{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "with open('myjournal.csv','w',newline='') as fichiercsv:\n",
    "    writer=csv.writer(fichiercsv)\n",
    "    writer.writerow(['Date', 'Cahier', 'événement', 'heures_travail'])\n",
    "    writer.writerow(['04/01/2022', 'projet_arduino', 'rdv_UCA', '8'])\n",
    "    writer.writerow(['05/01/2022', 'partition_bach', 'zoom_XR', '12'])\n",
    "    writer.writerow(['06/01/2022', 'cello_vocalise', 'rdv_CIRM', '9'])\n",
    "    writer.writerow(['07/01/2022', 'formation_éthique', 'cours_en_ligne', '14'])\n",
    "    writer.writerow(['08/01/2022', 'projet_collège', '', '13'])\n",
    "    writer.writerow(['09/01/2022', 'partition_bach', 'zoom_prof', '8'])\n",
    "    writer.writerow(['10/01/2022', 'cello_vocalise', 'répétition_CRR',  '6'])\n",
    "    writer.writerow(['11/01/2022', 'formation_articles', 'concert_CRR', '10'])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "04/01/2022 projet_arduino\n",
      "05/01/2022 partition_bach\n",
      "06/01/2022 cello_vocalise\n",
      "07/01/2022 formation_éthique\n",
      "08/01/2022 projet_collège\n",
      "09/01/2022 partition_bach\n",
      "10/01/2022 cello_vocalise\n",
      "11/01/2022 formation_articles\n"
     ]
    }
   ],
   "source": [
    "\n",
    " with open('myjournal.csv', newline='') as fichiercsv:\n",
    "     reader = csv.DictReader(fichiercsv)\n",
    "     for row in reader:\n",
    "         print(row['Date'], row['Cahier'])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rdv_UCA\n",
      "zoom_XR\n",
      "rdv_CIRM\n",
      "cours_en_ligne\n",
      "\n",
      "zoom_prof\n",
      "répétition_CRR\n",
      "concert_CRR\n"
     ]
    }
   ],
   "source": [
    "\n",
    " with open('myjournal.csv', newline='') as fichiercsv:\n",
    "     reader = csv.DictReader(fichiercsv)\n",
    "     for row in reader:\n",
    "         print(row['événement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Date,Cahier,événement,heures_travail\\n']\n",
      "['04/01/2022,projet_arduino,rdv_UCA,8\\n', '05/01/2022,partition_bach,zoom_XR,12\\n', '06/01/2022,cello_vocalise,rdv_CIRM,9\\n', '07/01/2022,formation_éthique,cours_en_ligne,14\\n', '08/01/2022,projet_collège,,13\\n', '09/01/2022,partition_bach,zoom_prof,8\\n', '10/01/2022,cello_vocalise,répétition_CRR,6\\n', '11/01/2022,formation_articles,concert_CRR,10\\n']\n"
     ]
    }
   ],
   "source": [
    "with open('myjournal.csv') as file:\n",
    "    content = file.readlines()\n",
    "header = content[:1]\n",
    "rows = content[1:]\n",
    "print(header)\n",
    "print(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Cahier</th>\n",
       "      <th>événement</th>\n",
       "      <th>heures_travail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>projet_arduino</td>\n",
       "      <td>rdv_UCA</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/01/2022</td>\n",
       "      <td>partition_bach</td>\n",
       "      <td>zoom_XR</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06/01/2022</td>\n",
       "      <td>cello_vocalise</td>\n",
       "      <td>rdv_CIRM</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07/01/2022</td>\n",
       "      <td>formation_éthique</td>\n",
       "      <td>cours_en_ligne</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/01/2022</td>\n",
       "      <td>projet_collège</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>09/01/2022</td>\n",
       "      <td>partition_bach</td>\n",
       "      <td>zoom_prof</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>cello_vocalise</td>\n",
       "      <td>répétition_CRR</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11/01/2022</td>\n",
       "      <td>formation_articles</td>\n",
       "      <td>concert_CRR</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date              Cahier       événement  heures_travail\n",
       "0  04/01/2022      projet_arduino         rdv_UCA               8\n",
       "1  05/01/2022      partition_bach         zoom_XR              12\n",
       "2  06/01/2022      cello_vocalise        rdv_CIRM               9\n",
       "3  07/01/2022   formation_éthique  cours_en_ligne              14\n",
       "4  08/01/2022      projet_collège             NaN              13\n",
       "5  09/01/2022      partition_bach       zoom_prof               8\n",
       "6  10/01/2022      cello_vocalise  répétition_CRR               6\n",
       "7  11/01/2022  formation_articles     concert_CRR              10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.read_csv(\"myjournal.csv\")\n",
    "data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Cahier', 'événement', 'heures_travail'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('cello_vocalise', 7.5)\n",
      "('formation_articles', 10.0)\n",
      "('formation_éthique', 14.0)\n",
      "('partition_bach', 10.0)\n",
      "('projet_arduino', 8.0)\n",
      "('projet_collège', 13.0)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data= pd.read_csv(\"myjournal.csv\")\n",
    "\n",
    "groupby_gender = data.groupby('Cahier')\n",
    "for gender, value in groupby_gender['heures_travail']:\n",
    "     print((gender, value.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "80\n",
      "14\n",
      "6\n",
      "8\n",
      "9.5\n",
      "2.7774602993176543\n",
      "7.714285714285714\n"
     ]
    }
   ],
   "source": [
    "data= pd.read_csv(\"myjournal.csv\")\n",
    "\n",
    "mean1 = data['heures_travail'].mean()\n",
    "sum1 = data['heures_travail'].sum()\n",
    "max1 = data['heures_travail'].max()\n",
    "min1 = data['heures_travail'].min()\n",
    "count1 = data['heures_travail'].count()\n",
    "median1 = data['heures_travail'].median() \n",
    "std1 = data['heures_travail'].std() \n",
    "var1 = data['heures_travail'].var() \n",
    "\n",
    "print(mean1)\n",
    "print(sum1)\n",
    "print(max1)\n",
    "print(min1)\n",
    "print(count1)\n",
    "print(median1)\n",
    "print(std1)\n",
    "print(var1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{partition_bach: 2 } {# of rows: 2 }\n"
     ]
    }
   ],
   "source": [
    "my_reader = open('myjournal.csv', encoding = 'utf-8')\n",
    "rows = 0\n",
    "partition_bach = 0\n",
    "\n",
    "for record in my_reader:\n",
    "    if record.count('partition_bach') > 0:\n",
    "        rows += 1\n",
    "        partition_bach += record.count('partition_bach')\n",
    "\n",
    "print('{partition_bach: %d } {# of rows: %d }' % (partition_bach, rows))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{projet: 2 } {# of rows: 2 }\n"
     ]
    }
   ],
   "source": [
    "my_reader = open('myjournal.csv', encoding = 'utf-8')\n",
    "rows = 0\n",
    "projet = 0\n",
    "\n",
    "for record in my_reader:\n",
    "    if record.count('projet') > 0:\n",
    "        rows += 1\n",
    "        projet += record.count('projet')\n",
    "\n",
    "print('{projet: %d } {# of rows: %d }' % (projet, rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{arduino: 1 } {# of rows: 1 }\n"
     ]
    }
   ],
   "source": [
    "my_reader = open('myjournal.csv', encoding = 'utf-8')\n",
    "rows = 0\n",
    "arduino = 0\n",
    "\n",
    "for record in my_reader:\n",
    "    if record.count('arduino') > 0:\n",
    "        rows += 1\n",
    "        arduino += record.count('arduino')\n",
    "\n",
    "print('{arduino: %d } {# of rows: %d }' % (arduino, rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "ht = data['heures_travail']\n",
    "\n",
    "plt.plot((ht))\n",
    "plt.show()"
   ]
  },
  {
   "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
}