03_Intro_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GitHub repository: https://github.com/jorgemauricio/tutorial_python\n",
    "\n",
    "### Instructor: Jorge Mauricio\n",
    "\n",
    "### Base de datos: Stanford Open Policing Project ([Montana](https://openpolicing.stanford.edu/data)) \n",
    "\n",
    "para tu comodidad esta información ya se encuentra preprocesada en la siguiente ruta [LINK](https://drive.google.com/open?id=1JZ8vtFv4W8TaxrWZSGIZSeFyGVLQE_me) , solo es necesario que descargues el archivo y lo coloques en la siguiente ruta: `data/Montana.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# librerías\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# leer csv\n",
    "df = pd.read_csv(\"data/Montana.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>state</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_fips</th>\n",
       "      <th>fine_grained_location</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>...</th>\n",
       "      <th>lon</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>city</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>vehicle_year</th>\n",
       "      <th>vehicle_make</th>\n",
       "      <th>vehicle_model</th>\n",
       "      <th>vehicle_style</th>\n",
       "      <th>search_reason</th>\n",
       "      <th>stop_outcome_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>MT-2009-00001</td>\n",
       "      <td>MT</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>02:10</td>\n",
       "      <td>CASCADE</td>\n",
       "      <td>Cascade County</td>\n",
       "      <td>30013.0</td>\n",
       "      <td>US 89 N MM10 (SB)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>-111.802932</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1994</td>\n",
       "      <td>FORD</td>\n",
       "      <td>EXPLORER</td>\n",
       "      <td>SPORT UTILITY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRAFFIC CITATION,WARNING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>MT-2009-00002</td>\n",
       "      <td>MT</td>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>11:34</td>\n",
       "      <td>MISSOULA</td>\n",
       "      <td>Missoula County</td>\n",
       "      <td>30063.0</td>\n",
       "      <td>HWY 93 SO AND ANNS LANE S/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.081142</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1996</td>\n",
       "      <td>GMC</td>\n",
       "      <td>TK</td>\n",
       "      <td>TRUCK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INFFRACTION ARREST,WARNING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>MT-2009-00003</td>\n",
       "      <td>MT</td>\n",
       "      <td>2009-01-03</td>\n",
       "      <td>11:36</td>\n",
       "      <td>MISSOULA</td>\n",
       "      <td>Missoula County</td>\n",
       "      <td>30063.0</td>\n",
       "      <td>P007 HWY 93 MM 77 N/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.073505</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1999</td>\n",
       "      <td>GMC</td>\n",
       "      <td>YUKON</td>\n",
       "      <td>SPORT UTILITY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INFFRACTION ARREST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>MT-2009-00004</td>\n",
       "      <td>MT</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>10:33</td>\n",
       "      <td>MISSOULA</td>\n",
       "      <td>Missoula County</td>\n",
       "      <td>30063.0</td>\n",
       "      <td>P007 HWY 93 MM 81 S/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.079027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2002</td>\n",
       "      <td>HOND</td>\n",
       "      <td>CR-V</td>\n",
       "      <td>SPORT UTILITY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INFFRACTION ARREST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>MT-2009-00005</td>\n",
       "      <td>MT</td>\n",
       "      <td>2009-01-04</td>\n",
       "      <td>10:46</td>\n",
       "      <td>MISSOULA</td>\n",
       "      <td>Missoula County</td>\n",
       "      <td>30063.0</td>\n",
       "      <td>P007 HWY 93 MM 81 N/B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.079150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1992</td>\n",
       "      <td>TOYT</td>\n",
       "      <td>TERCEL</td>\n",
       "      <td>SEDAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INFFRACTION ARREST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id state   stop_date stop_time location_raw      county_name  \\\n",
       "0  MT-2009-00001    MT  2009-01-01     02:10      CASCADE   Cascade County   \n",
       "1  MT-2009-00002    MT  2009-01-02     11:34     MISSOULA  Missoula County   \n",
       "2  MT-2009-00003    MT  2009-01-03     11:36     MISSOULA  Missoula County   \n",
       "3  MT-2009-00004    MT  2009-01-04     10:33     MISSOULA  Missoula County   \n",
       "4  MT-2009-00005    MT  2009-01-04     10:46     MISSOULA  Missoula County   \n",
       "\n",
       "   county_fips        fine_grained_location  police_department driver_gender  \\\n",
       "0      30013.0            US 89 N MM10 (SB)                NaN             F   \n",
       "1      30063.0  HWY 93 SO AND ANNS LANE S/B                NaN             M   \n",
       "2      30063.0        P007 HWY 93 MM 77 N/B                NaN             M   \n",
       "3      30063.0        P007 HWY 93 MM 81 S/B                NaN             F   \n",
       "4      30063.0        P007 HWY 93 MM 81 N/B                NaN             M   \n",
       "\n",
       "   ...         lon  ethnicity city out_of_state vehicle_year vehicle_make  \\\n",
       "0  ... -111.802932          N  NaN        False         1994         FORD   \n",
       "1  ... -114.081142          N  NaN        False         1996          GMC   \n",
       "2  ... -114.073505          N  NaN        False         1999          GMC   \n",
       "3  ... -114.079027        NaN  NaN        False         2002         HOND   \n",
       "4  ... -114.079150        NaN  NaN        False         1992         TOYT   \n",
       "\n",
       "   vehicle_model  vehicle_style search_reason            stop_outcome_raw  \n",
       "0       EXPLORER  SPORT UTILITY           NaN    TRAFFIC CITATION,WARNING  \n",
       "1             TK          TRUCK           NaN  INFFRACTION ARREST,WARNING  \n",
       "2          YUKON  SPORT UTILITY           NaN          INFFRACTION ARREST  \n",
       "3           CR-V  SPORT UTILITY           NaN          INFFRACTION ARREST  \n",
       "4         TERCEL          SEDAN           NaN          INFFRACTION ARREST  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estructura del dataFrame Montana\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(825118, 33)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cantidad de columnas y filas\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        object\n",
       "state                     object\n",
       "stop_date                 object\n",
       "stop_time                 object\n",
       "location_raw              object\n",
       "county_name               object\n",
       "county_fips              float64\n",
       "fine_grained_location     object\n",
       "police_department        float64\n",
       "driver_gender             object\n",
       "driver_age_raw           float64\n",
       "driver_age               float64\n",
       "driver_race_raw           object\n",
       "driver_race               object\n",
       "violation_raw             object\n",
       "violation                 object\n",
       "search_conducted            bool\n",
       "search_type_raw           object\n",
       "search_type               object\n",
       "contraband_found          object\n",
       "stop_outcome              object\n",
       "is_arrested                 bool\n",
       "lat                      float64\n",
       "lon                      float64\n",
       "ethnicity                 object\n",
       "city                      object\n",
       "out_of_state              object\n",
       "vehicle_year              object\n",
       "vehicle_make              object\n",
       "vehicle_model             object\n",
       "vehicle_style             object\n",
       "search_reason             object\n",
       "stop_outcome_raw          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tipos de datos de cada columna\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            0\n",
       "state                         0\n",
       "stop_date                    11\n",
       "stop_time                    11\n",
       "location_raw                  4\n",
       "county_name                4056\n",
       "county_fips                4056\n",
       "fine_grained_location      3741\n",
       "police_department        825118\n",
       "driver_gender               119\n",
       "driver_age_raw                0\n",
       "driver_age                 3480\n",
       "driver_race_raw             106\n",
       "driver_race                2739\n",
       "violation_raw                73\n",
       "violation                    73\n",
       "search_conducted              0\n",
       "search_type_raw          822092\n",
       "search_type              822092\n",
       "contraband_found           3026\n",
       "stop_outcome                 53\n",
       "is_arrested                   0\n",
       "lat                         436\n",
       "lon                         436\n",
       "ethnicity                    98\n",
       "city                     549630\n",
       "out_of_state               4699\n",
       "vehicle_year               6610\n",
       "vehicle_make               4268\n",
       "vehicle_model             23805\n",
       "vehicle_style             65362\n",
       "search_reason            822309\n",
       "stop_outcome_raw             53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contabilizar los valores nulos\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remover columnas que solo contienen valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eliminar la columna de \"police_department\"\n",
    "df.drop(\"police_department\", axis=\"columns\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(825118, 32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contabilizar columnas y filas\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'state', 'stop_date', 'stop_time', 'location_raw', 'county_name',\n",
       "       'county_fips', 'fine_grained_location', 'driver_gender',\n",
       "       'driver_age_raw', 'driver_age', 'driver_race_raw', 'driver_race',\n",
       "       'violation_raw', 'violation', 'search_conducted', 'search_type_raw',\n",
       "       'search_type', 'contraband_found', 'stop_outcome', 'is_arrested', 'lat',\n",
       "       'lon', 'ethnicity', 'city', 'out_of_state', 'vehicle_year',\n",
       "       'vehicle_make', 'vehicle_model', 'vehicle_style', 'search_reason',\n",
       "       'stop_outcome_raw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checar columnas\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(825118, 32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# método alternativo\n",
    "df.dropna(axis='columns', how='all').shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTA: \n",
    "- Existe más de una forma de eliminar datos nulos\n",
    "- Verificar los argumentos de cada método"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Qué género tiene un mayor número de detenciones por exceso de velocidad?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    225327\n",
       "F    111598\n",
       "Name: driver_gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número de mujeres y hombres que son detenidos por exceso de velocidad\n",
    "df[df[\"violation\"] == 'Speeding'][\"driver_gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.668775\n",
       "F    0.331225\n",
       "Name: driver_gender, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Porcentaje de mujeres y hombres que son detenidos por exceso de velocidad\n",
    "df[df[\"violation\"] == 'Speeding'][\"driver_gender\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    0.668775\n",
       "F    0.331225\n",
       "Name: driver_gender, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# forma alternativa\n",
    "df.loc[df[\"violation\"] == 'Speeding', \"driver_gender\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Speeding                                      0.404612\n",
       "License,Speeding                              0.066251\n",
       "Paperwork,Speeding                            0.053619\n",
       "Lights                                        0.050501\n",
       "License,Paperwork,Speeding                    0.032053\n",
       "                                                ...   \n",
       "Equipment,Safe movement,Truck                 0.000002\n",
       "DUI,Lights,Registration/plates                0.000002\n",
       "Other (non-mapped),Safe movement,Seat belt    0.000002\n",
       "Other (non-mapped),Paperwork,Safe movement    0.000002\n",
       "Other,Other (non-mapped),Safe movement        0.000002\n",
       "Name: violation, Length: 330, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cuando un hombre es detenido, que porcentaje es por exceso de velocidad?\n",
    "df[df['driver_gender'] == \"M\"][\"violation\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Speeding                                      0.416364\n",
       "License,Speeding                              0.080838\n",
       "Paperwork,Speeding                            0.062131\n",
       "Lights                                        0.054774\n",
       "License,Paperwork,Speeding                    0.040507\n",
       "                                                ...   \n",
       "License,Other,Other (non-mapped)              0.000004\n",
       "Lights,Registration/plates,Stop sign/light    0.000004\n",
       "License,Other (non-mapped),Safe movement      0.000004\n",
       "License,Seat belt,Truck                       0.000004\n",
       "Equipment,License,Truck                       0.000004\n",
       "Name: violation, Length: 273, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cuando una mujer es detenida, que porcentaje es por exceso de velocidad?\n",
    "df[df['driver_gender'] == \"F\"][\"violation\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "driver_gender  violation                             \n",
       "F              Speeding                                  0.416364\n",
       "               License,Speeding                          0.080838\n",
       "               Paperwork,Speeding                        0.062131\n",
       "               Lights                                    0.054774\n",
       "               License,Paperwork,Speeding                0.040507\n",
       "                                                           ...   \n",
       "M              Other (non-mapped),Stop sign/light        0.000002\n",
       "               Other,Other (non-mapped),Safe movement    0.000002\n",
       "               Other,Stop sign/light,Truck               0.000002\n",
       "               Paperwork,Registration/plates,Truck       0.000002\n",
       "               Speeding,Stop sign/light,Truck            0.000002\n",
       "Name: violation, Length: 603, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# porcentaje agrupado por genero\n",
    "df.groupby(\"driver_gender\")[\"violation\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'state', 'stop_date', 'stop_time', 'location_raw', 'county_name',\n",
       "       'county_fips', 'fine_grained_location', 'driver_gender',\n",
       "       'driver_age_raw', 'driver_age', 'driver_race_raw', 'driver_race',\n",
       "       'violation_raw', 'violation', 'search_conducted', 'search_type_raw',\n",
       "       'search_type', 'contraband_found', 'stop_outcome', 'is_arrested', 'lat',\n",
       "       'lon', 'ethnicity', 'city', 'out_of_state', 'vehicle_year',\n",
       "       'vehicle_make', 'vehicle_model', 'vehicle_style', 'search_reason',\n",
       "       'stop_outcome_raw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# año con el número mayor de detenciones\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combinar celdas de fecha y tiempo\n",
    "combined = df[\"stop_date\"] + \" \" + df['stop_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convertir a columna de tiempo\n",
    "df['fecha'] = pd.to_datetime(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                               object\n",
       "state                            object\n",
       "stop_date                        object\n",
       "stop_time                        object\n",
       "location_raw                     object\n",
       "county_name                      object\n",
       "county_fips                     float64\n",
       "fine_grained_location            object\n",
       "driver_gender                    object\n",
       "driver_age_raw                  float64\n",
       "driver_age                      float64\n",
       "driver_race_raw                  object\n",
       "driver_race                      object\n",
       "violation_raw                    object\n",
       "violation                        object\n",
       "search_conducted                   bool\n",
       "search_type_raw                  object\n",
       "search_type                      object\n",
       "contraband_found                 object\n",
       "stop_outcome                     object\n",
       "is_arrested                        bool\n",
       "lat                             float64\n",
       "lon                             float64\n",
       "ethnicity                        object\n",
       "city                             object\n",
       "out_of_state                     object\n",
       "vehicle_year                     object\n",
       "vehicle_make                     object\n",
       "vehicle_model                    object\n",
       "vehicle_style                    object\n",
       "search_reason                    object\n",
       "stop_outcome_raw                 object\n",
       "fecha                    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corroborar tipo de dato de la columna\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010.0    124285\n",
       "2011.0    122839\n",
       "2012.0    117487\n",
       "2015.0    115935\n",
       "2013.0    114283\n",
       "2014.0    109747\n",
       "2016.0    102097\n",
       "2009.0     18434\n",
       "Name: fecha, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# año con mayor número de detenciones\n",
    "df['fecha'].dt.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     13137\n",
       "8     12421\n",
       "7     12271\n",
       "5     11953\n",
       "10    11791\n",
       "3     10650\n",
       "6     10568\n",
       "4     10279\n",
       "2      9084\n",
       "11     7882\n",
       "1      7321\n",
       "12     6928\n",
       "Name: fecha, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mes con mayor número de detenciones del 2010\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      7321\n",
       "2      9084\n",
       "3     10650\n",
       "4     10279\n",
       "5     11953\n",
       "6     10568\n",
       "7     12271\n",
       "8     12421\n",
       "9     13137\n",
       "10    11791\n",
       "11     7882\n",
       "12     6928\n",
       "Name: fecha, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ordenar datos por índice\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121546210>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gráficar los datos (línea)\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a33790>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gráficar los datos (barra)\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts().sort_index().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1226cd7d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gráficar los datos (barra horizontal)\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts().sort_index().plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a3ffd0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# aplicando estilo\n",
    "plt.style.use(\"seaborn\")\n",
    "# gráficar los datos (barra horizontal)\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts().sort_index().plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122c82ad0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gráficar los datos (barra y título)\n",
    "df[df['fecha'].dt.year == 2010]['fecha'].dt.month.value_counts().sort_index().plot(kind=\"barh\", title=\"Detenciones 2010\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122e36390>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma de edades\n",
    "df['driver_age'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 825118 entries, 0 to 825117\n",
      "Data columns (total 33 columns):\n",
      "id                       825118 non-null object\n",
      "state                    825118 non-null object\n",
      "stop_date                825107 non-null object\n",
      "stop_time                825107 non-null object\n",
      "location_raw             825114 non-null object\n",
      "county_name              821062 non-null object\n",
      "county_fips              821062 non-null float64\n",
      "fine_grained_location    821377 non-null object\n",
      "driver_gender            824999 non-null object\n",
      "driver_age_raw           825118 non-null float64\n",
      "driver_age               821638 non-null float64\n",
      "driver_race_raw          825012 non-null object\n",
      "driver_race              822379 non-null object\n",
      "violation_raw            825045 non-null object\n",
      "violation                825045 non-null object\n",
      "search_conducted         825118 non-null bool\n",
      "search_type_raw          3026 non-null object\n",
      "search_type              3026 non-null object\n",
      "contraband_found         822092 non-null object\n",
      "stop_outcome             825065 non-null object\n",
      "is_arrested              825118 non-null bool\n",
      "lat                      824682 non-null float64\n",
      "lon                      824682 non-null float64\n",
      "ethnicity                825020 non-null object\n",
      "city                     275488 non-null object\n",
      "out_of_state             820419 non-null object\n",
      "vehicle_year             818508 non-null object\n",
      "vehicle_make             820850 non-null object\n",
      "vehicle_model            801313 non-null object\n",
      "vehicle_style            759756 non-null object\n",
      "search_reason            2809 non-null object\n",
      "stop_outcome_raw         825065 non-null object\n",
      "fecha                    825107 non-null datetime64[ns]\n",
      "dtypes: bool(2), datetime64[ns](1), float64(5), object(25)\n",
      "memory usage: 196.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}