03_Data_Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Cirugía de datos\"\n",
    "\n",
    "Es el proceso de transformar y mapear datos de un dataset raw (en bruto) en otro formato con la intención de hacerlo más apropiado y valioso para una variedad de propósitos posteriores, como el análisis.\n",
    "\n",
    "Este proceso puede incluir visualización de datos, agregación de datos, entrenamiento de un modelo estadístico, así como muchos otros usos potenciales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/customer-churn-model/Customer Churn Model.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
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       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
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       "      <td>False.</td>\n",
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       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
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       "      <td>False.</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
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       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  Eve Charge  \\\n",
       "0             25     265.1        110       45.07  ...         99       16.78   \n",
       "1             26     161.6        123       27.47  ...        103       16.62   \n",
       "2              0     243.4        114       41.38  ...        110       10.30   \n",
       "3              0     299.4         71       50.90  ...         88        5.26   \n",
       "4              0     166.7        113       28.34  ...        122       12.61   \n",
       "\n",
       "   Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0       244.7           91         11.01       10.0           3         2.70   \n",
       "1       254.4          103         11.45       13.7           3         3.70   \n",
       "2       162.6          104          7.32       12.2           5         3.29   \n",
       "3       196.9           89          8.86        6.6           7         1.78   \n",
       "4       186.9          121          8.41       10.1           3         2.73   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "0               1  False.  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seleccionar \"n\" columnas para un nuevo subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Account Length', 'Area Code', 'Phone', 'Int'l Plan',\n",
       "       'VMail Plan', 'VMail Message', 'Day Mins', 'Day Calls', 'Day Charge',\n",
       "       'Eve Mins', 'Eve Calls', 'Eve Charge', 'Night Mins', 'Night Calls',\n",
       "       'Night Charge', 'Intl Mins', 'Intl Calls', 'Intl Charge',\n",
       "       'CustServ Calls', 'Churn?'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Account Length</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>107</td>\n",
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       "      <td>16.62</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>10.30</td>\n",
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      "text/plain": [
       "   Account Length     Phone  Eve Charge\n",
       "0             128  382-4657       16.78\n",
       "1             107  371-7191       16.62\n",
       "2             137  358-1921       10.30\n",
       "3              84  375-9999        5.26\n",
       "4              75  330-6626       12.61"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnas_deseadas = ['Account Length', 'Phone', 'Eve Charge']\n",
    "subset = data[columnas_deseadas]\n",
    "subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
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       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
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       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
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       "      <td>False.</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
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       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
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       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>37.98</td>\n",
       "      <td>220.6</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>37.09</td>\n",
       "      <td>348.5</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MO</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>26.69</td>\n",
       "      <td>103.1</td>\n",
       "      <td>94</td>\n",
       "      <td>8.76</td>\n",
       "      <td>211.8</td>\n",
       "      <td>96</td>\n",
       "      <td>9.53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LA</td>\n",
       "      <td>117</td>\n",
       "      <td>408</td>\n",
       "      <td>335-4719</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>31.37</td>\n",
       "      <td>351.6</td>\n",
       "      <td>80</td>\n",
       "      <td>29.89</td>\n",
       "      <td>215.8</td>\n",
       "      <td>90</td>\n",
       "      <td>9.71</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WV</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>330-8173</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>37</td>\n",
       "      <td>258.6</td>\n",
       "      <td>43.96</td>\n",
       "      <td>222.0</td>\n",
       "      <td>111</td>\n",
       "      <td>18.87</td>\n",
       "      <td>326.4</td>\n",
       "      <td>97</td>\n",
       "      <td>14.69</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IN</td>\n",
       "      <td>65</td>\n",
       "      <td>415</td>\n",
       "      <td>329-6603</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>129.1</td>\n",
       "      <td>21.95</td>\n",
       "      <td>228.5</td>\n",
       "      <td>83</td>\n",
       "      <td>19.42</td>\n",
       "      <td>208.8</td>\n",
       "      <td>111</td>\n",
       "      <td>9.40</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6</td>\n",
       "      <td>3.43</td>\n",
       "      <td>4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RI</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>344-9403</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>187.7</td>\n",
       "      <td>31.91</td>\n",
       "      <td>163.4</td>\n",
       "      <td>148</td>\n",
       "      <td>13.89</td>\n",
       "      <td>196.0</td>\n",
       "      <td>94</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.1</td>\n",
       "      <td>5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>168</td>\n",
       "      <td>408</td>\n",
       "      <td>363-1107</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>128.8</td>\n",
       "      <td>21.90</td>\n",
       "      <td>104.9</td>\n",
       "      <td>71</td>\n",
       "      <td>8.92</td>\n",
       "      <td>141.1</td>\n",
       "      <td>128</td>\n",
       "      <td>6.35</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MT</td>\n",
       "      <td>95</td>\n",
       "      <td>510</td>\n",
       "      <td>394-8006</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>156.6</td>\n",
       "      <td>26.62</td>\n",
       "      <td>247.6</td>\n",
       "      <td>75</td>\n",
       "      <td>21.05</td>\n",
       "      <td>192.3</td>\n",
       "      <td>115</td>\n",
       "      <td>8.65</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IA</td>\n",
       "      <td>62</td>\n",
       "      <td>415</td>\n",
       "      <td>366-9238</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>120.7</td>\n",
       "      <td>20.52</td>\n",
       "      <td>307.2</td>\n",
       "      <td>76</td>\n",
       "      <td>26.11</td>\n",
       "      <td>203.0</td>\n",
       "      <td>99</td>\n",
       "      <td>9.14</td>\n",
       "      <td>13.1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.54</td>\n",
       "      <td>4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NY</td>\n",
       "      <td>161</td>\n",
       "      <td>415</td>\n",
       "      <td>351-7269</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>332.9</td>\n",
       "      <td>56.59</td>\n",
       "      <td>317.8</td>\n",
       "      <td>97</td>\n",
       "      <td>27.01</td>\n",
       "      <td>160.6</td>\n",
       "      <td>128</td>\n",
       "      <td>7.23</td>\n",
       "      <td>5.4</td>\n",
       "      <td>9</td>\n",
       "      <td>1.46</td>\n",
       "      <td>4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ID</td>\n",
       "      <td>85</td>\n",
       "      <td>408</td>\n",
       "      <td>350-8884</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>27</td>\n",
       "      <td>196.4</td>\n",
       "      <td>33.39</td>\n",
       "      <td>280.9</td>\n",
       "      <td>90</td>\n",
       "      <td>23.88</td>\n",
       "      <td>89.3</td>\n",
       "      <td>75</td>\n",
       "      <td>4.02</td>\n",
       "      <td>13.8</td>\n",
       "      <td>4</td>\n",
       "      <td>3.73</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>VT</td>\n",
       "      <td>93</td>\n",
       "      <td>510</td>\n",
       "      <td>386-2923</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>190.7</td>\n",
       "      <td>32.42</td>\n",
       "      <td>218.2</td>\n",
       "      <td>111</td>\n",
       "      <td>18.55</td>\n",
       "      <td>129.6</td>\n",
       "      <td>121</td>\n",
       "      <td>5.83</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.19</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VA</td>\n",
       "      <td>76</td>\n",
       "      <td>510</td>\n",
       "      <td>356-2992</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>33</td>\n",
       "      <td>189.7</td>\n",
       "      <td>32.25</td>\n",
       "      <td>212.8</td>\n",
       "      <td>65</td>\n",
       "      <td>18.09</td>\n",
       "      <td>165.7</td>\n",
       "      <td>108</td>\n",
       "      <td>7.46</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TX</td>\n",
       "      <td>73</td>\n",
       "      <td>415</td>\n",
       "      <td>373-2782</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>224.4</td>\n",
       "      <td>38.15</td>\n",
       "      <td>159.5</td>\n",
       "      <td>88</td>\n",
       "      <td>13.56</td>\n",
       "      <td>192.8</td>\n",
       "      <td>74</td>\n",
       "      <td>8.68</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.51</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FL</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>396-5800</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>155.1</td>\n",
       "      <td>26.37</td>\n",
       "      <td>239.7</td>\n",
       "      <td>93</td>\n",
       "      <td>20.37</td>\n",
       "      <td>208.8</td>\n",
       "      <td>133</td>\n",
       "      <td>9.40</td>\n",
       "      <td>10.6</td>\n",
       "      <td>4</td>\n",
       "      <td>2.86</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CO</td>\n",
       "      <td>77</td>\n",
       "      <td>408</td>\n",
       "      <td>393-7984</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>62.4</td>\n",
       "      <td>10.61</td>\n",
       "      <td>169.9</td>\n",
       "      <td>121</td>\n",
       "      <td>14.44</td>\n",
       "      <td>209.6</td>\n",
       "      <td>64</td>\n",
       "      <td>9.43</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6</td>\n",
       "      <td>1.54</td>\n",
       "      <td>5</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AZ</td>\n",
       "      <td>130</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1958</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>31.11</td>\n",
       "      <td>72.9</td>\n",
       "      <td>99</td>\n",
       "      <td>6.20</td>\n",
       "      <td>181.8</td>\n",
       "      <td>78</td>\n",
       "      <td>8.18</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SC</td>\n",
       "      <td>111</td>\n",
       "      <td>415</td>\n",
       "      <td>350-2565</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>110.4</td>\n",
       "      <td>18.77</td>\n",
       "      <td>137.3</td>\n",
       "      <td>102</td>\n",
       "      <td>11.67</td>\n",
       "      <td>189.6</td>\n",
       "      <td>105</td>\n",
       "      <td>8.53</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>VA</td>\n",
       "      <td>132</td>\n",
       "      <td>510</td>\n",
       "      <td>343-4696</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>81.1</td>\n",
       "      <td>13.79</td>\n",
       "      <td>245.2</td>\n",
       "      <td>72</td>\n",
       "      <td>20.84</td>\n",
       "      <td>237.0</td>\n",
       "      <td>115</td>\n",
       "      <td>10.67</td>\n",
       "      <td>10.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NE</td>\n",
       "      <td>174</td>\n",
       "      <td>415</td>\n",
       "      <td>331-3698</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>124.3</td>\n",
       "      <td>21.13</td>\n",
       "      <td>277.1</td>\n",
       "      <td>112</td>\n",
       "      <td>23.55</td>\n",
       "      <td>250.7</td>\n",
       "      <td>115</td>\n",
       "      <td>11.28</td>\n",
       "      <td>15.5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WY</td>\n",
       "      <td>57</td>\n",
       "      <td>408</td>\n",
       "      <td>357-3817</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>39</td>\n",
       "      <td>213.0</td>\n",
       "      <td>36.21</td>\n",
       "      <td>191.1</td>\n",
       "      <td>112</td>\n",
       "      <td>16.24</td>\n",
       "      <td>182.7</td>\n",
       "      <td>115</td>\n",
       "      <td>8.22</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MT</td>\n",
       "      <td>54</td>\n",
       "      <td>408</td>\n",
       "      <td>418-6412</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>134.3</td>\n",
       "      <td>22.83</td>\n",
       "      <td>155.5</td>\n",
       "      <td>100</td>\n",
       "      <td>13.22</td>\n",
       "      <td>102.1</td>\n",
       "      <td>68</td>\n",
       "      <td>4.59</td>\n",
       "      <td>14.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>MO</td>\n",
       "      <td>20</td>\n",
       "      <td>415</td>\n",
       "      <td>353-2630</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>32.30</td>\n",
       "      <td>258.2</td>\n",
       "      <td>84</td>\n",
       "      <td>21.95</td>\n",
       "      <td>181.5</td>\n",
       "      <td>102</td>\n",
       "      <td>8.17</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HI</td>\n",
       "      <td>49</td>\n",
       "      <td>510</td>\n",
       "      <td>410-7789</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>119.3</td>\n",
       "      <td>20.28</td>\n",
       "      <td>215.1</td>\n",
       "      <td>109</td>\n",
       "      <td>18.28</td>\n",
       "      <td>178.7</td>\n",
       "      <td>90</td>\n",
       "      <td>8.04</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>WI</td>\n",
       "      <td>114</td>\n",
       "      <td>415</td>\n",
       "      <td>373-7308</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>137.1</td>\n",
       "      <td>23.31</td>\n",
       "      <td>155.7</td>\n",
       "      <td>125</td>\n",
       "      <td>13.23</td>\n",
       "      <td>247.6</td>\n",
       "      <td>94</td>\n",
       "      <td>11.14</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>IL</td>\n",
       "      <td>71</td>\n",
       "      <td>510</td>\n",
       "      <td>330-7137</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>186.1</td>\n",
       "      <td>31.64</td>\n",
       "      <td>198.6</td>\n",
       "      <td>140</td>\n",
       "      <td>16.88</td>\n",
       "      <td>206.5</td>\n",
       "      <td>80</td>\n",
       "      <td>9.29</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5</td>\n",
       "      <td>3.73</td>\n",
       "      <td>4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3305</th>\n",
       "      <td>IN</td>\n",
       "      <td>58</td>\n",
       "      <td>415</td>\n",
       "      <td>406-8445</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>22</td>\n",
       "      <td>224.1</td>\n",
       "      <td>38.10</td>\n",
       "      <td>238.8</td>\n",
       "      <td>85</td>\n",
       "      <td>20.30</td>\n",
       "      <td>174.2</td>\n",
       "      <td>86</td>\n",
       "      <td>7.84</td>\n",
       "      <td>11.5</td>\n",
       "      <td>7</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>AL</td>\n",
       "      <td>106</td>\n",
       "      <td>408</td>\n",
       "      <td>404-5283</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>29</td>\n",
       "      <td>83.6</td>\n",
       "      <td>14.21</td>\n",
       "      <td>203.9</td>\n",
       "      <td>131</td>\n",
       "      <td>17.33</td>\n",
       "      <td>229.5</td>\n",
       "      <td>73</td>\n",
       "      <td>10.33</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>OK</td>\n",
       "      <td>172</td>\n",
       "      <td>408</td>\n",
       "      <td>398-3632</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>203.9</td>\n",
       "      <td>34.66</td>\n",
       "      <td>234.0</td>\n",
       "      <td>123</td>\n",
       "      <td>19.89</td>\n",
       "      <td>160.7</td>\n",
       "      <td>65</td>\n",
       "      <td>7.23</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4</td>\n",
       "      <td>4.81</td>\n",
       "      <td>4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>IA</td>\n",
       "      <td>45</td>\n",
       "      <td>415</td>\n",
       "      <td>399-5763</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>211.3</td>\n",
       "      <td>35.92</td>\n",
       "      <td>165.7</td>\n",
       "      <td>97</td>\n",
       "      <td>14.08</td>\n",
       "      <td>265.9</td>\n",
       "      <td>72</td>\n",
       "      <td>11.97</td>\n",
       "      <td>13.3</td>\n",
       "      <td>6</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>VT</td>\n",
       "      <td>100</td>\n",
       "      <td>408</td>\n",
       "      <td>340-9449</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>219.4</td>\n",
       "      <td>37.30</td>\n",
       "      <td>225.7</td>\n",
       "      <td>102</td>\n",
       "      <td>19.18</td>\n",
       "      <td>255.3</td>\n",
       "      <td>95</td>\n",
       "      <td>11.49</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.24</td>\n",
       "      <td>4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>NY</td>\n",
       "      <td>94</td>\n",
       "      <td>415</td>\n",
       "      <td>363-1123</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>190.4</td>\n",
       "      <td>32.37</td>\n",
       "      <td>92.0</td>\n",
       "      <td>107</td>\n",
       "      <td>7.82</td>\n",
       "      <td>224.8</td>\n",
       "      <td>108</td>\n",
       "      <td>10.12</td>\n",
       "      <td>13.6</td>\n",
       "      <td>17</td>\n",
       "      <td>3.67</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>LA</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>361-2170</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>147.7</td>\n",
       "      <td>25.11</td>\n",
       "      <td>283.3</td>\n",
       "      <td>83</td>\n",
       "      <td>24.08</td>\n",
       "      <td>188.3</td>\n",
       "      <td>124</td>\n",
       "      <td>8.47</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>SC</td>\n",
       "      <td>181</td>\n",
       "      <td>408</td>\n",
       "      <td>406-6304</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>229.9</td>\n",
       "      <td>39.08</td>\n",
       "      <td>144.4</td>\n",
       "      <td>93</td>\n",
       "      <td>12.27</td>\n",
       "      <td>262.4</td>\n",
       "      <td>110</td>\n",
       "      <td>11.81</td>\n",
       "      <td>14.2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>ID</td>\n",
       "      <td>127</td>\n",
       "      <td>408</td>\n",
       "      <td>392-5090</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>102.8</td>\n",
       "      <td>17.48</td>\n",
       "      <td>143.7</td>\n",
       "      <td>95</td>\n",
       "      <td>12.21</td>\n",
       "      <td>191.4</td>\n",
       "      <td>97</td>\n",
       "      <td>8.61</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3314</th>\n",
       "      <td>MO</td>\n",
       "      <td>89</td>\n",
       "      <td>415</td>\n",
       "      <td>373-7713</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>178.7</td>\n",
       "      <td>30.38</td>\n",
       "      <td>233.7</td>\n",
       "      <td>74</td>\n",
       "      <td>19.86</td>\n",
       "      <td>131.9</td>\n",
       "      <td>120</td>\n",
       "      <td>5.94</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>ME</td>\n",
       "      <td>149</td>\n",
       "      <td>415</td>\n",
       "      <td>392-1376</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>18</td>\n",
       "      <td>148.5</td>\n",
       "      <td>25.25</td>\n",
       "      <td>114.5</td>\n",
       "      <td>106</td>\n",
       "      <td>9.73</td>\n",
       "      <td>178.3</td>\n",
       "      <td>98</td>\n",
       "      <td>8.02</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>MS</td>\n",
       "      <td>103</td>\n",
       "      <td>510</td>\n",
       "      <td>390-6388</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>29</td>\n",
       "      <td>164.1</td>\n",
       "      <td>27.90</td>\n",
       "      <td>219.1</td>\n",
       "      <td>96</td>\n",
       "      <td>18.62</td>\n",
       "      <td>220.3</td>\n",
       "      <td>108</td>\n",
       "      <td>9.91</td>\n",
       "      <td>12.3</td>\n",
       "      <td>9</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>SD</td>\n",
       "      <td>163</td>\n",
       "      <td>415</td>\n",
       "      <td>379-7290</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>197.2</td>\n",
       "      <td>33.52</td>\n",
       "      <td>188.5</td>\n",
       "      <td>113</td>\n",
       "      <td>16.02</td>\n",
       "      <td>211.1</td>\n",
       "      <td>94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>OK</td>\n",
       "      <td>52</td>\n",
       "      <td>415</td>\n",
       "      <td>397-9928</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>124.9</td>\n",
       "      <td>21.23</td>\n",
       "      <td>300.5</td>\n",
       "      <td>118</td>\n",
       "      <td>25.54</td>\n",
       "      <td>192.5</td>\n",
       "      <td>106</td>\n",
       "      <td>8.66</td>\n",
       "      <td>11.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>WY</td>\n",
       "      <td>89</td>\n",
       "      <td>415</td>\n",
       "      <td>378-6924</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>115.4</td>\n",
       "      <td>19.62</td>\n",
       "      <td>209.9</td>\n",
       "      <td>115</td>\n",
       "      <td>17.84</td>\n",
       "      <td>280.9</td>\n",
       "      <td>112</td>\n",
       "      <td>12.64</td>\n",
       "      <td>15.9</td>\n",
       "      <td>6</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>GA</td>\n",
       "      <td>122</td>\n",
       "      <td>510</td>\n",
       "      <td>411-5677</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>23.80</td>\n",
       "      <td>196.4</td>\n",
       "      <td>77</td>\n",
       "      <td>16.69</td>\n",
       "      <td>120.1</td>\n",
       "      <td>133</td>\n",
       "      <td>5.40</td>\n",
       "      <td>9.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.62</td>\n",
       "      <td>4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>VT</td>\n",
       "      <td>60</td>\n",
       "      <td>415</td>\n",
       "      <td>400-2738</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>193.9</td>\n",
       "      <td>32.96</td>\n",
       "      <td>85.0</td>\n",
       "      <td>110</td>\n",
       "      <td>7.23</td>\n",
       "      <td>210.1</td>\n",
       "      <td>134</td>\n",
       "      <td>9.45</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>MD</td>\n",
       "      <td>62</td>\n",
       "      <td>408</td>\n",
       "      <td>409-1856</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>321.1</td>\n",
       "      <td>54.59</td>\n",
       "      <td>265.5</td>\n",
       "      <td>122</td>\n",
       "      <td>22.57</td>\n",
       "      <td>180.5</td>\n",
       "      <td>72</td>\n",
       "      <td>8.12</td>\n",
       "      <td>11.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.11</td>\n",
       "      <td>4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>IN</td>\n",
       "      <td>117</td>\n",
       "      <td>415</td>\n",
       "      <td>362-5899</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>118.4</td>\n",
       "      <td>20.13</td>\n",
       "      <td>249.3</td>\n",
       "      <td>97</td>\n",
       "      <td>21.19</td>\n",
       "      <td>227.0</td>\n",
       "      <td>56</td>\n",
       "      <td>10.22</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>WV</td>\n",
       "      <td>159</td>\n",
       "      <td>415</td>\n",
       "      <td>377-1164</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>169.8</td>\n",
       "      <td>28.87</td>\n",
       "      <td>197.7</td>\n",
       "      <td>105</td>\n",
       "      <td>16.80</td>\n",
       "      <td>193.7</td>\n",
       "      <td>82</td>\n",
       "      <td>8.72</td>\n",
       "      <td>11.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3.13</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>OH</td>\n",
       "      <td>78</td>\n",
       "      <td>408</td>\n",
       "      <td>368-8555</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>193.4</td>\n",
       "      <td>32.88</td>\n",
       "      <td>116.9</td>\n",
       "      <td>88</td>\n",
       "      <td>9.94</td>\n",
       "      <td>243.3</td>\n",
       "      <td>109</td>\n",
       "      <td>10.95</td>\n",
       "      <td>9.3</td>\n",
       "      <td>4</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>OH</td>\n",
       "      <td>96</td>\n",
       "      <td>415</td>\n",
       "      <td>347-6812</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>106.6</td>\n",
       "      <td>18.12</td>\n",
       "      <td>284.8</td>\n",
       "      <td>87</td>\n",
       "      <td>24.21</td>\n",
       "      <td>178.9</td>\n",
       "      <td>92</td>\n",
       "      <td>8.05</td>\n",
       "      <td>14.9</td>\n",
       "      <td>7</td>\n",
       "      <td>4.02</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>SC</td>\n",
       "      <td>79</td>\n",
       "      <td>415</td>\n",
       "      <td>348-3830</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>134.7</td>\n",
       "      <td>22.90</td>\n",
       "      <td>189.7</td>\n",
       "      <td>68</td>\n",
       "      <td>16.12</td>\n",
       "      <td>221.4</td>\n",
       "      <td>128</td>\n",
       "      <td>9.96</td>\n",
       "      <td>11.8</td>\n",
       "      <td>5</td>\n",
       "      <td>3.19</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>AZ</td>\n",
       "      <td>192</td>\n",
       "      <td>415</td>\n",
       "      <td>414-4276</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>36</td>\n",
       "      <td>156.2</td>\n",
       "      <td>26.55</td>\n",
       "      <td>215.5</td>\n",
       "      <td>126</td>\n",
       "      <td>18.32</td>\n",
       "      <td>279.1</td>\n",
       "      <td>83</td>\n",
       "      <td>12.56</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>WV</td>\n",
       "      <td>68</td>\n",
       "      <td>415</td>\n",
       "      <td>370-3271</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>231.1</td>\n",
       "      <td>39.29</td>\n",
       "      <td>153.4</td>\n",
       "      <td>55</td>\n",
       "      <td>13.04</td>\n",
       "      <td>191.3</td>\n",
       "      <td>123</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>2.59</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>RI</td>\n",
       "      <td>28</td>\n",
       "      <td>510</td>\n",
       "      <td>328-8230</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>180.8</td>\n",
       "      <td>30.74</td>\n",
       "      <td>288.8</td>\n",
       "      <td>58</td>\n",
       "      <td>24.55</td>\n",
       "      <td>191.9</td>\n",
       "      <td>91</td>\n",
       "      <td>8.64</td>\n",
       "      <td>14.1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>CT</td>\n",
       "      <td>184</td>\n",
       "      <td>510</td>\n",
       "      <td>364-6381</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>213.8</td>\n",
       "      <td>36.35</td>\n",
       "      <td>159.6</td>\n",
       "      <td>84</td>\n",
       "      <td>13.57</td>\n",
       "      <td>139.2</td>\n",
       "      <td>137</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>TN</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>400-4344</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>234.4</td>\n",
       "      <td>39.85</td>\n",
       "      <td>265.9</td>\n",
       "      <td>82</td>\n",
       "      <td>22.60</td>\n",
       "      <td>241.4</td>\n",
       "      <td>77</td>\n",
       "      <td>10.86</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0       KS             128        415  382-4657         no        yes   \n",
       "1       OH             107        415  371-7191         no        yes   \n",
       "2       NJ             137        415  358-1921         no         no   \n",
       "3       OH              84        408  375-9999        yes         no   \n",
       "4       OK              75        415  330-6626        yes         no   \n",
       "5       AL             118        510  391-8027        yes         no   \n",
       "6       MA             121        510  355-9993         no        yes   \n",
       "7       MO             147        415  329-9001        yes         no   \n",
       "8       LA             117        408  335-4719         no         no   \n",
       "9       WV             141        415  330-8173        yes        yes   \n",
       "10      IN              65        415  329-6603         no         no   \n",
       "11      RI              74        415  344-9403         no         no   \n",
       "12      IA             168        408  363-1107         no         no   \n",
       "13      MT              95        510  394-8006         no         no   \n",
       "14      IA              62        415  366-9238         no         no   \n",
       "15      NY             161        415  351-7269         no         no   \n",
       "16      ID              85        408  350-8884         no        yes   \n",
       "17      VT              93        510  386-2923         no         no   \n",
       "18      VA              76        510  356-2992         no        yes   \n",
       "19      TX              73        415  373-2782         no         no   \n",
       "20      FL             147        415  396-5800         no         no   \n",
       "21      CO              77        408  393-7984         no         no   \n",
       "22      AZ             130        415  358-1958         no         no   \n",
       "23      SC             111        415  350-2565         no         no   \n",
       "24      VA             132        510  343-4696         no         no   \n",
       "25      NE             174        415  331-3698         no         no   \n",
       "26      WY              57        408  357-3817         no        yes   \n",
       "27      MT              54        408  418-6412         no         no   \n",
       "28      MO              20        415  353-2630         no         no   \n",
       "29      HI              49        510  410-7789         no         no   \n",
       "...    ...             ...        ...       ...        ...        ...   \n",
       "3303    WI             114        415  373-7308         no        yes   \n",
       "3304    IL              71        510  330-7137        yes         no   \n",
       "3305    IN              58        415  406-8445         no        yes   \n",
       "3306    AL             106        408  404-5283         no        yes   \n",
       "3307    OK             172        408  398-3632         no         no   \n",
       "3308    IA              45        415  399-5763         no         no   \n",
       "3309    VT             100        408  340-9449        yes         no   \n",
       "3310    NY              94        415  363-1123         no         no   \n",
       "3311    LA             128        415  361-2170         no         no   \n",
       "3312    SC             181        408  406-6304         no         no   \n",
       "3313    ID             127        408  392-5090         no         no   \n",
       "3314    MO              89        415  373-7713         no         no   \n",
       "3315    ME             149        415  392-1376         no        yes   \n",
       "3316    MS             103        510  390-6388         no        yes   \n",
       "3317    SD             163        415  379-7290        yes         no   \n",
       "3318    OK              52        415  397-9928         no         no   \n",
       "3319    WY              89        415  378-6924         no         no   \n",
       "3320    GA             122        510  411-5677        yes         no   \n",
       "3321    VT              60        415  400-2738         no         no   \n",
       "3322    MD              62        408  409-1856         no         no   \n",
       "3323    IN             117        415  362-5899         no         no   \n",
       "3324    WV             159        415  377-1164         no         no   \n",
       "3325    OH              78        408  368-8555         no         no   \n",
       "3326    OH              96        415  347-6812         no         no   \n",
       "3327    SC              79        415  348-3830         no         no   \n",
       "3328    AZ             192        415  414-4276         no        yes   \n",
       "3329    WV              68        415  370-3271         no         no   \n",
       "3330    RI              28        510  328-8230         no         no   \n",
       "3331    CT             184        510  364-6381        yes         no   \n",
       "3332    TN              74        415  400-4344         no        yes   \n",
       "\n",
       "      VMail Message  Day Mins  Day Charge  Eve Mins  Eve Calls  Eve Charge  \\\n",
       "0                25     265.1       45.07     197.4         99       16.78   \n",
       "1                26     161.6       27.47     195.5        103       16.62   \n",
       "2                 0     243.4       41.38     121.2        110       10.30   \n",
       "3                 0     299.4       50.90      61.9         88        5.26   \n",
       "4                 0     166.7       28.34     148.3        122       12.61   \n",
       "5                 0     223.4       37.98     220.6        101       18.75   \n",
       "6                24     218.2       37.09     348.5        108       29.62   \n",
       "7                 0     157.0       26.69     103.1         94        8.76   \n",
       "8                 0     184.5       31.37     351.6         80       29.89   \n",
       "9                37     258.6       43.96     222.0        111       18.87   \n",
       "10                0     129.1       21.95     228.5         83       19.42   \n",
       "11                0     187.7       31.91     163.4        148       13.89   \n",
       "12                0     128.8       21.90     104.9         71        8.92   \n",
       "13                0     156.6       26.62     247.6         75       21.05   \n",
       "14                0     120.7       20.52     307.2         76       26.11   \n",
       "15                0     332.9       56.59     317.8         97       27.01   \n",
       "16               27     196.4       33.39     280.9         90       23.88   \n",
       "17                0     190.7       32.42     218.2        111       18.55   \n",
       "18               33     189.7       32.25     212.8         65       18.09   \n",
       "19                0     224.4       38.15     159.5         88       13.56   \n",
       "20                0     155.1       26.37     239.7         93       20.37   \n",
       "21                0      62.4       10.61     169.9        121       14.44   \n",
       "22                0     183.0       31.11      72.9         99        6.20   \n",
       "23                0     110.4       18.77     137.3        102       11.67   \n",
       "24                0      81.1       13.79     245.2         72       20.84   \n",
       "25                0     124.3       21.13     277.1        112       23.55   \n",
       "26               39     213.0       36.21     191.1        112       16.24   \n",
       "27                0     134.3       22.83     155.5        100       13.22   \n",
       "28                0     190.0       32.30     258.2         84       21.95   \n",
       "29                0     119.3       20.28     215.1        109       18.28   \n",
       "...             ...       ...         ...       ...        ...         ...   \n",
       "3303             26     137.1       23.31     155.7        125       13.23   \n",
       "3304              0     186.1       31.64     198.6        140       16.88   \n",
       "3305             22     224.1       38.10     238.8         85       20.30   \n",
       "3306             29      83.6       14.21     203.9        131       17.33   \n",
       "3307              0     203.9       34.66     234.0        123       19.89   \n",
       "3308              0     211.3       35.92     165.7         97       14.08   \n",
       "3309              0     219.4       37.30     225.7        102       19.18   \n",
       "3310              0     190.4       32.37      92.0        107        7.82   \n",
       "3311              0     147.7       25.11     283.3         83       24.08   \n",
       "3312              0     229.9       39.08     144.4         93       12.27   \n",
       "3313              0     102.8       17.48     143.7         95       12.21   \n",
       "3314              0     178.7       30.38     233.7         74       19.86   \n",
       "3315             18     148.5       25.25     114.5        106        9.73   \n",
       "3316             29     164.1       27.90     219.1         96       18.62   \n",
       "3317              0     197.2       33.52     188.5        113       16.02   \n",
       "3318              0     124.9       21.23     300.5        118       25.54   \n",
       "3319              0     115.4       19.62     209.9        115       17.84   \n",
       "3320              0     140.0       23.80     196.4         77       16.69   \n",
       "3321              0     193.9       32.96      85.0        110        7.23   \n",
       "3322              0     321.1       54.59     265.5        122       22.57   \n",
       "3323              0     118.4       20.13     249.3         97       21.19   \n",
       "3324              0     169.8       28.87     197.7        105       16.80   \n",
       "3325              0     193.4       32.88     116.9         88        9.94   \n",
       "3326              0     106.6       18.12     284.8         87       24.21   \n",
       "3327              0     134.7       22.90     189.7         68       16.12   \n",
       "3328             36     156.2       26.55     215.5        126       18.32   \n",
       "3329              0     231.1       39.29     153.4         55       13.04   \n",
       "3330              0     180.8       30.74     288.8         58       24.55   \n",
       "3331              0     213.8       36.35     159.6         84       13.57   \n",
       "3332             25     234.4       39.85     265.9         82       22.60   \n",
       "\n",
       "      Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
       "0          244.7           91         11.01       10.0           3   \n",
       "1          254.4          103         11.45       13.7           3   \n",
       "2          162.6          104          7.32       12.2           5   \n",
       "3          196.9           89          8.86        6.6           7   \n",
       "4          186.9          121          8.41       10.1           3   \n",
       "5          203.9          118          9.18        6.3           6   \n",
       "6          212.6          118          9.57        7.5           7   \n",
       "7          211.8           96          9.53        7.1           6   \n",
       "8          215.8           90          9.71        8.7           4   \n",
       "9          326.4           97         14.69       11.2           5   \n",
       "10         208.8          111          9.40       12.7           6   \n",
       "11         196.0           94          8.82        9.1           5   \n",
       "12         141.1          128          6.35       11.2           2   \n",
       "13         192.3          115          8.65       12.3           5   \n",
       "14         203.0           99          9.14       13.1           6   \n",
       "15         160.6          128          7.23        5.4           9   \n",
       "16          89.3           75          4.02       13.8           4   \n",
       "17         129.6          121          5.83        8.1           3   \n",
       "18         165.7          108          7.46       10.0           5   \n",
       "19         192.8           74          8.68       13.0           2   \n",
       "20         208.8          133          9.40       10.6           4   \n",
       "21         209.6           64          9.43        5.7           6   \n",
       "22         181.8           78          8.18        9.5          19   \n",
       "23         189.6          105          8.53        7.7           6   \n",
       "24         237.0          115         10.67       10.3           2   \n",
       "25         250.7          115         11.28       15.5           5   \n",
       "26         182.7          115          8.22        9.5           3   \n",
       "27         102.1           68          4.59       14.7           4   \n",
       "28         181.5          102          8.17        6.3           6   \n",
       "29         178.7           90          8.04       11.1           1   \n",
       "...          ...          ...           ...        ...         ...   \n",
       "3303       247.6           94         11.14       11.5           7   \n",
       "3304       206.5           80          9.29       13.8           5   \n",
       "3305       174.2           86          7.84       11.5           7   \n",
       "3306       229.5           73         10.33        8.1           3   \n",
       "3307       160.7           65          7.23       17.8           4   \n",
       "3308       265.9           72         11.97       13.3           6   \n",
       "3309       255.3           95         11.49       12.0           4   \n",
       "3310       224.8          108         10.12       13.6          17   \n",
       "3311       188.3          124          8.47        6.9           5   \n",
       "3312       262.4          110         11.81       14.2           4   \n",
       "3313       191.4           97          8.61       10.0           5   \n",
       "3314       131.9          120          5.94        9.1           4   \n",
       "3315       178.3           98          8.02        6.5           4   \n",
       "3316       220.3          108          9.91       12.3           9   \n",
       "3317       211.1           94          9.50        7.8           8   \n",
       "3318       192.5          106          8.66       11.6           4   \n",
       "3319       280.9          112         12.64       15.9           6   \n",
       "3320       120.1          133          5.40        9.7           4   \n",
       "3321       210.1          134          9.45       13.2           8   \n",
       "3322       180.5           72          8.12       11.5           2   \n",
       "3323       227.0           56         10.22       13.6           3   \n",
       "3324       193.7           82          8.72       11.6           4   \n",
       "3325       243.3          109         10.95        9.3           4   \n",
       "3326       178.9           92          8.05       14.9           7   \n",
       "3327       221.4          128          9.96       11.8           5   \n",
       "3328       279.1           83         12.56        9.9           6   \n",
       "3329       191.3          123          8.61        9.6           4   \n",
       "3330       191.9           91          8.64       14.1           6   \n",
       "3331       139.2          137          6.26        5.0          10   \n",
       "3332       241.4           77         10.86       13.7           4   \n",
       "\n",
       "      Intl Charge  CustServ Calls  Churn?  \n",
       "0            2.70               1  False.  \n",
       "1            3.70               1  False.  \n",
       "2            3.29               0  False.  \n",
       "3            1.78               2  False.  \n",
       "4            2.73               3  False.  \n",
       "5            1.70               0  False.  \n",
       "6            2.03               3  False.  \n",
       "7            1.92               0  False.  \n",
       "8            2.35               1  False.  \n",
       "9            3.02               0  False.  \n",
       "10           3.43               4   True.  \n",
       "11           2.46               0  False.  \n",
       "12           3.02               1  False.  \n",
       "13           3.32               3  False.  \n",
       "14           3.54               4  False.  \n",
       "15           1.46               4   True.  \n",
       "16           3.73               1  False.  \n",
       "17           2.19               3  False.  \n",
       "18           2.70               1  False.  \n",
       "19           3.51               1  False.  \n",
       "20           2.86               0  False.  \n",
       "21           1.54               5   True.  \n",
       "22           2.57               0  False.  \n",
       "23           2.08               2  False.  \n",
       "24           2.78               0  False.  \n",
       "25           4.19               3  False.  \n",
       "26           2.57               0  False.  \n",
       "27           3.97               3  False.  \n",
       "28           1.70               0  False.  \n",
       "29           3.00               1  False.  \n",
       "...           ...             ...     ...  \n",
       "3303         3.11               2  False.  \n",
       "3304         3.73               4   True.  \n",
       "3305         3.11               2  False.  \n",
       "3306         2.19               1  False.  \n",
       "3307         4.81               4  False.  \n",
       "3308         3.59               1  False.  \n",
       "3309         3.24               4  False.  \n",
       "3310         3.67               2  False.  \n",
       "3311         1.86               2  False.  \n",
       "3312         3.83               2  False.  \n",
       "3313         2.70               1  False.  \n",
       "3314         2.46               1  False.  \n",
       "3315         1.76               0  False.  \n",
       "3316         3.32               0  False.  \n",
       "3317         2.11               1  False.  \n",
       "3318         3.13               2  False.  \n",
       "3319         4.29               3  False.  \n",
       "3320         2.62               4   True.  \n",
       "3321         3.56               3  False.  \n",
       "3322         3.11               4   True.  \n",
       "3323         3.67               5   True.  \n",
       "3324         3.13               1  False.  \n",
       "3325         2.51               2  False.  \n",
       "3326         4.02               1  False.  \n",
       "3327         3.19               2  False.  \n",
       "3328         2.67               2  False.  \n",
       "3329         2.59               3  False.  \n",
       "3330         3.81               2  False.  \n",
       "3331         1.35               2  False.  \n",
       "3332         3.70               0  False.  \n",
       "\n",
       "[3333 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Obtener las columnas deseadas, cuando las que no se quieren son pocas\n",
    "columnas_no_deseadas = ['Account Leght', 'Vmail Message', 'Day Calls']\n",
    "all_columnas = data.columns.values.tolist()\n",
    "\n",
    "#Obtener todas las columnas que se quieren\n",
    "sublist = [x for x in all_columnas if x not in columnas_no_deseadas]\n",
    "\n",
    "subset2 = data[sublist]\n",
    "subset2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>VMail Message</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>Eve Mins</th>\n",
       "      <th>State</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>2.70</td>\n",
       "      <td>16.78</td>\n",
       "      <td>11.01</td>\n",
       "      <td>45.07</td>\n",
       "      <td>yes</td>\n",
       "      <td>91</td>\n",
       "      <td>10.0</td>\n",
       "      <td>99</td>\n",
       "      <td>244.7</td>\n",
       "      <td>no</td>\n",
       "      <td>197.4</td>\n",
       "      <td>KS</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>265.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>3.70</td>\n",
       "      <td>16.62</td>\n",
       "      <td>11.45</td>\n",
       "      <td>27.47</td>\n",
       "      <td>yes</td>\n",
       "      <td>103</td>\n",
       "      <td>13.7</td>\n",
       "      <td>103</td>\n",
       "      <td>254.4</td>\n",
       "      <td>no</td>\n",
       "      <td>195.5</td>\n",
       "      <td>OH</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>161.6</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>3.29</td>\n",
       "      <td>10.30</td>\n",
       "      <td>7.32</td>\n",
       "      <td>41.38</td>\n",
       "      <td>no</td>\n",
       "      <td>104</td>\n",
       "      <td>12.2</td>\n",
       "      <td>110</td>\n",
       "      <td>162.6</td>\n",
       "      <td>no</td>\n",
       "      <td>121.2</td>\n",
       "      <td>NJ</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>1.78</td>\n",
       "      <td>5.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>50.90</td>\n",
       "      <td>no</td>\n",
       "      <td>89</td>\n",
       "      <td>6.6</td>\n",
       "      <td>88</td>\n",
       "      <td>196.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>61.9</td>\n",
       "      <td>OH</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>299.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>2.73</td>\n",
       "      <td>12.61</td>\n",
       "      <td>8.41</td>\n",
       "      <td>28.34</td>\n",
       "      <td>no</td>\n",
       "      <td>121</td>\n",
       "      <td>10.1</td>\n",
       "      <td>122</td>\n",
       "      <td>186.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>148.3</td>\n",
       "      <td>OK</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>166.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.75</td>\n",
       "      <td>9.18</td>\n",
       "      <td>37.98</td>\n",
       "      <td>no</td>\n",
       "      <td>118</td>\n",
       "      <td>6.3</td>\n",
       "      <td>101</td>\n",
       "      <td>203.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>220.6</td>\n",
       "      <td>AL</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>2.03</td>\n",
       "      <td>29.62</td>\n",
       "      <td>9.57</td>\n",
       "      <td>37.09</td>\n",
       "      <td>yes</td>\n",
       "      <td>118</td>\n",
       "      <td>7.5</td>\n",
       "      <td>108</td>\n",
       "      <td>212.6</td>\n",
       "      <td>no</td>\n",
       "      <td>348.5</td>\n",
       "      <td>MA</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>218.2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>1.92</td>\n",
       "      <td>8.76</td>\n",
       "      <td>9.53</td>\n",
       "      <td>26.69</td>\n",
       "      <td>no</td>\n",
       "      <td>96</td>\n",
       "      <td>7.1</td>\n",
       "      <td>94</td>\n",
       "      <td>211.8</td>\n",
       "      <td>yes</td>\n",
       "      <td>103.1</td>\n",
       "      <td>MO</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>408</td>\n",
       "      <td>335-4719</td>\n",
       "      <td>2.35</td>\n",
       "      <td>29.89</td>\n",
       "      <td>9.71</td>\n",
       "      <td>31.37</td>\n",
       "      <td>no</td>\n",
       "      <td>90</td>\n",
       "      <td>8.7</td>\n",
       "      <td>80</td>\n",
       "      <td>215.8</td>\n",
       "      <td>no</td>\n",
       "      <td>351.6</td>\n",
       "      <td>LA</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>184.5</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>330-8173</td>\n",
       "      <td>3.02</td>\n",
       "      <td>18.87</td>\n",
       "      <td>14.69</td>\n",
       "      <td>43.96</td>\n",
       "      <td>yes</td>\n",
       "      <td>97</td>\n",
       "      <td>11.2</td>\n",
       "      <td>111</td>\n",
       "      <td>326.4</td>\n",
       "      <td>yes</td>\n",
       "      <td>222.0</td>\n",
       "      <td>WV</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>258.6</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>415</td>\n",
       "      <td>329-6603</td>\n",
       "      <td>3.43</td>\n",
       "      <td>19.42</td>\n",
       "      <td>9.40</td>\n",
       "      <td>21.95</td>\n",
       "      <td>no</td>\n",
       "      <td>111</td>\n",
       "      <td>12.7</td>\n",
       "      <td>83</td>\n",
       "      <td>208.8</td>\n",
       "      <td>no</td>\n",
       "      <td>228.5</td>\n",
       "      <td>IN</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>129.1</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>344-9403</td>\n",
       "      <td>2.46</td>\n",
       "      <td>13.89</td>\n",
       "      <td>8.82</td>\n",
       "      <td>31.91</td>\n",
       "      <td>no</td>\n",
       "      <td>94</td>\n",
       "      <td>9.1</td>\n",
       "      <td>148</td>\n",
       "      <td>196.0</td>\n",
       "      <td>no</td>\n",
       "      <td>163.4</td>\n",
       "      <td>RI</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>187.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>408</td>\n",
       "      <td>363-1107</td>\n",
       "      <td>3.02</td>\n",
       "      <td>8.92</td>\n",
       "      <td>6.35</td>\n",
       "      <td>21.90</td>\n",
       "      <td>no</td>\n",
       "      <td>128</td>\n",
       "      <td>11.2</td>\n",
       "      <td>71</td>\n",
       "      <td>141.1</td>\n",
       "      <td>no</td>\n",
       "      <td>104.9</td>\n",
       "      <td>IA</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>128.8</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>510</td>\n",
       "      <td>394-8006</td>\n",
       "      <td>3.32</td>\n",
       "      <td>21.05</td>\n",
       "      <td>8.65</td>\n",
       "      <td>26.62</td>\n",
       "      <td>no</td>\n",
       "      <td>115</td>\n",
       "      <td>12.3</td>\n",
       "      <td>75</td>\n",
       "      <td>192.3</td>\n",
       "      <td>no</td>\n",
       "      <td>247.6</td>\n",
       "      <td>MT</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>156.6</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>415</td>\n",
       "      <td>366-9238</td>\n",
       "      <td>3.54</td>\n",
       "      <td>26.11</td>\n",
       "      <td>9.14</td>\n",
       "      <td>20.52</td>\n",
       "      <td>no</td>\n",
       "      <td>99</td>\n",
       "      <td>13.1</td>\n",
       "      <td>76</td>\n",
       "      <td>203.0</td>\n",
       "      <td>no</td>\n",
       "      <td>307.2</td>\n",
       "      <td>IA</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>120.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>415</td>\n",
       "      <td>351-7269</td>\n",
       "      <td>1.46</td>\n",
       "      <td>27.01</td>\n",
       "      <td>7.23</td>\n",
       "      <td>56.59</td>\n",
       "      <td>no</td>\n",
       "      <td>128</td>\n",
       "      <td>5.4</td>\n",
       "      <td>97</td>\n",
       "      <td>160.6</td>\n",
       "      <td>no</td>\n",
       "      <td>317.8</td>\n",
       "      <td>NY</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>332.9</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>27</td>\n",
       "      <td>85</td>\n",
       "      <td>408</td>\n",
       "      <td>350-8884</td>\n",
       "      <td>3.73</td>\n",
       "      <td>23.88</td>\n",
       "      <td>4.02</td>\n",
       "      <td>33.39</td>\n",
       "      <td>yes</td>\n",
       "      <td>75</td>\n",
       "      <td>13.8</td>\n",
       "      <td>90</td>\n",
       "      <td>89.3</td>\n",
       "      <td>no</td>\n",
       "      <td>280.9</td>\n",
       "      <td>ID</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>196.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>510</td>\n",
       "      <td>386-2923</td>\n",
       "      <td>2.19</td>\n",
       "      <td>18.55</td>\n",
       "      <td>5.83</td>\n",
       "      <td>32.42</td>\n",
       "      <td>no</td>\n",
       "      <td>121</td>\n",
       "      <td>8.1</td>\n",
       "      <td>111</td>\n",
       "      <td>129.6</td>\n",
       "      <td>no</td>\n",
       "      <td>218.2</td>\n",
       "      <td>VT</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>190.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33</td>\n",
       "      <td>76</td>\n",
       "      <td>510</td>\n",
       "      <td>356-2992</td>\n",
       "      <td>2.70</td>\n",
       "      <td>18.09</td>\n",
       "      <td>7.46</td>\n",
       "      <td>32.25</td>\n",
       "      <td>yes</td>\n",
       "      <td>108</td>\n",
       "      <td>10.0</td>\n",
       "      <td>65</td>\n",
       "      <td>165.7</td>\n",
       "      <td>no</td>\n",
       "      <td>212.8</td>\n",
       "      <td>VA</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>189.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>415</td>\n",
       "      <td>373-2782</td>\n",
       "      <td>3.51</td>\n",
       "      <td>13.56</td>\n",
       "      <td>8.68</td>\n",
       "      <td>38.15</td>\n",
       "      <td>no</td>\n",
       "      <td>74</td>\n",
       "      <td>13.0</td>\n",
       "      <td>88</td>\n",
       "      <td>192.8</td>\n",
       "      <td>no</td>\n",
       "      <td>159.5</td>\n",
       "      <td>TX</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>224.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>396-5800</td>\n",
       "      <td>2.86</td>\n",
       "      <td>20.37</td>\n",
       "      <td>9.40</td>\n",
       "      <td>26.37</td>\n",
       "      <td>no</td>\n",
       "      <td>133</td>\n",
       "      <td>10.6</td>\n",
       "      <td>93</td>\n",
       "      <td>208.8</td>\n",
       "      <td>no</td>\n",
       "      <td>239.7</td>\n",
       "      <td>FL</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>155.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>408</td>\n",
       "      <td>393-7984</td>\n",
       "      <td>1.54</td>\n",
       "      <td>14.44</td>\n",
       "      <td>9.43</td>\n",
       "      <td>10.61</td>\n",
       "      <td>no</td>\n",
       "      <td>64</td>\n",
       "      <td>5.7</td>\n",
       "      <td>121</td>\n",
       "      <td>209.6</td>\n",
       "      <td>no</td>\n",
       "      <td>169.9</td>\n",
       "      <td>CO</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>62.4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1958</td>\n",
       "      <td>2.57</td>\n",
       "      <td>6.20</td>\n",
       "      <td>8.18</td>\n",
       "      <td>31.11</td>\n",
       "      <td>no</td>\n",
       "      <td>78</td>\n",
       "      <td>9.5</td>\n",
       "      <td>99</td>\n",
       "      <td>181.8</td>\n",
       "      <td>no</td>\n",
       "      <td>72.9</td>\n",
       "      <td>AZ</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>415</td>\n",
       "      <td>350-2565</td>\n",
       "      <td>2.08</td>\n",
       "      <td>11.67</td>\n",
       "      <td>8.53</td>\n",
       "      <td>18.77</td>\n",
       "      <td>no</td>\n",
       "      <td>105</td>\n",
       "      <td>7.7</td>\n",
       "      <td>102</td>\n",
       "      <td>189.6</td>\n",
       "      <td>no</td>\n",
       "      <td>137.3</td>\n",
       "      <td>SC</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>110.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>510</td>\n",
       "      <td>343-4696</td>\n",
       "      <td>2.78</td>\n",
       "      <td>20.84</td>\n",
       "      <td>10.67</td>\n",
       "      <td>13.79</td>\n",
       "      <td>no</td>\n",
       "      <td>115</td>\n",
       "      <td>10.3</td>\n",
       "      <td>72</td>\n",
       "      <td>237.0</td>\n",
       "      <td>no</td>\n",
       "      <td>245.2</td>\n",
       "      <td>VA</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>81.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>415</td>\n",
       "      <td>331-3698</td>\n",
       "      <td>4.19</td>\n",
       "      <td>23.55</td>\n",
       "      <td>11.28</td>\n",
       "      <td>21.13</td>\n",
       "      <td>no</td>\n",
       "      <td>115</td>\n",
       "      <td>15.5</td>\n",
       "      <td>112</td>\n",
       "      <td>250.7</td>\n",
       "      <td>no</td>\n",
       "      <td>277.1</td>\n",
       "      <td>NE</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>124.3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39</td>\n",
       "      <td>57</td>\n",
       "      <td>408</td>\n",
       "      <td>357-3817</td>\n",
       "      <td>2.57</td>\n",
       "      <td>16.24</td>\n",
       "      <td>8.22</td>\n",
       "      <td>36.21</td>\n",
       "      <td>yes</td>\n",
       "      <td>115</td>\n",
       "      <td>9.5</td>\n",
       "      <td>112</td>\n",
       "      <td>182.7</td>\n",
       "      <td>no</td>\n",
       "      <td>191.1</td>\n",
       "      <td>WY</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>408</td>\n",
       "      <td>418-6412</td>\n",
       "      <td>3.97</td>\n",
       "      <td>13.22</td>\n",
       "      <td>4.59</td>\n",
       "      <td>22.83</td>\n",
       "      <td>no</td>\n",
       "      <td>68</td>\n",
       "      <td>14.7</td>\n",
       "      <td>100</td>\n",
       "      <td>102.1</td>\n",
       "      <td>no</td>\n",
       "      <td>155.5</td>\n",
       "      <td>MT</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>134.3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>415</td>\n",
       "      <td>353-2630</td>\n",
       "      <td>1.70</td>\n",
       "      <td>21.95</td>\n",
       "      <td>8.17</td>\n",
       "      <td>32.30</td>\n",
       "      <td>no</td>\n",
       "      <td>102</td>\n",
       "      <td>6.3</td>\n",
       "      <td>84</td>\n",
       "      <td>181.5</td>\n",
       "      <td>no</td>\n",
       "      <td>258.2</td>\n",
       "      <td>MO</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>510</td>\n",
       "      <td>410-7789</td>\n",
       "      <td>3.00</td>\n",
       "      <td>18.28</td>\n",
       "      <td>8.04</td>\n",
       "      <td>20.28</td>\n",
       "      <td>no</td>\n",
       "      <td>90</td>\n",
       "      <td>11.1</td>\n",
       "      <td>109</td>\n",
       "      <td>178.7</td>\n",
       "      <td>no</td>\n",
       "      <td>215.1</td>\n",
       "      <td>HI</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>119.3</td>\n",
       "      <td>False.</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>26</td>\n",
       "      <td>114</td>\n",
       "      <td>415</td>\n",
       "      <td>373-7308</td>\n",
       "      <td>3.11</td>\n",
       "      <td>13.23</td>\n",
       "      <td>11.14</td>\n",
       "      <td>23.31</td>\n",
       "      <td>yes</td>\n",
       "      <td>94</td>\n",
       "      <td>11.5</td>\n",
       "      <td>125</td>\n",
       "      <td>247.6</td>\n",
       "      <td>no</td>\n",
       "      <td>155.7</td>\n",
       "      <td>WI</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>137.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>510</td>\n",
       "      <td>330-7137</td>\n",
       "      <td>3.73</td>\n",
       "      <td>16.88</td>\n",
       "      <td>9.29</td>\n",
       "      <td>31.64</td>\n",
       "      <td>no</td>\n",
       "      <td>80</td>\n",
       "      <td>13.8</td>\n",
       "      <td>140</td>\n",
       "      <td>206.5</td>\n",
       "      <td>yes</td>\n",
       "      <td>198.6</td>\n",
       "      <td>IL</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>186.1</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3305</th>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "      <td>415</td>\n",
       "      <td>406-8445</td>\n",
       "      <td>3.11</td>\n",
       "      <td>20.30</td>\n",
       "      <td>7.84</td>\n",
       "      <td>38.10</td>\n",
       "      <td>yes</td>\n",
       "      <td>86</td>\n",
       "      <td>11.5</td>\n",
       "      <td>85</td>\n",
       "      <td>174.2</td>\n",
       "      <td>no</td>\n",
       "      <td>238.8</td>\n",
       "      <td>IN</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>224.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>29</td>\n",
       "      <td>106</td>\n",
       "      <td>408</td>\n",
       "      <td>404-5283</td>\n",
       "      <td>2.19</td>\n",
       "      <td>17.33</td>\n",
       "      <td>10.33</td>\n",
       "      <td>14.21</td>\n",
       "      <td>yes</td>\n",
       "      <td>73</td>\n",
       "      <td>8.1</td>\n",
       "      <td>131</td>\n",
       "      <td>229.5</td>\n",
       "      <td>no</td>\n",
       "      <td>203.9</td>\n",
       "      <td>AL</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>408</td>\n",
       "      <td>398-3632</td>\n",
       "      <td>4.81</td>\n",
       "      <td>19.89</td>\n",
       "      <td>7.23</td>\n",
       "      <td>34.66</td>\n",
       "      <td>no</td>\n",
       "      <td>65</td>\n",
       "      <td>17.8</td>\n",
       "      <td>123</td>\n",
       "      <td>160.7</td>\n",
       "      <td>no</td>\n",
       "      <td>234.0</td>\n",
       "      <td>OK</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>203.9</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>415</td>\n",
       "      <td>399-5763</td>\n",
       "      <td>3.59</td>\n",
       "      <td>14.08</td>\n",
       "      <td>11.97</td>\n",
       "      <td>35.92</td>\n",
       "      <td>no</td>\n",
       "      <td>72</td>\n",
       "      <td>13.3</td>\n",
       "      <td>97</td>\n",
       "      <td>265.9</td>\n",
       "      <td>no</td>\n",
       "      <td>165.7</td>\n",
       "      <td>IA</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>211.3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>408</td>\n",
       "      <td>340-9449</td>\n",
       "      <td>3.24</td>\n",
       "      <td>19.18</td>\n",
       "      <td>11.49</td>\n",
       "      <td>37.30</td>\n",
       "      <td>no</td>\n",
       "      <td>95</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102</td>\n",
       "      <td>255.3</td>\n",
       "      <td>yes</td>\n",
       "      <td>225.7</td>\n",
       "      <td>VT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>219.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>415</td>\n",
       "      <td>363-1123</td>\n",
       "      <td>3.67</td>\n",
       "      <td>7.82</td>\n",
       "      <td>10.12</td>\n",
       "      <td>32.37</td>\n",
       "      <td>no</td>\n",
       "      <td>108</td>\n",
       "      <td>13.6</td>\n",
       "      <td>107</td>\n",
       "      <td>224.8</td>\n",
       "      <td>no</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>190.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>361-2170</td>\n",
       "      <td>1.86</td>\n",
       "      <td>24.08</td>\n",
       "      <td>8.47</td>\n",
       "      <td>25.11</td>\n",
       "      <td>no</td>\n",
       "      <td>124</td>\n",
       "      <td>6.9</td>\n",
       "      <td>83</td>\n",
       "      <td>188.3</td>\n",
       "      <td>no</td>\n",
       "      <td>283.3</td>\n",
       "      <td>LA</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>147.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>408</td>\n",
       "      <td>406-6304</td>\n",
       "      <td>3.83</td>\n",
       "      <td>12.27</td>\n",
       "      <td>11.81</td>\n",
       "      <td>39.08</td>\n",
       "      <td>no</td>\n",
       "      <td>110</td>\n",
       "      <td>14.2</td>\n",
       "      <td>93</td>\n",
       "      <td>262.4</td>\n",
       "      <td>no</td>\n",
       "      <td>144.4</td>\n",
       "      <td>SC</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>229.9</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>408</td>\n",
       "      <td>392-5090</td>\n",
       "      <td>2.70</td>\n",
       "      <td>12.21</td>\n",
       "      <td>8.61</td>\n",
       "      <td>17.48</td>\n",
       "      <td>no</td>\n",
       "      <td>97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>95</td>\n",
       "      <td>191.4</td>\n",
       "      <td>no</td>\n",
       "      <td>143.7</td>\n",
       "      <td>ID</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>102.8</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3314</th>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>415</td>\n",
       "      <td>373-7713</td>\n",
       "      <td>2.46</td>\n",
       "      <td>19.86</td>\n",
       "      <td>5.94</td>\n",
       "      <td>30.38</td>\n",
       "      <td>no</td>\n",
       "      <td>120</td>\n",
       "      <td>9.1</td>\n",
       "      <td>74</td>\n",
       "      <td>131.9</td>\n",
       "      <td>no</td>\n",
       "      <td>233.7</td>\n",
       "      <td>MO</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>178.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>18</td>\n",
       "      <td>149</td>\n",
       "      <td>415</td>\n",
       "      <td>392-1376</td>\n",
       "      <td>1.76</td>\n",
       "      <td>9.73</td>\n",
       "      <td>8.02</td>\n",
       "      <td>25.25</td>\n",
       "      <td>yes</td>\n",
       "      <td>98</td>\n",
       "      <td>6.5</td>\n",
       "      <td>106</td>\n",
       "      <td>178.3</td>\n",
       "      <td>no</td>\n",
       "      <td>114.5</td>\n",
       "      <td>ME</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>148.5</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>29</td>\n",
       "      <td>103</td>\n",
       "      <td>510</td>\n",
       "      <td>390-6388</td>\n",
       "      <td>3.32</td>\n",
       "      <td>18.62</td>\n",
       "      <td>9.91</td>\n",
       "      <td>27.90</td>\n",
       "      <td>yes</td>\n",
       "      <td>108</td>\n",
       "      <td>12.3</td>\n",
       "      <td>96</td>\n",
       "      <td>220.3</td>\n",
       "      <td>no</td>\n",
       "      <td>219.1</td>\n",
       "      <td>MS</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>164.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>415</td>\n",
       "      <td>379-7290</td>\n",
       "      <td>2.11</td>\n",
       "      <td>16.02</td>\n",
       "      <td>9.50</td>\n",
       "      <td>33.52</td>\n",
       "      <td>no</td>\n",
       "      <td>94</td>\n",
       "      <td>7.8</td>\n",
       "      <td>113</td>\n",
       "      <td>211.1</td>\n",
       "      <td>yes</td>\n",
       "      <td>188.5</td>\n",
       "      <td>SD</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>197.2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>415</td>\n",
       "      <td>397-9928</td>\n",
       "      <td>3.13</td>\n",
       "      <td>25.54</td>\n",
       "      <td>8.66</td>\n",
       "      <td>21.23</td>\n",
       "      <td>no</td>\n",
       "      <td>106</td>\n",
       "      <td>11.6</td>\n",
       "      <td>118</td>\n",
       "      <td>192.5</td>\n",
       "      <td>no</td>\n",
       "      <td>300.5</td>\n",
       "      <td>OK</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>124.9</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>415</td>\n",
       "      <td>378-6924</td>\n",
       "      <td>4.29</td>\n",
       "      <td>17.84</td>\n",
       "      <td>12.64</td>\n",
       "      <td>19.62</td>\n",
       "      <td>no</td>\n",
       "      <td>112</td>\n",
       "      <td>15.9</td>\n",
       "      <td>115</td>\n",
       "      <td>280.9</td>\n",
       "      <td>no</td>\n",
       "      <td>209.9</td>\n",
       "      <td>WY</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>115.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>510</td>\n",
       "      <td>411-5677</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.69</td>\n",
       "      <td>5.40</td>\n",
       "      <td>23.80</td>\n",
       "      <td>no</td>\n",
       "      <td>133</td>\n",
       "      <td>9.7</td>\n",
       "      <td>77</td>\n",
       "      <td>120.1</td>\n",
       "      <td>yes</td>\n",
       "      <td>196.4</td>\n",
       "      <td>GA</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>415</td>\n",
       "      <td>400-2738</td>\n",
       "      <td>3.56</td>\n",
       "      <td>7.23</td>\n",
       "      <td>9.45</td>\n",
       "      <td>32.96</td>\n",
       "      <td>no</td>\n",
       "      <td>134</td>\n",
       "      <td>13.2</td>\n",
       "      <td>110</td>\n",
       "      <td>210.1</td>\n",
       "      <td>no</td>\n",
       "      <td>85.0</td>\n",
       "      <td>VT</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>193.9</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>408</td>\n",
       "      <td>409-1856</td>\n",
       "      <td>3.11</td>\n",
       "      <td>22.57</td>\n",
       "      <td>8.12</td>\n",
       "      <td>54.59</td>\n",
       "      <td>no</td>\n",
       "      <td>72</td>\n",
       "      <td>11.5</td>\n",
       "      <td>122</td>\n",
       "      <td>180.5</td>\n",
       "      <td>no</td>\n",
       "      <td>265.5</td>\n",
       "      <td>MD</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>321.1</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>415</td>\n",
       "      <td>362-5899</td>\n",
       "      <td>3.67</td>\n",
       "      <td>21.19</td>\n",
       "      <td>10.22</td>\n",
       "      <td>20.13</td>\n",
       "      <td>no</td>\n",
       "      <td>56</td>\n",
       "      <td>13.6</td>\n",
       "      <td>97</td>\n",
       "      <td>227.0</td>\n",
       "      <td>no</td>\n",
       "      <td>249.3</td>\n",
       "      <td>IN</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>118.4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>415</td>\n",
       "      <td>377-1164</td>\n",
       "      <td>3.13</td>\n",
       "      <td>16.80</td>\n",
       "      <td>8.72</td>\n",
       "      <td>28.87</td>\n",
       "      <td>no</td>\n",
       "      <td>82</td>\n",
       "      <td>11.6</td>\n",
       "      <td>105</td>\n",
       "      <td>193.7</td>\n",
       "      <td>no</td>\n",
       "      <td>197.7</td>\n",
       "      <td>WV</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>169.8</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>408</td>\n",
       "      <td>368-8555</td>\n",
       "      <td>2.51</td>\n",
       "      <td>9.94</td>\n",
       "      <td>10.95</td>\n",
       "      <td>32.88</td>\n",
       "      <td>no</td>\n",
       "      <td>109</td>\n",
       "      <td>9.3</td>\n",
       "      <td>88</td>\n",
       "      <td>243.3</td>\n",
       "      <td>no</td>\n",
       "      <td>116.9</td>\n",
       "      <td>OH</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>193.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>415</td>\n",
       "      <td>347-6812</td>\n",
       "      <td>4.02</td>\n",
       "      <td>24.21</td>\n",
       "      <td>8.05</td>\n",
       "      <td>18.12</td>\n",
       "      <td>no</td>\n",
       "      <td>92</td>\n",
       "      <td>14.9</td>\n",
       "      <td>87</td>\n",
       "      <td>178.9</td>\n",
       "      <td>no</td>\n",
       "      <td>284.8</td>\n",
       "      <td>OH</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>106.6</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>415</td>\n",
       "      <td>348-3830</td>\n",
       "      <td>3.19</td>\n",
       "      <td>16.12</td>\n",
       "      <td>9.96</td>\n",
       "      <td>22.90</td>\n",
       "      <td>no</td>\n",
       "      <td>128</td>\n",
       "      <td>11.8</td>\n",
       "      <td>68</td>\n",
       "      <td>221.4</td>\n",
       "      <td>no</td>\n",
       "      <td>189.7</td>\n",
       "      <td>SC</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>134.7</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>36</td>\n",
       "      <td>192</td>\n",
       "      <td>415</td>\n",
       "      <td>414-4276</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.32</td>\n",
       "      <td>12.56</td>\n",
       "      <td>26.55</td>\n",
       "      <td>yes</td>\n",
       "      <td>83</td>\n",
       "      <td>9.9</td>\n",
       "      <td>126</td>\n",
       "      <td>279.1</td>\n",
       "      <td>no</td>\n",
       "      <td>215.5</td>\n",
       "      <td>AZ</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>156.2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>415</td>\n",
       "      <td>370-3271</td>\n",
       "      <td>2.59</td>\n",
       "      <td>13.04</td>\n",
       "      <td>8.61</td>\n",
       "      <td>39.29</td>\n",
       "      <td>no</td>\n",
       "      <td>123</td>\n",
       "      <td>9.6</td>\n",
       "      <td>55</td>\n",
       "      <td>191.3</td>\n",
       "      <td>no</td>\n",
       "      <td>153.4</td>\n",
       "      <td>WV</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>231.1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>510</td>\n",
       "      <td>328-8230</td>\n",
       "      <td>3.81</td>\n",
       "      <td>24.55</td>\n",
       "      <td>8.64</td>\n",
       "      <td>30.74</td>\n",
       "      <td>no</td>\n",
       "      <td>91</td>\n",
       "      <td>14.1</td>\n",
       "      <td>58</td>\n",
       "      <td>191.9</td>\n",
       "      <td>no</td>\n",
       "      <td>288.8</td>\n",
       "      <td>RI</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>180.8</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>510</td>\n",
       "      <td>364-6381</td>\n",
       "      <td>1.35</td>\n",
       "      <td>13.57</td>\n",
       "      <td>6.26</td>\n",
       "      <td>36.35</td>\n",
       "      <td>no</td>\n",
       "      <td>137</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84</td>\n",
       "      <td>139.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>159.6</td>\n",
       "      <td>CT</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>213.8</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>25</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>400-4344</td>\n",
       "      <td>3.70</td>\n",
       "      <td>22.60</td>\n",
       "      <td>10.86</td>\n",
       "      <td>39.85</td>\n",
       "      <td>yes</td>\n",
       "      <td>77</td>\n",
       "      <td>13.7</td>\n",
       "      <td>82</td>\n",
       "      <td>241.4</td>\n",
       "      <td>no</td>\n",
       "      <td>265.9</td>\n",
       "      <td>TN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>234.4</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      VMail Message  Account Length  Area Code     Phone  Intl Charge  \\\n",
       "0                25             128        415  382-4657         2.70   \n",
       "1                26             107        415  371-7191         3.70   \n",
       "2                 0             137        415  358-1921         3.29   \n",
       "3                 0              84        408  375-9999         1.78   \n",
       "4                 0              75        415  330-6626         2.73   \n",
       "5                 0             118        510  391-8027         1.70   \n",
       "6                24             121        510  355-9993         2.03   \n",
       "7                 0             147        415  329-9001         1.92   \n",
       "8                 0             117        408  335-4719         2.35   \n",
       "9                37             141        415  330-8173         3.02   \n",
       "10                0              65        415  329-6603         3.43   \n",
       "11                0              74        415  344-9403         2.46   \n",
       "12                0             168        408  363-1107         3.02   \n",
       "13                0              95        510  394-8006         3.32   \n",
       "14                0              62        415  366-9238         3.54   \n",
       "15                0             161        415  351-7269         1.46   \n",
       "16               27              85        408  350-8884         3.73   \n",
       "17                0              93        510  386-2923         2.19   \n",
       "18               33              76        510  356-2992         2.70   \n",
       "19                0              73        415  373-2782         3.51   \n",
       "20                0             147        415  396-5800         2.86   \n",
       "21                0              77        408  393-7984         1.54   \n",
       "22                0             130        415  358-1958         2.57   \n",
       "23                0             111        415  350-2565         2.08   \n",
       "24                0             132        510  343-4696         2.78   \n",
       "25                0             174        415  331-3698         4.19   \n",
       "26               39              57        408  357-3817         2.57   \n",
       "27                0              54        408  418-6412         3.97   \n",
       "28                0              20        415  353-2630         1.70   \n",
       "29                0              49        510  410-7789         3.00   \n",
       "...             ...             ...        ...       ...          ...   \n",
       "3303             26             114        415  373-7308         3.11   \n",
       "3304              0              71        510  330-7137         3.73   \n",
       "3305             22              58        415  406-8445         3.11   \n",
       "3306             29             106        408  404-5283         2.19   \n",
       "3307              0             172        408  398-3632         4.81   \n",
       "3308              0              45        415  399-5763         3.59   \n",
       "3309              0             100        408  340-9449         3.24   \n",
       "3310              0              94        415  363-1123         3.67   \n",
       "3311              0             128        415  361-2170         1.86   \n",
       "3312              0             181        408  406-6304         3.83   \n",
       "3313              0             127        408  392-5090         2.70   \n",
       "3314              0              89        415  373-7713         2.46   \n",
       "3315             18             149        415  392-1376         1.76   \n",
       "3316             29             103        510  390-6388         3.32   \n",
       "3317              0             163        415  379-7290         2.11   \n",
       "3318              0              52        415  397-9928         3.13   \n",
       "3319              0              89        415  378-6924         4.29   \n",
       "3320              0             122        510  411-5677         2.62   \n",
       "3321              0              60        415  400-2738         3.56   \n",
       "3322              0              62        408  409-1856         3.11   \n",
       "3323              0             117        415  362-5899         3.67   \n",
       "3324              0             159        415  377-1164         3.13   \n",
       "3325              0              78        408  368-8555         2.51   \n",
       "3326              0              96        415  347-6812         4.02   \n",
       "3327              0              79        415  348-3830         3.19   \n",
       "3328             36             192        415  414-4276         2.67   \n",
       "3329              0              68        415  370-3271         2.59   \n",
       "3330              0              28        510  328-8230         3.81   \n",
       "3331              0             184        510  364-6381         1.35   \n",
       "3332             25              74        415  400-4344         3.70   \n",
       "\n",
       "      Eve Charge  Night Charge  Day Charge VMail Plan  Night Calls  Intl Mins  \\\n",
       "0          16.78         11.01       45.07        yes           91       10.0   \n",
       "1          16.62         11.45       27.47        yes          103       13.7   \n",
       "2          10.30          7.32       41.38         no          104       12.2   \n",
       "3           5.26          8.86       50.90         no           89        6.6   \n",
       "4          12.61          8.41       28.34         no          121       10.1   \n",
       "5          18.75          9.18       37.98         no          118        6.3   \n",
       "6          29.62          9.57       37.09        yes          118        7.5   \n",
       "7           8.76          9.53       26.69         no           96        7.1   \n",
       "8          29.89          9.71       31.37         no           90        8.7   \n",
       "9          18.87         14.69       43.96        yes           97       11.2   \n",
       "10         19.42          9.40       21.95         no          111       12.7   \n",
       "11         13.89          8.82       31.91         no           94        9.1   \n",
       "12          8.92          6.35       21.90         no          128       11.2   \n",
       "13         21.05          8.65       26.62         no          115       12.3   \n",
       "14         26.11          9.14       20.52         no           99       13.1   \n",
       "15         27.01          7.23       56.59         no          128        5.4   \n",
       "16         23.88          4.02       33.39        yes           75       13.8   \n",
       "17         18.55          5.83       32.42         no          121        8.1   \n",
       "18         18.09          7.46       32.25        yes          108       10.0   \n",
       "19         13.56          8.68       38.15         no           74       13.0   \n",
       "20         20.37          9.40       26.37         no          133       10.6   \n",
       "21         14.44          9.43       10.61         no           64        5.7   \n",
       "22          6.20          8.18       31.11         no           78        9.5   \n",
       "23         11.67          8.53       18.77         no          105        7.7   \n",
       "24         20.84         10.67       13.79         no          115       10.3   \n",
       "25         23.55         11.28       21.13         no          115       15.5   \n",
       "26         16.24          8.22       36.21        yes          115        9.5   \n",
       "27         13.22          4.59       22.83         no           68       14.7   \n",
       "28         21.95          8.17       32.30         no          102        6.3   \n",
       "29         18.28          8.04       20.28         no           90       11.1   \n",
       "...          ...           ...         ...        ...          ...        ...   \n",
       "3303       13.23         11.14       23.31        yes           94       11.5   \n",
       "3304       16.88          9.29       31.64         no           80       13.8   \n",
       "3305       20.30          7.84       38.10        yes           86       11.5   \n",
       "3306       17.33         10.33       14.21        yes           73        8.1   \n",
       "3307       19.89          7.23       34.66         no           65       17.8   \n",
       "3308       14.08         11.97       35.92         no           72       13.3   \n",
       "3309       19.18         11.49       37.30         no           95       12.0   \n",
       "3310        7.82         10.12       32.37         no          108       13.6   \n",
       "3311       24.08          8.47       25.11         no          124        6.9   \n",
       "3312       12.27         11.81       39.08         no          110       14.2   \n",
       "3313       12.21          8.61       17.48         no           97       10.0   \n",
       "3314       19.86          5.94       30.38         no          120        9.1   \n",
       "3315        9.73          8.02       25.25        yes           98        6.5   \n",
       "3316       18.62          9.91       27.90        yes          108       12.3   \n",
       "3317       16.02          9.50       33.52         no           94        7.8   \n",
       "3318       25.54          8.66       21.23         no          106       11.6   \n",
       "3319       17.84         12.64       19.62         no          112       15.9   \n",
       "3320       16.69          5.40       23.80         no          133        9.7   \n",
       "3321        7.23          9.45       32.96         no          134       13.2   \n",
       "3322       22.57          8.12       54.59         no           72       11.5   \n",
       "3323       21.19         10.22       20.13         no           56       13.6   \n",
       "3324       16.80          8.72       28.87         no           82       11.6   \n",
       "3325        9.94         10.95       32.88         no          109        9.3   \n",
       "3326       24.21          8.05       18.12         no           92       14.9   \n",
       "3327       16.12          9.96       22.90         no          128       11.8   \n",
       "3328       18.32         12.56       26.55        yes           83        9.9   \n",
       "3329       13.04          8.61       39.29         no          123        9.6   \n",
       "3330       24.55          8.64       30.74         no           91       14.1   \n",
       "3331       13.57          6.26       36.35         no          137        5.0   \n",
       "3332       22.60         10.86       39.85        yes           77       13.7   \n",
       "\n",
       "      Eve Calls  Night Mins Int'l Plan  Eve Mins State  Intl Calls  \\\n",
       "0            99       244.7         no     197.4    KS           3   \n",
       "1           103       254.4         no     195.5    OH           3   \n",
       "2           110       162.6         no     121.2    NJ           5   \n",
       "3            88       196.9        yes      61.9    OH           7   \n",
       "4           122       186.9        yes     148.3    OK           3   \n",
       "5           101       203.9        yes     220.6    AL           6   \n",
       "6           108       212.6         no     348.5    MA           7   \n",
       "7            94       211.8        yes     103.1    MO           6   \n",
       "8            80       215.8         no     351.6    LA           4   \n",
       "9           111       326.4        yes     222.0    WV           5   \n",
       "10           83       208.8         no     228.5    IN           6   \n",
       "11          148       196.0         no     163.4    RI           5   \n",
       "12           71       141.1         no     104.9    IA           2   \n",
       "13           75       192.3         no     247.6    MT           5   \n",
       "14           76       203.0         no     307.2    IA           6   \n",
       "15           97       160.6         no     317.8    NY           9   \n",
       "16           90        89.3         no     280.9    ID           4   \n",
       "17          111       129.6         no     218.2    VT           3   \n",
       "18           65       165.7         no     212.8    VA           5   \n",
       "19           88       192.8         no     159.5    TX           2   \n",
       "20           93       208.8         no     239.7    FL           4   \n",
       "21          121       209.6         no     169.9    CO           6   \n",
       "22           99       181.8         no      72.9    AZ          19   \n",
       "23          102       189.6         no     137.3    SC           6   \n",
       "24           72       237.0         no     245.2    VA           2   \n",
       "25          112       250.7         no     277.1    NE           5   \n",
       "26          112       182.7         no     191.1    WY           3   \n",
       "27          100       102.1         no     155.5    MT           4   \n",
       "28           84       181.5         no     258.2    MO           6   \n",
       "29          109       178.7         no     215.1    HI           1   \n",
       "...         ...         ...        ...       ...   ...         ...   \n",
       "3303        125       247.6         no     155.7    WI           7   \n",
       "3304        140       206.5        yes     198.6    IL           5   \n",
       "3305         85       174.2         no     238.8    IN           7   \n",
       "3306        131       229.5         no     203.9    AL           3   \n",
       "3307        123       160.7         no     234.0    OK           4   \n",
       "3308         97       265.9         no     165.7    IA           6   \n",
       "3309        102       255.3        yes     225.7    VT           4   \n",
       "3310        107       224.8         no      92.0    NY          17   \n",
       "3311         83       188.3         no     283.3    LA           5   \n",
       "3312         93       262.4         no     144.4    SC           4   \n",
       "3313         95       191.4         no     143.7    ID           5   \n",
       "3314         74       131.9         no     233.7    MO           4   \n",
       "3315        106       178.3         no     114.5    ME           4   \n",
       "3316         96       220.3         no     219.1    MS           9   \n",
       "3317        113       211.1        yes     188.5    SD           8   \n",
       "3318        118       192.5         no     300.5    OK           4   \n",
       "3319        115       280.9         no     209.9    WY           6   \n",
       "3320         77       120.1        yes     196.4    GA           4   \n",
       "3321        110       210.1         no      85.0    VT           8   \n",
       "3322        122       180.5         no     265.5    MD           2   \n",
       "3323         97       227.0         no     249.3    IN           3   \n",
       "3324        105       193.7         no     197.7    WV           4   \n",
       "3325         88       243.3         no     116.9    OH           4   \n",
       "3326         87       178.9         no     284.8    OH           7   \n",
       "3327         68       221.4         no     189.7    SC           5   \n",
       "3328        126       279.1         no     215.5    AZ           6   \n",
       "3329         55       191.3         no     153.4    WV           4   \n",
       "3330         58       191.9         no     288.8    RI           6   \n",
       "3331         84       139.2        yes     159.6    CT          10   \n",
       "3332         82       241.4         no     265.9    TN           4   \n",
       "\n",
       "      CustServ Calls  Day Mins  Churn?  \n",
       "0                  1     265.1  False.  \n",
       "1                  1     161.6  False.  \n",
       "2                  0     243.4  False.  \n",
       "3                  2     299.4  False.  \n",
       "4                  3     166.7  False.  \n",
       "5                  0     223.4  False.  \n",
       "6                  3     218.2  False.  \n",
       "7                  0     157.0  False.  \n",
       "8                  1     184.5  False.  \n",
       "9                  0     258.6  False.  \n",
       "10                 4     129.1   True.  \n",
       "11                 0     187.7  False.  \n",
       "12                 1     128.8  False.  \n",
       "13                 3     156.6  False.  \n",
       "14                 4     120.7  False.  \n",
       "15                 4     332.9   True.  \n",
       "16                 1     196.4  False.  \n",
       "17                 3     190.7  False.  \n",
       "18                 1     189.7  False.  \n",
       "19                 1     224.4  False.  \n",
       "20                 0     155.1  False.  \n",
       "21                 5      62.4   True.  \n",
       "22                 0     183.0  False.  \n",
       "23                 2     110.4  False.  \n",
       "24                 0      81.1  False.  \n",
       "25                 3     124.3  False.  \n",
       "26                 0     213.0  False.  \n",
       "27                 3     134.3  False.  \n",
       "28                 0     190.0  False.  \n",
       "29                 1     119.3  False.  \n",
       "...              ...       ...     ...  \n",
       "3303               2     137.1  False.  \n",
       "3304               4     186.1   True.  \n",
       "3305               2     224.1  False.  \n",
       "3306               1      83.6  False.  \n",
       "3307               4     203.9  False.  \n",
       "3308               1     211.3  False.  \n",
       "3309               4     219.4  False.  \n",
       "3310               2     190.4  False.  \n",
       "3311               2     147.7  False.  \n",
       "3312               2     229.9  False.  \n",
       "3313               1     102.8  False.  \n",
       "3314               1     178.7  False.  \n",
       "3315               0     148.5  False.  \n",
       "3316               0     164.1  False.  \n",
       "3317               1     197.2  False.  \n",
       "3318               2     124.9  False.  \n",
       "3319               3     115.4  False.  \n",
       "3320               4     140.0   True.  \n",
       "3321               3     193.9  False.  \n",
       "3322               4     321.1   True.  \n",
       "3323               5     118.4   True.  \n",
       "3324               1     169.8  False.  \n",
       "3325               2     193.4  False.  \n",
       "3326               1     106.6  False.  \n",
       "3327               2     134.7  False.  \n",
       "3328               2     156.2  False.  \n",
       "3329               3     231.1  False.  \n",
       "3330               2     180.8  False.  \n",
       "3331               2     213.8  False.  \n",
       "3332               0     234.4  False.  \n",
       "\n",
       "[3333 rows x 20 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#De forma alternativa, se puede hacer la diferencias por conjuntos\n",
    "\n",
    "a = set(columnas_no_deseadas)\n",
    "b = set(all_columnas)\n",
    "sublist2 = list(b - a)\n",
    "subset3 = data[sublist2]\n",
    "subset3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seleccionar \"n\" filas para un nuevo subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IN</td>\n",
       "      <td>65</td>\n",
       "      <td>415</td>\n",
       "      <td>329-6603</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>129.1</td>\n",
       "      <td>137</td>\n",
       "      <td>21.95</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>19.42</td>\n",
       "      <td>208.8</td>\n",
       "      <td>111</td>\n",
       "      <td>9.40</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6</td>\n",
       "      <td>3.43</td>\n",
       "      <td>4</td>\n",
       "      <td>True.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RI</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>344-9403</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>187.7</td>\n",
       "      <td>127</td>\n",
       "      <td>31.91</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>13.89</td>\n",
       "      <td>196.0</td>\n",
       "      <td>94</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.1</td>\n",
       "      <td>5</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>168</td>\n",
       "      <td>408</td>\n",
       "      <td>363-1107</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>128.8</td>\n",
       "      <td>96</td>\n",
       "      <td>21.90</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>8.92</td>\n",
       "      <td>141.1</td>\n",
       "      <td>128</td>\n",
       "      <td>6.35</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MT</td>\n",
       "      <td>95</td>\n",
       "      <td>510</td>\n",
       "      <td>394-8006</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>156.6</td>\n",
       "      <td>88</td>\n",
       "      <td>26.62</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>21.05</td>\n",
       "      <td>192.3</td>\n",
       "      <td>115</td>\n",
       "      <td>8.65</td>\n",
       "      <td>12.3</td>\n",
       "      <td>5</td>\n",
       "      <td>3.32</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "10    IN              65        415  329-6603         no         no   \n",
       "11    RI              74        415  344-9403         no         no   \n",
       "12    IA             168        408  363-1107         no         no   \n",
       "13    MT              95        510  394-8006         no         no   \n",
       "\n",
       "    VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  \\\n",
       "10              0     129.1        137       21.95  ...         83   \n",
       "11              0     187.7        127       31.91  ...        148   \n",
       "12              0     128.8         96       21.90  ...         71   \n",
       "13              0     156.6         88       26.62  ...         75   \n",
       "\n",
       "    Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
       "10       19.42       208.8          111          9.40       12.7           6   \n",
       "11       13.89       196.0           94          8.82        9.1           5   \n",
       "12        8.92       141.1          128          6.35       11.2           2   \n",
       "13       21.05       192.3          115          8.65       12.3           5   \n",
       "\n",
       "    Intl Charge  CustServ Calls  Churn?  \n",
       "10         3.43               4   True.  \n",
       "11         2.46               0  False.  \n",
       "12         3.02               1  False.  \n",
       "13         3.32               3  False.  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se considera del límite inferior hasta 1 menos del límite superior del rango en el corchete\n",
    "data[10:14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>RI</td>\n",
       "      <td>28</td>\n",
       "      <td>510</td>\n",
       "      <td>328-8230</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>180.8</td>\n",
       "      <td>109</td>\n",
       "      <td>30.74</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>24.55</td>\n",
       "      <td>191.9</td>\n",
       "      <td>91</td>\n",
       "      <td>8.64</td>\n",
       "      <td>14.1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>CT</td>\n",
       "      <td>184</td>\n",
       "      <td>510</td>\n",
       "      <td>364-6381</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>213.8</td>\n",
       "      <td>105</td>\n",
       "      <td>36.35</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>13.57</td>\n",
       "      <td>139.2</td>\n",
       "      <td>137</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>TN</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>400-4344</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>234.4</td>\n",
       "      <td>113</td>\n",
       "      <td>39.85</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>22.60</td>\n",
       "      <td>241.4</td>\n",
       "      <td>77</td>\n",
       "      <td>10.86</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "3330    RI              28        510  328-8230         no         no   \n",
       "3331    CT             184        510  364-6381        yes         no   \n",
       "3332    TN              74        415  400-4344         no        yes   \n",
       "\n",
       "      VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  \\\n",
       "3330              0     180.8        109       30.74  ...         58   \n",
       "3331              0     213.8        105       36.35  ...         84   \n",
       "3332             25     234.4        113       39.85  ...         82   \n",
       "\n",
       "      Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  \\\n",
       "3330       24.55       191.9           91          8.64       14.1   \n",
       "3331       13.57       139.2          137          6.26        5.0   \n",
       "3332       22.60       241.4           77         10.86       13.7   \n",
       "\n",
       "      Intl Calls  Intl Charge  CustServ Calls  Churn?  \n",
       "3330           6         3.81               2  False.  \n",
       "3331          10         1.35               2  False.  \n",
       "3332           4         3.70               0  False.  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Desde una fila en adelante\n",
    "\n",
    "data[3330:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>State</th>\n",
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       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
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       "      <td>no</td>\n",
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       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>98</td>\n",
       "      <td>37.98</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>88</td>\n",
       "      <td>37.09</td>\n",
       "      <td>...</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MO</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>79</td>\n",
       "      <td>26.69</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>8.76</td>\n",
       "      <td>211.8</td>\n",
       "      <td>96</td>\n",
       "      <td>9.53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "5    AL             118        510  391-8027        yes         no   \n",
       "6    MA             121        510  355-9993         no        yes   \n",
       "7    MO             147        415  329-9001        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  Eve Charge  \\\n",
       "0             25     265.1        110       45.07  ...         99       16.78   \n",
       "1             26     161.6        123       27.47  ...        103       16.62   \n",
       "2              0     243.4        114       41.38  ...        110       10.30   \n",
       "3              0     299.4         71       50.90  ...         88        5.26   \n",
       "4              0     166.7        113       28.34  ...        122       12.61   \n",
       "5              0     223.4         98       37.98  ...        101       18.75   \n",
       "6             24     218.2         88       37.09  ...        108       29.62   \n",
       "7              0     157.0         79       26.69  ...         94        8.76   \n",
       "\n",
       "   Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0       244.7           91         11.01       10.0           3         2.70   \n",
       "1       254.4          103         11.45       13.7           3         3.70   \n",
       "2       162.6          104          7.32       12.2           5         3.29   \n",
       "3       196.9           89          8.86        6.6           7         1.78   \n",
       "4       186.9          121          8.41       10.1           3         2.73   \n",
       "5       203.9          118          9.18        6.3           6         1.70   \n",
       "6       212.6          118          9.57        7.5           7         2.03   \n",
       "7       211.8           96          9.53        7.1           6         1.92   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "0               1  False.  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  \n",
       "5               0  False.  \n",
       "6               3  False.  \n",
       "7               0  False.  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hasta una fila hacia atráz\n",
    "\n",
    "data[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 21)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usuarios con Day Mins > 300\n",
    "\n",
    "data1 = data[data['Day Mins'] > 300]\n",
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 21)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usuarios de Nueva York (State = 'NY')\n",
    "data2 = data[data['State'] == 'NY']\n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 21)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Más de una condición a cumplir\n",
    "# AND -> &\n",
    "# Buscamos los usuarios que cumplan con 2 condiciones, que hablen más de 300 minutos y sean de New York\n",
    "data3 = data[(data['Day Mins'] > 300) & (data['State'] == 'NY')]\n",
    "data3.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesante, sólo dos usuarios!!!\n",
    "\n",
    "¿Qué ocurre si cambiamos la condición por un \"OR\"? Pues bien, deberíamos tener un conjunto más grande de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124, 21)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Más de una condición a cumplir\n",
    "# OR -> |\n",
    "# Buscamos los usuarios que cumplan con; que hablen más de 300 minutos ó bien que sean de New York\n",
    "\n",
    "data4 = data[(data['Day Mins'] > 300) | (data['State'] == 'NY')]\n",
    "data4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Se puede obtener un subset donde se combinen condiciones sobre filas y columnas?\n",
    "La respuesta es sí, vamos a por ello!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subconjuntos con loc e iloc y creación de nuevas columnas\n",
    "\n",
    "Se pide obtener los valores de los 50 primeros individuos con respecto a minutos del día, de noche y longitud de la cuenta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Day Mins</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Account Length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>265.1</td>\n",
       "      <td>244.7</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161.6</td>\n",
       "      <td>254.4</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>243.4</td>\n",
       "      <td>162.6</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>299.4</td>\n",
       "      <td>196.9</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166.7</td>\n",
       "      <td>186.9</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day Mins  Night Mins  Account Length\n",
       "0     265.1       244.7             128\n",
       "1     161.6       254.4             107\n",
       "2     243.4       162.6             137\n",
       "3     299.4       196.9              84\n",
       "4     166.7       186.9              75"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primera forma\n",
    "\n",
    "subset_first_50 = data[['Day Mins', 'Night Mins', 'Account Length']][:50]\n",
    "subset_first_50.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>391-8027</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>355-9993</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>329-9001</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335-4719</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>330-8173</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Phone Int'l Plan VMail Plan\n",
       "1  371-7191         no        yes\n",
       "2  358-1921         no         no\n",
       "3  375-9999        yes         no\n",
       "4  330-6626        yes         no\n",
       "5  391-8027        yes         no\n",
       "6  355-9993         no        yes\n",
       "7  329-9001        yes         no\n",
       "8  335-4719         no         no\n",
       "9  330-8173        yes        yes"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segunda forma, con iloc\n",
    "\n",
    "data.iloc[1:10, 3:6] # Las filas entre la 1 a la 10, y las columnas en las posiciones 3 a la 6 (la 6 sin incluir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>391-8027</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>98</td>\n",
       "      <td>37.98</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>355-9993</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>88</td>\n",
       "      <td>37.09</td>\n",
       "      <td>...</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MO</td>\n",
       "      <td>147</td>\n",
       "      <td>415</td>\n",
       "      <td>329-9001</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>79</td>\n",
       "      <td>26.69</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>8.76</td>\n",
       "      <td>211.8</td>\n",
       "      <td>96</td>\n",
       "      <td>9.53</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LA</td>\n",
       "      <td>117</td>\n",
       "      <td>408</td>\n",
       "      <td>335-4719</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>184.5</td>\n",
       "      <td>97</td>\n",
       "      <td>31.37</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>29.89</td>\n",
       "      <td>215.8</td>\n",
       "      <td>90</td>\n",
       "      <td>9.71</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WV</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>330-8173</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>37</td>\n",
       "      <td>258.6</td>\n",
       "      <td>84</td>\n",
       "      <td>43.96</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>18.87</td>\n",
       "      <td>326.4</td>\n",
       "      <td>97</td>\n",
       "      <td>14.69</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "5    AL             118        510  391-8027        yes         no   \n",
       "6    MA             121        510  355-9993         no        yes   \n",
       "7    MO             147        415  329-9001        yes         no   \n",
       "8    LA             117        408  335-4719         no         no   \n",
       "9    WV             141        415  330-8173        yes        yes   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  Eve Charge  \\\n",
       "1             26     161.6        123       27.47  ...        103       16.62   \n",
       "2              0     243.4        114       41.38  ...        110       10.30   \n",
       "3              0     299.4         71       50.90  ...         88        5.26   \n",
       "4              0     166.7        113       28.34  ...        122       12.61   \n",
       "5              0     223.4         98       37.98  ...        101       18.75   \n",
       "6             24     218.2         88       37.09  ...        108       29.62   \n",
       "7              0     157.0         79       26.69  ...         94        8.76   \n",
       "8              0     184.5         97       31.37  ...         80       29.89   \n",
       "9             37     258.6         84       43.96  ...        111       18.87   \n",
       "\n",
       "   Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "1       254.4          103         11.45       13.7           3         3.70   \n",
       "2       162.6          104          7.32       12.2           5         3.29   \n",
       "3       196.9           89          8.86        6.6           7         1.78   \n",
       "4       186.9          121          8.41       10.1           3         2.73   \n",
       "5       203.9          118          9.18        6.3           6         1.70   \n",
       "6       212.6          118          9.57        7.5           7         2.03   \n",
       "7       211.8           96          9.53        7.1           6         1.92   \n",
       "8       215.8           90          9.71        8.7           4         2.35   \n",
       "9       326.4           97         14.69       11.2           5         3.02   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  \n",
       "5               0  False.  \n",
       "6               3  False.  \n",
       "7               0  False.  \n",
       "8               1  False.  \n",
       "9               0  False.  \n",
       "\n",
       "[9 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Más ejemplos\n",
    "data.iloc[:, 3:6] # Todas las filas para las columnas de la 3 a la 6\n",
    "data.iloc[1:10, :] # Todas las columnas, para los registros del 1 al 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Day Mins</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Account Length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>265.1</td>\n",
       "      <td>244.7</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161.6</td>\n",
       "      <td>254.4</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>243.4</td>\n",
       "      <td>162.6</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>299.4</td>\n",
       "      <td>196.9</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166.7</td>\n",
       "      <td>186.9</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>223.4</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>218.2</td>\n",
       "      <td>212.6</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>157.0</td>\n",
       "      <td>211.8</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>184.5</td>\n",
       "      <td>215.8</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>258.6</td>\n",
       "      <td>326.4</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>129.1</td>\n",
       "      <td>208.8</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Day Mins  Night Mins  Account Length\n",
       "0      265.1       244.7             128\n",
       "1      161.6       254.4             107\n",
       "2      243.4       162.6             137\n",
       "3      299.4       196.9              84\n",
       "4      166.7       186.9              75\n",
       "5      223.4       203.9             118\n",
       "6      218.2       212.6             121\n",
       "7      157.0       211.8             147\n",
       "8      184.5       215.8             117\n",
       "9      258.6       326.4             141\n",
       "10     129.1       208.8              65"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usando loc, para ingresar los nombres de las columnas\n",
    "\n",
    "data.loc[:10, ['Day Mins', 'Night Mins', 'Account Length']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crear una nueva columna\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    707.2\n",
       "1    611.5\n",
       "2    527.2\n",
       "3    558.2\n",
       "4    501.9\n",
       "Name: Total Mins, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aquí un ejemplo para agregar una columna con los minutos totales \n",
    "\n",
    "data['Total Mins'] = data['Day Mins'] + data['Night Mins'] + data['Eve Mins']\n",
    "data['Total Mins'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    300\n",
       "1    329\n",
       "2    328\n",
       "3    248\n",
       "4    356\n",
       "Name: Total Calls, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Otro ejemplo para agregar una columna con las llamadas totales \n",
    "data['Total Calls'] = data['Day Calls'] + data['Night Calls'] + data['Eve Calls']\n",
    "data['Total Calls'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3333, 23)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "      <th>Total Mins</th>\n",
       "      <th>Total Calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "      <td>707.2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "      <td>611.5</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "      <td>527.2</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "      <td>558.2</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "      <td>501.9</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Night Mins  \\\n",
       "0             25     265.1        110       45.07  ...       244.7   \n",
       "1             26     161.6        123       27.47  ...       254.4   \n",
       "2              0     243.4        114       41.38  ...       162.6   \n",
       "3              0     299.4         71       50.90  ...       196.9   \n",
       "4              0     166.7        113       28.34  ...       186.9   \n",
       "\n",
       "   Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0           91         11.01       10.0           3         2.70   \n",
       "1          103         11.45       13.7           3         3.70   \n",
       "2          104          7.32       12.2           5         3.29   \n",
       "3           89          8.86        6.6           7         1.78   \n",
       "4          121          8.41       10.1           3         2.73   \n",
       "\n",
       "   CustServ Calls  Churn?  Total Mins  Total Calls  \n",
       "0               1  False.       707.2          300  \n",
       "1               1  False.       611.5          329  \n",
       "2               0  False.       527.2          328  \n",
       "3               2  False.       558.2          248  \n",
       "4               3  False.       501.9          356  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos evidenciar que creamos las dos columnas al final de nuestro dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generación aleatoria de números"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "21\n",
      "34\n",
      "47\n",
      "37\n",
      "91\n",
      "98\n",
      "2\n",
      "31\n",
      "71\n"
     ]
    }
   ],
   "source": [
    "#Generar 10 números aleatorios con random\n",
    "i=0\n",
    "while i < 10:\n",
    "    print(np.random.randint(1,100))\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3792106797823498\n",
      "0.39990153763983916\n",
      "0.08558157363440766\n",
      "0.8286610276578852\n",
      "0.5429550987016851\n",
      "0.47842378849684064\n",
      "0.2422158395336389\n",
      "0.0879273909248035\n",
      "0.5279648605320951\n",
      "0.4162943419136004\n"
     ]
    }
   ],
   "source": [
    "## La forma más clásica de generar un número aleatorio es entre 0 y 1. \n",
    "## Se generan 10 números\n",
    "i=0\n",
    "while i < 10:\n",
    "    print(np.random.random())\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n",
      "13\n",
      "17\n",
      "24\n",
      "94\n",
      "13\n",
      "45\n",
      "56\n",
      "55\n",
      "31\n"
     ]
    }
   ],
   "source": [
    "## Existe una librería que genera una lista de número de forma aleatoria dentro de un rango\n",
    "\n",
    "import random\n",
    "\n",
    "# Imprimir 10 números\n",
    "\n",
    "for i in range(10):\n",
    "    print(random.randrange(1, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shuffling -> \"es como barajar un mazo de cartas, altera el orden\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(100)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 87, 44, 92,  3,  9, 13, 96, 93, 61, 67, 95, 34,  1, 19, 68, 94,\n",
       "       26,  2,  6, 72,  5, 42, 82, 27, 30, 89, 90, 74, 99, 98, 51, 20, 88,\n",
       "       43, 58, 60, 65, 62, 77, 29, 64,  7, 52, 81, 31, 36, 54, 85, 22, 33,\n",
       "       55, 32, 10, 18, 71, 79, 40, 97, 53,  0, 80,  8, 23, 76, 17, 70, 83,\n",
       "       14, 16, 25, 24, 86, 56, 37, 45, 63, 59, 41, 38, 91, 73, 78, 21, 50,\n",
       "       69, 28, 84, 35, 46, 11, 48, 75, 15, 57, 39, 47, 49,  4, 66])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.shuffle(a)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La semilla -> Seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9862768288615988\n",
      "0.8733919458206546\n",
      "0.5097455249715815\n",
      "0.27183571428207576\n",
      "0.33691872774596354\n"
     ]
    }
   ],
   "source": [
    "## Fijamos una semilla\n",
    "\n",
    "np.random.seed(2020)\n",
    "for i in range(5):\n",
    "    print(np.random.random())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Para qué sirve? Para replicar los resultados obtenidos, es muy importante que se establezca al principio del trabajo.\n",
    "\n",
    "Antes de comenzar un experimento, se recomienda establecer una semilla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones de distribución de probabilidades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribución uniforme\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Librerías\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generamos  n muestras entre a y b, distribuidas de forma uniforme\n",
    "\n",
    "a = 1 # Límite inferior\n",
    "b = 100 # Límite superior\n",
    "n = 200 # Cantidad de muestras a generar\n",
    "data = np.random.uniform(a,b,n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficamos los datos generados\n",
    "\n",
    "plt.hist(data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No parece tan uniforme, ¿por qué? Porque tomamos muy pocas muestras, probemos con 1.000.000 de muestras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1000000 # Cantidad de muestras a generar\n",
    "data2 = np.random.uniform(a,b,n)\n",
    "\n",
    "# Graficamos los datos generados\n",
    "\n",
    "plt.hist(data2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora si parece una distribución uniforme !! :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribución Normal\n",
    "\n",
    "<img src=\"Distribución_Normal.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1000000 # Cantidad de muestras a generar\n",
    "data3 = np.random.randn(n) # Generador de muestras\n",
    "\n",
    "# Graficamos los datos generados\n",
    "plt.hist(data3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x230bae89518>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar la función de distribución acumulada\n",
    "\n",
    "x = range(1, 1000001) # Generar el eje x\n",
    "plt.plot(x, sorted(data3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## La simulación de Monte Carlo\n",
    "\n",
    "### ¿Cuál es la probabilidad de que al elijir un punto cualquiera al azar dentro del cuadrado, caiga dentro del círculo?\n",
    "\n",
    "\n",
    "<img src=\"Montecarlo.png\">\n",
    "\n",
    "### Y bien, si multiplicamos a esta probabilidad por $4$ se puede obtener una aproximación de $\\pi$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objetivo = simular el cálculo del valor de $\\pi$\n",
    "\n",
    "Procedimiento:\n",
    "\n",
    "1. Generamos dos números aleatorios $x$ e $y$ entre 0 y 1 (tienen que ser equiprobables)\n",
    "2. Calculamos $x^2 + y^2$:\n",
    "    \n",
    "    2.1. Si el valor es inferior a 1 $\\rightarrow$ estamos dentro del círculo. \n",
    "    \n",
    "    2.2. Si el valor es superior a 1 $\\rightarrow$ estamos fuera del círculo.\n",
    "    \n",
    "\n",
    "3. Calculamos el número total de veces que están dentro del círculo y lo dividimos entre el número total de intentos para obtener una aproximación de la probabilidad de caer dentro del círculo\n",
    "4. Usamos dicha probabilidad para aproximar el valor de $\\pi$.\n",
    "5. Repetimos el experimento un número n de veces, para obtener diferentes aproximaciones de $\\pi$.\n",
    "6. Calculamos el promedio de los experimentos anteriores para dar un valor final de $\\pi$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1376000000000017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x230b8699358>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generamos una variable que guarde los valores de pi obtenidos\n",
    "pi_avg = 0\n",
    "n = 1000 # cantidad de muestras a generar\n",
    "pi_value_list =[] # guardar los valores de pi obtenidos\n",
    "m = 100 # cantidad de experimentos  realizar\n",
    "\n",
    "for i in range(m): # Calcular el experimento m veces\n",
    "    value = 0 # para guardar el número de veces que acertamos dentro del círculo\n",
    "    x = np.random.uniform(0,1,n).tolist() # Valores uniformes guardados en una lista, para x\n",
    "    y = np.random.uniform(0,1,n).tolist() # Valores uniformes guardados en una lista, para y\n",
    "    for j in range(n): # Hacer un recorrido para obtener el valor x cuadrado más y cuadrado\n",
    "        z = x[j]*x[j] + y[j]*y[j]\n",
    "        if z<=1:\n",
    "            value+=1 # Incrementamos en uno el número de casos favorables\n",
    "    float_value = float(value)\n",
    "    pi_value = float_value * 4 / n # Valor de casos favorables dividido en casos totales\n",
    "    pi_value_list.append(pi_value) # Agregar a la lista un nuevo valor obtenido de pi\n",
    "    pi_avg += pi_value # Acumular el valor de pi\n",
    "\n",
    "pi = pi_avg/m # Obtener el promedio de todos los experimentos\n",
    "print(pi) # Mostrar el valor de pi obtenido\n",
    "plt.plot(pi_value_list) # Graficar los valores de pi obtenidos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Y si creamos una función que realice este experimento? Let's do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pi_montecarlo(n=1000, n_exp=100):\n",
    "    # Generamos una variable que guarde los valores de pi obtenidos\n",
    "    pi_avg = 0\n",
    "    pi_value_list =[] # guardar los valores de pi obtenidos\n",
    "    m = n_exp # cantidad de experimentos  realizar\n",
    "\n",
    "    for i in range(m): # Calcular el experimento m veces\n",
    "        value = 0 # para guardar el número de veces que acertamos dentro del círculo\n",
    "        x = np.random.uniform(0,1,n).tolist() # n Valores uniformes guardados en una lista, para x\n",
    "        y = np.random.uniform(0,1,n).tolist() # n Valores uniformes guardados en una lista, para y\n",
    "        for j in range(n): # Hacer un recorrido para obtener el valor x cuadrado más y cuadrado\n",
    "            z = x[j]*x[j] + y[j]*y[j]\n",
    "            if z<=1:\n",
    "                value+=1 # Incrementamos en uno el número de casos favorables\n",
    "        float_value = float(value)\n",
    "        pi_value = float_value * 4 / n # Valor de casos favorables dividido en casos totales\n",
    "        pi_value_list.append(pi_value) # Agregar a la lista un nuevo valor obtenido de pi\n",
    "        pi_avg += pi_value # Acumular el valor de pi\n",
    "\n",
    "    pi = pi_avg/m # Obtener el promedio de todos los experimentos\n",
    "    print(pi) # Mostrar el valor de pi obtenido\n",
    "    fig = plt.plot(pi_value_list) # Graficar los valores de pi obtenidos\n",
    "    return (pi, fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.140576\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3.140576, [<matplotlib.lines.Line2D at 0x230b6e1b898>])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pi_montecarlo(10000, 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sensacional!! Continuemos con la generación de data sets \"dummy\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000387</td>\n",
       "      <td>1.497605</td>\n",
       "      <td>18.512680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000015</td>\n",
       "      <td>2.499402</td>\n",
       "      <td>7.797526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.827719</td>\n",
       "      <td>-10.056537</td>\n",
       "      <td>5.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.673379</td>\n",
       "      <td>-0.190753</td>\n",
       "      <td>11.762609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.000087</td>\n",
       "      <td>1.500650</td>\n",
       "      <td>18.504083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.674366</td>\n",
       "      <td>3.182166</td>\n",
       "      <td>25.271306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.913072</td>\n",
       "      <td>13.713005</td>\n",
       "      <td>31.999993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A               B               C\n",
       "count  1000000.000000  1000000.000000  1000000.000000\n",
       "mean         0.000387        1.497605       18.512680\n",
       "std          1.000015        2.499402        7.797526\n",
       "min         -4.827719      -10.056537        5.000075\n",
       "25%         -0.673379       -0.190753       11.762609\n",
       "50%         -0.000087        1.500650       18.504083\n",
       "75%          0.674366        3.182166       25.271306\n",
       "max          4.913072       13.713005       31.999993"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construyamos un dataframe con todo lo que hemos aprendido sobre distribuciones\n",
    "\n",
    "# Cantidad de muestras\n",
    "n = 1000000\n",
    "\n",
    "data_1 = pd.DataFrame(\n",
    "    {\n",
    "     'A' : np.random.randn(n), # Distribución normal (0,1)\n",
    "     'B' : 1.5 + 2.5*np.random.randn(n), # Distribución normal (1.5, 2.5)\n",
    "     'C' : np.random.uniform(5, 32, n) # Distribución Uniforme entre 5 y 32\n",
    "    } \n",
    ")\n",
    "# Para todas las variables, se generó n muestras. Revisemos\n",
    "data_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5.00000e+01, 1.93300e+03, 2.63880e+04, 1.47231e+05, 3.41541e+05,\n",
       "        3.28130e+05, 1.31417e+05, 2.17890e+04, 1.47800e+03, 4.30000e+01]),\n",
       " array([-4.82771871, -3.85363966, -2.8795606 , -1.90548154, -0.93140249,\n",
       "         0.04267657,  1.01675562,  1.99083468,  2.96491374,  3.93899279,\n",
       "         4.91307185]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Revisemos las distrbuciones\n",
    "\n",
    "# Distribución normal (0,1)\n",
    "plt.hist(data_1['A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.00000e+02, 3.10600e+03, 3.51070e+04, 1.68267e+05, 3.45984e+05,\n",
       "        3.07824e+05, 1.18721e+05, 1.94850e+04, 1.36300e+03, 4.30000e+01]),\n",
       " array([-10.0565369 ,  -7.6795827 ,  -5.30262849,  -2.92567428,\n",
       "         -0.54872008,   1.82823413,   4.20518834,   6.58214254,\n",
       "          8.95909675,  11.33605096,  13.71300516]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribución normal (1.5, 2.5)\n",
    "plt.hist(data_1['B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 99717.,  99855.,  99967., 100400.,  99924.,  99536., 100000.,\n",
       "         99921.,  99904., 100776.]),\n",
       " array([ 5.00007513,  7.70006693, 10.40005873, 13.10005054, 15.80004234,\n",
       "        18.50003414, 21.20002594, 23.90001775, 26.60000955, 29.30000135,\n",
       "        31.99999315]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribución Uniforme entre 5 y 32\n",
    "plt.hist(data_1['C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  Eve Charge  \\\n",
       "0             25     265.1        110       45.07  ...         99       16.78   \n",
       "1             26     161.6        123       27.47  ...        103       16.62   \n",
       "2              0     243.4        114       41.38  ...        110       10.30   \n",
       "3              0     299.4         71       50.90  ...         88        5.26   \n",
       "4              0     166.7        113       28.34  ...        122       12.61   \n",
       "\n",
       "   Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0       244.7           91         11.01       10.0           3         2.70   \n",
       "1       254.4          103         11.45       13.7           3         3.70   \n",
       "2       162.6          104          7.32       12.2           5         3.29   \n",
       "3       196.9           89          8.86        6.6           7         1.78   \n",
       "4       186.9          121          8.41       10.1           3         2.73   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "0               1  False.  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2 = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/customer-churn-model/Customer Churn Model.txt')\n",
    "data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colum_names = data_2.columns.values.tolist()\n",
    "a = len(colum_names)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State</td>\n",
       "      <td>0.018997</td>\n",
       "      <td>0.209223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Account Length</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>0.711497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Area Code</td>\n",
       "      <td>-0.280073</td>\n",
       "      <td>0.710835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phone</td>\n",
       "      <td>-0.888329</td>\n",
       "      <td>0.476497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Int'l Plan</td>\n",
       "      <td>2.091324</td>\n",
       "      <td>0.341038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VMail Plan</td>\n",
       "      <td>0.569381</td>\n",
       "      <td>0.472723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VMail Message</td>\n",
       "      <td>-0.605941</td>\n",
       "      <td>0.256080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Day Mins</td>\n",
       "      <td>0.447846</td>\n",
       "      <td>0.129207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Day Calls</td>\n",
       "      <td>0.699854</td>\n",
       "      <td>0.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Day Charge</td>\n",
       "      <td>0.046414</td>\n",
       "      <td>0.837945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Eve Mins</td>\n",
       "      <td>1.681305</td>\n",
       "      <td>0.163688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Eve Calls</td>\n",
       "      <td>-0.639666</td>\n",
       "      <td>0.798479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Eve Charge</td>\n",
       "      <td>1.098624</td>\n",
       "      <td>0.288328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Night Mins</td>\n",
       "      <td>-1.138158</td>\n",
       "      <td>0.438249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Night Calls</td>\n",
       "      <td>-1.883176</td>\n",
       "      <td>0.983024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Night Charge</td>\n",
       "      <td>0.366658</td>\n",
       "      <td>0.240144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Intl Mins</td>\n",
       "      <td>1.768024</td>\n",
       "      <td>0.241438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Intl Calls</td>\n",
       "      <td>1.998162</td>\n",
       "      <td>0.700124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Intl Charge</td>\n",
       "      <td>1.695546</td>\n",
       "      <td>0.881544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CustServ Calls</td>\n",
       "      <td>-0.019813</td>\n",
       "      <td>0.159834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Churn?</td>\n",
       "      <td>0.002418</td>\n",
       "      <td>0.476904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Column Name         A         B\n",
       "0            State  0.018997  0.209223\n",
       "1   Account Length  0.140282  0.711497\n",
       "2        Area Code -0.280073  0.710835\n",
       "3            Phone -0.888329  0.476497\n",
       "4       Int'l Plan  2.091324  0.341038\n",
       "5       VMail Plan  0.569381  0.472723\n",
       "6    VMail Message -0.605941  0.256080\n",
       "7         Day Mins  0.447846  0.129207\n",
       "8        Day Calls  0.699854  0.448251\n",
       "9       Day Charge  0.046414  0.837945\n",
       "10        Eve Mins  1.681305  0.163688\n",
       "11       Eve Calls -0.639666  0.798479\n",
       "12      Eve Charge  1.098624  0.288328\n",
       "13      Night Mins -1.138158  0.438249\n",
       "14     Night Calls -1.883176  0.983024\n",
       "15    Night Charge  0.366658  0.240144\n",
       "16       Intl Mins  1.768024  0.241438\n",
       "17      Intl Calls  1.998162  0.700124\n",
       "18     Intl Charge  1.695546  0.881544\n",
       "19  CustServ Calls -0.019813  0.159834\n",
       "20          Churn?  0.002418  0.476904"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pd.DataFrame (\n",
    "    {\n",
    "        'Column Name': colum_names,\n",
    "        'A' : np.random.randn(a),\n",
    "        'B' : np.random.uniform(0,1,a)\n",
    "    }\n",
    ")\n",
    "\n",
    "new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy Data Sets con datos categóricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hombre',\n",
       " 'Hombre',\n",
       " 'Hombre',\n",
       " 'Hombre',\n",
       " 'Mujer',\n",
       " 'Mujer',\n",
       " 'Mujer',\n",
       " 'Mujer',\n",
       " 'Mujer',\n",
       " 'Mujer']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generamos dos variables categóricas\n",
    "\n",
    "gender = ['Mujer', 'Hombre']\n",
    "income = ['Pobre', 'Clase media', 'Rico']\n",
    "\n",
    "n = 500 # Cantidad de muestras\n",
    "\n",
    "# Creamos las listas para guardar los datos aleatorios\n",
    "\n",
    "gender_data = []\n",
    "income_data = []\n",
    "\n",
    "# Ciclo para obtener n muestras sobre nuestras variables\n",
    "for i in range(0, 500):\n",
    "    gender_data.append(np.random.choice(gender))\n",
    "    income_data.append(np.random.choice(income))\n",
    "\n",
    "# Revisemos los primeros 10 registros\n",
    "gender_data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Rico',\n",
       " 'Clase media',\n",
       " 'Pobre',\n",
       " 'Clase media',\n",
       " 'Clase media',\n",
       " 'Clase media',\n",
       " 'Rico',\n",
       " 'Rico',\n",
       " 'Rico',\n",
       " 'Clase media']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisemos los primeros 10 registros\n",
    "income_data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generamos nuevas variables, pero ahora numéricas\n",
    "# Con distribuciones normales\n",
    "# Z -> N(0,1)\n",
    "\n",
    "# N(m, s) -> m + s *z\n",
    "height = 160 + 30 * np.random.randn(n)\n",
    "weight = 65 + 25 * np.random.randn(n)\n",
    "age = 30 + 12 * np.random.randn(n)\n",
    "income = 18000 + 3500 * np.random.randn(n)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Rico</td>\n",
       "      <td>33.346201</td>\n",
       "      <td>178.854874</td>\n",
       "      <td>45.385946</td>\n",
       "      <td>20813.470719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>113.361591</td>\n",
       "      <td>141.806900</td>\n",
       "      <td>13.956049</td>\n",
       "      <td>15429.476751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Pobre</td>\n",
       "      <td>61.085082</td>\n",
       "      <td>188.536748</td>\n",
       "      <td>23.820786</td>\n",
       "      <td>13117.313909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>18.778331</td>\n",
       "      <td>140.283566</td>\n",
       "      <td>42.871327</td>\n",
       "      <td>17759.165610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>61.181933</td>\n",
       "      <td>121.918602</td>\n",
       "      <td>13.601447</td>\n",
       "      <td>23901.455692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
       "0  Hombre             Rico   33.346201  178.854874  45.385946  20813.470719\n",
       "1  Hombre      Clase media  113.361591  141.806900  13.956049  15429.476751\n",
       "2  Hombre            Pobre   61.085082  188.536748  23.820786  13117.313909\n",
       "3  Hombre      Clase media   18.778331  140.283566  42.871327  17759.165610\n",
       "4   Mujer      Clase media   61.181933  121.918602  13.601447  23901.455692"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos el dataframe\n",
    "\n",
    "data_3 = pd.DataFrame(\n",
    "    {\n",
    "        'Sexo': gender_data,\n",
    "        'Estado Economico': income_data,\n",
    "        'Peso': weight,\n",
    "        'Altura': height,\n",
    "        'Edad': age,\n",
    "        'Ingresos': income\n",
    "    }\n",
    ")\n",
    "\n",
    "data_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrupación de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hombre': Int64Index([  0,   1,   2,   3,  11,  12,  18,  20,  21,  23,\n",
       "             ...\n",
       "             484, 486, 487, 488, 491, 492, 494, 496, 497, 498],\n",
       "            dtype='int64', length=257),\n",
       " 'Mujer': Int64Index([  4,   5,   6,   7,   8,   9,  10,  13,  14,  15,\n",
       "             ...\n",
       "             477, 478, 479, 480, 485, 489, 490, 493, 495, 499],\n",
       "            dtype='int64', length=243)}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agrupados_sexo = data_3.groupby('Sexo')\n",
    "agrupados_sexo.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hombre\n",
      "       Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "0    Hombre             Rico   33.346201  178.854874  45.385946  20813.470719\n",
      "1    Hombre      Clase media  113.361591  141.806900  13.956049  15429.476751\n",
      "2    Hombre            Pobre   61.085082  188.536748  23.820786  13117.313909\n",
      "3    Hombre      Clase media   18.778331  140.283566  42.871327  17759.165610\n",
      "11   Hombre      Clase media   75.303962  154.932648  34.281199  19046.369405\n",
      "12   Hombre      Clase media   62.728649  166.651245  29.479539  17420.128140\n",
      "18   Hombre      Clase media   84.737532  182.552952  38.527954  20331.740509\n",
      "20   Hombre      Clase media   71.459400  145.489352  25.602147  22406.519566\n",
      "21   Hombre             Rico   48.845505  171.255861  36.341825  17866.847612\n",
      "23   Hombre      Clase media   79.984957  171.061312  30.883470  21461.458889\n",
      "24   Hombre             Rico   69.516723  161.285463  14.448105  16987.956652\n",
      "25   Hombre      Clase media   62.429489  104.401969  41.774141  14537.604535\n",
      "27   Hombre      Clase media   58.603480  208.998805  28.943294  24312.776440\n",
      "31   Hombre      Clase media   72.877127  159.872874  36.165485  18896.238479\n",
      "32   Hombre             Rico   60.457446  108.682354  32.892411  18915.110284\n",
      "33   Hombre      Clase media   60.016595  129.018628  23.147194  20817.312735\n",
      "34   Hombre             Rico   56.810920  131.873287  53.618540  14463.387698\n",
      "36   Hombre             Rico   86.511228  173.451385  30.681507  24208.054556\n",
      "37   Hombre             Rico   65.079106  130.523457  31.906173  16176.374604\n",
      "40   Hombre            Pobre   77.669863   98.649087  12.787209  17043.917381\n",
      "41   Hombre            Pobre   69.902331  185.231122  23.337743  14492.387956\n",
      "42   Hombre            Pobre   56.935984  192.875052  10.721323  18044.052640\n",
      "45   Hombre             Rico   98.355855  177.743133  42.185538  17108.076404\n",
      "52   Hombre             Rico   16.357960  161.415063  23.518715  17956.664940\n",
      "54   Hombre             Rico   90.269989  164.009535  40.801611  19810.412952\n",
      "55   Hombre            Pobre   80.401239  125.134461  43.219089  19964.814527\n",
      "58   Hombre            Pobre   72.370506  186.351808  31.013231  18443.760802\n",
      "61   Hombre             Rico  119.871572  139.603747  17.506358  21872.829970\n",
      "62   Hombre            Pobre   82.419144  174.803364  16.645850  11925.884175\n",
      "65   Hombre             Rico   91.592110  207.311949  43.121437  22271.013418\n",
      "..      ...              ...         ...         ...        ...           ...\n",
      "442  Hombre             Rico  103.539213  176.223837  46.228944  16721.681442\n",
      "446  Hombre            Pobre   41.594394  197.881419  32.933740  16207.016934\n",
      "447  Hombre            Pobre   71.898854  145.728011  37.305398  20894.032053\n",
      "448  Hombre      Clase media   68.221683  154.474879  26.519632  27312.252693\n",
      "455  Hombre            Pobre   88.389670  172.966093  34.949802  13803.713538\n",
      "456  Hombre            Pobre   76.368381  157.970215  21.940201  23526.498047\n",
      "457  Hombre      Clase media   58.723643  133.664295  34.027605  23433.524772\n",
      "459  Hombre      Clase media   47.539380  165.524175  26.547940  20599.364920\n",
      "462  Hombre            Pobre  108.146843  172.085807  16.258728  17816.842928\n",
      "465  Hombre      Clase media   79.784361  109.010498  29.763316  17210.205430\n",
      "466  Hombre      Clase media   42.912054  176.443106  47.247449  17251.478254\n",
      "468  Hombre            Pobre   37.788970  158.703511  21.134936  20197.266842\n",
      "469  Hombre             Rico   81.108639  164.816241  44.916621  19450.230796\n",
      "471  Hombre             Rico   49.753602  175.617097  30.343496  15476.742356\n",
      "473  Hombre      Clase media   68.577303  132.877746  28.270287  23072.652798\n",
      "474  Hombre            Pobre   46.004514  177.100838  41.477903  16277.318072\n",
      "476  Hombre            Pobre   48.053292  172.373627  32.744511  18838.871788\n",
      "481  Hombre            Pobre   29.028383  189.397032  47.450977  23384.297552\n",
      "482  Hombre             Rico   90.728441  153.570766  17.133635  18595.162047\n",
      "483  Hombre             Rico    8.476032  185.060423  61.716743  23285.320191\n",
      "484  Hombre      Clase media   86.395015  200.089372  40.268949  12000.580239\n",
      "486  Hombre      Clase media   80.325793  186.310876   9.222875  16280.049197\n",
      "487  Hombre             Rico  103.346903  136.358536  29.166839  14705.324995\n",
      "488  Hombre             Rico   62.548446  164.920059  12.892936  19948.486048\n",
      "491  Hombre            Pobre   97.932800  133.694422  23.530031  15053.198346\n",
      "492  Hombre            Pobre   84.575073  184.811158  34.008297  14011.828009\n",
      "494  Hombre             Rico   56.315027  169.029406  26.561931  15756.636840\n",
      "496  Hombre             Rico   54.667400  170.864904  26.502169  18674.708651\n",
      "497  Hombre             Rico   68.628059  134.685613  27.459753  22361.539315\n",
      "498  Hombre      Clase media   20.073167  159.974501  26.578268  18438.432852\n",
      "\n",
      "[257 rows x 6 columns]\n",
      "Mujer\n",
      "      Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "4    Mujer      Clase media   61.181933  121.918602  13.601447  23901.455692\n",
      "5    Mujer      Clase media   64.653648  202.156947  14.276626  22072.023957\n",
      "6    Mujer             Rico   33.608932  149.200442  19.499976  15909.629024\n",
      "7    Mujer             Rico   86.882174  176.266911  24.902984  20061.186877\n",
      "8    Mujer             Rico   93.163432  176.640838  22.585424  22440.940719\n",
      "9    Mujer      Clase media   46.400017  200.391440  42.941121  18356.377285\n",
      "10   Mujer            Pobre  -10.591598  164.383082  32.754846  16986.821945\n",
      "13   Mujer      Clase media   58.006239  149.233307  50.702530  15716.073638\n",
      "14   Mujer      Clase media   40.584091  186.890421  40.545193  19532.323031\n",
      "15   Mujer      Clase media   58.376074  161.419205  46.065908   8883.606484\n",
      "16   Mujer             Rico   31.307101  153.446700  18.829034  22110.670594\n",
      "17   Mujer             Rico   40.944907  134.548742  18.890701  14543.559477\n",
      "19   Mujer      Clase media   49.984521  124.648857  38.683582  21873.367663\n",
      "22   Mujer             Rico   75.237019  101.762481  24.728259  10947.541907\n",
      "26   Mujer            Pobre   39.406656  185.019067  -1.263022  18275.040730\n",
      "28   Mujer             Rico  107.653876  175.474834  42.926472  17171.668712\n",
      "29   Mujer             Rico   82.082029  161.359900  16.809352  16155.491060\n",
      "30   Mujer            Pobre   49.792379  138.004594  54.549133  14002.537593\n",
      "35   Mujer      Clase media  113.068925  151.408024  24.790545  14588.801427\n",
      "38   Mujer            Pobre   44.146773  130.819010  38.053475  16099.272054\n",
      "39   Mujer             Rico   41.869646  197.515862  42.097703  21657.207445\n",
      "43   Mujer      Clase media   73.714083  179.204190  38.324433  18116.352179\n",
      "44   Mujer      Clase media   79.519129  127.801687  11.768651  21049.313755\n",
      "46   Mujer             Rico   29.211076  167.447367  38.544269  19384.407549\n",
      "47   Mujer            Pobre   72.723934  153.714213  46.077096  18237.589463\n",
      "48   Mujer      Clase media   34.190581  106.739274  25.692490  18521.332356\n",
      "49   Mujer      Clase media   65.159198  140.627270  43.060404  18618.656504\n",
      "50   Mujer            Pobre   76.306046  178.668915   2.754252  20120.687602\n",
      "51   Mujer             Rico   13.666734  131.516817  31.524921  17073.394765\n",
      "53   Mujer             Rico   88.061979  142.096469  33.169495  20735.733463\n",
      "..     ...              ...         ...         ...        ...           ...\n",
      "440  Mujer      Clase media   99.731452  124.392941  37.549346  18926.785817\n",
      "441  Mujer      Clase media  100.175276  167.803278  20.802833  20746.853248\n",
      "443  Mujer             Rico   93.952518  114.858146   1.974032  14196.975239\n",
      "444  Mujer             Rico   59.199541  159.346651  34.747025  14991.943446\n",
      "445  Mujer            Pobre   64.633631  162.921895  12.030290  18690.264318\n",
      "449  Mujer            Pobre   64.397138  249.001372  29.056784  17067.067374\n",
      "450  Mujer            Pobre   49.764190  169.281061  23.944155  23545.258029\n",
      "451  Mujer      Clase media   33.518895  140.315611   2.410050  17851.931866\n",
      "452  Mujer             Rico   45.976064  148.955804  37.476934  12155.950668\n",
      "453  Mujer      Clase media   42.668972  173.107395  28.296817  17545.223136\n",
      "454  Mujer             Rico   86.700874  199.352994  44.115582  13951.983880\n",
      "458  Mujer      Clase media   70.019016  184.194487  30.955388  22317.994180\n",
      "460  Mujer             Rico   50.142045  172.648798  35.764421  20492.246108\n",
      "461  Mujer             Rico   74.561375  110.133710  18.076238  11975.177376\n",
      "463  Mujer      Clase media   97.708714  163.941644  21.955239  16721.414211\n",
      "464  Mujer            Pobre  109.477794  168.664001  28.741450  16189.254200\n",
      "467  Mujer            Pobre   88.191577  190.574839  26.991766  22009.234525\n",
      "470  Mujer            Pobre   72.331017  177.284841  16.654716  16937.490203\n",
      "472  Mujer            Pobre   33.872953  197.178530  33.316359  24088.810742\n",
      "475  Mujer            Pobre   36.891275  142.793779  28.438586  13206.245954\n",
      "477  Mujer             Rico   43.690748  171.307994  61.710629  14848.942000\n",
      "478  Mujer      Clase media   95.152456  161.146638  24.924640  12964.438548\n",
      "479  Mujer      Clase media   25.233586  157.248114  18.161252  18299.639012\n",
      "480  Mujer      Clase media   97.983450  177.863890  31.581959  19295.551864\n",
      "485  Mujer      Clase media   27.559226  125.476923  27.978056  17174.897929\n",
      "489  Mujer      Clase media   75.076798  208.668954  33.940396  13341.673461\n",
      "490  Mujer            Pobre  119.141026  182.532712  53.067710  14771.470981\n",
      "493  Mujer             Rico  102.140518  102.495762  31.202585  17172.427267\n",
      "495  Mujer             Rico   41.865146  137.059163  20.238401  21707.185946\n",
      "499  Mujer            Pobre   32.392374  159.457208  37.390841  20971.174066\n",
      "\n",
      "[243 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Revisemos los datos agrupados\n",
    "\n",
    "for nombre, grupo in agrupados_sexo:\n",
    "    print(nombre)\n",
    "    print(grupo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>61.181933</td>\n",
       "      <td>121.918602</td>\n",
       "      <td>13.601447</td>\n",
       "      <td>23901.455692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>64.653648</td>\n",
       "      <td>202.156947</td>\n",
       "      <td>14.276626</td>\n",
       "      <td>22072.023957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Rico</td>\n",
       "      <td>33.608932</td>\n",
       "      <td>149.200442</td>\n",
       "      <td>19.499976</td>\n",
       "      <td>15909.629024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Rico</td>\n",
       "      <td>86.882174</td>\n",
       "      <td>176.266911</td>\n",
       "      <td>24.902984</td>\n",
       "      <td>20061.186877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Rico</td>\n",
       "      <td>93.163432</td>\n",
       "      <td>176.640838</td>\n",
       "      <td>22.585424</td>\n",
       "      <td>22440.940719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sexo Estado Economico       Peso      Altura       Edad      Ingresos\n",
       "4  Mujer      Clase media  61.181933  121.918602  13.601447  23901.455692\n",
       "5  Mujer      Clase media  64.653648  202.156947  14.276626  22072.023957\n",
       "6  Mujer             Rico  33.608932  149.200442  19.499976  15909.629024\n",
       "7  Mujer             Rico  86.882174  176.266911  24.902984  20061.186877\n",
       "8  Mujer             Rico  93.163432  176.640838  22.585424  22440.940719"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisar por ejemplo a las mujeres\n",
    "agrupados_sexo.get_group('Mujer').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupemos por más de una categoría\n",
    "\n",
    "doble_agrupacion = data_3.groupby(['Sexo', 'Estado Economico'])\n",
    "len(doble_agrupacion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Hombre', 'Clase media')\n",
      "       Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "1    Hombre      Clase media  113.361591  141.806900  13.956049  15429.476751\n",
      "3    Hombre      Clase media   18.778331  140.283566  42.871327  17759.165610\n",
      "11   Hombre      Clase media   75.303962  154.932648  34.281199  19046.369405\n",
      "12   Hombre      Clase media   62.728649  166.651245  29.479539  17420.128140\n",
      "18   Hombre      Clase media   84.737532  182.552952  38.527954  20331.740509\n",
      "20   Hombre      Clase media   71.459400  145.489352  25.602147  22406.519566\n",
      "23   Hombre      Clase media   79.984957  171.061312  30.883470  21461.458889\n",
      "25   Hombre      Clase media   62.429489  104.401969  41.774141  14537.604535\n",
      "27   Hombre      Clase media   58.603480  208.998805  28.943294  24312.776440\n",
      "31   Hombre      Clase media   72.877127  159.872874  36.165485  18896.238479\n",
      "33   Hombre      Clase media   60.016595  129.018628  23.147194  20817.312735\n",
      "66   Hombre      Clase media   64.350584  128.344812   7.426064  20741.219434\n",
      "70   Hombre      Clase media   47.886301  196.167892  42.330173  19824.013403\n",
      "80   Hombre      Clase media   57.048224  219.346323  23.717325  19562.343124\n",
      "94   Hombre      Clase media    8.966734   80.530788  23.367649  15133.907726\n",
      "98   Hombre      Clase media   47.185271  199.193409  24.530735   8909.287626\n",
      "99   Hombre      Clase media   71.476700  200.842925  31.454117  20160.135655\n",
      "105  Hombre      Clase media   32.225120  152.711303  27.918561  18364.197371\n",
      "124  Hombre      Clase media   74.761507  204.088213  49.406206  11597.170994\n",
      "129  Hombre      Clase media   67.281228  169.837196  28.169565  16265.567708\n",
      "133  Hombre      Clase media   89.181271  142.470584  30.704459  22017.648418\n",
      "142  Hombre      Clase media   28.292559  173.433782  24.778627  15895.079742\n",
      "145  Hombre      Clase media   61.410065  197.841557  31.118750  17320.943796\n",
      "152  Hombre      Clase media   53.749082  168.421335  22.101205  17781.669488\n",
      "154  Hombre      Clase media   72.023968  162.920286  18.457637  14033.748332\n",
      "156  Hombre      Clase media   65.336157  155.785436  35.479325  21987.594938\n",
      "160  Hombre      Clase media   71.144149  132.204203  12.764324  19102.775778\n",
      "161  Hombre      Clase media   37.942915  180.842012  17.680896  20779.873425\n",
      "164  Hombre      Clase media   80.652286  145.304557  26.188991  19192.399928\n",
      "169  Hombre      Clase media   76.205452  153.226189  17.707988  22512.223437\n",
      "..      ...              ...         ...         ...        ...           ...\n",
      "287  Hombre      Clase media   88.748308  140.149895  29.236052  20365.924913\n",
      "308  Hombre      Clase media   81.219473  149.104467  19.677962  19565.458058\n",
      "314  Hombre      Clase media   49.450741  143.969513  22.803757  21101.770904\n",
      "319  Hombre      Clase media    9.147273  158.091097  30.191266  18735.870473\n",
      "322  Hombre      Clase media   51.968495  161.703072  20.344285  22535.798177\n",
      "323  Hombre      Clase media   90.864908  142.916965  23.535837  25300.872208\n",
      "326  Hombre      Clase media   67.977048  132.207643  32.298729  15487.221855\n",
      "327  Hombre      Clase media   55.051324  151.487822  51.491398  11012.318715\n",
      "347  Hombre      Clase media   71.992141  121.685631  40.952213  19893.233672\n",
      "359  Hombre      Clase media   58.944362  199.319895  37.835658  23116.424249\n",
      "360  Hombre      Clase media   70.616643  191.267992  30.131206  17096.895779\n",
      "366  Hombre      Clase media   52.693533  160.531939  30.533238  19534.868425\n",
      "385  Hombre      Clase media   42.880893  180.775584  45.527138  18664.358558\n",
      "387  Hombre      Clase media   -5.373007  176.380157  30.847368  16180.989149\n",
      "391  Hombre      Clase media   98.191714  182.413066   1.615836  24189.954460\n",
      "400  Hombre      Clase media   81.951277  144.534233  18.555595  15909.665281\n",
      "406  Hombre      Clase media   89.684805  114.757779  24.998551  17398.578530\n",
      "422  Hombre      Clase media   67.315244  136.296781  36.193550  18140.892134\n",
      "424  Hombre      Clase media   89.240180  164.426038  55.068285  23698.578183\n",
      "429  Hombre      Clase media  102.189079  181.429941  22.637755  12896.111635\n",
      "434  Hombre      Clase media   42.981332  130.723606  30.946256  25408.387951\n",
      "448  Hombre      Clase media   68.221683  154.474879  26.519632  27312.252693\n",
      "457  Hombre      Clase media   58.723643  133.664295  34.027605  23433.524772\n",
      "459  Hombre      Clase media   47.539380  165.524175  26.547940  20599.364920\n",
      "465  Hombre      Clase media   79.784361  109.010498  29.763316  17210.205430\n",
      "466  Hombre      Clase media   42.912054  176.443106  47.247449  17251.478254\n",
      "473  Hombre      Clase media   68.577303  132.877746  28.270287  23072.652798\n",
      "484  Hombre      Clase media   86.395015  200.089372  40.268949  12000.580239\n",
      "486  Hombre      Clase media   80.325793  186.310876   9.222875  16280.049197\n",
      "498  Hombre      Clase media   20.073167  159.974501  26.578268  18438.432852\n",
      "\n",
      "[79 rows x 6 columns]\n",
      "('Hombre', 'Pobre')\n",
      "       Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "2    Hombre            Pobre   61.085082  188.536748  23.820786  13117.313909\n",
      "40   Hombre            Pobre   77.669863   98.649087  12.787209  17043.917381\n",
      "41   Hombre            Pobre   69.902331  185.231122  23.337743  14492.387956\n",
      "42   Hombre            Pobre   56.935984  192.875052  10.721323  18044.052640\n",
      "55   Hombre            Pobre   80.401239  125.134461  43.219089  19964.814527\n",
      "58   Hombre            Pobre   72.370506  186.351808  31.013231  18443.760802\n",
      "62   Hombre            Pobre   82.419144  174.803364  16.645850  11925.884175\n",
      "69   Hombre            Pobre   70.757442  154.480758   1.709324  17079.587651\n",
      "71   Hombre            Pobre   87.442687  132.773588   5.099336  16298.200814\n",
      "73   Hombre            Pobre   42.533171  178.977269  21.317267  26278.260626\n",
      "76   Hombre            Pobre   37.319988  176.070722  16.651778  22445.772691\n",
      "82   Hombre            Pobre   76.512764  177.551124  25.448195  20954.493649\n",
      "84   Hombre            Pobre   66.055105  182.142501  28.101791  13953.708303\n",
      "85   Hombre            Pobre   56.185612  140.270492  34.417336  17906.470182\n",
      "92   Hombre            Pobre  107.116220  125.632428  31.992832  20915.430153\n",
      "95   Hombre            Pobre   33.433925  162.257569  31.687011  17540.187899\n",
      "102  Hombre            Pobre   70.903128  149.995923  21.932181  14234.073705\n",
      "109  Hombre            Pobre   28.737584  149.920924  29.940454  14305.137371\n",
      "122  Hombre            Pobre   65.116991  163.406115  14.057269  16741.430195\n",
      "125  Hombre            Pobre   64.251038  173.279617  26.199963  17501.476751\n",
      "132  Hombre            Pobre   66.967745  143.426572  41.531643  16352.819683\n",
      "140  Hombre            Pobre   81.031157  177.495365  41.780760  14937.911594\n",
      "173  Hombre            Pobre   53.358389  160.238799  35.973861  23711.590006\n",
      "182  Hombre            Pobre   59.556525  152.899703  23.486796  19427.459654\n",
      "187  Hombre            Pobre   87.943293  170.310458  44.367865  20185.988784\n",
      "192  Hombre            Pobre   45.822645  208.196153  22.984887  10325.578441\n",
      "195  Hombre            Pobre   87.792280  197.831857  39.269062  18269.166510\n",
      "197  Hombre            Pobre   79.201295  120.028044  53.335600  17146.715584\n",
      "198  Hombre            Pobre   82.644864  130.847526  35.936828  20922.353342\n",
      "200  Hombre            Pobre   50.244830  156.895947  39.538906  16306.172838\n",
      "..      ...              ...         ...         ...        ...           ...\n",
      "361  Hombre            Pobre   59.721446  195.300500  20.889848  13772.671426\n",
      "371  Hombre            Pobre   63.974942  135.769918  45.714749  14409.635481\n",
      "372  Hombre            Pobre   66.770001  129.397401  25.189691  20270.104056\n",
      "377  Hombre            Pobre   43.439737  176.989470  42.170180  10211.492785\n",
      "380  Hombre            Pobre   -0.594288  199.450061  40.133187  13637.259992\n",
      "381  Hombre            Pobre   52.240939  176.529729  28.708246  14266.177331\n",
      "386  Hombre            Pobre   36.147466  124.448146  18.230693  10172.365944\n",
      "396  Hombre            Pobre   10.236110  108.413935  50.891757  19176.227971\n",
      "397  Hombre            Pobre   95.835724  143.780147  27.516728  16250.348906\n",
      "403  Hombre            Pobre   36.064231  119.758987  40.155327  24448.713357\n",
      "407  Hombre            Pobre   65.940690  167.834534  30.420582  21660.033196\n",
      "408  Hombre            Pobre   43.827918  173.436901  37.223913  18440.188485\n",
      "410  Hombre            Pobre   57.754743  238.765490  32.645179  14054.745414\n",
      "412  Hombre            Pobre   95.145725  137.006191  38.568241  10620.904879\n",
      "419  Hombre            Pobre   82.893335  137.083879  45.023777  20039.627346\n",
      "423  Hombre            Pobre   74.161769  104.032057  34.285255  17483.285051\n",
      "425  Hombre            Pobre   62.442591  149.205524  23.412649  21823.116103\n",
      "430  Hombre            Pobre   89.740363  127.463811  37.137120  20859.814350\n",
      "437  Hombre            Pobre   69.696650  181.710060  32.928282  19270.545601\n",
      "446  Hombre            Pobre   41.594394  197.881419  32.933740  16207.016934\n",
      "447  Hombre            Pobre   71.898854  145.728011  37.305398  20894.032053\n",
      "455  Hombre            Pobre   88.389670  172.966093  34.949802  13803.713538\n",
      "456  Hombre            Pobre   76.368381  157.970215  21.940201  23526.498047\n",
      "462  Hombre            Pobre  108.146843  172.085807  16.258728  17816.842928\n",
      "468  Hombre            Pobre   37.788970  158.703511  21.134936  20197.266842\n",
      "474  Hombre            Pobre   46.004514  177.100838  41.477903  16277.318072\n",
      "476  Hombre            Pobre   48.053292  172.373627  32.744511  18838.871788\n",
      "481  Hombre            Pobre   29.028383  189.397032  47.450977  23384.297552\n",
      "491  Hombre            Pobre   97.932800  133.694422  23.530031  15053.198346\n",
      "492  Hombre            Pobre   84.575073  184.811158  34.008297  14011.828009\n",
      "\n",
      "[92 rows x 6 columns]\n",
      "('Hombre', 'Rico')\n",
      "       Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "0    Hombre             Rico   33.346201  178.854874  45.385946  20813.470719\n",
      "21   Hombre             Rico   48.845505  171.255861  36.341825  17866.847612\n",
      "24   Hombre             Rico   69.516723  161.285463  14.448105  16987.956652\n",
      "32   Hombre             Rico   60.457446  108.682354  32.892411  18915.110284\n",
      "34   Hombre             Rico   56.810920  131.873287  53.618540  14463.387698\n",
      "36   Hombre             Rico   86.511228  173.451385  30.681507  24208.054556\n",
      "37   Hombre             Rico   65.079106  130.523457  31.906173  16176.374604\n",
      "45   Hombre             Rico   98.355855  177.743133  42.185538  17108.076404\n",
      "52   Hombre             Rico   16.357960  161.415063  23.518715  17956.664940\n",
      "54   Hombre             Rico   90.269989  164.009535  40.801611  19810.412952\n",
      "61   Hombre             Rico  119.871572  139.603747  17.506358  21872.829970\n",
      "65   Hombre             Rico   91.592110  207.311949  43.121437  22271.013418\n",
      "67   Hombre             Rico  116.414782  187.965914  26.974854  21512.618242\n",
      "83   Hombre             Rico   83.954548  181.313569  20.586196  21407.691451\n",
      "87   Hombre             Rico   84.336987  143.520413  27.696125  21248.373752\n",
      "88   Hombre             Rico   71.976589  139.786804  34.705702  24534.843353\n",
      "97   Hombre             Rico   50.614080  164.790056  29.132660  19756.732213\n",
      "101  Hombre             Rico   46.458550  145.552165  26.427287  17600.660775\n",
      "112  Hombre             Rico   58.028707  201.535258  33.085161  23547.997045\n",
      "128  Hombre             Rico   43.930688  158.616771  51.235194  19351.295016\n",
      "137  Hombre             Rico   24.057271  167.834114  28.061627  13979.456075\n",
      "143  Hombre             Rico   73.166341  139.562934  46.144969  18209.886378\n",
      "144  Hombre             Rico   50.759390  188.077390  46.749676  17110.042990\n",
      "146  Hombre             Rico   92.844424  154.015235  33.895451  21374.180733\n",
      "158  Hombre             Rico   48.379885  177.556822  35.051575  17185.182022\n",
      "176  Hombre             Rico   78.243839  120.968502  38.849695  20598.864745\n",
      "194  Hombre             Rico   59.628517  221.169610  28.883151  16531.852308\n",
      "196  Hombre             Rico   68.474471  136.375295  25.028265  10451.648858\n",
      "202  Hombre             Rico   52.015486  108.919569  34.883346  21533.348317\n",
      "208  Hombre             Rico   40.210148  164.197095  52.882622  12108.463127\n",
      "..      ...              ...         ...         ...        ...           ...\n",
      "370  Hombre             Rico   60.990121  145.784753  43.416678  18951.725008\n",
      "374  Hombre             Rico   12.467165  175.911061  24.315441  19070.713085\n",
      "375  Hombre             Rico   73.547270  187.090573  24.154273  22561.767189\n",
      "376  Hombre             Rico   66.418609  151.011497   9.401122  22299.116326\n",
      "379  Hombre             Rico   35.335811   98.880741  40.228614  12615.221418\n",
      "382  Hombre             Rico   97.878594  209.525001  24.580961  11753.881638\n",
      "383  Hombre             Rico   83.377093  142.466191  31.729866  14508.470685\n",
      "389  Hombre             Rico   66.066553  177.017183  38.720558  18858.171229\n",
      "390  Hombre             Rico   85.919046  178.720784  46.043863  17262.071495\n",
      "392  Hombre             Rico   65.279682  144.962402  37.032584  17292.637167\n",
      "394  Hombre             Rico    6.244182  114.856046  23.071098  16436.944615\n",
      "395  Hombre             Rico  113.731133  166.918998  55.689046  15169.863621\n",
      "402  Hombre             Rico   68.820446  194.641838  32.837234  14146.996708\n",
      "404  Hombre             Rico   54.466563  259.014456  22.598526  20062.349218\n",
      "414  Hombre             Rico   61.059092  185.772039  28.812479  21312.380597\n",
      "416  Hombre             Rico   22.317768  121.992236  42.344332  21257.369922\n",
      "426  Hombre             Rico   83.217765  124.498500  33.173307  13472.989111\n",
      "427  Hombre             Rico   47.336768  163.141672  25.802270  19297.591520\n",
      "435  Hombre             Rico   93.497424  131.653485  18.024017  21811.938090\n",
      "439  Hombre             Rico   29.188015  151.794286  23.554060  21776.334150\n",
      "442  Hombre             Rico  103.539213  176.223837  46.228944  16721.681442\n",
      "469  Hombre             Rico   81.108639  164.816241  44.916621  19450.230796\n",
      "471  Hombre             Rico   49.753602  175.617097  30.343496  15476.742356\n",
      "482  Hombre             Rico   90.728441  153.570766  17.133635  18595.162047\n",
      "483  Hombre             Rico    8.476032  185.060423  61.716743  23285.320191\n",
      "487  Hombre             Rico  103.346903  136.358536  29.166839  14705.324995\n",
      "488  Hombre             Rico   62.548446  164.920059  12.892936  19948.486048\n",
      "494  Hombre             Rico   56.315027  169.029406  26.561931  15756.636840\n",
      "496  Hombre             Rico   54.667400  170.864904  26.502169  18674.708651\n",
      "497  Hombre             Rico   68.628059  134.685613  27.459753  22361.539315\n",
      "\n",
      "[86 rows x 6 columns]\n",
      "('Mujer', 'Clase media')\n",
      "      Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "4    Mujer      Clase media   61.181933  121.918602  13.601447  23901.455692\n",
      "5    Mujer      Clase media   64.653648  202.156947  14.276626  22072.023957\n",
      "9    Mujer      Clase media   46.400017  200.391440  42.941121  18356.377285\n",
      "13   Mujer      Clase media   58.006239  149.233307  50.702530  15716.073638\n",
      "14   Mujer      Clase media   40.584091  186.890421  40.545193  19532.323031\n",
      "15   Mujer      Clase media   58.376074  161.419205  46.065908   8883.606484\n",
      "19   Mujer      Clase media   49.984521  124.648857  38.683582  21873.367663\n",
      "35   Mujer      Clase media  113.068925  151.408024  24.790545  14588.801427\n",
      "43   Mujer      Clase media   73.714083  179.204190  38.324433  18116.352179\n",
      "44   Mujer      Clase media   79.519129  127.801687  11.768651  21049.313755\n",
      "48   Mujer      Clase media   34.190581  106.739274  25.692490  18521.332356\n",
      "49   Mujer      Clase media   65.159198  140.627270  43.060404  18618.656504\n",
      "56   Mujer      Clase media   45.364629  173.188047  32.123878  26850.031568\n",
      "64   Mujer      Clase media   98.127548   97.993416  41.071870  16587.400568\n",
      "75   Mujer      Clase media   61.935919  127.210796  39.480257  12950.379215\n",
      "78   Mujer      Clase media  103.752724  172.713283  29.421074  17410.745220\n",
      "93   Mujer      Clase media   64.142693  182.284956  20.152621  19535.513508\n",
      "96   Mujer      Clase media   67.088138  178.164939  30.300313  21062.260116\n",
      "100  Mujer      Clase media   69.128470  110.085675  41.432815  21777.022006\n",
      "114  Mujer      Clase media   84.935086  144.519525  15.224136  12965.931147\n",
      "115  Mujer      Clase media   71.699327  138.291325  43.926612   8867.894779\n",
      "120  Mujer      Clase media   82.914338  140.542303  16.436442  16210.734443\n",
      "130  Mujer      Clase media  105.015537  175.033642  33.122502  13890.115979\n",
      "131  Mujer      Clase media   40.435816  184.394914  48.267761  27458.032466\n",
      "138  Mujer      Clase media  130.575444  155.250332  29.936659  14617.028658\n",
      "139  Mujer      Clase media   97.930520  179.434862  46.500604  19071.790553\n",
      "147  Mujer      Clase media   69.487956  148.540740   7.564566  17249.554327\n",
      "162  Mujer      Clase media   25.338716  133.880423  11.115671  22896.393558\n",
      "168  Mujer      Clase media   81.486771  171.591400  32.257755  15261.740626\n",
      "170  Mujer      Clase media   59.593067  111.613783  48.476343  16390.113744\n",
      "..     ...              ...         ...         ...        ...           ...\n",
      "291  Mujer      Clase media   85.008406  145.444154  38.334064  19347.815824\n",
      "292  Mujer      Clase media   94.019245  134.350068  46.648633  13670.372656\n",
      "295  Mujer      Clase media   73.199613  101.294508  20.045955  16901.596893\n",
      "299  Mujer      Clase media   32.295227  139.460676  25.564197  22687.438810\n",
      "324  Mujer      Clase media   17.742725  200.695972  24.428239  23429.755194\n",
      "330  Mujer      Clase media   30.220156  188.649133  28.094260  15818.383084\n",
      "336  Mujer      Clase media   93.890000  184.234879  29.807593  17174.265518\n",
      "338  Mujer      Clase media  113.385329  151.312295  18.044763  12155.339938\n",
      "352  Mujer      Clase media   51.534470  145.830080  25.199894  20505.413871\n",
      "355  Mujer      Clase media    4.043933  157.121423  37.843929  23832.230627\n",
      "364  Mujer      Clase media   60.656545  108.589947  13.200165  22114.201757\n",
      "365  Mujer      Clase media   35.191447  100.928075  47.861974  18285.758426\n",
      "393  Mujer      Clase media   61.446876  194.486431  43.421100  16679.748892\n",
      "399  Mujer      Clase media   37.445672  119.887149  34.798536  21728.827447\n",
      "405  Mujer      Clase media   40.947007  147.064771  32.093009  18760.792263\n",
      "411  Mujer      Clase media   75.054157  134.985146  23.549993  16699.311266\n",
      "420  Mujer      Clase media    2.971024  198.889518  31.823646  19281.399606\n",
      "433  Mujer      Clase media   90.072923  143.939242  31.986288  16445.277829\n",
      "436  Mujer      Clase media   88.200555  154.997915  56.282319  18470.944226\n",
      "440  Mujer      Clase media   99.731452  124.392941  37.549346  18926.785817\n",
      "441  Mujer      Clase media  100.175276  167.803278  20.802833  20746.853248\n",
      "451  Mujer      Clase media   33.518895  140.315611   2.410050  17851.931866\n",
      "453  Mujer      Clase media   42.668972  173.107395  28.296817  17545.223136\n",
      "458  Mujer      Clase media   70.019016  184.194487  30.955388  22317.994180\n",
      "463  Mujer      Clase media   97.708714  163.941644  21.955239  16721.414211\n",
      "478  Mujer      Clase media   95.152456  161.146638  24.924640  12964.438548\n",
      "479  Mujer      Clase media   25.233586  157.248114  18.161252  18299.639012\n",
      "480  Mujer      Clase media   97.983450  177.863890  31.581959  19295.551864\n",
      "485  Mujer      Clase media   27.559226  125.476923  27.978056  17174.897929\n",
      "489  Mujer      Clase media   75.076798  208.668954  33.940396  13341.673461\n",
      "\n",
      "[81 rows x 6 columns]\n",
      "('Mujer', 'Pobre')\n",
      "      Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "10   Mujer            Pobre  -10.591598  164.383082  32.754846  16986.821945\n",
      "26   Mujer            Pobre   39.406656  185.019067  -1.263022  18275.040730\n",
      "30   Mujer            Pobre   49.792379  138.004594  54.549133  14002.537593\n",
      "38   Mujer            Pobre   44.146773  130.819010  38.053475  16099.272054\n",
      "47   Mujer            Pobre   72.723934  153.714213  46.077096  18237.589463\n",
      "50   Mujer            Pobre   76.306046  178.668915   2.754252  20120.687602\n",
      "57   Mujer            Pobre   60.671523  188.907236  21.466557  18478.147033\n",
      "68   Mujer            Pobre   67.675218  146.485817  64.969351  14187.588761\n",
      "79   Mujer            Pobre   67.102038  183.796384  19.839221  16390.748120\n",
      "81   Mujer            Pobre   73.132096  173.157547  18.556288  17312.467989\n",
      "86   Mujer            Pobre   91.403481  191.307139  50.406562  21086.182126\n",
      "89   Mujer            Pobre   49.646347  180.556228  17.887297  11225.830889\n",
      "90   Mujer            Pobre   57.777721  151.619749  34.111365  18354.466639\n",
      "91   Mujer            Pobre   55.742644  135.803350  15.772402  15436.605684\n",
      "108  Mujer            Pobre   62.114820  131.517826  38.310871  15349.566255\n",
      "110  Mujer            Pobre   71.887446  198.862489  20.647546  15948.680061\n",
      "113  Mujer            Pobre  103.361196  195.142596  38.342012  17138.131235\n",
      "119  Mujer            Pobre   94.590317  168.283630  30.226372  16066.098630\n",
      "123  Mujer            Pobre  109.520847  136.027557  24.912961  22143.892592\n",
      "134  Mujer            Pobre   74.481450  121.425482  12.092921  15359.534872\n",
      "148  Mujer            Pobre   17.796603   95.094290  30.708652  16173.731107\n",
      "151  Mujer            Pobre   75.925932  175.197635  16.825644  23149.792189\n",
      "153  Mujer            Pobre   70.568414  147.140718  33.488144  20937.014220\n",
      "155  Mujer            Pobre   31.786478  160.203282  29.597544  18782.679045\n",
      "165  Mujer            Pobre   98.274208  190.128148  27.672008  19425.572798\n",
      "166  Mujer            Pobre  114.648416  170.062984  32.773839  18605.464690\n",
      "167  Mujer            Pobre   72.554366  172.525115  31.724301  19176.305684\n",
      "172  Mujer            Pobre   57.997615  152.128856   9.594383  14117.488612\n",
      "175  Mujer            Pobre   66.712121  160.602845  28.864015  12479.717766\n",
      "181  Mujer            Pobre   43.944735  235.766951  35.267650  18677.880788\n",
      "..     ...              ...         ...         ...        ...           ...\n",
      "302  Mujer            Pobre   30.776725  134.271993  31.757981  17859.706356\n",
      "304  Mujer            Pobre   20.108745  169.877812  36.629530  18146.761216\n",
      "307  Mujer            Pobre  116.380398  185.737326  38.881247  13267.640852\n",
      "312  Mujer            Pobre   74.154441  131.784818  33.347065  25294.664929\n",
      "313  Mujer            Pobre   44.979655  166.705233  19.526992  22092.057808\n",
      "331  Mujer            Pobre  107.687219  182.559672  35.416117  23597.034636\n",
      "340  Mujer            Pobre   45.164912  151.071697  26.818868  24482.602061\n",
      "346  Mujer            Pobre   57.030863  190.312031  38.961817  20116.748299\n",
      "350  Mujer            Pobre   34.407265  141.618760  61.726136  16561.227174\n",
      "351  Mujer            Pobre   96.894760  126.818514  23.008546  17178.331404\n",
      "357  Mujer            Pobre   89.065085  165.223819  29.161048  15654.498717\n",
      "362  Mujer            Pobre   28.321485  185.642064  12.490321  19375.575669\n",
      "378  Mujer            Pobre   58.232071  169.290022  38.206853  19410.948416\n",
      "388  Mujer            Pobre   51.883060  160.213965  41.910733  23496.387514\n",
      "401  Mujer            Pobre   76.288865  180.731817   9.291078  20232.583975\n",
      "415  Mujer            Pobre   53.583740  195.956124  35.039425  19477.961940\n",
      "417  Mujer            Pobre   41.845083  200.927158  26.287957  15435.433191\n",
      "418  Mujer            Pobre  131.582906  214.067832  29.114408  22642.499332\n",
      "431  Mujer            Pobre   71.324358  170.236415  24.868960  18308.660041\n",
      "438  Mujer            Pobre  139.203832  188.501848  22.332462  20642.608067\n",
      "445  Mujer            Pobre   64.633631  162.921895  12.030290  18690.264318\n",
      "449  Mujer            Pobre   64.397138  249.001372  29.056784  17067.067374\n",
      "450  Mujer            Pobre   49.764190  169.281061  23.944155  23545.258029\n",
      "464  Mujer            Pobre  109.477794  168.664001  28.741450  16189.254200\n",
      "467  Mujer            Pobre   88.191577  190.574839  26.991766  22009.234525\n",
      "470  Mujer            Pobre   72.331017  177.284841  16.654716  16937.490203\n",
      "472  Mujer            Pobre   33.872953  197.178530  33.316359  24088.810742\n",
      "475  Mujer            Pobre   36.891275  142.793779  28.438586  13206.245954\n",
      "490  Mujer            Pobre  119.141026  182.532712  53.067710  14771.470981\n",
      "499  Mujer            Pobre   32.392374  159.457208  37.390841  20971.174066\n",
      "\n",
      "[72 rows x 6 columns]\n",
      "('Mujer', 'Rico')\n",
      "      Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
      "6    Mujer             Rico   33.608932  149.200442  19.499976  15909.629024\n",
      "7    Mujer             Rico   86.882174  176.266911  24.902984  20061.186877\n",
      "8    Mujer             Rico   93.163432  176.640838  22.585424  22440.940719\n",
      "16   Mujer             Rico   31.307101  153.446700  18.829034  22110.670594\n",
      "17   Mujer             Rico   40.944907  134.548742  18.890701  14543.559477\n",
      "22   Mujer             Rico   75.237019  101.762481  24.728259  10947.541907\n",
      "28   Mujer             Rico  107.653876  175.474834  42.926472  17171.668712\n",
      "29   Mujer             Rico   82.082029  161.359900  16.809352  16155.491060\n",
      "39   Mujer             Rico   41.869646  197.515862  42.097703  21657.207445\n",
      "46   Mujer             Rico   29.211076  167.447367  38.544269  19384.407549\n",
      "51   Mujer             Rico   13.666734  131.516817  31.524921  17073.394765\n",
      "53   Mujer             Rico   88.061979  142.096469  33.169495  20735.733463\n",
      "59   Mujer             Rico   38.547975  207.020269   9.969273   9722.436525\n",
      "60   Mujer             Rico   50.727924  142.963689  29.801149  14702.150243\n",
      "63   Mujer             Rico   68.625278  184.021786  14.924959  15452.909141\n",
      "72   Mujer             Rico   17.611407  136.276460  39.151419  14323.478741\n",
      "74   Mujer             Rico   46.572765  104.903638  32.986976  18970.547004\n",
      "77   Mujer             Rico  101.703429  162.607418  16.496269  25289.762143\n",
      "103  Mujer             Rico   17.837618  164.985492  38.589024  20144.321696\n",
      "104  Mujer             Rico   63.712303  118.309269  42.510729  13758.034751\n",
      "106  Mujer             Rico   53.087430  156.916570  47.908183  16421.371433\n",
      "107  Mujer             Rico   50.913768  143.597978  40.674485  16462.990536\n",
      "111  Mujer             Rico   32.258007  181.982122  29.797028  13971.303493\n",
      "116  Mujer             Rico   61.537137  146.049930  44.545353  15664.961388\n",
      "117  Mujer             Rico   57.631477  122.243479  33.176179  21160.182441\n",
      "118  Mujer             Rico   82.469598  131.582969  -3.962558  24931.742458\n",
      "121  Mujer             Rico   91.615903  137.385965   6.142971  25276.314099\n",
      "126  Mujer             Rico   83.887658  178.816849   6.637029  18798.361813\n",
      "127  Mujer             Rico   79.304307  191.432436  36.514161  18456.447229\n",
      "135  Mujer             Rico   53.411018  112.259004  36.701914  24274.772720\n",
      "..     ...              ...         ...         ...        ...           ...\n",
      "317  Mujer             Rico   26.833444  105.435801  47.206224  21848.467222\n",
      "320  Mujer             Rico   83.081458  181.869307  43.670882  17490.104736\n",
      "321  Mujer             Rico   73.198535  130.524214  34.623432  23210.756383\n",
      "328  Mujer             Rico   39.032603  133.837154  29.088720  16872.422268\n",
      "329  Mujer             Rico  102.637227  182.814687  37.744539  17131.033159\n",
      "335  Mujer             Rico   68.375668  195.219389  29.080453  16587.816787\n",
      "339  Mujer             Rico   41.027510  151.082450  12.017843  15493.777919\n",
      "342  Mujer             Rico   79.749131  149.294771  32.529454  19491.172735\n",
      "344  Mujer             Rico  113.286186  207.576025  28.371911  16571.799871\n",
      "345  Mujer             Rico   60.543742  129.409801  37.710709  15808.173674\n",
      "349  Mujer             Rico   75.865513   50.767222  34.020550  16220.874223\n",
      "353  Mujer             Rico   83.715826  139.801328  26.940822  17754.396893\n",
      "356  Mujer             Rico   66.576031  140.543020  23.445791  20786.779439\n",
      "373  Mujer             Rico   47.247790  186.086650  38.339240  14677.254733\n",
      "384  Mujer             Rico   26.127955  206.462144  28.998159  17618.740882\n",
      "398  Mujer             Rico   57.986185  157.145931  15.488776  16676.766602\n",
      "409  Mujer             Rico   75.624786  135.965823  37.072278  19643.919862\n",
      "413  Mujer             Rico   82.387699  178.722721  17.650421  18727.713406\n",
      "421  Mujer             Rico   61.428313  110.925679  11.079925  15396.424417\n",
      "428  Mujer             Rico   35.376707  133.171485  43.384477  14627.529236\n",
      "432  Mujer             Rico   72.209276  192.124342  12.846644  20314.362548\n",
      "443  Mujer             Rico   93.952518  114.858146   1.974032  14196.975239\n",
      "444  Mujer             Rico   59.199541  159.346651  34.747025  14991.943446\n",
      "452  Mujer             Rico   45.976064  148.955804  37.476934  12155.950668\n",
      "454  Mujer             Rico   86.700874  199.352994  44.115582  13951.983880\n",
      "460  Mujer             Rico   50.142045  172.648798  35.764421  20492.246108\n",
      "461  Mujer             Rico   74.561375  110.133710  18.076238  11975.177376\n",
      "477  Mujer             Rico   43.690748  171.307994  61.710629  14848.942000\n",
      "493  Mujer             Rico  102.140518  102.495762  31.202585  17172.427267\n",
      "495  Mujer             Rico   41.865146  137.059163  20.238401  21707.185946\n",
      "\n",
      "[90 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Revisemos los datos agrupados\n",
    "\n",
    "for nombre, grupo in doble_agrupacion:\n",
    "    print(nombre)\n",
    "    print(grupo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operación sobre datos agrupados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>5013.312283</td>\n",
       "      <td>12436.794040</td>\n",
       "      <td>2257.234591</td>\n",
       "      <td>1.455356e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>5741.598002</td>\n",
       "      <td>14828.038511</td>\n",
       "      <td>2697.589791</td>\n",
       "      <td>1.586103e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>5372.297226</td>\n",
       "      <td>13440.993632</td>\n",
       "      <td>2683.046478</td>\n",
       "      <td>1.578362e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>5236.669807</td>\n",
       "      <td>12549.745940</td>\n",
       "      <td>2455.612164</td>\n",
       "      <td>1.457918e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>4681.788903</td>\n",
       "      <td>12054.681951</td>\n",
       "      <td>2132.585335</td>\n",
       "      <td>1.310266e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>5598.311570</td>\n",
       "      <td>13922.070760</td>\n",
       "      <td>2663.986143</td>\n",
       "      <td>1.617598e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Peso        Altura         Edad      Ingresos\n",
       "Sexo   Estado Economico                                                      \n",
       "Hombre Clase media       5013.312283  12436.794040  2257.234591  1.455356e+06\n",
       "       Pobre             5741.598002  14828.038511  2697.589791  1.586103e+06\n",
       "       Rico              5372.297226  13440.993632  2683.046478  1.578362e+06\n",
       "Mujer  Clase media       5236.669807  12549.745940  2455.612164  1.457918e+06\n",
       "       Pobre             4681.788903  12054.681951  2132.585335  1.310266e+06\n",
       "       Rico              5598.311570  13922.070760  2663.986143  1.617598e+06"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>63.459649</td>\n",
       "      <td>157.427773</td>\n",
       "      <td>28.572590</td>\n",
       "      <td>18422.226299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>62.408674</td>\n",
       "      <td>161.174332</td>\n",
       "      <td>29.321628</td>\n",
       "      <td>17240.245195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>62.468572</td>\n",
       "      <td>156.290624</td>\n",
       "      <td>31.198215</td>\n",
       "      <td>18353.048110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>64.650245</td>\n",
       "      <td>154.935135</td>\n",
       "      <td>30.316200</td>\n",
       "      <td>17998.993417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>65.024846</td>\n",
       "      <td>167.426138</td>\n",
       "      <td>29.619241</td>\n",
       "      <td>18198.141683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>62.203462</td>\n",
       "      <td>154.689675</td>\n",
       "      <td>29.599846</td>\n",
       "      <td>17973.311188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Peso      Altura       Edad      Ingresos\n",
       "Sexo   Estado Economico                                                \n",
       "Hombre Clase media       63.459649  157.427773  28.572590  18422.226299\n",
       "       Pobre             62.408674  161.174332  29.321628  17240.245195\n",
       "       Rico              62.468572  156.290624  31.198215  18353.048110\n",
       "Mujer  Clase media       64.650245  154.935135  30.316200  17998.993417\n",
       "       Pobre             65.024846  167.426138  29.619241  18198.141683\n",
       "       Rico              62.203462  154.689675  29.599846  17973.311188"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sexo    Estado Economico\n",
       "Hombre  Clase media         79\n",
       "        Pobre               92\n",
       "        Rico                86\n",
       "Mujer   Clase media         81\n",
       "        Pobre               72\n",
       "        Rico                90\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Clase media</th>\n",
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       "      <td>63.459649</td>\n",
       "      <td>23.902090</td>\n",
       "      <td>-5.373007</td>\n",
       "      <td>50.709618</td>\n",
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       "      <td>113.361591</td>\n",
       "      <td>79.0</td>\n",
       "      <td>157.427773</td>\n",
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       "      <td>79.0</td>\n",
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       "      <td>18664.358558</td>\n",
       "      <td>20670.292177</td>\n",
       "      <td>27312.252693</td>\n",
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       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>92.0</td>\n",
       "      <td>62.408674</td>\n",
       "      <td>23.941858</td>\n",
       "      <td>-0.594288</td>\n",
       "      <td>43.730873</td>\n",
       "      <td>64.684014</td>\n",
       "      <td>78.052721</td>\n",
       "      <td>123.060167</td>\n",
       "      <td>92.0</td>\n",
       "      <td>161.174332</td>\n",
       "      <td>...</td>\n",
       "      <td>37.244284</td>\n",
       "      <td>53.335600</td>\n",
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       "      <td>19766.556926</td>\n",
       "      <td>26278.260626</td>\n",
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       "      <td>82.690483</td>\n",
       "      <td>119.871572</td>\n",
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       "      <td>37.461040</td>\n",
       "      <td>61.716743</td>\n",
       "      <td>86.0</td>\n",
       "      <td>18353.048110</td>\n",
       "      <td>3440.538566</td>\n",
       "      <td>10451.648858</td>\n",
       "      <td>15861.784249</td>\n",
       "      <td>18581.682900</td>\n",
       "      <td>21255.120880</td>\n",
       "      <td>26768.083564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>81.0</td>\n",
       "      <td>64.650245</td>\n",
       "      <td>28.358615</td>\n",
       "      <td>2.971024</td>\n",
       "      <td>42.565867</td>\n",
       "      <td>64.653648</td>\n",
       "      <td>88.200555</td>\n",
       "      <td>130.575444</td>\n",
       "      <td>81.0</td>\n",
       "      <td>154.935135</td>\n",
       "      <td>...</td>\n",
       "      <td>38.683582</td>\n",
       "      <td>56.282319</td>\n",
       "      <td>81.0</td>\n",
       "      <td>17998.993417</td>\n",
       "      <td>3549.832838</td>\n",
       "      <td>8867.894779</td>\n",
       "      <td>15818.383084</td>\n",
       "      <td>17851.931866</td>\n",
       "      <td>20234.182284</td>\n",
       "      <td>27458.032466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>72.0</td>\n",
       "      <td>65.024846</td>\n",
       "      <td>29.179655</td>\n",
       "      <td>-10.591598</td>\n",
       "      <td>44.771435</td>\n",
       "      <td>65.672876</td>\n",
       "      <td>77.007572</td>\n",
       "      <td>139.203832</td>\n",
       "      <td>72.0</td>\n",
       "      <td>167.426138</td>\n",
       "      <td>...</td>\n",
       "      <td>35.492270</td>\n",
       "      <td>64.969351</td>\n",
       "      <td>72.0</td>\n",
       "      <td>18198.141683</td>\n",
       "      <td>3247.672597</td>\n",
       "      <td>11225.830889</td>\n",
       "      <td>16090.978698</td>\n",
       "      <td>18192.175340</td>\n",
       "      <td>20148.661695</td>\n",
       "      <td>25294.664929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>90.0</td>\n",
       "      <td>62.203462</td>\n",
       "      <td>25.902806</td>\n",
       "      <td>8.305391</td>\n",
       "      <td>41.236919</td>\n",
       "      <td>61.482725</td>\n",
       "      <td>82.449123</td>\n",
       "      <td>113.286186</td>\n",
       "      <td>90.0</td>\n",
       "      <td>154.689675</td>\n",
       "      <td>...</td>\n",
       "      <td>38.238079</td>\n",
       "      <td>61.710629</td>\n",
       "      <td>90.0</td>\n",
       "      <td>17973.311188</td>\n",
       "      <td>3530.933151</td>\n",
       "      <td>9722.436525</td>\n",
       "      <td>15463.126335</td>\n",
       "      <td>17671.063410</td>\n",
       "      <td>20447.775218</td>\n",
       "      <td>25289.762143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Peso                                              \\\n",
       "                        count       mean        std        min        25%   \n",
       "Sexo   Estado Economico                                                     \n",
       "Hombre Clase media       79.0  63.459649  23.902090  -5.373007  50.709618   \n",
       "       Pobre             92.0  62.408674  23.941858  -0.594288  43.730873   \n",
       "       Rico              86.0  62.468572  26.900411  -9.920644  46.609467   \n",
       "Mujer  Clase media       81.0  64.650245  28.358615   2.971024  42.565867   \n",
       "       Pobre             72.0  65.024846  29.179655 -10.591598  44.771435   \n",
       "       Rico              90.0  62.203462  25.902806   8.305391  41.236919   \n",
       "\n",
       "                                                          Altura              \\\n",
       "                               50%        75%         max  count        mean   \n",
       "Sexo   Estado Economico                                                        \n",
       "Hombre Clase media       67.281228  80.155375  113.361591   79.0  157.427773   \n",
       "       Pobre             64.684014  78.052721  123.060167   92.0  161.174332   \n",
       "       Rico              61.028891  82.690483  119.871572   86.0  156.290624   \n",
       "Mujer  Clase media       64.653648  88.200555  130.575444   81.0  154.935135   \n",
       "       Pobre             65.672876  77.007572  139.203832   72.0  167.426138   \n",
       "       Rico              61.482725  82.449123  113.286186   90.0  154.689675   \n",
       "\n",
       "                         ...       Edad            Ingresos                \\\n",
       "                         ...        75%        max    count          mean   \n",
       "Sexo   Estado Economico  ...                                                \n",
       "Hombre Clase media       ...  34.154402  61.627814     79.0  18422.226299   \n",
       "       Pobre             ...  37.244284  53.335600     92.0  17240.245195   \n",
       "       Rico              ...  37.461040  61.716743     86.0  18353.048110   \n",
       "Mujer  Clase media       ...  38.683582  56.282319     81.0  17998.993417   \n",
       "       Pobre             ...  35.492270  64.969351     72.0  18198.141683   \n",
       "       Rico              ...  38.238079  61.710629     90.0  17973.311188   \n",
       "\n",
       "                                                                  \\\n",
       "                                 std           min           25%   \n",
       "Sexo   Estado Economico                                            \n",
       "Hombre Clase media       3633.789522   8909.287626  16272.808453   \n",
       "       Pobre             3696.776907   6099.473523  14407.276102   \n",
       "       Rico              3440.538566  10451.648858  15861.784249   \n",
       "Mujer  Clase media       3549.832838   8867.894779  15818.383084   \n",
       "       Pobre             3247.672597  11225.830889  16090.978698   \n",
       "       Rico              3530.933151   9722.436525  15463.126335   \n",
       "\n",
       "                                                                   \n",
       "                                  50%           75%           max  \n",
       "Sexo   Estado Economico                                            \n",
       "Hombre Clase media       18664.358558  20670.292177  27312.252693  \n",
       "       Pobre             17390.486786  19766.556926  26278.260626  \n",
       "       Rico              18581.682900  21255.120880  26768.083564  \n",
       "Mujer  Clase media       17851.931866  20234.182284  27458.032466  \n",
       "       Pobre             18192.175340  20148.661695  25294.664929  \n",
       "       Rico              17671.063410  20447.775218  25289.762143  \n",
       "\n",
       "[6 rows x 32 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>79.0</td>\n",
       "      <td>18422.226299</td>\n",
       "      <td>3633.789522</td>\n",
       "      <td>8909.287626</td>\n",
       "      <td>16272.808453</td>\n",
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       "      <td>20670.292177</td>\n",
       "      <td>27312.252693</td>\n",
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       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>92.0</td>\n",
       "      <td>17240.245195</td>\n",
       "      <td>3696.776907</td>\n",
       "      <td>6099.473523</td>\n",
       "      <td>14407.276102</td>\n",
       "      <td>17390.486786</td>\n",
       "      <td>19766.556926</td>\n",
       "      <td>26278.260626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>86.0</td>\n",
       "      <td>18353.048110</td>\n",
       "      <td>3440.538566</td>\n",
       "      <td>10451.648858</td>\n",
       "      <td>15861.784249</td>\n",
       "      <td>18581.682900</td>\n",
       "      <td>21255.120880</td>\n",
       "      <td>26768.083564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>81.0</td>\n",
       "      <td>17998.993417</td>\n",
       "      <td>3549.832838</td>\n",
       "      <td>8867.894779</td>\n",
       "      <td>15818.383084</td>\n",
       "      <td>17851.931866</td>\n",
       "      <td>20234.182284</td>\n",
       "      <td>27458.032466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>72.0</td>\n",
       "      <td>18198.141683</td>\n",
       "      <td>3247.672597</td>\n",
       "      <td>11225.830889</td>\n",
       "      <td>16090.978698</td>\n",
       "      <td>18192.175340</td>\n",
       "      <td>20148.661695</td>\n",
       "      <td>25294.664929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>90.0</td>\n",
       "      <td>17973.311188</td>\n",
       "      <td>3530.933151</td>\n",
       "      <td>9722.436525</td>\n",
       "      <td>15463.126335</td>\n",
       "      <td>17671.063410</td>\n",
       "      <td>20447.775218</td>\n",
       "      <td>25289.762143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count          mean          std           min  \\\n",
       "Sexo   Estado Economico                                                   \n",
       "Hombre Clase media        79.0  18422.226299  3633.789522   8909.287626   \n",
       "       Pobre              92.0  17240.245195  3696.776907   6099.473523   \n",
       "       Rico               86.0  18353.048110  3440.538566  10451.648858   \n",
       "Mujer  Clase media        81.0  17998.993417  3549.832838   8867.894779   \n",
       "       Pobre              72.0  18198.141683  3247.672597  11225.830889   \n",
       "       Rico               90.0  17973.311188  3530.933151   9722.436525   \n",
       "\n",
       "                                  25%           50%           75%  \\\n",
       "Sexo   Estado Economico                                             \n",
       "Hombre Clase media       16272.808453  18664.358558  20670.292177   \n",
       "       Pobre             14407.276102  17390.486786  19766.556926   \n",
       "       Rico              15861.784249  18581.682900  21255.120880   \n",
       "Mujer  Clase media       15818.383084  17851.931866  20234.182284   \n",
       "       Pobre             16090.978698  18192.175340  20148.661695   \n",
       "       Rico              15463.126335  17671.063410  20447.775218   \n",
       "\n",
       "                                  max  \n",
       "Sexo   Estado Economico                \n",
       "Hombre Clase media       27312.252693  \n",
       "       Pobre             26278.260626  \n",
       "       Rico              26768.083564  \n",
       "Mujer  Clase media       27458.032466  \n",
       "       Pobre             25294.664929  \n",
       "       Rico              25289.762143  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion['Ingresos'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Sexo</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>1.455356e+06</td>\n",
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       "      <th>Pobre</th>\n",
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       "      <td>28.905208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>1.310266e+06</td>\n",
       "      <td>29.619241</td>\n",
       "      <td>27.561274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>1.617598e+06</td>\n",
       "      <td>29.599846</td>\n",
       "      <td>31.355662</td>\n",
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      "text/plain": [
       "                             Ingresos       Edad     Altura\n",
       "Sexo   Estado Economico                                    \n",
       "Hombre Clase media       1.455356e+06  28.572590  27.826528\n",
       "       Pobre             1.586103e+06  29.321628  26.983848\n",
       "       Rico              1.578362e+06  31.198215  29.242582\n",
       "Mujer  Clase media       1.457918e+06  30.316200  28.905208\n",
       "       Pobre             1.310266e+06  29.619241  27.561274\n",
       "       Rico              1.617598e+06  29.599846  31.355662"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.aggregate(\n",
    "    {\n",
    "        'Ingresos': np.sum,\n",
    "        'Edad': np.mean,\n",
    "        'Altura': np.std\n",
    "    }\n",
    ")"
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  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Pobre</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
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       "      <th>Pobre</th>\n",
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       "    <tr>\n",
       "      <th>Rico</th>\n",
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       "                              Edad    Altura\n",
       "Sexo   Estado Economico                     \n",
       "Hombre Clase media       28.572590  5.693621\n",
       "       Pobre             29.321628  6.005722\n",
       "       Rico              31.198215  5.375972\n",
       "Mujer  Clase media       30.316200  5.393508\n",
       "       Pobre             29.619241  6.117318\n",
       "       Rico              29.599846  4.961027"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.aggregate(\n",
    "    {\n",
    "        'Edad': np.mean,\n",
    "        'Altura': lambda h: np.mean(h)/np.std(h)\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Peso</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Altura</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Edad</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Ingresos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</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 rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>5013.312283</td>\n",
       "      <td>63.459649</td>\n",
       "      <td>23.902090</td>\n",
       "      <td>12436.794040</td>\n",
       "      <td>157.427773</td>\n",
       "      <td>27.826528</td>\n",
       "      <td>2257.234591</td>\n",
       "      <td>28.572590</td>\n",
       "      <td>10.887438</td>\n",
       "      <td>1.455356e+06</td>\n",
       "      <td>18422.226299</td>\n",
       "      <td>3633.789522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>5741.598002</td>\n",
       "      <td>62.408674</td>\n",
       "      <td>23.941858</td>\n",
       "      <td>14828.038511</td>\n",
       "      <td>161.174332</td>\n",
       "      <td>26.983848</td>\n",
       "      <td>2697.589791</td>\n",
       "      <td>29.321628</td>\n",
       "      <td>11.085650</td>\n",
       "      <td>1.586103e+06</td>\n",
       "      <td>17240.245195</td>\n",
       "      <td>3696.776907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>5372.297226</td>\n",
       "      <td>62.468572</td>\n",
       "      <td>26.900411</td>\n",
       "      <td>13440.993632</td>\n",
       "      <td>156.290624</td>\n",
       "      <td>29.242582</td>\n",
       "      <td>2683.046478</td>\n",
       "      <td>31.198215</td>\n",
       "      <td>11.365471</td>\n",
       "      <td>1.578362e+06</td>\n",
       "      <td>18353.048110</td>\n",
       "      <td>3440.538566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>5236.669807</td>\n",
       "      <td>64.650245</td>\n",
       "      <td>28.358615</td>\n",
       "      <td>12549.745940</td>\n",
       "      <td>154.935135</td>\n",
       "      <td>28.905208</td>\n",
       "      <td>2455.612164</td>\n",
       "      <td>30.316200</td>\n",
       "      <td>11.803545</td>\n",
       "      <td>1.457918e+06</td>\n",
       "      <td>17998.993417</td>\n",
       "      <td>3549.832838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>4681.788903</td>\n",
       "      <td>65.024846</td>\n",
       "      <td>29.179655</td>\n",
       "      <td>12054.681951</td>\n",
       "      <td>167.426138</td>\n",
       "      <td>27.561274</td>\n",
       "      <td>2132.585335</td>\n",
       "      <td>29.619241</td>\n",
       "      <td>12.312163</td>\n",
       "      <td>1.310266e+06</td>\n",
       "      <td>18198.141683</td>\n",
       "      <td>3247.672597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>5598.311570</td>\n",
       "      <td>62.203462</td>\n",
       "      <td>25.902806</td>\n",
       "      <td>13922.070760</td>\n",
       "      <td>154.689675</td>\n",
       "      <td>31.355662</td>\n",
       "      <td>2663.986143</td>\n",
       "      <td>29.599846</td>\n",
       "      <td>12.935859</td>\n",
       "      <td>1.617598e+06</td>\n",
       "      <td>17973.311188</td>\n",
       "      <td>3530.933151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Peso                              Altura  \\\n",
       "                                 sum       mean        std           sum   \n",
       "Sexo   Estado Economico                                                    \n",
       "Hombre Clase media       5013.312283  63.459649  23.902090  12436.794040   \n",
       "       Pobre             5741.598002  62.408674  23.941858  14828.038511   \n",
       "       Rico              5372.297226  62.468572  26.900411  13440.993632   \n",
       "Mujer  Clase media       5236.669807  64.650245  28.358615  12549.745940   \n",
       "       Pobre             4681.788903  65.024846  29.179655  12054.681951   \n",
       "       Rico              5598.311570  62.203462  25.902806  13922.070760   \n",
       "\n",
       "                                                       Edad             \\\n",
       "                               mean        std          sum       mean   \n",
       "Sexo   Estado Economico                                                  \n",
       "Hombre Clase media       157.427773  27.826528  2257.234591  28.572590   \n",
       "       Pobre             161.174332  26.983848  2697.589791  29.321628   \n",
       "       Rico              156.290624  29.242582  2683.046478  31.198215   \n",
       "Mujer  Clase media       154.935135  28.905208  2455.612164  30.316200   \n",
       "       Pobre             167.426138  27.561274  2132.585335  29.619241   \n",
       "       Rico              154.689675  31.355662  2663.986143  29.599846   \n",
       "\n",
       "                                        Ingresos                             \n",
       "                               std           sum          mean          std  \n",
       "Sexo   Estado Economico                                                      \n",
       "Hombre Clase media       10.887438  1.455356e+06  18422.226299  3633.789522  \n",
       "       Pobre             11.085650  1.586103e+06  17240.245195  3696.776907  \n",
       "       Rico              11.365471  1.578362e+06  18353.048110  3440.538566  \n",
       "Mujer  Clase media       11.803545  1.457918e+06  17998.993417  3549.832838  \n",
       "       Pobre             12.312163  1.310266e+06  18198.141683  3247.672597  \n",
       "       Rico              12.935859  1.617598e+06  17973.311188  3530.933151  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.aggregate([np.sum, np.mean, np.std])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>2.671948</td>\n",
       "      <td>5.693621</td>\n",
       "      <td>2.641132</td>\n",
       "      <td>5.102096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>2.620960</td>\n",
       "      <td>6.005722</td>\n",
       "      <td>2.659501</td>\n",
       "      <td>4.689142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>2.335836</td>\n",
       "      <td>5.375972</td>\n",
       "      <td>2.761100</td>\n",
       "      <td>5.365640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>2.293943</td>\n",
       "      <td>5.393508</td>\n",
       "      <td>2.584400</td>\n",
       "      <td>5.101969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>2.244069</td>\n",
       "      <td>6.117318</td>\n",
       "      <td>2.422572</td>\n",
       "      <td>5.642764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>2.414871</td>\n",
       "      <td>4.961027</td>\n",
       "      <td>2.301020</td>\n",
       "      <td>5.118761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Peso    Altura      Edad  Ingresos\n",
       "                         <lambda>  <lambda>  <lambda>  <lambda>\n",
       "Sexo   Estado Economico                                        \n",
       "Hombre Clase media       2.671948  5.693621  2.641132  5.102096\n",
       "       Pobre             2.620960  6.005722  2.659501  4.689142\n",
       "       Rico              2.335836  5.375972  2.761100  5.365640\n",
       "Mujer  Clase media       2.293943  5.393508  2.584400  5.101969\n",
       "       Pobre             2.244069  6.117318  2.422572  5.642764\n",
       "       Rico              2.414871  4.961027  2.301020  5.118761"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.aggregate([lambda x: np.mean(x) / np.std(x)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformación de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Zscore\n",
    "\n",
    "zscore = lambda x: (x-x.mean())/x.std() # A cada valore le quito el valor premio y divido por su desviación típica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.082599</td>\n",
       "      <td>0.771623</td>\n",
       "      <td>1.248319</td>\n",
       "      <td>0.715127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.087765</td>\n",
       "      <td>-0.561366</td>\n",
       "      <td>-1.342514</td>\n",
       "      <td>-0.823589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.055284</td>\n",
       "      <td>1.014029</td>\n",
       "      <td>-0.496213</td>\n",
       "      <td>-1.115277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.869348</td>\n",
       "      <td>-0.616110</td>\n",
       "      <td>1.313324</td>\n",
       "      <td>-0.182471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.122302</td>\n",
       "      <td>-1.142235</td>\n",
       "      <td>-1.416079</td>\n",
       "      <td>1.662744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Peso    Altura      Edad  Ingresos\n",
       "0 -1.082599  0.771623  1.248319  0.715127\n",
       "1  2.087765 -0.561366 -1.342514 -0.823589\n",
       "2 -0.055284  1.014029 -0.496213 -1.115277\n",
       "3 -1.869348 -0.616110  1.313324 -0.182471\n",
       "4 -0.122302 -1.142235 -1.416079  1.662744"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doble_agrupacion.transform(zscore).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.346201</td>\n",
       "      <td>178.854874</td>\n",
       "      <td>45.385946</td>\n",
       "      <td>20813.470719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>113.361591</td>\n",
       "      <td>141.806900</td>\n",
       "      <td>13.956049</td>\n",
       "      <td>15429.476751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.085082</td>\n",
       "      <td>188.536748</td>\n",
       "      <td>23.820786</td>\n",
       "      <td>13117.313909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.778331</td>\n",
       "      <td>140.283566</td>\n",
       "      <td>42.871327</td>\n",
       "      <td>17759.165610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.181933</td>\n",
       "      <td>121.918602</td>\n",
       "      <td>13.601447</td>\n",
       "      <td>23901.455692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Peso      Altura       Edad      Ingresos\n",
       "0   33.346201  178.854874  45.385946  20813.470719\n",
       "1  113.361591  141.806900  13.956049  15429.476751\n",
       "2   61.085082  188.536748  23.820786  13117.313909\n",
       "3   18.778331  140.283566  42.871327  17759.165610\n",
       "4   61.181933  121.918602  13.601447  23901.455692"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Con valores perdidos\n",
    "\n",
    "fill_na_mean = lambda x : x.fillna(x.mean())\n",
    "doble_agrupacion.transform(fill_na_mean).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operaciones diversas y útiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Hombre</td>\n",
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       "      <td>33.346201</td>\n",
       "      <td>178.854874</td>\n",
       "      <td>45.385946</td>\n",
       "      <td>20813.470719</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>113.361591</td>\n",
       "      <td>141.806900</td>\n",
       "      <td>13.956049</td>\n",
       "      <td>15429.476751</td>\n",
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       "      <th>2</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Pobre</td>\n",
       "      <td>61.085082</td>\n",
       "      <td>188.536748</td>\n",
       "      <td>23.820786</td>\n",
       "      <td>13117.313909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>61.181933</td>\n",
       "      <td>121.918602</td>\n",
       "      <td>13.601447</td>\n",
       "      <td>23901.455692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Rico</td>\n",
       "      <td>33.608932</td>\n",
       "      <td>149.200442</td>\n",
       "      <td>19.499976</td>\n",
       "      <td>15909.629024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Pobre</td>\n",
       "      <td>-10.591598</td>\n",
       "      <td>164.383082</td>\n",
       "      <td>32.754846</td>\n",
       "      <td>16986.821945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
       "0   Hombre             Rico   33.346201  178.854874  45.385946  20813.470719\n",
       "1   Hombre      Clase media  113.361591  141.806900  13.956049  15429.476751\n",
       "2   Hombre            Pobre   61.085082  188.536748  23.820786  13117.313909\n",
       "4    Mujer      Clase media   61.181933  121.918602  13.601447  23901.455692\n",
       "6    Mujer             Rico   33.608932  149.200442  19.499976  15909.629024\n",
       "10   Mujer            Pobre  -10.591598  164.383082  32.754846  16986.821945"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Primera fila de cada grupo\n",
    "doble_agrupacion.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>75.076798</td>\n",
       "      <td>208.668954</td>\n",
       "      <td>33.940396</td>\n",
       "      <td>13341.673461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Pobre</td>\n",
       "      <td>84.575073</td>\n",
       "      <td>184.811158</td>\n",
       "      <td>34.008297</td>\n",
       "      <td>14011.828009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Rico</td>\n",
       "      <td>41.865146</td>\n",
       "      <td>137.059163</td>\n",
       "      <td>20.238401</td>\n",
       "      <td>21707.185946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Rico</td>\n",
       "      <td>68.628059</td>\n",
       "      <td>134.685613</td>\n",
       "      <td>27.459753</td>\n",
       "      <td>22361.539315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>20.073167</td>\n",
       "      <td>159.974501</td>\n",
       "      <td>26.578268</td>\n",
       "      <td>18438.432852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Pobre</td>\n",
       "      <td>32.392374</td>\n",
       "      <td>159.457208</td>\n",
       "      <td>37.390841</td>\n",
       "      <td>20971.174066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sexo Estado Economico       Peso      Altura       Edad      Ingresos\n",
       "489   Mujer      Clase media  75.076798  208.668954  33.940396  13341.673461\n",
       "492  Hombre            Pobre  84.575073  184.811158  34.008297  14011.828009\n",
       "495   Mujer             Rico  41.865146  137.059163  20.238401  21707.185946\n",
       "497  Hombre             Rico  68.628059  134.685613  27.459753  22361.539315\n",
       "498  Hombre      Clase media  20.073167  159.974501  26.578268  18438.432852\n",
       "499   Mujer            Pobre  32.392374  159.457208  37.390841  20971.174066"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Última fila de cada grupo\n",
    "doble_agrupacion.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Hombre</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>81.334896</td>\n",
       "      <td>208.016617</td>\n",
       "      <td>27.813146</td>\n",
       "      <td>18749.311183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>37.061894</td>\n",
       "      <td>147.210041</td>\n",
       "      <td>38.761118</td>\n",
       "      <td>18044.235768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>40.995727</td>\n",
       "      <td>172.776297</td>\n",
       "      <td>30.073019</td>\n",
       "      <td>26768.083564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mujer</th>\n",
       "      <th>Clase media</th>\n",
       "      <td>112.710087</td>\n",
       "      <td>180.991135</td>\n",
       "      <td>14.704474</td>\n",
       "      <td>17113.816865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pobre</th>\n",
       "      <td>50.861898</td>\n",
       "      <td>171.424717</td>\n",
       "      <td>29.145658</td>\n",
       "      <td>14728.658398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>21.677604</td>\n",
       "      <td>166.956600</td>\n",
       "      <td>42.844271</td>\n",
       "      <td>20526.553283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Peso      Altura       Edad      Ingresos\n",
       "Sexo   Estado Economico                                                 \n",
       "Hombre Clase media        81.334896  208.016617  27.813146  18749.311183\n",
       "       Pobre              37.061894  147.210041  38.761118  18044.235768\n",
       "       Rico               40.995727  172.776297  30.073019  26768.083564\n",
       "Mujer  Clase media       112.710087  180.991135  14.704474  17113.816865\n",
       "       Pobre              50.861898  171.424717  29.145658  14728.658398\n",
       "       Rico               21.677604  166.956600  42.844271  20526.553283"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## n-ésima fila de cada grupo\n",
    "n = 32\n",
    "doble_agrupacion.nth(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Hombre</th>\n",
       "      <th>Pobre</th>\n",
       "      <td>76.368381</td>\n",
       "      <td>157.970215</td>\n",
       "      <td>21.940201</td>\n",
       "      <td>23526.498047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rico</th>\n",
       "      <td>54.667400</td>\n",
       "      <td>170.864904</td>\n",
       "      <td>26.502169</td>\n",
       "      <td>18674.708651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mujer</th>\n",
       "      <th>Rico</th>\n",
       "      <td>86.700874</td>\n",
       "      <td>199.352994</td>\n",
       "      <td>44.115582</td>\n",
       "      <td>13951.983880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Peso      Altura       Edad      Ingresos\n",
       "Sexo   Estado Economico                                                \n",
       "Hombre Pobre             76.368381  157.970215  21.940201  23526.498047\n",
       "       Rico              54.667400  170.864904  26.502169  18674.708651\n",
       "Mujer  Rico              86.700874  199.352994  44.115582  13951.983880"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## n-ésima fila de cada grupo\n",
    "n = 84\n",
    "doble_agrupacion.nth(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Qué ocurrió con las demás clasificaciones? No se muestran porque existen menos de 84 registros para esas categorías."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Sexo</th>\n",
       "      <th>Estado Economico</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Altura</th>\n",
       "      <th>Edad</th>\n",
       "      <th>Ingresos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Rico</td>\n",
       "      <td>33.346201</td>\n",
       "      <td>178.854874</td>\n",
       "      <td>45.385946</td>\n",
       "      <td>20813.470719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>113.361591</td>\n",
       "      <td>141.806900</td>\n",
       "      <td>13.956049</td>\n",
       "      <td>15429.476751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Pobre</td>\n",
       "      <td>61.085082</td>\n",
       "      <td>188.536748</td>\n",
       "      <td>23.820786</td>\n",
       "      <td>13117.313909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hombre</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>18.778331</td>\n",
       "      <td>140.283566</td>\n",
       "      <td>42.871327</td>\n",
       "      <td>17759.165610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mujer</td>\n",
       "      <td>Clase media</td>\n",
       "      <td>61.181933</td>\n",
       "      <td>121.918602</td>\n",
       "      <td>13.601447</td>\n",
       "      <td>23901.455692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sexo Estado Economico        Peso      Altura       Edad      Ingresos\n",
       "0  Hombre             Rico   33.346201  178.854874  45.385946  20813.470719\n",
       "1  Hombre      Clase media  113.361591  141.806900  13.956049  15429.476751\n",
       "2  Hombre            Pobre   61.085082  188.536748  23.820786  13117.313909\n",
       "3  Hombre      Clase media   18.778331  140.283566  42.871327  17759.165610\n",
       "4   Mujer      Clase media   61.181933  121.918602  13.601447  23901.455692"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjuntos de entrenamiento y testing\n",
    "\n",
    "Para realizar un modelo predictivo de Machine Learning, un dataset en general se divide en un subconjunto de entrenamiento y otro de testing.\n",
    "\n",
    "- El conjunto de entrenamiento (generalmente el 75% del dataset original) es para construir el modelo.\n",
    "\n",
    "        - El modelo tiende ajustarse muy bien con respecto a este conjunto de entrenamiento (overfitting)\n",
    "        \n",
    "- El conjunto de testing (generalmente el 25% del dataset original) se utiliza para comprobar la eficacia de dicho modelo.\n",
    "\n",
    "El método más efectivo para crear estos subconjuntos, es elegir de forma aleatoria del dataset original!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividir utilizando la distribución normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3333"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/customer-churn-model/Customer Churn Model.txt')\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  6.,  35., 151., 431., 763., 876., 629., 327.,  94.,  21.]),\n",
       " array([-3.63962874, -2.95716081, -2.27469288, -1.59222495, -0.90975702,\n",
       "        -0.22728909,  0.45517884,  1.13764677,  1.82011469,  2.50258262,\n",
       "         3.18505055]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.random.randn(len(data))\n",
    "plt.hist(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Generamos una condición para obtener un punto de corte entre los conjuntos\n",
    "\n",
    "check = (a<0.8)\n",
    "check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3333 artists>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Revisemos gráficamente los conjuntos de datos que se pueden generar\n",
    "plt.bar(check, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2636 697\n"
     ]
    }
   ],
   "source": [
    "# Dividimos la muestra\n",
    "training = data[check]\n",
    "testing = data[~check]\n",
    "print(len(training), len(testing))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Con la librería sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Es muy sencillo con esta librería\n",
    "\n",
    "train, test = train_test_split(data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2666 667\n"
     ]
    }
   ],
   "source": [
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando una función de shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Desde sklearn\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ordenar de forma aleatoria el dataset\n",
    "data = sklearn.utils.shuffle(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hacer la división\n",
    "pje_corte = int(0.75*len(data))\n",
    "train_data = data[:pje_corte]\n",
    "test_data = data[pje_corte+1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2499 833\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data), len(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenar y apendizar data sets\n",
    "\n",
    "¿Qué ocurre cuando tienes los datasets separados, con los mismos atributos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_wine = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/wine/winequality-red.csv', sep=';')\n",
    "red_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
       "       'quality'], dtype=object)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_wine.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_wine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/wine/winequality-white.csv', sep=';')\n",
    "white_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4898, 12)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En python, tenemos dos tipos de ejes:\n",
    "* axis = 0 denota el eje horizontal (filas)\n",
    "* axis = 1 denota el eje vertical (columnas)\n",
    "\n",
    "Para este caso, donde tenemos la misma cantidad de columnas y en el mismo orden, entonces juntaremos ambos datasets de forma horizontal, es decir, agregaremos las filas de ambos datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 12)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## El ´termino es \"apilar\", donde primero pondremos el data set red_wine y le agregaremos el data set  white_wine\n",
    "wine_data = pd.concat([red_wine, white_wine], axis=0)\n",
    "wine_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
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       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
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       "      <td>3.51</td>\n",
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       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Qué ocurre cuando tienes cientos datos distribuidos? ¿Cómo se unen?\n",
    "\n",
    "Este es el caso cuando tenemos registros por día de algún sistema o medición en particular. \n",
    "\n",
    "En este caso tenemos cerca de 300 bases con respecto a los niveles de contaminación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Date</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>nitrate</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  sulfate  nitrate  ID\n",
       "0  2003-01-01      NaN      NaN   1\n",
       "1  2003-01-02      NaN      NaN   1\n",
       "2  2003-01-03      NaN      NaN   1\n",
       "3  2003-01-04      NaN      NaN   1\n",
       "4  2003-01-05      NaN      NaN   1"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primero revisemos la data que tiene\n",
    "\n",
    "data = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/distributed-data/001.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1461, 4)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Procedimiento:\n",
    "\n",
    "1. Importar el primer fichero (base)\n",
    "2. Realizar un bucle/ciclo para ir recorriendo todos y cada uno de los ficheros\n",
    "    * Importante tener una consistencia en el nombre de los ficheros\n",
    "    * Importamos los ficheros uno a uno\n",
    "    * Cada uno de ellos debe apendizarse (añadirse al final) del primer fichero que ya habíamos cargado\n",
    "3. Repetimos el bucle hasta que queden ficheros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = '/Users/fsanmartin/python-ml-course-master/datasets/distributed-data/'\n",
    "\n",
    "data = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/distributed-data/001.csv')\n",
    "\n",
    "for i in range(2, 333):\n",
    "    if i < 10:\n",
    "        filename = '00'+str(i)\n",
    "    elif i >= 10 and i < 100:\n",
    "        filename = '0' + str(i)\n",
    "    else:\n",
    "        filename = str(i)\n",
    "        \n",
    "    file = filepath + filename + '.csv'\n",
    "    \n",
    "    temp_data = pd.read_csv(file)\n",
    "    \n",
    "    data = pd.concat([data, temp_data], axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(772087, 4)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisemos la información\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joins de datasets\n",
    "\n",
    "El caso en que tenemos \"n\" tablas, donde se unen a través de algún identificador (primary key). Es decir, una base de datos relacional.\n",
    "\n",
    "En el caso de python, se unen datasets*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10/01/2000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
       "\n",
       "   Silver Medals  Bronze Medals  Total Medals  \n",
       "0              0              0             8  \n",
       "1              0              2             8  \n",
       "2              2              0             6  \n",
       "3              2              3             6  \n",
       "4              1              3             6  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Este dataset tiene un detalle con la codificación (generalmente se utiliza utf-8)\n",
    "data_main = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/athletes/Medals.csv', encoding='ISO-8859-1')\n",
    "data_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6956"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisemos los atletas únicos\n",
    "\n",
    "a = data_main['Athlete'].unique().tolist()\n",
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8618, 8)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtenemos la base con los países asociados a los atletas\n",
    "\n",
    "data_country = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/athletes/Athelete_Country_Map.csv', encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Athlete</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alicia Coutts</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Missy Franklin</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete        Country\n",
       "0    Michael Phelps  United States\n",
       "1  Natalie Coughlin  United States\n",
       "2     Aleksey Nemov         Russia\n",
       "3     Alicia Coutts      Australia\n",
       "4    Missy Franklin  United States"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, tenemos dos bases con informaciones distintas, pero con los atletas en común.\n",
    "\n",
    "Revisemos que tengamos la misma cantidad de registros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6970"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_country.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6956"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Por qué existen más registros en la segunda tabla? Quizás puede ser que un mismo atleta olímpico haya jugado para dos países diferentes a lo largo de la historia y además haya ganado medallas. En un caso en concreto, puede ser que un país haya cambiado de nombre (por ejemplo, la división de Yugoslavia en nuevos países)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "David Musulbes      2\n",
       "Vanja Udovicic      2\n",
       "Gyuzel Manyurova    2\n",
       "Slobodan Nikic      2\n",
       "Chen Jing           2\n",
       "Iván García         2\n",
       "Dejan Savic         2\n",
       "Richard Thompson    2\n",
       "Aleksandar apic    2\n",
       "Matt Wells          2\n",
       "Name: Athlete, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_country['Athlete'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como comprobamos, existen atletas que han participado representando distintos países."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, tenemos una base que asocia el atleta con su deporte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sports = pd.read_csv('/Users/fsanmartin/python-ml-course-master/datasets/athletes/Athelete_Sports_Map.csv', encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Athlete</th>\n",
       "      <th>Sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aleksey Nemov</td>\n",
       "      <td>Gymnastics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alicia Coutts</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Missy Franklin</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete       Sport\n",
       "0    Michael Phelps    Swimming\n",
       "1  Natalie Coughlin    Swimming\n",
       "2     Aleksey Nemov  Gymnastics\n",
       "3     Alicia Coutts    Swimming\n",
       "4    Missy Franklin    Swimming"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sports.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6975, 2)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sports.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aquí también, tenemos más registros. Esto quiere decir que un mismo atleta pudo haber participado a 2 deportes olímpicos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chen Jing           2\n",
       "Matt Ryan           2\n",
       "Li Ting             2\n",
       "Matt Wells          2\n",
       "Ryan Bailey         2\n",
       "Jang Seong-Ho       2\n",
       "Richard Thompson    2\n",
       "Yang Wei            2\n",
       "Kim Nam-Sun         2\n",
       "Nataliya Ivanova    2\n",
       "Name: Athlete, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sports['Athlete'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Y cómo unimos estos 3 datasets?\n",
    "\n",
    "En este caso, tenemos la columna Athlete como punto en compun de los data sets. Lo hacemos con \"merge\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separamos el campo de unión\n",
    "key = 'Athlete'\n",
    "data_main_country = pd.merge(left = data_main, right = data_country,\n",
    "                            left_on=key, right_on = key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
       "\n",
       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
       "0              0              0             8  United States  \n",
       "1              0              2             8  United States  \n",
       "2              2              0             6  United States  \n",
       "3              2              3             6  United States  \n",
       "4              2              1             5  United States  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Y qué ocurrió con la dimensión del dataframe? Lo que se utilizó, es una técnica \"innerjoin\" donde se hace la combinación de ambos datasets, por lo tanto si un mismo atleta se relaciona con más de un país, entonces se crearán registros para cada país con los mismos resultados obtenidos en el mismo año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8657, 9)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Qué hacemos entonces? Se recomienda tener una relación uno a uno, en este caso, cada atleta se relacione con un solo país. Por lo tanto se procederá a eliminar los registros duplicados de la base que relaciona el atleta con su país."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_country_dp = data_country.drop_duplicates(subset='Athlete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_main_country_dp = pd.merge(left = data_main, right = data_country_dp,\n",
    "                            left_on=key, right_on = key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8618, 9)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_main_country_dp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De esta forma corregimos el problema de los registros duplicados. \n",
    "\n",
    "Para la base que relaciona el deporte con el atleta, debemos hacer lo mismo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sports_dp = data_sports.drop_duplicates(subset='Athlete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final = pd.merge(left = data_main_country_dp, right = data_sports_dp,\n",
    "                            left_on=key, right_on = key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>Athlete</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "      <th>Closing Ceremony Date</th>\n",
       "      <th>Gold Medals</th>\n",
       "      <th>Silver Medals</th>\n",
       "      <th>Bronze Medals</th>\n",
       "      <th>Total Medals</th>\n",
       "      <th>Country</th>\n",
       "      <th>Sport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Phelps</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>08/12/2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>08/24/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Natalie Coughlin</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08/29/2004</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "      <td>Swimming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
       "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
       "\n",
       "   Silver Medals  Bronze Medals  Total Medals        Country     Sport  \n",
       "0              0              0             8  United States  Swimming  \n",
       "1              0              2             8  United States  Swimming  \n",
       "2              2              0             6  United States  Swimming  \n",
       "3              2              3             6  United States  Swimming  \n",
       "4              2              1             5  United States  Swimming  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enhorabuena !! Hemos juntados 3 datasets con un campo en común."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tipos de Joins (Uniones) teoría"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Join\n",
    "\n",
    "\n",
    "* Devuelve un data frame con las filas que tienen valor tanto en el primero como en el segundo data frame que estamos uniendo\n",
    "* El número de filas será igual al número de filas comunes que tengan ambos data sets\n",
    "    * Data Set A tiene 60 gilas\n",
    "    * Data Set B tiene 50 filas\n",
    "    * Ambos comparte 30 filas\n",
    "    * Entonces A Inner Join B tendrá 30 filas\n",
    "* En términos de teoría de conjuntos, se trata de la intersección de los dos conjuntos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='/Users/fsanmartin/python-ml-course-master/notebooks/resources/inner-join.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Join\n",
    "\n",
    "\n",
    "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la izquierda, sin importar si tienen correspondencia en el de la derecha o no\n",
    "* Las filas del data frame final que no corresponden a ninguna fila del data frame derecho, tendrán NA's en las columnas del data frame derecho\n",
    "* El número de filas será igual al número de filas del data frame izquierdo\n",
    "    * Data Set A tiene 60 gilas\n",
    "    * Data Set B tiene 50 filas\n",
    "    * Entonces A Left Join B tendrá 60 filas\n",
    "* En términos de teoría de conjuntos, se trata del propio data set de la izquierda quien, además tiene la intersección en su interior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='/Users/fsanmartin/python-ml-course-master/notebooks/resources/left-join.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Right Join\n",
    "\n",
    "\n",
    "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la derecha, sin importar si tienen correspondencia en el de la izquierda o no\n",
    "* Las filas del data frame final que no corresponden a ninguna fila del data frame izquierdo, tendrán NA's en las columnas del data frame izquierdo\n",
    "* El número de filas será igual al número de filas del data frame derecho\n",
    "    * Data Set A tiene 60 gilas\n",
    "    * Data Set B tiene 50 filas\n",
    "    * Entonces A Right Join B tendrá 50 filas\n",
    "* En términos de teoría de conjuntos, se trata del propio data set de la derecha quien, además tiene la intersección en su interior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H38nt5fyx9kuodcFkBSkpgs4nam1Plp2utb70TttwcbtFk+WvK+apv7x59fIbixjaByU7f2jy2h+uWGR0QWWXrXsAXWDSu6o6Qep8ota6ceJm80lvd0YK9k7bmrK4uD0CDLvWINB/art5G+G39b5sq0yQ6MXsJiV8312bzJ5j5uPuXyWx5fHk9iQ1FJAn0H6CxM5Eeq+QvDz+N93ke6PLoq532ejFbNEv6NF322wg8FkSXelYSG8e+pj/50cOXG4uTew9ZU5U/Plai0LVDKl3Yc/cam5x/qrIQexmLrB/PwunbTNpopoMgeYU7a3mZdzdr1YvRpanqhnS8aebp7Cflgkza+Xks81T3s+yu7ARBOYSuPIP95k5HytkNjTyZ7d9rJVUZtNcRd8bcsi95T9SbKg6to8QcNmmvks00k2Vu9y7QGAnGyKXbYoxr+qUTRYtrIEACORGAIKUW8TQXxAomAAEqeDgwjUQyI2AtyC51zxyc1J7f93zdu19Rf9AICaBUUFKcVErprOabIPtvGi44o0L2vMYDtVy2abKz1FBGuo4toMACIBADAIQpBhUYRMEQGAWgSBBwnWkWYy9KrnTZa8KKAQCBRKYFKRU55IFsu65BLY9JKMbXNHG9aNRVME7XbYp83JSkII9QwUQAAEQmEkgWJBw2jaTtEc19yjlURxFQKA4Al6ClHIKVxxx4hDYEiADq65Y43RtANLMzS7b1PnoJUgz/UQ1EAABEAgiMEuQcNoWxDiosHu0CqpYcGEwiRdcbWy9BSn1VC5eSNJbBlv/GOB0zZ9VaEkNeegtSKHOoTwIgAAIhBIIEiRXQXHaFop6vLzLVts0erzncfeCRTy+GtkGCVI8NLAMAtMEcLo2zWhuCfeAONeGRL1FgoRZkkQIeBsaj158T+NtBYP62AYLkhYljReqdJbBdpg9ZkfDbJbu0ZR3wYJEnccsiRKRW695hlCz73IZxFvSzHaWIGlSVB55vlvBth87zI76TKS2aMu3WYJEYWCWRInIrWs+msl52bVUo89dAvHWtLOdLUjalDVeCNe3DLaHzDE7OmQhvaQxz2YLEoWDWRIlIreu/agm56kxNfkqyc3HVg5sFwmSRoX1CUwOZcDWNH/vfjeHUGXZR635tUiQaCQwS6JE5NZzOLot9bYGH5cymls/F7aLBYkqLURpbsr061G2uSRV35PpLdQ3zI6mmfmWoGxpXvnaWaPcYkGyndTs4BoQY7ZRI1uIUbyM0p5PIoJE8WGWRInIrdOjnZzldJZK9CkdzW7LubEVEyTtytsNU15rNbHF7ChebuaQR2KCZDG6DmOWJJtYLtvcjnpjJEryZczPFPtyZCsqSBQ6RIkSkVvPMdmo99QHzI4oofnrlK17QJtvNX5NcUGijkOU5IJI2dKkk2spviXad4iRHHPKluaNXEvylsQFyXaRAoAoyQWOsqXJJ9fSepYgRvFY03yJ15KM5SiCZLuWGwgZnOtYyZ1tCSK6TqTDW8mdbTRBoigxS6JE5NZzSkLaV8yO4uVBjgeuqIJEgUCU5JKPsqUDXa4lOUu0jxCjeGxpfsi1FNdSVEGyXadgIEpyAaVs6YCXa2m5Jdo3iNFypq0FypbmRVsuh9/ogmQhUEAQJbnUoGxpcsq1NN8S7RPEaD5LWpOypflAy2tfX0WQLAQKCqIklxqULU1SuZbCLWnqS3jvddcoke1qgqQ7tOjdWgQwO4pHmh6Y4rUUz/KqgkSBYZYkF1jKVsPRk/YBYiQXb8qWxl+upXUtrSpI1jUKDqIkF3DKliatXEvTlmjbEKNpZr4lKFsad187GsttGmf2UnSME6JEXUnhftQ2ObZrCQIdLNbRtdqOClWBcY5taWNm9RlSG1cOJDeQ2vL49SfAseWS2d+iX0muDYiRH7upUhxbLs5TdrTvTyZIFgwHFKIkkzIcWy6pZVrj/1oIxEiGLhc3Lr4yraW1klSQrOscWIiSTFJwbLnkXtoaZxNitJTqfn2OLRdXmdbSW0kuSBYBBxiiJJMcHFsuyee2xtmCGM2l2a3HseXi2a2V91qyi9pD2KgQlR6AIQ4xtlO2S4WDDpil9mL4nKtNyraWcaBOkGwC0YFjt9USEOtrzH8c21AhoYPF9jfURkwfc7bNsa0p91WcstEE4gLADSRaD+vTBDi23CAYssSVhRgN0QrbzrHl4hVmNa/SKgXJIuQCAVGSSS6OLTcYaGtcGYgRpTRvnWPLxWme9XxqqTxlo/g4IaoxWJSLxDrHlooMN1hoGYm+1GiDY1tzbmchSDZRuYFjt9ccPOu/xL8htkO2IUZDZPy3c0Jka9eez9kIUhtqbvDUHsSWzdJfji21CTGiRMLXIUbDzLITJOvK0MCBMA0H2nfPEFsIkS/B4XIQomE27Z4sBemg85tNu9j5hTB1cHitDAkRrQxhokSm1yFE04zaElkL0oETEKYWRfDvmBBZYR/aD2GaRj0kRLYmDpo8vyIEybo2NHDsPgTfUuj+C+E1VhbC1OVq1yBEfSa+W4oRpNbhscEDYVom3GNsIUwQonYMLvktTpBaGGODx5apSZykWUzZq0mcxmZDteVZO/aW/BYrSC2UqcFTctJM+b5UlKfsW7alihOEqB1hsr/FC5KLy2cALR2kbnsplqd8jOXfVLuWRe7iBBGKn9FVCVKL02fw2LKxBm/bD4lfbb749icHcZoSoDZ+OeRJ21ftv1UKkhsU3wHU1kmZfDn11fIK7W9KkfIVHw150PahxN/qBckNaugAcuvaZSmxWtIPqT5Q35auL/HJti0lVqHC4/qtla3bx9yXIUgjEVw6iEZMi+3KdZCArVgKFGUIghQYzpQDKVfx8UUMtr6kyi0HQRKKreRgKl14QpGDbSixfMtDkPKNHXoOAsURUPsJ2+JIwyEQAIFJAhCkSUQoAAIgsBaB/wNkSesUWEaSRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='/Users/fsanmartin/python-ml-course-master/notebooks/resources/right-join.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outer Join (full)\n",
    "\n",
    "\n",
    "* Devuelve un data frame con las filas de ambos, reemplazando las ausencias de uno o de otro con NA's en la región específica.\n",
    "* Las filas del data frame final que no corresponden a ninguna fila del data frame izquierdo (o derecho), tendrán NA's en las columnas del data frame izquierdo (o derecho)\n",
    "* El número de filas será igual al máximo número de filas de ambos data frames\n",
    "    * Data Set A tiene 60 gilas\n",
    "    * Data Set B tiene 50 filas\n",
    "    * Ambos comparten 30 filas\n",
    "    * Entonces A Outer Join B tendrá 60 + 50 - 30 = 80 filas\n",
    "* En términos de teoría de conjuntos, se trata de una unión de conjuntos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='/Users/fsanmartin/python-ml-course-master/notebooks/resources/outer-join.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}