2016-2017/Notes/Manip 503.ipynb

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    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-notebook\")\n",
    "#plt.style.use('ggplot')\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "import seaborn as sns\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "\n",
    "from repytex.tools import extract_flat_marks, get_class_ws, digest_flat_df, term, evaluation\n",
    "from repytex.tools.bareme import tranform_scale\n",
    "from repytex.tools.marks_plottings import *"
   ]
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    "from jyquickhelper import add_notebook_menu\n",
    "add_notebook_menu()"
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    "ws = get_class_ws(\"503\")\n",
    "flat = extract_flat_marks(ws)"
   ]
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     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "quest_pov, exo_pov, eval_pov = digest_flat_df(flat)"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "array(['DS1', 'DS2', 'DS3', 'DS4', 'DS5', 'DS6', 'Calcul mental T2', 'DM1',\n",
       "       'DS7', 'DS8', 'DS9', 'DS10', 'Calcul mental T1', 'CMT3'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "flat[\"Nom\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "## DS1"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "ds1_flat = flat[flat[\"Nom\"]==\"DS1\"]"
   ]
  },
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   "execution_count": 7,
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     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
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   ],
   "source": [
    "ds1_quest, ds1_exo, ds1_eval =  digest_flat_df(ds1_flat)"
   ]
  },
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Nom</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Bareme</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mark</th>\n",
       "      <th>Normalized</th>\n",
       "      <th>Mark_barem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>DS1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>12 / 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>DS1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>12 / 22</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>DS1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.70</td>\n",
       "      <td>15,5 / 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>DS1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10,5 / 22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>DS1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10,5 / 22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                  Eleve  Nom  Trimestre  Bareme       Date  Mark  \\\n",
       "0      0  ABDILLAH Nourouzamane  DS1          1    22.0 2016-10-01  12.0   \n",
       "1      1        ABDOU Mouhamadi  DS1          1    22.0 2016-10-01  12.0   \n",
       "2      2       ABOUDOU Amayoune  DS1          1    22.0 2016-10-01  15.5   \n",
       "3      3          AHAMED Tansia  DS1          1    22.0 2016-10-01  10.5   \n",
       "4      4          AHMED Yancoub  DS1          1    22.0 2016-10-01  10.5   \n",
       "\n",
       "   Normalized Mark_barem  \n",
       "0        0.55    12 / 22  \n",
       "1        0.55    12 / 22  \n",
       "2        0.70  15,5 / 22  \n",
       "3        0.48  10,5 / 22  \n",
       "4        0.48  10,5 / 22  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    "ds1_eval.head()"
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XCe/67W9/q5CQEAUEBCgwMFAvvvii6ZJ8zty5c5Wfn686deooJydHknT69GnN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\nAAAY9n+4NMFLhWTz8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982f105fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marks_hist(ds1_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 4,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Répartition des notes sur chaques exercices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 11,
        "hidden": false,
        "row": 4,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f982cca3f60>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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VCYfDeuGFF/S73/3O7SitXjgc1r59+/Tggw9qxowZWr58uY4cOeJ2rFbpww8/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\n6v8AeHZJJOPtuM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982ccdddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds1_exo.pivot(\"Eleve\", \"Exercice\", \"Normalized\").plot.hist(alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 8,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Diagramme moustache par exercice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 11,
        "hidden": false,
        "row": 12,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f982ca855c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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L4p577omrr766kIEBoN00fGRcr9ejv78/duzYEXv37o2DBw/GiRMnZq0ZHByMxYsXx2OP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\nTIwBIJkYA0AyMQaAZGIMAMn+Bz0sINV70UCLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982ca910f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds1_exo.pivot(\"Eleve\", \"Exercice\", \"Normalized\").plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 15,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Axes paralleles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 9,
        "hidden": false,
        "row": 15,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f982c93fd68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZaQK1sjEzx0no7rGiq9uCgSFz1mPm5Qmor41hpUaQ1ddFUVcTzx6u0W4MgEDs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\nlyDhRRAEQRAE4SVIeBEEQRAEQXiJ/w+7+x7SlhuM0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982c9aa198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parallel_on(ds1_exo, \"Exercice\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 19,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "# DS2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "quest_DS2 = quest_pov[quest_pov[\"Nom\"] == \"DS2\"]\n",
    "exo_DS2 = exo_pov[exo_pov[\"Nom\"] == \"DS2\"]\n",
    "eval_DS2 = eval_pov[eval_pov[\"Nom\"] == \"DS2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 23,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "On enlèves les absents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "eval_DS2 = eval_DS2[eval_DS2[\"Mark\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 11,
        "hidden": false,
        "row": 23,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f982c7c1908>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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WLl061WM1lVdeeSVef/31KBQKsWjRoti6dWvMnj17qsea1vbv3x/Hjx+Pzs7OGBgYiIiI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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982c8ac630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_DS2[\"Mark\"].hist(bins = 20, range = (0,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 7,
        "hidden": false,
        "row": 24,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.00\n",
       "mean      7.48\n",
       "std       1.64\n",
       "min       2.50\n",
       "25%       6.50\n",
       "50%       7.50\n",
       "75%       9.00\n",
       "max      10.00\n",
       "Name: Mark, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_DS2[eval_DS2[\"Mark\"]>0][\"Mark\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 27,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "# Trimestre 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "flat_T2 = flat[flat[\"Trimestre\"] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "quest_T2, exo_T2, eval_T2 = digest_flat_df(flat_T2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 31,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DS4', 'DS5', 'DS6', 'Calcul mental T2', 'DM1', 'CMT3'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_T2[\"Nom\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 4,
        "hidden": false,
        "row": 31,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## DS4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "ds4_flat = flat_T2[flat_T2[\"Nom\"]==\"DS4\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "ds4_quest, ds4_exo, ds4_eval = digest_flat_df(ds4_flat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "Le devoir est sur 10 et personne ne peut avoir au dessus!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds4_eval = tranform_scale(ds4_eval, 10, \"min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2016-12-02T00:00:00.000000000')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds4_eval[\"Date\"].unique()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogramme des résultats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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G2SzLsopb6aGHHlJQUJAkqX///urXr5/xYAAqphOxnQtdfiB6tiRp9ZfX/pRUow+SvJIJ\n5d+v95vSKOv9pkSnjp9++mmFhIQoOztbzzzzjOx2u1q3bn3VbRwOh1cCSpLdbvfqeDci5rD0mMPS\ns9vtXh3vRn082BdLx+FweH0Or7Zvl+jUcUhIiCQpODhYXbp00aFDh7yTDACACq7Yor148aLy8vI8\n///+++/VuHFj48EAAKgIij11nJ2dreeff16S5HK51LNnT0VFRRkPBgBARVBs0darV09LliwpiywA\nAFQ4vL0HAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyi\naAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACD\nKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAoBIXrdvt1uOPP65FixaZzAMAQIVS\n4qL98MMPFR4ebjILAAAVTomKNiMjQ8nJyerbt6/pPAAAVCiBJVlp3bp1GjdunPLy8ko8sN1uv+5Q\nZTHejYg5LD3msHy5kR+PG+2+n/DiWJfnrqzmsNii/eabbxQcHKzmzZtr//79JR7Y4XCUKtgv2e12\nr453I2IOS485LD1v/2K7UR8P9sXScTgcXp/Dq+3bxRbtwYMHlZSUpG+//VYFBQXKy8vTihUrNH36\ndK8FBACgoiq2aO+++27dfffdkqT9+/dr27ZtlCwAACXE+2gBADCoRC+GuqxNmzZq06aNqSwAAFQ4\nHNECAGAQRQsAgEEULQAABlG0AAAYRNECAGAQRQsAgEEULQAABlG0AAAYRNECAGAQRQsAgEEULQAA\nBlG0AAAYRNECAGAQRQsAgEEULQAABlG0AAAYRNECAGAQRQsAgEEULQAABlG0AAAYRNECAGAQRQsA\ngEEULQAABlG0AAAYRNECAGAQRQsAgEEULQAABgUWt0JBQYHmzZsnp9Mpl8ul6OhojR49uiyyAQDg\n94ot2sqVK2vevHmqWrWqnE6n5s6dq6ioKEVERJRFPgAA/Fqxp45tNpuqVq0qSXK5XHK5XLLZbMaD\nAQBQERR7RCtJbrdbs2bN0g8//KA777xTrVq1Mp0LAIAKoURFW6lSJS1ZskS5ubl6/vnndfz4cTVu\n3Piq29jtdq8ENDXejYg5LD3msHzxl8fjRGxnr43V6IMkSf5z373lhBfHujx3ZTWHJSray2rUqKE2\nbdpo7969xRatw+EoVbBfstvtXh3vRsQclh5zWHre/sV2Iz4eDoeDfbGUTMzh1fbtYp+j/emnn5Sb\nmyvp0iuQv//+e4WHh3stHAAAFVmxR7RZWVlauXKl3G63LMtSt27d1KlTp7LIBgCA3yu2aJs0aaLF\nixeXRRYAACocPhkKAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACD\nKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDA\nIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAoMDiVjh79qxWrlypc+fO\nyWazqV+/fho8eHBZZAMAwO8VW7QBAQEaP368mjdvrry8PM2ePVvt2rVTw4YNyyIfAAB+rdhTx3Xq\n1FHz5s0lSdWqVVN4eLgyMzONBwMAoCK4pudo09PTdeTIEbVs2dJUHgAAKpRiTx1fdvHiRcXHx2vi\nxImqXr16sevb7fZSBTM93o3IH+bwRGxnr4zT6IMkI+P5wxzeSC4/Ht5+nL3thBfHunyfb7R90Z/n\nsERF63Q6FR8frzvuuENdu3Yt0cAOh6NUwX7Jbrd7dbwb0Y02h96+rw6H44abQxO8/YvNxONc3rEv\nlp6JObzavl3sqWPLspSQkKDw8HANGTLEa6EAALgRFHtEe/DgQe3cuVONGzfWY489JkkaO3asOnbs\naDwcAAD+rtiiveWWW/TWW2+VRRYAACocPhkKAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyi\naAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACD\nKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDAIIoWAACDKFoAAAyiaAEAMIiiBQDA\noMDiVli1apWSk5MVHBys+Pj4ssgEAECFUewR7e9+9zvNmTOnLLIAAFDhFFu0rVu3VlBQUFlkAQCg\nwin21PH1stvtXh3Pdf9dXhmn0QdJXhnHH3l7Dk/EdvbKeIXG9NJ4l/c/b4/n7f3ayBx6aUxT43mT\nqcfZ63PoldEuuZyxvP9O9Ic59PbP828xVrQOh8NrY3lzMryZy5+U9zn09pgmxrPb7eV6//GXOfT2\nmDfSeJfHLO8/z95kcg7Lqqd41TEAAAZRtAAAGFTsqePly5frwIEDOn/+vKZOnarRo0crJiamLLIB\nAOD3ii3aGTNmlEUOAAAqJE4dAwBgEEULAIBBFC0AAAZRtAAAGETRAgBgEEULAIBBFC0AAAZRtAAA\nGETRAgBgEEULAIBBFC0AAAZRtAAAGGTsi98BwNtyXJbi0y/qaIFbNkmP79+vNm3aeJYfL3BpyZmL\nOpTv1n2hN2l0nSq+Cwv8fxQtAL+x8uxFdakeoHkNqulny5KzSZNCy2tWsumhsKranev0UUKgKE4d\nA/ALOS5LKXkuDapVWZJU2WZTUFBQoXXqBFbSLVUDFOCLgMBv4IgWgF/4welWcIBNS9Iv6nC+WxFV\nA/T/8vJUrVo1X0cDroojWgB+wWVJafluDQ2uotWNa6iqTdq4caOvYwHFomgB+IWwQJvCAm26teql\nE8O9ggL1z3/+08epgOJRtAD8QkhgJYUFVtKJArckKfmCS02bNvVtKKAEeI4WgN94OOwmPXsmTz9b\nUoPKlTT7nnv03nvvyZ1doKHBVZTpdGvaiQu64LZks0mbzxVoTZMaqlHJ5uvouIFRtAD8RsubArSq\nUQ3P5YCaNXXXXXfJte0VSZeOet9sFvRbmwM+waljAAAMomgBADCIogUAwCCKFgAAgyhaAAAMomgB\nADCoRG/v2bt3r9auXSu3262+fftq+PDhpnMBAFAhFHtE63a7tWbNGs2ZM0fLli3TF198oZMnT5ZF\nNgAA/F6xRXvo0CHVr19f9erVU2BgoLp3767ExMSyyAYAgN8rtmgzMzMVGhrquRwaGqrMzEyjoQAA\nqChslmVZV1vhyy+/1N69ezV16lRJ0s6dO5WWlqbJkyeXSUAAAPxZsUe0ISEhysjI8FzOyMhQSEiI\n0VAAAFQUxRZtixYtdPr0aaWnp8vpdGr37t3q3LlzWWQDAMDvFXvqWJKSk5O1fv16ud1u9enTRyNG\njCiLbAAA+L0SFS0AALg+fDIUAAAGUbQAABhE0QIAYBBFCwCAQRQtAAAGUbQAABhE0QIAYBBFCwCA\nQRQtAAAGUbQAABhE0QIAYBBFCwCAQRQtAAAGUbQAABhE0QIAYBBFCwCAQRQtAAAGUbQAABhUbop2\n586duvPOO9W/f3+99NJLRZYXFBRoxowZ6t+/v0aNGqWTJ0/6ICVQmMvl0vDhw/XAAw8UWbZ27VoN\nHjxYQ4cO1YQJE3Tq1CnPsltvvVXDhg3TsGHDNHXq1LKMXCZ++uknTZ8+XQMHDtSgQYP07bffFlpu\nWZaeeeYZ9e/fX0OHDtX+/ft9lNScf/3rX57HeNiwYerYsaPWrVtXZL2vvvpKw4YNU2xsrMaNGydJ\nOn36tMaPH6/BgwcrNjZW69ev96z/4osv6o477vCMu2PHjrK6S34nLi5O3bp105AhQzzXnTt3Tvfd\nd58GDBig++67T9nZ2VfcdsuWLRowYIAGDBigLVu2lC6IVQ44nU6rb9++1vHjx638/Hxr6NChVlpa\nWqF1/vKXv1hPPvmkZVmW9f7771uPPPKIL6IChbz66qvWzJkzrSlTphRZtmfPHuvChQuWZVnW66+/\nXmifjYqKKrOMvvD4449bb731lmVZlpWfn29lZ2cXWv75559bkydPttxut/Xtt99aI0eO9EXMMuN0\nOq3u3btbJ0+eLHR9dna2NWjQIOvUqVOWZVnW2bNnLcuyrDNnzlj79u2zLMuyzp8/bw0YMMDzO3HF\nihXWK6+8Uobp/dfXX39t7du3z4qNjfVc99xzz1mrV6+2LMuyVq9ebS1evLjIdllZWVZMTIyVlZVl\nnTt3zoqJibHOnTt33TnKxRHt999/ryZNmqhRo0aqUqWKYmNj9dlnnxVaZ/v27fr9738vSbrzzju1\nZ88eWZbli7iAJOmHH37Q559/rpEjR15xeXR0tKpVqyZJioqK0g8//FCW8Xzm/PnzSkxM9MxLlSpV\nVKtWrULrfPbZZxo+fLhsNpuioqL0008/KT093Rdxy8SePXvUqFEjhYeHF7p+27Zt6t+/v+x2uyQp\nNDRUklS3bl21adNGkhQUFKTmzZvrzJkzZRu6AujSpYuCg4MLXXd535Ok4cOH69NPPy2y3a5du9Sj\nRw/Vrl1bwcHB6tGjh/7xj39cd45yUbRnzpxR/fr1PZfr1atXZKc6c+aMGjRoIEkKDAxUzZo1lZWV\nVaY5gV9auHChHnvsMVWqVPyP0dtvv61evXp5Lufn52vEiBEaPXr0FX/Q/dnJkycVEhKiuLg4DR8+\nXE888YQuXLhQaJ1f/8zXr1+/QhfJBx98UOj05WVHjx7VTz/9pPHjx2vEiBHaunVrkXVOnjyp1NRU\ntW/f3nPd66+/rqFDhyouLu43T33iyjIyMlS3bl1JUlhYmDIyMoqsU5JOuhblomgBf/Pf//3fCgkJ\nUdu2bYtd991339W+ffv0hz/8odD2mzdvVnx8vBYuXKjjx4+bjFumnE6nDhw4oLFjx2rr1q2qVq3a\nFV93caMoKCjQ9u3bNXDgwCLLXC6X9u/fr9WrV+uVV17RqlWrdOTIEc/y3NxcTZ8+XXPmzFFQUJAk\naezYsfrkk0/07rvvqm7dulq0aFGZ3ZeKxmazyWazGb+dQFMDOxyOEq8bEBCgo0ePerZJS0tTtWrV\nPJftdruCg4OVkpIit9stl8ul7Oxs5eXlXdPt3MjsdjtzVUq/nMOdO3fqk08+0fbt21VQUKALFy7o\noYce0hNPPFFom2+++UZ//vOftXz5cp09e7bQMofDoYCAALVt21ZffPGFAgON/TiWKZvNpptvvllh\nYWFyOBzq3Lmz3njjDTkcDs8cBgUFKTU11XPK9PILxSriPrpr1y61bNlSBQUFRe5f9erV1b59e507\nd06S1Lp1a+3Zs0c33XSTnE6n4uLi1KtXL7Vt27bQtgEBAXI4HOrdu7fi4uIq5Lx5S3p6un7++WfP\nHNWuXVspKSm67bbblJKSouDg4CLzV6VKFR04cMBz/b/+9S9FRUVddZ4v78tXUi6OaG+55RadOnVK\np0+f1s8//6zt27ere/fuhdbp3r27PvroI0nSjh071KFDhzL5SwS4kvvvv1+bNm3Sm2++qblz56pD\nhw5FSjYtLU1Lly7VggULVKdOHc/158+fV0FBgSQpOztb+/btU5MmTco0v0khISGqW7eu5yg9OTlZ\nTZs2LbRO9+7d9fHHH8uyLB04cEA1atTwPD9Z0Wzfvl0xMTFXXNajRw+lpKTI5XLp4sWLSk1NVZMm\nTWRZlhYvXqwmTZpo9OjRhbb55anOf/zjH2rWrJnR/BXNL7vko48+KtI10qXndpOSknT+/HmdP39e\nSUlJ6tKly3XfZrn4EzogIEDTp0/X448/LrfbrUGDBqlZs2Z69dVXFRkZqVGjRik2NlYLFy7UPffc\no1q1aunJJ5/0dWygiMv7bI8ePZSQkKC8vDw99dRTki49z7NgwQIdO3ZMS5culc1mk2VZGjt2bJEi\n8nfTp0/XggUL5HQ61aBBA82aNUvvvfeegoOD1bt3b0VHR+urr77SuHHjdNNNN2nWrFm+jmxEXl6e\nvvnmG82cOdNz3XvvvSdJuuuuu9SkSRPdfvvtmjx5smw2m2JjY9WsWTOlpKTok08+UfPmzT1POfzh\nD39QdHS2fj8gAAAGh0lEQVS0Vq9erWPHjsnpdKp+/fqFxkZhTz/9tPbu3avs7GyNGjVKEydO1Nix\nYzV//nwNGDBAoaGhmjdvniTp4MGDeu+99/TYY4+pVq1aGj9+vOetd/fee2+RF/RdC5tl6KW73jyV\nwWnP0mMOS485LD3m0DuYx9Lz9hyW+1PHAABUVBQtAAAGUbQAABhULl4MBVQkrkfuli7k+DpGufR6\n9EL1+nKOr2P4XvUgBbzwhq9ToIxQtIC3XchRwMvv+TpFuZTz13PMjSTX/Xf5OgLKEKeOAQAwiKIF\nAMAgihYAAIMoWgAADPKLoi3JN6QAAFBSZdkrflG0+/fv93UEAEAFUpa94hdFCwCAv6JoAQAwiKIF\nAMAgihYAAIOMfQTj1b6b73rwkWWlc8LXASqAa5lDb+//Fcc55kaX9qXS/E7j59k7ympfNFa03v5S\nYj4ftXT4oujSK+kcuu6/i7m+CubmktL8TuPn2QvCw/nidwAAKgKKFgAAgyhaAAAMomgBADCIogUA\nwCC/KNo2bdr4OgIAoAIpy17xi6Ldt2+fryMAACqQsuwVvyhaAAD8FUULAIBBFC0AAAZRtAAAGGTs\ns46BGxlfgnFlQdEL5br/Xl/H8L3qQb5OgDJE0QJexhdg/LZ77HY5HG19HQMoU5w6BgDAIIoWAACD\nKFoAAAyiaAEAMIiiBQDAIIoWAACDbJZlWb4OAQBARcURLQAABlG0AAAYRNECAGAQRQsAgEEULQAA\nBlG0AAAYVK6/vWfv3r1au3at3G63+vbtq+HDh/s6kt85e/asVq5cqXPnzslms6lfv34aPHiwr2P5\nJbfbrdmzZyskJESzZ8/2dRy/k5ubq4SEBJ04cUI2m00PPvigIiIifB3Lr7z//vvavn27bDabGjVq\npGnTpqlKlSq+jlWurVq1SsnJyQoODlZ8fLwkKScnR8uWLdOPP/6osLAw/fGPf1RQkLmvLiy3R7Ru\nt1tr1qzRnDlztGzZMn3xxRc6efKkr2P5nYCAAI0fP17Lli3TggUL9NFHHzGP1+nDDz9UeHi4r2P4\nrbVr1yoqKkrLly/XkiVLmMtrlJmZqb/97W9atGiR4uPj5Xa7tXv3bl/HKvd+97vfac6cOYWu27p1\nq2677TatWLFCt912m7Zu3Wo0Q7kt2kOHDql+/fqqV6+eAgMD1b17dyUmJvo6lt+pU6eOmjdvLkmq\nVq2awsPDlZmZ6eNU/icjI0PJycnq27evr6P4pQsXLig1NVUxMTGSpMDAQNWoUcPHqfyP2+1WQUGB\nXC6XCgoKVKdOHV9HKvdat25d5Gg1MTFRvXv3liT17t3beLeU21PHmZmZCg0N9VwODQ1VWlqaDxP5\nv/T0dB05ckQtW7b0dRS/s27dOo0bN055eXm+juKX0tPTVatWLa1atUrHjh1T8+bNNXHiRFWtWtXX\n0fxGSEiIhg4dqgcffFBVqlRR+/bt1b59e1/H8kvZ2dmeP1Jq166t7Oxso7dXbo9o4V0XL15UfHy8\nJk6cqOrVq/s6jl/55ptvFBwc7DkzgGvncrl05MgRDRgwQIsXL9ZNN91k/HRdRZOTk6PExEStXLlS\nq1ev1sWLF7Vz505fx/J7NptNNpvN6G2U26INCQlRRkaG53JGRoZCQkJ8mMh/OZ1OxcfH64477lDX\nrl19HcfvHDx4UElJSXrooYe0fPly7du3TytWrPB1LL8SGhqq0NBQtWrVSpIUHR2tI0eO+DiVf0lJ\nSVHdunVVq1YtBQYGqmvXrvrnP//p61h+KTg4WFlZWZKkrKws1apVy+jtlduibdGihU6fPq309HQ5\nnU7t3r1bnTt39nUsv2NZlhISEhQeHq4hQ4b4Oo5fuvvuu5WQkKCVK1dqxowZatu2raZPn+7rWH6l\ndu3aCg0NlcPhkHSpNBo2bOjjVP7l5ptvVlpamvLz82VZllJSUnhB2XXq3LmzduzYIUnasWOHunTp\nYvT2yvW39yQnJ2v9+vVyu93q06ePRowY4etIfud//ud/NHfuXDVu3NhzemTs2LHq2LGjj5P5p/37\n92vbtm28vec6HD16VAkJCXI6napbt66mTZtm9C0VFdFbb72l3bt3KyAgQE2bNtXUqVNVuXJlX8cq\n15YvX64DBw7o/PnzCg4O1ujRo9WlSxctW7ZMZ8+eLZO395TrogUAwN+V21PHAABUBBQtAAAGUbQA\nABhE0QIAYBBFCwCAQRQtAAAGUbQAABhE0QIAYND/AjBGsahzE5aMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982ae87550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_hist, ax_box) = plt.subplots(2, sharex=True, \n",
    "                                    gridspec_kw={\"height_ratios\": (.85, .15)})\n",
    "marks_hist(ds4_eval, ax = ax_hist, rwidth=0.9)\n",
    "ds4_desc = ds4_eval[\"Mark\"].describe()\n",
    "m = ds4_desc[\"mean\"]\n",
    "ax_hist.plot([m,m], [0,6])\n",
    "ds4_eval[\"Mark\"].plot.box(ax = ax_box, vert=False, widths = 0.6)\n",
    "ax_hist.annotate(round(ds4_desc[\"mean\"],1), xy=(ds4_desc[\"mean\"] + 0.2, 0.3))\n",
    "ax_box.set_yticklabels(\"\")\n",
    "for e in [\"min\", \"25%\", \"50%\", \"75%\", \"max\"]:\n",
    "    ax_box.annotate(ds4_desc[e], xy=(ds4_desc[e] - 0.2,1.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.00\n",
       "mean      6.14\n",
       "std       2.71\n",
       "min       0.00\n",
       "25%       4.25\n",
       "50%       6.00\n",
       "75%       7.62\n",
       "max      10.00\n",
       "Name: Mark, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds4_desc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 46,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## DS5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds5_flat = flat_T2[flat_T2[\"Nom\"]==\"DS5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "ds5_quest, ds5_exo, ds5_eval = digest_flat_df(ds5_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds5_eval = tranform_scale(ds5_eval, 10, 'min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017-01-18T00:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds5_eval[\"Date\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve  Mark\n",
       "0         ABDILLAH Nourouzamane   7.5\n",
       "1               ABDOU Mouhamadi   9.5\n",
       "2              ABOUDOU Amayoune  10.0\n",
       "3                 AHAMED Tansia   6.0\n",
       "4                 AHMED Yancoub  10.0\n",
       "5                   ALI Cynthia   9.0\n",
       "6     ANDRIAMAHAZAKA Néni Erika   7.5\n",
       "7            ATTOUMANI Antibati   4.0\n",
       "8   ATTOUMANI OUSSENI Jeannette   5.0\n",
       "9              CHAMASSE Nadjima   9.5\n",
       "10         CHARMANE RAFION Elda  10.0\n",
       "11                    DAOU Naël   5.5\n",
       "12                DARMINE Sadya   7.5\n",
       "13             HAMIDOU Fayssoil   6.5\n",
       "14             HOUMADI Mouhouyi   9.5\n",
       "15            MADI SAID Zaynati  10.0\n",
       "16                  MALIDE Elza  10.0\n",
       "17      MOUHAMADI ANDILI Issina  10.0\n",
       "18                 MOUSSA Samra   8.0\n",
       "19            OUSSENI Kaïssoune   5.5\n",
       "20             OUSSENI Saandati  10.0\n",
       "21                   SAID Amina  10.0\n",
       "22                 SAID Charfia   9.0\n",
       "23                SAID Hachimia   5.5\n",
       "24                   SAID Nasra  10.0\n",
       "25               SALIM Laïlouna   8.0\n",
       "26              SIDI Yansilouna   7.5\n",
       "27              SOILIHI Nadjdat   6.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds5_eval[[\"Eleve\", \"Mark\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 50,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## DS6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds6_flat = flat_T2[flat_T2[\"Nom\"]==\"DS6\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "ds6_quest, ds6_exo, ds6_eval = digest_flat_df(ds6_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds6_eval = tranform_scale(ds6_eval, 10, 'min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017-02-01T00:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds6_eval[\"Date\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve  Mark\n",
       "0         ABDILLAH Nourouzamane   6.0\n",
       "1               ABDOU Mouhamadi   8.5\n",
       "2              ABOUDOU Amayoune   9.5\n",
       "3                 AHAMED Tansia   6.5\n",
       "4                 AHMED Yancoub   7.5\n",
       "5                   ALI Cynthia   9.0\n",
       "6     ANDRIAMAHAZAKA Néni Erika   7.5\n",
       "7            ATTOUMANI Antibati   0.0\n",
       "8   ATTOUMANI OUSSENI Jeannette   8.0\n",
       "9              CHAMASSE Nadjima   6.0\n",
       "10         CHARMANE RAFION Elda   8.0\n",
       "11                    DAOU Naël   8.0\n",
       "12                DARMINE Sadya   7.5\n",
       "13             HAMIDOU Fayssoil   0.0\n",
       "14             HOUMADI Mouhouyi   8.0\n",
       "15            MADI SAID Zaynati   8.5\n",
       "16                  MALIDE Elza   6.0\n",
       "17      MOUHAMADI ANDILI Issina   8.0\n",
       "18                 MOUSSA Samra   9.0\n",
       "19            OUSSENI Kaïssoune   7.5\n",
       "20             OUSSENI Saandati   9.5\n",
       "21                   SAID Amina   9.5\n",
       "22                 SAID Charfia   9.0\n",
       "23                SAID Hachimia   5.5\n",
       "24                   SAID Nasra  10.0\n",
       "25               SALIM Laïlouna   7.0\n",
       "26              SIDI Yansilouna   7.0\n",
       "27              SOILIHI Nadjdat   6.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds6_eval[[\"Eleve\", \"Mark\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 50,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Calcul mental T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CMT2_flat = flat_T2[flat_T2[\"Nom\"]==\"Calcul mental T2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "CMT2_quest, CMT2_exo, CMT2_eval = digest_flat_df(CMT2_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "CMT2_eval =  tranform_scale(CMT2_eval, 20, 'prop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark_barem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>11 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>16 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>19 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>14 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>19 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>19 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>10 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>19 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>18 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>18 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>11,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>10 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>11 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>16,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>11 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>18 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>9,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>18 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>19 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>14 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>8 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>19,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>14 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>12,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>4,5 / 20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve Mark_barem\n",
       "0         ABDILLAH Nourouzamane    11 / 20\n",
       "1               ABDOU Mouhamadi    16 / 20\n",
       "2              ABOUDOU Amayoune    19 / 20\n",
       "3                 AHAMED Tansia    14 / 20\n",
       "4                 AHMED Yancoub    15 / 20\n",
       "5                   ALI Cynthia    19 / 20\n",
       "6     ANDRIAMAHAZAKA Néni Erika    19 / 20\n",
       "7            ATTOUMANI Antibati    10 / 20\n",
       "8   ATTOUMANI OUSSENI Jeannette    19 / 20\n",
       "9              CHAMASSE Nadjima    18 / 20\n",
       "10         CHARMANE RAFION Elda    18 / 20\n",
       "11                    DAOU Naël  11,5 / 20\n",
       "12                DARMINE Sadya    10 / 20\n",
       "13             HAMIDOU Fayssoil    11 / 20\n",
       "14             HOUMADI Mouhouyi  16,5 / 20\n",
       "15            MADI SAID Zaynati    15 / 20\n",
       "16                  MALIDE Elza    11 / 20\n",
       "17      MOUHAMADI ANDILI Issina    18 / 20\n",
       "18                 MOUSSA Samra    15 / 20\n",
       "19            OUSSENI Kaïssoune   9,5 / 20\n",
       "20             OUSSENI Saandati    18 / 20\n",
       "21                   SAID Amina    19 / 20\n",
       "22                 SAID Charfia    14 / 20\n",
       "23                SAID Hachimia     8 / 20\n",
       "24                   SAID Nasra  19,5 / 20\n",
       "25               SALIM Laïlouna    14 / 20\n",
       "26              SIDI Yansilouna  12,5 / 20\n",
       "27              SOILIHI Nadjdat   4,5 / 20"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CMT2_eval[[\"Eleve\", \"Mark_barem\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 4,
        "hidden": false,
        "row": 50,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## DM1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dm1_flat = flat_T2[flat_T2[\"Nom\"]==\"DM1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "dm1_quest, dm1_exo, dm1_eval = digest_flat_df(dm1_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark_barem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>2,5 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>3 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>1,5 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>2 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>2 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>4 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>0 / 5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve Mark_barem\n",
       "0         ABDILLAH Nourouzamane      4 / 5\n",
       "1               ABDOU Mouhamadi      3 / 5\n",
       "2              ABOUDOU Amayoune      3 / 5\n",
       "3                 AHAMED Tansia      3 / 5\n",
       "4                 AHMED Yancoub      3 / 5\n",
       "5                   ALI Cynthia      4 / 5\n",
       "6     ANDRIAMAHAZAKA Néni Erika      3 / 5\n",
       "7            ATTOUMANI Antibati      0 / 5\n",
       "8   ATTOUMANI OUSSENI Jeannette      3 / 5\n",
       "9              CHAMASSE Nadjima      3 / 5\n",
       "10         CHARMANE RAFION Elda      3 / 5\n",
       "11                    DAOU Naël    2,5 / 5\n",
       "12                DARMINE Sadya      3 / 5\n",
       "13             HAMIDOU Fayssoil    1,5 / 5\n",
       "14             HOUMADI Mouhouyi      0 / 5\n",
       "15            MADI SAID Zaynati      4 / 5\n",
       "16                  MALIDE Elza      4 / 5\n",
       "17      MOUHAMADI ANDILI Issina      2 / 5\n",
       "18                 MOUSSA Samra      2 / 5\n",
       "19            OUSSENI Kaïssoune      0 / 5\n",
       "20             OUSSENI Saandati      4 / 5\n",
       "21                   SAID Amina      0 / 5\n",
       "22                 SAID Charfia      4 / 5\n",
       "23                SAID Hachimia      4 / 5\n",
       "24                   SAID Nasra      4 / 5\n",
       "25               SALIM Laïlouna      0 / 5\n",
       "26              SIDI Yansilouna      0 / 5\n",
       "27              SOILIHI Nadjdat      0 / 5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm1_eval[[\"Eleve\", \"Mark_barem\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
        "col": 0,
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        "row": 54,
        "width": 4
       },
       "report_default": {
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       }
      }
     }
    }
   },
   "source": [
    "## Bilan du 2e trimestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
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        "col": 0,
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        "row": 58,
        "width": 11
       },
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      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bareme</th>\n",
       "      <th>Commentaire</th>\n",
       "      <th>Competence</th>\n",
       "      <th>Date</th>\n",
       "      <th>Domaine</th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Exercice</th>\n",
       "      <th>Niveau</th>\n",
       "      <th>Nom</th>\n",
       "      <th>Note</th>\n",
       "      <th>Question</th>\n",
       "      <th>Trimestre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1080</th>\n",
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       "      <td>Cal</td>\n",
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       "      <td>Ega</td>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Ega</td>\n",
       "      <td>ABDOU Mohamed</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1à3</td>\n",
       "      <td>2</td>\n",
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       "      <th>1082</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Calculs à trou</td>\n",
       "      <td>Cal</td>\n",
       "      <td>2016-12-02 00:00:00</td>\n",
       "      <td>Ega</td>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DS4</td>\n",
       "      <td>3</td>\n",
       "      <td>1à3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Calculs à trou</td>\n",
       "      <td>Cal</td>\n",
       "      <td>2016-12-02 00:00:00</td>\n",
       "      <td>Ega</td>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DS4</td>\n",
       "      <td>3</td>\n",
       "      <td>1à3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Calculs à trou</td>\n",
       "      <td>Cal</td>\n",
       "      <td>2016-12-02 00:00:00</td>\n",
       "      <td>Ega</td>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DS4</td>\n",
       "      <td>1</td>\n",
       "      <td>1à3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bareme     Commentaire Competence                 Date Domaine  \\\n",
       "1080     1.5  Calculs à trou        Cal  2016-12-02 00:00:00     Ega   \n",
       "1081     1.5  Calculs à trou        Cal  2016-12-02 00:00:00     Ega   \n",
       "1082     1.5  Calculs à trou        Cal  2016-12-02 00:00:00     Ega   \n",
       "1083     1.5  Calculs à trou        Cal  2016-12-02 00:00:00     Ega   \n",
       "1084     1.5  Calculs à trou        Cal  2016-12-02 00:00:00     Ega   \n",
       "\n",
       "                      Eleve  Exercice  Niveau  Nom Note Question  Trimestre  \n",
       "1080  ABDILLAH Nourouzamane         1       1  DS4    2      1à3          2  \n",
       "1081          ABDOU Mohamed         1       1  DS4  NaN      1à3          2  \n",
       "1082        ABDOU Mouhamadi         1       1  DS4    3      1à3          2  \n",
       "1083       ABOUDOU Amayoune         1       1  DS4    3      1à3          2  \n",
       "1084          AHAMED Tansia         1       1  DS4    1      1à3          2  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_T2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_student_info = [\"Bareme\", \"Commentaire\", \"Competence\", \"Date\", \"Domaine\", \"Exercice\", \"Niveau\", \"Nom\", \"Question\", \"Trimestre\"]\n",
    "T2_uniq_quest = quest_T2[not_student_info].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nombre d'évaluations par compétences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def my_autopct(values):\n",
    "    def my_autopct(pct):\n",
    "        total = sum(values)\n",
    "        val = int(round(pct*total/100.0))\n",
    "        return f\"{val}\"\n",
    "        #return '{p:.2f}%  ({v:d})'.format(p=pct,v=val)\n",
    "    return my_autopct\n",
    "\n",
    "def pivot_table_to_pie(pv):\n",
    "    nbr_pies = len(pv.columns)\n",
    "    nbr_cols = min(3, nbr_pies)\n",
    "    nbr_rows = max(nbr_pies % nbr_cols,1)\n",
    "    f, axs = plt.subplots(nbr_rows, nbr_cols, figsize = (4*nbr_cols,4*nbr_rows))\n",
    "    for (c, ax) in zip(pv, axs.flatten()):\n",
    "        datas = pv[c]\n",
    "        explode = [0.1]*len(datas)\n",
    "        pv[c].plot(kind=\"pie\",\n",
    "                             ax=ax,\n",
    "                             use_index = False,\n",
    "                             title = f\"{c} (total={datas.sum()})\",\n",
    "                             legend = False,\n",
    "                             autopct=my_autopct(datas),\n",
    "                             explode = explode,\n",
    "                            )\n",
    "        ax.set_ylabel(\"\")\n",
    "    for i in range(nbr_pies//nbr_cols, nbr_cols*nbr_rows):\n",
    "        axs.flat[i].axis(\"off\")\n",
    "    return (f, axs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "comp_eff_pts = pd.pivot_table(T2_uniq_quest,\n",
    "              index = \"Competence\",\n",
    "              #columns = \"Level\",\n",
    "              values = \"Bareme\",\n",
    "              aggfunc=[len,np.sum],\n",
    "              fill_value=0)\n",
    "#comp_eff_pts.columns = [\"Effectifs\", \"Points\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effectifs des évaluations et points attribués"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x7f982acef400>,\n",
       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f982acefcf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f982acb28d0>], dtype=object))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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fTuH97LPPMHv2bPTv3x9yuRwnTpyAj48P1q5di4ULF2LEiBEQi8Xo1q2bdgAmoJmn2r9/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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982acef400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot_table_to_pie(comp_eff_pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_count_pv = pd.pivot_table(quest_T2,\n",
    "              index = \"Competence\",\n",
    "              columns = \"Level\",\n",
    "              values = \"Trimestre\",\n",
    "              aggfunc=len,\n",
    "              fill_value=0)\n",
    "comp_count_pv.rename({\"Cal\": \"Calculer\",\n",
    "                              \"Com\": \"Communiquer\",\n",
    "                              \"Mod\": \"Modéliser\",\n",
    "                              \"Rai\": \"Raisonner\",\n",
    "                              \"Rep\": \"Représenter\",\n",
    "                             },\n",
    "                    inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Répartition des résultats des évaluations par compétences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/.virtualenvs/enseignement/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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PlJaWonv37uDz+RCJRJgxYwYmTZokPv/x40ekpaWBTqdjy5YtYLPZWL16NWbMmIHDhw+D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Tqaj48PoqKiEB0djaSkJKSkpMDHx8flcRBCnKO4uBhffPEFBgwYgOTkZKSkpNCmLyeipNbBOjo6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\niMjn/X/O26c2loK99wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f982ac6f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, a = pivot_table_to_pie(comp_count_pv.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trimestre 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds7_flat = flat[flat[\"Nom\"]==\"DS7\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "ds7_quest, ds7_exo, ds7_eval = digest_flat_df(ds7_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.00\n",
       "mean      6.46\n",
       "std       1.62\n",
       "min       4.00\n",
       "25%       5.00\n",
       "50%       6.50\n",
       "75%       7.50\n",
       "max       9.50\n",
       "Name: Mark, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds7_eval[\"Mark\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark_barem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>4 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABDOU Mouhamadi</td>\n",
       "      <td>7,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>7,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>6,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>6,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>4 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>4 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>6,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>6 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>7 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>8,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>8 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>8,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>8,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>7,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>9,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>6,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>6 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>9,5 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>7 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>7 / 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>5 / 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve Mark_barem\n",
       "0         ABDILLAH Nourouzamane     4 / 11\n",
       "1               ABDOU Mouhamadi   7,5 / 11\n",
       "2              ABOUDOU Amayoune   7,5 / 11\n",
       "3                 AHAMED Tansia   6,5 / 11\n",
       "4                 AHMED Yancoub   6,5 / 11\n",
       "5                   ALI Cynthia     5 / 11\n",
       "6     ANDRIAMAHAZAKA Néni Erika     4 / 11\n",
       "7            ATTOUMANI Antibati     5 / 11\n",
       "8   ATTOUMANI OUSSENI Jeannette     4 / 11\n",
       "9              CHAMASSE Nadjima     5 / 11\n",
       "10         CHARMANE RAFION Elda   6,5 / 11\n",
       "11                    DAOU Naël     5 / 11\n",
       "12                DARMINE Sadya     6 / 11\n",
       "13             HAMIDOU Fayssoil     5 / 11\n",
       "14             HOUMADI Mouhouyi     7 / 11\n",
       "15            MADI SAID Zaynati   8,5 / 11\n",
       "16                  MALIDE Elza     8 / 11\n",
       "17      MOUHAMADI ANDILI Issina   8,5 / 11\n",
       "18                 MOUSSA Samra   8,5 / 11\n",
       "19            OUSSENI Kaïssoune     5 / 11\n",
       "20             OUSSENI Saandati   7,5 / 11\n",
       "21                   SAID Amina   9,5 / 11\n",
       "22                 SAID Charfia   6,5 / 11\n",
       "23                SAID Hachimia     6 / 11\n",
       "24                   SAID Nasra   9,5 / 11\n",
       "25               SALIM Laïlouna     7 / 11\n",
       "26              SIDI Yansilouna     7 / 11\n",
       "27              SOILIHI Nadjdat     5 / 11"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds7_eval[[\"Eleve\", \"Mark_barem\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CMT3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cmT3_flat = flat[flat[\"Nom\"]==\"CMT3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:485: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Mark\"] = compute_marks(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:486: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Level\"] = compute_level(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:487: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Latex_rep\"] = compute_latex_rep(df)\n",
      "/home/lafrite/scripts/Repytex/repytex/tools/df_marks_manip.py:488: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[\"Normalized\"] = compute_normalized(df)\n"
     ]
    }
   ],
   "source": [
    "cmT3_quest, cmT3_exo, cmT3_eval = digest_flat_df(cmT3_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.00\n",
       "mean     20.52\n",
       "std       3.74\n",
       "min      12.00\n",
       "25%      18.00\n",
       "50%      21.00\n",
       "75%      23.00\n",
       "max      28.00\n",
       "Name: Mark, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmT3_eval[\"Mark\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark_barem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>18 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>21 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>23 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>16 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>21 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>21 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>16 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>21 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>25 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>23 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>20 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>18 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>19 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>23 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>23 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>22 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>22 / 24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>17 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>13 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>25 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>26 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>22 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>18 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>28 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>20 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>21 / 28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>12 / 28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve Mark_barem\n",
       "0         ABDILLAH Nourouzamane    18 / 28\n",
       "1              ABOUDOU Amayoune    21 / 28\n",
       "2                 AHAMED Tansia    23 / 28\n",
       "3                 AHMED Yancoub    16 / 28\n",
       "4                   ALI Cynthia    21 / 28\n",
       "5     ANDRIAMAHAZAKA Néni Erika    21 / 28\n",
       "6            ATTOUMANI Antibati    16 / 28\n",
       "7   ATTOUMANI OUSSENI Jeannette    21 / 28\n",
       "8              CHAMASSE Nadjima    25 / 28\n",
       "9          CHARMANE RAFION Elda    23 / 28\n",
       "10                    DAOU Naël    20 / 28\n",
       "11                DARMINE Sadya    18 / 28\n",
       "12             HAMIDOU Fayssoil    19 / 28\n",
       "13             HOUMADI Mouhouyi    23 / 28\n",
       "14            MADI SAID Zaynati    23 / 28\n",
       "15                  MALIDE Elza    22 / 28\n",
       "16      MOUHAMADI ANDILI Issina    22 / 24\n",
       "17                 MOUSSA Samra    17 / 28\n",
       "18            OUSSENI Kaïssoune    13 / 28\n",
       "19             OUSSENI Saandati    25 / 28\n",
       "20                   SAID Amina    26 / 28\n",
       "21                 SAID Charfia    22 / 28\n",
       "22                SAID Hachimia    18 / 28\n",
       "23                   SAID Nasra    28 / 28\n",
       "24               SALIM Laïlouna    20 / 28\n",
       "25              SIDI Yansilouna    21 / 28\n",
       "26              SOILIHI Nadjdat    12 / 28"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmT3_eval[[\"Eleve\", \"Mark_barem\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CMT3_eval =  tranform_scale(cmT3_eval, 20, 'prop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.00\n",
       "mean     14.91\n",
       "std       2.72\n",
       "min       9.00\n",
       "25%      13.00\n",
       "50%      15.00\n",
       "75%      16.50\n",
       "max      20.00\n",
       "Name: Mark, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CMT3_eval[\"Mark\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Eleve</th>\n",
       "      <th>Mark_barem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABDILLAH Nourouzamane</td>\n",
       "      <td>13 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOUDOU Amayoune</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AHAMED Tansia</td>\n",
       "      <td>16,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHMED Yancoub</td>\n",
       "      <td>11,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALI Cynthia</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANDRIAMAHAZAKA Néni Erika</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ATTOUMANI Antibati</td>\n",
       "      <td>11,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ATTOUMANI OUSSENI Jeannette</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHAMASSE Nadjima</td>\n",
       "      <td>18 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHARMANE RAFION Elda</td>\n",
       "      <td>16,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAOU Naël</td>\n",
       "      <td>14,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DARMINE Sadya</td>\n",
       "      <td>13 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HAMIDOU Fayssoil</td>\n",
       "      <td>14 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HOUMADI Mouhouyi</td>\n",
       "      <td>16,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MADI SAID Zaynati</td>\n",
       "      <td>16,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MALIDE Elza</td>\n",
       "      <td>16 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MOUHAMADI ANDILI Issina</td>\n",
       "      <td>18,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MOUSSA Samra</td>\n",
       "      <td>12,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OUSSENI Kaïssoune</td>\n",
       "      <td>9,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OUSSENI Saandati</td>\n",
       "      <td>18 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SAID Amina</td>\n",
       "      <td>19 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAID Charfia</td>\n",
       "      <td>16 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SAID Hachimia</td>\n",
       "      <td>13 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SAID Nasra</td>\n",
       "      <td>20 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SALIM Laïlouna</td>\n",
       "      <td>14,5 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SIDI Yansilouna</td>\n",
       "      <td>15 / 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SOILIHI Nadjdat</td>\n",
       "      <td>9 / 20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Eleve Mark_barem\n",
       "0         ABDILLAH Nourouzamane    13 / 20\n",
       "1              ABOUDOU Amayoune    15 / 20\n",
       "2                 AHAMED Tansia  16,5 / 20\n",
       "3                 AHMED Yancoub  11,5 / 20\n",
       "4                   ALI Cynthia    15 / 20\n",
       "5     ANDRIAMAHAZAKA Néni Erika    15 / 20\n",
       "6            ATTOUMANI Antibati  11,5 / 20\n",
       "7   ATTOUMANI OUSSENI Jeannette    15 / 20\n",
       "8              CHAMASSE Nadjima    18 / 20\n",
       "9          CHARMANE RAFION Elda  16,5 / 20\n",
       "10                    DAOU Naël  14,5 / 20\n",
       "11                DARMINE Sadya    13 / 20\n",
       "12             HAMIDOU Fayssoil    14 / 20\n",
       "13             HOUMADI Mouhouyi  16,5 / 20\n",
       "14            MADI SAID Zaynati  16,5 / 20\n",
       "15                  MALIDE Elza    16 / 20\n",
       "16      MOUHAMADI ANDILI Issina  18,5 / 20\n",
       "17                 MOUSSA Samra  12,5 / 20\n",
       "18            OUSSENI Kaïssoune   9,5 / 20\n",
       "19             OUSSENI Saandati    18 / 20\n",
       "20                   SAID Amina    19 / 20\n",
       "21                 SAID Charfia    16 / 20\n",
       "22                SAID Hachimia    13 / 20\n",
       "23                   SAID Nasra    20 / 20\n",
       "24               SALIM Laïlouna  14,5 / 20\n",
       "25              SIDI Yansilouna    15 / 20\n",
       "26              SOILIHI Nadjdat     9 / 20"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CMT3_eval[[\"Eleve\", \"Mark_barem\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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