Neurocognition_WAIS_USH2.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import scipy as sp\n",
    "from datetime import datetime\n",
    "from IPython.display import display\n",
    "import plotly\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from IPython.core.display import display, HTML\n",
    "from plotly.subplots import make_subplots\n",
    "from IPython.display import display\n",
    "autoscroll_set_on_load = True\n",
    "autoscroll_starting_threshold = -1\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "df = pd.read_excel('data/LIGHT4DEAF_Neurocognition_Avril_2020_1_FULL_ADULTES_20200504.xlsx')"
   ]
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    "HTML('''<script>\n",
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   "source": [
    "df = df[df[\"Investigateur\"].notnull()]\n",
    "\n",
    "cardinal = len(df)\n",
    "\n",
    "now = datetime.now()\n",
    "df['Date de naissance du patient'] = pd.to_datetime(df['Date de naissance du patient'], format='%m/%d/%y')\n",
    "df['Date de naissance du patient'] = df['Date de naissance du patient'].where(df['Date de naissance du patient'] < now, df['Date de naissance du patient'] -  np.timedelta64(100, 'Y')) \n",
    "df['age'] = (now - df['Date de naissance du patient']).astype('<m8[Y]')\n",
    "\n",
    "#Patients adultes : type\n",
    "#df_type1 = df[df[\"Type\"]=='Type 1']\n",
    "#df_type2 = df[df[\"Type\"]=='Type 2']\n",
    "#df_type3 = df[df[\"Type\"]=='Type 3']\n",
    "#df_type_nan = df[df[\"Type\"].isna()]"
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   "source": [
    "df = df[df[\"Type\"]=='Type 2']"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<h1>L i g h t 4 d e a f</h1>"
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     "metadata": {},
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       "<h2>Neurocognition - WAIS</h2>"
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       "<h3>Description de l'échantillon</h3>"
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      "text/html": [
       "<h4>1. Données : </h4>"
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    "display(HTML('<h1>L i g h t 4 d e a f</h1>'))\n",
    "display(HTML('<h2>Neurocognition - WAIS</h2>'))\n",
    "display(HTML(\"<h3>Description de l'échantillon</h3>\"))\n",
    "display(HTML(\"<h4>1. Données : </h4>\"))\n"
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   "cell_type": "code",
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   "source": [
    "cols = df.columns.values.tolist()\n",
    "cols.index('WAIS')\n",
    "cols.index('Autre commentaire WAIS')\n",
    "arr = cols[cols.index('WAIS'):cols.index('Autre commentaire WAIS')]\n",
    "arr.insert(0,\"ID_SECONDAIRE\")\n",
    "arr.insert(1,\"age\")\n",
    "arr.insert(2,\"Type\")"
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   "cell_type": "code",
   "execution_count": 84,
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   "source": [
    "df_wais = df.loc[:,arr]"
   ]
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   "outputs": [],
   "source": [
    "df_wais.loc[df_wais['Type']==\"Type 2\", 'Type_color']=\"#B3D9FF\"\n",
    "#df_wais.loc[df_wais['Type']==\"Type 1\", 'Type_color']=\"#33ffc9\"\n",
    "#df_wais.loc[df_wais['Type']==\"Type 3\", 'Type_color']=\"#FFC433\"\n",
    "#df_wais.loc[df_wais['Type'].isna(), 'Type_color']=\"#C5C5C5\"\n",
    "#df_wais['Type'].fillna('Type inconnu', inplace= True)"
   ]
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       "<p>Nombre de patients Type 2 ayant effectué le test (WAIS = Oui) : 24/56 patients</p>"
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    "display(HTML('<p>Nombre de patients Type 2 ayant effectué le test (WAIS = Oui) : '+str(len(df_wais[df_wais['WAIS']=='Oui']))+'/'+str(cardinal)+' patients</p>'))"
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   "source": [
    "columns= ['Calcul','Note standard Similitudes','Note standard Vocabulaire', 'Note standard Information',\n",
    "                             'Note standard Cubes','Note standard Matrices', 'Note standard Puzzles visuels',\n",
    "                              'Note standard Mémoires des chiffres', 'Note standard Arithmétique',\n",
    "                                'Note standard Symboles','Note standard Code'\n",
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    "myarr = [['Effectif', 'Moyenne', 'Moy. âge', 'Médiane', 'Min.', 'Max.']]\n",
    "for i in columns[1:]:\n",
    "    effectif = len(df_wais[df_wais[i].notna()])\n",
    "    avg_age = round(df_wais[df_wais[i].notna()]['age'].mean())\n",
    "    avg = round(df_wais[i].mean(),2)\n",
    "    median = round(df_wais[i].median(),2)\n",
    "    max_val = df_wais[i].max()\n",
    "    min_val = df_wais[i].min()\n",
    "    myarr.append([effectif, avg, avg_age, median, max_val, min_val])\n",
    "\n",
    "wais_test =  go.Table(\n",
    "                header=dict(values=list(columns),\n",
    "                align='left'),\n",
    "                cells=dict(values= myarr,align='left'))\n",
    "\n",
    "fig = go.Figure(data=wais_test)\n",
    "fig.update_layout(width=1000, height=400)\n",
    "fig.show()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
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     "end_time": "2020-11-26T13:42:39.561174Z",
     "start_time": "2020-11-26T13:42:39.556370Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>2. Tests </h4>"
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       "<IPython.core.display.HTML object>"
      ]
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    {
     "data": {
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      ]
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    "display(HTML('<h5>2.1. WAIS IVC</h5>'))"
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   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:41.263500Z",
     "start_time": "2020-11-26T13:42:41.259761Z"
    }
   },
   "outputs": [],
   "source": [
    "age_moy = df_wais[df_wais['Total des notes standards ICV'].notna()]['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:43.605534Z",
     "start_time": "2020-11-26T13:42:43.601926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p>Moyenne d'âge :48.79</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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   ],
   "source": [
    "display(HTML(\"<p>Moyenne d'âge :\"+str(round(age_moy,2))+\"</p>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:44.316656Z",
     "start_time": "2020-11-26T13:42:44.312986Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub= df_wais.iloc[:,[0,1,2,-1,6,7,8,9,10,11,12,13]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
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     "end_time": "2020-11-26T13:42:44.888347Z",
     "start_time": "2020-11-26T13:42:44.885751Z"
    }
   },
   "outputs": [],
   "source": [
    "subplot_titles = df_wais_sub.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
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     "end_time": "2020-11-26T13:42:45.472039Z",
     "start_time": "2020-11-26T13:42:45.283324Z"
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   },
   "outputs": [],
   "source": [
    "fig_wais_sub = make_subplots(\n",
    "    rows=4, cols=2,\n",
    "    shared_xaxes=False,\n",
    "    row_heights=[0.20, 0.20, 0.20, 0.20],\n",
    "    column_widths=[0.5, 0.5],\n",
    "    subplot_titles= subplot_titles[4:],\n",
    "    x_title='Âge',\n",
    "    y_title='Score',\n",
    "    vertical_spacing= 0.04 )\n",
    "df_wais_sub = df_wais_sub.sort_values(by=['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
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     "end_time": "2020-11-26T13:42:46.177407Z",
     "start_time": "2020-11-26T13:42:46.084619Z"
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[4]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=1, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[5]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=1, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[6]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=2, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[7]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=2, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[8]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=1)\n",
    "                                                             \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[9]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=2)\n",
    "    \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[10]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=4, col=1)\n",
    "    \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[11]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=4, col=2)\n",
    "    \n",
    "fig_wais_sub.update_layout(height=1500, width=800,\n",
    "                   showlegend=False\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:47.619622Z",
     "start_time": "2020-11-26T13:42:47.599862Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub.agg(['min', 'max', 'mean', 'median', 'count'])\n",
    "#\n",
    "df_wais_sub_agg.index.name = \"Aggrégation\"\n",
    "df_wais_sub_agg.reset_index(inplace=True)\n",
    "#\n",
    "df_wais_sub_agg = df_wais_sub_agg.replace({'Aggrégation' : {\"min\":\"Min\",\"max\":\"Max\",\"mean\":\"Moyenne\", \"median\":\"Médiane\", \"count\":\"Effectif\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:48.473588Z",
     "start_time": "2020-11-26T13:42:48.469961Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.drop(columns= ['ID_SECONDAIRE','Type', 'Type_color', 'age' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:49.159507Z",
     "start_time": "2020-11-26T13:42:49.154684Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:49.827109Z",
     "start_time": "2020-11-26T13:42:49.819995Z"
    }
   },
   "outputs": [],
   "source": [
    "wais_sub =  go.Table(\n",
    "                header=dict(values=list(df_wais_sub_agg.columns[:]),\n",
    "                align='left'),\n",
    "                cells=dict(values= [df_wais_sub_agg.iloc[:,0], df_wais_sub_agg.iloc[:,1], df_wais_sub_agg.iloc[:,2], df_wais_sub_agg.iloc[:,3]\n",
    "                           , df_wais_sub_agg.iloc[:,4], df_wais_sub_agg.iloc[:,5], df_wais_sub_agg.iloc[:,6], df_wais_sub_agg.iloc[:,7]\n",
    "                           , df_wais_sub_agg.iloc[:,8]], align='left'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:42:50.950296Z",
     "start_time": "2020-11-26T13:42:50.695646Z"
    },
    "scrolled": true
   },
   "outputs": [
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   "source": [
    "fig = go.Figure(data=wais_sub)\n",
    "fig.update_layout(width=1000, height=1000)\n",
    "fig.show()"
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     "data": {
      "text/html": [
       "<h5>2.1. WAIS IRP</h5>"
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   "execution_count": 101,
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     "end_time": "2020-11-26T13:42:56.868789Z",
     "start_time": "2020-11-26T13:42:56.865151Z"
    }
   },
   "outputs": [],
   "source": [
    "age_moy = df_wais[df_wais['Total des notes standards IRP'].notna()]['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
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     "end_time": "2020-11-26T13:42:57.589887Z",
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   "outputs": [
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     "data": {
      "text/html": [
       "<p>Moyenne d'âge : 40.14</p>"
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    "display(HTML(\"<p>Moyenne d'âge : \"+str(round(age_moy,2))+\"</p>\"))"
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   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
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     "end_time": "2020-11-26T13:42:58.266766Z",
     "start_time": "2020-11-26T13:42:58.263081Z"
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   "outputs": [],
   "source": [
    "df_wais_sub= df_wais.iloc[:,[0,1,2,-1,14, 15, 16, 17, 18, 19, 20, 21]]"
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  {
   "cell_type": "code",
   "execution_count": 104,
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     "start_time": "2020-11-26T13:42:59.184620Z"
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    "subplot_titles = df_wais_sub.columns.values.tolist()"
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   "execution_count": 105,
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     "end_time": "2020-11-26T13:43:00.109645Z",
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    "fig_wais_sub = make_subplots(\n",
    "    rows=4, cols=2,\n",
    "    shared_xaxes=False,\n",
    "    row_heights=[0.20, 0.20, 0.20, 0.20],\n",
    "    column_widths=[0.5, 0.5],\n",
    "    subplot_titles= subplot_titles[4:],\n",
    "    x_title='Âge',\n",
    "    y_title='Score',\n",
    "    vertical_spacing= 0.04 )\n",
    "df_wais_sub = df_wais_sub.sort_values(by=['age'])"
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   "execution_count": 106,
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     "start_time": "2020-11-26T13:43:00.727836Z"
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
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       "var gd = document.getElementById('2d150e82-6b66-43b3-b926-671ce4440f32');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[4]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=1, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[5]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=1, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[6]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=2, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[7]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=2, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[8]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=1)\n",
    "                                                             \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[9]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=2)\n",
    "    \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[10]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=4, col=1)\n",
    "    \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[11]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=4, col=2)\n",
    "    \n",
    "fig_wais_sub.update_layout(height=1500, width=800,\n",
    "                   showlegend=False\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:02.711450Z",
     "start_time": "2020-11-26T13:43:02.692066Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub.agg(['min', 'max', 'mean', 'median', 'count'])\n",
    "#\n",
    "df_wais_sub_agg.index.name = \"Aggrégation\"\n",
    "df_wais_sub_agg.reset_index(inplace=True)\n",
    "#\n",
    "df_wais_sub_agg = df_wais_sub_agg.replace({'Aggrégation' : {\"min\":\"Min\",\"max\":\"Max\",\"mean\":\"Moyenne\", \"median\":\"Médiane\", \"count\":\"Effectif\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:03.650830Z",
     "start_time": "2020-11-26T13:43:03.647280Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.drop(columns= ['ID_SECONDAIRE','Type', 'Type_color', 'age' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:04.577267Z",
     "start_time": "2020-11-26T13:43:04.572598Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.round(2)"
   ]
  },
  {
   "cell_type": "code",
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     "end_time": "2020-11-26T13:43:05.202633Z",
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    "wais_sub =  go.Table(\n",
    "                header=dict(values=list(df_wais_sub_agg.columns[:]),\n",
    "                align='left'),\n",
    "                cells=dict(values= [df_wais_sub_agg.iloc[:,0], df_wais_sub_agg.iloc[:,1], df_wais_sub_agg.iloc[:,2], df_wais_sub_agg.iloc[:,3]\n",
    "                           , df_wais_sub_agg.iloc[:,4], df_wais_sub_agg.iloc[:,5], df_wais_sub_agg.iloc[:,6], df_wais_sub_agg.iloc[:,7]\n",
    "                           , df_wais_sub_agg.iloc[:,8]], align='left'))"
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   "source": [
    "fig = go.Figure(data=wais_sub)\n",
    "fig.update_layout(width=1000, height=400)\n",
    "fig.show()"
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     "start_time": "2020-11-26T13:43:08.202403Z"
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   "outputs": [
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     "data": {
      "text/html": [
       "<h5>2.1. WAIS IMT</h5>"
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   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:08.951923Z",
     "start_time": "2020-11-26T13:43:08.948038Z"
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   },
   "outputs": [],
   "source": [
    "age_moy = df_wais[df_wais['Total des notes standards IMT'].notna()]['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:09.566373Z",
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   "outputs": [
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     "data": {
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       "<p>Moyenne d'âge : 46.9</p>"
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   ],
   "source": [
    "display(HTML(\"<p>Moyenne d'âge : \"+str(round(age_moy,2))+\"</p>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:10.242700Z",
     "start_time": "2020-11-26T13:43:10.238864Z"
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   },
   "outputs": [],
   "source": [
    "df_wais_sub= df_wais.iloc[:,[0,1,2,-1,22, 23, 24, 25, 26, 27, 28]]"
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  },
  {
   "cell_type": "code",
   "execution_count": 116,
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     "end_time": "2020-11-26T13:43:11.135773Z",
     "start_time": "2020-11-26T13:43:11.133298Z"
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   },
   "outputs": [],
   "source": [
    "subplot_titles = df_wais_sub.columns.values.tolist()"
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  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:12.090983Z",
     "start_time": "2020-11-26T13:43:11.899601Z"
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   "source": [
    "fig_wais_sub = make_subplots(\n",
    "    rows=4, cols=2,\n",
    "    shared_xaxes=False,\n",
    "    row_heights=[0.20, 0.20, 0.20, 0.20],\n",
    "    column_widths=[0.5, 0.5],\n",
    "    subplot_titles= subplot_titles[4:],\n",
    "    x_title='Âge',\n",
    "    y_title='Score',\n",
    "    vertical_spacing= 0.04 )\n",
    "df_wais_sub = df_wais_sub.sort_values(by=['age'])"
   ]
  },
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   "execution_count": 118,
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     "end_time": "2020-11-26T13:43:12.956798Z",
     "start_time": "2020-11-26T13:43:12.875251Z"
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\"x2\", \"domain\": [0.78, 1.0]}, \"yaxis3\": {\"anchor\": \"x3\", \"domain\": [0.52, 0.74]}, \"yaxis4\": {\"anchor\": \"x4\", \"domain\": [0.52, 0.74]}, \"yaxis5\": {\"anchor\": \"x5\", \"domain\": [0.26, 0.48]}, \"yaxis6\": {\"anchor\": \"x6\", \"domain\": [0.26, 0.48]}, \"yaxis7\": {\"anchor\": \"x7\", \"domain\": [0.0, 0.22]}, \"yaxis8\": {\"anchor\": \"x8\", \"domain\": [0.0, 0.22]}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('5d27458e-94cb-4431-951a-95ba2f98e1a9');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[4]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=1, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[5]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=1, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[6]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=2, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[7]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=2, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[8]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=1)\n",
    "                                                             \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[9]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=2)\n",
    "    \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[10]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=4, col=1)\n",
    "    \n",
    "fig_wais_sub.update_layout(height=1500, width=800,\n",
    "                   showlegend=False\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:15.184195Z",
     "start_time": "2020-11-26T13:43:15.166016Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub.agg(['min', 'max', 'mean', 'median', 'count'])\n",
    "#\n",
    "df_wais_sub_agg.index.name = \"Aggrégation\"\n",
    "df_wais_sub_agg.reset_index(inplace=True)\n",
    "#\n",
    "df_wais_sub_agg = df_wais_sub_agg.replace({'Aggrégation' : {\"min\":\"Min\",\"max\":\"Max\",\"mean\":\"Moyenne\", \"median\":\"Médiane\", \"count\":\"Effectif\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:15.987097Z",
     "start_time": "2020-11-26T13:43:15.983748Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.drop(columns= ['ID_SECONDAIRE','Type', 'Type_color', 'age' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:16.651007Z",
     "start_time": "2020-11-26T13:43:16.646046Z"
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   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.round(2)"
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  {
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    "wais_sub =  go.Table(\n",
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   "source": [
    "fig = go.Figure(data=wais_sub)\n",
    "fig.update_layout(width=1000, height=400)\n",
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       "<h5>2.1. WAIS IVT</h5>"
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   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:21.063895Z",
     "start_time": "2020-11-26T13:43:21.060269Z"
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   },
   "outputs": [],
   "source": [
    "age_moy = df_wais[df_wais['Total des notes standards IVT'].notna()]['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
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     "end_time": "2020-11-26T13:43:21.688044Z",
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       "<p>Moyenne d'âge : 41.31</p>"
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    "display(HTML(\"<p>Moyenne d'âge : \"+str(round(age_moy,2))+\"</p>\"))"
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  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
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     "end_time": "2020-11-26T13:43:22.350911Z",
     "start_time": "2020-11-26T13:43:22.347058Z"
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   "outputs": [],
   "source": [
    "df_wais_sub= df_wais.iloc[:,[0,1,2,-1, 29, 30, 31, 32, 33, 34, 35]]"
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  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
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     "end_time": "2020-11-26T13:43:23.327666Z",
     "start_time": "2020-11-26T13:43:23.324972Z"
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   "outputs": [],
   "source": [
    "subplot_titles = df_wais_sub.columns.values.tolist()"
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   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:24.205613Z",
     "start_time": "2020-11-26T13:43:24.026471Z"
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    "fig_wais_sub = make_subplots(\n",
    "    rows=4, cols=2,\n",
    "    shared_xaxes=False,\n",
    "    row_heights=[0.20, 0.20, 0.20, 0.20],\n",
    "    column_widths=[0.5, 0.5],\n",
    "    subplot_titles= subplot_titles[4:],\n",
    "    x_title='Âge',\n",
    "    y_title='Score',\n",
    "    vertical_spacing= 0.04 )\n",
    "df_wais_sub = df_wais_sub.sort_values(by=['age'])"
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   "execution_count": 130,
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     "start_time": "2020-11-26T13:43:24.833577Z"
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('6ce56afd-4643-4763-9219-1ce6a9cf1285');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[4]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=1, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[5]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=1, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[6]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "              row=2, col=1)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[7]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=2, col=2)\n",
    "\n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[8]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=1)\n",
    "                                                             \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[9]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=3, col=2)\n",
    "    \n",
    "fig_wais_sub.add_trace(go.Scatter(x= df_wais_sub['age'], y=df_wais_sub[subplot_titles[10]], mode=\"markers+text\",\n",
    "            marker={'size': 10, \"color\": df_wais_sub['Type_color'] },\n",
    "            hovertext= df_wais_sub['ID_SECONDAIRE'] +\", \"+ df_wais_sub['Type']),\n",
    "            row=4, col=1)\n",
    "    \n",
    "fig_wais_sub.update_layout(height=1500, width=800,\n",
    "                   showlegend=False\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:26.829993Z",
     "start_time": "2020-11-26T13:43:26.812518Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub.agg(['min', 'max', 'mean', 'median', 'count'])\n",
    "#\n",
    "df_wais_sub_agg.index.name = \"Aggrégation\"\n",
    "df_wais_sub_agg.reset_index(inplace=True)\n",
    "#\n",
    "df_wais_sub_agg = df_wais_sub_agg.replace({'Aggrégation' : {\"min\":\"Min\",\"max\":\"Max\",\"mean\":\"Moyenne\", \"median\":\"Médiane\",  \"count\":\"Effectif\"}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
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     "end_time": "2020-11-26T13:43:27.777173Z",
     "start_time": "2020-11-26T13:43:27.773424Z"
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   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.drop(columns= ['ID_SECONDAIRE','Type', 'Type_color', 'age' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:28.477826Z",
     "start_time": "2020-11-26T13:43:28.473664Z"
    }
   },
   "outputs": [],
   "source": [
    "df_wais_sub_agg = df_wais_sub_agg.round(2)"
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  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:29.132743Z",
     "start_time": "2020-11-26T13:43:29.126431Z"
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   },
   "outputs": [],
   "source": [
    "wais_sub =  go.Table(\n",
    "                header=dict(values=list(df_wais_sub_agg.columns[:]),\n",
    "                align='left'),\n",
    "                cells=dict(values= [df_wais_sub_agg.iloc[:,0], df_wais_sub_agg.iloc[:,1], df_wais_sub_agg.iloc[:,2], df_wais_sub_agg.iloc[:,3]\n",
    "                           , df_wais_sub_agg.iloc[:,4], df_wais_sub_agg.iloc[:,5], df_wais_sub_agg.iloc[:,6], df_wais_sub_agg.iloc[:,7]], align='left'))"
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(data=wais_sub)\n",
    "fig.update_layout(width=1000, height=1000)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:31.772371Z",
     "start_time": "2020-11-26T13:43:31.768942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>4. WAIS complet</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML('<h4>4. WAIS complet</h4>'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:39.914771Z",
     "start_time": "2020-11-26T13:43:39.893472Z"
    }
   },
   "outputs": [],
   "source": [
    "wais_complet = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IVT'].notna())&\n",
    "                        (df_wais['Total des notes standards IMT'].notna())&\n",
    "                        (df_wais['Total des notes standards IRP'].notna())])\n",
    "\n",
    "ICV_IVT = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IVT'].notna())])\n",
    "\n",
    "ICV_IMT = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IMT'].notna())])\n",
    "\n",
    "ICV_IRP = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IRP'].notna())])\n",
    "\n",
    "IVT_IMT = len(df_wais[(df_wais['Total des notes standards IVT'].notna())&\n",
    "                       (df_wais['Total des notes standards IMT'].notna())])\n",
    "\n",
    "IVT_IRP = len(df_wais[(df_wais['Total des notes standards IVT'].notna())&\n",
    "                       (df_wais['Total des notes standards IRP'].notna())])\n",
    "\n",
    "IMT_IRP = len(df_wais[(df_wais['Total des notes standards IMT'].notna())&\n",
    "                       (df_wais['Total des notes standards IRP'].notna())])\n",
    "\n",
    "ICV_IVT_IMT = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IVT'].notna())&\n",
    "                        (df_wais['Total des notes standards IMT'].notna())])\n",
    "\n",
    "ICV_IVT_IRP = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IVT'].notna())&\n",
    "                        (df_wais['Total des notes standards IRP'].notna())])\n",
    "\n",
    "ICV_IMT_IRP = len(df_wais[(df_wais['Total des notes standards ICV'].notna())&\n",
    "                       (df_wais['Total des notes standards IMT'].notna())&\n",
    "                        (df_wais['Total des notes standards IRP'].notna())])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-26T13:43:40.914891Z",
     "start_time": "2020-11-26T13:43:40.909460Z"
    }
   },
   "outputs": [],
   "source": [
    "wais_sub =  go.Table(\n",
    "                header=dict(values=list(['Tests effectués', 'Effectif']),\n",
    "                align='left'),\n",
    "                cells=dict(values= [['ICV+IVT', 'ICV+IMT', 'ICV+IRP', 'IVT+IMT', 'IVT+IRP', 'IMT+IRP',\n",
    "                                     'ICV+IVT+IMT', 'ICV+IVT+IRP', 'ICV+IMT+IRP',\n",
    "                                     'ICV+IVT+IMT+IRP'],\n",
    "                                    [ICV_IVT, ICV_IMT, ICV_IRP, IVT_IMT, IVT_IRP, IMT_IRP, ICV_IVT_IMT, ICV_IVT_IRP, ICV_IMT_IRP, wais_complet]], align='left'))"
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   "execution_count": 139,
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    "ExecuteTime": {
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     "start_time": "2020-11-26T13:43:42.166717Z"
    },
    "scrolled": true
   },
   "outputs": [
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