Utils.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
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   "execution_count": 2,
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    "# Occurrence classification\n",
    "\n",
    "def filtro_contem(coluna, regex):\n",
    "    return coluna.str.contains(regex, case=False)\n",
    "\n",
    "def classify_occurrences(df):\n",
    "    \n",
    "    class_col = df['Class'].astype(str)\n",
    "\n",
    "    #domesticos\n",
    "    family = filtro_contem(class_col, r'(?:\\b201[1-6])')\n",
    "\n",
    "    #ofensas sexuais e estupro\n",
    "    sex_offense = filtro_contem(class_col, r'(?:\\b171[1-8]$)')\n",
    "    rape_crime = filtro_contem(class_col, r'(?:\\b21\\d$)')\n",
    "\n",
    "    #lacerny\n",
    "    larceny = filtro_contem(class_col, r'(?:\\b6[1-3][1-9])')\n",
    "\n",
    "    #robbery\n",
    "    robbery = filtro_contem(class_col, r\"(?:\\b3[1-4][1-8])\")\n",
    "\n",
    "    auto_theft = filtro_contem(class_col, r'\\b7[1-3]')\n",
    "\n",
    "    burg = filtro_contem(class_col, r\"(?:\\b5[1-3][1-9])\")\n",
    "    #drogas/posse de drogas/\n",
    "    drug = filtro_contem(class_col, r\"(?:\\b18[1-6][1-8])\")\n",
    "\n",
    "    #ameaça de violencia\n",
    "    assault = filtro_contem(class_col, r\"(?:\\b8[1-2][1-5])\")\n",
    "\n",
    "    #AGGRAVATED ASSAULT\n",
    "    agg_assault = filtro_contem(class_col, r\"(?:\\b4[1-4][1-5])\")\n",
    "\n",
    "    #vandalismo ou dano objetos/propriedades/ incendio criminoso\n",
    "    vandalism= filtro_contem(class_col, r\"(?:\\b14[1-2][1-7]|\\b1431)\")\n",
    "\n",
    "    #falsificacao cartao de credito/documentos/cheques etc \n",
    "    forgery = filtro_contem(class_col, r\"(?:\\b101[1-4])\")\n",
    "\n",
    "    #fraude de checks\n",
    "    badcheck = filtro_contem(class_col, r\"(?:\\b11[1-2][1-3])\")\n",
    "\n",
    "    #miscleaneos não crimes (infracao, mau compartamento, delito, pessoa/veiculo suspeito)\n",
    "    #morte naturallost property|porperty\n",
    "    #perda de posse/abandono de propriedade/recuperaçao\n",
    "    #desaparecimento, incendio, vadiagem (menor de idade), trespassing, incidente doente mental\n",
    "\n",
    "    misc = filtro_contem(class_col, r\"(?:\\b29\\d\\d)\")\n",
    "\n",
    "    misc_trafic = filtro_contem(class_col, r\"(?:\\b28\\d\\d)\")\n",
    "\n",
    "    #leis bebidas alcolicas\n",
    "    liquor = filtro_contem(class_col, r\"(?:\\b221[1-7])\")\n",
    "\n",
    "    #suicide\n",
    "    suicide = filtro_contem(class_col, r\"(?:\\b261[1-6]|\\b262[1-6])\")\n",
    "\n",
    "    #disparo de arma de fogo\n",
    "    weapon_offense = filtro_contem(class_col, r\"(?:\\b15[1-4][1-3])\")\n",
    "\n",
    "    #outros crimes class 27xx\n",
    "    #violaccao ordem de restrição \n",
    "    other_offenses = filtro_contem(class_col, r\"(?:\\b27\\d\\d)\")\n",
    "\n",
    "    #homicidios\n",
    "    homicide = filtro_contem(class_col, r\"(?:\\b11[1-6]$)\")\n",
    "\n",
    "    #desvio de verba\n",
    "    embezzle = filtro_contem(class_col, r\"(?:\\b121[1-4]|\\b122[1-4])\")\n",
    "\n",
    "    #incidentes, acidentes animals\n",
    "    animal = filtro_contem(class_col, r\"(?:\\b3[0-1]\\d{2})\")\n",
    "\n",
    "    #crimes por juvenis class 2313 e 2314\n",
    "    juvenile = filtro_contem(class_col, r\"(?:\\b211[1-4])\")\n",
    "\n",
    "    #conduta desordeira\n",
    "    disordely = filtro_contem(class_col, r\"(?:\\b241[1-3])\")\n",
    "\n",
    "    # roubo de propriedade\n",
    "    stolen_prop = filtro_contem(class_col, r\"(?:\\b1311)\")\n",
    "\n",
    "    arson = filtro_contem(class_col, r\"(?:\\b9[1-3][1-3])\")\n",
    "\n",
    "    vice = filtro_contem(class_col, r'(?:\\b161[3-4])')\n",
    "    \n",
    "    categorias = [family,\n",
    "                  sex_offense,\n",
    "                  rape_crime,\n",
    "                  larceny,\n",
    "                  robbery,\n",
    "                  badcheck,\n",
    "                  drug,\n",
    "                  assault,\n",
    "                  agg_assault,\n",
    "                  vandalism, \n",
    "                  forgery,\n",
    "                  misc,\n",
    "                  liquor,\n",
    "                  suicide,\n",
    "                  weapon_offense,\n",
    "                  homicide,\n",
    "                  embezzle,\n",
    "                  animal,\n",
    "                  juvenile,\n",
    "                  disordely,\n",
    "                  stolen_prop,\n",
    "                  auto_theft,\n",
    "                  burg,\n",
    "                  misc_trafic,\n",
    "                  arson,\n",
    "                  vice,\n",
    "                  other_offenses]\n",
    "\n",
    "    cat_str = ['Family offenses',\n",
    "                  'Sex offenses',\n",
    "                  'Rape',\n",
    "                  'Larceny',\n",
    "                  'Robbery',\n",
    "                  'Bad checks',\n",
    "                  'Drugs',\n",
    "                  'Assault',\n",
    "                  'Aggravated assault',\n",
    "                  'Vandalism', \n",
    "                  'Forgery',\n",
    "                  'Miscelaneous',\n",
    "                  'Liquor laws',\n",
    "                  'Suicide',\n",
    "                  'Weapon_offenses',\n",
    "                  'Homicide',\n",
    "                  'Embezzle',\n",
    "                  'Animal offenses',\n",
    "                  'Juvenile',\n",
    "                  'Disordely conduct',\n",
    "                  'Stolen property',\n",
    "                  'Auto theft',\n",
    "                  'Burglary',\n",
    "                  'Miscelaneous traffic',\n",
    "                  'Arson',\n",
    "                  'Vice',\n",
    "                  'Other offenses']\n",
    "    \n",
    "    df['Category'] = ''\n",
    "    for categoria, nome_categoria in zip(categorias, cat_str):\n",
    "        df.loc[categoria, 'Category'] = nome_categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "source": [
    "# Dataset conversion\n",
    "\n",
    "def dataset_conversion(df):\n",
    "    df['Dispatch Date / Time'] = pd.to_datetime(df['Dispatch Date / Time'])\n",
    "    df['Start Date / Time'] = pd.to_datetime(df['Start Date / Time'])\n",
    "    df['End Date / Time'] = pd.to_datetime(df['End Date / Time'])\n",
    "    \n",
    "    df['Zip Code'] = pd.to_numeric(df['Zip Code'].fillna(-1), downcast='integer')\n",
    "    df['PRA'] = pd.to_numeric(df['PRA'].fillna(-1), downcast='integer')    \n",
    "    df['Address Number'] = pd.to_numeric(df['Address Number'].fillna(-1), downcast='integer') "
   ]
  }
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