{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caso Aplicado\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image info](images/logo_terremoto.png )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EL dataset `terremotos.csv` contiene la información de los terremotos de los países durante el año 2000 al 2011. Debido a que la información de este dataset es relativamente fácil de trabajar, hemos creado un dataset denominado `terremotos_contaminados.csv` que posee información contaminada en cada una de sus columnas. De esta forma se podrá ilustrar los distintos inconvenientes que se puede tener en el análisis exploratorio de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bases del experimento\n",
    "\n",
    "Lo primero es identificar las variables que influyen en el estudio y la naturaleza de esta.\n",
    "\n",
    "* **País**: \n",
    "    * Descripción: País del evento sísmico. \n",
    "    * Tipo de dato: *string*\n",
    "    * Limitantes: No pueden haber nombre de ciudades, comunas o pueblos.\n",
    "* **Año**: \n",
    "    * Descripción: Año del evento sísmico. \n",
    "    * Tipo de dato: *integer*. \n",
    "    * Limitantes: el año debe estar entre al año 2000 y 2011.\n",
    "* **Magnitud**: \n",
    "    * Descripción: Magnitud del evento sísmico. \n",
    "    * Tipo de dato: *float*. \n",
    "    * Limitantes: la magnitud puede estar entre 5 y 9.6. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **¿ Por qué no puede ser un terremoto con una intensidad mayor a 9.6?**. Esto se debe a que el terremoto con mayor magnitud registrado por la humanidad es de 9.6, ocurrido en Chile (Valdivia) durante el año 1960. Por lo tanto, entre mayor conocimiento se tenga del fenómeno en estudio, más restrictivo se vulve el análisis exploratorio y más sentido tienen los resultados obtenidos.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos\n",
    "\n",
    "El conjunto de datos consta de cuatro columnas:\n",
    " * País\n",
    " * Año\n",
    " * Magnitud\n",
    " * Información\n",
    " \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checklist del experimento\n",
    "\n",
    "Dado que conocemos el fenómeno en estudio, vayamos realizando un checklist de los procesos para hacer un correcto EDA.\n",
    "\n",
    "**1. ¿ Qué pregunta (s) estás tratando de resolver (o probar que estás equivocado)?**\n",
    "\n",
    "El objetivo es encontrar el terremoto de mayor magnitud por país en los distintos años.\n",
    "\n",
    "\n",
    "**2. ¿ Qué tipo de datos tienes ?**\n",
    "\n",
    "Los tipos de variables que tiene el conjunto de datos son:\n",
    "\n",
    "* Categóricas: País, Información.\n",
    "* Numéricas: Año, Magnitud.\n",
    "\n",
    "\n",
    "![image info](images/categorias.png )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cargar librerias\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.max_columns', 500)  # Ver más columnas de los dataframes\n",
    "\n",
    "# Ver gráficos de matplotlib en jupyter notebook/lab\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Año</th>\n",
       "      <th>Pais</th>\n",
       "      <th>Magnitud</th>\n",
       "      <th>Informacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>6</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>7</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>6.5</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>6.8</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año              Pais Magnitud      Informacion\n",
       "0  2000            Turkey        6  info no valiosa\n",
       "1  2000      Turkmenistan        7  info no valiosa\n",
       "2  2000        Azerbaijan      6.5  info no valiosa\n",
       "3  2000       Azerbaijan       6.8  info no valiosa\n",
       "4  2000  Papua New Guinea        8  info no valiosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cargar datos\n",
    "terremotos_data = pd.read_csv(os.path.join(\"data\",\"terremotos_contaminados.csv\"), sep=\",\")\n",
    "terremotos_data.head()"
   ]
  },
  {
   "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>año</th>\n",
       "      <th>pais</th>\n",
       "      <th>magnitud</th>\n",
       "      <th>informacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>6</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>7</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>6.5</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>6.8</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8</td>\n",
       "      <td>info no valiosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    año              pais magnitud      informacion\n",
       "0  2000            Turkey        6  info no valiosa\n",
       "1  2000      Turkmenistan        7  info no valiosa\n",
       "2  2000        Azerbaijan      6.5  info no valiosa\n",
       "3  2000       Azerbaijan       6.8  info no valiosa\n",
       "4  2000  Papua New Guinea        8  info no valiosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalizar columnas (minusculas y sin espacios)\n",
    "\n",
    "terremotos_data.columns = terremotos_data.columns.str.lower().str.strip()\n",
    "terremotos_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resumen de la informacion\n",
    "\n",
    "def resumen_por_columna(df,cols):\n",
    "    pd_series = df[cols]\n",
    "    \n",
    "    # elementos distintos \n",
    "    l_unique = pd_series.unique()\n",
    "    \n",
    "    # elementos vacios\n",
    "    \n",
    "    l_vacios = pd_series[pd_series.isna()]\n",
    "    \n",
    "    df_info = pd.DataFrame({\n",
    "        'columna': [cols],\n",
    "        'unicos': [len(l_unique)],\n",
    "        'vacios': [len(l_vacios)]\n",
    "    })\n",
    "    \n",
    "    return df_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>columna</th>\n",
       "      <th>unicos</th>\n",
       "      <th>vacios</th>\n",
       "      <th>% vacios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>año</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pais</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>magnitud</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>0.013158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>informacion</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.035088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       columna  unicos  vacios  % vacios\n",
       "0          año      17       2  0.008772\n",
       "1         pais      75       2  0.008772\n",
       "2     magnitud      46       3  0.013158\n",
       "3  informacion       4       8  0.035088"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "\n",
    "for col in terremotos_data.columns:\n",
    "    aux_df = resumen_por_columna(terremotos_data,col)\n",
    "    frames.append(aux_df)\n",
    "    \n",
    "df_info = pd.concat(frames).reset_index(drop=True)\n",
    "df_info['% vacios'] = df_info['vacios']/len(terremotos_data)\n",
    "df_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.figure(figsize=(7,5))\n",
    "\n",
    "plotting = sns.barplot(\n",
    "    x=\"columna\",\n",
    "    y=\"unicos\",\n",
    "    data=df_info.sort_values('unicos'),\n",
    "    palette=\"Greys_d\",\n",
    "    linewidth=3\n",
    ")\n",
    "\n",
    "for container in plotting.containers:\n",
    "    plotting.bar_label(container,fontsize=12)\n",
    "\n",
    "plt.title(\"Valores Distintos\")\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xticks(fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**¿Qué falta en los datos y cómo los maneja?**\n",
    "\n",
    "En este caso, se tiene la información suficiente para poder realizar el experimento, solo falta ver que los datos de la muestra no esten lo suficientemente contaminados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**¿Qué hacer con los datos faltantes, outliers o información mal inputada?**\n",
    "\n",
    "Este caso es más interesante, y se necesita ir detallando columna por columna los distintos análisis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columna: Año\n",
    "Los años distintos en la muestra son:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2000', '2001', 'dos mil uno', '2002', '2003', '2004', '2005',\n",
       "       '2006', '2007', '2008', '2009', '2010', '2011', '1997', '1990',\n",
       "       '1999', nan], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data['año'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot: año\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(7,4))\n",
    "\n",
    "plotting = sns.countplot(\n",
    "    y=\"año\",\n",
    "    data=terremotos_data,\n",
    "    order=terremotos_data['año'].value_counts().index,\n",
    "    palette=\"Greys_d\",\n",
    "    linewidth=3\n",
    ")\n",
    "\n",
    "for container in plotting.containers:\n",
    "    plotting.bar_label(container,fontsize=12)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se presentan las siguientes anomalidades:\n",
    "\n",
    "* **Años sin importancia**: Se ha establecido que los años de estudios son desde el año 2000 al 2011.\n",
    "* **Nombres mal escritos**: en este caso sabemos que 'dos mil uno' corresponde a '2001'.\n",
    "* **Datos vacío**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora la pregunta es, ¿ qué debemos hacer primero?. Lo primero es corregir la información, dar un formato estándar a los datos y luego filtrar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**a) Corrección**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '1997', '1990', '1999', '0'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data.loc[terremotos_data['año']=='dos mil uno','año'] = '2001'\n",
    "terremotos_data.loc[terremotos_data['año'].isnull(),'año'] = '0'\n",
    "\n",
    "terremotos_data['año'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**b) Formato**\n",
    "\n",
    "El formato de los años es *integer*, por lo tanto se le debe dar ese formato."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "terremotos_data['año'] = terremotos_data['año'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**c) Filtro**\n",
    "\n",
    "Se ha establecido que los años de estudios son desde el año 2000 al 2011, por lo tanto los años en estudios deberian ser:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011]\n"
     ]
    }
   ],
   "source": [
    "anios_estudio = [x for x in range(2000,2011+1)]\n",
    "print(anios_estudio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por lo tanto ya tenemo nuestro primer filtro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_anio = terremotos_data['año'].isin(anios_estudio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columna: País\n",
    "\n",
    "Los paises distintos en la muestra son:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Afghanistan',\n",
       " 'Afghanistan ',\n",
       " 'Algeria',\n",
       " 'Algeria ',\n",
       " 'Argentina',\n",
       " 'Azerbaijan',\n",
       " 'Azerbaijan ',\n",
       " 'Bangladesh',\n",
       " 'Burma ',\n",
       " 'Chile',\n",
       " 'Chile ',\n",
       " 'China',\n",
       " 'China ',\n",
       " 'Colombia',\n",
       " 'Costa Rica',\n",
       " 'Costa Rica ',\n",
       " 'Democratic Republic of the Congo',\n",
       " 'Democratic Republic of the Congo ',\n",
       " 'Dominican Republic',\n",
       " 'Ecuador',\n",
       " 'El Salvador ',\n",
       " 'Greece',\n",
       " 'Greece ',\n",
       " 'Guadeloupe',\n",
       " 'Guatemala',\n",
       " 'Haiti ',\n",
       " 'India',\n",
       " 'India ',\n",
       " 'Indonesia',\n",
       " 'Indonesia ',\n",
       " 'Iran',\n",
       " 'Iran ',\n",
       " 'Iran, 2005 Qeshm earthquake',\n",
       " 'Italy',\n",
       " 'Italy ',\n",
       " 'Japan',\n",
       " 'Japan ',\n",
       " 'Kazakhstan',\n",
       " 'Kyrgyzstan ',\n",
       " 'Martinique',\n",
       " 'Mexico ',\n",
       " 'Morocco',\n",
       " 'Morocco ',\n",
       " 'Mozambique',\n",
       " 'New Zealand',\n",
       " 'New Zealand ',\n",
       " 'Nicaragua',\n",
       " 'Pakistan',\n",
       " 'Pakistan ',\n",
       " 'Panama',\n",
       " 'Papua New Guinea',\n",
       " 'Peru',\n",
       " 'Peru ',\n",
       " 'Philippines',\n",
       " 'Russian Federation',\n",
       " 'Rwanda',\n",
       " 'Samoa ',\n",
       " 'Serbia',\n",
       " 'Slovenia',\n",
       " 'Solomon Islands ',\n",
       " 'Taiwan',\n",
       " 'Taiwan ',\n",
       " 'Tajikistan',\n",
       " 'Tajikistan ',\n",
       " 'Tanzania',\n",
       " 'Tanzania ',\n",
       " 'Turkey',\n",
       " 'Turkey ',\n",
       " 'Turkmenistan',\n",
       " 'United States ',\n",
       " 'Venezuela',\n",
       " 'Vietnam',\n",
       " 'arica',\n",
       " nan,\n",
       " 'shile'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(terremotos_data['pais'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot: pais\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(14,14))\n",
    "\n",
    "plotting = sns.countplot(\n",
    "    y=\"pais\",\n",
    "    data=terremotos_data,\n",
    "    order=terremotos_data.pais.value_counts().index,\n",
    "    palette=\"Greys_d\",\n",
    "    linewidth=3\n",
    ")\n",
    "\n",
    "for container in plotting.containers:\n",
    "    plotting.bar_label(container,fontsize=12)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se presentan las siguientes anomalidades:\n",
    "\n",
    "* **Formato de los nombres**: no se le ha aplicado *strip()* y *lower()*, por lo cual tenemos casos como: 'Turkey' y 'Turkey ' como elementos diferentes.\n",
    "* **Nombres mal escritos**: en este caso sabemos que 'shile' corresponde a 'Chile' e 'Iran, 2005 Qeshm earthquake' corrsponde a 'Iran'.\n",
    "* **Datos vacío**\n",
    "\n",
    "\n",
    "Se solucionará cada uno de estos inconvenientes:\n",
    "\n",
    "**Corrección de los nombres**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "terremotos_data.loc[terremotos_data['pais']=='arica','pais'] = 'chile'\n",
    "terremotos_data.loc[terremotos_data['pais']=='shile','pais'] = 'chile'\n",
    "terremotos_data.loc[terremotos_data['pais']=='Iran, 2005 Qeshm earthquake','pais'] = 'iran'\n",
    "terremotos_data.loc[terremotos_data['pais'].isnull(),'pais'] = 'sin_nombre'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Formato**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correccion formato de nombre \n",
    "terremotos_data['pais'] = terremotos_data['pais'].str.lower().str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtro**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_pais = terremotos_data['pais']!='sin_nombre'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columna: Magnitud\n",
    "\n",
    "Las magnitudes distintas en la muestra son:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6', '7', '6.5', '6.8', '8', '5.7', '6.4', '5.5', '6.3', '5.4',\n",
       "       '6.1', '6.7', '7.9', '7.2', '7.5', '5.3', '5.9', '9.7', '5.8',\n",
       "       '4.7', '7.6', '8.4', '5', '5.6', '6.6', '6.2', '7.1', '7.3', '5.1',\n",
       "       '5.2', '8.3', '6.9', '9.1', '4.9', '7.8', '8.6', '7.7', '7.4',\n",
       "       '8.5', '8.1', '8.8', '9', '-10', nan, '2002-Tanzania-5.8',\n",
       "       '2003-japan-8.5'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data['magnitud'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot: magnitud\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "plotting = sns.countplot(\n",
    "    y=\"magnitud\",\n",
    "    data=terremotos_data,\n",
    "    order=terremotos_data['magnitud'].value_counts().index,\n",
    "    palette=\"Greys_d\",\n",
    "    linewidth=3\n",
    ")\n",
    "\n",
    "for container in plotting.containers:\n",
    "    plotting.bar_label(container,fontsize=10)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se presentan las siguientes anomalidades:\n",
    "\n",
    "* **Magnitudes sin importancia**: Se ha establecido que las magnitudes de un terremoto son de 5 a 9.6.\n",
    "* **Datos con información comprimida**: Debido a una inputación incorrecta de los datos o a una mala lectura, la información se concentra en una celda.\n",
    "* **Datos vacío**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Corrección de las magnitudes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# caso: tradicional\n",
    "terremotos_data.loc[terremotos_data['magnitud']=='2002-Tanzania-5.8','magnitud'] = '0'\n",
    "terremotos_data.loc[terremotos_data['magnitud']=='2003-japan-8.5','magnitud'] = '0'\n",
    "terremotos_data.loc[terremotos_data['magnitud'].isnull(),'magnitud'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# caso: informacion comprimida\n",
    "terremotos_data.loc[-1] = [2002,'tanzania','5.8','-']\n",
    "terremotos_data.loc[-2] = [2003,'japan','8.5','-']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>año</th>\n",
       "      <th>pais</th>\n",
       "      <th>magnitud</th>\n",
       "      <th>informacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0</td>\n",
       "      <td>sin_nombre</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0</td>\n",
       "      <td>sin_nombre</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2005</td>\n",
       "      <td>chile</td>\n",
       "      <td>8</td>\n",
       "      <td>valiosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>2002</td>\n",
       "      <td>tanzania</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>2003</td>\n",
       "      <td>japan</td>\n",
       "      <td>8.5</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      año        pais magnitud informacion\n",
       "225     0  sin_nombre        0         NaN\n",
       "226     0  sin_nombre        0         NaN\n",
       "227  2005       chile        8     valiosa\n",
       "228  2002    tanzania      5.8           -\n",
       "229  2003       japan      8.5           -"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data = terremotos_data.reset_index(drop=True)\n",
    "terremotos_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Corrección formato de las magnitudes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6. ,   7. ,   6.5,   6.8,   8. ,   5.7,   6.4,   5.5,   6.3,\n",
       "         5.4,   6.1,   6.7,   7.9,   7.2,   7.5,   5.3,   5.9,   9.7,\n",
       "         5.8,   4.7,   7.6,   8.4,   5. ,   5.6,   6.6,   6.2,   7.1,\n",
       "         7.3,   5.1,   5.2,   8.3,   6.9,   9.1,   4.9,   7.8,   8.6,\n",
       "         7.7,   7.4,   8.5,   8.1,   8.8,   9. , -10. ,   0. ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data['magnitud'] = terremotos_data['magnitud'].astype(float)\n",
    "terremotos_data['magnitud'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**c) Filtro de las magnitudes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_mag_inf =  terremotos_data['magnitud']>=5\n",
    "mask_mag_sup =  terremotos_data['magnitud']<=9.6\n",
    "\n",
    "mask_mag = mask_mag_inf & mask_mag_sup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columna: Información\n",
    "\n",
    "La cantidad de elementos distintos para la columna **información** son:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['info no valiosa', 'info valiosa', nan, 'valiosa', '-'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data['informacion'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que esta columna no aporta información valiosa al estudio, quedando excluida para cualquier análisis importante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "terremotos_data.drop('informacion', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>año</th>\n",
       "      <th>pais</th>\n",
       "      <th>magnitud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>turkey</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>turkmenistan</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>azerbaijan</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>azerbaijan</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>papua new guinea</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    año              pais  magnitud\n",
       "0  2000            turkey       6.0\n",
       "1  2000      turkmenistan       7.0\n",
       "2  2000        azerbaijan       6.5\n",
       "3  2000        azerbaijan       6.8\n",
       "4  2000  papua new guinea       8.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terremotos_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. ¿Se puede sacar más provecho a los datos ?**\n",
    "\n",
    "Una vez realizado toda la limpieza de datos, debemos filtrar la información que se considere importante.\n",
    "\n",
    "* **Años**: Años desde el 2000 al 2011.\n",
    "* **País**: Paises con nombre distinto de *sin_nombre*\n",
    "* **Magnitud**: Magnitud entre 5 y 9.6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>año</th>\n",
       "      <th>pais</th>\n",
       "      <th>magnitud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>turkey</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>turkmenistan</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>azerbaijan</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>azerbaijan</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>papua new guinea</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    año              pais  magnitud\n",
       "0  2000            turkey       6.0\n",
       "1  2000      turkmenistan       7.0\n",
       "2  2000        azerbaijan       6.5\n",
       "3  2000        azerbaijan       6.8\n",
       "4  2000  papua new guinea       8.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aplicar filtros\n",
    "terremotos_data_filtrado = terremotos_data[mask_anio & mask_pais & mask_mag]\n",
    "terremotos_data_filtrado.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos cuánta información se perdio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de filas dataset sin filtro: 230\n",
      "Cantidad de filas dataset con filtro: 212\n"
     ]
    }
   ],
   "source": [
    "print('Cantidad de filas dataset sin filtro:',len(terremotos_data))\n",
    "print('Cantidad de filas dataset con filtro:',len(terremotos_data_filtrado))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora veamos el el resumen de la información para el nuevo dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>columna</th>\n",
       "      <th>unicos</th>\n",
       "      <th>vacios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>año</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pais</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>magnitud</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    columna  unicos  vacios\n",
       "0       año      12       0\n",
       "1      pais      50       0\n",
       "2  magnitud      39       0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "\n",
    "for col in terremotos_data_filtrado.columns:\n",
    "    aux_df = resumen_por_columna(terremotos_data_filtrado,col)\n",
    "    frames.append(aux_df)\n",
    "    \n",
    "df_info = pd.concat(frames).reset_index(drop=True)\n",
    "df_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, podemos responder la pregunta del inicio:\n",
    "\n",
    "### Solución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-8c1a5edb19d8>:6: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n",
      "  aggfunc=pd.np.max)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>año</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pais</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>afghanistan</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.5</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>algeria</th>\n",
       "      <td>5.7</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.8</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.2</td>\n",
       "      <td></td>\n",
       "      <td>5.5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>argentina</th>\n",
       "      <td>7.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.1</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>azerbaijan</th>\n",
       "      <td>6.8</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>bangladesh</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.6</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>burma</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>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chile</th>\n",
       "      <td></td>\n",
       "      <td>6.3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.0</td>\n",
       "      <td></td>\n",
       "      <td>7.7</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.8</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>china</th>\n",
       "      <td>5.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colombia</th>\n",
       "      <td>6.5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.9</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>costa rica</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>democratic republic of the congo</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.9</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dominican republic</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.4</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>ecuador</th>\n",
       "      <td>5.5</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>el salvador</th>\n",
       "      <td></td>\n",
       "      <td>7.6</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>greece</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guadeloupe</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.3</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>guatemala</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.4</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>haiti</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>7.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>india</th>\n",
       "      <td></td>\n",
       "      <td>7.6</td>\n",
       "      <td>6.5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indonesia</th>\n",
       "      <td>7.9</td>\n",
       "      <td></td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iran</th>\n",
       "      <td>5.3</td>\n",
       "      <td></td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>italy</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.9</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>japan</th>\n",
       "      <td>6.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td></td>\n",
       "      <td>8.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td></td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td></td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kazakhstan</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.0</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>kyrgyzstan</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>6.9</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>martinique</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7.4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mexico</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7.5</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>morocco</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.3</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>mozambique</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new zealand</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nicaragua</th>\n",
       "      <td>5.4</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>pakistan</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.3</td>\n",
       "      <td></td>\n",
       "      <td>5.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td></td>\n",
       "      <td>5.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>panama</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.5</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>papua new guinea</th>\n",
       "      <td>8.0</td>\n",
       "      <td></td>\n",
       "      <td>7.6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.1</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>peru</th>\n",
       "      <td></td>\n",
       "      <td>8.4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7.5</td>\n",
       "      <td></td>\n",
       "      <td>8.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>philippines</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td></td>\n",
       "      <td>7.1</td>\n",
       "      <td></td>\n",
       "      <td>5.3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>russian federation</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7.3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwanda</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>5.3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samoa</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>8.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serbia</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.7</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>slovenia</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.2</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>solomon islands</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>taiwan</th>\n",
       "      <td>6.4</td>\n",
       "      <td></td>\n",
       "      <td>7.1</td>\n",
       "      <td></td>\n",
       "      <td>5.2</td>\n",
       "      <td></td>\n",
       "      <td>7.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tajikistan</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tanzania</th>\n",
       "      <td>6.4</td>\n",
       "      <td></td>\n",
       "      <td>5.5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.8</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>turkey</th>\n",
       "      <td>6.0</td>\n",
       "      <td></td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turkmenistan</th>\n",
       "      <td>7.0</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>united states</th>\n",
       "      <td></td>\n",
       "      <td>6.8</td>\n",
       "      <td></td>\n",
       "      <td>6.6</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>venezuela</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vietnam</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5.3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "año                              2000 2001 2002 2003 2004 2005 2006 2007 2008  \\\n",
       "pais                                                                            \n",
       "afghanistan                       6.3  5.0  7.3  5.8  6.5  6.5                  \n",
       "algeria                           5.7            6.8            5.2       5.5   \n",
       "argentina                         7.2                 6.1                       \n",
       "azerbaijan                        6.8                                           \n",
       "bangladesh                                       5.6                            \n",
       "burma                                                                           \n",
       "chile                                  6.3                 8.0       7.7        \n",
       "china                             5.9  5.6  5.5  6.3  5.3  5.2  5.0  6.1  7.9   \n",
       "colombia                          6.5                                     5.9   \n",
       "costa rica                                            6.4                       \n",
       "democratic republic of the congo            6.2                           5.9   \n",
       "dominican republic                               6.4                            \n",
       "ecuador                           5.5                                           \n",
       "el salvador                            7.6                                      \n",
       "greece                                      6.2                           6.4   \n",
       "guadeloupe                                            6.3                       \n",
       "guatemala                                        6.4                            \n",
       "haiti                                                                           \n",
       "india                                  7.6  6.5            5.1  5.3  5.1        \n",
       "indonesia                         7.9       7.5  6.9  9.1  8.6  7.7  8.5  7.3   \n",
       "iran                              5.3       6.5  6.6  6.3  6.4  6.1             \n",
       "italy                                       5.9                                 \n",
       "japan                             6.1  6.8       8.3  6.6  6.6       6.7  6.9   \n",
       "kazakhstan                                       6.0                            \n",
       "kyrgyzstan                                                                6.9   \n",
       "martinique                                                           7.4        \n",
       "mexico                                           7.5                            \n",
       "morocco                                               6.3                       \n",
       "mozambique                                                      7.0             \n",
       "new zealand                                           5.4            6.6        \n",
       "nicaragua                         5.4                                           \n",
       "pakistan                                    6.3       5.4  7.6       5.2  6.4   \n",
       "panama                                           6.5                            \n",
       "papua new guinea                  8.0       7.6            6.1                  \n",
       "peru                                   8.4                 7.5       8.0        \n",
       "philippines                                 7.5  6.5       7.1       5.3        \n",
       "russian federation                               7.3                 6.2        \n",
       "rwanda                                                                    5.3   \n",
       "samoa                                                                           \n",
       "serbia                                      5.7                                 \n",
       "slovenia                                              5.2                       \n",
       "solomon islands                                                      8.1        \n",
       "taiwan                            6.4       7.1       5.2       7.0             \n",
       "tajikistan                                  5.2                 5.6  5.2        \n",
       "tanzania                          6.4       5.5            6.8                  \n",
       "turkey                            6.0       6.5  6.3  5.6  5.9                  \n",
       "turkmenistan                      7.0                                           \n",
       "united states                          6.8       6.6                            \n",
       "venezuela                                                       5.5             \n",
       "vietnam                                                    5.3                  \n",
       "\n",
       "año                              2009 2010 2011  \n",
       "pais                                             \n",
       "afghanistan                                      \n",
       "algeria                                          \n",
       "argentina                                        \n",
       "azerbaijan                                       \n",
       "bangladesh                                       \n",
       "burma                                       6.8  \n",
       "chile                                  8.8       \n",
       "china                             5.7  6.9  5.4  \n",
       "colombia                                         \n",
       "costa rica                        6.1            \n",
       "democratic republic of the congo                 \n",
       "dominican republic                               \n",
       "ecuador                                          \n",
       "el salvador                                      \n",
       "greece                                           \n",
       "guadeloupe                                       \n",
       "guatemala                                        \n",
       "haiti                                  7.0       \n",
       "india                                       6.9  \n",
       "indonesia                         7.6            \n",
       "iran                                             \n",
       "italy                             6.2            \n",
       "japan                             6.4       9.0  \n",
       "kazakhstan                                       \n",
       "kyrgyzstan                                       \n",
       "martinique                                       \n",
       "mexico                                           \n",
       "morocco                                          \n",
       "mozambique                                       \n",
       "new zealand                                 6.3  \n",
       "nicaragua                                        \n",
       "pakistan                                         \n",
       "panama                                           \n",
       "papua new guinea                                 \n",
       "peru                                             \n",
       "philippines                                      \n",
       "russian federation                               \n",
       "rwanda                                           \n",
       "samoa                             8.1            \n",
       "serbia                                           \n",
       "slovenia                                         \n",
       "solomon islands                                  \n",
       "taiwan                                           \n",
       "tajikistan                                       \n",
       "tanzania                                         \n",
       "turkey                                 6.1  7.1  \n",
       "turkmenistan                                     \n",
       "united states                                    \n",
       "venezuela                                        \n",
       "vietnam                                          "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# formato wide\n",
    "terremotos_data_filtrado.pivot_table(index=\"pais\", \n",
    "                                     columns=\"año\",\n",
    "                                     values=\"magnitud\", \n",
    "                                     fill_value='', \n",
    "                                     aggfunc=pd.np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿ Podemos sacar más información ?. Por supuesto que se puede, no obstante, siempre se debe ser preciso con la información que se "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusión del caso**\n",
    "\n",
    "* El análisis exploratorio de datos (EDA) es una metodología que sirve para asegurarse de la calidad de los datos.\n",
    "* A medida que se tiene más expertice en el tema, mejor es el análisis de datos y por tanto, mejor son los resultados obtenidos.\n",
    "* No existe un procedimiento estándar para realizar el EDA, pero siempre se debe tener una claridad mental con:\n",
    "    * Problema que se quiere resolverlo\n",
    "    * Cómo resolver el problema\n",
    "    * Posibles problemas de la muestra (datos perdidos, ouliers, etc.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Referencia\n",
    "\n",
    "1. [Detailed exploratory data analysis with python](https://www.kaggle.com/ekami66/detailed-exploratory-data-analysis-with-python)\n",
    "2. [Exploratory Data Analysis (EDA) and Data Visualization with Python](https://kite.com/blog/python/data-analysis-visualization-python/)"
   ]
  }
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