{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traitement du fichier de données\n",
    "## Chargement de quelques librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lecture des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Date', 'Number of emails', 'Number that require answers']\n",
      "['2023-08-02', '3', '0']\n",
      "['2023-08-03', '2', '0']\n",
      "['2023-08-04', '2', '0']\n",
      "['2023-08-05', '0', '0']\n",
      "['2023-08-06', '1', '0']\n",
      "['2023-08-07', '3', '2']\n",
      "['2023-08-08', '5', '3']\n"
     ]
    }
   ],
   "source": [
    "filename = \"Emails_par_jours.csv\"\n",
    "\n",
    "file = open(filename,\"r\")\n",
    "data = file.readlines()\n",
    "Nlines = len(data)\n",
    "for line in data :\n",
    "    print(line[0:-1].split(\",\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mettre les données dans des tableaux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2023    8    2    3    0]\n",
      " [2023    8    3    2    0]\n",
      " [2023    8    4    2    0]\n",
      " [2023    8    5    0    0]\n",
      " [2023    8    6    1    0]\n",
      " [2023    8    7    3    2]\n",
      " [2023    8    8    2    0]]\n"
     ]
    }
   ],
   "source": [
    "names = data[0]\n",
    "names = names[0:-1].split(\",\")\n",
    "Ncol = len(names)\n",
    "\n",
    "\n",
    "datatab = np.zeros((Nlines-1, Ncol),dtype=int)\n",
    "for i in range(1,Nlines) :\n",
    "    line = data[i]\n",
    "    line = line[0:-1].split(\",\")\n",
    "    for j in range(Ncol) :\n",
    "        datatab[i-1,j] = int(line[j])\n",
    "print(datatab)\n",
    "year = datatab[:,0]\n",
    "month = datatab[:,1]\n",
    "day = datatab[:,2]\n",
    "Nemail = datatab[:,3]\n",
    "Nanswer = datatab[:,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Afficher les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Redefine Nlines to not take into account name row\n",
    "Nlines = len(year)\n",
    "\n",
    "# Create labels for figures\n",
    "datelabels = []\n",
    "for i in range(Nlines) :\n",
    "    yyyy = str(year[i])\n",
    "    mm = str(month[i])\n",
    "    dd = str(day[i])\n",
    "    date_string = yyyy+\"-\"+mm+\"-\"+dd\n",
    "    datelabels.append(date_string)\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "I_x = [i for i in range(Nlines)]\n",
    "ax.plot(I_x,Nemail)\n",
    "ax.set_xticks(I_x)\n",
    "ax.set_xticklabels(datelabels)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Number of emails\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8571428571428572"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(Nemail)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(Nemail)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traitement des données avec pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Number of emails</th>\n",
       "      <th>Number that require answers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-02</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-08-03</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-04</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-08-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-08-07</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Number of emails  Number that require answers\n",
       "0  2023-08-02                 3                            0\n",
       "1  2023-08-03                 2                            0\n",
       "2  2023-08-04                 2                            0\n",
       "3  2023-08-05                 0                            0\n",
       "4  2023-08-06                 1                            0\n",
       "5  2023-08-07                 3                            2\n",
       "6  2023-08-08                 5                            3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(filename)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of emails</th>\n",
       "      <th>Number that require answers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-08-02</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-03</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-04</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-05</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-06</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-07</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-08</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Number of emails  Number that require answers\n",
       "Date                                                     \n",
       "2023-08-02                 3                            0\n",
       "2023-08-03                 2                            0\n",
       "2023-08-04                 2                            0\n",
       "2023-08-05                 0                            0\n",
       "2023-08-06                 1                            0\n",
       "2023-08-07                 3                            2\n",
       "2023-08-08                 5                            3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.to_datetime(raw_data['Date'],format='%Y-%m-%d')\n",
    "raw_data['Date'] = date\n",
    "data = raw_data.set_index('Date')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6799aa65f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Number of emails'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}