stat_evalsum.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import join\n",
    "\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import ceil\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# data load\n",
    "data_dir='data'\n",
    "\n",
    "path_t0 = join(data_dir, '0_elikagai_texto.xlsx')\n",
    "path_u0 = join(data_dir, '0_elikagai_resumenesu.xlsx')\n",
    "path_p0 = join(data_dir, '0_elikagai_resumenesp.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# data read and load to data frame\n",
    "# pip3 install xlrd if errors\n",
    "\n",
    "df_t0 = pd.read_excel(path_t0, header=0, index_col=0)\n",
    "df_u0 = pd.read_excel(path_u0, header=0, index_col=1)\n",
    "df_p0 = pd.read_excel(path_p0, header=0, index_col=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>EDU-Kop</th>\n",
       "      <th>TOKEN</th>\n",
       "      <th>UZKop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOK</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EDU-Kop  TOKEN  UZKop\n",
       "DOK                       \n",
       "0         17    145      1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "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>EGILE</th>\n",
       "      <th>KEN-UZkop</th>\n",
       "      <th>KEN-EDU_Dok</th>\n",
       "      <th>KEN-TOK_Dok</th>\n",
       "      <th>%KEN-EDU</th>\n",
       "      <th>%KEN-TOK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KODEA</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>8</th>\n",
       "      <td>Castro Ruiz de Loizaga, nagorecastro</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>29.41</td>\n",
       "      <td>33.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Etxeandia Etxebarria, June</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>23.53</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Martinez Diaz, Yaiza</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>11.76</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rodriguez Casado, Uxue</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>inuntziaga barrenetxea, Leire</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>23.53</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Fernandez  del campo , Nerea12345</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11.76</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IbaÃ±ez Zabala, Nerea2020</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>17.65</td>\n",
       "      <td>31.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GarcÃ­a JimÃ©nez, Alba</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>11.76</td>\n",
       "      <td>22.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pesquera Sanz, Ainhoa</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>17.65</td>\n",
       "      <td>14.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Paredes Berganza, Itsaso</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>86</td>\n",
       "      <td>64.71</td>\n",
       "      <td>59.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Garcia Borge, Irati</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>61</td>\n",
       "      <td>52.94</td>\n",
       "      <td>42.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Ingunza Bilbao, Maitane</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "      <td>29.41</td>\n",
       "      <td>43.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>, mikelontxio</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>11.76</td>\n",
       "      <td>15.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Galicia Garcia, Itsasne</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>URRESTIZALA DE ANDRÃƒâ€°S, LUCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>23.53</td>\n",
       "      <td>16.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Gerrikaetxebarria Abrisketa, Maider</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>74</td>\n",
       "      <td>58.82</td>\n",
       "      <td>51.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gonzalez Granado, saidagonzalez</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>64.71</td>\n",
       "      <td>52.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>irazola agorria, inaxio</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>58.82</td>\n",
       "      <td>44.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LoroÃ±o , Ainara</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>29.41</td>\n",
       "      <td>20.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>FernÃ¡ndez DomÃ­nguez, Amets</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>35.29</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mato Escorza, asiermato</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>41.18</td>\n",
       "      <td>44.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Hierro Rangil, Asier</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>17.65</td>\n",
       "      <td>14.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Feijoo , Maria</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>35.29</td>\n",
       "      <td>24.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Iturralde , Rakel</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>29.41</td>\n",
       "      <td>20.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>garate martinez, nagore</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>58.82</td>\n",
       "      <td>44.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Gonzalez Roncero, Leire</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>17.65</td>\n",
       "      <td>14.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Echeandia , iratietxe</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>23.53</td>\n",
       "      <td>33.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CaÃ±ones ExpÃ³sito, elenacaÃ±ones</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>23.53</td>\n",
       "      <td>20.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ranero Gonzalez, karmele</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>29.41</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>FernÃ¡ndez , Maider</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "      <td>35.29</td>\n",
       "      <td>33.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>De La Cruz Fernandez, Anne De La Cruz</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>53</td>\n",
       "      <td>52.94</td>\n",
       "      <td>36.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>, Araitz Bengoetxea</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>41.18</td>\n",
       "      <td>47.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Milito Abella, Nerea</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>23.53</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Avedillo Carro, Irene</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "      <td>29.41</td>\n",
       "      <td>43.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Guibelondo Lage, Janire</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>29.41</td>\n",
       "      <td>38.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Cerro Barredo, Nahia</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>23.53</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Amo Aguilera, Sara</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>bollar perez, Jonebollar</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Alonso Sanchez, Enara Alonso</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Bilbao Zabala, Ane</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>23.53</td>\n",
       "      <td>21.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Apraiz Olarza, Karmele</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>47.06</td>\n",
       "      <td>40.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>GonzÃ¡lez PÃ©rez, Enara Gonzalez</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>23.53</td>\n",
       "      <td>32.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Almeida Santamaria, Sandra</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>73</td>\n",
       "      <td>52.94</td>\n",
       "      <td>50.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Azkue Martinez, Araitz</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "      <td>35.29</td>\n",
       "      <td>24.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>MartÃ­n MartÃ­nez, MarÃ­a M</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>11.76</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>, Saragil</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>23.53</td>\n",
       "      <td>32.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>alaez Escanciano, Saraalaez</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Arribas Quintanilla, Udane</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>bollar perez, jone</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5.88</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Alaez Escanciano, Saraalaez1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>23.53</td>\n",
       "      <td>16.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Garcia Martinez, itziar</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>62</td>\n",
       "      <td>29.41</td>\n",
       "      <td>42.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>fernandez velasco, leirefernandez</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>17.65</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Etxebarria Diego, Maialen</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>29.41</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Alonso Sanchez, enaraalonso</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>, Anne</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>35.29</td>\n",
       "      <td>27.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Martin , Maialen</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>41.18</td>\n",
       "      <td>35.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>, Martin</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>61</td>\n",
       "      <td>52.94</td>\n",
       "      <td>42.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Heredia corrales, Beatxuizar</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Arrausi BolaÃ±os, ainaraarrausi</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>41.18</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Andrade Carrasco, Joseba</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       EGILE  KEN-UZkop  KEN-EDU_Dok  \\\n",
       "KODEA                                                                  \n",
       "8       Castro Ruiz de Loizaga, nagorecastro          0            5   \n",
       "9                Etxeandia Etxebarria, June           0            4   \n",
       "10                      Martinez Diaz, Yaiza          0            2   \n",
       "11                    Rodriguez Casado, Uxue          0            2   \n",
       "12            inuntziaga barrenetxea, Leire           0            4   \n",
       "13         Fernandez  del campo , Nerea12345          0            2   \n",
       "14                 IbaÃ±ez Zabala, Nerea2020          0            3   \n",
       "15                   GarcÃ­a JimÃ©nez, Alba           0            2   \n",
       "16                     Pesquera Sanz, Ainhoa          0            3   \n",
       "17                  Paredes Berganza, Itsaso          0           11   \n",
       "18                       Garcia Borge, Irati          0            9   \n",
       "19                   Ingunza Bilbao, Maitane          0            5   \n",
       "20                             , mikelontxio          1            2   \n",
       "21                   Galicia Garcia, Itsasne          0            1   \n",
       "22          URRESTIZALA DE ANDRÃƒâ€°S, LUCIA          0            4   \n",
       "23       Gerrikaetxebarria Abrisketa, Maider          0           10   \n",
       "24           Gonzalez Granado, saidagonzalez          0           11   \n",
       "25                   irazola agorria, inaxio          0           10   \n",
       "26                          LoroÃ±o , Ainara          0            5   \n",
       "27              FernÃ¡ndez DomÃ­nguez, Amets          0            6   \n",
       "28                   Mato Escorza, asiermato          0            7   \n",
       "29                      Hierro Rangil, Asier          0            3   \n",
       "30                            Feijoo , Maria          0            6   \n",
       "31                         Iturralde , Rakel          0            5   \n",
       "32                   garate martinez, nagore          0           10   \n",
       "33                   Gonzalez Roncero, Leire          0            3   \n",
       "34                     Echeandia , iratietxe          0            4   \n",
       "35         CaÃ±ones ExpÃ³sito, elenacaÃ±ones          0            4   \n",
       "36                  Ranero Gonzalez, karmele          0            5   \n",
       "37                       FernÃ¡ndez , Maider          0            6   \n",
       "...                                      ...        ...          ...   \n",
       "81     De La Cruz Fernandez, Anne De La Cruz          0            9   \n",
       "82                       , Araitz Bengoetxea          0            7   \n",
       "83                      Milito Abella, Nerea          0            4   \n",
       "84                     Avedillo Carro, Irene          0            5   \n",
       "85                   Guibelondo Lage, Janire          0            5   \n",
       "86                     Cerro Barredo, Nahia           0            4   \n",
       "87                        Amo Aguilera, Sara          0            2   \n",
       "88                  bollar perez, Jonebollar          0            1   \n",
       "90              Alonso Sanchez, Enara Alonso          0            1   \n",
       "91                        Bilbao Zabala, Ane          0            4   \n",
       "92                    Apraiz Olarza, Karmele          0            8   \n",
       "93          GonzÃ¡lez PÃ©rez, Enara Gonzalez          1            4   \n",
       "94                Almeida Santamaria, Sandra          1            9   \n",
       "95                    Azkue Martinez, Araitz          0            6   \n",
       "96               MartÃ­n MartÃ­nez, MarÃ­a M          0            2   \n",
       "97                                 , Saragil          0            4   \n",
       "98               alaez Escanciano, Saraalaez          0            5   \n",
       "99                Arribas Quintanilla, Udane          0            5   \n",
       "100                       bollar perez, jone          0            1   \n",
       "101             Alaez Escanciano, Saraalaez1          0            4   \n",
       "102                  Garcia Martinez, itziar          0            5   \n",
       "103        fernandez velasco, leirefernandez          0            3   \n",
       "104               Etxebarria Diego, Maialen           0            5   \n",
       "106              Alonso Sanchez, enaraalonso          0            1   \n",
       "110                                   , Anne          0            6   \n",
       "113                         Martin , Maialen          0            7   \n",
       "114                                 , Martin          0            9   \n",
       "115             Heredia corrales, Beatxuizar          0            1   \n",
       "116          Arrausi BolaÃ±os, ainaraarrausi          0            7   \n",
       "117                 Andrade Carrasco, Joseba          0            1   \n",
       "\n",
       "       KEN-TOK_Dok  %KEN-EDU  %KEN-TOK  \n",
       "KODEA                                   \n",
       "8               48     29.41     33.10  \n",
       "9               42     23.53     28.97  \n",
       "10              26     11.76     17.93  \n",
       "11              27     11.76     18.62  \n",
       "12              38     23.53     26.21  \n",
       "13              17     11.76     11.72  \n",
       "14              45     17.65     31.03  \n",
       "15              33     11.76     22.76  \n",
       "16              21     17.65     14.48  \n",
       "17              86     64.71     59.31  \n",
       "18              61     52.94     42.07  \n",
       "19              63     29.41     43.45  \n",
       "20              22     11.76     15.17  \n",
       "21              15      5.88     10.34  \n",
       "22              24     23.53     16.55  \n",
       "23              74     58.82     51.03  \n",
       "24              76     64.71     52.41  \n",
       "25              64     58.82     44.14  \n",
       "26              30     29.41     20.69  \n",
       "27              32     35.29     22.07  \n",
       "28              65     41.18     44.83  \n",
       "29              21     17.65     14.48  \n",
       "30              36     35.29     24.83  \n",
       "31              30     29.41     20.69  \n",
       "32              64     58.82     44.14  \n",
       "33              21     17.65     14.48  \n",
       "34              48     23.53     33.10  \n",
       "35              30     23.53     20.69  \n",
       "36              38     29.41     26.21  \n",
       "37              48     35.29     33.10  \n",
       "...            ...       ...       ...  \n",
       "81              53     52.94     36.55  \n",
       "82              69     41.18     47.59  \n",
       "83              27     23.53     18.62  \n",
       "84              63     29.41     43.45  \n",
       "85              56     29.41     38.62  \n",
       "86              32     23.53     22.07  \n",
       "87              27     11.76     18.62  \n",
       "88              15      5.88     10.34  \n",
       "90              15      5.88     10.34  \n",
       "91              31     23.53     21.38  \n",
       "92              59     47.06     40.69  \n",
       "93              47     23.53     32.41  \n",
       "94              73     52.94     50.34  \n",
       "95              35     35.29     24.14  \n",
       "96               6     11.76      4.14  \n",
       "97              47     23.53     32.41  \n",
       "98              26     29.41     17.93  \n",
       "99              26     29.41     17.93  \n",
       "100             18      5.88     12.41  \n",
       "101             24     23.53     16.55  \n",
       "102             62     29.41     42.76  \n",
       "103             32     17.65     22.07  \n",
       "104             38     29.41     26.21  \n",
       "106             15      5.88     10.34  \n",
       "110             40     35.29     27.59  \n",
       "113             52     41.18     35.86  \n",
       "114             61     52.94     42.07  \n",
       "115             15      5.88     10.34  \n",
       "116             38     41.18     26.21  \n",
       "117             15      5.88     10.34  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_u0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EGILE</th>\n",
       "      <th>KEN-UZkop</th>\n",
       "      <th>KEN-EDU_Dok</th>\n",
       "      <th>KEN-TOK_Dok</th>\n",
       "      <th>%KEN-EDU</th>\n",
       "      <th>%KEN-TOK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KODEA</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>169</th>\n",
       "      <td>, sanfermin2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "      <td>29.41</td>\n",
       "      <td>43.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>, sanfermin3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>, sanfermin4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>, sanfermin5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>17.65</td>\n",
       "      <td>21.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>, sanfermin6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>23.53</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>, sanfermin7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>29.41</td>\n",
       "      <td>24.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>, sanfermin8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>23.53</td>\n",
       "      <td>35.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>, sanfermin9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>59</td>\n",
       "      <td>29.41</td>\n",
       "      <td>40.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>, sanfermin10</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>47.06</td>\n",
       "      <td>34.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>, sanfermin11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>, sanfermin12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>17.65</td>\n",
       "      <td>13.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>, sanfermin13</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "      <td>29.41</td>\n",
       "      <td>43.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>, sanfermin14</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>29.41</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>, sanfermin16</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>29.41</td>\n",
       "      <td>26.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>, sanfermin17</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>73</td>\n",
       "      <td>52.94</td>\n",
       "      <td>50.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>, sanfermin18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>, sanfermin19</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>23.53</td>\n",
       "      <td>15.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>, sanfermin20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5.88</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>, sanfermin21</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>70</td>\n",
       "      <td>52.94</td>\n",
       "      <td>48.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>, sanfermin22</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>23.53</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>, sanfermin23</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>23.53</td>\n",
       "      <td>35.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>, sanfermin24</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>17.65</td>\n",
       "      <td>13.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>, sanfermin25</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>29.41</td>\n",
       "      <td>19.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>, sanfermin26</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>35.29</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>, sanfermin27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>, sanfermin28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>17.65</td>\n",
       "      <td>15.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>, sanfermin29</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>17.65</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>, sanfermin30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11.76</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>, sanfermin31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>11.76</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>, sanfermin32</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>59</td>\n",
       "      <td>41.18</td>\n",
       "      <td>40.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>, sanfermin73</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>17.65</td>\n",
       "      <td>13.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>, sanfermin74</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>17.65</td>\n",
       "      <td>15.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>, sanfermin75</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>17.65</td>\n",
       "      <td>7.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>, sanfermin76</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11.76</td>\n",
       "      <td>11.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>, sanfermin77</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>, sanfermin78</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>11.76</td>\n",
       "      <td>22.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>, sanfermin79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5.88</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>, sanfermin80</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>17.65</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>, sanfermin81</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>, sanfermin82</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>29.41</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>, sanfermin83</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>41.18</td>\n",
       "      <td>31.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>, sanfermin84</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>17.65</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>, sanfermin85</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>17.65</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>, sanfermin86</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>, sanfermin87</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>, sanfermin88</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>, sanfermin89</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>, sanfermin90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>, sanfermin91</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>17.65</td>\n",
       "      <td>22.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>, sanfermin92</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>, sanfermin93</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>23.53</td>\n",
       "      <td>26.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>, sanfermin94</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>11.76</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>, sanfermin95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>, sanfermin96</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>5.88</td>\n",
       "      <td>10.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>, sanfermin97</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>35.29</td>\n",
       "      <td>32.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>, sanfermin98</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>23.53</td>\n",
       "      <td>16.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>, sanfermin99</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>11.76</td>\n",
       "      <td>20.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>, sanfermin100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.88</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>, sanfermin107</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>11.76</td>\n",
       "      <td>14.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>, sanfermin108</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>23.53</td>\n",
       "      <td>31.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EGILE  KEN-UZkop  KEN-EDU_Dok  KEN-TOK_Dok  %KEN-EDU  \\\n",
       "KODEA                                                                   \n",
       "169       , sanfermin2          0            5           63     29.41   \n",
       "170       , sanfermin3          0            1           15      5.88   \n",
       "171       , sanfermin4          0            1           15      5.88   \n",
       "172       , sanfermin5          0            3           31     17.65   \n",
       "173       , sanfermin6          0            4           29     23.53   \n",
       "174       , sanfermin7          0            5           35     29.41   \n",
       "175       , sanfermin8          0            4           51     23.53   \n",
       "176       , sanfermin9          0            5           59     29.41   \n",
       "177      , sanfermin10          0            8           50     47.06   \n",
       "178      , sanfermin11          0            1           15      5.88   \n",
       "179      , sanfermin12          0            3           20     17.65   \n",
       "180      , sanfermin13          0            5           63     29.41   \n",
       "181      , sanfermin14          0            5           32     29.41   \n",
       "183      , sanfermin16          0            5           38     29.41   \n",
       "184      , sanfermin17          0            9           73     52.94   \n",
       "185      , sanfermin18          0            2           27     11.76   \n",
       "186      , sanfermin19          0            4           23     23.53   \n",
       "187      , sanfermin20          0            1           18      5.88   \n",
       "188      , sanfermin21          0            9           70     52.94   \n",
       "189      , sanfermin22          0            4           32     23.53   \n",
       "190      , sanfermin23          0            4           51     23.53   \n",
       "191      , sanfermin24          0            3           20     17.65   \n",
       "192      , sanfermin25          0            5           28     29.41   \n",
       "193      , sanfermin26          0            6           42     35.29   \n",
       "194      , sanfermin27          0            2           27     11.76   \n",
       "195      , sanfermin28          0            3           23     17.65   \n",
       "196      , sanfermin29          0            3           29     17.65   \n",
       "197      , sanfermin30          0            2           17     11.76   \n",
       "198      , sanfermin31          0            2           26     11.76   \n",
       "199      , sanfermin32          0            7           59     41.18   \n",
       "...                ...        ...          ...          ...       ...   \n",
       "240      , sanfermin73          0            3           20     17.65   \n",
       "241      , sanfermin74          0            3           23     17.65   \n",
       "242      , sanfermin75          0            3           11     17.65   \n",
       "243      , sanfermin76          0            2           17     11.76   \n",
       "244      , sanfermin77          0            1           15      5.88   \n",
       "245      , sanfermin78          0            2           33     11.76   \n",
       "246      , sanfermin79          0            1           18      5.88   \n",
       "247      , sanfermin80          0            3           29     17.65   \n",
       "248      , sanfermin81          0            2           27     11.76   \n",
       "249      , sanfermin82          0            5           29     29.41   \n",
       "250      , sanfermin83          0            7           45     41.18   \n",
       "251      , sanfermin84          0            3           18     17.65   \n",
       "252      , sanfermin85          0            3           32     17.65   \n",
       "253      , sanfermin86          0            5           26     29.41   \n",
       "254      , sanfermin87          0            5           26     29.41   \n",
       "255      , sanfermin88          0            2           27     11.76   \n",
       "256      , sanfermin89          0            1           15      5.88   \n",
       "257      , sanfermin90          0            1           15      5.88   \n",
       "258      , sanfermin91          1            3           33     17.65   \n",
       "259      , sanfermin92          0            2           27     11.76   \n",
       "260      , sanfermin93          0            4           39     23.53   \n",
       "261      , sanfermin94          0            2           27     11.76   \n",
       "262      , sanfermin95          0            1           15      5.88   \n",
       "263      , sanfermin96          0            1           15      5.88   \n",
       "264      , sanfermin97          0            6           47     35.29   \n",
       "265      , sanfermin98          0            4           24     23.53   \n",
       "266      , sanfermin99          0            2           30     11.76   \n",
       "267     , sanfermin100          0            1            4      5.88   \n",
       "274     , sanfermin107          0            2           21     11.76   \n",
       "275     , sanfermin108          0            4           46     23.53   \n",
       "\n",
       "       %KEN-TOK  \n",
       "KODEA            \n",
       "169       43.45  \n",
       "170       10.34  \n",
       "171       10.34  \n",
       "172       21.38  \n",
       "173       20.00  \n",
       "174       24.14  \n",
       "175       35.17  \n",
       "176       40.69  \n",
       "177       34.48  \n",
       "178       10.34  \n",
       "179       13.79  \n",
       "180       43.45  \n",
       "181       22.07  \n",
       "183       26.21  \n",
       "184       50.34  \n",
       "185       18.62  \n",
       "186       15.86  \n",
       "187       12.41  \n",
       "188       48.28  \n",
       "189       22.07  \n",
       "190       35.17  \n",
       "191       13.79  \n",
       "192       19.31  \n",
       "193       28.97  \n",
       "194       18.62  \n",
       "195       15.86  \n",
       "196       20.00  \n",
       "197       11.72  \n",
       "198       17.93  \n",
       "199       40.69  \n",
       "...         ...  \n",
       "240       13.79  \n",
       "241       15.86  \n",
       "242        7.59  \n",
       "243       11.72  \n",
       "244       10.34  \n",
       "245       22.76  \n",
       "246       12.41  \n",
       "247       20.00  \n",
       "248       18.62  \n",
       "249       20.00  \n",
       "250       31.03  \n",
       "251       12.41  \n",
       "252       22.07  \n",
       "253       17.93  \n",
       "254       17.93  \n",
       "255       18.62  \n",
       "256       10.34  \n",
       "257       10.34  \n",
       "258       22.76  \n",
       "259       18.62  \n",
       "260       26.90  \n",
       "261       18.62  \n",
       "262       10.34  \n",
       "263       10.34  \n",
       "264       32.41  \n",
       "265       16.55  \n",
       "266       20.69  \n",
       "267        2.76  \n",
       "274       14.48  \n",
       "275       31.72  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p0"
   ]
  },
  {
   "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>KEN-UZkop</th>\n",
       "      <th>KEN-EDU_Dok</th>\n",
       "      <th>KEN-TOK_Dok</th>\n",
       "      <th>%KEN-EDU</th>\n",
       "      <th>%KEN-TOK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.110000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>43.530000</td>\n",
       "      <td>29.881500</td>\n",
       "      <td>30.020400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.314466</td>\n",
       "      <td>2.665454</td>\n",
       "      <td>21.040344</td>\n",
       "      <td>15.679925</td>\n",
       "      <td>14.511539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>4.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>17.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>29.410000</td>\n",
       "      <td>27.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>42.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>64.710000</td>\n",
       "      <td>69.660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        KEN-UZkop  KEN-EDU_Dok  KEN-TOK_Dok    %KEN-EDU    %KEN-TOK\n",
       "count  100.000000   100.000000   100.000000  100.000000  100.000000\n",
       "mean     0.110000     5.080000    43.530000   29.881500   30.020400\n",
       "std      0.314466     2.665454    21.040344   15.679925   14.511539\n",
       "min      0.000000     1.000000     6.000000    5.880000    4.140000\n",
       "25%      0.000000     3.000000    26.000000   17.650000   17.930000\n",
       "50%      0.000000     5.000000    40.000000   29.410000   27.590000\n",
       "75%      0.000000     7.000000    61.000000   41.180000   42.070000\n",
       "max      1.000000    11.000000   101.000000   64.710000   69.660000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_u0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>KEN-UZkop</th>\n",
       "      <th>KEN-EDU_Dok</th>\n",
       "      <th>KEN-TOK_Dok</th>\n",
       "      <th>%KEN-EDU</th>\n",
       "      <th>%KEN-TOK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>32.350000</td>\n",
       "      <td>21.410500</td>\n",
       "      <td>22.309500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.219043</td>\n",
       "      <td>2.101082</td>\n",
       "      <td>16.877679</td>\n",
       "      <td>12.360097</td>\n",
       "      <td>11.640416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>2.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.760000</td>\n",
       "      <td>13.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>19.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>29.410000</td>\n",
       "      <td>26.382500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.820000</td>\n",
       "      <td>57.930000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        KEN-UZkop  KEN-EDU_Dok  KEN-TOK_Dok    %KEN-EDU    %KEN-TOK\n",
       "count  100.000000   100.000000   100.000000  100.000000  100.000000\n",
       "mean     0.050000     3.640000    32.350000   21.410500   22.309500\n",
       "std      0.219043     2.101082    16.877679   12.360097   11.640416\n",
       "min      0.000000     1.000000     4.000000    5.880000    2.760000\n",
       "25%      0.000000     2.000000    20.000000   11.760000   13.790000\n",
       "50%      0.000000     3.000000    28.500000   17.650000   19.655000\n",
       "75%      0.000000     5.000000    38.250000   29.410000   26.382500\n",
       "max      1.000000    10.000000    84.000000   58.820000   57.930000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*Universidad*\n",
      " KEN-UZkop      2.531015\n",
      "KEN-EDU_Dok    0.317395\n",
      "KEN-TOK_Dok    0.356393\n",
      "%KEN-EDU       0.317349\n",
      "%KEN-TOK       0.356255\n",
      "dtype: float64\n",
      "*Primaria*\n",
      " KEN-UZkop      4.192637\n",
      "KEN-EDU_Dok    0.949746\n",
      "KEN-TOK_Dok    1.049952\n",
      "%KEN-EDU       0.949458\n",
      "%KEN-TOK       1.049710\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Skewness is usually described as a measure of a dataset’s symmetry – or lack of symmetry.   A perfectly symmetrical data set will have a skewness of 0.\n",
    "# The normal distribution has a skewness of 0. \n",
    "\n",
    "#The rule of thumb seems to be:\n",
    "\n",
    "#If the skewness is between -0.5 and 0.5, the data are fairly symmetrical\n",
    "#If the skewness is between -1 and – 0.5 or between 0.5 and 1, the data are moderately skewed\n",
    "#If the skewness is less than -1 or greater than 1, the data are highly skewed\n",
    "\n",
    "print('*Universidad*\\n', df_u0.skew())\n",
    "print('*Primaria*\\n', df_p0.skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*Universidad*\n",
      " KEN-UZkop      4.495564\n",
      "KEN-EDU_Dok   -0.554764\n",
      "KEN-TOK_Dok   -0.646226\n",
      "%KEN-EDU      -0.554654\n",
      "%KEN-TOK      -0.646173\n",
      "dtype: float64\n",
      "*Primaria*\n",
      " KEN-UZkop      15.895778\n",
      "KEN-EDU_Dok     0.651232\n",
      "KEN-TOK_Dok     0.686452\n",
      "%KEN-EDU        0.650588\n",
      "%KEN-TOK        0.685633\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# So, kurtosis is all about the tails of the distribution – not the peakedness or flatness.\n",
    "# It measures the tail-heaviness of the distribution.\n",
    "\n",
    "#the kurtosis of a normal distribution is 0.\n",
    "# This is really the excess kurtosis, but most software packages refer to it as simply kurtosis.\n",
    "# The last equation is used here. \n",
    "# So, if a dataset has a positive kurtosis, it has more in the tails than the normal distribution.\n",
    "# If a dataset has a negative kurtosis, it has less in the tails than the normal distribution. \n",
    "\n",
    "df_u0.kurt()\n",
    "print('*Universidad*\\n', df_u0.kurt())\n",
    "print('*Primaria*\\n', df_p0.kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_u0['KEN-EDU_Dok'], label=\"Universidad\", hist=False,);\n",
    "sns.distplot(df_p0['KEN-EDU_Dok'], label=\"Primaria\", hist=False,);\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": [
    "sns.distplot(df_u0['KEN-TOK_Dok'], label=\"Universidad\", hist=False,);\n",
    "sns.distplot(df_p0['KEN-TOK_Dok'], label=\"Primaria\", hist=False,);\n",
    "plt.legend();"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}