"Pour la suite de l'analyse il est plus simple d'affecter une note numérique à chaque évaluation. Pour se faire il a été décidé d'utiliser la table de conversion _symétrique_ suivante :\n",
"\n",
"| Sigle | Valeur associée |\n",
"|-------|-----------------|\n",
"| ++ | +3 |\n",
"| + | +1 |\n",
"| - | -1 |\n",
"| -- | -3 |\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"list_answers = [\"--\",\"-\",\"+\",\"++\"]\n",
"list_coeffs = [-3,-1,1,3]\n",
"\n",
"def get_nb_eval(row):\n",
" return sum([row[\"stats\"][\"1\"][i] for i in list_answers])\n",
"\n",
"def get_mark_q(row, q): \n",
" if type(q) is int:\n",
" q = str(q)\n",
" return sum([row[\"stats\"][q][list_answers[i]] * list_coeffs[i] for i in range(len(list_coeffs))]) / get_nb_eval(row)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tmp = data_pd.apply((lambda r : get_mark_q(r,10)), axis=1)\n",
"\n",
"graph_data = [\n",
" go.Scatter(\n",
" x=tmp.sort_values(ascending = False).index,\n",
" y=tmp.sort_values(ascending = False),\n",
" )\n",
"]\n",
"\n",
"layout = go.Layout(\n",
" title=\"Visualisation de l'appréciation globale de chaque UV (question 10) sous forme de note moyenne\"\n",