From c125e3e5385a03655404ccdb52aabfa81be0b47d Mon Sep 17 00:00:00 2001 From: f969076062e7e87452e3220760453367 Date: Tue, 5 May 2020 10:31:26 +0000 Subject: [PATCH] Sauvegarde --- module3/exo3/exercice.ipynb | 273 ++++++++++++++++++++++++++++++++++-- 1 file changed, 261 insertions(+), 12 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 59d9399..fd164a2 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -518,7 +518,7 @@ } ], "source": [ - "raw_data = pd.read_csv(data_url)\n", + "raw_data = pd.read_csv(data_url,header=0)\n", "raw_data" ] }, @@ -533,16 +533,16 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 74, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -560,9 +560,9 @@ } ], "source": [ - "X=raw_data['Year']\n", - "Y1=raw_data['Wages']\n", - "Y2=raw_data['Wheat']\n", + "X=raw_data['Year'] #Contient les années\n", + "Y1=raw_data['Wages'] #Contient le salaire correspondant à chaque année\n", + "Y2=raw_data['Wheat'] #Contient le prix du blé correspondant à chaque année\n", "\n", "plt.grid(True)\n", "plt.plot(X, Y1,\"r\",label='Salaire (Shillings/Semaine)',linewidth=2)\n", @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -596,7 +596,7 @@ "" ] }, - "execution_count": 108, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -635,16 +635,16 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 109, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -673,6 +673,255 @@ "ax2.plot(X, Y2, \"b\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Étude du pouvoir d'achat des ouvriers\n", + "\n", + "Dans cette dernière partie, on utilise les données pour déterminer de quelle manière à évoluer le pouvoir d'achat des ouvriers au cours du temps.\n", + "\n", + "Dans un premier temps, afin de rendre les données plus actuelles, on va ramener le prix du blé au kilogramme. Un quart de boisseau de blé étant équivalent à 6,8 kg de blé, on divise le prix d'un quart de boisseau de blé par 6,8 pour obtenir le prix au kilo (arrondi à deux décimales près). Pour cela on introduit une variable \"PK\" tel que :" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 6.03\n", + "1 6.62\n", + "2 6.18\n", + "3 7.21\n", + "4 6.10\n", + "5 6.91\n", + "6 9.41\n", + "7 3.97\n", + "8 4.85\n", + "9 4.71\n", + "10 4.85\n", + "11 5.15\n", + "12 4.85\n", + "13 6.62\n", + "14 4.85\n", + "15 5.74\n", + "16 7.79\n", + "17 6.18\n", + "18 5.96\n", + "19 6.84\n", + "20 4.71\n", + "21 5.44\n", + "22 6.32\n", + "23 5.15\n", + "24 3.97\n", + "25 5.88\n", + "26 7.35\n", + "27 4.41\n", + "28 4.71\n", + "29 6.47\n", + "30 4.85\n", + "31 4.26\n", + "32 5.74\n", + "33 3.82\n", + "34 4.71\n", + "35 3.97\n", + "36 4.04\n", + "37 4.56\n", + "38 5.22\n", + "39 4.56\n", + "40 6.32\n", + "41 6.91\n", + "42 6.47\n", + "43 6.76\n", + "44 6.18\n", + "45 6.99\n", + "46 11.18\n", + "47 11.62\n", + "48 11.91\n", + "49 14.56\n", + "50 11.47\n", + "51 7.94\n", + "52 7.94\n", + "Name: Wheat, dtype: float64\n" + ] + } + ], + "source": [ + "PK = round(Y2/6.8,2)\n", + "print(PK)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le pouvoir d'achat d'un ouvrier se définit par la quantité de blé qu'il peut acheter avec son salaire par semaine (en kg, arrondi à deux décimales près). On définit donc la variable pouvoir d'achat \"PA\" tel que :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0.83\n", + "1 0.76\n", + "2 0.82\n", + "3 0.71\n", + "4 0.84\n", + "5 0.76\n", + "6 0.59\n", + "7 1.41\n", + "8 1.17\n", + "9 1.23\n", + "10 1.22\n", + "11 1.17\n", + "12 1.26\n", + "13 0.94\n", + "14 1.30\n", + "15 1.11\n", + "16 0.83\n", + "17 1.05\n", + "18 1.11\n", + "19 0.99\n", + "20 1.44\n", + "21 1.27\n", + "22 1.11\n", + "23 1.42\n", + "24 1.91\n", + "25 1.36\n", + "26 1.16\n", + "27 2.04\n", + "28 2.12\n", + "29 1.70\n", + "30 2.42\n", + "31 2.93\n", + "32 2.26\n", + "33 3.48\n", + "34 2.89\n", + "35 3.53\n", + "36 3.59\n", + "37 3.29\n", + "38 3.01\n", + "39 3.62\n", + "40 2.78\n", + "41 2.68\n", + "42 3.01\n", + "43 3.11\n", + "44 3.72\n", + "45 3.65\n", + "46 2.46\n", + "47 2.45\n", + "48 2.48\n", + "49 2.06\n", + "50 NaN\n", + "51 NaN\n", + "52 NaN\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "PA = round(Y1/PK,2)\n", + "print(PA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il ne reste plus qu'à tracer l'évolution de ce pouvoir d'achat au cours du temps :" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,\"Pouvoir d'achat (kg de blé)\")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.grid(True)\n", + "plt.plot(X, PA, \"r\")\n", + "plt.xlabel('Années')\n", + "plt.ylabel('Pouvoir d\\'achat (kg de blé)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il peut être remarqué que le pouvoir d'achat a globalement augmenté, un ouvrier pouvant acheter un peu plus d'1 kg de blé avec son salaire hébdomadaire au début du 17ème siècle contre plus de 3 kg au milieu du 18ème siècle. Cependant le pouvoir d'achat semble avoir commencé à diminuer à partir de la fin du 18ème siècle." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(PK[0:6],raw_data['Wages'][0:6],label='< 1600')\n", + "plt.scatter(PK[7:16],raw_data['Wages'][7:16],label='1600 à 1649')\n", + "plt.scatter(PK[17:26],raw_data['Wages'][17:26],label='1650 à 1699')\n", + "plt.scatter(PK[27:36],raw_data['Wages'][27:36],label='1700 à 1749')\n", + "plt.scatter(PK[37:46],raw_data['Wages'][37:46],label='1750 à 1799')\n", + "plt.scatter(PK[47:52],raw_data['Wages'][47:52],label='1800 à 1849')\n", + "plt.xlabel('Prix du blé (Schillings/Kg)')\n", + "plt.ylabel('Salaires (Schillings)')\n", + "plt.legend()" + ] + }, { "cell_type": "code", "execution_count": null, -- 2.18.1