Ayudantías/T4a/Ay_T4a.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Taller 4a\n",
    "Importamos librerías necesarias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misión 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos la base de datos y visualizamos su formato:"
   ]
  },
  {
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>O3</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>Environmental_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>24.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>29.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>30.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day     O3  PM2.5 Environmental_risk\n",
       "0  2008      1    1  29.63    NaN                NaN\n",
       "1  2008      1    2  21.46    NaN                NaN\n",
       "2  2008      1    3  24.25    NaN                NaN\n",
       "3  2008      1    4  29.04    NaN                NaN\n",
       "4  2008      1    5  30.17    NaN                NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>O3</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2984.000000</td>\n",
       "      <td>2984.000000</td>\n",
       "      <td>2984.000000</td>\n",
       "      <td>2878.000000</td>\n",
       "      <td>2725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011.592493</td>\n",
       "      <td>6.419236</td>\n",
       "      <td>15.715818</td>\n",
       "      <td>16.313767</td>\n",
       "      <td>28.814594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.357040</td>\n",
       "      <td>3.487660</td>\n",
       "      <td>8.804066</td>\n",
       "      <td>7.995680</td>\n",
       "      <td>17.479388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.392500</td>\n",
       "      <td>16.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.960000</td>\n",
       "      <td>23.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>22.357500</td>\n",
       "      <td>37.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>56.300000</td>\n",
       "      <td>126.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year        Month          Day           O3        PM2.5\n",
       "count  2984.000000  2984.000000  2984.000000  2878.000000  2725.000000\n",
       "mean   2011.592493     6.419236    15.715818    16.313767    28.814594\n",
       "std       2.357040     3.487660     8.804066     7.995680    17.479388\n",
       "min    2008.000000     1.000000     1.000000     1.000000     5.000000\n",
       "25%    2010.000000     3.000000     8.000000     9.392500    16.170000\n",
       "50%    2012.000000     6.000000    16.000000    16.960000    23.210000\n",
       "75%    2014.000000     9.000000    23.000000    22.357500    37.210000\n",
       "max    2016.000000    12.000000    31.000000    56.300000   126.200000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "display(df.head())\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misión 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores extremos\n",
    "Visualizamos los datos para ``O3``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"O3\"].hist(bins=50)\n",
    "plt.show()\n",
    "df.boxplot(column = \"O3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay ciertos datos que se escapan bastante del máximo no atípico mostrado en el diagrama de caja, además de parecer que son pocos en cantidad. En efecto, los siguientes son los datos cuya medición de ``O3`` es mayor que 40:"
   ]
  },
  {
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>O3</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>Environmental_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1715</th>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>48.57</td>\n",
       "      <td>26.23</td>\n",
       "      <td>medio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>52.86</td>\n",
       "      <td>43.88</td>\n",
       "      <td>medio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>55.18</td>\n",
       "      <td>31.46</td>\n",
       "      <td>medio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>56.30</td>\n",
       "      <td>13.69</td>\n",
       "      <td>extremo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>49.50</td>\n",
       "      <td>95.88</td>\n",
       "      <td>Alto</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Month  Day     O3  PM2.5 Environmental_risk\n",
       "1715  2012      9   11  48.57  26.23              medio\n",
       "1716  2012      9   12  52.86  43.88              medio\n",
       "1717  2012      9   13  55.18  31.46              medio\n",
       "1718  2012      9   14  56.30  13.69            extremo\n",
       "2195  2014      1    4  49.50  95.88               Alto"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df[df[\"O3\"] > 40])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como son solo 5 datos de un total de 2984, se decidirá eliminar estos valores extremos de la base de datos para este caso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df[\"O3\"] > 40].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volvemos a visualizar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"O3\"].hist(bins=50)\n",
    "plt.show()\n",
    "df.boxplot(column = \"O3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizamos los datos para ``PM2.5``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"PM2.5\"].hist(bins=50)\n",
    "plt.show()\n",
    "df.boxplot(column = \"PM2.5\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para este caso, no es tan identificable un conjunto de datos que disten significativamente del máximo no atípico. Además, la distribución tiene un comportamiento similar a una Log-Normal, por lo que para este caso utilizaremos una transformación logarítmica para anular el efecto de los valores extremos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"PM2.5_log\"] = np.log(df[\"PM2.5\"])\n",
    "df[\"PM2.5_log\"].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora la distribución se ve mucho más cerca de una normal y el efecto de los valores extremos ha disminuido significativamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datos faltantes\n",
    "Veamos cuántos datos faltantes hay en cada columna de interés:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° de datos df: 2979\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "O3       106\n",
       "PM2.5    259\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"N° de datos df: {len(df)}\")\n",
    "display(df[[\"O3\", \"PM2.5\"]].apply(lambda x: sum(x.isnull()), axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un nuevos DataFrame ``df2`` en el que se completan los datos faltantes con la media:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° de datos df2: 2979\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "O3           0\n",
       "PM2.5_log    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2[\"O3\"].fillna(df2[\"O3\"].mean(), inplace=True)\n",
    "media_PM25 = df2[\"PM2.5\"].mean()\n",
    "df2[\"PM2.5\"].fillna(media_PM25, inplace=True)\n",
    "df2[\"PM2.5_log\"].fillna(np.log(media_PM25), inplace=True)\n",
    "print(f\"N° de datos df2: {len(df2)}\")\n",
    "display(df2[[\"O3\", \"PM2.5_log\"]].apply(lambda x: sum(x.isnull()), axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un nuevos DataFrame ``df3`` en el que se eliminan los datos faltantes en vez de completar con la media:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° de datos df3: 2651\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "O3           0\n",
       "PM2.5_log    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = df.copy()\n",
    "df3.dropna(subset=[\"O3\", \"PM2.5_log\"], inplace=True)\n",
    "print(f\"N° de datos df3: {len(df3)}\")\n",
    "display(df3[[\"O3\", \"PM2.5_log\"]].apply(lambda x: sum(x.isnull()), axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misión 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estadísticos pedidos para ``df2``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>O3</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.250821</td>\n",
       "      <td>28.789938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.716669</td>\n",
       "      <td>16.667372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.650000</td>\n",
       "      <td>126.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.868417</td>\n",
       "      <td>2.485593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corr O3 - PM2.5</th>\n",
       "      <td>-0.475489</td>\n",
       "      <td>-0.475489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        O3       PM2.5\n",
       "mean             16.250821   28.789938\n",
       "std               7.716669   16.667372\n",
       "min               1.000000    5.000000\n",
       "max              39.650000  126.200000\n",
       "kurtosis         -0.868417    2.485593\n",
       "corr O3 - PM2.5  -0.475489   -0.475489"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats_df2 = df2[[\"O3\", \"PM2.5\"]].describe().drop(labels=[\"count\",\"25%\",\"50%\",\"75%\"])\n",
    "stats_df2.loc[\"kurtosis\"] = df2[[\"O3\", \"PM2.5\"]].kurtosis()\n",
    "corr_03_PM25 = df2[\"O3\"].corr(df2[\"PM2.5\"])\n",
    "stats_df2.loc[\"corr O3 - PM2.5\"] = [corr_03_PM25, corr_03_PM25]\n",
    "display(stats_df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estadísticos pedidos para ``df3``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>O3</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.892422</td>\n",
       "      <td>28.962003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.890672</td>\n",
       "      <td>17.560376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.650000</td>\n",
       "      <td>126.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-0.944448</td>\n",
       "      <td>1.937068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corr O3 - PM2.5</th>\n",
       "      <td>-0.495540</td>\n",
       "      <td>-0.495540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        O3       PM2.5\n",
       "mean             15.892422   28.962003\n",
       "std               7.890672   17.560376\n",
       "min               1.000000    5.000000\n",
       "max              39.650000  126.200000\n",
       "kurtosis         -0.944448    1.937068\n",
       "corr O3 - PM2.5  -0.495540   -0.495540"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats_df3 = df3[[\"O3\", \"PM2.5\"]].describe().drop(labels=[\"count\",\"25%\",\"50%\",\"75%\"])\n",
    "stats_df3.loc[\"kurtosis\"] = df3[[\"O3\", \"PM2.5\"]].kurtosis()\n",
    "corr_03_PM25 = df3[\"O3\"].corr(df3[\"PM2.5\"])\n",
    "stats_df3.loc[\"corr O3 - PM2.5\"] = [corr_03_PM25, corr_03_PM25]\n",
    "display(stats_df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misión 3\n",
    "### Histograma de PM2.5\n",
    "Para ``df2``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2[\"PM2.5\"].hist(bins=50) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ``df3``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3[\"PM2.5\"].hist(bins=50) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot de O3 por mes\n",
    "Para ``df2``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.boxplot(column=\"O3\", by = \"Month\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ``df3``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.boxplot(column=\"O3\", by = \"Month\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolución promedio de O3 y PM2.5 por año"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ``df2``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2[[\"Year\", \"O3\", \"PM2.5\"]].groupby(\"Year\").mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ``df3``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3[[\"Year\", \"O3\", \"PM2.5\"]].groupby(\"Year\").mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misión 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identifiquemos la cantidad de datos nulos y los niveles de riesgos ambientales presentes con su frecuencia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores NaN df2: 328\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medio      1602\n",
       "Bajo        750\n",
       "nulo        134\n",
       "Alto        105\n",
       "extremo      60\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Valores NaN df2: {df2['Environmental_risk'].isna().sum()}\")\n",
    "display(df2[\"Environmental_risk\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores NaN df3: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medio      1602\n",
       "Bajo        750\n",
       "nulo        134\n",
       "Alto        105\n",
       "extremo      60\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Valores NaN df3: {df3['Environmental_risk'].isna().sum()}\")\n",
    "display(df3[\"Environmental_risk\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ``df3``, al eliminar los datos faltantes de ``O3`` y ``PM2.5`` coincidentemente no quedaron valores NaN en ``Environmental_risk``, mientras que en ``df2`` sí quedaron. En cualquier caso, lo relevante a notar es que existe una categoría ``nulo`` en ``Environmental_risk``, la cual podría hacer referencia a que es un dato faltante o desconocido, por lo que lo transformaremos a valor NaN para que sea más sencillo trabajar con ellos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[\"Environmental_risk\"].replace(\"nulo\", np.nan, inplace=True)\n",
    "df3[\"Environmental_risk\"].replace(\"nulo\", np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores NaN df2: 462\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medio      1602\n",
       "Bajo        750\n",
       "Alto        105\n",
       "extremo      60\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Valores NaN df2: {df2['Environmental_risk'].isna().sum()}\")\n",
    "display(df2[\"Environmental_risk\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores NaN df3: 134\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medio      1602\n",
       "Bajo        750\n",
       "Alto        105\n",
       "extremo      60\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Valores NaN df3: {df3['Environmental_risk'].isna().sum()}\")\n",
    "display(df3[\"Environmental_risk\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualicemos el comportamiento de los niveles de riesgo ambiental respecto a las variables ``O3`` y ``PM2.5`` (se hará todo usando ``df3``, con ``df2`` se obtienen los mismos comportamientos):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.boxplot(column=\"O3\", by = \"Environmental_risk\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.boxplot(column=\"PM2.5\", by = \"Environmental_risk\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La última figura no logra mostrar un comportamiento claro del nivel de riesgo respecto a la cantidad de ``PM2.5``, debido a los valores extremos presentes. Sin embargo, la transformación logarítmica implementada en la Misión 1 nos podría ayudar en este caso:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.boxplot(column=\"PM2.5_log\", by = \"Environmental_risk\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mucho mejor :)\n",
    "\n",
    "Hay dos categorías que quizás son más fácil de identificar respecto a las otras usando las variables ``O3`` y ``PM2.5_log``: ``Bajo`` y ``extremo``. Ambas poseen datos con un valor bajo los 2.9 aprox de ``PM2.5_log``, pero la primera tiene datos con un valor bajo los 27 aprox de ``O3``, mientras que la segunda sobre este valor. Por otro lado, pareciera no haber tantos datos de las categorías ``Alto`` y ``medio`` en esos rangos. En efecto, si determinamos las frecuencias de los niveles de riesgo para estos rangos se obtiene lo siguiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bajo    690\n",
       "Alto     22\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1 = (df3[\"O3\"] < 27) & (df3[\"PM2.5_log\"] < 2.9)\n",
    "df3[cond1][\"Environmental_risk\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "extremo    51\n",
       "Alto       14\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond2 = (df3[\"O3\"] > 27) & (df3[\"PM2.5_log\"] < 2.9)\n",
    "df3[cond2][\"Environmental_risk\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para el primer rango, se tiene una probabilidad de 96.91% que la observación tenga un nivel de riesgo ``Bajo``, mientras que para el segundo rango, se tiene una probabilidad de 78.46% que la observación tenga un nivel de riesgo ``extremo``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La categoría más compleja de desagregar pareciera ser el nivel de riesgo ``Alto``. Su rango más favorable pareciera ser para valores sobre los 2.5 y bajo los 3.7 de ``PM2.5_log`` y sobre los 25 de ``O3``. Sin embargo, determinando las frecuencias de los niveles de riesgo para este rango se obtiene lo siguiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medio      150\n",
       "Alto        85\n",
       "extremo     56\n",
       "Bajo        40\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[(df3[\"O3\"] > 25) & (df3[\"PM2.5_log\"] > 2.5) & ((df3[\"PM2.5_log\"] < 3.7))][\"Environmental_risk\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La categoría ``medio`` es preponderante en ese rango y lo es también en la base de datos en general, de acuerdo con las frecuencias totales obtenidas al comienzo de la misión. De hecho, para los datos que no se encuentran en los rangos definidos para los niveles ``Bajo`` y ``extremo`` se tiene que hay un 92.07% de observaciones con nivel de riesgo ``medio`` como se observa a continuación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medio      1602\n",
       "Alto         69\n",
       "Bajo         60\n",
       "extremo       9\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond3 = ~(cond1 | cond2)\n",
    "df3[cond3][\"Environmental_risk\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De esta manera, el esquema de asignación plnateado es el siguiente:\n",
    "\n",
    "- Si la cantidad de ``O3`` es mayor que 27 y la cantidad de ``PM2.5`` es menor que 2.9, asignar nivel ``extremo``.\n",
    "- Si la cantidad de ``O3`` es menor que 27 y la cantidad de ``PM2.5`` es menor que 2.9, asignar nivel ``Bajo``.\n",
    "- Asignar nivel ``medio`` en otro caso.\n",
    "\n",
    "Aplicamos el esquema definido para rellenar los datos faltantes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.loc[cond1, \"Environmental_risk\"] = df3.loc[cond1, \"Environmental_risk\"].fillna(\"Bajo\")\n",
    "df3.loc[cond2, \"Environmental_risk\"] = df3.loc[cond2, \"Environmental_risk\"].fillna(\"extremo\")\n",
    "df3.loc[cond3, \"Environmental_risk\"] = df3.loc[cond3, \"Environmental_risk\"].fillna(\"medio\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tras completar los datos se tienen las siguientes frecuencias de los niveles de riesgo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores NaN df3: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medio      1633\n",
       "Bajo        853\n",
       "Alto        105\n",
       "extremo      60\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Valores NaN df3: {df3['Environmental_risk'].isna().sum()}\")\n",
    "display(df3[\"Environmental_risk\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rellenamos para ``df2``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores NaN df2: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "medio      1937\n",
       "Bajo        877\n",
       "Alto        105\n",
       "extremo      60\n",
       "Name: Environmental_risk, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond1 = (df2[\"O3\"] < 27) & (df2[\"PM2.5_log\"] < 2.9)\n",
    "cond2 = (df2[\"O3\"] > 27) & (df2[\"PM2.5_log\"] < 2.9)\n",
    "cond3 = ~(cond1 | cond2)\n",
    "df2.loc[cond1, \"Environmental_risk\"] = df2.loc[cond1, \"Environmental_risk\"].fillna(\"Bajo\")\n",
    "df2.loc[cond2, \"Environmental_risk\"] = df2.loc[cond2, \"Environmental_risk\"].fillna(\"extremo\")\n",
    "df2.loc[cond3, \"Environmental_risk\"] = df2.loc[cond3, \"Environmental_risk\"].fillna(\"medio\")\n",
    "print(f\"Valores NaN df2: {df2['Environmental_risk'].isna().sum()}\")\n",
    "display(df2[\"Environmental_risk\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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