canvas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LearnLissajous course evaluation\n",
    "\n",
    "The objective here is to generate insights based on the resulting data from the course application to a group of heterogenous students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just reading the files\n",
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "directoryPath = \"./LearnLissajous/\"\n",
    "results = {}\n",
    "for file_name in glob.glob(directoryPath+'*.csv'):\n",
    "    x = pd.read_csv(file_name, low_memory=False)\n",
    "    username = file_name.split(\"/\")[-1][:-4]\n",
    "    results[username] =  x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result for a certain student"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>index</th>\n",
       "      <th>student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>daniel-saravali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>davibai1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>estknowledge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>felipehu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>fishboy1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>frederico_schiffner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22</td>\n",
       "      <td>gui-lodi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>guipolezel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>h-lefundes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>joao-mosz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>joaogrudka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27</td>\n",
       "      <td>joaonado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>jvictorshiba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9</td>\n",
       "      <td>lari_mend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24</td>\n",
       "      <td>leokaneko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12</td>\n",
       "      <td>lucasgiacone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>marcelohtalmeida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6</td>\n",
       "      <td>matheus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>mto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>pedro-saito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>rccampos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15</td>\n",
       "      <td>reinaldoavelino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>renan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>terpomoj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14</td>\n",
       "      <td>tonybai2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>topcat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25</td>\n",
       "      <td>victor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>21</td>\n",
       "      <td>vitor-g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>williamcolcioni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>yan_abdul</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index              student\n",
       "0      26                   bo\n",
       "1      13      daniel-saravali\n",
       "2      16          davibai1006\n",
       "3      28         estknowledge\n",
       "4       8             felipehu\n",
       "5      17          fishboy1900\n",
       "6      29  frederico_schiffner\n",
       "7      22             gui-lodi\n",
       "8       0           guipolezel\n",
       "9       5           h-lefundes\n",
       "10     18            joao-mosz\n",
       "11     11           joaogrudka\n",
       "12     27             joaonado\n",
       "13     10         jvictorshiba\n",
       "14      9            lari_mend\n",
       "15     24            leokaneko\n",
       "16     12         lucasgiacone\n",
       "17     20     marcelohtalmeida\n",
       "18      6              matheus\n",
       "19     19                  mto\n",
       "20     23          pedro-saito\n",
       "21      4             rccampos\n",
       "22     15      reinaldoavelino\n",
       "23      3                renan\n",
       "24      2             terpomoj\n",
       "25     14          tonybai2003\n",
       "26      7               topcat\n",
       "27     25               victor\n",
       "28     21              vitor-g\n",
       "29     30      williamcolcioni\n",
       "30      1            yan_abdul"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results.keys(), columns=[\"student\"]).sort_values(\"student\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Type</th>\n",
       "      <th>Start time</th>\n",
       "      <th>End time</th>\n",
       "      <th>Graduation</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vamos nos conhecer um pouco</td>\n",
       "      <td>Lesson</td>\n",
       "      <td>2021-08-27 02:26:53</td>\n",
       "      <td>2021-08-27 02:26:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Introdução</td>\n",
       "      <td>Lesson</td>\n",
       "      <td>2021-08-27 02:31:37</td>\n",
       "      <td>2021-08-27 02:31:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avaliação de conhecimentos prévios</td>\n",
       "      <td>Quiz</td>\n",
       "      <td>2021-08-27 02:31:48</td>\n",
       "      <td>2021-08-27 02:37:25</td>\n",
       "      <td>Passed</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avaliação de conhecimentos prévios (Parte 2)</td>\n",
       "      <td>Quiz</td>\n",
       "      <td>2021-08-27 02:37:37</td>\n",
       "      <td>2021-08-27 02:40:36</td>\n",
       "      <td>Passed</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Construção geométrica: Curvas e Lugar geométrico</td>\n",
       "      <td>Lesson</td>\n",
       "      <td>2021-08-27 02:45:48</td>\n",
       "      <td>2021-08-27 02:45:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mecanismos que descrevem movimentos harmônicos</td>\n",
       "      <td>Lesson</td>\n",
       "      <td>2021-09-09 18:50:03</td>\n",
       "      <td>2021-09-09 18:50:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Projeto de Mecanismo para desenhar padrões de ...</td>\n",
       "      <td>Lesson</td>\n",
       "      <td>2021-09-09 18:51:33</td>\n",
       "      <td>2021-09-09 18:51:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avaliação Final (Parte 1)</td>\n",
       "      <td>Quiz</td>\n",
       "      <td>2021-09-09 18:51:44</td>\n",
       "      <td>2021-09-09 19:07:42</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Avaliação Final (Parte 2)</td>\n",
       "      <td>Quiz</td>\n",
       "      <td>2021-09-09 19:10:56</td>\n",
       "      <td>2021-09-09 20:04:50</td>\n",
       "      <td>Failed</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Avaliação Final (Parte 3)</td>\n",
       "      <td>Quiz</td>\n",
       "      <td>2021-09-09 20:45:59</td>\n",
       "      <td>2021-09-09 20:51:21</td>\n",
       "      <td>Passed</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Avaliação Final (Parte 4)</td>\n",
       "      <td>Quiz</td>\n",
       "      <td>2021-09-09 20:52:51</td>\n",
       "      <td>2021-09-09 21:12:16</td>\n",
       "      <td>Passed</td>\n",
       "      <td>Completed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title    Type  \\\n",
       "0                         Vamos nos conhecer um pouco  Lesson   \n",
       "1                                          Introdução  Lesson   \n",
       "2                  Avaliação de conhecimentos prévios    Quiz   \n",
       "3        Avaliação de conhecimentos prévios (Parte 2)    Quiz   \n",
       "4    Construção geométrica: Curvas e Lugar geométrico  Lesson   \n",
       "5      Mecanismos que descrevem movimentos harmônicos  Lesson   \n",
       "6   Projeto de Mecanismo para desenhar padrões de ...  Lesson   \n",
       "7                           Avaliação Final (Parte 1)    Quiz   \n",
       "8                           Avaliação Final (Parte 2)    Quiz   \n",
       "9                           Avaliação Final (Parte 3)    Quiz   \n",
       "10                          Avaliação Final (Parte 4)    Quiz   \n",
       "\n",
       "             Start time             End time Graduation     Status  \n",
       "0   2021-08-27 02:26:53  2021-08-27 02:26:53        NaN  Completed  \n",
       "1   2021-08-27 02:31:37  2021-08-27 02:31:37        NaN  Completed  \n",
       "2   2021-08-27 02:31:48  2021-08-27 02:37:25     Passed  Completed  \n",
       "3   2021-08-27 02:37:37  2021-08-27 02:40:36     Passed  Completed  \n",
       "4   2021-08-27 02:45:48  2021-08-27 02:45:48        NaN  Completed  \n",
       "5   2021-09-09 18:50:03  2021-09-09 18:50:03        NaN  Completed  \n",
       "6   2021-09-09 18:51:33  2021-09-09 18:51:33        NaN  Completed  \n",
       "7   2021-09-09 18:51:44  2021-09-09 19:07:42     Failed  Completed  \n",
       "8   2021-09-09 19:10:56  2021-09-09 20:04:50     Failed  Completed  \n",
       "9   2021-09-09 20:45:59  2021-09-09 20:51:21     Passed  Completed  \n",
       "10  2021-09-09 20:52:51  2021-09-09 21:12:16     Passed  Completed  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[\"joaogrudka\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Absolute analysis for av pair 12 and av pair 34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = pd.DataFrame()\n",
    "\n",
    "for student in list(results.keys()):\n",
    "\n",
    "    df = results[student][results[student][\"Type\"] == \"Quiz\"].copy()\n",
    "    df[\"student\"] = student\n",
    "    df = df.set_index(\"Title\")\n",
    "    df = df.drop(\"Avaliação de conhecimentos prévios\", errors=\"ignore\")\n",
    "    df = df.drop(\"Avaliação de conhecimentos prévios (Parte 2)\", errors=\"ignore\")\n",
    "    df = df.reset_index()\n",
    "\n",
    "    for av_pair_name, av_pair in {\n",
    "        \"quadrant_av12\": (\"Avaliação Final (Parte 1)\", \"Avaliação Final (Parte 2)\"),\n",
    "        \"quadrant_av34\": (\"Avaliação Final (Parte 3)\", \"Avaliação Final (Parte 4)\"),\n",
    "    }.items():\n",
    "        av1, av2 = av_pair\n",
    "\n",
    "        value_av1 = None\n",
    "        if len(df[df[\"Title\"] == av1][\"Graduation\"].tolist()) > 0:\n",
    "            value_av1 = df[df[\"Title\"] == av1][\"Graduation\"].tolist()[0]\n",
    "\n",
    "        value_av2 = None\n",
    "        if len(df[df[\"Title\"] == av2][\"Graduation\"].tolist()) > 0:\n",
    "            value_av2 = df[df[\"Title\"] == av2][\"Graduation\"].tolist()[0]\n",
    "\n",
    "        if value_av1 == \"Passed\" and value_av2 == \"Passed\":\n",
    "            df[av_pair_name] = \"TT\"\n",
    "        elif value_av1 == \"Failed\" and value_av2 == \"Passed\":\n",
    "            df[av_pair_name] = \"FT\"\n",
    "        elif value_av1 == \"Passed\" and value_av2 == \"Failed\":\n",
    "            df[av_pair_name] = \"TF\"\n",
    "        elif value_av1 == \"Failed\" and value_av2 == \"Failed\":\n",
    "            df[av_pair_name] = \"FF\"\n",
    "        \n",
    "    full_df = pd.concat([full_df, df])\n",
    "\n",
    "full_df\n",
    "\n",
    "q3_q4_q5_q6_df = full_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "full_df[full_df[\"Title\"] == \"Avaliação Final (Parte 1)\"][\"Graduation\"].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Passed or not\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students who passed the Avaliação Final (Parte 1)\", y=1.02);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "full_df[full_df[\"Title\"] == \"Avaliação Final (Parte 2)\"][\"Graduation\"].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Passed or not\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students who passed the Avaliação Final (Parte 2)\", y=1.02);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "full_df[full_df[\"Title\"] == \"Avaliação Final (Parte 3)\"][\"Graduation\"].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Passed or not\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students who passed the Avaliação Final (Parte 3)\", y=1.02);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "full_df[full_df[\"Title\"] == \"Avaliação Final (Parte 4)\"][\"Graduation\"].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Passed or not\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students who passed the Avaliação Final (Parte 4)\", y=1.02);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quadrants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_df[['student', 'quadrant_av12']].drop_duplicates().reset_index().drop('index', axis=1)['quadrant_av12'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Quadrant (av12)\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students in each Quadrant (av12)\", y=1.02);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_df[['student', 'quadrant_av34']].drop_duplicates().reset_index().drop('index', axis=1)['quadrant_av34'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Quadrant (av34)\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students in each quadrant\", y=1.02);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introductory questions separated analysis\n",
    "\n",
    "Since the 2 introductory questions were meant to measure the previous knowledge, before taking the course, they did not have a minimum value to pass.\n",
    "\n",
    "Thus we everybody passed.\n",
    "\n",
    "Now we need to observe how each student performed in the detailed result, not general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just reading the files\n",
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "directoryPath = \"./LearnLissajousIntro/\"\n",
    "results_question1 = {}\n",
    "results_question2 = {}\n",
    "for file_name in glob.glob(directoryPath+'*.csv'):\n",
    "    x = pd.read_csv(file_name, low_memory=False)\n",
    "    a = file_name.split(\"/\")[-1][:-4].split(\"_\")\n",
    "    username = a[0]\n",
    "    introductory_question_number = a[1][-1]\n",
    "    if introductory_question_number == \"1\":\n",
    "        results_question1[username] =  x\n",
    "    if introductory_question_number == \"2\":\n",
    "        results_question2[username] =  x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>daniel-saravali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>davibai1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>estknowledge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>felipehu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>fellipy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "      <td>fishboy1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>gui-lodi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>guipolezel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>h-lefundes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25</td>\n",
       "      <td>joaogrudka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>joaonado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14</td>\n",
       "      <td>jvictorshiba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>leokaneko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>lucasgiacone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>lukinhasloko123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22</td>\n",
       "      <td>marcelohtalmeida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11</td>\n",
       "      <td>matheus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>mto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>rccampos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>reinaldoavelino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12</td>\n",
       "      <td>renan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13</td>\n",
       "      <td>terpomoj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>tonybai2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>topcat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>vitor-g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10</td>\n",
       "      <td>williamcolcioni</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index           student\n",
       "0      24   daniel-saravali\n",
       "1       9       davibai1006\n",
       "2       8      estknowledge\n",
       "3      23          felipehu\n",
       "4       5           fellipy\n",
       "5      19       fishboy1900\n",
       "6       7          gui-lodi\n",
       "7      15        guipolezel\n",
       "8       1        h-lefundes\n",
       "9      25        joaogrudka\n",
       "10     21          joaonado\n",
       "11     14      jvictorshiba\n",
       "12     18         leokaneko\n",
       "13     17      lucasgiacone\n",
       "14      6   lukinhasloko123\n",
       "15     22  marcelohtalmeida\n",
       "16     11           matheus\n",
       "17     20               mto\n",
       "18      3          rccampos\n",
       "19      4   reinaldoavelino\n",
       "20     12             renan\n",
       "21     13          terpomoj\n",
       "22      0       tonybai2003\n",
       "23     16            topcat\n",
       "24      2           vitor-g\n",
       "25     10   williamcolcioni"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results_question2.keys(), columns=[\"student\"]).sort_values(\"student\").reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregated result for all students about the 2 introductory questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_students = list(set(list(results_question1.keys()) + list(results_question2.keys())))\n",
    "\n",
    "full_df = None\n",
    "\n",
    "students_that_did_not_take_both_tests = []\n",
    "\n",
    "for student in all_students:\n",
    "    if student not in results_question1 or student not in results_question2:\n",
    "        students_that_did_not_take_both_tests += [student]\n",
    "        continue # skipping students that did not take both tests\n",
    "\n",
    "    student_quadrant = \"--\"\n",
    "    if results_question1[student]['Correct'][0] == True and results_question2[student]['Correct'][0] == True:\n",
    "        student_quadrant = \"TT\"\n",
    "    elif  results_question1[student]['Correct'][0] == False and results_question2[student]['Correct'][0] == True:\n",
    "        student_quadrant = \"FT\"\n",
    "    elif  results_question1[student]['Correct'][0] == True and results_question2[student]['Correct'][0] == False:\n",
    "        student_quadrant = \"TF\"\n",
    "    elif  results_question1[student]['Correct'][0] == False and results_question2[student]['Correct'][0] == False:\n",
    "        student_quadrant = \"FF\"\n",
    "\n",
    "    df = pd.concat([results_question2[student], results_question1[student]])\n",
    "    df[\"student\"] = student\n",
    "    df[\"quadrant\"] = student_quadrant\n",
    "    if full_df is None:\n",
    "        full_df = df\n",
    "    else:\n",
    "        full_df = pd.concat([full_df, df])\n",
    "df_final = full_df.reset_index().drop(['index'], axis=1)\n",
    "df_final\n",
    "\n",
    "q1_q2_df = df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result graphs for the first 2 questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHz6MFi1awMXFBX369JF9+fTs2RO//PILZs2ahaVLl8LR0RFdunRBjx49ULHih6+QJ0+eIDo6WrbP+WO5B1HGxsZCIpHk2aSd3+e/pD4+DTL3tX779i10dXXx5MkTAChwV1vdunWVlgUAjI2N8+wuUuR1V6aPv7dyX5PifG99/J4lJiYiNTW1wO8u4MP7XdB3lyo+C4q+t8p+/UvyPV7U57Qgointhg0b4v79+8jIyCiVIxYLWrst6OhQoYz9LHmjRo1w4sQJhIeH49y5c/jjjz+we/dueHp64rvvvivxfEuy1g/8+/osXLhQoXPpc/d/llSPHj3g4OCA06dP4/z589i6dSs2btyIoKAg9O7du8D7GRsbw8zMDFevXkWTJk2Qnp4uWztNTk5GREQErl+/jiZNmuRZsD41szI9e/YMo0ePRv369TFz5kzUrl0bWlpauHfvHpYuXSq3xhwYGIiBAwfizJkzOHfuHBYtWoSff/4Z27Ztg7m5ObS1tbFt2zZcvXpV9nmaNm0aNm3ahB07dkBbWxs5OTkwNzfHN998k2+ej/+YUaWP/wDMlfsZzH3u69evl/3R8V/KPiq4oPkV9borkzK+t/671bK0FbRsffx9VJz3tjRf//wU9TktiGhK29XVFTdv3sSJEyfQp0+fQqfN/QsmOjoa7du3lxsXFRUldx6lvr5+vpuIPj7KvDiK8+X936wfi4qKQuXKlUv8l7eOjg66d++O7t27IysrC4GBgdixYwfGjx8PExOTQnPq6+vn+Ss8IyMjz9WmateuLXcUaq7cv3hz5f6Fa2BgACcnpxI9n48V9TobGRlh6NChGDp0KKRSKYYMGYJVq1YVWtrAh03aZ8+eRbNmzdCgQQMYGhrC0NAQNWvWxJUrV3Djxg3079+/RJlNTU1x4cIFJCcny50y9OTJE+Tk5Ch0jq8gCIiJiZE7iyL385N7/9OnTyMjIwNr1qyRm2dBuxcaN26Mxo0b44svvkBERAQGDhyIzZs3Y968eQA+fMG0adMGbdq0wYwZM7Bjxw7Mnj0bv/76K/r06YN69erh3r17aNu2baHvi6mpKQRBwNOnT+W+GPP7/OdHGX8Y5a7Z1a5du1S+nAtT1Otemor72hoYGEBHR6fA7y4AhX6ei/NZ0NPTy3erwMff08V9b5X1+qvye/xjotmnPXToUJiYmGDRokV4/PhxnvEpKSlYvnw5AMDa2ho1atTA7t275U7JuXbtGu7evYtOnTrJhtWtWxdRUVFITEyUDYuIiMCNGzdKnDV3X6Iim3KNjY1hZWWFgwcPIikpSTb86dOnOHPmDDp06KDQuZ8fe/PmjdztihUrwsLCAsC/m8Ryc+a3MNStWzfPaRt79uzJ85ets7Mz7ty5I7fv/M2bNwgLC5ObrkOHDtDT08OaNWuQkZGR5/H++/orqqDXOTs7W7b5N5eenh7q1Kmj0OZABwcHxMXFYf/+/XJHvzo4OGDXrl2QSqUlvqhCp06dkJOTk+ciFps2bZKNV8TWrVvlbm/btg0SiQTOzs4A/l2z+u9f7RkZGdi2bZvc/VJSUvKcKtaoUSNoaWnJXquPP0sAYGVlBeDfzew9e/ZEfHw8duzYkWfajIwM2X66jh07yvJ+nF8RxVm2CtKtWzdUqFABq1evznetpiSfxeJS5HUHgMqVK3/Scy0OHR0dSKVShde+K1SogA4dOuD333/H06dPZcOTkpJw8OBB2fdwQYrzWahXr55sS1eu169f47fffpObTtH3Vtmvv6q+x/MjmjVtPT09rF69GuPGjUP//v3h7u4OGxsbaGhoIDIyEmFhYdDX18fUqVOhqamJ6dOnIyAgAJ6enujTpw8SExOxdetWmJiYYOzYsbL5Dho0CJs3b4aPjw8GDRqEhIQE7Nq1C+bm5grvQ/+YtbU1AGDOnDno2LEjKlasCBcXF1SuXDnf6WfMmAEfHx94eHhgyJAhslMFtLS0MHXq1BJl8PHxgYGBgezUsKdPn2Lbtm2wtLSUnVqVm3PZsmXo3bs3NDU14ejoCENDQwwePBjfffcdJk2aBCcnJ0RERODcuXN5/lr09fXFoUOH4OvrC29vb1SuXBm7d+9GzZo15T7surq6mD17NqZNm4b+/fujV69eqFGjBl68eIGzZ8+icePGWLhwYbGeo7a2Nho3boxjx46hQYMGqFatGurUqYMGDRqgY8eOcHNzk23GvnHjBs6ePZvnoJf85O7Xjo6Oxpdffik3PPePkfwOQlOEi4sL2rVrh1WrVuHFixdo1qwZLl++jJMnT8LDw0P2h1VhNDU1cfXqVXz11Vey02Ry75+7T619+/bQ1NTE+PHj4eHhgYyMDBw6dCjPJrlLly5h9uzZ6Natm+y+x44dw7t379CzZ08AwE8//YQrV66gU6dOMDU1xdu3b7Fr1y5UrlxZ9kdGnz59cOLECcyZMwdXr15Fy5YtIQgCoqOjcfz4caxYsQJt2rRB06ZN4e7ujp07dyI5OVmW/+MtMwUp6D23tbVV8B348AfpV199hcWLF+PFixfo3Lkz9PT08Pz5c5w+fRo9e/ZU+iWIP6bI6w58WEYvXryIjRs3ombNmjAwMCjwuIFPZW1tjWPHjmH+/PmwtbWFhoYGevXqVeh9/ve//+HcuXPw9PSEp6cntLS0sGfPHtm58oUpzmehZ8+eWLp0KSZOnAgvLy+kpaVh586daNCgAe7duyebTtH3VhWvvyq+x/MjmtIGABsbG4SFhcmuPX706FEIggAzMzN4eHjIHXXYr18/6OjoYO3atVi6dCl0dHTg7OyMadOmyZ2D2ahRIyxatAgrV65EUFAQzM3NsXjxYoSFhZX4Uolubm4YOXIkjh49Kst4+vTpAkvb0dERISEhWLlyJVauXIkKFSrAwcEBX331VYkPyvDw8EBYWBi2bNmClJQUGBsbY+DAgfjyyy9lX9w2Njb46quvsGPHDsycOVO2BmhoaIghQ4bg+fPn+OWXX3D27Fm0bNkSmzZtynNdYmNjY4SGhmLevHlYt24dqlWrhqFDh8LY2DjP/s2ePXvC2NgYa9aswaZNm5Ceng5jY2O0aNEiz9G1ipo/fz7mzZuHhQsXIiMjA/3798ecOXPg6emJCxcu4MyZM8jKykKdOnUQEBCg0KVuzczMZOdtfrymDXz4zJT0PH+JRILg4GCsWrUKR48exaFDh1CrVi34+/vD19dX4Xls2LAB33//PZYsWQIdHR34+PjIfTE0aNAAq1evxvLly7FkyRJUr14d/fr1Q+vWrWXnoAMfLiDRsWNH/Pnnn9i7dy+0tLRgbm6O1atXy6633LlzZ7x8+RIHDhxAYmIiqlWrBnt7e/j5+ck2f2poaCA4OBhbtmzBwYMHcfr0aWhra8vOUf/vBTAWLFiA6tWr48iRIzhz5gzatGmDdevWybYSFCW/97w4pQ18+KPWzMwMmzdvxs8//wxBEGBiYgJHR0d07969WPMqCUVed+DDhZS+/fZbBAcH4/3792jdurXKStvT0xN///03jhw5gm3btkEQhCJLu1GjRti5cyeWLVuG9evXQxAEWFtbY968eQptjVL0s1C9enUEBwdj4cKFWLJkCerUqQN/f3/ExMTIlTag2HuritdfFd/j+ZEIZe0IKiIqUGBgII4ePYo7d+6oOwqRylhaWmLixIkq3+IhRqLZp01ERPS5Y2kTERGJBEubiIhIJLhPm4iISCS4pk1ERCQSLG0iIiKRYGkTERGJBEubiIhIJFjaREREIsHSJiIiEgmWNhERkUiwtImIiERCVL/yVVrevHmHnBxec0adDA11kZCQou4YRGrHZUH9NDQkqF69irpjAGBp5ysnR2BplwF8D4g+4LJAubh5nIiISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikRDFedoxMTHYuHEjbt26hcjISDRs2BBhYWF5pktPT8e6detw6NAhvHr1CtWrV4ezszPmzZunhtRERETKJYrSjoyMRHh4OGxtbZGTkwNByHuhgZycHEyYMAFPnjzB+PHjYWZmhlevXuHu3btqSExERKR8oihtV1dXdOnSBQAQGBiYbxHv27cPf/31F44dOwYTExPZ8D59+pRaTiIiIlUSxT5tDY2iY+7duxfdu3eXK2wiIqLyRBSlXZTMzEzcv38fpqamCAgIgL29Pezs7DBhwgS8ePFC3fGIiIiUolyUdlJSEjIzM7F+/XrEx8dj5cqVmD9/PiIiIjBu3DhkZWWpOyIREdEnE8U+7aLk5OQAAKpUqYLVq1dDW1sbAFCvXj0MGjQIv/32G3r06KHw/AwNdVWSs7RkZGajkmYFdcf4ZEZGVdUd4ZOUl/eB1E/sywIpT7kobT09PUgkErRo0UJW2ABgY2MDXV1dREZGFqu0ExJSRP1TeEZGVdH7q0PqjvHZO7KsL+LiktUdg0TOyKgqP0dqpqEhKTMrc+Vi87iOjg5MTU3zHSeRSJCenl7KiYiIiJSvXJQ2ALi4uOD69etIS0uTDbt9+zaSk5NhZWWlxmRERETKIYrN46mpqQgPDwcAxMbGIiUlBSdOnADwYRO4qakpfHx8cPjwYYwfPx6jRo2CVCrF8uXLYWFhga5du6ozPhERkVKIorQTEhIwZcoUuWG5t4OCgjBgwADUqlULoaGhCAoKwpQpU1CpUiU4OzsjMDAQmpqa6ohNRESkVKIo7Tp16uDhw4dFTtekSRNs2bKlFBIRERGVvnKzT5uIiKi8Y2kTERGJBEubiIhIJFjaREREIsHSJiIiEgmWNhERkUiwtImIiESCpU1ERCQSLG0iIiKRYGkTERGJBEubiIhIJFjaREREIsHSJiIiEgmWNhERkUiwtImIiESCpU1ERCQSLG0iIiKRYGkTERGJBEubiIhIJFjaREREIsHSJiIiEgmWNhERkUiwtImIiESCpU1ERCQSLG0iIiKRYGkTERGJhChKOyYmBt9++y369u2LZs2awd3dvdDp7969i6ZNm8Le3r6UEhIREaleRXUHUERkZCTCw8Nha2uLnJwcCIJQ4LQ5OTn4/vvvYWBggPfv35diSiIiItUSxZq2q6srwsPDsXLlSlhZWRU67Z49e5CcnIyBAweWUjoiIqLSIYrS1tBQLGZiYiKWL1+Ob775BpqamipORUREVLpEUdqKWrp0KVq0aIGOHTuqOwoREZHSiWKftiJu3ryJo0ePIiwsTN1RiIiIVKJclHZ2djZmz56NUaNGoW7dup88P0NDXSWkIgKMjKqqOwKVA/wcUa5yUdp79uxBXFwcPD09IZVKAQDp6ekAAKlUikqVKkFbW1vh+SUkpCAnp+Aj1Ms6LuBlR1xcsrojkMgZGVXl50jNNDQkZWZlrlyUdlRUFOLj4/Pdl92qVSt4e3vjm2++UUMyIiIi5SkXpT1ixAh06dJFbtiBAwdw7NgxrF+/HjVr1lRTMiIiIuURRWmnpqYiPDwcABAbG4uUlBScOHECAGBjYwMzMzOYmZnJ3efKlSuoUKEC2rRpU+p5iYiIVEEUpZ2QkIApU6bIDcu9HRQUhAEDBqgjFhERUakSRWnXqVMHDx8+LNZ9Jk2ahEmTJqkoERERUekrVxdXISIiKs9Y2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCJRUd0BFBETE4ONGzfi1q1biIyMRMOGDREWFiYbn52djZCQEISHh+PRo0fIzs6GhYUFJk6ciLZt26oxORERkfKIYk07MjIS4eHhMDMzQ6NGjfKMT0tLw9q1a9GkSRMEBQXhhx9+gImJCUaPHo3ff/9dDYmJiIiUTxRr2q6urujSpQsAIDAwEHfv3pUbr62tjdOnT0NfX182rH379njy5AlCQkLg4uJSqnmJiIhUQeE17WvXrmH79u1yw44ePYpu3brByckJ8+fPR05OjtIDAoCGRuExK1SoIFfYACCRSNCkSRO8fv1aJZmIiIhKm8KlvWLFCly9elV2Ozo6GgEBAdDQ0ICVlRW2bduG0NBQlYQsiZycHNy8eTPfzelERERipPDm8UePHsHV1VV2+9ChQ9DW1sbevXuhq6uLwMBA7Nu3D6NGjVJFzmLbunUroqOjMXfu3GLf19BQVwWJ6HNkZFRV3RGoHODniHIpXNopKSnQ09OT3T579iycnJygq/uh4Fq2bImTJ08qP2EJXLlyBUuWLMGYMWPg4OBQ7PsnJKQgJ0dQQbLSwQW87IiLS1Z3BBI5I6Oq/BypmYaGpMyszCm8edzY2BiPHj0CAPzzzz948OAB2rdvLxufkpICTU1N5ScspoiICEyYMAFdunTB9OnT1R2HiIhIaRRe03Zzc8P27duRmZmJW7duQUtLC507d5aNj4iIQJ06dVQSUlFPnz6Fr68vmjVrhsWLF0Mikag1DxERkTIpXNqTJk1CfHw8Dh8+DF1dXQQFBcHQ0BDAh7XsX3/9FcOHD1dZ0KLExcVhzJgxqFGjBn766SdUqlRJbVmIiIhUQeHSrly5MpYsWVLguD///BM6OjpKC/ZfqampCA8PBwDExsYiJSUFJ06cAADY2NjA0NAQvr6+SEhIQGBgoGwzfi47OzuV5CIiIipNCpf2zJkzMXToUNja2uYZp6GhgejoaOzcuRNBQUFKDQgACQkJmDJlityw3NtBQUFo3bo1IiIiAAB+fn557v/w4UOlZyIiIiptCpf2gQMH4OTklG9pA8Dz589x8OBBlZR2nTp1iixeFjMREZV3Srv2+Js3b7gfmYiISIUKXdO+evUqLl++LLv922+/ISYmJs90UqkUx44dQ5MmTZSfkIiIiAAUUdqXL19GcHAwgA/X8v7111/x66+/5jtt48aN8c033yg/IREREQEoorR9fX0xfPhwCIIAJycnzJ49G25ubnLTSCQS6OjoQEtLS6VBiYiIPneFlra2tja0tbUBAKdPn4aBgYHKTusiIiKiwil89LipqakqcxAREVERFC5tQRCwe/du/PLLL3j27BmkUmmeaSQSCe7fv6/UgERERPSBwqW9ePFibN68GU2bNkWfPn2gr6+vylxERET0EYVL++DBg3Bzc8OKFStUmYeIiIgKoPDFVdLS0uDk5KTKLERERFQIhUu7bdu2uHPnjiqzEBERUSEULu3vvvsOt27dws8//4z4+HhVZiIiIqJ8KLxPu2vXrhAEAStXrsTKlStRsWJFaGjId75EIsFff/2l7IxERESEYpR2z549IZFIVJmFiIiICqFwaS9cuFCVOYiIiKgISvtpTiIiIlKtYpV2dHQ0pk2bhg4dOsDa2hoXL14EACQmJmLmzJm4deuWSkISERFRMUo7IiICgwYNwoULF2Bvb4/s7GzZOAMDA0RGRmLnzp0qCUlERETF2Ke9dOlSGBkZYe/evcjMzMzzu9odOnTA0aNHlR6QiIiIPlB4Tfv69evw8PBA1apV8z2KvHbt2nj9+rVSwxEREdG/irVPu1KlSgWOi4+Ph5aW1icHIiIiovwpXNrW1tb4/fff8x2XmZmJo0ePwtbWVmnBiIiISJ7Cpf3FF1/g/PnzmDVrFiIiIgAAr1+/xp9//olRo0bhyZMn+OKLL1QWlIiI6HOn8IFo7du3x+LFizFv3jzs27cPABAYGAhBEKCnp4fFixejZcuWKgtKRET0uVO4tAGgd+/e6NKlC86fP48nT54gJycH9erVQ/v27aGrq6uqjERERIRiljYA6OjooEuXLqrIQkRERIUosLRfvHhRohnWrl27xGEKEhMTg40bN+LWrVuIjIxEw4YNERYWlme68PBw/Pjjj3j06BFMTEwwcuRIeHl5KT0PERGROhRY2q6uriX6Va8HDx58UqD8REZGIjw8HLa2tsjJyYEgCHmmuXnzJiZMmIC+ffsiICAAN27cwIIFC1CxYkUMGzZM6ZmIiIhKW4GlvWDBArnSzsnJQWhoKF69egV3d3fUr18fwIfrkR89ehS1atVS2Vqtq6urbJN8YGAg7t69m2ea1atXo1mzZliwYAEAwNHRES9fvsTq1avh4eGR57e/iYiIxKbA0h4wYIDc7Z9//hkZGRn49ddfoa+vLzdu0qRJGDZsGOLj41USsqjCzcjIwKVLl/DVV1/JDXd3d8eePXtw79492NjYqCQbERFRaVF49XPXrl3w8PDIU9gAUL16dQwZMkRtPxjy9OlTZGZmolGjRnLDGzduDACIiopSRywiIiKlUvjo8aSkJLx7967A8e/fv0dSUpIyMhXb27dvAQB6enpyw3Nv545XlKEhT18j5TAyqqruCJ+1jMxsVNKsoO4Yn0zsn6Py8j6UBQqXtq2tLUJDQ+Hs7Axra2u5cXfu3MHWrVvRvHlzpQdUh4SEFOTk5D3YTSzEvoCXJ3FxyeqO8FkzMqqK3l8dUneMz96RZX1FvSxoaEjKzMqcwqX97bffwsvLC4MHD4aNjQ3MzMwAfDgd686dO9DX18f//d//qSxoYXI32UulUrnhubfz26RPREQkNgrv0zY3N8eRI0fg5eUFqVSKkydP4uTJk5BKpfD29saRI0dk+5BLW7169aCpqZln3/WjR48AAA0bNlRHLCIiIqUq1hXRatSoga+//hpff/21qvKUSKVKleDo6Ijjx49j1KhRsuFhYWEwMjKClZWV+sIREREpSbEvY6oOqampCA8PBwDExsYiJSUFJ06cAADY2NjA1NQUfn5+GDFiBGbNmoXevXvjxo0b2Lt3L7799lueo01EROWCwqU9c+bMIqeRSCSyi5soU0JCAqZMmSI3LPd2UFAQBgwYAHt7e/z000/44YcfcPDgQRgbG2PmzJm8GhoREZUbCpf25cuX8wzLyclBXFwcsrOzYWBgAB0dHaWGy1WnTh08fPiwyOmcnZ3h7OyskgxERETqpnBpnzlzJt/hmZmZ2L17N7Zs2YKQkBClBSMiIiJ5n7yzV1NTEyNGjEC7du0wd+5cZWQiIiKifCjtCK0mTZrg6tWrypodERERfURppX3hwgWV7dMmIiKiYuzTDg4Oznd4cnIyrl69ivv372PcuHFKC0ZERETyPrm09fX1UbduXcyePRtDhgxRWjAiIiKSp3BpR0REqDIHERERFUHhfdpXr15FYmJigeMTExN5IBoREZEKKVza3t7eOH/+fIHjL126BG9vb6WEIiIiorwULm1BKPz3pTMyMniNbyIiIhUqdJ92SkqK3G9UJyUl4cWLF3mmk0qlCAsLg4mJifITEhEREYAiSnvz5s1YvXo1gH9/DKSgHwQRBAFfffWV8hMSERERgCJKu127dqhcuTIEQcCSJUvQq1evPL9NLZFIoKOjA2tra1hbW6s0LBER0ees0NK2t7eHvb09gA+/ad21a1dYWlqWSjAiIiKSp/B52hMnTsx3+OvXr5GcnIxGjRopLRQRERHlpfDh3nv27MHMmTPlhs2dOxfOzs5wd3dH//79Cz2Pm4iIiD6NwqW9Y8cOaGtry25fvnwZ27dvh7u7O6ZOnYonT55gzZo1KglJRERExdg8/vz5cwwePFh2+9ixY6hZsyYWLVoEDQ0NJCcn4/jx4/j6669VEpSIiOhzp/CadnZ2NjQ1NWW3L1y4gI4dO8ouqGJmZobXr18rPyEREREBKEZp16lTBxcvXgQA3LlzB8+ePUP79u1l4+Pj46Grq6v8hERERASgGJvHhw4dirlz5+LRo0f4559/UKtWLTg7O8vGX79+Hebm5ioJSURERMUo7eHDh6NSpUoIDw+HtbU1fH19oaWlBeDD5U0TEhIwbNgwlQUlIiL63Clc2gAwePBguYPRclWrVg379+9XWigiIiLKiz/LRUREJBIsbSIiIpFgaRMREYlEuSrtU6dOYdCgQbC3t0e7du0wadIkPHnyRN2xiIiIlKLA0o6IiEBycnJpZvkkFy9exMSJE9GwYUMEBwdj1qxZiIqKwujRo5GSkqLueERERJ+swNLu378//vjjD9ltb29v2cVVyqKwsDDUrl0bixYtQrt27dCjRw8EBQXhxYsXuH79urrjERERfbICS1tbWxupqamy21euXEF8fHyphCqJrKwsVKlSBRKJRDasatWqakxERESkXAWep920aVNs3LgRGRkZssuTXrt2DdnZ2YXOsF+/fkoNqKj+/fsjLCwMW7duRd++fSGVSrFo0SI0atQIbdu2VUsmIiIiZZIIgiDkN+LBgwf43//+h5iYmA8TSiQoYNJ/ZyaR4MGDB8pPqaAzZ85g2rRpePfuHQDAwsICGzZsgImJidoyqUvvrw6pO8Jn78iyvuqOQOCyUBZwWVCeQte0T5w4gdevXyM+Ph4DBw7ElClT0KFDh9LMp7AbN24gICAAgwYNgqurK5KSkvDTTz/hyy+/zPNb4EVJSEhBTk7hf6CUZUZG3C1QVsTFiedgzvKIy0LZIeZlQUNDAkPDsvGDWIVexlQikcDExAQmJibo378/nJycYG1tXVrZimXevHlo06aN3O9529nZoVOnTjh06BA8PDzUmI6IiOjTKXzt8aCgINm/BUHAmzdvAADVq1eXO/hLXR4/fgxXV1e5YTVr1kT16tXx9OlTNaUiIiJSnmL9YEhMTAx++OEHnD17VnZkuY6ODpydnfG///0PZmZmKgmpiNq1a+PevXtyw2JjY/HmzRuYmpqqKRUREZHyKFzakZGRGDZsGNLS0uDq6opGjRoB+LCGe+rUKZw/fx7bt29H48aNVRa2MMOHD8fcuXMxd+5cdO7cGUlJSfj5559haGiIHj16qCUTERGRMilc2suWLYO2tjb27duXZ4366dOn8PT0xLJly7BmzRqlh1TE8OHDoampiR07dmD//v2oUqUKbG1t8eOPP6J69epqyURERKRMCpf2tWvX4OPjk+8m8Hr16sHT0xMhISFKDVccEokEHh4ePOCMiIjKLYV/MCQ7OxtaWloFjtfW1i7ywitERERUcgqXtpWVFfbs2QOpVJpnnFQqxZ49e8rs6WBERETlgcKbxydPnowxY8agW7du6N+/Pxo0aAAAiIqKwqFDhyCVSjF37lyVBSUiIvrcKVzarVu3xoYNG7Bo0aI8+66bNWuG5cuXo1WrVkoPSERERB8U6zxtR0dHHDhwAHFxcXjx4gWAD+dHGxkZqSQcERER/atYpZ3LyMiIRU1ERFTKFD4QjYiIiNSLpU1ERCQSLG0iIiKRYGkTERGJhMKl/eLFC6SlpRU4Pi0tTXZEORERESmfwqXduXNn/PbbbwWOP3PmDDp37qyUUERERJSXwqUtCEKh47OysiCRSD45EBEREeWvWPu0Cyrl5ORk/PnnnzAwMFBKKCIiIsqr0IurBAcHY/Xq1QA+FPb06dMxffr0fKcVBAGjRo1SekAiIiL6oNDStrGxgaenJwRBwI4dO9CuXTvUr19fbhqJRAIdHR1YW1vDzc1NlVmJiIg+a4WWtrOzM5ydnQEAqampGDp0KGxtbUslGBEREclT+NrjQUFBqsxBRERERSjWD4ZkZ2fj3LlzePbsGd6+fZvniHKJRAI/Pz+lBiQiIqIPFC7tO3fuYPLkyXj16lWBp3+xtImIiFRH4dKePXs20tLSsHr1ajg4OEBPT0+VuYiIiOgjCpf2w4cPMXXqVLi6uqoyDxERERVA4Yur1KxZs8irohEREZHqKFza48aNw549e5CSkqLKPERERFQAhTePJyUloXLlyujatSu6deuGWrVqQUNDvvMlEgl8fX2VHpKIiIiKUdrLli2T/XvXrl35TlMWSvvgwYMIDQ3Fo0ePoKOjg2bNmmHZsmW8LjoREYmewqV9+vRpVeZQip9//hnr1q3DuHHjEBAQgOTkZFy+fBmZmZnqjkZERPTJFC5tU1NTVeb4ZFFRUQgODkZwcDBcXFxkw7t06aLGVERERMpTrJ/mLMv279+P2rVryxU2ERFReaLwmrarq2uBv6edSyKR4NSpU58cqiRu3boFS0tL/PTTT9i+fTuSkpLQtGlTzJgxA61bt1ZLJiIiImVSuLRbt26dp7Szs7Px4sUL3LhxA40bN0azZs2UHlBRcXFxuHv3LiIiIvDNN99AV1cXISEh8PX1xbFjx1CnTh21ZSMiIlIGhUt74cKFBY6LiIiAj48PevfurZRQJSEIAt6/f48dO3agadOmAIBWrVqhc+fO2LhxI7777juF52VoqKuqmPSZMTKqqu4IRGUClwXlKNavfBWkSZMm8PDwwNKlS7F//35lzLLY9PT0UK1aNVlhA4COjg5sbW0RGRlZrHklJKQgJ0e8V3/jwlF2xMUlqzvCZ43LQtkh5mVBQ0NSZlbmlHYgmqGhIR49eqSs2RWbubl5gePS09NLMQkREZFqKKW037x5g3379qFmzZrKmF2JuLi4ICkpCffu3ZMNe//+Pf766y9YWVmpLRcREZGyKLx53NvbO9/hycnJiIqKQmZmJhYvXqy0YMXVpUsXNG/eHJMnT8bUqVNRpUoVhISEIC0tDaNHj1ZbLiIiImVRuLTz+4UviUSCOnXqoG3bthg4cCAaNWqk1HDFoaGhgbVr12Lx4sWYPXs20tPTYWtri9DQUJiZmaktFxERkbIoXNpbt25VZQ6lMDAwKPQodyIiIjErN1dEIyIiKu+KdcpXUlIS1q1bh/DwcMTGxgL4cE1yFxcX+Pr6olq1aqrISERERCjGmvbLly/Rv39/hISEQFtbG25ubnBzc4OOjg42bNiA/v374+XLl6rMSkRE9FlTeE176dKlePv2LUJDQ/Ncy/vatWv44osvsHTpUrnf3SYiIiLlUXhN+9y5c/D29s73xzccHBwwYsQInDt3TqnhiIiI6F8Kl3ZaWhoMDAwKHG9oaIi0tDSlhCIiIqK8FC5tc3NzHDlyBBkZGXnGZWRk4PDhw2jcuLFSwxEREdG/FN6nPW7cOEyZMgUDBw7E0KFD0aBBAwBAdHQ0du3ahUePHmHlypUqC0pERPS5U7i0u3XrhkWLFmHJkiWYO3eu7Le1BUFAjRo1sHDhQnTt2lVlQYmIiD53xTpPu2/fvujVqxfu3r2LFy9eAABq164Na2trVKyolF/5JCIiogIUu2krVqwIOzs72NnZqSAOERERFaTQA9Fev36N7t27Y/ny5YXOZPny5ejRowcSExOVGo6IiIj+VWhpb926FW/fvsXYsWMLncnYsWORlJQkih8VISIiEqtCSzs8PBw9e/aErq5uoTPR1dVFr169cObMGaWGIyIion8VWtpPnz6FpaWlQjOysLBATEyMUkIRERFRXoWWtkQiQU5OjkIzysnJkZ0GRkRERMpXaGmbmpri9u3bCs3ozp07MDU1VUooIiIiyqvQ0u7UqROOHj2Kx48fFzqTx48fIywsDC4uLkoNR0RERP8qtLTHjBmDKlWqYOTIkQgLC0NWVpbc+KysLISFhWHkyJHQ1dXF6NGjVRqWiIjoc1boxVUMDAywfv16+Pn5Yfr06Zg1axYaNGiAKlWq4N27d4iOjkZ6ejqMjY2xevXqQn8FjIiIiD5NkVdEs7KyQlhYGHbu3Inff/8djx8/RkpKCnR1ddG0aVO4urpi6NChqFq1amnkJSIi+mwpdBlTXV1djB07tsiLrBAREZHqKPx72kRERKReLG0iIiKRYGkTERGJBEubiIhIJMptab979w4dO3aEpaUl7ty5o+44REREn6zclnZwcDCys7PVHYOIiEhpymVp//3339i1axcmT56s7ihERERKUy5Le86cORg+fDjq16+v7ihERERKU+5K++DBg4iJicGXX36p7ihERERKVa5KOzk5GUuWLMH06dNRpUoVdcchIiJSKoUuYyoWP/74I8zMzNCnT59Pmo+hoa6SEtHnzsiI1+QnArgsKEu5Ke3IyEjs2rULISEhkEqlAID379/L/p/7IyeKSEhIQU6OoLKsqsaFo+yIi0tWd4TPGpeFskPMy4KGhqTMrMyVm9KOiYlBVlYWvL2984zz9vZGkyZNcOjQITUkIyIiUo5yU9otWrRAaGio3LAHDx4gKCgIs2fPhpWVlZqSERERKUe5KW0DAwO0adMm33FWVlawsbEp5URERETKVa6OHiciIirPys2adn7atGmDhw8fqjsGERGRUnBNm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCJRUd0BlOX48eM4cuQI7t27h7dv36Ju3boYNmwYhg4dCg0N/m1CRETiV25Ke9OmTahduzZmzJgBQ0NDXL58GfPnz8ezZ88QEBCg7nhERESfrNyU9po1a2BgYCC77ejoiPfv32P79u2YOnUqKlWqpMZ0REREn67cbDf+b2Hnatq0KdLT05GUlFT6gYiIiJSs3JR2fq5fv45q1arB0NBQ3VGIiIg+WbnZPP6xO3fuYP/+/fDz80OFChWKdV9DQ10VpaLPjZFRVXVHICoTuCwoR7ks7bi4OEyePBk2NjYYO3Zsse+fkJCCnBxBBclKBxeOsiMuLlndET5rXBbKDjEvCxoakjKzMlfuNo8nJydj7Nix0NbWxs8//wxNTU11RyIiIlKKcrWmnZ6eji+//BIJCQnYtWsXqlevru5IRERESlNuSjsrKwtTpkzBw4cPsXXrVpiamqo7EhERkVKVm9KeM2cOfv/9d0yfPh1paWn466+/ZOPMzc2hq1s29kcQERGVVLkp7XPnzgEAlixZkmdcaGgo2rRpU9qRiIiIlKrclPaZM2fUHYGIiEilyt3R40REROUVS5uIiEgkWNpEREQiwdImIiISCZY2ERGRSLC0iYiIRIKlTUREJBIsbSIiIpFgaRMREYkES5uIiEgkWNpEREQiwdImIiISCZY2ERGRSLC0iYiIRIKlTUREJBIsbSIiIpFgaRMREYkES5uIiEgkWNpEREQiwdImIiISCZY2ERGRSLC0iYiIRIKlTUREJBIsbSIiIpFgaRMREYkES5uIiEgkylVpP3nyBD4+PrC3t4ejoyPmzp2L1NRUdcciIiJSiorqDqAsUqkU3t7eqF27NlasWIHExEQEBQUhMTERy5cvV3c8IiKiT1ZuSnvXrl2QSqU4ePAgDAwMAAAVKlTAtGnTMGHCBDRu3FjNCYmIiD5Nudk8/ueff8LR0VFW2ADQrVs3VKpUCX/++acakxERESlHuVnTfvz4MQYOHCg3rFKlSqhXrx6ioqKKNS8NDYkyo6mFcXUddUcglI/PkthxWSgbxLwslKXs5aa0pVIp9PT08gzX09PD27dvizWv6tWrKCuW2myc5abuCATA0FBX3RE+e1wWygYuC8pRbjaPExERlXflprT19PQglUrzDJdKpdDX11dDIiIiIuUqN6XdqFEjPH78WG5YRkYGnj59ioYNG6opFRERkfKUm9Lu2LEjLl26hDdv3siG/fbbb8jIyICzs7MakxERESmHRBAEQd0hlEEqlcLd3R2mpqaYMGECEhISsHDhQrRt25YXVyEionKh3JQ2AERHR2PevHm4fv06tLS00KtXL0yfPh06Ojzlg4iIxK9clTYREVF5Vm72aRMREZV3LG0iIiKRKDdXRKOyYdWqVQgODs4z3MTERKFrwHt5eaFy5cpYu3atKuIRlTpLS8sip5k4cSImTZpUCmlI7FjapHTa2trYsmWL3LBKlSqpKQ2Reu3evVvutoeHB7y8vODu7i4bVrNmzdKORSLF0ial09DQgJ2dnbpjEJUJ+S0LtWrVKnQZSUtLg7a2tupCkWhxnzaVirS0NMydOxfdu3eHra0tXFxc8PXXXyMpKanQ+/3zzz+YOnUqnJycYGNjA1dXV3z77bdy00RFRWHixIlo1aoVbG1tMXr0aERGRqrw2RApz/79+2FpaYlbt27B19cXdnZ2mDNnDp4/fw5LS0ucOHFCbvpVq1bB3t5eblhKSgrmzZuHDh06wNraGr1798apU6dK82lQKeGaNqlEVlaW3O3U1FRkZmZiypQpqFGjBl69eoV169bB19cXv/zyS4HzmTFjBv755x/MmjULNWrUwMuXL3H9+nXZ+OfPn2PYsGFo0KAB5s2bB01NTYSEhMDb2xu//vorqlatqrLnSKRMU6dOxaBBg+Dj4wMtLS2F75eZmYkxY8bg1atXmDRpEkxNTXHy5ElMmjQJ27ZtQ8uWLVWYmkobS5uU7v3797CyspIb9v3332POnDmy21lZWTA3N0e/fv1w7969PNPnun37Nvz9/dGzZ0/ZsL59+8r+HRwcjCpVqmDz5s2yzYmtW7dGly5dsHXrVkyYMEGZT41IZYYMGYLx48fLbj9//lyh+x05cgR3797FgQMHZAe9tWvXDrGxsVi1ahU2b96sirikJixtUjptbW1s27ZNbpipqSkOHjyILVu24MmTJ3j//r1s3JMnTwos7WbNmiEkJAQaGhpo164d6tevLzf+3Llz6NGjBypWrChbu9fW1oadnR1u376t3CdGpEKurq4lut/58+dhYWGBRo0ayW3hateuHVavXq2seFRGsLRJ6TQ0NGBjYyM37LfffkNAQAAGDx6M//3vf6hWrRqkUil8fX2Rnp5e4LyWL1+OH3/8EStXrsScOXNgZmaGKVOmoFevXgCAN2/eIDQ0FKGhoXnu26RJE+U+MSIVqlGjRonul5iYiAcPHhT4h69UKoWent6nRKMyhKVNpeLEiRNo0qQJ5s2bJxt29+7dIu9nbGyMBQsWQBAE3Lt3D+vXr8e0adNgaWkJc3Nz6Ovrw9nZGZ6ennnuy6NvScxy92tnZmbKDX/79q3cbX19fVhaWmL+/Pn5zqdy5cqqCUhqwdKmUpGWlpbnXO0jR44ofH+JRAJra2tMmzYNJ06cQFRUFMzNzeHk5IS///4bzZo1Q4UKFZQdm0htDA0NoampiUePHsmGZWdn48KFC3LTtWvXDuHh4TA2NoaJiUlpx6RSxtKmUuHk5IQ5c+Zg1apVaNmyJS5cuIAzZ84Uep/k5GSMGTMGffv2RYMGDZCdnY2dO3eiSpUqsLW1BQBMmTIFgwYNwujRo+Hh4QEjIyPEx8fj5s2baNCgQb5r4ERioKGhgW7dumH79u2oV68eatSogd27dyM1NVVuur59+2LPnj0YMWIExowZg4YNGyI5ORl///034uLi8N1336npGZAqsLSpVAwdOhTPnz/Hrl27EBISAkdHR6xcuRL9+vUr8D5aWlpo0qQJtm/fjhcvXkBLSwtWVlbYuHGjbI2ibt262Lt3L1asWIF58+YhOTkZRkZGsLOzQ58+fUrp2RGpxjfffIPMzEwEBQVBW1sbI0aMQNOmTRESEiKbplKlSti8eTNWr16N9evX4/Xr19DX14eFhQUGDx6sxvSkCvxpTiIiIpHgFdGIiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEgqVNREQkEixtIiIikWBpExERiQRLm4iISCRY2kRERCLB0iYiIhIJljYREZFIsLSJiIhEoqK6AxCJWWJiIkJCQvD7778jNjYWgiCgXr16cHZ2hpeXF0xMTNQdsdi2b98OHR0dDBgwQN1RlO7IkSNISEjAqFGj1B2FqEQkgiAI6g5BJEb37t3D2LFjkZycDHd3d9jY2EBDQwMPHz7EsWPHUK1aNZw8eVLdMYvN3d0d1atXx9atW9UdRem++OILREZG4syZM+qOQlQiXNMmKoHk5GT4+fkBAPbv34/GjRvLjff398f69euV8ljv379H5cqV8x2XmpoKHR0dpTyOOog9P1Fp4z5tohLYtWsXXr58iYCAgDyFDQBVq1aFv7+/3LCTJ09iwIABaN68Odq0aQN/f3+8ePFCbprAwEDY2Njg+fPnGD9+PFq0aIEvvvgCAODq6gofHx9cvHgRgwcPho2NDTZs2AAAyMjIQHBwMNzc3GBtbY0OHTpgwYIFSE1NzZPt6NGjGDJkCOzs7ODg4IBhw4bh1KlTsseIjIzElStXYGlpCUtLS7i6uhb5euzcuVO2taFdu3b49ttvkZSUJDeNl5cXunfvjgcPHsDLywt2dnaYPXt2gfPcv38/LC0tceXKFQQFBcHR0RF2dnbw8/NDYmJisTN4eXnhjz/+QGxsrOy5WVpaFvnciMoSrmkTlcCZM2egpaWFHj16KDT9oUOHMGPGDFhZWcHf3x9v3rxBaGgorl+/jgMHDsDAwEA2rSAI8PHxgY2NDWbMmIEKFSrIxj19+hSTJ0/G4MGDMWjQINSqVQuCIMDPzw9Xr17F4MGDYW5ujsePH2PHjh149OgRNm7cCIlEAgD46aefsGLFCln5aWtr4969ezh37hy6dOmCr7/+GnPnzkXlypUxfvx4AECVKlUKfW6583R0dISHhweePn2KHTt24NatW9i7dy8qVaokmzY5ORk+Pj5wc3ODu7s7qlatWuRrt2DBAujr62PixImIjY3Fli1bMGfOHPz444/FyjB+/HgkJyfj1atXmDlzpkLvG1GZIxBRsbVq1Uro06ePQtNmZGQITk5OQo8ePYTU1FTZ8EuXLgkWFhbCwoULZcMCAgIECwsLYcGCBXnm4+LiIlhYWAinTp2SG3748GHB0tJSuHz5stzwQ4cOCRYWFsLZs2cFQRCEmJgYoUmTJsL48eOFrKwsuWlzcnJk/+7Vq5cwYsQIhZ5bQkKCYGVlJYwcOVJunvv27RMsLCyErVu3yoaNGDFCsLCwELZs2aLQvHPnMXLkSLl8CxYsEJo2bSpIpdJiZxg3bpzg4uKi0OMTlUXcPE5UAikpKUWugea6e/cu4uPjMWzYMGhra8uGt2nTBlZWVvjjjz/y3MfT0zPfedWsWROdO3eWG3b8+HHUr18f5ubmSExMlP3XunVrSCQSXL58GQDw22+/IScnBxMmTJBbewcgWxMvrgsXLiAzMxPe3t5y8+zbty9q1KiR57lVrFgRHh4exXqMQYMGyeVzcHBAdnY2YmNjS5SBSMy4eZyoBHR1dfHu3TuFps3db92gQYM84xo1apTnCHMNDQ2YmprmO6+6devmGfbkyRNER0ejbdu2+d4nISEBwIdN6wDy3QdfUgU9twoVKsDMzExWrLmMjY2hpaVVrMeoVauW3G09PT0AgFQqLVEGIjFjaROVQMOGDXH//n1kZGTI7bNVhooVK6JixfwXzfwKLycnB+bm5vjmm2/yvY+xsbFS832K4hY2gDxbBXIJPFuVPkPcPE5UAq6urkhPT8eJEyeKnLZ27doAgOjo6DzjoqKiClyrVlS9evWQlJSEtm3bwsnJKc9/5ubmsukAIDIystD5FWdTeUHPLScnBzExMZ/83JSdoaS7AYjKCpY2UQkMHToUJiYmWLRoER4/fpxnfEpKCpYvXw4AsLa2Ro0aNbB7926kp6fLprl27Rru3r2LTp06fVKWnj17Ij4+Hjt27MgzLiMjAykpKQCArl27QkNDA6tXr0Z2drbcdP9da9XR0cHbt28VemwnJydoampi69atyMnJkQ0/fPgw4uPj4eLiUpKnVCzFyaCjowOpVMq1dBItbh4nKgE9PT2sXr0a48aNQ//+/eWuiBYZGYmwsDDo6+tj6tSp0NTUxPTp0xEQEABPT0/06dMHiYmJ2Lp1K0xMTDB27NhPytKnTx+cOHECc+bMwdWrV9GyZUsIgoDo6GgcP34cK1asQJs2bVCvXj34+flh1apV8PT0RNeuXaGjo4N79+5BS0sL3333HYAPf2Ts2LEDwcHBqF+/PipXrlzgudoGBgaYMGECVqxYgTFjxqBLly549uwZtm/fjiZNmmDw4MGf9NwUUZwM1tbWOHbsGObPnw9bW1toaGigV69eKs9IpCwsbaISsrGxQVhYmOza40ePHoUgCDAzM4OHhwe8vLxk0/br1w86OjpYu3Ytli5dCh0dHTg7O2PatGly52iXhIaGBoKDg7FlyxYcPHgQp0+fhra2NurWrQtPT0+5C4hMnDgRderUQWhoKFauXAktLS2Ym5vD19dXNo2fnx9evnyJTZs2ISUlBaampoVeYGXChAmoXr06tm3bhoULF0JPTw/9+/eHv7+/0vf3f2oGT09P/P333zhy5Ai2bdsGQRBY2iQqvPY4ERGRSHCfNhERkUiwtImIiESCpU1ERCQSLG0iIiKRYGkTERGJBEubiIhIJFjaREREIsHSJiIiEgmWNhERkUiwtImIiETi/wFz7OMNkeQbeAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "df_final[df_final[\"Question\"] == \"Frequência Angular com Lissajous\"][\"Correct\"].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Correct or not\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students who passed the first intro question\", y=1.02);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.4)\n",
    "df_final[df_final[\"Question\"] == \"Frequência angular com Lissajous 2\"][\"Correct\"].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Correct or not\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students who passed the second intro question\", y=1.02);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final[['student', 'quadrant']].drop_duplicates().reset_index().drop('index', axis=1)['quadrant'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, stacked=True)\n",
    "plt.xlabel(\"Quadrant (Question 1 and 2 right or wrong)\", labelpad=14)\n",
    "plt.ylabel(\"Count of students\", labelpad=14)\n",
    "plt.title(\"Count of students in each quadrant\", y=1.02);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Enhancement analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1 = q1_q2_df.copy()[[\"Question\", \"Correct\", \"student\"]]\n",
    "q1 = q1[q1[\"Question\"] == \"Frequência Angular com Lissajous\"].set_index(\"student\")\n",
    "q1.columns = [\"Title\", \"Graduation\"]\n",
    "q1 = q1.add_suffix(\"_q1\")\n",
    "\n",
    "q2 = q1_q2_df.copy()[[\"Question\", \"Correct\", \"student\"]]\n",
    "q2 = q2[q2[\"Question\"] == \"Frequência angular com Lissajous 2\"].set_index(\"student\")\n",
    "q2.columns = [\"Title\", \"Graduation\"]\n",
    "q2 = q2.add_suffix(\"_q2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "q3 = q3_q4_q5_q6_df.copy()[[\"Title\", \"Graduation\", \"student\"]]\n",
    "q4 = q3_q4_q5_q6_df.copy()[[\"Title\", \"Graduation\", \"student\"]]\n",
    "q5 = q3_q4_q5_q6_df.copy()[[\"Title\", \"Graduation\", \"student\"]]\n",
    "q6 = q3_q4_q5_q6_df.copy()[[\"Title\", \"Graduation\", \"student\"]]\n",
    "\n",
    "q3[\"Graduation\"] = q3[\"Graduation\"].map({\"Passed\": True, \"Failed\": False})\n",
    "q4[\"Graduation\"] = q4[\"Graduation\"].map({\"Passed\": True, \"Failed\": False})\n",
    "q5[\"Graduation\"] = q5[\"Graduation\"].map({\"Passed\": True, \"Failed\": False})\n",
    "q6[\"Graduation\"] = q6[\"Graduation\"].map({\"Passed\": True, \"Failed\": False})\n",
    "\n",
    "q3 = q3[q3[\"Title\"] == \"Avaliação Final (Parte 1)\"].set_index(\"student\").add_suffix(\"_q3\")\n",
    "q4 = q4[q4[\"Title\"] == \"Avaliação Final (Parte 2)\"].set_index(\"student\").add_suffix(\"_q4\")\n",
    "q5 = q5[q5[\"Title\"] == \"Avaliação Final (Parte 3)\"].set_index(\"student\").add_suffix(\"_q5\")\n",
    "q6 = q6[q6[\"Title\"] == \"Avaliação Final (Parte 4)\"].set_index(\"student\").add_suffix(\"_q6\")\n",
    "\n",
    "df_enhancement_q2_q4_q5 = pd.concat([q2, q4, q5], axis=1, join=\"inner\")[[\"Title_q2\", \"Title_q4\", \"Title_q5\", \"Graduation_q2\", \"Graduation_q4\", \"Graduation_q5\"]]\n",
    "df_enhancement_q1_q3_q6 = pd.concat([q1, q3, q6], axis=1, join=\"inner\")[[\"Title_q1\", \"Title_q3\", \"Title_q6\", \"Graduation_q1\", \"Graduation_q3\", \"Graduation_q6\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enhancement in graphic view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Graduation_q1</th>\n",
       "      <th>Graduation_q3</th>\n",
       "      <th>Graduation_q6</th>\n",
       "      <th>Enhancement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>renan</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estknowledge</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reinaldoavelino</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felipehu</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matheus</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terpomoj</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitor-g</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rccampos</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joaonado</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daniel-saravali</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joaogrudka</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>davibai1006</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fishboy1900</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lucasgiacone</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gui-lodi</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topcat</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>williamcolcioni</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tonybai2003</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Graduation_q1 Graduation_q3 Graduation_q6  Enhancement\n",
       "student                                                                \n",
       "renan                    False         False         False        False\n",
       "estknowledge              True         False         False        False\n",
       "reinaldoavelino          False         False          True         True\n",
       "felipehu                 False          True         False         True\n",
       "matheus                   True         False          True        False\n",
       "terpomoj                  True         False          True        False\n",
       "vitor-g                  False         False          True         True\n",
       "rccampos                  True         False          True        False\n",
       "joaonado                  True          True          True        False\n",
       "daniel-saravali           True          True         False        False\n",
       "joaogrudka                True         False          True        False\n",
       "davibai1006              False         False         False        False\n",
       "fishboy1900              False         False         False        False\n",
       "lucasgiacone              True          True          True        False\n",
       "gui-lodi                 False          True          True         True\n",
       "topcat                   False         False          True         True\n",
       "williamcolcioni          False          True         False         True\n",
       "tonybai2003              False         False          True         True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def q3_q5_condition(s):\n",
    "    if (s[\"Graduation_q3\"] == True or s[\"Graduation_q6\"] == True) and s[\"Graduation_q1\"] == False:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "df_enhancement_q1_q3_q6 = df_enhancement_q1_q3_q6[[\"Graduation_q1\", \"Graduation_q3\", \"Graduation_q6\"]]\n",
    "df_enhancement_q1_q3_q6[\"Enhancement\"] = df_enhancement_q1_q3_q6.apply(q3_q5_condition, axis=1)\n",
    "df_enhancement_q1_q3_q6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduation_q1  Graduation_q3  Graduation_q6  Enhancement\n",
       "False          False          True           True           4\n",
       "True           False          True           False          4\n",
       "False          False          False          False          3\n",
       "               True           False          True           2\n",
       "True           True           True           False          2\n",
       "False          True           True           True           1\n",
       "True           False          False          False          1\n",
       "               True           False          False          1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enhancement_q1_q3_q6.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    11\n",
       "True      7\n",
       "Name: Enhancement, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enhancement_q1_q3_q6[\"Enhancement\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'38.89%'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"\"\"{df_enhancement_q1_q3_q6[\"Enhancement\"].value_counts()[1]/len(df_enhancement_q1_q3_q6[\"Enhancement\"])*100:.2f}%\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enhancement in Algebraic view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Graduation_q2</th>\n",
       "      <th>Graduation_q4</th>\n",
       "      <th>Graduation_q5</th>\n",
       "      <th>Enhancement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>renan</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>estknowledge</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reinaldoavelino</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felipehu</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matheus</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terpomoj</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitor-g</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rccampos</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joaonado</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daniel-saravali</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joaogrudka</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>davibai1006</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fishboy1900</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lucasgiacone</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gui-lodi</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topcat</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>williamcolcioni</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tonybai2003</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Graduation_q2 Graduation_q4 Graduation_q5  Enhancement\n",
       "student                                                                \n",
       "renan                    False          True         False         True\n",
       "estknowledge             False         False          True         True\n",
       "reinaldoavelino          False         False         False        False\n",
       "felipehu                 False         False         False        False\n",
       "matheus                  False          True         False         True\n",
       "terpomoj                 False         False         False        False\n",
       "vitor-g                   True         False          True        False\n",
       "rccampos                 False         False         False        False\n",
       "joaonado                 False         False          True         True\n",
       "daniel-saravali           True         False         False        False\n",
       "joaogrudka                True         False          True        False\n",
       "davibai1006              False         False          True         True\n",
       "fishboy1900              False         False         False        False\n",
       "lucasgiacone              True         False          True        False\n",
       "gui-lodi                 False         False          True         True\n",
       "topcat                    True         False          True        False\n",
       "williamcolcioni          False         False         False        False\n",
       "tonybai2003               True         False         False        False"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def q4_q5_condition(s):\n",
    "    if (s[\"Graduation_q4\"] == True or s[\"Graduation_q5\"] == True) and s[\"Graduation_q2\"] == False:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "df_enhancement_q2_q4_q5 = df_enhancement_q2_q4_q5[[\"Graduation_q2\", \"Graduation_q4\", \"Graduation_q5\"]]\n",
    "df_enhancement_q2_q4_q5[\"Enhancement\"] = df_enhancement_q2_q4_q5.apply(q4_q5_condition, axis=1)\n",
    "df_enhancement_q2_q4_q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduation_q2  Graduation_q4  Graduation_q5  Enhancement\n",
       "False          False          False          False          6\n",
       "                              True           True           4\n",
       "True           False          True           False          4\n",
       "False          True           False          True           2\n",
       "True           False          False          False          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enhancement_q2_q4_q5.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    12\n",
       "True      6\n",
       "Name: Enhancement, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_enhancement_q2_q4_q5[\"Enhancement\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'33.33%'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"\"\"{df_enhancement_q2_q4_q5[\"Enhancement\"].value_counts()[1]/len(df_enhancement_q2_q4_q5[\"Enhancement\"])*100:.2f}%\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Open Connection ###\n"
     ]
    }
   ],
   "source": [
    "import pigeon\n",
    "\n",
    "ws = pigeon.WebSocketClient(api_key='1lw9SI3R.KHixHAueoIJFXt3pyNB3JXaQ', insruments=['PETR4', 'VALE3'])\n",
    "ws.run(on_message=lambda message: print(message))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>candle_period</th>\n",
       "      <th>candle_time</th>\n",
       "      <th>prev_close_price</th>\n",
       "      <th>open_price</th>\n",
       "      <th>close_price</th>\n",
       "      <th>high_price</th>\n",
       "      <th>low_price</th>\n",
       "      <th>open_time</th>\n",
       "      <th>close_time</th>\n",
       "      <th>vwap</th>\n",
       "      <th>volume</th>\n",
       "      <th>financial_volume</th>\n",
       "      <th>amount_trades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T13:03:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>28.99</td>\n",
       "      <td>28.89</td>\n",
       "      <td>28.99</td>\n",
       "      <td>28.78</td>\n",
       "      <td>2022-09-29 13:03:56.321000+00:00</td>\n",
       "      <td>2022-09-29 13:03:59.946000+00:00</td>\n",
       "      <td>28.952195</td>\n",
       "      <td>486500</td>\n",
       "      <td>14085243</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T13:04:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>28.89</td>\n",
       "      <td>28.70</td>\n",
       "      <td>28.95</td>\n",
       "      <td>28.65</td>\n",
       "      <td>2022-09-29 13:04:00.053000+00:00</td>\n",
       "      <td>2022-09-29 13:04:59.925000+00:00</td>\n",
       "      <td>28.782891</td>\n",
       "      <td>910200</td>\n",
       "      <td>26198187</td>\n",
       "      <td>1546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T13:05:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>28.70</td>\n",
       "      <td>28.93</td>\n",
       "      <td>28.95</td>\n",
       "      <td>28.70</td>\n",
       "      <td>2022-09-29 13:05:00.073000+00:00</td>\n",
       "      <td>2022-09-29 13:05:59.769000+00:00</td>\n",
       "      <td>28.856553</td>\n",
       "      <td>587700</td>\n",
       "      <td>16958996</td>\n",
       "      <td>748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T13:06:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>28.93</td>\n",
       "      <td>28.83</td>\n",
       "      <td>28.93</td>\n",
       "      <td>28.81</td>\n",
       "      <td>2022-09-29 13:06:00.017000+00:00</td>\n",
       "      <td>2022-09-29 13:06:59.872000+00:00</td>\n",
       "      <td>28.892176</td>\n",
       "      <td>336800</td>\n",
       "      <td>9730885</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T13:07:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>28.84</td>\n",
       "      <td>28.89</td>\n",
       "      <td>28.91</td>\n",
       "      <td>28.81</td>\n",
       "      <td>2022-09-29 13:07:00.013000+00:00</td>\n",
       "      <td>2022-09-29 13:07:55.556000+00:00</td>\n",
       "      <td>28.866195</td>\n",
       "      <td>130100</td>\n",
       "      <td>3755492</td>\n",
       "      <td>250</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T15:41:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.22</td>\n",
       "      <td>29.22</td>\n",
       "      <td>29.23</td>\n",
       "      <td>29.21</td>\n",
       "      <td>2022-09-29 15:41:00.071000+00:00</td>\n",
       "      <td>2022-09-29 15:41:59.856000+00:00</td>\n",
       "      <td>29.219817</td>\n",
       "      <td>114600</td>\n",
       "      <td>3348591</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T15:42:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.22</td>\n",
       "      <td>29.26</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.22</td>\n",
       "      <td>2022-09-29 15:42:00.151000+00:00</td>\n",
       "      <td>2022-09-29 15:42:59.753000+00:00</td>\n",
       "      <td>29.238310</td>\n",
       "      <td>211800</td>\n",
       "      <td>6192674</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T15:43:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.26</td>\n",
       "      <td>29.22</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.21</td>\n",
       "      <td>2022-09-29 15:43:00.218000+00:00</td>\n",
       "      <td>2022-09-29 15:43:59.709000+00:00</td>\n",
       "      <td>29.242306</td>\n",
       "      <td>87600</td>\n",
       "      <td>2561626</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T15:44:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.22</td>\n",
       "      <td>29.26</td>\n",
       "      <td>29.26</td>\n",
       "      <td>29.20</td>\n",
       "      <td>2022-09-29 15:44:00.195000+00:00</td>\n",
       "      <td>2022-09-29 15:44:58.724000+00:00</td>\n",
       "      <td>29.222117</td>\n",
       "      <td>141700</td>\n",
       "      <td>4140774</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>PETR4</td>\n",
       "      <td>1m</td>\n",
       "      <td>2022-09-29T15:45:00Z</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.25</td>\n",
       "      <td>29.27</td>\n",
       "      <td>29.29</td>\n",
       "      <td>29.24</td>\n",
       "      <td>2022-09-29 15:45:00.148000+00:00</td>\n",
       "      <td>2022-09-29 15:45:58.550000+00:00</td>\n",
       "      <td>29.267870</td>\n",
       "      <td>131000</td>\n",
       "      <td>3834091</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>163 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ticker candle_period           candle_time  prev_close_price  open_price  \\\n",
       "0    PETR4            1m  2022-09-29T13:03:00Z             29.27       28.99   \n",
       "1    PETR4            1m  2022-09-29T13:04:00Z             29.27       28.89   \n",
       "2    PETR4            1m  2022-09-29T13:05:00Z             29.27       28.70   \n",
       "3    PETR4            1m  2022-09-29T13:06:00Z             29.27       28.93   \n",
       "4    PETR4            1m  2022-09-29T13:07:00Z             29.27       28.84   \n",
       "..     ...           ...                   ...               ...         ...   \n",
       "158  PETR4            1m  2022-09-29T15:41:00Z             29.27       29.22   \n",
       "159  PETR4            1m  2022-09-29T15:42:00Z             29.27       29.22   \n",
       "160  PETR4            1m  2022-09-29T15:43:00Z             29.27       29.26   \n",
       "161  PETR4            1m  2022-09-29T15:44:00Z             29.27       29.22   \n",
       "162  PETR4            1m  2022-09-29T15:45:00Z             29.27       29.25   \n",
       "\n",
       "     close_price  high_price  low_price                         open_time  \\\n",
       "0          28.89       28.99      28.78  2022-09-29 13:03:56.321000+00:00   \n",
       "1          28.70       28.95      28.65  2022-09-29 13:04:00.053000+00:00   \n",
       "2          28.93       28.95      28.70  2022-09-29 13:05:00.073000+00:00   \n",
       "3          28.83       28.93      28.81  2022-09-29 13:06:00.017000+00:00   \n",
       "4          28.89       28.91      28.81  2022-09-29 13:07:00.013000+00:00   \n",
       "..           ...         ...        ...                               ...   \n",
       "158        29.22       29.23      29.21  2022-09-29 15:41:00.071000+00:00   \n",
       "159        29.26       29.27      29.22  2022-09-29 15:42:00.151000+00:00   \n",
       "160        29.22       29.27      29.21  2022-09-29 15:43:00.218000+00:00   \n",
       "161        29.26       29.26      29.20  2022-09-29 15:44:00.195000+00:00   \n",
       "162        29.27       29.29      29.24  2022-09-29 15:45:00.148000+00:00   \n",
       "\n",
       "                           close_time       vwap  volume  financial_volume  \\\n",
       "0    2022-09-29 13:03:59.946000+00:00  28.952195  486500          14085243   \n",
       "1    2022-09-29 13:04:59.925000+00:00  28.782891  910200          26198187   \n",
       "2    2022-09-29 13:05:59.769000+00:00  28.856553  587700          16958996   \n",
       "3    2022-09-29 13:06:59.872000+00:00  28.892176  336800           9730885   \n",
       "4    2022-09-29 13:07:55.556000+00:00  28.866195  130100           3755492   \n",
       "..                                ...        ...     ...               ...   \n",
       "158  2022-09-29 15:41:59.856000+00:00  29.219817  114600           3348591   \n",
       "159  2022-09-29 15:42:59.753000+00:00  29.238310  211800           6192674   \n",
       "160  2022-09-29 15:43:59.709000+00:00  29.242306   87600           2561626   \n",
       "161  2022-09-29 15:44:58.724000+00:00  29.222117  141700           4140774   \n",
       "162  2022-09-29 15:45:58.550000+00:00  29.267870  131000           3834091   \n",
       "\n",
       "     amount_trades  \n",
       "0              628  \n",
       "1             1546  \n",
       "2              748  \n",
       "3              266  \n",
       "4              250  \n",
       "..             ...  \n",
       "158            297  \n",
       "159            425  \n",
       "160            301  \n",
       "161            251  \n",
       "162            309  \n",
       "\n",
       "[163 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_candles = pigeon.IntradayCandles(api_key='1lw9SI3R.KHixHAueoIJFXt3pyNB3JXaQ')\n",
    "int_candles.get_intraday_candles(ticker='PETR4', candle_period='1m', mode='absolute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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