{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Selection process for CS50 Puyo\n",
    "This notebook contains information about the selection process applied to the candidates of CS50 Puyo.\n",
    "The process started with 18 candidates competing for 8 available seats. The candidates took a test and were interviewed by the instructor of the course. In the end, 4 men and 4 women with the highest scores were chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Test\n",
    "The test consisted of 20 questions, 17 questions about mathematical problems and 3 open-ended questions. The math section was graded over 82 points and the questions section over 18 points.\n",
    "### Math Section\n",
    "The **average grade in this section is 12.8 points.**\n",
    "Because this section is graded over 82, most scores are significantly low. There are 9 students with scores above the average.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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du3Zrrv/6669n8ODB69UzaNAgJk+ezEsvvQRUPi+YP39+03/4G9HkCD0z/xAR44HpwErg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\ne44ePZrRo0e3+Dq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a423a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "c_orange = (255/255, 128/255, 14/255)\n",
    "student_numbers = list(range(19))\n",
    "data = pd.read_csv('Applicants.csv', encoding='ansi')\n",
    "math_scores = data['Math (82)']\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "ax.bar(data.index,math_scores)\n",
    "ax.axhline(math_scores.mean(), color=c_orange)\n",
    "ax.text(15.8, 14, 'Average', size=9.5)\n",
    "ax.axhline(82, color=c_orange)\n",
    "ax.text(12.8, 79, 'Max possible score', size=9.5)\n",
    "ax.set_title('Scores in math section', size=14, weight='bold')\n",
    "ax.tick_params(bottom='off', labelbottom='off', left='off')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_ylim(0, 85)\n",
    "ax.set_xlabel('Student')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Questions Section\n",
    "This section comprised 3 questions. The criteria for grading these questions were critical thinking and creativity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ckkOSnJ/kxiRnd8qI+wGHAX/Zyrfr+kz79CSn93z2qiT/017fMckRrWXBRUle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\nQgAAAACD+R/LKbw7FEwo+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a423a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_questions = []\n",
    "data_questions.append(data['Internet (4)'])\n",
    "data_questions.append(data['Principal (7)'])\n",
    "data_questions.append(data['Crisis (7)'])\n",
    "questions = ['How would you explain the internet\\nto a 3 year old?\\n4 points', \n",
    "             'What three things would you change/enhance/implement\\nat your high school if you were the principal?\\n7 points',\n",
    "             'How could you solve humankind’s biggest crisis\\ngiven $1 billion and a spacecraft?\\n7 points']\n",
    "fig = plt.figure(figsize=(20,6))\n",
    "for i in range(3):\n",
    "    ax = fig.add_subplot(1, 3, i+1)\n",
    "    ax.set_title(questions[i])\n",
    "    ax.bar(data.index, data_questions[i])\n",
    "    ax.tick_params(bottom='off', labelbottom='off', left='off')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.axhline(data_questions[i].mean(), color=c_orange)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Total Scores in Test\n",
    "The candidates are from the 1st grade and 2nd grade of high school. To compensate for this difference, a weight is given such that the candidates of 1st grade receive a 15% increase in their total score.\n",
    "\n",
    "The **highest total score is 42.6 points** and the **average is 28.4 points.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PP5wddtiBTTbZhJQSRx55JBdddNFy85g1axYbbLDBsvu33XYbDzzwAPfeey/D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a42ce3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "data['Weighted Total Test'] = data['Total'] * data['Weight']\n",
    "total_test = data['Weighted Total Test']\n",
    "ax.bar(data.index,total_test)\n",
    "ax.axhline(total_test.mean(), color=c_orange)\n",
    "ax.text(15.8, 30, 'Average', size=9.5)\n",
    "ax.set_title('Total Scores in Test\\nMath section + open-ended questions', size=15, weight='bold')\n",
    "ax.tick_params(bottom='off', labelbottom='off', left='off')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_xlabel('Student')\n",
    "ax.set_ylim(0, 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Interview\n",
    "The personal interview comprised 10 questions and lasted 30 minutes. Six questions were the same for all candidates and four questions were selected randomly from a pool. The criteria for grading the interview were structure, argumentation, and use of examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zSY5McsJ2zvmWJG/KZPjlvZL81+Fcn0zyjCR/PLzWUzP5QPG1FdT9u0leMty5/9eT3D/J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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a3b29208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_green = (63/255,124/255,123/255)\n",
    "c_purple = (130/255,32/255,74/255)\n",
    "data_interview = []\n",
    "data_interview.append(data['Structure (50)'])\n",
    "data_interview.append(data['Argumentative (20)'])\n",
    "data_interview.append(data['Examples (30)'])\n",
    "criteria = ['Structure\\n50 points', \n",
    "             'Argumentation\\n20 points',\n",
    "             'Examples\\n30 points']\n",
    "fig = plt.figure(figsize=(20,6))\n",
    "for i in range(0,3):\n",
    "    ax = fig.add_subplot(1, 3, i+1)\n",
    "    ax.set_title(criteria[i])\n",
    "    ax.bar(data.index, data_interview[i], color=c_green)\n",
    "    ax.tick_params(bottom='off', labelbottom='off', left='off')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.axhline(data_interview[i].mean(), color=c_purple)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Total Scores in Interview\n",
    "All candidates did well in the interview, the **average score is 92.3 points.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a43d9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "total_interview = data['Total.1']\n",
    "ax.bar(data.index,total_interview, color=c_green)\n",
    "ax.axhline(total_interview.mean(), color=c_purple)\n",
    "ax.text(15.8, 94, 'Average', size=9.5)\n",
    "ax.set_title('Total Scores in Interview', size=15, weight='bold')\n",
    "ax.tick_params(bottom='off', labelbottom='off', left='off')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_xlabel('Student')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Personal traits\n",
    "Based on the interview, the following traits were evaluated in the candidates and given a weight of 5 points.\n",
    "* Curious\n",
    "* Responsible\n",
    "* Dedicated\n",
    "* Organized\n",
    "* Patient\n",
    "* Asks good questions\n",
    "* Hard-working\n",
    "* Honest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a43d9ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(data.index, data['Traits'])\n",
    "ax.set_title('Personal traits\\n5 points', size=15, weight='bold')\n",
    "ax.tick_params(bottom='on', labelbottom='on', left='off')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_xlabel('Student')\n",
    "ax.set_xticks(student_numbers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Results\n",
    "The final scores are calculated in the following way:\n",
    "* 60% test score\n",
    "* 35% interview score\n",
    "* 5% personal traits score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a48548d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "total = data['Total Results'] = (data['Weighted Total Test'] * 0.6) + (data['Total.1'] * 0.35) + (data['Traits'] * 0.05)\n",
    "ax.bar(data.index,total, color='magenta')\n",
    "#ax.axhline(total.mean(), color='black')\n",
    "ax.set_title('FINAL SCORES', size=16, weight='bold')\n",
    "ax.tick_params(bottom='off', labelbottom='off', left='off')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_xlabel('Student')\n",
    "ax.set_ylim(0,100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The selected students are:\n",
    "\n",
    "* Brigitte Pérez\n",
    "* Sebastián Illicachi\n",
    "* Dayra Yasacama\n",
    "* Daniel Velin\n",
    "* Christian Santamaría\n",
    "* Yadira De La Torre\n",
    "* Edwin León\n",
    "* Thaily Ribadeneira"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "sorted_data = data.sort_values('Total Results', ascending=False)\n",
    "all_gender = sorted_data['Gender']\n",
    "female_final = sorted_data[all_gender == 'F']['Name'].head(4)\n",
    "male_final = sorted_data[all_gender == 'M']['Name'].head(4)\n",
    "names_final = pd.concat([female_final, male_final])\n",
    "all_selected = sorted_data.loc[names_final.index]\n",
    "#print(all_selected.sort_values('Total Results', ascending=False)['Name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Additional information\n",
    "### Personality Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a7a5b28208>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a5b8ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7a5b8b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "personalities_count = Counter(data['Personality'])\n",
    "df = pd.DataFrame.from_dict(personalities_count, orient='index')\n",
    "df.plot(kind='bar', legend=False, title='Distribution of personality types\\nAll applicants', rot=0)\n",
    "\n",
    "personalities_selected_count = Counter(all_selected['Personality'])\n",
    "df = pd.DataFrame.from_dict(personalities_selected_count, orient='index')\n",
    "df.plot(kind='bar', legend=False, title='Distribution of personality types\\nSucessfull applicants', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Course and Gender Average Scores\n",
    "\n",
    "\n",
    " | 1st grade | 2nd grade | All\n",
    "--- |: ---: | ---\n",
    "**Female** | 52.9 | 45.9 | 49\n",
    "**Male** | 53 | 48.5 | 50\n",
    "**All** | 53 | 47.3 | 49.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "data['Course'] = data['Class'].str.slice(0,1)\n",
    "all_first_course = data[data['Course'] == '1']\n",
    "all_second_course = data[data['Course'] == '2']\n",
    "# print(data['Total Results'].mean())\n",
    "# print(all_first_course['Total Results'].mean())\n",
    "# print(all_second_course['Total Results'].mean())\n",
    "# print(data[data['Gender'] == 'F']['Total Results'].mean())\n",
    "# print(data[data['Gender'] == 'M']['Total Results'].mean())\n",
    "# print(all_first_course[all_first_course['Gender'] == 'F']['Total Results'].mean())\n",
    "# print(all_first_course[all_first_course['Gender'] == 'M']['Total Results'].mean())\n",
    "# print(all_second_course[all_second_course['Gender'] == 'F']['Total Results'].mean())\n",
    "# print(all_second_course[all_second_course['Gender'] == 'M']['Total Results'].mean())"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
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