Muertes y Lesionados por Ciudad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import unicodedata\n",
    "def strip_accents(s):\n",
    "   return ''.join(c for c in unicodedata.normalize('NFD', s)\n",
    "                  if unicodedata.category(c) != 'Mn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"25_mayo.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data  = data[data.columns[0:45]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Ciudad', 'Mes', 'Fecha', 'Hora', 'Longitud', 'Latitud', 'Calle',\n",
       "       'Cruce', 'Sentido', 'V1_Tipo de Vehículo', 'V1_Estatus Licencia',\n",
       "       'V1_Género', 'V1_Dictamen', 'V1_Edad', 'V1_Uso de Cinturón',\n",
       "       'V1_Arresto', 'V1_No. De Lesionados', 'V1_No. De Fallecidos',\n",
       "       'V2_Tipo de Vehículo', 'V2_Estatus Licencia', 'V2_Género',\n",
       "       'V2_Dictamen', 'V2_Edad', 'V2_Uso de Cinturón', 'V2_Arresto',\n",
       "       'V2_No. De Lesionados', 'V2_No. De Fallecidos', 'V3_Tipo de Vehículo',\n",
       "       'V3_Estatus Licencia', 'V3_Género', 'V3_Dictamen', 'V3_Edad',\n",
       "       'V3_Uso de Cinturón', 'V3_Arresto', 'V3_No. De Lesionados',\n",
       "       'V3_No. De Fallecidos', 'Tipo de incidente', 'No. De Infracción',\n",
       "       'Causa', 'Situación climática', 'Situación del Pavimento',\n",
       "       'Tipo de vía', 'Número  total de lesionados',\n",
       "       'Número total de muertos'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:1: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data[\"Mes\"] = data[\"Mes\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marzo        6148\n",
       "febrero      5507\n",
       "abril        5114\n",
       "noviembre    3148\n",
       "diciembre    3061\n",
       "enero        2273\n",
       "arbil           1\n",
       "mayo            1\n",
       "Name: Mes, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Mes\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:1: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/lib/python3.6/site-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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/usr/local/lib/python3.6/site-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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "monterrey         6751\n",
       "san pedro         4348\n",
       "san nicolas       4123\n",
       "apodaca           2328\n",
       "santa catarina    2052\n",
       "escobedo          1926\n",
       "garcia            1890\n",
       "guadalupe         1834\n",
       "nan                  3\n",
       "                     1\n",
       "Name: Ciudad, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Ciudad\"] = data[\"Ciudad\"].str.lower()\n",
    "data[\"Ciudad\"] = data[\"Ciudad\"].str.strip()\n",
    "data[\"Ciudad\"] = data[\"Ciudad\"].map(lambda x: strip_accents(str(x)))\n",
    "data[\"Ciudad\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
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      "2017-03-31 00:00:00\n",
      "22300\n",
      "2017-04-03 00:00:00\n",
      "22400\n",
      "2017-04-07 00:00:00\n",
      "22500\n",
      "2017-04-11 00:00:00\n",
      "22600\n",
      "2017-04-17 00:00:00\n",
      "22700\n",
      "2017-04-22 00:00:00\n",
      "22800\n",
      "2017-04-26 00:00:00\n",
      "22900\n",
      "2017-04-29 00:00:00\n",
      "23000\n",
      "2017-02-03 00:00:00\n",
      "23100\n",
      "2017-02-08 00:00:00\n",
      "23200\n",
      "2017-02-11 00:00:00\n",
      "23300\n",
      "2017-02-16 00:00:00\n",
      "23400\n",
      "2017-02-20 00:00:00\n",
      "23500\n",
      "2017-02-22 00:00:00\n",
      "23600\n",
      "2017-02-25 00:00:00\n",
      "23700\n",
      "2017-02-28 00:00:00\n",
      "23800\n",
      "2017-03-04 00:00:00\n",
      "23900\n",
      "2017-03-08 00:00:00\n",
      "24000\n",
      "2017-03-11 00:00:00\n",
      "24100\n",
      "2017-03-16 00:00:00\n",
      "24200\n",
      "2017-03-20 00:00:00\n",
      "24300\n",
      "2017-03-23 00:00:00\n",
      "24400\n",
      "2017-03-28 00:00:00\n",
      "24500\n",
      "2017-03-31 00:00:00\n",
      "24600\n",
      "2017-04-03 00:00:00\n",
      "24700\n",
      "2017-04-06 00:00:00\n",
      "24800\n",
      "2017-04-13 00:00:00\n",
      "24900\n",
      "2017-04-20 00:00:00\n",
      "25000\n",
      "2017-04-25 00:00:00\n",
      "25100\n",
      "2017-04-28 00:00:00\n",
      "25200\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(data[\"Fecha\"]), 100):\n",
    "    print(data[\"Fecha\"][i])\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(data, open(\"data.pickle\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "percances = []\n",
    "for ciudad in data[\"Ciudad\"].unique():\n",
    "    ciudad_obj = {\"Ciudad\":ciudad}\n",
    "    data_ciudad = data[data[\"Ciudad\"] == ciudad]\n",
    "    counts_ciudad = data_ciudad[\"Mes\"].value_counts()\n",
    "    for mes in counts_ciudad.keys():\n",
    "        ciudad_obj[mes] = counts_ciudad[mes]\n",
    "    percances.append(ciudad_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "percances_df = pd.DataFrame(percances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     14554\n",
       "1.0       746\n",
       "2.0       128\n",
       "3.0        36\n",
       "4.0        28\n",
       "5.0        11\n",
       "7.0         3\n",
       "21.0        2\n",
       "10.0        2\n",
       "9.0         2\n",
       "6.0         2\n",
       "8.0         1\n",
       "Name: Número  total de lesionados, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Número  total de lesionados\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    15370\n",
       "1.0       35\n",
       "2.0        2\n",
       "4.0        1\n",
       "Name: Número total de muertos, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Número total de muertos\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1398.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Número  total de lesionados\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Número total de muertos\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "muertes = []\n",
    "for ciudad in data[\"Ciudad\"].unique():\n",
    "    ciudad_obj = {\"Ciudad\":ciudad}\n",
    "    data_ciudad = data[data[\"Ciudad\"] == ciudad]\n",
    "    counts_ciudad = data_ciudad[\"Mes\"].value_counts()\n",
    "    for mes in counts_ciudad.keys():\n",
    "        ciudad_obj[mes] = data_ciudad[data_ciudad[\"Mes\"]==mes][\"Número total de muertos\"].sum()\n",
    "    muertes.append(ciudad_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lesionados = []\n",
    "for ciudad in data[\"Ciudad\"].unique():\n",
    "    ciudad_obj = {\"Ciudad\":ciudad}\n",
    "    data_ciudad = data[data[\"Ciudad\"] == ciudad]\n",
    "    counts_ciudad = data_ciudad[\"Mes\"].value_counts()\n",
    "    for mes in counts_ciudad.keys():\n",
    "        ciudad_obj[mes] = data_ciudad[data_ciudad[\"Mes\"]==mes][\"Número  total de lesionados\"].sum()\n",
    "    lesionados.append(ciudad_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "lesionados_df = pd.DataFrame(lesionados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "muertes_df = pd.DataFrame(muertes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ciudad       apodacaescobedogarciaguadalupesan nicolassan p...\n",
       "abril                                                       12\n",
       "arbil                                                     None\n",
       "diciembre                                                    5\n",
       "enero                                                        2\n",
       "febrero                                                      8\n",
       "marzo                                                       14\n",
       "mayo                                                         0\n",
       "noviembre                                                    2\n",
       "dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(muertes_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25256"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ciudad</th>\n",
       "      <th>abril</th>\n",
       "      <th>arbil</th>\n",
       "      <th>diciembre</th>\n",
       "      <th>enero</th>\n",
       "      <th>febrero</th>\n",
       "      <th>marzo</th>\n",
       "      <th>mayo</th>\n",
       "      <th>noviembre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apodaca</td>\n",
       "      <td>305.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>439.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>escobedo</td>\n",
       "      <td>266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>358.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>garcia</td>\n",
       "      <td>315.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>280.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>guadalupe</td>\n",
       "      <td>455.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>san nicolas</td>\n",
       "      <td>702.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>san pedro</td>\n",
       "      <td>618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>707.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>831.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>santa catarina</td>\n",
       "      <td>344.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>358.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>monterrey</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>2459.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ciudad   abril  arbil  diciembre  enero  febrero   marzo  mayo  \\\n",
       "0         apodaca   305.0    NaN      433.0  415.0    388.0   348.0   NaN   \n",
       "1        escobedo   266.0    NaN      358.0  307.0    300.0   367.0   NaN   \n",
       "2          garcia   315.0    NaN      280.0  272.0    357.0   359.0   1.0   \n",
       "3       guadalupe   455.0    NaN      125.0   89.0    493.0   553.0   NaN   \n",
       "4     san nicolas   702.0    1.0      800.0  270.0    648.0   835.0   NaN   \n",
       "5       san pedro   618.0    NaN      707.0  668.0    767.0   831.0   NaN   \n",
       "6  santa catarina   344.0    NaN      358.0  252.0    370.0   396.0   NaN   \n",
       "7             nan     NaN    NaN        NaN    NaN      NaN     NaN   NaN   \n",
       "8       monterrey  2108.0    NaN        NaN    NaN   2184.0  2459.0   NaN   \n",
       "9                     1.0    NaN        NaN    NaN      NaN     NaN   NaN   \n",
       "\n",
       "   noviembre  \n",
       "0      439.0  \n",
       "1      328.0  \n",
       "2      306.0  \n",
       "3      119.0  \n",
       "4      867.0  \n",
       "5      757.0  \n",
       "6      332.0  \n",
       "7        NaN  \n",
       "8        NaN  \n",
       "9        NaN  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percances_df"
   ]
  }
 ],
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