{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Améliorer son journal de bord" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Formatage des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mes données de suivi de températures sont stockées dans le module2/exo4/ en fichier csv, sous le nome *Exemple_Suivi de la température chamvre froide avril 2010* " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Consignes de l'exercice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " + Produire quelques statistiques de base (de votre choix) sur ces données.\n", " + Produire une représentation graphique (de votre choix) de ces données." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistiques de base" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "T = arr = [5,6.3,5.3,4.5,6.7,4.4,4.5,8.6,4.5,4.7,7.2,5.3,4.5,6.9,4.9,4.8,7.1,4.9,4.9,6.5]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "111.5\n" ] } ], "source": [ "s = np.sum(T)\n", "print (s)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.575\n" ] } ], "source": [ "m = np.mean(arr)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.6\n" ] } ], "source": [ "max = np.max(arr)\n", "print (max)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.4\n" ] } ], "source": [ "min = np.min(arr)\n", "print (min)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.386875\n", "1.20825014837683\n" ] } ], "source": [ "v = variance = np.var(arr)\n", "e = np.std([arr], ddof=1)\n", "print (v)\n", "print (e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Représentation graphique" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "T = arr = [5,6.3,5.3,4.5,6.7,4.4,4.5,8.6,4.5,4.7,7.2,5.3,4.5,6.9,4.9,4.8,7.1,4.9,4.9,6.5]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.grid(True)\n", "plt.plot(T, marker='o')\n", "plt.axis([0,20,4,9])\n", "plt.xlabel('Numéro de mesure')\n", "plt.ylabel('Température relevée(°C)')\n", "plt.xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test chargement des données" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SUIVI DE LA TEMPERATURE DE LA CHAMBRE FROIDE DU 01/04/2010 AU 30/04/2010Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
0NaNNaNNaNNaNNaNNaN
1NaNMesureDateHeure[°C] chambre froideNaN
2NaN8582010-04-01 00:00:0000:17:335NaN
3NaN8592010-04-01 00:00:0002:17:336.3NaN
4NaN8602010-04-01 00:00:0004:17:335.3NaN
5NaN8612010-04-01 00:00:0006:17:334.5NaN
6NaN8622010-04-01 00:00:0008:17:336.7NaN
7NaN8632010-04-01 00:00:0010:17:334.4NaN
8NaN8642010-04-01 00:00:0012:17:334.5NaN
9NaN8652010-04-01 00:00:0014:17:338.6NaN
10NaN8662010-04-01 00:00:0016:17:334.5NaN
11NaN8672010-04-01 00:00:0018:17:334.7NaN
12NaN8682010-04-01 00:00:0020:17:337.2NaN
13NaN8692010-04-01 00:00:0022:17:335.3NaN
14NaN8702010-04-02 00:00:0000:17:334.5NaN
15NaN8712010-04-02 00:00:0002:17:336.9NaN
16NaN8722010-04-02 00:00:0004:17:334.9NaN
17NaN8732010-04-02 00:00:0006:17:334.8NaN
18NaN8742010-04-02 00:00:0008:17:337.1NaN
19NaN8752010-04-02 00:00:0010:17:334.9NaN
20NaN8762010-04-02 00:00:0012:17:334.9NaN
21NaN8772010-04-02 00:00:0014:17:336.5NaN
22NaN8782010-04-02 00:00:0016:17:336.1NaN
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"21 NaN \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", ".. ... \n", "337 NaN \n", "338 NaN \n", "339 NaN \n", "340 NaN \n", "341 NaN \n", "342 NaN \n", "343 NaN \n", "344 NaN \n", "345 NaN \n", "346 NaN \n", "347 NaN \n", "348 NaN \n", "349 NaN \n", "350 NaN \n", "351 NaN \n", "352 NaN \n", "353 NaN \n", "354 NaN \n", "355 NaN \n", "356 NaN \n", "357 NaN \n", "358 NaN \n", "359 NaN \n", "360 NaN \n", "361 NaN \n", "362 NaN \n", "363 NaN \n", "364 NaN \n", "365 NaN \n", "366 NaN \n", "\n", "[367 rows x 6 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "data = pd.read_excel('Suivi de la température chambre froide avril 2010.xlsx')\n", "data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: xlrd in /opt/conda/lib/python3.6/site-packages (1.2.0)\r\n" ] } ], "source": [ "!pip install xlrd" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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