recopytex/templates/tpl_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Markdown as md\n",
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from datetime import datetime\n",
    "from recopytex import flat_clear_csv, pp_q_scores\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "tribe = \"308\"\n",
    "assessment = \"DM1\"\n",
    "date = \"15/09/16\"\n",
    "csv_file = Path(f\"../sheets/{tribe}/160915_{assessment}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# DM1 (15/09/16) pour 308"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if date is None:\n",
    "    display(md(f\"# {assessment} pour {tribe}\"))\n",
    "else:\n",
    "    display(md(f\"# {assessment} ({date}) pour {tribe}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_scores = pd.read_csv(csv_file)\n",
    "scores = flat_clear_csv(stack_scores).dropna(subset=[\"Score\"])\n",
    "scores = pp_q_scores(scores)"
   ]
  },
  {
   "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></th>\n",
       "      <th>Note</th>\n",
       "      <th>Bareme</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exercice</th>\n",
       "      <th>Eleve</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>ABDOU Asmahane</th>\n",
       "      <td>3.67</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABOU Roihim</th>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED BOINALI Kouraichia</th>\n",
       "      <td>1.33</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED Rahada</th>\n",
       "      <td>2.67</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALI SAID Anchourati</th>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Note  Bareme\n",
       "Exercice Eleve                                 \n",
       "1        ABDOU Asmahane            3.67     6.0\n",
       "         ABOU Roihim               0.00     6.0\n",
       "         AHMED BOINALI Kouraichia  1.33     6.0\n",
       "         AHMED Rahada              2.67     6.0\n",
       "         ALI SAID Anchourati       0.00     6.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exercises_scores = scores.groupby([\"Exercice\", \"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
    "exercises_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Note</th>\n",
       "      <th>Bareme</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eleve</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDOU Asmahane</th>\n",
       "      <td>5.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABOU Roihim</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED BOINALI Kouraichia</th>\n",
       "      <td>2.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED Rahada</th>\n",
       "      <td>6.33</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALI SAID Anchourati</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSANE Noussouraniya</th>\n",
       "      <td>4.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BACAR Issiaka</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BACAR Samina</th>\n",
       "      <td>3.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAIHANE Said</th>\n",
       "      <td>5.33</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMBO Houzaimati</th>\n",
       "      <td>5.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAOUD Anzilati</th>\n",
       "      <td>5.17</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAOUD Talaenti</th>\n",
       "      <td>5.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DARKAOUI Rachma</th>\n",
       "      <td>5.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHAKIOINE Nabaouya</th>\n",
       "      <td>1.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJANFAR Soioutinour</th>\n",
       "      <td>5.33</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRISSA Ibrahim</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HACHIM SIDI Assani</th>\n",
       "      <td>7.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAFIDHUI Zalifa</th>\n",
       "      <td>5.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Marie</th>\n",
       "      <td>6.67</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Sania</th>\n",
       "      <td>5.33</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAANDHUI Halouoi</th>\n",
       "      <td>7.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MASSONDI Nasma</th>\n",
       "      <td>7.33</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAIDALI Irichad</th>\n",
       "      <td>5.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Note  Bareme\n",
       "Eleve                                 \n",
       "ABDOU Asmahane            5.00    12.0\n",
       "ABOU Roihim               0.00    12.0\n",
       "AHMED BOINALI Kouraichia  2.67    12.0\n",
       "AHMED Rahada              6.33    12.0\n",
       "ALI SAID Anchourati       0.00    12.0\n",
       "ASSANE Noussouraniya      4.67    12.0\n",
       "BACAR Issiaka             0.00    12.0\n",
       "BACAR Samina              3.67    12.0\n",
       "CHAIHANE Said             5.33    12.0\n",
       "COMBO Houzaimati          5.00    12.0\n",
       "DAOUD Anzilati            5.17    12.0\n",
       "DAOUD Talaenti            5.67    12.0\n",
       "DARKAOUI Rachma           5.67    12.0\n",
       "DHAKIOINE Nabaouya        1.00    12.0\n",
       "DJANFAR Soioutinour       5.33    12.0\n",
       "DRISSA Ibrahim            0.00    12.0\n",
       "HACHIM SIDI Assani        7.00    12.0\n",
       "HAFIDHUI Zalifa           5.67    12.0\n",
       "HOUMADI Marie             6.67    12.0\n",
       "HOUMADI Sania             5.33    12.0\n",
       "MAANDHUI Halouoi          7.00    12.0\n",
       "MASSONDI Nasma            7.33    12.0\n",
       "SAIDALI Irichad           5.00    12.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assessment_scores = scores.groupby([\"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
    "assessment_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23.00\n",
       "mean      4.33\n",
       "std       2.45\n",
       "min       0.00\n",
       "25%       3.17\n",
       "50%       5.17\n",
       "75%       5.67\n",
       "max       7.33\n",
       "Name: Note, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assessment_scores[\"Note\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ce24e90d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "assessment_scores[\"Note\"].plot.kde()\n",
    "assessment_scores[\"Note\"].plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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