Histogramme

module2/exo2/exercice.ipynb

@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -178,14 +178,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
     "# mean = 14.11\n",
     "# std = 4.33\n",
-    "# number of points = 1\n"
+    "# number of points = 100"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

module2/exo3/exercice.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,47 +12,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14.113000000000001\n",
-      "2.8\n",
-      "14.5\n",
-      "4.334094455301447\n"
-     ]
-    }
-   ],
-   "source": [
-    "moyenne_A = np.mean(A, 0)\n",
-    "print(moyenne_A)\n",
-    "minimum_A = np.min(A, 0)\n",
-    "print(minimum_A)\n",
-    "médiane_A = np.median(A)\n",
-    "print(médiane_A)\n",
-    "écartype2_A = np.std(A,0,ddof = 1)\n",
-    "print(écartype2_A)"
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAADF5JREFUeJzt3U+IpHV+x/H3x9UIUQ/KtDODUTsRCZFAxtBIwBAMi4vxoh52yRw2E1gYDyso7CHiZb0EJKwmlyCMKDsB17CgRg+SrIhgFoJsjwzrmNmNyzJu1JmeFgMqAYPON4cqSWOmp6rrT9f2t98vKKrq6af6+T089Lurn36ep1JVSJJ2vosWPQBJ0mwYdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNTEy6EmuTfJqkpNJ3kpy/3D6w0neS3J8eLtz/sOVJG0mo04sSrIf2F9VbyS5AjgG3A18A/ikqr43/2FKkka5eNQMVXUaOD18/HGSk8A1kyxsz549tby8PMlLJWnXOnbs2AdVtTRqvpFB3yjJMnAz8DpwK3Bfkr8AVoHvVNV/Xej1y8vLrK6ubmWRkrTrJXlnnPnG/qdoksuBZ4EHquoj4HHgBuAAg3fwj27yusNJVpOsrq+vj7s4SdIWjRX0JJcwiPnTVfUcQFWtVdXnVXUOeAK45XyvraojVbVSVStLSyP/YpAkTWico1wCPAmcrKrHNkzfv2G2e4ATsx+eJGlc4+xDvxX4JvBmkuPDaQ8BB5McAAo4Bdw7lxFKksYyzlEuPwZyni+9NPvhSJIm5ZmiktSEQZekJgy6JDVh0CWpCYMuLdi+fcsk2fbbvn3Li151zdiWTv2XNHtra+8wOPp3u5d7voPXtJP5Dl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrC66FLDD5kYnBdcmnnMugSi/uQiQE/aEKz4S4XSWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktTEyKAnuTbJq0lOJnkryf3D6VcleTnJ28P7K+c/XEnSZsZ5h/4Z8J2q+j3gj4BvJ7kJeBB4papuBF4ZPpckLcjIoFfV6ap6Y/j4Y+AkcA1wF3B0ONtR4O55DVKSNNqW9qEnWQZuBl4H9lbVaRhEH7h6k9ccTrKaZHV9fX260UqSNjV20JNcDjwLPFBVH437uqo6UlUrVbWytLQ0yRglSWMYK+hJLmEQ86er6rnh5LUk+4df3w+cnc8QJUnjGOcolwBPAier6rENX3oRODR8fAh4YfbDkySNa5zPFL0V+CbwZpLjw2kPAY8AP0zyLeBXwNfnM0RJ0jhGBr2qfszmn2L71dkOR5I0Kc8UlaQmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1MTLoSZ5KcjbJiQ3THk7yXpLjw9ud8x2mJGmUcd6hfx+44zzT/7aqDgxvL812WJKkrRoZ9Kp6DfhwG8YiSZrCNPvQ70vy0+EumSs3mynJ4SSrSVbX19enWJx2g337lkmy7Tepg0mD/jhwA3AAOA08utmMVXWkqlaqamVpaWnCxWm3WFt7B6gF3KSdb6KgV9VaVX1eVeeAJ4BbZjssSdJWTRT0JPs3PL0HOLHZvJKk7XHxqBmSPAPcBuxJ8i7wXeC2JAcY/K16Crh3jmOUJI1hZNCr6uB5Jj85h7FIkqbgmaKS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpiZEfEi2pq0tJspAl7917PWfOnFrIsjsz6NKu9SlQC1ny2tpifpF05y4XSWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktTEyKAneSrJ2SQnNky7KsnLSd4e3l8532FKkkYZ5x3694E7vjTtQeCVqroReGX4XJK0QCODXlWvAR9+afJdwNHh46PA3TMelyRpiybdh763qk4DDO+vnt2QJEmTmPs/RZMcTrKaZHV9fX3ei9MM7Nu3TJKF3CRNbtKgryXZDzC8P7vZjFV1pKpWqmplaWlpwsVpO62tvcPgk2wWcZM0qUmD/iJwaPj4EPDCbIYjSZrUOIctPgP8G/C7Sd5N8i3gEeD2JG8Dtw+fS5IWaOSHRFfVwU2+9NUZj0WSNAXPFJWkJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTYy8OJckzd6lC/lAk717r+fMmVPbvtztYtAlLcCnLOIDTdbWen8qlrtcJKkJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhNTfUh0klPAx8DnwGdVtTKLQUmStm6qoA/9aVV9MIPvI0magrtcJKmJaYNewI+SHEtyeBYDkiRNZtpdLrdW1ftJrgZeTvKzqnpt4wzD0B8GuO6666ZcnCRpM1O9Q6+q94f3Z4HngVvOM8+RqlqpqpWlpaVpFidJuoCJg57ksiRXfPEY+BpwYlYDkyRtzTS7XPYCzyf54vv8oKr+eSajkiRt2cRBr6pfAn8ww7FIkqbgYYuS1IRBl6QmDLokNTGLU//b27dvmbW1d7Z9uRdd9JucO/ff275cSTuTQR/DIOa17cs9dy4LWS5kAcuUNC13uUhSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWri4kUPQJK2z6UkWciS9+69njNnTs11GQZd0i7yKVALWfLa2vx/kbjLRZKaMOiS1IRBl6Qmpgp6kjuS/DzJL5I8OKtBSZK2buKgJ/kK8PfAnwE3AQeT3DSrgUmStmaad+i3AL+oql9W1f8A/wjcNZthSZK2apqgXwP854bn7w6nSZIWYJrj0M93UOX/O8AzyWHg8PDpJ0l+PuHy9gAfTPjaGVjIyQh7IAta58WcfIHrvM1c5220J5l4na8fZ6Zpgv4ucO2G578FvP/lmarqCHBkiuUAkGS1qlam/T47ieu8O7jOu8N2rPM0u1x+AtyY5LeT/Abw58CLsxmWJGmrJn6HXlWfJbkP+BfgK8BTVfXWzEYmSdqSqa7lUlUvAS/NaCyjTL3bZgdynXcH13l3mPs6p2oxF6qRJM2Wp/5LUhM7Iui78RIDSU4leTPJ8SSrix7PPCR5KsnZJCc2TLsqyctJ3h7eX7nIMc7aJuv8cJL3htv6eJI7FznGWUpybZJXk5xM8laS+4fT227nC6zz3Lfzr/0ul+ElBv4DuJ3BoZI/AQ5W1b8vdGBzluQUsFJVCzz2fr6S/AnwCfAPVfX7w2l/A3xYVY8Mf3lfWVV/tchxztIm6/ww8ElVfW+RY5uHJPuB/VX1RpIrgGPA3cBf0nQ7X2Cdv8Gct/NOeIfuJQaaqqrXgA+/NPku4Ojw8VEGPwhtbLLObVXV6ap6Y/j4Y+AkgzPK227nC6zz3O2EoO/WSwwU8KMkx4Zn2+4We6vqNAx+MICrFzye7XJfkp8Od8m02f2wUZJl4GbgdXbJdv7SOsOct/NOCPpYlxho6Naq+kMGV7P89vBPdfX0OHADcAA4DTy62OHMXpLLgWeBB6rqo0WPZzucZ53nvp13QtDHusRAN1X1/vD+LPA8g11Pu8HacB/kF/sizy54PHNXVWtV9XlVnQOeoNm2TnIJg7A9XVXPDSe33s7nW+ft2M47Iei77hIDSS4b/jOFJJcBXwNOXPhVbbwIHBo+PgS8sMCxbIsvwjZ0D422dZIATwInq+qxDV9qu503W+ft2M6/9ke5AAwP7/k7/u8SA3+94CHNVZLfYfCuHAZn8/6g4zoneQa4jcGVNNeA7wL/BPwQuA74FfD1qmrzT8RN1vk2Bn+GF3AKuPeL/cs7XZI/Bv4VeBM4N5z8EIN9yi238wXW+SBz3s47IuiSpNF2wi4XSdIYDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUxP8CJA5j0ihFYq8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100\n"
-     ]
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "Size_A = np.size(A)\n",
-    "print(Size_A)"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.hist(A, color='b',range = (0, 25), edgecolor='black')\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {