From d23b2740f03933a2fd84763823095066a46aa6bf Mon Sep 17 00:00:00 2001 From: 082aa0aa9507b80b621099010e33de9d <082aa0aa9507b80b621099010e33de9d@app-learninglab.inria.fr> Date: Wed, 26 Jun 2024 10:44:03 +0000 Subject: [PATCH] n --- module2/exo2/exercice.ipynb | 80 +++++++++++++++++++++++++++++++++---- 1 file changed, 73 insertions(+), 7 deletions(-) diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb index 773095b..32286a6 100644 --- a/module2/exo2/exercice.ipynb +++ b/module2/exo2/exercice.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -11,6 +11,7 @@ "text": [ "Mean: 14.113000000000001\n", "Standard_variation: 4.312369534258399\n", + "4.31\n", "Min: 2.8\n", "Median: 14.5\n", "Max: 23.4\n" @@ -21,23 +22,88 @@ "import numpy as np\n", "import pandas as pd\n", "\n", + "\n", "#input dataset\n", - "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n", + "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,\n", + " 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n", + " 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n", + " 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n", + " 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3,\n", + " 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,\n", + " 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n", "\n", + "#convert to array\n", + "data_1 = np.array(data)\n", "#mean\n", - "print(\"Mean:\", np.mean(data))\n", + "print(\"Mean:\", np.mean(data_1))\n", "\n", "#standard_variation\n", - "print(\"Standard_variation:\", np.std(data))\n", + "print(\"Standard_variation:\", np.std(data_1))\n", + "a = round(np.std(data_1),2)\n", + "print(a)\n", "\n", "#minimum\n", - "print(\"Min:\", np.min(data))\n", + "print(\"Min:\", np.min(data_1))\n", "\n", "#median\n", - "print(\"Median:\", np.median(data))\n", + "print(\"Median:\", np.median(data_1))\n", "\n", "#maximum\n", - "print(\"Max:\", np.max(data))" + "print(\"Max:\", np.max(data_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 14.113000000000001\n", + "Standard Deviation: 4.312369534258399\n", + "Minimum: 2.8\n", + "Median: 14.5\n", + "Maximum: 23.4\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Input the dataset\n", + "data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,\n", + " 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n", + " 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n", + " 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n", + " 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3,\n", + " 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,\n", + " 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n", + "\n", + "# Convert to a numpy array\n", + "np_data = np.array(data)\n", + "\n", + "# Compute the mean\n", + "mean_value = np.mean(np_data)\n", + "print(f\"Mean: {mean_value}\")\n", + "\n", + "# Compute the standard deviation\n", + "std_deviation = np.std(np_data)\n", + "print(f\"Standard Deviation: {std_deviation}\")\n", + "\n", + "# Compute the minimum\n", + "min_value = np.min(np_data)\n", + "print(f\"Minimum: {min_value}\")\n", + "\n", + "# Compute the median\n", + "median_value = np.median(np_data)\n", + "print(f\"Median: {median_value}\")\n", + "\n", + "# Compute the maximum\n", + "max_value = np.max(np_data)\n", + "print(f\"Maximum: {max_value}\")\n" ] }, { -- 2.18.1