S6_clase1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e0bda55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29222ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('indice_2017.csv',index_col=['Fecha']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d8256a3",
   "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>Hora</th>\n",
       "      <th>Noroeste Ozono</th>\n",
       "      <th>Noroeste diÛxido de azufre</th>\n",
       "      <th>Noroeste diÛxido de nitrÛgeno</th>\n",
       "      <th>Noroeste monÛxido de carbono</th>\n",
       "      <th>Noroeste PM10</th>\n",
       "      <th>Noreste Ozono</th>\n",
       "      <th>Noreste diÛxido de azufre</th>\n",
       "      <th>Noreste diÛxido de nitrÛgeno</th>\n",
       "      <th>Noreste monÛxido de carbono</th>\n",
       "      <th>...</th>\n",
       "      <th>Suroeste Ozono</th>\n",
       "      <th>Suroeste diÛxido de azufre</th>\n",
       "      <th>Suroeste diÛxido de nitrÛgeno</th>\n",
       "      <th>Suroeste monÛxido de carbono</th>\n",
       "      <th>Suroeste PM10</th>\n",
       "      <th>Sureste Ozono</th>\n",
       "      <th>Sureste diÛxido de azufre</th>\n",
       "      <th>Sureste diÛxido de nitrÛgeno</th>\n",
       "      <th>Sureste monÛxido de carbono</th>\n",
       "      <th>Sureste PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</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",
       "      <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>2017-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>11</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>12</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>13</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>14</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>15</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>16</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>17</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>18</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>20</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>23</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>24</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Hora  Noroeste Ozono  Noroeste diÛxido de azufre   \n",
       "Fecha                                                          \n",
       "2017-01-01     1             9.0                          18  \\\n",
       "2017-01-01     2             5.0                          15   \n",
       "2017-01-01     3             6.0                          12   \n",
       "2017-01-01     4             3.0                           9   \n",
       "2017-01-01     5             3.0                           8   \n",
       "2017-01-01     6             4.0                           6   \n",
       "2017-01-01     7             4.0                           6   \n",
       "2017-01-01     8             4.0                           5   \n",
       "2017-01-01     9            11.0                           5   \n",
       "2017-01-01    10            23.0                           5   \n",
       "2017-01-01    11            39.0                           4   \n",
       "2017-01-01    12            48.0                           4   \n",
       "2017-01-01    13            61.0                           4   \n",
       "2017-01-01    14            90.0                           4   \n",
       "2017-01-01    15            92.0                           3   \n",
       "2017-01-01    16           100.0                           3   \n",
       "2017-01-01    17            88.0                           3   \n",
       "2017-01-01    18            67.0                           3   \n",
       "2017-01-01    19            42.0                           3   \n",
       "2017-01-01    20            26.0                           3   \n",
       "2017-01-01    21            22.0                           3   \n",
       "2017-01-01    22            23.0                           3   \n",
       "2017-01-01    23            18.0                           3   \n",
       "2017-01-01    24             6.0                           3   \n",
       "\n",
       "            Noroeste diÛxido de nitrÛgeno  Noroeste monÛxido de carbono   \n",
       "Fecha                                                                     \n",
       "2017-01-01                           18.0                          12.0  \\\n",
       "2017-01-01                           17.0                          13.0   \n",
       "2017-01-01                           16.0                          13.0   \n",
       "2017-01-01                           17.0                          13.0   \n",
       "2017-01-01                           16.0                          13.0   \n",
       "2017-01-01                           15.0                          13.0   \n",
       "2017-01-01                           15.0                          12.0   \n",
       "2017-01-01                           13.0                          12.0   \n",
       "2017-01-01                           16.0                          11.0   \n",
       "2017-01-01                           23.0                          11.0   \n",
       "2017-01-01                           18.0                          11.0   \n",
       "2017-01-01                           10.0                          10.0   \n",
       "2017-01-01                            8.0                           9.0   \n",
       "2017-01-01                            6.0                           7.0   \n",
       "2017-01-01                            6.0                           7.0   \n",
       "2017-01-01                            7.0                           6.0   \n",
       "2017-01-01                            9.0                           5.0   \n",
       "2017-01-01                           11.0                           5.0   \n",
       "2017-01-01                           10.0                           5.0   \n",
       "2017-01-01                           17.0                           5.0   \n",
       "2017-01-01                           19.0                           5.0   \n",
       "2017-01-01                           22.0                           6.0   \n",
       "2017-01-01                           26.0                           6.0   \n",
       "2017-01-01                           25.0                           7.0   \n",
       "\n",
       "            Noroeste PM10  Noreste Ozono  Noreste diÛxido de azufre   \n",
       "Fecha                                                                 \n",
       "2017-01-01           80.0            8.0                          5  \\\n",
       "2017-01-01           86.0            4.0                          5   \n",
       "2017-01-01           94.0            4.0                          4   \n",
       "2017-01-01          100.0            4.0                          4   \n",
       "2017-01-01          103.0            4.0                          3   \n",
       "2017-01-01          106.0            4.0                          3   \n",
       "2017-01-01          108.0            4.0                          3   \n",
       "2017-01-01          110.0            5.0                          4   \n",
       "2017-01-01          111.0            8.0                          4   \n",
       "2017-01-01          113.0           21.0                          4   \n",
       "2017-01-01          114.0           37.0                          4   \n",
       "2017-01-01          115.0           55.0                          4   \n",
       "2017-01-01          115.0           63.0                          5   \n",
       "2017-01-01          113.0           80.0                          5   \n",
       "2017-01-01          112.0           96.0                          5   \n",
       "2017-01-01          112.0           94.0                          5   \n",
       "2017-01-01          112.0           67.0                          5   \n",
       "2017-01-01          112.0           46.0                          4   \n",
       "2017-01-01          112.0           34.0                          5   \n",
       "2017-01-01          113.0           25.0                          5   \n",
       "2017-01-01          112.0           18.0                          5   \n",
       "2017-01-01          112.0           14.0                          5   \n",
       "2017-01-01          112.0            7.0                          4   \n",
       "2017-01-01          112.0            6.0                          4   \n",
       "\n",
       "            Noreste diÛxido de nitrÛgeno  Noreste monÛxido de carbono  ...   \n",
       "Fecha                                                                  ...   \n",
       "2017-01-01                          22.0                         11.0  ...  \\\n",
       "2017-01-01                          21.0                         12.0  ...   \n",
       "2017-01-01                          21.0                         14.0  ...   \n",
       "2017-01-01                          21.0                         15.0  ...   \n",
       "2017-01-01                          20.0                         18.0  ...   \n",
       "2017-01-01                          19.0                         19.0  ...   \n",
       "2017-01-01                          17.0                         19.0  ...   \n",
       "2017-01-01                          17.0                         20.0  ...   \n",
       "2017-01-01                          21.0                         22.0  ...   \n",
       "2017-01-01                          26.0                         22.0  ...   \n",
       "2017-01-01                          19.0                         20.0  ...   \n",
       "2017-01-01                          10.0                         18.0  ...   \n",
       "2017-01-01                           8.0                         15.0  ...   \n",
       "2017-01-01                           5.0                         14.0  ...   \n",
       "2017-01-01                           6.0                         12.0  ...   \n",
       "2017-01-01                           8.0                         10.0  ...   \n",
       "2017-01-01                           8.0                          8.0  ...   \n",
       "2017-01-01                          12.0                          6.0  ...   \n",
       "2017-01-01                          16.0                          5.0  ...   \n",
       "2017-01-01                          20.0                          5.0  ...   \n",
       "2017-01-01                          24.0                          6.0  ...   \n",
       "2017-01-01                          27.0                          7.0  ...   \n",
       "2017-01-01                          28.0                          8.0  ...   \n",
       "2017-01-01                          26.0                         10.0  ...   \n",
       "\n",
       "            Suroeste Ozono  Suroeste diÛxido de azufre   \n",
       "Fecha                                                    \n",
       "2017-01-01            21.0                           2  \\\n",
       "2017-01-01            25.0                           2   \n",
       "2017-01-01            24.0                           2   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            21.0                           1   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            25.0                           1   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            24.0                           1   \n",
       "2017-01-01            38.0                           1   \n",
       "2017-01-01            48.0                           1   \n",
       "2017-01-01            71.0                           1   \n",
       "2017-01-01            80.0                           1   \n",
       "2017-01-01            86.0                           1   \n",
       "2017-01-01            84.0                           1   \n",
       "2017-01-01            67.0                           1   \n",
       "2017-01-01            45.0                           1   \n",
       "2017-01-01            36.0                           1   \n",
       "2017-01-01            34.0                           1   \n",
       "2017-01-01            32.0                           1   \n",
       "2017-01-01            31.0                           1   \n",
       "2017-01-01            30.0                           1   \n",
       "2017-01-01            28.0                           1   \n",
       "\n",
       "            Suroeste diÛxido de nitrÛgeno  Suroeste monÛxido de carbono   \n",
       "Fecha                                                                     \n",
       "2017-01-01                           18.0                           5.0  \\\n",
       "2017-01-01                           17.0                           5.0   \n",
       "2017-01-01                           16.0                           6.0   \n",
       "2017-01-01                           17.0                           7.0   \n",
       "2017-01-01                           17.0                           7.0   \n",
       "2017-01-01                           16.0                           8.0   \n",
       "2017-01-01                           14.0                           8.0   \n",
       "2017-01-01                           13.0                           8.0   \n",
       "2017-01-01                           13.0                           8.0   \n",
       "2017-01-01                           15.0                           8.0   \n",
       "2017-01-01                           10.0                           8.0   \n",
       "2017-01-01                            6.0                           7.0   \n",
       "2017-01-01                            5.0                           6.0   \n",
       "2017-01-01                            6.0                           6.0   \n",
       "2017-01-01                            5.0                           5.0   \n",
       "2017-01-01                            5.0                           5.0   \n",
       "2017-01-01                            5.0                           5.0   \n",
       "2017-01-01                            6.0                           5.0   \n",
       "2017-01-01                            7.0                           5.0   \n",
       "2017-01-01                           12.0                           5.0   \n",
       "2017-01-01                           21.0                           5.0   \n",
       "2017-01-01                           19.0                           5.0   \n",
       "2017-01-01                           21.0                           5.0   \n",
       "2017-01-01                           23.0                           5.0   \n",
       "\n",
       "            Suroeste PM10  Sureste Ozono  Sureste diÛxido de azufre   \n",
       "Fecha                                                                 \n",
       "2017-01-01           36.0            6.0                          2  \\\n",
       "2017-01-01           36.0            6.0                          2   \n",
       "2017-01-01           36.0           14.0                          2   \n",
       "2017-01-01           38.0            7.0                          2   \n",
       "2017-01-01           39.0            6.0                          2   \n",
       "2017-01-01           40.0            5.0                          2   \n",
       "2017-01-01           41.0            6.0                          2   \n",
       "2017-01-01           43.0            7.0                          2   \n",
       "2017-01-01           45.0           10.0                          2   \n",
       "2017-01-01           45.0           23.0                          2   \n",
       "2017-01-01           46.0           38.0                          2   \n",
       "2017-01-01           45.0           49.0                          2   \n",
       "2017-01-01           46.0           71.0                          2   \n",
       "2017-01-01           46.0           80.0                          2   \n",
       "2017-01-01           48.0           86.0                          2   \n",
       "2017-01-01           48.0           49.0                          2   \n",
       "2017-01-01           48.0           41.0                          2   \n",
       "2017-01-01           48.0           40.0                          2   \n",
       "2017-01-01           48.0           40.0                          2   \n",
       "2017-01-01           48.0           34.0                          2   \n",
       "2017-01-01           46.0           26.0                          2   \n",
       "2017-01-01           46.0           21.0                          2   \n",
       "2017-01-01           46.0           14.0                          2   \n",
       "2017-01-01           46.0           16.0                          2   \n",
       "\n",
       "            Sureste diÛxido de nitrÛgeno  Sureste monÛxido de carbono   \n",
       "Fecha                                                                   \n",
       "2017-01-01                          22.0                          9.0  \\\n",
       "2017-01-01                          18.0                         10.0   \n",
       "2017-01-01                          20.0                         11.0   \n",
       "2017-01-01                          17.0                         12.0   \n",
       "2017-01-01                          17.0                         12.0   \n",
       "2017-01-01                          17.0                         12.0   \n",
       "2017-01-01                          14.0                         12.0   \n",
       "2017-01-01                          16.0                         12.0   \n",
       "2017-01-01                          12.0                         12.0   \n",
       "2017-01-01                          10.0                         11.0   \n",
       "2017-01-01                           8.0                         10.0   \n",
       "2017-01-01                           6.0                          9.0   \n",
       "2017-01-01                           6.0                          8.0   \n",
       "2017-01-01                           5.0                          7.0   \n",
       "2017-01-01                           6.0                          6.0   \n",
       "2017-01-01                           5.0                          5.0   \n",
       "2017-01-01                           4.0                          5.0   \n",
       "2017-01-01                           5.0                          5.0   \n",
       "2017-01-01                           7.0                          5.0   \n",
       "2017-01-01                           9.0                          5.0   \n",
       "2017-01-01                          12.0                          5.0   \n",
       "2017-01-01                          13.0                          5.0   \n",
       "2017-01-01                          20.0                          5.0   \n",
       "2017-01-01                          19.0                          5.0   \n",
       "\n",
       "            Sureste PM10  \n",
       "Fecha                     \n",
       "2017-01-01          64.0  \n",
       "2017-01-01          68.0  \n",
       "2017-01-01          77.0  \n",
       "2017-01-01          87.0  \n",
       "2017-01-01          94.0  \n",
       "2017-01-01         101.0  \n",
       "2017-01-01         102.0  \n",
       "2017-01-01         104.0  \n",
       "2017-01-01         105.0  \n",
       "2017-01-01         107.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         109.0  \n",
       "2017-01-01         110.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         112.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         110.0  \n",
       "2017-01-01         109.0  \n",
       "\n",
       "[24 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec34aa12",
   "metadata": {},
   "outputs": [],
   "source": [
    "oz_h=df.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42c0ce11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hora</th>\n",
       "      <th>Noroeste Ozono</th>\n",
       "      <th>Noroeste diÛxido de azufre</th>\n",
       "      <th>Noroeste diÛxido de nitrÛgeno</th>\n",
       "      <th>Noroeste monÛxido de carbono</th>\n",
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       "      <th>Noreste Ozono</th>\n",
       "      <th>Noreste diÛxido de azufre</th>\n",
       "      <th>Noreste diÛxido de nitrÛgeno</th>\n",
       "      <th>Noreste monÛxido de carbono</th>\n",
       "      <th>...</th>\n",
       "      <th>Suroeste Ozono</th>\n",
       "      <th>Suroeste diÛxido de azufre</th>\n",
       "      <th>Suroeste diÛxido de nitrÛgeno</th>\n",
       "      <th>Suroeste monÛxido de carbono</th>\n",
       "      <th>Suroeste PM10</th>\n",
       "      <th>Sureste Ozono</th>\n",
       "      <th>Sureste diÛxido de azufre</th>\n",
       "      <th>Sureste diÛxido de nitrÛgeno</th>\n",
       "      <th>Sureste monÛxido de carbono</th>\n",
       "      <th>Sureste PM10</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>11</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>12</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>13</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>14</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>15</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>16</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>17</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>18</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>20</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>21</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>22</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>23</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>24</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Hora  Noroeste Ozono  Noroeste diÛxido de azufre   \n",
       "Fecha                                                          \n",
       "2017-01-01     1             9.0                          18  \\\n",
       "2017-01-01     2             5.0                          15   \n",
       "2017-01-01     3             6.0                          12   \n",
       "2017-01-01     4             3.0                           9   \n",
       "2017-01-01     5             3.0                           8   \n",
       "2017-01-01     6             4.0                           6   \n",
       "2017-01-01     7             4.0                           6   \n",
       "2017-01-01     8             4.0                           5   \n",
       "2017-01-01     9            11.0                           5   \n",
       "2017-01-01    10            23.0                           5   \n",
       "2017-01-01    11            39.0                           4   \n",
       "2017-01-01    12            48.0                           4   \n",
       "2017-01-01    13            61.0                           4   \n",
       "2017-01-01    14            90.0                           4   \n",
       "2017-01-01    15            92.0                           3   \n",
       "2017-01-01    16           100.0                           3   \n",
       "2017-01-01    17            88.0                           3   \n",
       "2017-01-01    18            67.0                           3   \n",
       "2017-01-01    19            42.0                           3   \n",
       "2017-01-01    20            26.0                           3   \n",
       "2017-01-01    21            22.0                           3   \n",
       "2017-01-01    22            23.0                           3   \n",
       "2017-01-01    23            18.0                           3   \n",
       "2017-01-01    24             6.0                           3   \n",
       "\n",
       "            Noroeste diÛxido de nitrÛgeno  Noroeste monÛxido de carbono   \n",
       "Fecha                                                                     \n",
       "2017-01-01                           18.0                          12.0  \\\n",
       "2017-01-01                           17.0                          13.0   \n",
       "2017-01-01                           16.0                          13.0   \n",
       "2017-01-01                           17.0                          13.0   \n",
       "2017-01-01                           16.0                          13.0   \n",
       "2017-01-01                           15.0                          13.0   \n",
       "2017-01-01                           15.0                          12.0   \n",
       "2017-01-01                           13.0                          12.0   \n",
       "2017-01-01                           16.0                          11.0   \n",
       "2017-01-01                           23.0                          11.0   \n",
       "2017-01-01                           18.0                          11.0   \n",
       "2017-01-01                           10.0                          10.0   \n",
       "2017-01-01                            8.0                           9.0   \n",
       "2017-01-01                            6.0                           7.0   \n",
       "2017-01-01                            6.0                           7.0   \n",
       "2017-01-01                            7.0                           6.0   \n",
       "2017-01-01                            9.0                           5.0   \n",
       "2017-01-01                           11.0                           5.0   \n",
       "2017-01-01                           10.0                           5.0   \n",
       "2017-01-01                           17.0                           5.0   \n",
       "2017-01-01                           19.0                           5.0   \n",
       "2017-01-01                           22.0                           6.0   \n",
       "2017-01-01                           26.0                           6.0   \n",
       "2017-01-01                           25.0                           7.0   \n",
       "\n",
       "            Noroeste PM10  Noreste Ozono  Noreste diÛxido de azufre   \n",
       "Fecha                                                                 \n",
       "2017-01-01           80.0            8.0                          5  \\\n",
       "2017-01-01           86.0            4.0                          5   \n",
       "2017-01-01           94.0            4.0                          4   \n",
       "2017-01-01          100.0            4.0                          4   \n",
       "2017-01-01          103.0            4.0                          3   \n",
       "2017-01-01          106.0            4.0                          3   \n",
       "2017-01-01          108.0            4.0                          3   \n",
       "2017-01-01          110.0            5.0                          4   \n",
       "2017-01-01          111.0            8.0                          4   \n",
       "2017-01-01          113.0           21.0                          4   \n",
       "2017-01-01          114.0           37.0                          4   \n",
       "2017-01-01          115.0           55.0                          4   \n",
       "2017-01-01          115.0           63.0                          5   \n",
       "2017-01-01          113.0           80.0                          5   \n",
       "2017-01-01          112.0           96.0                          5   \n",
       "2017-01-01          112.0           94.0                          5   \n",
       "2017-01-01          112.0           67.0                          5   \n",
       "2017-01-01          112.0           46.0                          4   \n",
       "2017-01-01          112.0           34.0                          5   \n",
       "2017-01-01          113.0           25.0                          5   \n",
       "2017-01-01          112.0           18.0                          5   \n",
       "2017-01-01          112.0           14.0                          5   \n",
       "2017-01-01          112.0            7.0                          4   \n",
       "2017-01-01          112.0            6.0                          4   \n",
       "\n",
       "            Noreste diÛxido de nitrÛgeno  Noreste monÛxido de carbono  ...   \n",
       "Fecha                                                                  ...   \n",
       "2017-01-01                          22.0                         11.0  ...  \\\n",
       "2017-01-01                          21.0                         12.0  ...   \n",
       "2017-01-01                          21.0                         14.0  ...   \n",
       "2017-01-01                          21.0                         15.0  ...   \n",
       "2017-01-01                          20.0                         18.0  ...   \n",
       "2017-01-01                          19.0                         19.0  ...   \n",
       "2017-01-01                          17.0                         19.0  ...   \n",
       "2017-01-01                          17.0                         20.0  ...   \n",
       "2017-01-01                          21.0                         22.0  ...   \n",
       "2017-01-01                          26.0                         22.0  ...   \n",
       "2017-01-01                          19.0                         20.0  ...   \n",
       "2017-01-01                          10.0                         18.0  ...   \n",
       "2017-01-01                           8.0                         15.0  ...   \n",
       "2017-01-01                           5.0                         14.0  ...   \n",
       "2017-01-01                           6.0                         12.0  ...   \n",
       "2017-01-01                           8.0                         10.0  ...   \n",
       "2017-01-01                           8.0                          8.0  ...   \n",
       "2017-01-01                          12.0                          6.0  ...   \n",
       "2017-01-01                          16.0                          5.0  ...   \n",
       "2017-01-01                          20.0                          5.0  ...   \n",
       "2017-01-01                          24.0                          6.0  ...   \n",
       "2017-01-01                          27.0                          7.0  ...   \n",
       "2017-01-01                          28.0                          8.0  ...   \n",
       "2017-01-01                          26.0                         10.0  ...   \n",
       "\n",
       "            Suroeste Ozono  Suroeste diÛxido de azufre   \n",
       "Fecha                                                    \n",
       "2017-01-01            21.0                           2  \\\n",
       "2017-01-01            25.0                           2   \n",
       "2017-01-01            24.0                           2   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            21.0                           1   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            25.0                           1   \n",
       "2017-01-01            22.0                           1   \n",
       "2017-01-01            24.0                           1   \n",
       "2017-01-01            38.0                           1   \n",
       "2017-01-01            48.0                           1   \n",
       "2017-01-01            71.0                           1   \n",
       "2017-01-01            80.0                           1   \n",
       "2017-01-01            86.0                           1   \n",
       "2017-01-01            84.0                           1   \n",
       "2017-01-01            67.0                           1   \n",
       "2017-01-01            45.0                           1   \n",
       "2017-01-01            36.0                           1   \n",
       "2017-01-01            34.0                           1   \n",
       "2017-01-01            32.0                           1   \n",
       "2017-01-01            31.0                           1   \n",
       "2017-01-01            30.0                           1   \n",
       "2017-01-01            28.0                           1   \n",
       "\n",
       "            Suroeste diÛxido de nitrÛgeno  Suroeste monÛxido de carbono   \n",
       "Fecha                                                                     \n",
       "2017-01-01                           18.0                           5.0  \\\n",
       "2017-01-01                           17.0                           5.0   \n",
       "2017-01-01                           16.0                           6.0   \n",
       "2017-01-01                           17.0                           7.0   \n",
       "2017-01-01                           17.0                           7.0   \n",
       "2017-01-01                           16.0                           8.0   \n",
       "2017-01-01                           14.0                           8.0   \n",
       "2017-01-01                           13.0                           8.0   \n",
       "2017-01-01                           13.0                           8.0   \n",
       "2017-01-01                           15.0                           8.0   \n",
       "2017-01-01                           10.0                           8.0   \n",
       "2017-01-01                            6.0                           7.0   \n",
       "2017-01-01                            5.0                           6.0   \n",
       "2017-01-01                            6.0                           6.0   \n",
       "2017-01-01                            5.0                           5.0   \n",
       "2017-01-01                            5.0                           5.0   \n",
       "2017-01-01                            5.0                           5.0   \n",
       "2017-01-01                            6.0                           5.0   \n",
       "2017-01-01                            7.0                           5.0   \n",
       "2017-01-01                           12.0                           5.0   \n",
       "2017-01-01                           21.0                           5.0   \n",
       "2017-01-01                           19.0                           5.0   \n",
       "2017-01-01                           21.0                           5.0   \n",
       "2017-01-01                           23.0                           5.0   \n",
       "\n",
       "            Suroeste PM10  Sureste Ozono  Sureste diÛxido de azufre   \n",
       "Fecha                                                                 \n",
       "2017-01-01           36.0            6.0                          2  \\\n",
       "2017-01-01           36.0            6.0                          2   \n",
       "2017-01-01           36.0           14.0                          2   \n",
       "2017-01-01           38.0            7.0                          2   \n",
       "2017-01-01           39.0            6.0                          2   \n",
       "2017-01-01           40.0            5.0                          2   \n",
       "2017-01-01           41.0            6.0                          2   \n",
       "2017-01-01           43.0            7.0                          2   \n",
       "2017-01-01           45.0           10.0                          2   \n",
       "2017-01-01           45.0           23.0                          2   \n",
       "2017-01-01           46.0           38.0                          2   \n",
       "2017-01-01           45.0           49.0                          2   \n",
       "2017-01-01           46.0           71.0                          2   \n",
       "2017-01-01           46.0           80.0                          2   \n",
       "2017-01-01           48.0           86.0                          2   \n",
       "2017-01-01           48.0           49.0                          2   \n",
       "2017-01-01           48.0           41.0                          2   \n",
       "2017-01-01           48.0           40.0                          2   \n",
       "2017-01-01           48.0           40.0                          2   \n",
       "2017-01-01           48.0           34.0                          2   \n",
       "2017-01-01           46.0           26.0                          2   \n",
       "2017-01-01           46.0           21.0                          2   \n",
       "2017-01-01           46.0           14.0                          2   \n",
       "2017-01-01           46.0           16.0                          2   \n",
       "\n",
       "            Sureste diÛxido de nitrÛgeno  Sureste monÛxido de carbono   \n",
       "Fecha                                                                   \n",
       "2017-01-01                          22.0                          9.0  \\\n",
       "2017-01-01                          18.0                         10.0   \n",
       "2017-01-01                          20.0                         11.0   \n",
       "2017-01-01                          17.0                         12.0   \n",
       "2017-01-01                          17.0                         12.0   \n",
       "2017-01-01                          17.0                         12.0   \n",
       "2017-01-01                          14.0                         12.0   \n",
       "2017-01-01                          16.0                         12.0   \n",
       "2017-01-01                          12.0                         12.0   \n",
       "2017-01-01                          10.0                         11.0   \n",
       "2017-01-01                           8.0                         10.0   \n",
       "2017-01-01                           6.0                          9.0   \n",
       "2017-01-01                           6.0                          8.0   \n",
       "2017-01-01                           5.0                          7.0   \n",
       "2017-01-01                           6.0                          6.0   \n",
       "2017-01-01                           5.0                          5.0   \n",
       "2017-01-01                           4.0                          5.0   \n",
       "2017-01-01                           5.0                          5.0   \n",
       "2017-01-01                           7.0                          5.0   \n",
       "2017-01-01                           9.0                          5.0   \n",
       "2017-01-01                          12.0                          5.0   \n",
       "2017-01-01                          13.0                          5.0   \n",
       "2017-01-01                          20.0                          5.0   \n",
       "2017-01-01                          19.0                          5.0   \n",
       "\n",
       "            Sureste PM10  \n",
       "Fecha                     \n",
       "2017-01-01          64.0  \n",
       "2017-01-01          68.0  \n",
       "2017-01-01          77.0  \n",
       "2017-01-01          87.0  \n",
       "2017-01-01          94.0  \n",
       "2017-01-01         101.0  \n",
       "2017-01-01         102.0  \n",
       "2017-01-01         104.0  \n",
       "2017-01-01         105.0  \n",
       "2017-01-01         107.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         108.0  \n",
       "2017-01-01         109.0  \n",
       "2017-01-01         110.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         112.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         111.0  \n",
       "2017-01-01         110.0  \n",
       "2017-01-01         109.0  \n",
       "\n",
       "[24 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oz_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dc36563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8760 entries, 2017-01-01 to 2017-12-31\n",
      "Data columns (total 26 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Hora                           8760 non-null   int64  \n",
      " 1   Noroeste Ozono                 8563 non-null   float64\n",
      " 2   Noroeste diÛxido de azufre     8760 non-null   int64  \n",
      " 3   Noroeste diÛxido de nitrÛgeno  8559 non-null   float64\n",
      " 4   Noroeste monÛxido de carbono   8402 non-null   float64\n",
      " 5   Noroeste PM10                  8758 non-null   float64\n",
      " 6   Noreste Ozono                  8566 non-null   float64\n",
      " 7   Noreste diÛxido de azufre      8760 non-null   int64  \n",
      " 8   Noreste diÛxido de nitrÛgeno   8564 non-null   float64\n",
      " 9   Noreste monÛxido de carbono    8415 non-null   float64\n",
      " 10  Noreste PM10                   8693 non-null   float64\n",
      " 11  Centro Ozono                   8559 non-null   float64\n",
      " 12  Centro diÛxido de azufre       8742 non-null   float64\n",
      " 13  Centro diÛxido de nitrÛgeno    8547 non-null   float64\n",
      " 14  Centro monÛxido de carbono     8417 non-null   float64\n",
      " 15  Centro PM10                    8760 non-null   int64  \n",
      " 16  Suroeste Ozono                 8563 non-null   float64\n",
      " 17  Suroeste diÛxido de azufre     8760 non-null   int64  \n",
      " 18  Suroeste diÛxido de nitrÛgeno  8563 non-null   float64\n",
      " 19  Suroeste monÛxido de carbono   8416 non-null   float64\n",
      " 20  Suroeste PM10                  8714 non-null   float64\n",
      " 21  Sureste Ozono                  8558 non-null   float64\n",
      " 22  Sureste diÛxido de azufre      8760 non-null   int64  \n",
      " 23  Sureste diÛxido de nitrÛgeno   8563 non-null   float64\n",
      " 24  Sureste monÛxido de carbono    8411 non-null   float64\n",
      " 25  Sureste PM10                   7877 non-null   float64\n",
      "dtypes: float64(20), int64(6)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d17127d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10), dpi=150)\n",
    "plt.plot(oz_h['Hora'],oz_h[\"Centro Ozono\"],lw = 2, color='black')\n",
    "plt.xlabel('Fecha', fontdict = {'fontsize':35 })\n",
    "plt.ylabel('O3',fontdict = {'fontsize':35 })\n",
    "plt.title('ozono centro CDMX (24 horas)', fontsize = 35)\n",
    "plt.tick_params(labelsize=30, pad=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04591846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hora                               0\n",
       "Noroeste Ozono                   197\n",
       "Noroeste diÛxido de azufre         0\n",
       "Noroeste diÛxido de nitrÛgeno    201\n",
       "Noroeste monÛxido de carbono     358\n",
       "Noroeste PM10                      2\n",
       "Noreste Ozono                    194\n",
       "Noreste diÛxido de azufre          0\n",
       "Noreste diÛxido de nitrÛgeno     196\n",
       "Noreste monÛxido de carbono      345\n",
       "Noreste PM10                      67\n",
       "Centro Ozono                     201\n",
       "Centro diÛxido de azufre          18\n",
       "Centro diÛxido de nitrÛgeno      213\n",
       "Centro monÛxido de carbono       343\n",
       "Centro PM10                        0\n",
       "Suroeste Ozono                   197\n",
       "Suroeste diÛxido de azufre         0\n",
       "Suroeste diÛxido de nitrÛgeno    197\n",
       "Suroeste monÛxido de carbono     344\n",
       "Suroeste PM10                     46\n",
       "Sureste Ozono                    202\n",
       "Sureste diÛxido de azufre          0\n",
       "Sureste diÛxido de nitrÛgeno     197\n",
       "Sureste monÛxido de carbono      349\n",
       "Sureste PM10                     883\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a13101d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3356bd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Centro Ozono</th>\n",
       "      <th>Centro diÛxido de azufre</th>\n",
       "      <th>Centro diÛxido de nitrÛgeno</th>\n",
       "      <th>Centro monÛxido de carbono</th>\n",
       "      <th>Centro PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</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>2017-01-01</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Centro Ozono  Centro diÛxido de azufre   \n",
       "Fecha                                                \n",
       "2017-01-01          12.0                       6.0  \\\n",
       "2017-01-01          19.0                       5.0   \n",
       "2017-01-01           4.0                       5.0   \n",
       "2017-01-01           2.0                       4.0   \n",
       "2017-01-01           2.0                       4.0   \n",
       "...                  ...                       ...   \n",
       "2017-12-31          34.0                       8.0   \n",
       "2017-12-31          27.0                       7.0   \n",
       "2017-12-31          16.0                       6.0   \n",
       "2017-12-31          17.0                       5.0   \n",
       "2017-12-31          16.0                       5.0   \n",
       "\n",
       "            Centro diÛxido de nitrÛgeno  Centro monÛxido de carbono   \n",
       "Fecha                                                                 \n",
       "2017-01-01                         22.0                         9.0  \\\n",
       "2017-01-01                         21.0                        10.0   \n",
       "2017-01-01                         20.0                        11.0   \n",
       "2017-01-01                         19.0                        11.0   \n",
       "2017-01-01                         18.0                        12.0   \n",
       "...                                 ...                         ...   \n",
       "2017-12-31                         26.0                         5.0   \n",
       "2017-12-31                         20.0                         5.0   \n",
       "2017-12-31                         32.0                         6.0   \n",
       "2017-12-31                         28.0                         7.0   \n",
       "2017-12-31                         25.0                         8.0   \n",
       "\n",
       "            Centro PM10  \n",
       "Fecha                    \n",
       "2017-01-01           61  \n",
       "2017-01-01           61  \n",
       "2017-01-01           64  \n",
       "2017-01-01           68  \n",
       "2017-01-01           71  \n",
       "...                 ...  \n",
       "2017-12-31           93  \n",
       "2017-12-31           93  \n",
       "2017-12-31           94  \n",
       "2017-12-31           94  \n",
       "2017-12-31           96  \n",
       "\n",
       "[8760 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[['Centro Ozono','Centro diÛxido de azufre',\n",
    "        'Centro diÛxido de nitrÛgeno',\n",
    "        'Centro monÛxido de carbono',\n",
    "       'Centro PM10']]\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "545eb0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8760 entries, 2017-01-01 to 2017-12-31\n",
      "Data columns (total 5 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Centro Ozono                 8559 non-null   float64\n",
      " 1   Centro diÛxido de azufre     8742 non-null   float64\n",
      " 2   Centro diÛxido de nitrÛgeno  8547 non-null   float64\n",
      " 3   Centro monÛxido de carbono   8417 non-null   float64\n",
      " 4   Centro PM10                  8760 non-null   int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 410.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a9a6e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c376ccdb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12c276a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Centro Ozono                   201\n",
       "Centro diÛxido de azufre        18\n",
       "Centro diÛxido de nitrÛgeno    213\n",
       "Centro monÛxido de carbono     343\n",
       "Centro PM10                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7653dc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.columns = ['ozono','dioxido_azufre',\n",
    "               'dioxido_nitrogeno',\n",
    "               'monoxido_carbono',\n",
    "               'PM10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "63ecbad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</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>2017-01-01</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono  PM10\n",
       "Fecha                                                                       \n",
       "2017-01-01   12.0             6.0               22.0               9.0    61\n",
       "2017-01-01   19.0             5.0               21.0              10.0    61\n",
       "2017-01-01    4.0             5.0               20.0              11.0    64\n",
       "2017-01-01    2.0             4.0               19.0              11.0    68\n",
       "2017-01-01    2.0             4.0               18.0              12.0    71\n",
       "...           ...             ...                ...               ...   ...\n",
       "2017-12-31   34.0             8.0               26.0               5.0    93\n",
       "2017-12-31   27.0             7.0               20.0               5.0    93\n",
       "2017-12-31   16.0             6.0               32.0               6.0    94\n",
       "2017-12-31   17.0             5.0               28.0               7.0    94\n",
       "2017-12-31   16.0             5.0               25.0               8.0    96\n",
       "\n",
       "[8760 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac9f6574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ozono                201\n",
       "dioxido_azufre        18\n",
       "dioxido_nitrogeno    213\n",
       "monoxido_carbono     343\n",
       "PM10                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac21013",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f0fb41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8559.000000</td>\n",
       "      <td>8742.000000</td>\n",
       "      <td>8547.000000</td>\n",
       "      <td>8417.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-88.538731</td>\n",
       "      <td>5.281972</td>\n",
       "      <td>-64.256113</td>\n",
       "      <td>-277.278128</td>\n",
       "      <td>65.200913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3417.419398</td>\n",
       "      <td>5.500423</td>\n",
       "      <td>2861.330413</td>\n",
       "      <td>5332.917303</td>\n",
       "      <td>25.989012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-100000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-100000.000000</td>\n",
       "      <td>-100000.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono   \n",
       "count    8559.000000     8742.000000        8547.000000       8417.000000  \\\n",
       "mean      -88.538731        5.281972         -64.256113       -277.278128   \n",
       "std      3417.419398        5.500423        2861.330413       5332.917303   \n",
       "min   -100000.000000        1.000000     -100000.000000    -100000.000000   \n",
       "25%         8.000000        2.000000          12.000000          5.000000   \n",
       "50%        18.000000        3.000000          16.000000          7.000000   \n",
       "75%        38.000000        6.000000          22.000000         10.000000   \n",
       "max       186.000000       42.000000          62.000000         25.000000   \n",
       "\n",
       "              PM10  \n",
       "count  8760.000000  \n",
       "mean     65.200913  \n",
       "std      25.989012  \n",
       "min      14.000000  \n",
       "25%      43.000000  \n",
       "50%      63.000000  \n",
       "75%      88.000000  \n",
       "max     121.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "04727063",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.replace(-100000,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f7fd59b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8552.000000</td>\n",
       "      <td>8742.000000</td>\n",
       "      <td>8540.000000</td>\n",
       "      <td>8405.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-6.759004</td>\n",
       "      <td>5.281972</td>\n",
       "      <td>17.658431</td>\n",
       "      <td>-134.901844</td>\n",
       "      <td>65.200913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1873.483246</td>\n",
       "      <td>5.500423</td>\n",
       "      <td>7.936012</td>\n",
       "      <td>3776.308093</td>\n",
       "      <td>25.989012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-99999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-99999.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono   \n",
       "count   8552.000000     8742.000000        8540.000000       8405.000000  \\\n",
       "mean      -6.759004        5.281972          17.658431       -134.901844   \n",
       "std     1873.483246        5.500423           7.936012       3776.308093   \n",
       "min   -99999.000000        1.000000           3.000000     -99999.000000   \n",
       "25%        8.000000        2.000000          12.000000          5.000000   \n",
       "50%       18.000000        3.000000          16.000000          7.000000   \n",
       "75%       38.000000        6.000000          22.000000         10.000000   \n",
       "max      186.000000       42.000000          62.000000         25.000000   \n",
       "\n",
       "              PM10  \n",
       "count  8760.000000  \n",
       "mean     65.200913  \n",
       "std      25.989012  \n",
       "min      14.000000  \n",
       "25%      43.000000  \n",
       "50%      63.000000  \n",
       "75%      88.000000  \n",
       "max     121.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b77ae27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.replace(-99999,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ec6aa02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8549.000000</td>\n",
       "      <td>8742.000000</td>\n",
       "      <td>8540.000000</td>\n",
       "      <td>8393.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.330097</td>\n",
       "      <td>5.281972</td>\n",
       "      <td>17.658431</td>\n",
       "      <td>7.880138</td>\n",
       "      <td>65.200913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.860361</td>\n",
       "      <td>5.500423</td>\n",
       "      <td>7.936012</td>\n",
       "      <td>3.501896</td>\n",
       "      <td>25.989012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono   \n",
       "count  8549.000000     8742.000000        8540.000000       8393.000000  \\\n",
       "mean     28.330097        5.281972          17.658431          7.880138   \n",
       "std      29.860361        5.500423           7.936012          3.501896   \n",
       "min       1.000000        1.000000           3.000000          1.000000   \n",
       "25%       8.000000        2.000000          12.000000          5.000000   \n",
       "50%      18.000000        3.000000          16.000000          7.000000   \n",
       "75%      38.000000        6.000000          22.000000         10.000000   \n",
       "max     186.000000       42.000000          62.000000         25.000000   \n",
       "\n",
       "              PM10  \n",
       "count  8760.000000  \n",
       "mean     65.200913  \n",
       "std      25.989012  \n",
       "min      14.000000  \n",
       "25%      43.000000  \n",
       "50%      63.000000  \n",
       "75%      88.000000  \n",
       "max     121.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67d0c720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ozono                211\n",
       "dioxido_azufre        18\n",
       "dioxido_nitrogeno    220\n",
       "monoxido_carbono     367\n",
       "PM10                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482bd074",
   "metadata": {},
   "source": [
    "Si queremos reemplazar varios valores al mismo tiempo, lo que hacemos es pasar una lista y después el valor sustituto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c18aded6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono  PM10\n",
       "Fecha                                                                       \n",
       "2017-01-01   12.0             6.0               22.0               9.0    61\n",
       "2017-01-01   19.0             5.0               21.0              10.0    61\n",
       "2017-01-01    4.0             5.0               20.0              11.0    64\n",
       "2017-01-01    2.0             4.0               19.0              11.0    68\n",
       "2017-01-01    2.0             4.0               18.0              12.0    71\n",
       "...           ...             ...                ...               ...   ...\n",
       "2017-12-31   34.0             8.0               26.0               5.0    93\n",
       "2017-12-31   27.0             7.0               20.0               5.0    93\n",
       "2017-12-31   16.0             6.0               32.0               6.0    94\n",
       "2017-12-31   17.0             5.0               28.0               7.0    94\n",
       "2017-12-31   16.0             5.0               25.0               8.0    96\n",
       "\n",
       "[8760 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.replace([-100000,-99999,np.nan])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9187b9ef",
   "metadata": {},
   "source": [
    "Para utilizar un sustituto distinto para cada valor, pasamos una lista de sustitutos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0beb231",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>61</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono  PM10\n",
       "Fecha                                                                       \n",
       "2017-01-01   12.0             6.0               22.0               9.0    61\n",
       "2017-01-01   19.0             5.0               21.0              10.0    61\n",
       "2017-01-01    4.0             5.0               20.0              11.0    64\n",
       "2017-01-01    2.0             4.0               19.0              11.0    68\n",
       "2017-01-01    2.0             4.0               18.0              12.0    71\n",
       "...           ...             ...                ...               ...   ...\n",
       "2017-12-31   34.0             8.0               26.0               5.0    93\n",
       "2017-12-31   27.0             7.0               20.0               5.0    93\n",
       "2017-12-31   16.0             6.0               32.0               6.0    94\n",
       "2017-12-31   17.0             5.0               28.0               7.0    94\n",
       "2017-12-31   16.0             5.0               25.0               8.0    96\n",
       "\n",
       "[8760 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.replace([-100000,-99999],[np.nan,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3fffd5",
   "metadata": {},
   "source": [
    "El argumento pasado puede ser también un diccionario:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8db4fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</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>2017-01-01</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono  PM10\n",
       "Fecha                                                                       \n",
       "2017-01-01   12.0             6.0               22.0               9.0    61\n",
       "2017-01-01   19.0             5.0               21.0              10.0    61\n",
       "2017-01-01    4.0             5.0               20.0              11.0    64\n",
       "2017-01-01    2.0             4.0               19.0              11.0    68\n",
       "2017-01-01    2.0             4.0               18.0              12.0    71\n",
       "...           ...             ...                ...               ...   ...\n",
       "2017-12-31   34.0             8.0               26.0               5.0    93\n",
       "2017-12-31   27.0             7.0               20.0               5.0    93\n",
       "2017-12-31   16.0             6.0               32.0               6.0    94\n",
       "2017-12-31   17.0             5.0               28.0               7.0    94\n",
       "2017-12-31   16.0             5.0               25.0               8.0    96\n",
       "\n",
       "[8760 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.replace({-100000:np.nan, -99999:0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a1150f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8549.000000</td>\n",
       "      <td>8742.000000</td>\n",
       "      <td>8540.000000</td>\n",
       "      <td>8393.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.330097</td>\n",
       "      <td>5.281972</td>\n",
       "      <td>17.658431</td>\n",
       "      <td>7.880138</td>\n",
       "      <td>65.200913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.860361</td>\n",
       "      <td>5.500423</td>\n",
       "      <td>7.936012</td>\n",
       "      <td>3.501896</td>\n",
       "      <td>25.989012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono   \n",
       "count  8549.000000     8742.000000        8540.000000       8393.000000  \\\n",
       "mean     28.330097        5.281972          17.658431          7.880138   \n",
       "std      29.860361        5.500423           7.936012          3.501896   \n",
       "min       1.000000        1.000000           3.000000          1.000000   \n",
       "25%       8.000000        2.000000          12.000000          5.000000   \n",
       "50%      18.000000        3.000000          16.000000          7.000000   \n",
       "75%      38.000000        6.000000          22.000000         10.000000   \n",
       "max     186.000000       42.000000          62.000000         25.000000   \n",
       "\n",
       "              PM10  \n",
       "count  8760.000000  \n",
       "mean     65.200913  \n",
       "std      25.989012  \n",
       "min      14.000000  \n",
       "25%      43.000000  \n",
       "50%      63.000000  \n",
       "75%      88.000000  \n",
       "max     121.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9ba858f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.fillna({'ozono': df1['ozono'].median(),\n",
    "           'dioxido_azufre': df1['dioxido_azufre'].median(),\n",
    "           'dioxido_nitrogeno': df1['dioxido_nitrogeno'].median(),\n",
    "           'monoxido_carbono': df1['monoxido_carbono'].median(),\n",
    "           'PM10':df1['PM10'].median()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b01f595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ozono                0\n",
       "dioxido_azufre       0\n",
       "dioxido_nitrogeno    0\n",
       "monoxido_carbono     0\n",
       "PM10                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e750cfbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ozono</th>\n",
       "      <th>dioxido_azufre</th>\n",
       "      <th>dioxido_nitrogeno</th>\n",
       "      <th>monoxido_carbono</th>\n",
       "      <th>PM10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "      <td>8760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.081279</td>\n",
       "      <td>5.277283</td>\n",
       "      <td>17.616781</td>\n",
       "      <td>7.843265</td>\n",
       "      <td>65.200913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.540999</td>\n",
       "      <td>5.495740</td>\n",
       "      <td>7.840010</td>\n",
       "      <td>3.432280</td>\n",
       "      <td>25.989012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ozono  dioxido_azufre  dioxido_nitrogeno  monoxido_carbono   \n",
       "count  8760.000000     8760.000000        8760.000000       8760.000000  \\\n",
       "mean     28.081279        5.277283          17.616781          7.843265   \n",
       "std      29.540999        5.495740           7.840010          3.432280   \n",
       "min       1.000000        1.000000           3.000000          1.000000   \n",
       "25%       8.000000        2.000000          12.000000          5.000000   \n",
       "50%      18.000000        3.000000          16.000000          7.000000   \n",
       "75%      37.000000        6.000000          22.000000         10.000000   \n",
       "max     186.000000       42.000000          62.000000         25.000000   \n",
       "\n",
       "              PM10  \n",
       "count  8760.000000  \n",
       "mean     65.200913  \n",
       "std      25.989012  \n",
       "min      14.000000  \n",
       "25%      43.000000  \n",
       "50%      63.000000  \n",
       "75%      88.000000  \n",
       "max     121.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "61b64d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.041666666666668"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['ozono'].iloc[0:24].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "38b009f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.833333333333332"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['ozono'].iloc[24:48].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be62162e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c56bb79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5a85ffe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "781ad4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['ozono'].iloc[24:48].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "91bca641",
   "metadata": {},
   "outputs": [],
   "source": [
    "O=[]\n",
    "F=[]\n",
    "\n",
    "for i in range(0,365):\n",
    "    F.append(df.index[i*24])\n",
    "    O.append((df1.iloc[i*24:(i+1)*24 ]['ozono']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f2800ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8c963b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ozono = pd.DataFrame({\n",
    "    'Fecha' : F,\n",
    "    'O3' : O,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "844aba3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Fecha</th>\n",
       "      <th>O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>29.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>21.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>29.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>17.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>20.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>26.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>24.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>17.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>21.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>36.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fecha         O3\n",
       "0    2017-01-01  29.041667\n",
       "1    2017-01-02  21.833333\n",
       "2    2017-01-03  29.250000\n",
       "3    2017-01-04  17.375000\n",
       "4    2017-01-05  20.291667\n",
       "..          ...        ...\n",
       "360  2017-12-27  26.791667\n",
       "361  2017-12-28  24.458333\n",
       "362  2017-12-29  17.708333\n",
       "363  2017-12-30  21.583333\n",
       "364  2017-12-31  36.750000\n",
       "\n",
       "[365 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ozono"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d9ff271",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "843b5b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "ozono['Fecha'] = pd.to_datetime(ozono['Fecha'])\n",
    "ozono = ozono.set_index(\"Fecha\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "55efcb04",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>29.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>21.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>29.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>17.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>20.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>26.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>24.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>17.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>21.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>36.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   O3\n",
       "Fecha                \n",
       "2017-01-01  29.041667\n",
       "2017-01-02  21.833333\n",
       "2017-01-03  29.250000\n",
       "2017-01-04  17.375000\n",
       "2017-01-05  20.291667\n",
       "...               ...\n",
       "2017-12-27  26.791667\n",
       "2017-12-28  24.458333\n",
       "2017-12-29  17.708333\n",
       "2017-12-30  21.583333\n",
       "2017-12-31  36.750000\n",
       "\n",
       "[365 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ozono"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d402e311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 365 entries, 2017-01-01 to 2017-12-31\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   O3      365 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 5.7 KB\n"
     ]
    }
   ],
   "source": [
    "ozono.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "51d2ed52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Qn93HSXXArvX5o48+kqNHj+oeu/3226Nal8jJ34ebbrpJ99jChQujnhfORh12HyfVYQDUYTvYu3ev7nhubq6MHj06prmnTp1q6j4Tza7vh92EwCBgwsSJEw2PmTnR/sILL+iOX3zxxdK1a1ezyzplzpw5pu7P69ra2uTZZ5/VPTZ79mzp0KFDglcUmtHjaPS4RyItLU1mz55t6v4AK1FX3YW6av+6WlRUpDt+7NixBK8EdkItdhdqsf1rMaBGHXYX6rA967DTHhfYA/XZXZxWB+xan5cuXao73rdvXxk6dGjUaxMR3ZP+IiJ/+ctfpK2tLaa54UzUYXdxWh0GQB22g4MHD+qOFxcXi98fW9yrT58+uuMHDhyIaV6r2PX9sJsQGARM6NChg+Tl5ekeM/pWnZ63335bd3zmzJnRLOuUadOmSWZmpmZ806ZNsmvXrpjmdqN33nlHdu7cqXvsxhtvTPBqQtu5c6d89dVXusdifd4Y3f6tt96KaV4gEtRVd6Guhr69HepqWlqa7rje8xzeQS12F2px6NvboRYDatRhd6EOh759suqwkx4X2Af12V2cVAfsXJ/Xr1+vOz527Nio19RuxIgRkp6erhk/dOiQrFq1Kub54TzUYXdxUh0GcBJ1OPmMzul07Ngx5rmzs7N1xwOBQMxzx8rO74fdhMAgYJLeH6xmbN++XSorK3WPTZ48Oaa509PTZfz48brH3n///ZjmdqOnn35ad/yMM86IuYWv1Ywev4EDB0phYWFMc48dO1aysrI049u2bdNtDw1YjbrqHtTVk+xcV41ayeu1YIe3UIvdg1p8kp1rMaCHOuwe1OGT7FaHnfS4wF6oz+7hpDpg5/psFPYZMGBATOsSOXlCvKSkRPfYu+++G/P8cCbqsHs4qQ4D+Bp1OLm6deumO15dXR3z3EeOHNEdz8/Pj3nuWNn5/bCbEBgETGhubpbDhw/rHjNK16utWLFCd7y4uFgKCgqiXlu7MWPG6I6vXLky5rndpKqqSl577TXdY3b8JpPR88bo8TYjNTVVRowYoXuM5w3ijbrqHtTVr9m5rhr9kWVFJwA4F7XYPajFX7NzLQbUqMPuQR3+mp3qsNMeF9gH9dk9nFYH7FyfjboJ5ebmxrCqrxn9bi1fvtyS+eEs1GH3cFodBnASdTj5RowYISkpKZrxyspKaWhoiGnujRs36o6Xl5fHNK8V7Px+2E0IDAImrFy5UlpaWnSP9evXL6I5Vq9erTs+fPjwqNd1OqPi9sUXX1gyv1ssXrxYmpqaNONpaWly7bXXJmFFofG8gVtRV92DuhrMjs+b2tpaefbZZ3WPXX755QleDeyEWuwe1OJgPG/gFNRh96AOB7PL88Zpjwvsg/rsHk6rA3Z+3hj9Tuh1aYmG0TxsSexN1GH3cFodBnASdTj5srKy5LzzztOMNzY2xtSBubm5Wf75z3/qHrvggguintcqPG8SQxtFBWDo5Zdf1h3Pz8+X0tLSiObYsmWL7nj//v2jXtfpjF6cN2/ebMn8bmHU+vyyyy6Trl27Jng14fG8gVtRV92DuhrMbs8bRVHk9ttvlz179miO9evXT2bMmJH4RcE2qMXuQS0OFu/nzfbt2+Xjjz+WVatWycGDB+XQoUNy4sQJ6dChg+Tn50vv3r1l2LBhMnbs2Ji3yoC7UYfdgzoczC7PG6c9LrAP6rN7OK0O2Pl5Y9RJ8Pjx47Es6ZRjx47pju/cuVOamppi3hYRzkIddg+n1WEAJ1GH7eHOO+/U3T3qwQcflEsuuUR8Pp/pOR9//HE5dOiQZnzSpEkyaNCgqNZpJZ43iUFgEIhQbW2t4RtaM4W4srJSd7ykpCTapUU0z4EDB6S+vl4yMzMtuR8nW716tWE63I6tz+vq6nRfsEXi/7wxer4CVqCuugd1NfJ5klFXDx48KLfffru89NJLmmN+v1+efPJJ8ftpPO5V1GL3oBZHPo8VtXjRokWGXVv1DB8+XK6//nqZO3eudO7cOeb7h3tQh92DOhz5PIl8T+y0xwX2QX12D6fVAbvX5y5duuiOV1VVxbKkU6qrq3XH29raZPv27TJgwABL7gf2Rx12D6fVYQAnUYft4/LLL5cpU6bIO++8EzS+bNky+dGPfiS//OUvTc33ySefyA9/+EPNeCAQMD1XPNj9/bCbcGYQiNBDDz1k+MfqTTfdFPE8u3fv1h3v3r17VOtSy87Olg4dOuge0+sq5EVPPfWU7nhRUZFMnTo1wasJz+g5I2Ld88ZonlD3DcSKuuoe1NXI50lUXa2rq5N//etf8p3vfEf69++vGxYUEfnNb34jEyZMSMiaYE/UYvegFkc+jxW1uLW11dT116xZI/Pnz5c+ffrIz3/+czlx4kTMa4A7UIfdgzoc+TyJ/KzBaY8L7IP67B5OqwN2r89GncDWrVsX05pERGpqakKepOWzam+hDruH0+owgJOow/bh8/lk4cKFUlxcrDn20EMPyY033ig1NTVh51EURZ566imZOnWqNDY2ao7/5Cc/kbPOOsuSNcfC7u+H3YTAIBCBdevWya9+9SvdYxMnTpRzzjkn4rmMvmlnZcvt/Px83fEjR45Ydh9O1dTUJH/+8591j82ZM8eWHZaMHjefz2dZdxKj54xV3wwF1Kir7kFd1RfPulpbWyvjx4/X/Ddu3DgZPny49O7dW7Kzs+W8886TRx99VHc7n44dO8qzzz4rd9xxR8zrgXNRi92DWqzPju9xjx8/Lvfee6+MGjVKKioqkrYO2AN12D2ow/qSXYed+LjAHqjP7uHEOmD3+jxixAjd8c8++yymNYmILF++XNra2gyP85z2DuqwezixDgOgDttRYWGhfPDBB1JWVqY59vTTT0tRUZHcdddd8sYbb8iuXbukvr5eTpw4IQcPHpRPPvlEHn74YRk8eLDcdNNNUldXp5nj+9//vvzkJz9JxI8Slt3fD7sJWxIDYTQ0NMi1114rzc3NmmN+v18eeeSRiOeqq6vTnUfkZPrdKp06ddIdN/oWgJcsWbJE99/B5/PJDTfckIQVhWf0uHXs2NGyP6aMnjONjY3S0NBg+M0MIBrUVXehruqLZ11taWmRpUuXRnXbQCAgV199tTzwwAPSt2/fqNcA56MWuwu1WJ+d3+OuXbtWxowZI2+99ZaMGjUqaetA8lCH3YU6rC/ZddiJjwuSj/rsLk6sA3avz0a7FBw4cEDee+89ueCCC6Je1+LFi0Med9NJfxijDruLE+sw4HXUYfvq27evrF69Wv7jP/5DnnjiiaAdTI4dOya//e1v5be//a2pObt27Sq/+93v5Morr7R6uVGz+/thNyG2D4Tx7W9/W9auXat77N///d8Nv1Gnp76+3vBYZmam6bUZycrKMn3/XvH000/rjk+aNMm2wQmjxy0Rz5lQ9w9Ei7rqLtRVfXarqxkZGXLfffdJZWWlLFq0yLaPDRKHWuwu1GJ98ajF6enpMnnyZPnpT38qr7zyimzatEkOHDggjY2NUltbKzt37pTPP/9cFixYIDNmzJCUFOPvaVZVVcm0adPkq6++imotcDbqsLtQh/Ul+z2xEx8XJB/12V2cWAfsXp/Ly8sNT/CbPUF9uoMHD8rzzz8f8jo8p72BOuwuTqzDgNdRh+0tKytLHnvsMdm6dav86Ec/koEDB0Y1z5AhQ+Thhx+WyspKW4UFRez/fthNCAwCIfz3f/+3PPPMM7rHhg0bJg8++KCp+YwS9CIS8kSSWUZzhbp/L9i5c6e89957usfmzZuX4NVEzuhxS8RzJtT9A9GgrroLddWY3epqY2Oj/PznP5errrpKnnvuuaBvnsF7qMXuQi02ZlUt9vl8Mm3aNHnppZfkyJEj8s4778hPfvITmTlzpgwcOFC6desm6enpkpWVJUVFRTJmzBi56667ZMmSJbJt2za54447xOfz6c5dVVUl//Zv/yYNDQ2mfz44F3XYXajDxpL5ntipjwuSi/rsLk6tA3avz4FAwPDf7/XXX5dXXnklqjV961vfCvue2OvPaS+gDruLU+sw4GXUYeeoqamJ+jxP79695brrrpOZM2dKx44dLV5Z7Oz+fthNCAwCBl599VX5wQ9+oHssOztbXnrpJUlPTzc1Z0tLi+GxRBQ4r4cDnnnmGWlra9OM5+bmyhVXXJGEFUXG6HmTqBdFrz9vYB3qqvtQV43Zsa62tbXJp59+KnPmzJH+/fvL+++/n5R1ILmoxe5DLTZmVS2uqKiQf/7zn3LFFVeE/PapnqKiInn00Ufln//8p+Tn5+teZ+PGjfLLX/7S1LxwLuqw+1CHjSXzPbFTHxckD/XZfZxaB5xQn+fPny+pqam6x2666SZZsWKFqfU88MADsmTJkrDX8/pz2u2ow+7j1DoMeBV12Bm2bt0ql19+uQwdOlR+9atfyZdffml6jh07dsg999wj/fv3lyuuuELWr18fh5VGzwnvh92CwCCg4//+7/9k9uzZum9kA4GALF68WMrKykzPG6rwtLa2mp7PiFERNfoj3gsURZGFCxfqHvvmN78pGRkZCV5R5IyeN4l4zoh4+3kD61BX3Ye6Glo862pubq4oiqL578SJE3Lo0CHZvHmz/PWvf5W7775biouLdefYvn27TJ48WX7xi1/EtBY4C7XYfajFoVlVi/v06RPzWqZOnSr//Oc/DQOHjzzyiFRVVcV8P7A36rD7UIdDS9ZnDU5+XJAc1Gf3cXIdcEJ97tWrl9x22226x6qrq+X888+XxYsXh53n6NGjcuutt8r9998f9roiImlpaRFdD85DHXYfJ9dhwIuow86wcOFCOfPMM+X1118XRVF0r+P3+6VXr14ybNgwGT16tPTt29dwK19FUeSVV16RUaNGySOPPBLPpZvihPfDbkFgEFBZsWKFXHbZZdLY2Kg55vP5ZOHChXLJJZdENXeoP2hDFSWzjOby8h/U77//vmzfvl33mN1bnxs9bol4zoS6fyBS1FV3oq6Gloy6mpKSIl27dpWysjKZNWuWPPzww1JZWSmvvPKKFBUVaa6vKIr8+Mc/pquVR1CL3YlaHJrd3uOWl5fLk08+qXusrq5OnnrqqQSvCIlEHXYn6nBoyarDTn5ckHjUZ3dych1wSn1++OGHZeTIkbrHampq5Nprr5XRo0fLggULZN26dVJVVSXNzc2yd+9e+fDDD+X73/++lJWVyRNPPBF02y5dukhOTo7uvASM3Ik67E5OrsOA11CHnWHBggUyb948qaur0xzz+Xxy2WWXyZtvvilVVVWya9cuWbNmjSxbtkwqKirk2LFjsnz5cvnhD38oeXl5mts3NTXJ/Pnz5Xvf+14ifpSwnPJ+2A2s69kIuMC6detk2rRpcvz4cd3jv/vd7+S6666Len6j9LaI6Bb3aBnN1aFDB8vuw2mefvpp3fHhw4cbfrBhxvjx42O6/bx58wz/SDJ63iTiOSPi7ecNYkdddS/qamh2qat+v19mzpwpF1xwgcyePVv+/ve/a65z7733yrnnnhvzvznsi1rsXtTi0OxSi0939dVXy4IFC+Tzzz/XHHvxxRfl7rvvTsKqEG/UYfeiDoeWrDoc78cF7kF9di/qc2hW1Of09HR56aWX5KyzzpKDBw/qXmfFihWmtidOSUmRxYsXy9y5c+XYsWOa40ZBQjgXddi9nFyHAS+hDjvDm2++aRjmKy0tleeff15Gjx5tePuUlBQZNWqUjBo1Su655x657bbb5M9//rPmegsWLJAePXoYbk19Ot4PuwOBQeD/++qrr2TKlCmG20D9+te/NmyzH6msrCxJS0uT5uZmzTGjF+Jo1NTU6I537tzZsvtwkmPHjsmSJUt0j914442W3MfSpUtjuv3kyZMNj+kl/UVEamtrRVEU8fl8Md23iPFzJiMjI+SbOSAU6qp7UVfDs1tdzc7OlpdfflmmTZsmH330UdCxtrY2ueWWW2TDhg2W/OywF2qxe1GLw7NbLW539913y6xZszTjq1atkuPHj0t2dnYSVoV4oQ67F3U4vGTU4UQ8LnAH6rN7UZ/Ds6o+l5SUyEcffSQXXXSRbNu2LaY1paWlyaJFi2TatGm6HY5ERHr06BHTfcBeqMPu5fQ6DHgFddgZqqurDWvngAED5JNPPjH1c2ZnZ8vixYulZ8+e8utf/1pz/L777pPp06fLsGHDQs7D+2F3YEtiQES2bdsmF1xwgRw4cED3+M9+9jOZP3++JfdlVLCPHDliyfwiIocPH9Yd79Kli2X34SR//vOfpaGhQTOenp4u11xzTRJWZI7R46YoiuGbOLMOHTqkO+6GN1JIDuqqu1FXw7NjXc3IyJCnnnpKt536pk2b5M0330zCqhBP1GJ3oxaHZ8daLCIyZcoUSUnRfn+zra1NVq9enYQVIV6ow+5GHQ4vGXXY6Y8LEoP67G5OrwNOq88DBgyQVatWyZw5c6I+eduvXz/56KOP5Bvf+Ia0tLTodhcUEenZs2dU88N+qMPu5vQ6DHgBddg5fv/73+t2c05PT5c333wz6r+vH374YTn//PM1483NzfLAAw9ENadVnPZ+2MkIDMLzduzYIZMmTZI9e/boHv/xj38s9957r2X3V1hYqDu+f/9+S+avqamR+vp6U/ftdkatz2fMmOGIoh/qcbPqeWP0htCrzxnEhrrqftTV8OxaV/v16yff/OY3dY/99a9/TfBqEE/UYvejFodn11qcnZ0tZ5xxhu6xysrKBK8G8UIddj/qcHjJqMNOf1wQf9Rn93N6HXBifc7JyZFnnnlGVqxYId/85jcj7spSVFQkv/71r2X9+vVy1llnicjJk7eKomiu6/f7pX///lGtD/ZCHXY/p9dhwO2ow87yhz/8QXf81ltvlX79+kU9r8/nk4cfflj32JIlS2T37t1Rzx0rJ74fdioCg/C03bt3y/nnny87d+7UPT5//nx58MEHLb3PPn366I5v377dkvmN5ikoKPBU+9R2a9eulRUrVugemzdvXoJXE52OHTtK165ddY/F+3nTt29fS+aHd1BX3Y+6Ghk719XLL79cd1y9VTGci1rsftTiyNi5FhcUFOiOG30jGs5CHXY/6nBkEl2H3fC4IL6oz+7nhjrg5PpcXl4uixcvlkOHDsk//vEP+elPfyrXX3+9TJ8+Xc477zyZMmWKfPOb35QHH3xQli5dKtu3b5f58+dLenr6qTm2bt2qO3dpaalkZGTEtD4kH3XY/dxQhwE3ow47y+bNm2XXrl26x66//vqY5x85cqQMGTJEM97W1ibvv/9+zPNHy8nvh51GuwcO4BH79++X888/X7Zt26Z7/I477tDdtz1WZWVluuObN2+2ZP4tW7aYul+3M/omU3FxsUyePNmy+9H71qOVysrKdE8ebt68WS6++OKY5+d5AytQV72BuhoZOz9vhg8frju+c+dOaWlp0d0mE85BLfYGanFk7Py8Meqq0NjYmOCVwGrUYW+gDkcm0c+bRD0ucCbqszdQnyMT7+dNZmamTJ8+XaZPn276ths3btQdb+9ACOeiDnuDW+ow4EbUYecxCmDn5uZKeXm5Jfdx/vnny4YNGzTjn3/+echQIu+H3YEOg/CkQ4cOyfnnn29YCG655Rb57W9/G5f7HjFihO74mjVrLJn/iy++0B0/88wzLZnfSZqbm2Xx4sW6x2644Qbx+51TAnnewO6oq95AXY2cnZ83Rl2tFEWRI0eOJHg1sBK12BuoxZGz8/OmpqZGdzwtLS3BK4GVqMPeQB2OXCKfN256XGA96rM3uKkOePl58/nnn+uOT5w4MbELgaWow97gpjoMuA112JkOHTqkO15YWCg+n8+S+ygqKtIdN9qyN1F43iQGr8zwnCNHjsgFF1wgmzZt0j1+ww03yB/+8AfLiqza6NGjdcd37NghBw8ejHn+ZcuW6Y6PHDky5rmd5vXXX9dNnvt8PrnhhhuSsKLoGT1vjB5vM1paWmT16tW6x7z4vIF51FXvoK5Gxu51taWlxfBYW1tbAlcCK1GLvYNaHBm712KjD/xycnISvBJYhTrsHdThyCS6DrvpcYG1qM/e4aY64Kb6bNY777yjGfP7/VF1K4Q9UIe9w011GHAT6rBz1dXV6Y5b+flhbm6u7vjx48ctu49oePn9cCIRGISnHD16VKZOnSrr1q3TPX7NNdfIk08+GbcXRBGRkpISKSkp0T323nvvxTR3c3Oz/Otf/9I9dv7558c0txMZtT6/4IILpHfv3gleTWwmTZqkO75p0ybZu3dvTHN//vnnUltbqxnv06eP4XMVaEdd9RbqamTsXldD/RGenZ2dwJXAKtRib6EWR8bOtbi5uVnWr1+veyzZa0N0qMPeQh2OTKLrsJseF1iH+uwtbqoDbqrPZnz++eeye/duzfi5554rhYWFSVgRYkUd9hY31WHALajDzpaXl6c7fuzYMcvuo7q6WnfcKEiYKF59P5xoBAbhGcePH5dp06bJqlWrdI9fddVV8uyzzyakJfbUqVN1x5csWRLTvG+//bbU19drxgcOHCjFxcUxze00e/bskbfeekv32I033pjg1cSud+/e0r9/f91jr776akxzGz3vpk2bFtO8cD/qKnW1HXU1mN3rqlHL9vz8fMnKykrwahArajG1uB21OJida/HSpUsNvyF8xhlnJHg1iBV1mDrcjjocLJF12G2PC6xBfaY+t3NiHXBLfTbrj3/8o+44ncmciTpMHW7nxDoMuAF12Pl1OD8/X3d8z549oiiKJfexa9cu3fFu3bpZMn+0vPp+ONEIDMIT6urq5KKLLjJsUfpv//ZvsmjRIgkEAglZz5VXXqk7/uabb0pVVVXU8z777LOm7s/NnnnmGd1tFfPy8mTmzJlJWFHsjB5Ho8c9EidOnJDnn3/e1P0BItRVL/5+UFcj44S6+tprr+mODx8+PMErQayoxfb4nUokanFk7F6LjU6ElpSU0DnFYajDyf99SjTqcGQSXYfd+LggNtRn6nM7J9cBN9RnMyoqKmTRokWa8Z49e8rs2bOTsCLEgjpMHW7n5DoMOBl12B11uFevXrrjR48eNQyCmmXU5bFHjx6WzB8Lr70fTgYCg3C9hoYGufTSS2Xp0qW6xy+77DL5y1/+IikpKQlb06RJk6SoqEgz3tDQII8++mhUc1ZUVOimoX0+n1x33XVRzelkzzzzjO74NddcI+np6YldjEWuu+463ZbQy5Ytk//7v/+Las7Fixfrtu0tLi6WCRMmRDUn3I+6Sl09HXU1mN3r6rZt2+TFF1/UPTZ58uQErwaxoBZTi09HLQ5m51q8cuVKwzrMSRRnoQ5Th09HHQ6W6DrsxscF0aM+U59P5+Q64Ib6bMa3v/1tOXHihGb8nnvukbS0tCSsCNGiDlOHT+fkOgw4FXXYPXV45MiR0qlTJ91jzz33XMzzL1u2TDZt2qR7bOLEiTHPHyuvvR9OCgVwsaamJmX69OmKiOj+d9FFFylNTU1JWduvf/1r3TV17NhR2blzp+n5LrvsMt35LrvssjisPtiECRN073vhwoVxv289H374oeFjvnr16qSsySqXXHKJ7s9VXl6unDhxwtRcx48fVwoLC3Xne+SRR+L0E3zN6DGqrKyM+30jetRV6ip11Viy62o4J06cUKZOnaq7Pr/fr+zYsSPZS0SEqMXUYmqxMTvX4oMHDyp9+/bVXZvP51PWr1+f1PUhctRh6jB12Fii67CbHxeYR32mPrutDji5Ppvx05/+VHddgwYNMv1zIrmow9Rht9VhM+z2uKgZPUack3MX6rD76vDll1+ue1/p6enKli1bop63tbVVOe+883Tnzs3NVVpaWiz8KaLnpvfDdqzDBAbhWidOnDAsoCKiTJs2TWlsbEza+mpqapSCggLdtY0bN87U2h599FHdeXw+n7J8+fI4/hQn2e1N8PXXX6+7nhEjRiRlPVb67LPPFJ/Pp/vzzZ8/P+J52tralG984xu683Tv3l2pra2N409xkh1fFBEadZW6Sl01ZnVdnTNnjvLFF1+Y/XE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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10), dpi=150)\n",
    "plt.plot(ozono.index,ozono[\"O3\"],lw=2, color='black')\n",
    "plt.xlabel('Fecha', fontdict = {'fontsize':35 })\n",
    "plt.ylabel('O3',fontdict = {'fontsize':35 })\n",
    "plt.title('ozono centro CDMX 2017', fontsize = 35)\n",
    "plt.tick_params(labelsize=30, pad=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32624fe2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "72170efd",
   "metadata": {},
   "source": [
    "# Discretización"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04faf5ae",
   "metadata": {},
   "source": [
    "Los datos continuos se suelen discretizar o, lo que es lo mismo, separar en «categorías» para su análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2447d6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "corredores = pd.read_csv('Corredores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6697a68e",
   "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>Pais</th>\n",
       "      <th>Corredor</th>\n",
       "      <th>Lugar</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Tiempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBR</td>\n",
       "      <td>3475</td>\n",
       "      <td>3592</td>\n",
       "      <td>Male</td>\n",
       "      <td>52</td>\n",
       "      <td>217.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>13594</td>\n",
       "      <td>13853</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>272.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRA</td>\n",
       "      <td>12012</td>\n",
       "      <td>12256</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>265.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MI</td>\n",
       "      <td>10236</td>\n",
       "      <td>10457</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>256.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NY</td>\n",
       "      <td>9476</td>\n",
       "      <td>9686</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>252.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>FRA</td>\n",
       "      <td>15972</td>\n",
       "      <td>16261</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>285.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>GBR</td>\n",
       "      <td>1090</td>\n",
       "      <td>1131</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>191.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>IRL</td>\n",
       "      <td>16419</td>\n",
       "      <td>16713</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>289.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>NY</td>\n",
       "      <td>13977</td>\n",
       "      <td>14241</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>276.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>GER</td>\n",
       "      <td>8527</td>\n",
       "      <td>8726</td>\n",
       "      <td>Male</td>\n",
       "      <td>55</td>\n",
       "      <td>240.966667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pais  Corredor  Lugar  Genero  Edad      Tiempo\n",
       "0    GBR      3475   3592    Male    52  217.483333\n",
       "1     NY     13594  13853  Female    40  272.550000\n",
       "2    FRA     12012  12256    Male    31  265.283333\n",
       "3     MI     10236  10457  Female    33  256.150000\n",
       "4     NY      9476   9686    Male    33  252.250000\n",
       "..   ...       ...    ...     ...   ...         ...\n",
       "995  FRA     15972  16261    Male    61  285.883333\n",
       "996  GBR      1090   1131    Male    37  191.916667\n",
       "997  IRL     16419  16713  Female    53  289.016667\n",
       "998   NY     13977  14241  Female    21  276.050000\n",
       "999  GER      8527   8726    Male    55  240.966667\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corredores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "56db7a4d",
   "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>Corredor</th>\n",
       "      <th>Lugar</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Tiempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11763.113000</td>\n",
       "      <td>11992.319000</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>268.570700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6761.839631</td>\n",
       "      <td>6858.858466</td>\n",
       "      <td>10.675391</td>\n",
       "      <td>53.550743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>147.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5936.000000</td>\n",
       "      <td>6095.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>233.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11722.000000</td>\n",
       "      <td>11962.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>262.841667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17600.500000</td>\n",
       "      <td>17921.750000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>294.854167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23290.000000</td>\n",
       "      <td>23662.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>566.783333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Corredor         Lugar         Edad       Tiempo\n",
       "count   1000.000000   1000.000000  1000.000000  1000.000000\n",
       "mean   11763.113000  11992.319000    39.100000   268.570700\n",
       "std     6761.839631   6858.858466    10.675391    53.550743\n",
       "min       53.000000     54.000000     5.000000   147.333333\n",
       "25%     5936.000000   6095.000000    31.000000   233.775000\n",
       "50%    11722.000000  11962.000000    38.000000   262.841667\n",
       "75%    17600.500000  17921.750000    46.000000   294.854167\n",
       "max    23290.000000  23662.000000    81.000000   566.783333"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corredores.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33671c23",
   "metadata": {},
   "source": [
    "Supongamos que queremos agrupar las edades de los corredores en grupos de 5 a 18 años, de 19 a 25 años, de 26 a 35 años, de 36 años a 60 años y de 60 años a 100 años. Para ello tenemos que usar **pandas.cut**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3e8b8d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "edades = corredores['Edad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e7e3b146",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [5,18,25,35,60,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0fabe4cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorias = pd.cut(edades,bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4bb957ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       (35, 60]\n",
       "1       (35, 60]\n",
       "2       (25, 35]\n",
       "3       (25, 35]\n",
       "4       (25, 35]\n",
       "         ...    \n",
       "995    (60, 100]\n",
       "996     (35, 60]\n",
       "997     (35, 60]\n",
       "998     (18, 25]\n",
       "999     (35, 60]\n",
       "Name: Edad, Length: 1000, dtype: category\n",
       "Categories (5, interval[int64, right]): [(5, 18] < (18, 25] < (25, 35] < (35, 60] < (60, 100]]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bff623",
   "metadata": {},
   "source": [
    "**pd.value_counts()** obtiene los recuentos de contenedores para el resultado de pandas.cut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5ca541fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Edad\n",
       "(35, 60]     519\n",
       "(25, 35]     363\n",
       "(18, 25]      76\n",
       "(60, 100]     40\n",
       "(5, 18]        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(categorias)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0026abf5",
   "metadata": {},
   "source": [
    "En la representación de cadena de texto de un intervalo, un paréntesis significa que el lado está abierto, mientras que el corchete significa que está cerrado. Se puede cambiar el lado que está cerrado pasando **right=False**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5265d88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [35, 60)\n",
       "1       [35, 60)\n",
       "2       [25, 35)\n",
       "3       [25, 35)\n",
       "4       [25, 35)\n",
       "         ...    \n",
       "995    [60, 100)\n",
       "996     [35, 60)\n",
       "997     [35, 60)\n",
       "998     [18, 25)\n",
       "999     [35, 60)\n",
       "Name: Edad, Length: 1000, dtype: category\n",
       "Categories (5, interval[int64, left]): [[5, 18) < [18, 25) < [25, 35) < [35, 60) < [60, 100)]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(edades,bins,right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e94b5c7a",
   "metadata": {},
   "source": [
    "Se puede anular el etiquetado de contenedor predeterminado basado en intervalos pasando una lista o un array a la opción labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7975c023",
   "metadata": {},
   "outputs": [],
   "source": [
    "nombres_grupos = ['Niño','joven_adulto','mediana_edad','Adulto',\n",
    "                 'tercera_edad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8b73d3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Adulto\n",
       "1            Adulto\n",
       "2      mediana_edad\n",
       "3      mediana_edad\n",
       "4      mediana_edad\n",
       "           ...     \n",
       "995    tercera_edad\n",
       "996          Adulto\n",
       "997          Adulto\n",
       "998    joven_adulto\n",
       "999          Adulto\n",
       "Name: Edad, Length: 1000, dtype: category\n",
       "Categories (5, object): ['Niño' < 'joven_adulto' < 'mediana_edad' < 'Adulto' < 'tercera_edad']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(edades,bins,labels=nombres_grupos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419b84a3",
   "metadata": {},
   "source": [
    "Si se pasa un número entero de contenedores a pandas.cut en lugar de bordes explícitos de contenedor, calculará contenedores de la misma longitud basándose en los valores mínimo y máximo de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0e9d5260",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.standard_normal(size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3b57c4c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.86533403,  1.54048197, -0.8551658 , -0.75869928, -0.22198037,\n",
       "        1.42512663,  0.34260773, -2.29425441, -0.72410026,  1.88864241,\n",
       "       -0.05902265, -0.6706976 ,  0.29217541, -1.83475246, -1.03487947,\n",
       "       -0.08325513,  1.76025473,  0.10270815, -0.98231872,  0.0520404 ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb21385",
   "metadata": {},
   "source": [
    "La opción precision=2 limita la precisión decimal a dos dígitos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fc3f1d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "group = pd.cut(data, 4, precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e38dc61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.25, -0.2]    7\n",
       "(-0.2, 0.84]     6\n",
       "(0.84, 1.89]     5\n",
       "(-2.3, -1.25]    2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2e68ffa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4d6f9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d479f74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d8a78126",
   "metadata": {},
   "source": [
    "# Detección y filtrado de valores atípicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "511cec6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = pd.DataFrame(np.random.standard_normal((1000, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "936dc1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.558448</td>\n",
       "      <td>0.102976</td>\n",
       "      <td>0.005625</td>\n",
       "      <td>0.082568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.045111</td>\n",
       "      <td>1.045289</td>\n",
       "      <td>0.485476</td>\n",
       "      <td>0.025952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.432224</td>\n",
       "      <td>-0.472566</td>\n",
       "      <td>0.832408</td>\n",
       "      <td>2.213827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.151996</td>\n",
       "      <td>-1.041452</td>\n",
       "      <td>0.358433</td>\n",
       "      <td>0.272873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.684100</td>\n",
       "      <td>0.844070</td>\n",
       "      <td>-0.911501</td>\n",
       "      <td>-1.188778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.398053</td>\n",
       "      <td>-0.132784</td>\n",
       "      <td>-0.288498</td>\n",
       "      <td>1.097303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-0.801528</td>\n",
       "      <td>0.474521</td>\n",
       "      <td>-0.870316</td>\n",
       "      <td>-0.092551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-0.474608</td>\n",
       "      <td>-0.511794</td>\n",
       "      <td>0.590471</td>\n",
       "      <td>-1.203040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.821878</td>\n",
       "      <td>-0.348507</td>\n",
       "      <td>-2.264538</td>\n",
       "      <td>-1.031698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.381770</td>\n",
       "      <td>-1.058486</td>\n",
       "      <td>-0.300008</td>\n",
       "      <td>0.773188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "0   -0.558448  0.102976  0.005625  0.082568\n",
       "1   -0.045111  1.045289  0.485476  0.025952\n",
       "2    0.432224 -0.472566  0.832408  2.213827\n",
       "3   -0.151996 -1.041452  0.358433  0.272873\n",
       "4    1.684100  0.844070 -0.911501 -1.188778\n",
       "..        ...       ...       ...       ...\n",
       "995  1.398053 -0.132784 -0.288498  1.097303\n",
       "996 -0.801528  0.474521 -0.870316 -0.092551\n",
       "997 -0.474608 -0.511794  0.590471 -1.203040\n",
       "998  0.821878 -0.348507 -2.264538 -1.031698\n",
       "999  0.381770 -1.058486 -0.300008  0.773188\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7a2ed6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.039536</td>\n",
       "      <td>-0.010798</td>\n",
       "      <td>0.031043</td>\n",
       "      <td>0.019642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.028474</td>\n",
       "      <td>1.022964</td>\n",
       "      <td>1.000711</td>\n",
       "      <td>1.003470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.817688</td>\n",
       "      <td>-3.618455</td>\n",
       "      <td>-3.259576</td>\n",
       "      <td>-3.445395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.602803</td>\n",
       "      <td>-0.721580</td>\n",
       "      <td>-0.664495</td>\n",
       "      <td>-0.653017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.007723</td>\n",
       "      <td>-0.016027</td>\n",
       "      <td>0.043482</td>\n",
       "      <td>0.051301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.705832</td>\n",
       "      <td>0.636284</td>\n",
       "      <td>0.678966</td>\n",
       "      <td>0.657567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.913186</td>\n",
       "      <td>3.053666</td>\n",
       "      <td>3.329851</td>\n",
       "      <td>3.809224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.039536    -0.010798     0.031043     0.019642\n",
       "std       1.028474     1.022964     1.000711     1.003470\n",
       "min      -2.817688    -3.618455    -3.259576    -3.445395\n",
       "25%      -0.602803    -0.721580    -0.664495    -0.653017\n",
       "50%       0.007723    -0.016027     0.043482     0.051301\n",
       "75%       0.705832     0.636284     0.678966     0.657567\n",
       "max       2.913186     3.053666     3.329851     3.809224"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c2f603",
   "metadata": {},
   "source": [
    "Supongamos que queremos encontrar valores en una de las columnas que sean mayores de 3 y menores que -3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "256beb48",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = datos[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "acffc57e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.005625\n",
       "1      0.485476\n",
       "2      0.832408\n",
       "3      0.358433\n",
       "4     -0.911501\n",
       "         ...   \n",
       "995   -0.288498\n",
       "996   -0.870316\n",
       "997    0.590471\n",
       "998   -2.264538\n",
       "999   -0.300008\n",
       "Name: 2, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8c757e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199    3.042633\n",
       "425   -3.259576\n",
       "555    3.329851\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col[col.abs() > 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a7e070",
   "metadata": {},
   "source": [
    "Para seleccionar todas las filas con un valor superior a 3 o –3, se puede emplear el método **any**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "332251c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.529744</td>\n",
       "      <td>2.066436</td>\n",
       "      <td>0.784442</td>\n",
       "      <td>3.081949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>-0.894895</td>\n",
       "      <td>3.053666</td>\n",
       "      <td>-0.186727</td>\n",
       "      <td>-0.762823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>-1.540912</td>\n",
       "      <td>3.041763</td>\n",
       "      <td>0.775283</td>\n",
       "      <td>-0.475222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>-0.601092</td>\n",
       "      <td>1.354714</td>\n",
       "      <td>-1.407018</td>\n",
       "      <td>3.666374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.913490</td>\n",
       "      <td>0.321483</td>\n",
       "      <td>3.042633</td>\n",
       "      <td>-1.014288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.009160</td>\n",
       "      <td>-1.575113</td>\n",
       "      <td>-0.509386</td>\n",
       "      <td>3.809224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>-0.390114</td>\n",
       "      <td>-3.137809</td>\n",
       "      <td>0.161104</td>\n",
       "      <td>-1.132148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>1.888708</td>\n",
       "      <td>-1.571501</td>\n",
       "      <td>-3.259576</td>\n",
       "      <td>1.255063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>-0.093859</td>\n",
       "      <td>1.229675</td>\n",
       "      <td>0.320181</td>\n",
       "      <td>-3.445395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>-0.567740</td>\n",
       "      <td>-0.021891</td>\n",
       "      <td>3.329851</td>\n",
       "      <td>-0.133877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>-0.152137</td>\n",
       "      <td>-3.618455</td>\n",
       "      <td>0.835416</td>\n",
       "      <td>-0.903067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "60   0.529744  2.066436  0.784442  3.081949\n",
       "91  -0.894895  3.053666 -0.186727 -0.762823\n",
       "129 -1.540912  3.041763  0.775283 -0.475222\n",
       "170 -0.601092  1.354714 -1.407018  3.666374\n",
       "199  0.913490  0.321483  3.042633 -1.014288\n",
       "318  0.009160 -1.575113 -0.509386  3.809224\n",
       "418 -0.390114 -3.137809  0.161104 -1.132148\n",
       "425  1.888708 -1.571501 -3.259576  1.255063\n",
       "538 -0.093859  1.229675  0.320181 -3.445395\n",
       "555 -0.567740 -0.021891  3.329851 -0.133877\n",
       "587 -0.152137 -3.618455  0.835416 -0.903067"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos[(datos.abs() > 3).any(axis='columns')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0045f93a",
   "metadata": {},
   "source": [
    "Los valores se pueden configurar con base a ciertos criterios. Podemos limitar los valores que quedenn fuera del intervalo –3 a 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ef5c8350",
   "metadata": {},
   "outputs": [],
   "source": [
    "datos[datos.abs() > 3] = np.sign(datos) * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9750bbe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.093859\n",
       "1    1.229675\n",
       "2    0.320181\n",
       "3   -3.000000\n",
       "Name: 538, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos.iloc[538]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490970ef",
   "metadata": {},
   "source": [
    "La sentencia **np.sign()** produce valores 1 y –1 según si los valores de data son positivos o negativos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aa0ce65e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3\n",
       "0   -1.0  1.0  1.0  1.0\n",
       "1   -1.0  1.0  1.0  1.0\n",
       "2    1.0 -1.0  1.0  1.0\n",
       "3   -1.0 -1.0  1.0  1.0\n",
       "4    1.0  1.0 -1.0 -1.0\n",
       "..   ...  ...  ...  ...\n",
       "995  1.0 -1.0 -1.0  1.0\n",
       "996 -1.0  1.0 -1.0 -1.0\n",
       "997 -1.0 -1.0  1.0 -1.0\n",
       "998  1.0 -1.0 -1.0 -1.0\n",
       "999  1.0 -1.0 -1.0  1.0\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sign(datos) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "000db23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "edades = pd.DataFrame(edades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa9206f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fb0e25eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10,4), dpi=100)\n",
    "sns.boxplot(data=edades, orient=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "74f9cb43",
   "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>Edad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Edad\n",
       "249     5"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edades[(edades < 18).any(axis='columns')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "13472061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pais               NaN\n",
       "Corredor         18260\n",
       "Lugar            18589\n",
       "Genero            Male\n",
       "Edad                 5\n",
       "Tiempo      302.683333\n",
       "Name: 249, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corredores.iloc[249]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ff8444c3",
   "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>Edad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Edad\n",
       "105    81\n",
       "152    76\n",
       "249     5"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edades[((edades < 18) | (edades >75)).any(axis='columns')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2d9aebf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pais          ITA\n",
       "Corredor    22743\n",
       "Lugar       23112\n",
       "Genero       Male\n",
       "Edad           81\n",
       "Tiempo      389.0\n",
       "Name: 105, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corredores.iloc[105]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "021396a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e70457",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e235564c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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