recopytex/templates/tpl_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
    "#import prettytable as pt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
    "scores = flat_clear_csv(stack_scores).dropna(subset=[\"Score\"])\n",
    "scores = pp_q_scores(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assessment_scores = scores.groupby([\"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
    "assessment_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assessment_scores[\"Note\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ae61e5cf8>"
      ]
     },
     "execution_count": 8,
     "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.hist(density=True)"
   ]
  },
  {
   "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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