_recid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000868</td>\n",
              "      <td>0.010017</td>\n",
              "      <td>0.010659</td>\n",
              "      <td>0.012445</td>\n",
              "      <td>0.002063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.000868</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.416816</td>\n",
              "      <td>0.137449</td>\n",
              "      <td>-0.158166</td>\n",
              "      <td>-0.082961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>decile_score</th>\n",
              "      <td>0.010017</td>\n",
              "      <td>-0.416816</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.439396</td>\n",
              "      <td>0.285313</td>\n",
              "      <td>0.148525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>priors_count</th>\n",
              "      <td>0.010659</td>\n",
              "      <td>0.137449</td>\n",
              "      <td>0.439396</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.268078</td>\n",
              "      <td>0.110125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_recid</th>\n",
              "      <td>0.012445</td>\n",
              "      <td>-0.158166</td>\n",
              "      <td>0.285313</td>\n",
              "      <td>0.268078</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.414759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_violent_recid</th>\n",
              "      <td>0.002063</td>\n",
              "      <td>-0.082961</td>\n",
              "      <td>0.148525</td>\n",
              "      <td>0.110125</td>\n",
              "      <td>0.414759</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        id       age  ...  is_recid  is_violent_recid\n",
              "id                1.000000  0.000868  ...  0.012445          0.002063\n",
              "age               0.000868  1.000000  ... -0.158166         -0.082961\n",
              "decile_score      0.010017 -0.416816  ...  0.285313          0.148525\n",
              "priors_count      0.010659  0.137449  ...  0.268078          0.110125\n",
              "is_recid          0.012445 -0.158166  ...  1.000000          0.414759\n",
              "is_violent_recid  0.002063 -0.082961  ...  0.414759          1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ln-9f_RDuMcR"
      },
      "id": "ln-9f_RDuMcR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reincidencias2[reincidencias2['is_recid']==-1 & reincidencias2['r_offense_date'].isnull()]\n",
        "#reincidencias2[(reincidencias2.isrecid==-1) & (reincidencias2.r_offense_date.isnull())]\n",
        "\n",
        "# Revisión de casos con categoria is recid -1 y sin fecha de reincidencia. \n",
        "reincidencias2[reincidencias2['is_recid']==-1 & pd.isnull(reincidencias2['is_recid'])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "C4zgiw0rYjsU",
        "outputId": "0c1ecc32-c647-411c-96ed-be4799d56a74"
      },
      "id": "C4zgiw0rYjsU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>id</th>\n",
              "      <th>sex</th>\n",
              "      <th>compas_screening_date</th>\n",
              "      <th>age</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>race</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>is_recid</th>\n",
              "      <th>r_offense_date</th>\n",
              "      <th>is_violent_recid</th>\n",
              "      <th>vr_offense_date</th>\n",
              "      <th>v_decile_score</th>\n",
              "      <th>v_score_text</th>\n",
              "      <th>score_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-08-14</td>\n",
              "      <td>69</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-01-13</td>\n",
              "      <td>23</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-03-26</td>\n",
              "      <td>43</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-11-30</td>\n",
              "      <td>44</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-08-30</td>\n",
              "      <td>43</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11750</th>\n",
              "      <td>11751</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-03-04</td>\n",
              "      <td>44</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11752</th>\n",
              "      <td>11753</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-09-22</td>\n",
              "      <td>47</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11754</th>\n",
              "      <td>11755</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-10-08</td>\n",
              "      <td>35</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11755</th>\n",
              "      <td>11756</td>\n",
              "      <td>Male</td>\n",
              "      <td>2013-12-03</td>\n",
              "      <td>29</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11756</th>\n",
              "      <td>11757</td>\n",
              "      <td>Male</td>\n",
              "      <td>2014-01-11</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Asian</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7335 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id   sex  ... v_score_text  score_text\n",
              "0          1  Male  ...          Low         Low\n",
              "4          5  Male  ...       Medium        High\n",
              "5          6  Male  ...          Low         Low\n",
              "6          7  Male  ...          Low         Low\n",
              "8          9  Male  ...          Low         Low\n",
              "...      ...   ...  ...          ...         ...\n",
              "11750  11751  Male  ...          Low         Low\n",
              "11752  11753  Male  ...          Low         Low\n",
              "11754  11755  Male  ...          Low         Low\n",
              "11755  11756  Male  ...          Low         Low\n",
              "11756  11757  Male  ...          Low         Low\n",
              "\n",
              "[7335 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TUJq3BecYjpj"
      },
      "id": "TUJq3BecYjpj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "iRFkh1xxYjm4"
      },
      "id": "iRFkh1xxYjm4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_99URp-4Yjj6"
      },
      "id": "_99URp-4Yjj6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "e7kzNrw7Yjgy"
      },
      "id": "e7kzNrw7Yjgy",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4fgdV13uYjdw"
      },
      "id": "4fgdV13uYjdw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ryIe9Uz_YjVK"
      },
      "id": "ryIe9Uz_YjVK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "daa382c0",
      "metadata": {
        "id": "daa382c0"
      },
      "outputs": [],
      "source": [
        "#Variables categoricas\n",
        "reincidencias['sex'] = reincidencias['sex'].astype('category')\n",
        "reincidencias['age_cat'] = reincidencias['age_cat'].astype('category')\n",
        "reincidencias['race'] = reincidencias['race'].astype('category')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b0113b34",
      "metadata": {
        "id": "b0113b34"
      },
      "source": [
        "\n",
        "JUV_FEL_COUNT Numero de delitos juveniles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cbac8ee1",
      "metadata": {
        "id": "cbac8ee1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "217ac279-8dae-4028-f255-dd39e3858c95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n",
            "11757\n",
            "0.12758356723653994\n"
          ]
        }
      ],
      "source": [
        "#la columna decile_score tiene valores -1 \n",
        "#Tratamiento para revisar esos valores\n",
        "numv = reincidencias[reincidencias['decile_score']==-1]['id'].count()\n",
        "totalregistros = reincidencias['id'].count()\n",
        "print(numv)\n",
        "print(totalregistros)\n",
        "\n",
        "porcentaje = (numv * 100 ) / totalregistros\n",
        "print(porcentaje)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d85bbd56",
      "metadata": {
        "id": "d85bbd56"
      },
      "outputs": [],
      "source": [
        "#Se procede a eliminar los datos con valores - 1 ya que corresponden a 0.12% \n",
        "#de la data y por lo tanto no afecta al analisis\n",
        "reincidencias2 = reincidencias[reincidencias['decile_score']!=-1]\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias2['decile_score'].unique()"
      ],
      "metadata": {
        "id": "evUjW3Eyd8PB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f2b990e-54ed-4672-9595-7a6e3e7e08b8"
      },
      "id": "evUjW3Eyd8PB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  5,  3,  4,  8,  6, 10,  9,  2,  7])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#se procede a transformar a variable categorica\n",
        "reincidencias2['decile_score'] = reincidencias2['decile_score'].astype('category')"
      ],
      "metadata": {
        "id": "NiJi4ISseDj6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "613f9d15-f968-45b3-dec9-16c545cd3f5c"
      },
      "id": "NiJi4ISseDj6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias2.info()"
      ],
      "metadata": {
        "id": "9O_bLG8IeXlh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "768321ba-0f49-4ef7-8679-ee690ef692b8"
      },
      "id": "9O_bLG8IeXlh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 11742 entries, 0 to 11756\n",
            "Data columns (total 47 columns):\n",
            " #   Column                   Non-Null Count  Dtype         \n",
            "---  ------                   --------------  -----         \n",
            " 0   id                       11742 non-null  int64         \n",
            " 1   name                     11742 non-null  object        \n",
            " 2   first                    11742 non-null  object        \n",
            " 3   last                     11742 non-null  object        \n",
            " 4   compas_screening_date    11742 non-null  datetime64[ns]\n",
            " 5   sex                      11742 non-null  category      \n",
            " 6   dob                      11742 non-null  datetime64[ns]\n",
            " 7   age                      11742 non-null  int64         \n",
            " 8   age_cat                  11742 non-null  category      \n",
            " 9   race                     11742 non-null  category      \n",
            " 10  juv_fel_count            11742 non-null  int64         \n",
            " 11  decile_score             11742 non-null  category      \n",
            " 12  juv_misd_count           11742 non-null  int64         \n",
            " 13  juv_other_count          11742 non-null  int64         \n",
            " 14  priors_count             11742 non-null  int64         \n",
            " 15  days_b_screening_arrest  10568 non-null  float64       \n",
            " 16  c_jail_in                10568 non-null  datetime64[ns]\n",
            " 17  c_jail_out               10568 non-null  datetime64[ns]\n",
            " 18  c_case_number            11004 non-null  object        \n",
            " 19  c_offense_date           9147 non-null   datetime64[ns]\n",
            " 20  c_arrest_date            1857 non-null   datetime64[ns]\n",
            " 21  c_days_from_compas       11004 non-null  float64       \n",
            " 22  c_charge_degree          11742 non-null  object        \n",
            " 23  c_charge_desc            10997 non-null  object        \n",
            " 24  is_recid                 11742 non-null  int64         \n",
            " 25  num_r_cases              0 non-null      float64       \n",
            " 26  r_case_number            3701 non-null   object        \n",
            " 27  r_charge_degree          11742 non-null  object        \n",
            " 28  r_days_from_arrest       2459 non-null   float64       \n",
            " 29  r_offense_date           3701 non-null   datetime64[ns]\n",
            " 30  r_charge_desc            3641 non-null   object        \n",
            " 31  r_jail_in                2459 non-null   datetime64[ns]\n",
            " 32  r_jail_out               2459 non-null   datetime64[ns]\n",
            " 33  is_violent_recid         11742 non-null  int64         \n",
            " 34  num_vr_cases             0 non-null      float64       \n",
            " 35  vr_case_number           882 non-null    object        \n",
            " 36  vr_charge_degree         882 non-null    object        \n",
            " 37  vr_offense_date          882 non-null    datetime64[ns]\n",
            " 38  vr_charge_desc           882 non-null    object        \n",
            " 39  v_type_of_assessment     11742 non-null  object        \n",
            " 40  v_decile_score           11742 non-null  int64         \n",
            " 41  v_score_text             11742 non-null  object        \n",
            " 42  v_screening_date         11742 non-null  datetime64[ns]\n",
            " 43  type_of_assessment       11742 non-null  object        \n",
            " 44  decile_score.1           11742 non-null  int64         \n",
            " 45  score_text               11742 non-null  object        \n",
            " 46  screening_date           11742 non-null  datetime64[ns]\n",
            "dtypes: category(4), datetime64[ns](12), float64(5), int64(10), object(16)\n",
            "memory usage: 4.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Continuamos con el analisis de la informacion de las columnas\n",
        "for ncolum in reincidencias2.columns :\n",
        "    revisa_columna(reincidencias2,ncolum)"
      ],
      "metadata": {
        "id": "_vLvvL0Zevx_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f294d7d5-7209-4616-f94a-69b55cba30eb"
      },
      "id": "_vLvvL0Zevx_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columna: \u001b[1m id \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean      5879.149208\n",
            "std       3393.345429\n",
            "min          1.000000\n",
            "25%       2940.250000\n",
            "50%       5878.500000\n",
            "75%       8817.750000\n",
            "max      11757.000000\n",
            "Name: id, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "4    5\n",
            "Name: id, dtype: int64\n",
            "-------------------------\n",
            "Columna: \u001b[1m name \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count          11742\n",
            "unique         11569\n",
            "top       john brown\n",
            "freq               4\n",
            "Name: name, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    miguel hernandez\n",
            "1        michael ryan\n",
            "2         kevon dixon\n",
            "3            ed philo\n",
            "4         marcu brown\n",
            "Name: name, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m first \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count       11742\n",
            "unique       4056\n",
            "top       michael\n",
            "freq          263\n",
            "Name: first, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0     miguel\n",
            "1    michael\n",
            "2      kevon\n",
            "3         ed\n",
            "4      marcu\n",
            "Name: first, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m last \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count        11742\n",
            "unique        5913\n",
            "top       williams\n",
            "freq           145\n",
            "Name: last, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    hernandez\n",
            "1         ryan\n",
            "2        dixon\n",
            "3        philo\n",
            "4        brown\n",
            "Name: last, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m compas_screening_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   11742\n",
            "unique                    704\n",
            "top       2013-03-20 00:00:00\n",
            "freq                       39\n",
            "first     2013-01-01 00:00:00\n",
            "last      2014-12-31 00:00:00\n",
            "Name: compas_screening_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   2013-08-14\n",
            "1   2014-12-31\n",
            "2   2013-01-27\n",
            "3   2013-04-14\n",
            "4   2013-01-13\n",
            "Name: compas_screening_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m sex \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11742\n",
            "unique        2\n",
            "top        Male\n",
            "freq       9325\n",
            "Name: sex, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Male', 'Female']\n",
            "Categories (2, object): ['Male', 'Female']\n",
            "-------------------------\n",
            "Columna: \u001b[1m dob \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   11742\n",
            "unique                   7790\n",
            "top       1990-05-02 00:00:00\n",
            "freq                        6\n",
            "first     1919-10-14 00:00:00\n",
            "last      1998-03-29 00:00:00\n",
            "Name: dob, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   1947-04-18\n",
            "1   1985-02-06\n",
            "2   1982-01-22\n",
            "3   1991-05-14\n",
            "4   1993-01-21\n",
            "Name: dob, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m age \u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean        35.131409\n",
            "std         12.013833\n",
            "min         18.000000\n",
            "25%         25.000000\n",
            "50%         32.000000\n",
            "75%         43.000000\n",
            "max         96.000000\n",
            "Name: age, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    69\n",
            "1    31\n",
            "2    34\n",
            "3    24\n",
            "4    23\n",
            "Name: age, dtype: int64\n",
            "-------------------------\n",
            "Columna: \u001b[1m age_cat \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count       11742\n",
            "unique          3\n",
            "top       25 - 45\n",
            "freq         6644\n",
            "Name: age_cat, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Greater than 45', '25 - 45', 'Less than 25']\n",
            "Categories (3, object): ['Greater than 45', '25 - 45', 'Less than 25']\n",
            "-------------------------\n",
            "Columna: \u001b[1m race \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                11742\n",
            "unique                   6\n",
            "top       African-American\n",
            "freq                  5807\n",
            "Name: race, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Other', 'Caucasian', 'African-American', 'Hispanic', 'Asian', 'Native American']\n",
            "Categories (6, object): ['Other', 'Caucasian', 'African-American', 'Hispanic', 'Asian',\n",
            "                         'Native American']\n",
            "-------------------------\n",
            "Columna: \u001b[1m juv_fel_count \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean         0.061659\n",
            "std          0.445607\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.000000\n",
            "max         20.000000\n",
            "Name: juv_fel_count, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0  2  1  3  8  4  5  9 20  6 13 10]\n",
            "-------------------------\n",
            "Columna: \u001b[1m decile_score \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11742\n",
            "unique       10\n",
            "top           1\n",
            "freq       2577\n",
            "Name: decile_score, dtype: int64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[1, 5, 3, 4, 8, 6, 10, 9, 2, 7]\n",
            "Categories (10, int64): [1, 5, 3, 4, ..., 10, 9, 2, 7]\n",
            "-------------------------\n",
            "Columna: \u001b[1m juv_misd_count \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean         0.076137\n",
            "std          0.450036\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.000000\n",
            "max         13.000000\n",
            "Name: juv_misd_count, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0  1  6 12  2  4  3  8  5 13  7]\n",
            "-------------------------\n",
            "Columna: \u001b[1m juv_other_count \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean         0.093681\n",
            "std          0.472293\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          0.000000\n",
            "max         17.000000\n",
            "Name: juv_other_count, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0  1  2  3  4 17  9 11  5  7  6 10]\n",
            "-------------------------\n",
            "Columna: \u001b[1m priors_count \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean         3.084909\n",
            "std          4.689620\n",
            "min          0.000000\n",
            "25%          0.000000\n",
            "50%          1.000000\n",
            "75%          4.000000\n",
            "max         43.000000\n",
            "Name: priors_count, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0  4  1  2 14  3  7  6  5 13  8  9 15 19 21 22 20 10 12 28 11 23 25 24\n",
            " 36 18 29 16 17 33 30 43 27 26 38 37 35 31 39]\n",
            "-------------------------\n",
            "Columna: \u001b[1m days_b_screening_arrest \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    10568.000000\n",
            "mean        -0.885125\n",
            "std         72.913170\n",
            "min       -597.000000\n",
            "25%         -1.000000\n",
            "50%         -1.000000\n",
            "75%         -1.000000\n",
            "max       1057.000000\n",
            "Name: days_b_screening_arrest, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 1174 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   -1.0\n",
            "1    NaN\n",
            "2   -1.0\n",
            "3   -1.0\n",
            "4    NaN\n",
            "Name: days_b_screening_arrest, dtype: float64\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_jail_in \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   10568\n",
            "unique                  10568\n",
            "top       2013-05-15 09:10:58\n",
            "freq                        1\n",
            "first     2013-01-01 01:31:55\n",
            "last      2016-03-11 10:26:16\n",
            "Name: c_jail_in, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 1174 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   2013-08-13 06:03:42\n",
            "1                   NaT\n",
            "2   2013-01-26 03:45:27\n",
            "3   2013-04-13 04:58:34\n",
            "4                   NaT\n",
            "Name: c_jail_in, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_jail_out \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   10568\n",
            "unique                  10508\n",
            "top       2014-02-12 10:41:00\n",
            "freq                        4\n",
            "first     2013-01-02 01:12:01\n",
            "last      2020-01-01 00:00:00\n",
            "Name: c_jail_out, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 1174 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   2013-08-14 05:41:20\n",
            "1                   NaT\n",
            "2   2013-02-05 05:36:53\n",
            "3   2013-04-14 07:02:04\n",
            "4                   NaT\n",
            "Name: c_jail_out, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_case_number \u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \u001b[1m Informacion: \u001b[0m \n",
            "count             11004\n",
            "unique            11004\n",
            "top       13012896CF10A\n",
            "freq                  1\n",
            "Name: c_case_number, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 738 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    13011352CF10A\n",
            "1              NaN\n",
            "2    13001275CF10A\n",
            "3    13005330CF10A\n",
            "4    13000570CF10A\n",
            "Name: c_case_number, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_offense_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                    9147\n",
            "unique                   1033\n",
            "top       2013-03-20 00:00:00\n",
            "freq                       29\n",
            "first     1987-11-07 00:00:00\n",
            "last      2014-12-30 00:00:00\n",
            "Name: c_offense_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 2595 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   2013-08-13\n",
            "1          NaT\n",
            "2   2013-01-26\n",
            "3   2013-04-13\n",
            "4   2013-01-12\n",
            "Name: c_offense_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_arrest_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                    1857\n",
            "unique                    801\n",
            "top       2013-02-06 00:00:00\n",
            "freq                        9\n",
            "first     1997-06-18 00:00:00\n",
            "last      2014-12-31 00:00:00\n",
            "Name: c_arrest_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 9885 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0   NaT\n",
            "1   NaT\n",
            "2   NaT\n",
            "3   NaT\n",
            "4   NaT\n",
            "Name: c_arrest_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_days_from_compas \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11004.000000\n",
            "mean        63.477463\n",
            "std        341.924246\n",
            "min          0.000000\n",
            "25%          1.000000\n",
            "50%          1.000000\n",
            "75%          2.000000\n",
            "max       9485.000000\n",
            "Name: c_days_from_compas, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 738 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    1.0\n",
            "1    NaN\n",
            "2    1.0\n",
            "3    1.0\n",
            "4    1.0\n",
            "Name: c_days_from_compas, dtype: float64\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_charge_degree \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11742\n",
            "unique        3\n",
            "top           F\n",
            "freq       7225\n",
            "Name: c_charge_degree, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['F' 'O' 'M']\n",
            "-------------------------\n",
            "Columna: \u001b[1m c_charge_desc \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                     10997\n",
            "unique                      531\n",
            "top       arrest case no charge\n",
            "freq                       1857\n",
            "Name: c_charge_desc, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 745 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0      Aggravated Assault w/Firearm\n",
            "1                               NaN\n",
            "2    Felony Battery w/Prior Convict\n",
            "3             Possession of Cocaine\n",
            "4            Possession of Cannabis\n",
            "Name: c_charge_desc, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m is_recid \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean         0.254301\n",
            "std          0.558071\n",
            "min         -1.000000\n",
            "25%          0.000000\n",
            "50%          0.000000\n",
            "75%          1.000000\n",
            "max          1.000000\n",
            "Name: is_recid, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 0 -1  1]\n",
            "-------------------------\n",
            "Columna: \u001b[1m num_r_cases \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    0.0\n",
            "mean     NaN\n",
            "std      NaN\n",
            "min      NaN\n",
            "25%      NaN\n",
            "50%      NaN\n",
            "75%      NaN\n",
            "max      NaN\n",
            "Name: num_r_cases, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 11742 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[nan]\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_case_number \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count              3701\n",
            "unique             3701\n",
            "top       15008216CF10A\n",
            "freq                  1\n",
            "Name: r_case_number, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 8041 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0              NaN\n",
            "1              NaN\n",
            "2    13009779CF10A\n",
            "3    13011511MM10A\n",
            "4              NaN\n",
            "Name: r_case_number, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_charge_degree \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count     11742\n",
            "unique        3\n",
            "top           O\n",
            "freq       8043\n",
            "Name: r_charge_degree, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['O' 'F' 'M']\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_days_from_arrest \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    2459.000000\n",
            "mean       20.418869\n",
            "std        74.368824\n",
            "min        -1.000000\n",
            "25%         0.000000\n",
            "50%         0.000000\n",
            "75%         1.000000\n",
            "max       993.000000\n",
            "Name: r_days_from_arrest, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 9283 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0    NaN\n",
            "1    NaN\n",
            "2    NaN\n",
            "3    0.0\n",
            "4    NaN\n",
            "Name: r_days_from_arrest, dtype: float64\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_offense_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                    3701\n",
            "unique                   1090\n",
            "top       2014-12-08 00:00:00\n",
            "freq                       12\n",
            "first     2013-01-03 00:00:00\n",
            "last      2016-03-29 00:00:00\n",
            "Name: r_offense_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 8041 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0          NaT\n",
            "1          NaT\n",
            "2   2013-07-05\n",
            "3   2013-06-16\n",
            "4          NaT\n",
            "Name: r_offense_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_charge_desc \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                          3641\n",
            "unique                          351\n",
            "top       Driving License Suspended\n",
            "freq                            279\n",
            "Name: r_charge_desc, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 8101 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
            "0                            NaN\n",
            "1                            NaN\n",
            "2    Felony Battery (Dom Strang)\n",
            "3    Driving Under The Influence\n",
            "4                            NaN\n",
            "Name: r_charge_desc, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_jail_in \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
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            "freq                        9\n",
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            "Name: r_jail_in, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 9283 \n",
            "*************************\n",
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            "Name: r_jail_in, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m r_jail_out \u001b[0m\n",
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            "top       2014-02-18 00:00:00\n",
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            "Name: r_jail_out, dtype: datetime64[ns]\n",
            "-------------------------\n",
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            "-------------------------\n",
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            "Name: num_vr_cases, dtype: float64 \n",
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            "*************************\n",
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            "[nan]\n",
            "-------------------------\n",
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            "Name: vr_case_number, dtype: object \n",
            "*************************\n",
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            "Name: vr_case_number, dtype: object\n",
            "-------------------------\n",
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            "Name: vr_charge_degree, dtype: object \n",
            "*************************\n",
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            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[nan '(F3)' '(F2)' '(F1)' '(M1)' '(MO3)' '(M2)' '(F6)' '(F7)' '(F5)']\n",
            "-------------------------\n",
            "Columna: \u001b[1m vr_offense_date \u001b[0m\n",
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            "freq                        6\n",
            "first     2013-01-28 00:00:00\n",
            "last      2016-03-13 00:00:00\n",
            "Name: vr_offense_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 10860 \n",
            "*************************\n",
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            "Name: vr_offense_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
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            "Name: vr_charge_desc, dtype: object \n",
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            "0                            NaN\n",
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            "4                            NaN\n",
            "Name: vr_charge_desc, dtype: object\n",
            "-------------------------\n",
            "Columna: \u001b[1m v_type_of_assessment \u001b[0m\n",
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            "Name: v_type_of_assessment, dtype: object \n",
            "*************************\n",
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            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Risk of Violence']\n",
            "-------------------------\n",
            "Columna: \u001b[1m v_decile_score \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count    11742.000000\n",
            "mean         3.573156\n",
            "std          2.499479\n",
            "min          1.000000\n",
            "25%          1.000000\n",
            "50%          3.000000\n",
            "75%          5.000000\n",
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            "Name: v_decile_score, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 1  2  3  6  9  5  4  7 10  8]\n",
            "-------------------------\n",
            "Columna: \u001b[1m v_score_text \u001b[0m\n",
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            "Name: v_score_text, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Low' 'Medium' 'High']\n",
            "-------------------------\n",
            "Columna: \u001b[1m v_screening_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   11742\n",
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            "top       2013-03-20 00:00:00\n",
            "freq                       39\n",
            "first     2013-01-01 00:00:00\n",
            "last      2014-12-31 00:00:00\n",
            "Name: v_screening_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
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            "1   2014-12-31\n",
            "2   2013-01-27\n",
            "3   2013-04-14\n",
            "4   2013-01-13\n",
            "Name: v_screening_date, dtype: datetime64[ns]\n",
            "-------------------------\n",
            "Columna: \u001b[1m type_of_assessment \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                  11742\n",
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            "freq                   11742\n",
            "Name: type_of_assessment, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Risk of Recidivism']\n",
            "-------------------------\n",
            "Columna: \u001b[1m decile_score.1 \u001b[0m\n",
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            "Name: decile_score.1, dtype: float64 \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "[ 1  5  3  4  8  6 10  9  2  7]\n",
            "-------------------------\n",
            "Columna: \u001b[1m score_text \u001b[0m\n",
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            "unique        3\n",
            "top         Low\n",
            "freq       6607\n",
            "Name: score_text, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m VALORES UNICOS: \u001b[0m \n",
            "['Low' 'Medium' 'High']\n",
            "-------------------------\n",
            "Columna: \u001b[1m screening_date \u001b[0m\n",
            " \u001b[1m Informacion: \u001b[0m \n",
            "count                   11742\n",
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            "top       2013-03-20 00:00:00\n",
            "freq                       39\n",
            "first     2013-01-01 00:00:00\n",
            "last      2014-12-31 00:00:00\n",
            "Name: screening_date, dtype: object \n",
            "*************************\n",
            " \u001b[1m NULOS: \u001b[0m 0 \n",
            "*************************\n",
            " \u001b[1m DATOS COLUMNA: \u001b[0m \n",
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            "2   2013-01-27\n",
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            "4   2013-01-13\n",
            "Name: screening_date, dtype: datetime64[ns]\n",
            "-------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DAYS_B_SCREENING_ARREST \n",
        "reincidencias2[reincidencias2['days_b_screening_arrest']==-597]\n"
      ],
      "metadata": {
        "id": "LQgXLb2wgMNu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "0501fdd4-d801-4c6a-f14d-e459b4eeaab4"
      },
      "id": "LQgXLb2wgMNu",
      "execution_count": null,
      "outputs": [
        {
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              "      <td>corey golden</td>\n",
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              "      <td>golden</td>\n",
              "      <td>2014-12-05</td>\n",
              "      <td>Male</td>\n",
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              "      <td>2013-09-26</td>\n",
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              "      <td>arrest case no charge</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2014-12-05</td>\n",
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            ],
            "text/plain": [
              "        id          name  first  ... decile_score.1 score_text screening_date\n",
              "6749  6750  corey golden  corey  ...              6     Medium     2014-12-05\n",
              "\n",
              "[1 rows x 47 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias2[reincidencias2['days_b_screening_arrest']>0]"
      ],
      "metadata": {
        "id": "d5dW5HC0jYkz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "ddc0a596-fb27-4069-88ea-fa9f07020b68"
      },
      "id": "d5dW5HC0jYkz",
      "execution_count": null,
      "outputs": [
        {
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              "      <th>type_of_assessment</th>\n",
              "      <th>decile_score.1</th>\n",
              "      <th>score_text</th>\n",
              "      <th>screening_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>bo bradac</td>\n",
              "      <td>bo</td>\n",
              "      <td>bradac</td>\n",
              "      <td>2013-11-04</td>\n",
              "      <td>Male</td>\n",
              "      <td>1994-06-10</td>\n",
              "      <td>21</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>428.0</td>\n",
              "      <td>2015-01-06 03:55:34</td>\n",
              "      <td>2015-01-07 03:38:44</td>\n",
              "      <td>13000017CF10A</td>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>NaT</td>\n",
              "      <td>308.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Insurance Fraud</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15002891MM10A</td>\n",
              "      <td>M</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>Battery</td>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15000258CF10A</td>\n",
              "      <td>(F2)</td>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>Aggrav Battery w/Deadly Weapon</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>5</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2013-11-04</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-11-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>samuel seraphin</td>\n",
              "      <td>samuel</td>\n",
              "      <td>seraphin</td>\n",
              "      <td>2014-06-03</td>\n",
              "      <td>Male</td>\n",
              "      <td>1985-03-25</td>\n",
              "      <td>31</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2014-06-25 02:15:57</td>\n",
              "      <td>2014-06-28 05:02:21</td>\n",
              "      <td>14004186CF10A</td>\n",
              "      <td>2014-03-24</td>\n",
              "      <td>NaT</td>\n",
              "      <td>71.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Felony Driving While Lic Suspd</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14009921MM10A</td>\n",
              "      <td>M</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2014-06-25</td>\n",
              "      <td>Criminal Mischief&gt;$200&lt;$1000</td>\n",
              "      <td>2014-06-25</td>\n",
              "      <td>2014-06-28</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-06-03</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-06-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>38</td>\n",
              "      <td>victoria soltau</td>\n",
              "      <td>victoria</td>\n",
              "      <td>soltau</td>\n",
              "      <td>2013-03-18</td>\n",
              "      <td>Female</td>\n",
              "      <td>1979-09-03</td>\n",
              "      <td>36</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>53.0</td>\n",
              "      <td>2013-05-10 11:15:09</td>\n",
              "      <td>2013-05-11 07:01:59</td>\n",
              "      <td>12018170CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2012-12-13</td>\n",
              "      <td>95.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-03-18</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-03-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>51</td>\n",
              "      <td>kurt fowks</td>\n",
              "      <td>kurt</td>\n",
              "      <td>fowks</td>\n",
              "      <td>2013-04-09</td>\n",
              "      <td>Male</td>\n",
              "      <td>1990-02-11</td>\n",
              "      <td>26</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>81.0</td>\n",
              "      <td>2013-06-29 06:05:49</td>\n",
              "      <td>2013-07-11 09:02:10</td>\n",
              "      <td>12022229TC10A</td>\n",
              "      <td>2012-03-23</td>\n",
              "      <td>NaT</td>\n",
              "      <td>382.0</td>\n",
              "      <td>M</td>\n",
              "      <td>Operating W/O Valid License</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>8</td>\n",
              "      <td>High</td>\n",
              "      <td>2013-04-09</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>8</td>\n",
              "      <td>High</td>\n",
              "      <td>2013-04-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>75</td>\n",
              "      <td>elliott knauff</td>\n",
              "      <td>elliott</td>\n",
              "      <td>knauff</td>\n",
              "      <td>2013-05-02</td>\n",
              "      <td>Male</td>\n",
              "      <td>1967-05-01</td>\n",
              "      <td>48</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>302.0</td>\n",
              "      <td>2014-02-28 11:19:21</td>\n",
              "      <td>2014-03-11 02:46:41</td>\n",
              "      <td>11010574CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2011-10-18</td>\n",
              "      <td>562.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14000740MM20A</td>\n",
              "      <td>M</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-02-01</td>\n",
              "      <td>Trespass Struct/Conveyance</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-05-02</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2013-05-02</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11597</th>\n",
              "      <td>11598</td>\n",
              "      <td>michael stenquist</td>\n",
              "      <td>michael</td>\n",
              "      <td>stenquist</td>\n",
              "      <td>2014-05-19</td>\n",
              "      <td>Male</td>\n",
              "      <td>1980-12-30</td>\n",
              "      <td>35</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>290.0</td>\n",
              "      <td>2015-03-05 10:05:57</td>\n",
              "      <td>2015-07-02 07:04:45</td>\n",
              "      <td>11020014CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013-08-28</td>\n",
              "      <td>264.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16003902TC20A</td>\n",
              "      <td>M</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016-01-21</td>\n",
              "      <td>Expired DL More Than 6 Months</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-05-19</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>9</td>\n",
              "      <td>High</td>\n",
              "      <td>2014-05-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11653</th>\n",
              "      <td>11654</td>\n",
              "      <td>wayne taylor</td>\n",
              "      <td>wayne</td>\n",
              "      <td>taylor</td>\n",
              "      <td>2014-06-22</td>\n",
              "      <td>Male</td>\n",
              "      <td>1969-08-13</td>\n",
              "      <td>46</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>2015-10-07 08:25:41</td>\n",
              "      <td>2015-12-03 08:05:17</td>\n",
              "      <td>14008606CF10A</td>\n",
              "      <td>2014-06-22</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Possession of Cocaine</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-06-22</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>9</td>\n",
              "      <td>High</td>\n",
              "      <td>2014-06-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11674</th>\n",
              "      <td>11675</td>\n",
              "      <td>daniel brown</td>\n",
              "      <td>daniel</td>\n",
              "      <td>brown</td>\n",
              "      <td>2014-06-02</td>\n",
              "      <td>Male</td>\n",
              "      <td>1990-11-16</td>\n",
              "      <td>25</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>175.0</td>\n",
              "      <td>2014-11-24 08:24:56</td>\n",
              "      <td>2015-02-10 11:58:49</td>\n",
              "      <td>13005759CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013-08-30</td>\n",
              "      <td>276.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>9</td>\n",
              "      <td>High</td>\n",
              "      <td>2014-06-02</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>10</td>\n",
              "      <td>High</td>\n",
              "      <td>2014-06-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11701</th>\n",
              "      <td>11702</td>\n",
              "      <td>mohamed elmoutawayei</td>\n",
              "      <td>mohamed</td>\n",
              "      <td>elmoutawayei</td>\n",
              "      <td>2013-04-16</td>\n",
              "      <td>Male</td>\n",
              "      <td>1991-07-12</td>\n",
              "      <td>24</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>155.0</td>\n",
              "      <td>2013-09-18 10:30:07</td>\n",
              "      <td>2013-11-01 10:14:00</td>\n",
              "      <td>10006095CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2012-10-12</td>\n",
              "      <td>186.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>8</td>\n",
              "      <td>High</td>\n",
              "      <td>2013-04-16</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>10</td>\n",
              "      <td>High</td>\n",
              "      <td>2013-04-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11720</th>\n",
              "      <td>11721</td>\n",
              "      <td>terrance marino</td>\n",
              "      <td>terrance</td>\n",
              "      <td>marino</td>\n",
              "      <td>2013-12-05</td>\n",
              "      <td>Male</td>\n",
              "      <td>1963-08-11</td>\n",
              "      <td>52</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>67.0</td>\n",
              "      <td>2014-02-10 01:08:36</td>\n",
              "      <td>2014-02-13 06:54:34</td>\n",
              "      <td>13004352CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013-04-22</td>\n",
              "      <td>227.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-12-05</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-12-05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>473 rows × 47 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id                  name  ... score_text screening_date\n",
              "12        13             bo bradac  ...        Low     2013-11-04\n",
              "19        20       samuel seraphin  ...        Low     2014-06-03\n",
              "37        38       victoria soltau  ...        Low     2013-03-18\n",
              "50        51            kurt fowks  ...       High     2013-04-09\n",
              "74        75        elliott knauff  ...     Medium     2013-05-02\n",
              "...      ...                   ...  ...        ...            ...\n",
              "11597  11598     michael stenquist  ...       High     2014-05-19\n",
              "11653  11654          wayne taylor  ...       High     2014-06-22\n",
              "11674  11675          daniel brown  ...       High     2014-06-02\n",
              "11701  11702  mohamed elmoutawayei  ...       High     2013-04-16\n",
              "11720  11721       terrance marino  ...        Low     2013-12-05\n",
              "\n",
              "[473 rows x 47 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reincidencias2[reincidencias2['days_b_screening_arrest']<0]"
      ],
      "metadata": {
        "id": "ofKwIro2j32K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "outputId": "2ce5e069-50b7-446e-ae62-1211c3e7a601"
      },
      "id": "ofKwIro2j32K",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>first</th>\n",
              "      <th>last</th>\n",
              "      <th>compas_screening_date</th>\n",
              "      <th>sex</th>\n",
              "      <th>dob</th>\n",
              "      <th>age</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>race</th>\n",
              "      <th>juv_fel_count</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>juv_misd_count</th>\n",
              "      <th>juv_other_count</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>days_b_screening_arrest</th>\n",
              "      <th>c_jail_in</th>\n",
              "      <th>c_jail_out</th>\n",
              "      <th>c_case_number</th>\n",
              "      <th>c_offense_date</th>\n",
              "      <th>c_arrest_date</th>\n",
              "      <th>c_days_from_compas</th>\n",
              "      <th>c_charge_degree</th>\n",
              "      <th>c_charge_desc</th>\n",
              "      <th>is_recid</th>\n",
              "      <th>num_r_cases</th>\n",
              "      <th>r_case_number</th>\n",
              "      <th>r_charge_degree</th>\n",
              "      <th>r_days_from_arrest</th>\n",
              "      <th>r_offense_date</th>\n",
              "      <th>r_charge_desc</th>\n",
              "      <th>r_jail_in</th>\n",
              "      <th>r_jail_out</th>\n",
              "      <th>is_violent_recid</th>\n",
              "      <th>num_vr_cases</th>\n",
              "      <th>vr_case_number</th>\n",
              "      <th>vr_charge_degree</th>\n",
              "      <th>vr_offense_date</th>\n",
              "      <th>vr_charge_desc</th>\n",
              "      <th>v_type_of_assessment</th>\n",
              "      <th>v_decile_score</th>\n",
              "      <th>v_score_text</th>\n",
              "      <th>v_screening_date</th>\n",
              "      <th>type_of_assessment</th>\n",
              "      <th>decile_score.1</th>\n",
              "      <th>score_text</th>\n",
              "      <th>screening_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>miguel hernandez</td>\n",
              "      <td>miguel</td>\n",
              "      <td>hernandez</td>\n",
              "      <td>2013-08-14</td>\n",
              "      <td>Male</td>\n",
              "      <td>1947-04-18</td>\n",
              "      <td>69</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-08-13 06:03:42</td>\n",
              "      <td>2013-08-14 05:41:20</td>\n",
              "      <td>13011352CF10A</td>\n",
              "      <td>2013-08-13</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Aggravated Assault w/Firearm</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-08-14</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-08-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kevon dixon</td>\n",
              "      <td>kevon</td>\n",
              "      <td>dixon</td>\n",
              "      <td>2013-01-27</td>\n",
              "      <td>Male</td>\n",
              "      <td>1982-01-22</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-01-26 03:45:27</td>\n",
              "      <td>2013-02-05 05:36:53</td>\n",
              "      <td>13001275CF10A</td>\n",
              "      <td>2013-01-26</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Felony Battery w/Prior Convict</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13009779CF10A</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>Felony Battery (Dom Strang)</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13009779CF10A</td>\n",
              "      <td>(F3)</td>\n",
              "      <td>2013-07-05</td>\n",
              "      <td>Felony Battery (Dom Strang)</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-01-27</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-01-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>ed philo</td>\n",
              "      <td>ed</td>\n",
              "      <td>philo</td>\n",
              "      <td>2013-04-14</td>\n",
              "      <td>Male</td>\n",
              "      <td>1991-05-14</td>\n",
              "      <td>24</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-04-13 04:58:34</td>\n",
              "      <td>2013-04-14 07:02:04</td>\n",
              "      <td>13005330CF10A</td>\n",
              "      <td>2013-04-13</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Possession of Cocaine</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13011511MM10A</td>\n",
              "      <td>M</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>Driving Under The Influence</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>2013-06-16</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-04-14</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-04-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>edward riddle</td>\n",
              "      <td>edward</td>\n",
              "      <td>riddle</td>\n",
              "      <td>2014-02-19</td>\n",
              "      <td>Male</td>\n",
              "      <td>1974-07-23</td>\n",
              "      <td>41</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2014-02-18 05:08:24</td>\n",
              "      <td>2014-02-24 12:18:30</td>\n",
              "      <td>14002304CF10A</td>\n",
              "      <td>2014-02-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Possession Burglary Tools</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14004485CF10A</td>\n",
              "      <td>F</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>Poss of Firearm by Convic Felo</td>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>2014-04-18</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-02-19</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>6</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2014-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>steven stewart</td>\n",
              "      <td>steven</td>\n",
              "      <td>stewart</td>\n",
              "      <td>2013-08-30</td>\n",
              "      <td>Male</td>\n",
              "      <td>1973-02-25</td>\n",
              "      <td>43</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-08-29 08:55:23</td>\n",
              "      <td>2013-08-30 08:42:13</td>\n",
              "      <td>13012216CF10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013-08-29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-08-30</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>4</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-08-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <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",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>11751</th>\n",
              "      <td>11752</td>\n",
              "      <td>rodrick phillips</td>\n",
              "      <td>rodrick</td>\n",
              "      <td>phillips</td>\n",
              "      <td>2013-02-27</td>\n",
              "      <td>Male</td>\n",
              "      <td>1953-02-24</td>\n",
              "      <td>63</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>2013-02-25 09:20:49</td>\n",
              "      <td>2013-02-26 01:21:51</td>\n",
              "      <td>13003941MM10A</td>\n",
              "      <td>2013-02-25</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2.0</td>\n",
              "      <td>M</td>\n",
              "      <td>Driving Under The Influence</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13004477CF10A</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2013-03-27</td>\n",
              "      <td>Violation License Restrictions</td>\n",
              "      <td>2013-03-28</td>\n",
              "      <td>2013-05-07</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-02-27</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-02-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11752</th>\n",
              "      <td>11753</td>\n",
              "      <td>patrick hamilton</td>\n",
              "      <td>patrick</td>\n",
              "      <td>hamilton</td>\n",
              "      <td>2013-09-22</td>\n",
              "      <td>Male</td>\n",
              "      <td>1968-05-02</td>\n",
              "      <td>47</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-09-21 04:06:00</td>\n",
              "      <td>2013-09-22 08:16:13</td>\n",
              "      <td>13013317CF10A</td>\n",
              "      <td>2013-09-21</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Burglary Structure Unoccup</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-09-22</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-09-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11753</th>\n",
              "      <td>11754</td>\n",
              "      <td>raymond hernandez</td>\n",
              "      <td>raymond</td>\n",
              "      <td>hernandez</td>\n",
              "      <td>2013-05-17</td>\n",
              "      <td>Male</td>\n",
              "      <td>1993-06-24</td>\n",
              "      <td>22</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2013-05-16 08:58:35</td>\n",
              "      <td>2013-05-18 07:59:30</td>\n",
              "      <td>13009569MM10A</td>\n",
              "      <td>2013-05-16</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>Battery</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14086081TC30A</td>\n",
              "      <td>M</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-10-11</td>\n",
              "      <td>Driving License Suspended</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>5</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2013-05-17</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>7</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2013-05-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11755</th>\n",
              "      <td>11756</td>\n",
              "      <td>scott lomagistro</td>\n",
              "      <td>scott</td>\n",
              "      <td>lomagistro</td>\n",
              "      <td>2013-12-03</td>\n",
              "      <td>Male</td>\n",
              "      <td>1986-12-04</td>\n",
              "      <td>29</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>-196.0</td>\n",
              "      <td>2013-05-21 03:12:58</td>\n",
              "      <td>2013-05-22 09:04:20</td>\n",
              "      <td>12018116MM10A</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2013-05-21</td>\n",
              "      <td>196.0</td>\n",
              "      <td>M</td>\n",
              "      <td>arrest case no charge</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-12-03</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>3</td>\n",
              "      <td>Low</td>\n",
              "      <td>2013-12-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11756</th>\n",
              "      <td>11757</td>\n",
              "      <td>chin yan</td>\n",
              "      <td>chin</td>\n",
              "      <td>yan</td>\n",
              "      <td>2014-01-11</td>\n",
              "      <td>Male</td>\n",
              "      <td>1982-02-19</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Asian</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2014-01-10 06:49:02</td>\n",
              "      <td>2014-01-11 01:45:55</td>\n",
              "      <td>14000455CF10A</td>\n",
              "      <td>2014-01-10</td>\n",
              "      <td>NaT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Grand Theft in the 3rd Degree</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>O</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaT</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-01-11</td>\n",
              "      <td>Risk of Recidivism</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>2014-01-11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8089 rows × 47 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id               name  ... score_text screening_date\n",
              "0          1   miguel hernandez  ...        Low     2013-08-14\n",
              "2          3        kevon dixon  ...        Low     2013-01-27\n",
              "3          4           ed philo  ...        Low     2013-04-14\n",
              "7          8      edward riddle  ...     Medium     2014-02-19\n",
              "8          9     steven stewart  ...        Low     2013-08-30\n",
              "...      ...                ...  ...        ...            ...\n",
              "11751  11752   rodrick phillips  ...        Low     2013-02-27\n",
              "11752  11753   patrick hamilton  ...        Low     2013-09-22\n",
              "11753  11754  raymond hernandez  ...     Medium     2013-05-17\n",
              "11755  11756   scott lomagistro  ...        Low     2013-12-03\n",
              "11756  11757           chin yan  ...        Low     2014-01-11\n",
              "\n",
              "[8089 rows x 47 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 112
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      ]
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        "\n",
        "\n",
        "reincidencias2['days_b_screening_arrest2']= reincidencias2['c_jail_out'] - reincidencias2['c_jail_in']\n",
        "\n",
        "np.datetime64()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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            "text/plain": [
              "numpy.datetime64('NaT')"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11742, 48)"
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