{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traitement du fichier de données\n",
    "## Chargement de quelques librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lecture des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Year', 'Month', 'Day', 'Number of emails', 'Number that require answers']\n",
      "['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": [
    "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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/Fn57KM98vodVeRoEqELPZzsPs+ewu8L82kNE+J8540jt053FT6+ltCIwgwC1SQSQ2y4azpQhcfzqhfVs2R9ct0BUqiXZniJ6REdwydj+TpfSabxBgBkcP3Waxc8EZhCgNokAUn8LxNjoSBYtyw+qWyAq1ZyKqhrWrN/HrLQBREe6L8yvPUb078F9s8fx+c7D/OH1rU6X8w2tahIi0ltExttVjIK+PaJZem06ew6f4L9yNAhQhYbVBSWcOl3H/MyBTpfiiCsnJnHdlIH85YMdvLYhsIIAW2wSIvKeiMSKSBxQCPxNRB60v7TQNXlwHD+/ZASvbdzPXz/a6XQ5Stkux1PEyP49GJsU63QpjrnjstFMSO7Jz3IK2XkwcIIA/dmT6GmMqQBmA38zxmQAF9hblvrhuUO4eEw/7nt1C7kaBKiC2Jb9FRQWlzN/kvvD/NojKiKcRxakEx4uLFqWx8nqwJgO70+TiBCRRCALeNnmepRFRPjDvAmk9O7KjcvzKavUIEAVnFbkFtElPIwr04IjzK89knt340/z09h6oJI7XtwQEIeb/WkS9wCvA9uNMbkiMgTYZm9ZCiA2OpJHF2RQfrKGHz+TH5AzH5Rqj1Ona3lh7V4uHNOP3kEU5tce00b05cfTzmBlXjErAiAIsMUmYYzJMcaMN8bcYH2/wxgzx/7SFMDoAbH89sqxfLrjMH/UIEAVZN7aVMqREzVkZYbGtRH+uumC4Zw7LJ47V29kw15ngwAjfL0gIg8DPvd1jDE/aW7DIpICPAX0B+qAJ4wxDzVaR4CHgJnACeD7xph8v6sPEfMyU8jbfYTH3vuS9IG9uXB0P6dLUqpDZHuKGNAzmnPOiHe6lIASHib8eX4alz38EYuW5/Hy4nPp2S3SkVqa25PwAHnNLC05DdxqjBkFTAFuFJHRjdaZAQyzluuBx1pVfQj5zRVjGDMglluyC9hzSIMAlfuVHD3JB9uCO8yvPfrERLH02nT2Ha3i1pwC6hy674zPJmGM+UdzS0sbNsbsq98rMMZUApuBxmemZgFPGa9PgV7WSXLVSHRkOI8tyEDQIEAVHFblFWOMd09ZNS1jUG9+deko3tpcyuMffOlIDc0dbvqzMeanIvISTRx2MsZc4e+HiEgqMBH4rNFLSUDDMzPF1nP7/N12KBnYpxsPZqXx/57y8N2/fs6gIAlBm52ezFlD+zhdhupEdXWG7LwivjW0DylxwfHv2C7f/1Yqnt1HeOD1raSl9OJbQzv30JzPJgH80/r6QHs+QERigFXAT63rLb72chNv+UZDEpHr8R6OYuDA0Lwis94Fo/vxy5kj+ccnuyk+4v7DTkdO1LB+bzmv3nRuSM+RDzWf7jxE0eGT3HbRCKdLCXgiwv1zxrN1fyWFReWB0ySMMXnW1/fbunERicTbIJYbY55rYpVioOG+ZjJQ0kQtTwBPAGRmZjo/cdhh108dyvVThzpdRodY9ulufv3CBtbvLWd8ci+ny1GdJDvXG+Z38ZjQCfNrj5ioCF5afA5du3R+rpU/sRzDRGSliGwSkR31ix/vE+CvwGZjjK8Yj9XAd8VrClBujNFDTSHkirQBREWEBcR8cNU5yk/W8OqG/VyZlhRyYX7t4USDAP8upvsb3llHp4FpeKe1/rPZd3idDXwHmC4iBdYyU0QWishCa501wA5gO/C/wA2tHYByt9joSGaOS2R1QUnAxBAoe60utML8QuS+EW7X3DmJel2NMW+LiBhjdgO/EZEPgbuae5Mx5iOaPufQcB0D3Oh3tSooZWWm8Pzavby2cR9XTUx2uhxlsxxPEaMSYxkzIHTD/NzEnz2JKhEJA7aJyGIRuQroa3NdKoScOTiOgXHdyM7VO/IFu837KlhXXM78zGSdqOAS/jSJnwLdgJ8AGXgPIX3PzqJUaAkLE7Iyk/nXjkPsPhQ4Ecmq49WH+c3SMD/X8Ce7KdcYc8wYU2yM+YExZrZ14ZtSHWZORjJhAiv1/t5B69TpWl4o2MtFGubnKv7MbsoUkedFJF9E1tUvnVGcCh2JPbsydXgCK/OKqXUofkDZ681NBziqYX6u48/hpuV4ZzjNAS5vsCjVobIyU9hXXsWH28qcLkXZINtTTFKvrpytYX6u4k+TKDPGrDbG7DTG7K5fbK9MhZwLRvUjrnsXsj16zUSw2Xv0JB9uK2OOhvm5jj9TYO8Skf8D3ga+uj2ajyuolWqzLhHeu5P989NdHD5eTZwetw4aX4X5ZegUZ7fxZ0/iB0AacAn/PtR0mZ1FqdA1f1IKNbWG59fudboU1UHq6gzZniLOPkPD/NzInz2JCcaYcbZXohQwon8PJiT3JMdTxH+cnapz6YPApzsOUXzkJD+7WMP83MifPYlPm7hZkFK2yZqUwpb9lawrdva2japjrPAUEathfq7lT5M4BygQka3W9Nf1OgVW2enyCQOIjgxjhZ7Adr3yE1aY30QN83Mrfw43XWJ7FUo1EBsdycyxibxUUMIdl452LP1Std/qwr1Un67TayNczJ8rrnfjvefDdOvxCX/ep1R7ZE1KofLUaV7doMnxbpbtKWZ0Yixjk3o6XYpqI3+uuL4L+DnwC+upSGCZnUUpdebgOAb16abXTLjYppIK1u8t10hwl/Nnj+Aq4ArgOIAxpgToYWdRSokIWZkpfLrjsIb+uVS2p4guEWHMShvgdCmqHfxpEtXWfR8MgIh0t7ckpbzmpHtD/3I8GvrnNlU1tTy/di8Xj+lPr256UaSb+dMkskXkL0AvEfkh8Bbeu8gpZav+PaP5tob+udKbmw5QfrKGrEy9wtrt/Dlx/QCwElgFjADuNMY8bHdhSoE39G9/RRUfaOifq2R7irxhfkM1zM/t/JkCizHmTeBNm2tR6hvOrw/9yy1i2gi9IaIbFB85wUfbD/KT6cMI0zA/19OprCqgdYkI46qJSby1+QCHjp1q+Q3KcavyvLlb8/RQU1DQJqECXlamhv65RV2dISeviLOHxpPcW8P8goHPJiEib1tf7++8cpT6phH9ezAhpRfZniK8E+1UoPqXFeaXpddGBI3m9iQSReTbwBUiMlFE0hsunVWgUgDzM1P44sAxCjX0L6CtyC2iZ9dILhrdz+lSVAdp7sT1ncDtQDLwYKPXDDDdrqKUauyyCYnc8/JGVuQWkZbSy+lyVBPKT9Tw2sb9XDMpRcP8gojPPQljzEpjzAzg98aYaY2WFhuEiDwpIqUissHH6+eJSLmIFFjLne0YhwpysdGRzByXyEuFJZysrnW6HNWEF+vD/PRQU1Dx5zqJe0XkChF5wFr8vSvd32k5QfZDY0yatdzj53ZViJqfmcKxU6dZs15D/wJRtqeIMQNiGTNAw/yCiT8Bf/cBNwGbrOUm67lmGWM+AA63u0KlLJMHx5GqoX8BaWNJORv2VmiYXxDyZwrspcCFxpgnjTFP4t07uLSDPv8sESkUkVdFZEwHbVMFKRFhXmYKn+08zK6DGvoXSLJzrTC/CUlOl6I6mL/XSTQ8U9hR+5L5wCBjzATgYeAFXyuKyPUi4hERT1mZxjOEsq9C//J0byJQVNXU8kJBCZeM6U/PbpFOl6M6mD9N4j5grYj8XUT+AeQB/93eDzbGVBhjjlmP1wCRItJk0Isx5gljTKYxJjMhIaG9H61crH/PaM4b0ZeVecWcrq1zuhwFvPFVmJ8eagpG/py4fgaYAjxnLWcZY55t7weLSH8REevxZKuWQ+3drgp+WZnJHKg4xYfbDjpdigJyrDC/bw3t43Qpygb+BvztA1a3ZsMi8gxwHhAvIsXAXXjvaocx5nFgLrBIRE4DJ4GrjV5Oq/wwfWQ/+nTvworcIqaN1NA/J9WH+d10vob5BSu/mkRbGGOuaeH1pcBSuz5fBa/60L+/f7KLQ8dO0ScmyumSQtbKPO8NoeZmaJhfsNKAP+VKWZNSOF2noX9Oqqsz5HiKOecMDfMLZs02CREJ83XFtFJOGt6vB2kpvViRq6F/Tvnky0PsPXpST1gHuWabhDGmDigUkYGdVI9Sfps/KYVtpccoKDrqdCkhaYWniF7dIrlojIb5BTN/DjclAhtF5G0RWV2/2F2YUi25bHwiXSPD9QpsBxw9Uc3rG/dzZVoSUREa5hfM/DlxfbftVSjVBj2+Cv3bxx2XjaZbF9vmYahGXiwo8Yb56aGmoOfPdRLvA7uASOtxLt6rpZVyXFZmshX6t9/pUkLKitwixibFMnpArNOlKJv5E/D3Q2Al8BfrqSSaidBQqjNNHhzH4PjuesipE23YW86mfRXM172IkODPOYkbgbOBCgBjzDZAr2BSAcEb+pfM5zsPs1ND/zpFtscb5neFhvmFBH+axCljTHX9NyISgffOdEoFhK9C/3RvwnZVNbW8sHYvM8ZqmF+o8KdJvC8ivwS6isiFQA7wkr1lKeW/frHRTNPQv07x+sb9VFSd1hPWIcSfJnE7UAasB34ErAF+bWdRSrXWvMwUSitP8cE2jZK3U46nmOTeXTlriIb5hYoW5wwaY+qsiPDP8B5m2qpBfCrQnD+qL/Ex3tC/6SP14i47FB32hvndfMFwDfMLIf7MbroU+BJYgjeQb7uIzLC7MKVaIzLcG/r39uZSDh475XQ5QWllXjEiMDdTw/xCiT+Hm/4ITDPGnGeM+TYwDfiTvWUp1XpZmVboX76G/nW02jrDyrxizh2WQFKvrk6XozqRP02i1BizvcH3O4BSm+pRqs2G9evBxIG9yPZo6F9H++TLg1aYn+5FhBqfTUJEZovIbLy5TWtE5Psi8j28M5tyO61CpVphfqY39G+thv51qBW53jC/C0fr+Z5Q09yexOXWEg0cAL6N905zZUBv2ytTqg0urQ/9y9VrJjrK0RPVvLHxgIb5hSifs5uMMT/ozEKU6gg9oiO5dHwiLxWWcOflGvrXEV5Yu5fqWg3zC1X+zG4aLCIPishzGhWu3CArM4Xj1bW8sm6f06W4njGGFZ5ixiX11DC/EOXPn1kvAH/Fey5CL2dVAW9Sam8Gx3cnx1PMPP3rt102llSweV8F91451ulSlEP8aRJVxpgltleiVAepD/37/Wtb2VF2jCEJMU6X5ForcouIigjjigkDnC5FOcSfKbAPichdInKWiKTXL7ZXplQ7zE1PJjxMyMkrdroU16qqqeXFAivMr6uG+YUqf/YkxgHfAabz78NNxvpeqYDUNzaaaSMSWJVXzK0XDici3J+/h1RDGuanwL8mcRUwpGFcuFJuMC8zhbc2l/L+F2WcP0rn97dWtqeIlLiuTNEwv5Dmz59XhUCv1m5YRJ4UkVIR2eDjdRGRJSKyXUTW6SEs1dGmj/x36J9qnaLDJ/h4+yHmZaRomF+I86dJ9AO2iMjrrZwC+3fgkmZenwEMs5brgcf82KZSfosMD2N2ejLvbCmlrFJD/1ojpz7ML0NjOEKdP4eb7mrLho0xH4hIajOrzAKesmLHPxWRXiKSaIzRye2qw2RlJvPEBzt4fm0x108d6nQ5rlBbZ1jpKWLqsAQGaJhfyPPnfhLv2/TZSUDD4wDF1nPaJFSHOaNvD9IH9iLbU8wPzx2CiB46acl7W0spKa/iV5eOdroUFQD8ueK6UkQqrKVKRGpFpKIDPrup39YmoztF5HoR8YiIp6xM7zymWmf+pBS2lx4jf4+G/rWkrPIUv3huPYPju3PB6L5Ol6MCQItNwhjTwxgTay3RwBy8Nx9qr2Kg4dy6ZKDERw1PGGMyjTGZCQkJHfDRKpRcOn4A3bpo6F9LTtfW8ZNn1lJRVcOjC9I1zE8B/p24/hpjzAt0zDUSq4HvWrOcpgDlej5C2SEmKoJLxyXy8roSjp867XQ5AevBN7/gXzsO8dsrxzEqUXOalFeL5ySse0rUCwMy8XFYqNH7nsEbLR4vIsV4T4BHAhhjHgfWADOB7cAJQFNnlW2yJqWQk1fMK+v36cVhTXhz0wEefe9LrpmcojOa1Nf4M7vp8gaPTwO78M5MapYx5poWXjfAjX58vlLtljmoN0Piu5PjKdIm0cieQye4JbuAsUmx3HX5GKfLUQHGn9lN+he+cj1v6F8K97+2hS/LjjFUQ/8Abz7TouV5CPDYggyiI/U8hPo6n01CRO5s5n3GGHOvDfUoZZuFrnpAAAAQ2UlEQVQ5GUk88MZWcjzF3D5jpNPlBITfrN7IxpIKnvx+Jilx3ZwuRwWg5k5cH29iAfhP4Oc216VUh+vbI5ppI/qyKr+Y07V6a5QcTxHP5hZx47ShTB+p2VaqaT6bhDHmj/UL8ATQFe/J5WeBIZ1Un1IdKiszmbLKU7y3NbSvt9lUUsGvX9jAt4b24ZYLRzhdjgpgzU6BFZE4EfktsA7voal0Y8zPjTGlnVKdUh1s2si+xMdEscITutdMVFTVcMPyPHp1i2TJNRMJ1wA/1QyfTUJE/gDkApXAOGPMb4wxRzqtMqVsEBkexpz0JN7ZUkppZZXT5XQ6Ywy3ZRdSfOQkj1ybTnxMlNMlqQDX3J7ErcAA4NdASYNojsoOiuVQyhHzMlOorTM8n7/X6VI63f9+uIM3Nh3g9hkjyUyNc7oc5QLNnZMIM8Z0bRTLEVv/fWcWqVRHOqNvDBmDepPtKcJ7uU5o+GzHIe5/bSszx/XnP88Z7HQ5yiX0no4qJM3PTOHLsuPk7wmNI6illVUsfmYtg+K6cf+c8ZqGq/ymTUKFpJnjE+nWJTwk7lp3uraOxU+vpbKqhkevS6dHdKTTJSkX0SahQlJMVASXjU/k5XX7gj707w9vbOXznYf576vGMbK/HilWraNNQoWsrMwUTlTX8sq64A0ffmPjfv7y/g6uPXMgs9M1uE+1njYJFbIyBvVmSEJ3soP0mondh45za04h45N7cudlepc51TbaJFTIEhGyMlPw7D7C9tJjTpfToapqalm4LJ8wER65Nl2D+1SbaZNQIW12ehLhYUJOXnDtTdz54gY276vgz/PTNLhPtYs2CRXSvgr9y9tLTZCE/mXnFpHtKebH089g2ki9T7VqH20SKuTNn5TCwWPBEfq3saScO17cwDlnxPPTC4Y7XY4KAtokVMibNiKBhB5Rrr9movxkDYuW5dO7WxceujpNg/tUh9AmoUJeRHgYs9OTeHere0P/jDHcllNIydGTPLIgnT4a3Kc6iDYJpfBeM1FbZ3jOpaF/f/lgB29uOsAvZ44iY1Bvp8tRQUSbhFLA0IQYMl0a+vevLw/x+9e2cOn4RH5wdqrT5aggo01CKUvWpBR2lB0nb7d7Qv9KK6r48TNrSY3vrsF9yhbaJJSyXDouke4uCv2rsYL7jp86zePXZRATFeF0SSoIaZNQytI9KoLLxg/glfX7OOaC0L8/vL6Vz3cd5r7Z4xjer4fT5aggZWuTEJFLRGSriGwXkdubeP08ESkXkQJrudPOepRqSdakZCv0r8TpUpr12ob9PPHBDr4zZRBXTkxyuhwVxGxrEiISDjwCzABGA9eISFMpYx8aY9Ks5R676lHKH+kDezM0oTvZnmKnS/Fp58Hj/CynkAkpvfj1ZaOcLkcFOTv3JCYD240xO4wx1cCzwCwbP0+pdqsP/cvbfYTtpZVOl/MNJ6trWbQsj/Bw4ZFrJxIVocF9yl52NokkoOEZwGLrucbOEpFCEXlVRMbYWI9SfpmdnuwN/QuwvQljDHe8uIGtByr58/w0kntrcJ+yn51Noqm5eI0noOcDg4wxE4CHgRea3JDI9SLiERFPWZn783VUYEvoEcX0kX1ZlV8cUKF/K3KLWJlXzI+nD+O8ERrcpzqHnU2iGEhp8H0y8LWzgcaYCmPMMevxGiBSROIbb8gY84QxJtMYk5mQkGBjyUp5zc9M4eCxat7dUup0KQBs2FvOnas3cu6weG46f5jT5agQYmeTyAWGichgEekCXA2sbriCiPQX6+ofEZls1XPIxpqU8st5VuhfINy1rvxEDQuX5dGnexceunqiBvepTmVbkzDGnAYWA68Dm4FsY8xGEVkoIgut1eYCG0SkEFgCXG3clomgglJEeBhz0pN5d2sZpRXOhf7V1RluyS7gQEUVjyxIJ657F8dqUaHJ1uskjDFrjDHDjTFDjTG/s5573BjzuPV4qTFmjDFmgjFmijHmEzvrUao1sjKTqa0zrHIw9O+x97/k7S2l/GrmKNIHanCf6nx6xbVSPgxJiGFSam9yHAr9++TLg/zxja1cPmEA3/tWaqd/vlKgTUKpZmVlprDj4HE8nRz6t7+8ip88s5bB8d35n9njNLhPOUabhFLNmGmF/mV3YuifN7gvnxPVtTx+XQbdNbhPOUibhFLN6B4VweUTOjf07/5Xt+DZfYT7Zo9jmAb3KYdpk1CqBfMyUzhRXcvLhfaH/r26fh//99FOvnfWIGalaXCfcp42CaVakD6wF2f0jbH9mokdZcf42cp1pKX04leXNpWFqVTn0yahVAu8oX/J5O85alvo38nqWm5Ynk9kuPDIgnS6ROivpgoM+i9RKT9cNTGZiDCxJULcGMOvnl/P1gOVPHT1RJJ6de3wz1CqrbRJKOWH+tC/52wI/Xv68z08t3YvN50/jKnDNZtMBRZtEkr5af4kb+jfOx0Y+reu+Ch3r97E1OEJ/GS6BvepwKNNQik/fXt4An17RHXYNRNHT1SzaFk+8TFd+PP8NMI0uE8FIG0SSvkpIjyMORnJvLu1lAPtDP2rqzPcvKKA0soqHr0uQ4P7VMDSJqFUK8zLSKbOwKr89p3AfvS97by7tYw7LhtNWkqvDqpOqY6nTUKpVhiSEMPk1DhyPMVtDv37ePtBHnzzC66YMIDvTBnUwRUq1bG0SSjVSlmTUth58Di5u1of+lcf3DckIYb7NLhPuYA2CaVaaea4/sRERbT6Cuya2jpufDqfqhoN7lPuoU1CqVbq1iWCyyck8sq6fVRW1fj9vvvWbCFv9xHunzueM/rG2FihUh1Hm4RSbTAvM4WTNbW8vG6fX+u/sm4fT368k+9/K5XLxg+wuTqlOo42CaXaYGJKL4b5Gfr3Zdkx/mtlIekDe/HLmaM6oTqlOo42CaXawBv6l8LaPUfZdsB36N+J6tMsWpZHVGS4BvcpV9J/sUq10VXpSVboX9N7E8YYfvnceraVHuOhq9NI7KnBfcp9tEko1UbxMVGcP6ovz+Xvpfr0N0P/ln22hxcKSrj5guGcO0yD+5Q7aZNQqh3mT0rh0PFvhv4VFh3l3pc2cd6IBBZPO8Oh6pRqP20SSrXD1GFW6F+DQ05Hjldzw/J8EnpE8acsDe5T7mZrkxCRS0Rkq4hsF5Hbm3hdRGSJ9fo6EUm3sx6lOlpEeBhzM5J5zwr9q6sz3JxdQFnlKR5dkE5vDe5TLmdbkxCRcOARYAYwGrhGRBrfuHcGMMxargces6sepewyLzOFOgMr84pZ+u523ttaxh2Xj2aCBvepIGBnLsBkYLsxZgeAiDwLzAI2NVhnFvCU8SalfSoivUQk0Rjj3xVKSgWAwfHdmTw4jr9+tJMjJ6q5amIS15050OmylOoQdh5uSgIazg0stp5r7TpKBbyszBQOH69mWN8YfnfVWA3uU0HDzj2Jpn5LGmcr+7MOInI93sNRDByof6GpwHPZ+ER2HzrO3IxkunXR4D4VPOzckygGUhp8nwyUtGEdjDFPGGMyjTGZCQk631wFnujIcG69aASD+nR3uhSlOpSdTSIXGCYig0WkC3A1sLrROquB71qznKYA5Xo+QimlAodt+8XGmNMishh4HQgHnjTGbBSRhdbrjwNrgJnAduAE8AO76lFKKdV6th48NcaswdsIGj73eIPHBrjRzhqUUkq1nV5xrZRSyidtEkoppXzSJqGUUsonbRJKKaV80iahlFLKJ/FOMHIPESkDdrfx7fHAwQ4sx0k6lsAULGMJlnGAjqXeIGNMq69Gdl2TaA8R8RhjMp2uoyPoWAJTsIwlWMYBOpb20sNNSimlfNImoZRSyqdQaxJPOF1AB9KxBKZgGUuwjAN0LO0SUucklFJKtU6o7UkopZRqDWNMwC147zHxLrAZ2AjcZD0fB7wJbLO+9raevxDIA9ZbX6c32NZrQKG1nceBcB+feY31/nXWe+KbWKcb8Aqwxdre/7h1LK3ZnhvG0mD91cAGl/9c3gO2AgXW0tel4+iC9/DIF3h/Z+a48WcC9GjwsyjAOwX1z24cS5t+p1r6ZXJiARKB9AY/oC+A0cDvgdut528H7rceTwQGWI/HAnsbbCvW+irAKuDqJj4vAiit/49lfc5vmlivGzCtwS/Ah8AMN47F3+25ZSzW67OBp/GvSQTsWPA2iUw3/65Yr90N/NZ6HEbLDT5gx9LofXnAVDeOpS1jDsjDTcaYfcaYfOtxJd5unATMAv5hrfYP4EprnbXGmPo72m0EokUkynqtwno+Au//2Js6CSPW0l28NyeOpek75J0wxrxrPa4G8vHeTc91Y2nF9lwxFhGJAW4BftvcGNwwltYI8HH8B3Cfte06Y0yzF4EF+Fi8bxAZBvTF+weiG8fS+n+HLXVNpxcgFdhjDeZoo9eONLH+XOCtRs+9DhzB+1emr121uUAFsA/4wNd6DdbvBewAhrh5LP5szw1jAf4EXGXV1eKeRICP5T28hwMKgDuwJpi4aRzW70cR8CDeP6ZygH5u/Zk0WP9O4AGX//tq3ZhbM9jOXoAYvLt2s63vm/0PDIwBvgSGNrGtaLy7ahc28Vok8DYwFG+XXQr8upm6IoBXgZ+6fSwtbc8NYwHSgJesx6m0okkE2lisdZOsrz2AN4Dvum0ceOMjDNZ5CLx7ef9068+kwXs2ARlu/ffVpjH7O9jOXqzBvA7c0uC5rUCi9TgR2NrgtWS8x/3Obmab37P+o4Tz75NQ9wCTgLcbrDcV7x31vrZeg9efBJYEw1gab8+NYwEW4d1l3gUUA9XAe24cSxPb+35LP5dAHAfe/wEdB8Ks9VKAjW7+mQATgC/c/Hvva71mx+HvgDtzsf6BPUWjGQTAH/j6SZ/fW4974T37P6fR+jENfiARwApgcROfNwDvrleC9f29wB991PZbvN08zM1j8Xd7bhhLo/ek4t+J64Aci7WN+pOKkcBKYKHbxmG99izWLB28zS7HjT+TBuv/D3B3S/+2AnksrR2zMYHbJM7Bu6u6jn93wZlAH7y7Stusr3HW+r/G+1dLw2lqfYF+QK61nY3Aw0CEj89ciPfk0jrgJaBPE+skW3VtbvA5/8+lY/F7e4E+lkbrp+JfkwjIsQDd8R6eqN/eQzR/Tikgx2GtNwjvMe91Vg0D3fgzabDuDmCkm/8f1pbfKb3iWimllE8BOQVWKaVUYNAmoZRSyidtEkoppXzSJqGUUsonbRJKKaV80iahVAtEpFZECkRko4gUisgtItLs746IpIrItZ1Vo1J20SahVMtOGmPSjDFj8EY6zwTuauE9qYA2CeV6ep2EUi0QkWPGmJgG3w/Be4FTPN4Lxv6J9yI48F4N+4mIfAqMAnbiTftcgveK3fOAKOARY8xfOm0QSrWRNgmlWtC4SVjPHQFGApVAnTGmyoqRfsYYkyki5wG3GWMus9a/Hu/Ng35rRUB/DMwzxuzs1MEo1UoRTheglEuJ9TUSWCoiaUAtMNzH+hcB40VkrvV9T2AY3j0NpQKWNgmlWsk63FSL9w5fdwEH8CaEhgFVvt4G/NgY83qnFKlUB9ET10q1gogk4L3P8FLjPVbbE9hnjKkDvoM3mhm8h6F6NHjr68AiEYm0tjNcRLqjVIDTPQmlWtZVRArwHlo6jfdE9YPWa48Cq0RkHt4b3x+3nl8HnBaRQuDveNNcU4F867aRZVi3rlQqkOmJa6WUUj7p4SallFI+aZNQSinlkzYJpZRSPmmTUEop5ZM2CaWUUj5pk1BKKeWTNgmllFI+aZNQSinl0/8H7uttW/FFEvcAAAAASUVORK5CYII=\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": "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
}