pair_visualizacion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# primero nuestras librerias!\n",
    "\n",
    "# Tratamiento de datos\n",
    "# -----------------------------------------------------------------------\n",
    "import pandas as pd\n",
    "\n",
    "# Visualización\n",
    "# ------------------------------------------------------------------------------\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Configuración\n",
    "# -----------------------------------------------------------------------\n",
    "pd.set_option('display.max_columns', None) # para poder visualizar todas las columnas de los DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>country</th>\n",
       "      <th>density</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>agricultural_land</th>\n",
       "      <th>land_area</th>\n",
       "      <th>armed_forces_size</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>calling_code</th>\n",
       "      <th>capital/major_city</th>\n",
       "      <th>co2-emissions</th>\n",
       "      <th>cpi</th>\n",
       "      <th>cpi_change</th>\n",
       "      <th>currency-code</th>\n",
       "      <th>fertility_rate</th>\n",
       "      <th>forested_area</th>\n",
       "      <th>gasoline_price</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gross_primary_education_enrollment</th>\n",
       "      <th>gross_tertiary_education_enrollment</th>\n",
       "      <th>infant_mortality</th>\n",
       "      <th>largest_city</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>maternal_mortality_ratio</th>\n",
       "      <th>minimum_wage</th>\n",
       "      <th>official_language</th>\n",
       "      <th>out_of_pocket_health_expenditure</th>\n",
       "      <th>physicians_per_thousand</th>\n",
       "      <th>population</th>\n",
       "      <th>population_labor_force_participation</th>\n",
       "      <th>tax_revenue</th>\n",
       "      <th>total_tax_rate</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>urban_population</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60.0</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.1</td>\n",
       "      <td>652.230</td>\n",
       "      <td>323.0</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8.672</td>\n",
       "      <td>149.90</td>\n",
       "      <td>2.3</td>\n",
       "      <td>AFN</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.910135e+10</td>\n",
       "      <td>104.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>47.9</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>64.5</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>Pashto</td>\n",
       "      <td>78.4</td>\n",
       "      <td>0.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>71.4</td>\n",
       "      <td>11.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105.0</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.1</td>\n",
       "      <td>28.748</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4.536</td>\n",
       "      <td>119.05</td>\n",
       "      <td>1.4</td>\n",
       "      <td>ALL</td>\n",
       "      <td>1.62</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.527808e+10</td>\n",
       "      <td>107.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>78.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>Albanian</td>\n",
       "      <td>56.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.7</td>\n",
       "      <td>18.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>12.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  density abbreviation  agricultural_land  land_area  \\\n",
       "0  Afghanistan     60.0           AF               58.1    652.230   \n",
       "1      Albania    105.0           AL               43.1     28.748   \n",
       "\n",
       "   armed_forces_size  birth_rate  calling_code capital/major_city  \\\n",
       "0              323.0       32.49          93.0              Kabul   \n",
       "1                9.0       11.78         355.0             Tirana   \n",
       "\n",
       "   co2-emissions     cpi  cpi_change currency-code  fertility_rate  \\\n",
       "0          8.672  149.90         2.3           AFN            4.47   \n",
       "1          4.536  119.05         1.4           ALL            1.62   \n",
       "\n",
       "   forested_area  gasoline_price           gdp  \\\n",
       "0            2.1            0.70  1.910135e+10   \n",
       "1           28.1            1.36  1.527808e+10   \n",
       "\n",
       "   gross_primary_education_enrollment  gross_tertiary_education_enrollment  \\\n",
       "0                               104.0                                  9.7   \n",
       "1                               107.0                                 55.0   \n",
       "\n",
       "   infant_mortality largest_city  life_expectancy  maternal_mortality_ratio  \\\n",
       "0              47.9        Kabul             64.5                     638.0   \n",
       "1               7.8       Tirana             78.5                      15.0   \n",
       "\n",
       "   minimum_wage official_language  out_of_pocket_health_expenditure  \\\n",
       "0          0.43            Pashto                              78.4   \n",
       "1          1.12          Albanian                              56.9   \n",
       "\n",
       "   physicians_per_thousand  population  population_labor_force_participation  \\\n",
       "0                     0.28         NaN                                  48.9   \n",
       "1                     1.20         NaN                                  55.7   \n",
       "\n",
       "   tax_revenue  total_tax_rate  unemployment_rate  urban_population  \\\n",
       "0          9.3            71.4              11.12               NaN   \n",
       "1         18.6            36.6              12.33               NaN   \n",
       "\n",
       "    latitude  longitude continent  \n",
       "0  33.939110  67.709953      Asia  \n",
       "1  41.153332  20.168331    Europe  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cargamos el dataframe que vamos a usar\n",
    "df = pd.read_csv(\"world_data_full_apply_nulos.csv\", index_col = 0) \n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. ¿Cómo se distribuye la tasa de natalidad en el conjunto de datos? ¿Hay algún patrón evidente?\n",
    "\n",
    "# Usamos un histograma para ver cómo se distribuyen los precios. \n",
    "# Si los datos no son muy dispersos, un gráfico de densidad también es útil.\n",
    "\n",
    "# creamos la gráfica de seaborn\n",
    "sns.histplot(x = \"birth_rate\", \n",
    "             data = df, \n",
    "             color = \"violet\", \n",
    "             kde = True, \n",
    "             bins = 50 )  #bins = intervalos\n",
    "\n",
    "# usando 'plt.xlabel()' cambiamos el nombre del eje x\n",
    "plt.xlabel(\"Tasa natalidad\")\n",
    "\n",
    "# usando el método 'plt.ylabel()' cambiamos el nombre del eje y\n",
    "plt.ylabel(\"Frecuencia\")\n",
    "\n",
    "# ponemos un título usando el método 'plt.title()'\n",
    "plt.title('Distribución de la tasa de natalidad');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. ¿Existe una relación aparente entre la tasa de mortalidad infantil y la esperanza de vida? \n",
    "# Proporciona una breve descripción de los patrones que observes.\n",
    "\n",
    "plt.scatter(x = \"infant_mortality\", \n",
    "            y = \"life_expectancy\", \n",
    "            data = df, \n",
    "            c = \"purple\", \n",
    "            s = 2, \n",
    "            marker = \"^\" )\n",
    "\n",
    "# ponemos los nombres de los ejes\n",
    "plt.xlabel(\"Mortalidad Infantil\")\n",
    "plt.ylabel(\"Promedio de esperanza de vida\")\n",
    "\n",
    "# ponemos un título usando el método 'plt.title()'\n",
    "# en este caso lo que hemos añadido es al parámetro 'fontsize' que nos permite cambiar el tamaño del título\n",
    "plt.title(\"Relación entre la mortalidad infantil y el promedio de esperanza de vida\", fontsize = 8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sí, encontramos una relación lineal negativa entre ambos datos: cuando existe una elevada mortalidad infantil, la esperanza de vida es baja y al contrario. También vemos que los datos están más condensados en zonas donde la mortalidad infantil es baja y la esperanza de vida alta y mucho más dispersos cuando la mortalidad infantil es superior a 25 y la esperanza de vida inferior a unos 67 años."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asia', 'Europe', 'Africa', 'Central America', 'South America',\n",
       "       'Oceania', 'North America', nan], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. ¿Cuál es el continente con la mayor cantidad de países en el conjunto de datos? Proporciona el porcentaje de países \n",
    "# en ese continente.\n",
    "\n",
    "df[\"continent\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "continent_order = df['continent'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df[\"continent\"].unique()\n",
    "\n",
    "sns.countplot(x = \"continent\", \n",
    "              data = df,\n",
    "              color = \"c\",\n",
    "              order = continent_order)\n",
    "              \n",
    "\n",
    "# giramos las etiquetas del eje \"x\", en este caso a 90º. En este caso solo vamos a usar el parámetro 'rotation', \n",
    "# más adelante veremos mas ejemplos para usar los otros parámetros. \n",
    "plt.xticks(rotation = 90)\n",
    "\n",
    "# cambiamos el nombre de los ejes usando los métodos 'plt.ylabel()' y 'plt.xlabel()' ya que estamos creando solo una gráfica\n",
    "plt.xlabel(\"Continente\")\n",
    "plt.ylabel(\"Nº países\")\n",
    "plt.title(\"Nº de países por continente\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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