n

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"
    ]
   },
   {