From 48defd270ab64ea75be93b88462d01a79a6f1fb2 Mon Sep 17 00:00:00 2001 From: 3d1cde3613956104173df2e357578f04 <3d1cde3613956104173df2e357578f04@app-learninglab.inria.fr> Date: Sat, 30 May 2020 16:47:39 +0000 Subject: [PATCH] version provisoire --- module3/exo3/exercice.ipynb | 636 ++++++++++++++++++++++++++++++++++-- 1 file changed, 602 insertions(+), 34 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 59975f5..8636871 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -7,6 +7,13 @@ "# Playfair analysis" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importation de la librairie et chargement des données." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -17,9 +24,16 @@ "playfair = pd.read_csv(\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On regarde le début et la fin du dataframe pour avoir un premier sentiment sur les données" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -43,7 +57,6 @@ " \n", " \n", " \n", - " Unnamed: 0\n", " Year\n", " Wheat\n", " Wages\n", @@ -51,54 +64,49 @@ " \n", " \n", " \n", - " 48\n", - " 49\n", - " 1805\n", - " 81.0\n", - " 29.5\n", + " 0\n", + " 1565\n", + " 41.0\n", + " 5.00\n", " \n", " \n", - " 49\n", - " 50\n", - " 1810\n", - " 99.0\n", - " 30.0\n", + " 1\n", + " 1570\n", + " 45.0\n", + " 5.05\n", " \n", " \n", - " 50\n", - " 51\n", - " 1815\n", - " 78.0\n", - " NaN\n", + " 2\n", + " 1575\n", + " 42.0\n", + " 5.08\n", " \n", " \n", - " 51\n", - " 52\n", - " 1820\n", - " 54.0\n", - " NaN\n", + " 3\n", + " 1580\n", + " 49.0\n", + " 5.12\n", " \n", " \n", - " 52\n", - " 53\n", - " 1821\n", - " 54.0\n", - " NaN\n", + " 4\n", + " 1585\n", + " 41.5\n", + " 5.15\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Unnamed: 0 Year Wheat Wages\n", - "48 49 1805 81.0 29.5\n", - "49 50 1810 99.0 30.0\n", - "50 51 1815 78.0 NaN\n", - "51 52 1820 54.0 NaN\n", - "52 53 1821 54.0 NaN" + " Year Wheat Wages\n", + "0 1565 41.0 5.00\n", + "1 1570 45.0 5.05\n", + "2 1575 42.0 5.08\n", + "3 1580 49.0 5.12\n", + "4 1585 41.5 5.15" ] }, - "execution_count": 5, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -197,6 +205,15 @@ "playfair.tail()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme il n'y a pas beaucoup de données on peut vérifier la qualité par de simples graphiques.\n", + "\n", + "On importe matplotlib et on visualise les deux variables principales; on ne constate pas de valeurs anormales. La variabilité du prix du blé est plus grande que celle des salaires. Cela parait normal. " + ] + }, { "cell_type": "code", "execution_count": 7, @@ -270,6 +287,557 @@ "plt.plot(playfair['Wages'])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On prend l'année comme index. Cela permettra que l'année figure comme abscice dans les graphiques" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Les 3 NaN du salaire correspondent aux trois dernières observations." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 53 entries, 0 to 52\n", + "Data columns (total 3 columns):\n", + "Year 53 non-null int64\n", + "Wheat 53 non-null float64\n", + "Wages 50 non-null float64\n", + "dtypes: float64(2), int64(1)\n", + "memory usage: 1.3 KB\n" + ] + } + ], + "source": [ + "playfair.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les années vont de 5 en 5 sauf la dernière qui a les mêmes valeurs que l'avant dernière. On élimine donc la dernière observation qui n'apporte rien. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 52 entries, 0 to 51\n", + "Data columns (total 3 columns):\n", + "Year 52 non-null int64\n", + "Wheat 52 non-null float64\n", + "Wages 50 non-null float64\n", + "dtypes: float64(2), int64(1)\n", + "memory usage: 1.3 KB\n" + ] + } + ], + "source": [ + "playfair=playfair[:-1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO vérifier que les années vont de 5 en 5" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "playfair.plot.bar(y='Wheat')" + ] + }, { "cell_type": "code", "execution_count": null, -- 2.18.1