diff --git a/module2/exo4/app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module2/exo4/temps_videos.csv b/module2/exo4/app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module2/exo4/temps_videos.csv new file mode 100644 index 0000000000000000000000000000000000000000..39a8515c0354dbbc797681ff148cdb49976ba0b9 --- /dev/null +++ b/module2/exo4/app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module2/exo4/temps_videos.csv @@ -0,0 +1,570 @@ + + + + + + + + + + + + + + + + + + +module2/exo4/temps_videos.csv · master · b2c48a7ab4afbff5f4d26650b09eb6b4 / mooc-rr · GitLab + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + diff --git a/module2/exo4/exercice.ipynb b/module2/exo4/exercice.ipynb index 912115f75360abbc909c11d036d7168db4fd3693..909347d3995f9b2de8063aa6d3a5400adda3f56e 100644 --- a/module2/exo4/exercice.ipynb +++ b/module2/exo4/exercice.ipynb @@ -64,47 +64,199 @@ "metadata": {}, "source": [ "### Exercie 4 : journal de bord et statistiques au choix\n", - "En profite pour me familiariser avec les raccourcis clavier qui s'avèrent particulièrement pratiques ! Choix d'une organisation chronologique pas nécessairement optimale mais semblant adaptée à la situation. Récriture des informations essentielles. Le temps des vidéos (parcours Jupyter) est compilé dans un fichier csv qui sera utilisé pour la réponse à cet exercice (par manque d'originalité probablement). " + "En profite pour me familiariser avec les raccourcis clavier qui s'avèrent particulièrement pratiques ! Choix d'une organisation chronologique pas nécessairement optimale mais semblant adaptée à la situation. Récriture des informations essentielles. Le temps des vidéos (parcours Jupyter) est compilé dans un fichier [csv](https://app-learninglab.inria.fr/moocrr/gitlab/b2c48a7ab4afbff5f4d26650b09eb6b4/mooc-rr/blob/master/module2/exo4/temps_videos.csv) qui sera utilisé pour la réponse à cet exercice (par manque d'originalité probablement). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Import du fichier csv, calcul de staitistiques de base et affichage d'un histogramme" + "Import des données" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "data_set = np.mat('[1 0 118 ; 1 1 469 ; 1 1 468 ; 1 1 554 ; 1 1 560 ; 1 1 394 ; 1 2 560 ; 1 2 253 ; 1 3 366 ; 1 4 512 ; 1 4 701 ; 1 4 700 ; 1 4 602 ; 1 5 485 ; 1 5 345 ; 1 5 316 ; 2 0 251 ; 2 1 408 ; 2 2 702 ; 2 3 494 ; 2 4 553 ; 2 5 447 ; 2 6 676 ; 2 7 505 ; 2 7 584 ; 2 7 209 ; 2 7 189]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sur la série totale (Jupyter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statistiques de base" + ] + }, + { + "cell_type": "code", + "execution_count": 60, "metadata": {}, "outputs": [ { - "ename": "OSError", - "evalue": "temps_videos.csv not found.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtemps_videos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenfromtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'temps_videos.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m';'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mskip_header\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mgenfromtxt\u001b[0;34m(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows, encoding)\u001b[0m\n\u001b[1;32m 1698\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1699\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbasestring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1700\u001b[0;31m \u001b[0mfhd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1701\u001b[0m \u001b[0mown_fhd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1702\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0mds\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s not found.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mOSError\u001b[0m: temps_videos.csv not found." + "name": "stdout", + "output_type": "stream", + "text": [ + "Temps_total = 12421 s\n", + "Min = 118 s\n", + "Max = 702 s\n", + "Moy = 460.037037037037 s\n" ] } ], "source": [ - "import numpy as np\n", - "temps_videos = np.genfromtxt('\\temps_videos.csv', delimiter=';',skip_header=1)" + "Temps_total = np.sum(data_set[:,2])\n", + "print('Temps_total = {0} s'.format(Temps_total))\n", + "Min = np.min(data_set[:,2])\n", + "print('Min = {0} s'.format(Min))\n", + "Max = np.max(data_set[:,2])\n", + "print('Max = {0} s'.format(Max))\n", + "Moy = np.mean(data_set[:,2])\n", + "print('Moy = {0} s'.format(Moy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affichage d'un histogramme" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1., 2., 2., 2., 3., 2., 5., 5., 1., 4.]),\n", + " array([118. , 176.4, 234.8, 293.2, 351.6, 410. , 468.4, 526.8, 585.2,\n", + " 643.6, 702. ]),\n", + " )" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(data_set[:,2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sur les modules 1 et 2 séparemment" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "Module_1 = np.mat('[1 0 118 ; 1 1 469 ; 1 1 468 ; 1 1 554 ; 1 1 560 ; 1 1 394 ; 1 2 560 ; 1 2 253 ; 1 3 366 ; 1 4 512 ; 1 4 701 ; 1 4 700 ; 1 4 602 ; 1 5 485 ; 1 5 345 ; 1 5 316]')\n", + "Module_2 = np.mat('[2 0 251 ; 2 1 408 ; 2 2 702 ; 2 3 494 ; 2 4 553 ; 2 5 447 ; 2 6 676 ; 2 7 505 ; 2 7 584 ; 2 7 209 ; 2 7 189]')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true, + "hideOutput": true + }, + "source": [ + "Pour le module 1" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temps_total_1 = 7403 s\n", + "Min_1 = 118 s\n", + "Max_1 = 701 s\n", + "Moy_1 = 462.6875 s\n" + ] + } + ], + "source": [ + "Temps_total_1 = np.sum(Module_1[:,2])\n", + "print('Temps_total_1 = {0} s'.format(Temps_total_1))\n", + "Min_1 = np.min(Module_1[:,2])\n", + "print('Min_1 = {0} s'.format(Min_1))\n", + "Max_1 = np.max(Module_1[:,2])\n", + "print('Max_1 = {0} s'.format(Max_1))\n", + "Moy_1 = np.mean(Module_1[:,2])\n", + "print('Moy_1 = {0} s'.format(Moy_1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour le module 2" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temps_total_2 = 5018 s\n", + "Min_2 = 189 s\n", + "Max_2 = 702 s\n", + "Moy_2 = 456.1818181818182 s\n" + ] + } + ], + "source": [ + "Temps_total_2 = np.sum(Module_2[:,2])\n", + "print('Temps_total_2 = {0} s'.format(Temps_total_2))\n", + "Min_2 = np.min(Module_2[:,2])\n", + "print('Min_2 = {0} s'.format(Min_2))\n", + "Max_2 = np.max(Module_2[:,2])\n", + "print('Max_2 = {0} s'.format(Max_2))\n", + "Moy_2 = np.mean(Module_2[:,2])\n", + "print('Moy_2 = {0} s'.format(Moy_2))" + ] } ], "metadata": {