diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..f63f9aab9d3daf28c6642e8f9641f8ac1b07eb74 100644
--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
@@ -1,5 +1,121 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import statistics \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",
+    "mean = statistics.mean(data)\n",
+    "std = statistics.stdev(data)\n",
+    "median = statistics.median(data)\n",
+    "m_ax = max(data)\n",
+    "m_in = min(data)\n",
+    "\n",
+    "std_2 = stdev(data)\n",
+    "\n",
+    "std_2 = round(std_2,2)\n",
+    "median = round(median,2)\n",
+    "mean = round(mean, 2)\n",
+    "std = round(std, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(m_ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(m_in)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +132,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module2/exo3/exercice.ipynb b/module2/exo3/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..457651d7a5399d22ab75fa0f871e4785259587c6 100644
--- a/module2/exo3/exercice.ipynb
+++ b/module2/exo3/exercice.ipynb
@@ -1,5 +1,64 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\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",
+    "plt.plot(data,color = 'blue',linewidth=1)\n",
+    "plt.grid(linestyle=':')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHQRJREFUeJzt3X9s2+d9J/D3h7Ed+sfkH2NtS+dasmOpmDrv5DRuBrgwqgRVvbqAJyMHe4ELAzYQGa2hJBh6DQ4Yrv8MK3a3H3/cMKzDiuaAa4ehW5eiKLIrjA0+YIUxm7DKWGVEM2Z4klkRmmlwjCnaPD73h0hPii09z0cS9fAR3y9AoKXIfD7ftx9+9AnF75dijAEREYUv4rsAIiJaHWzoRETrBBs6EdE6wYZORLROsKETEa0TbOhEROsEGzoR0TrBhk5EtE6woRMRrRMb1nKxWCxmenp61nJJIqLg3bhxY8YY8wnb961pQ+/p6cH169fXcslVlU6n8dxzz/kuo+UxJztm5IY5zRGRD12+j0+5KOzatct3CUFgTnbMyA1z0mFDV3jw4IHvEoLAnOyYkRvmpMOGrhCJMC4XzMmOGblhTjpMS2Hjxo2+SwgCc7JjRm6Ykw4bukKpVPJdQhCYkx0zcsOcdNjQFWKxmO8SgsCc7JiRG+akw4auMDk56buEIDAnO2bkhjnpsKErHDp0yHcJQWBOdszIDXPSYUNXuHXrlu8SgsCc7JiRG+akI2v5JtEvvPCCCflMUaJm2Lu3B9PTTicCrqo9e7rxy19m1nxd0hORG8aYF2zfxwld4caNG75LCAJzspuf0VwzN2v+4eOHiBb3kg4ndCLPRARzTXbNV8ZaPv5p+TihNwGnBTfMyY4ZuWFOOpzQiTzjhE42nNCbIJFI+C4hCMzJjhm5YU46bOgKfX19vksIAnOyY0ZumJMOG7pCNpv1XUIQmJMdM3LDnHTY0BX27Nnju4QgMCc7ZuSGOemwoSvcv3/fdwlBYE52zMgNc9JhQ1eIRqO+SwgCc7JjRm6Ykw4bOhHROsGGrjA7O+u7hCAwJztm5IY56bChK+zYscN3CUFgTnbMyA1z0mFDV5ienvZdQhCYkx0zcsOcdKwNXUQ+KSL/KCK/EJFbIvJ6/eu7ROSnIpKq3+5sfrl+7d+/33cJQWBOdszIDXPScZnQqwB+1xjzawB+E8DXRKQfwFsArhhjegFcqX++rk1MTPguIQjMyY4ZuWFOOuqLc4nIOwD+R/3j88aYnIh0AvgnY8ynlvq7vDgX0ZN4cS6yacrFuUSkB8ARANcA7DHG5ACgfrtbX2ZYeClPN8zJjhm5YU46zg1dRLYB+FsAbxhjioq/95qIXBeR67lcDjMzM8jlcpiamkKhUEA6nUa5XMb4+DhqtRri8TiAf/+HjMfjqNVqGB8fR7lcRjqdRqFQwNTUFBr3l8lkUCqVkEwmUa1WMTY2tuA+GreJRAKVSgWpVArFYhHZbBb5fB75fB7ZbBbFYhGpVAqVSuXxVd7m38dnPvMZjI2NoVqtIplMolQqIZPJBH1MAFb9mPr7+4M7pq6ug3j99dchInjzzTchInj99dfxzDPP4Ctf+Qo+8YlP4Mtf/jL6+vrwuc99Dp/97Gfx6U9/Gl/84hfR1dWFs2fPYvPmzbh06dKC+2jcXrx4Edu3b8fp06fR09ODr3/96zhy5AiOHDmCwcFBdHcXMTycQkdHBRcuzB3TG2/cWHA7MjKGaLSKM2eS6OwsYWgog/7+GRw9msOxY1Po7S3g5Mk0YrEyzp0bRyRSw+hofMF9jI7GEYnUcO7cOGKxWMv/Ox08eLDtH0/lchmunJ5yEZGNAH4M4B+MMX9c/9r7aLOnXBpNnZYWYk5r/bTHG2/cwJ/+aSMjPuWymBD3UjO4PuVibegyt9PfBnDPGPPGvK//NwD/aoz5loi8BWCXMeY/L3VfoTd0Wr/8PY8NsKGTzWo+h34MwFcAvCQiN+sfXwLwLQBfEJEUgC/UP1/XGv+bRktjTnYjI8zIBfeSDt+CTqFarWLDhg2+y2h5Iea01hN6NFrF7GwjI07oiwlxLzUD34KuCW7fvu27hCAwJ7tTp5iRC+4lHTZ0hX379vkuIQjMye7qVWbkgntJhw1dYWZmxncJQWBOdocPMyMX3Es6bOgK27Zt811CEJiT3eQkM3LBvaTDhq7w6NEj3yUEgTnZbd3KjFxwL+mwoSvUajXfJQSBOdlt2sSMXHAv6bChK2zZssV3CUFgTnb5PDNywb2kw4aucO/ePd8lBIE52fX1MSMX3Es6bOgKXV1dvksIAnOyu3aNGbngXtJhQ1e4c+eO7xKCwJzsTpxgRi64l3R46r9CrVZDJMKfgTYh5rTWp/5HIjXUao2MeOr/YkLcS83AU/+b4ObNm75LCAJzsrt8mRm54F7S4YROBF4+l1obJ/Qm4NthuWFOdo13EKKlcS/pcEInAid0am2c0Jug8b5/tDTmZNd4r09aGveSDid0Bf7G3U2IOfFVLq0pxL3UDJzQmyCZTPouIQjMye7VV5mRC+4lHTZ0hQMHDvguIQjMye7dd5mRC+4lHTZ0hbt37/ouIQjMye7FF5mRC+4lHTZ0hV27dvkuIQjMyW5ighm54F7SYUNXePDgge8SgsCc7HbvZkYuuJd02NAV+Nt2N8zJ7uFDZuSCe0mHaSls3LjRdwlBYE52H33EjFxwL+mwoSuUSiXfJQSBOdnt28eMXHAv6bChK8RiMd8lBIE52SUSzMgF95IOG7rC5OSk7xKCwJzsjh9nRi64l3TY0BUOHTrku4QgMCe7d95hRi64l3TY0BVu3brlu4QgMCe78+eZkQvuJR1enIsIvHwutTZenKsJeLF9N8zJjm9w4YZ7SYcTOhE4oVNr44TeBJwW3Kwkp717eyAia/6x1jihu+FjTocTOrUUf5MyJ3RqXZzQmyCRSPguIQjMye7CBWbkgntJhw1doa+vz3cJQWBOdj/4ATNywb2kw4aukM1mfZcQBOZk9/LLzMgF95IOG7rCnj17fJcQBOZkF48zIxfcSzrWhi4i3xGRvIi8N+9r3xSRKRG5Wf/4UnPLbA3379/3XUIQmJPdwYPMyAX3ko7LhP5dACee8vU/McYM1D9+srpltaZoNOq7hCAwJ7tCgRm54F7SsTZ0Y8xVAPfWoBYiIlqBlTyHfllEfl5/SmbnqlXUwmZnZ32XEATmZLdzJzNywb2ks9yG/ucAngMwACAH4I8W+0YReU1ErovI9Vwuh5mZGeRyOUxNTaFQKCCdTqNcLmN8fBy1Wg3xeBzAv58hFo/HUavVMD4+jnK5jHQ6jUKhgKmpKTTuL5PJoFQqIZlMolqtYmxsbMF9NG4TiQQqlQpSqRSKxSKy2Szy+Tzy+Tyy2SyKxSJSqRQqlcrj17/Ov48dO3ZgbGwM1WoVyWQSpVIJmUwm6GMCsOrHFI1GV3RMFy4k0NFRwfBwCt3dRQwOZjEwkMfAQB6Dg1l0dxcxPJxCR0fl8eu5G2deNm5HRsYQjVZx5kwSnZ0lDA1l0N8/g6NHczh2bAq9vQWcPJlGLFbGuXPjiEQiGB2NL7iP0dE4IpEazp0bRyxWxsmTafT2FnDs2BSOHs2hv38GQ0MZdHaWcOZMEtFoFSMjY0+t5+PH9Ku/Wp53TINNOqaa5ZhiLb/3IpFI2z+eyuUyXDmdKSoiPQB+bIz5dc1/+7jQzxRNpVLo7e31XUbLW0lO7XKm6PBwCj/8YSMjnim6GD7m5jT1TFER6Zz36TCA9xb73vVk//79vksIAnOyu3KFGbngXtJxedni9wH8DMCnRGRSRC4C+EMRSYjIzwEMAnizyXW2hImJCd8lBIE52b3yCjNywb2kw4tzUUtpl6dcWmPt1n/Khebw4lxNwEt5umFOdrx8rhvuJR1O6NRSOKGv7bqc0MPACb0JOC24YU52nNDdcC/pcEKnlsIJfW3X5YQeBk7oTdA4GYGWxpzsGicg0dK4l3Q4oStUq1Vs2LDBdxktbyU5tcuEHo1WMTvbyIgT+mL4mJvDCb0Jbt++7buEIDAnu1OnmJEL7iUdNnSFffv2+S4hCMzJ7upVZuSCe0mHDV1hZmbGdwlBYE52hw8zIxfcSzps6Arbtm3zXUIQmJPd5CQzcsG9pMOGrvDo0SPfJQSBOdlt3cqMXHAv6bChK9RqNd8lBIE52W3axIxccC/psKErbNmyxXcJQWBOdvk8M3LBvaTDhq5w7x7fWtUFc7Lr62NGLriXdNjQFbq6unyXEATmZHftGjNywb2kw4aucOfOHd8lBIE52Z04wYxccC/p8NR/hVqthkiEPwNtVpJTu5z6H4nUUKs1MuKp/4vhY24OT/1vgps3b/ouIQjMye7yZWbkgntJhxM6tZR2mdBbY+3Wn9BpDif0JuDF9t0wJzu+wYUb7iUdTujUUjihr+26nNDDwAm9CeLxuO8SgsCc7EZHmZEL7iUdTugK/I27G77KxY6vcnHDx9wcTuhNkEwmfZcQBOZk9+qrzMgF95IOG7rCgQMHfJcQBOZk9+67zMgF95IOG7rC3bt3fZcQBOZk9+KLzMgF95IOG7rCrl27fJcQBOZkNzHBjFxwL+mwoSs8ePDAdwlBYE52u3czIxfcSzps6Ar8bbsb5mT38CEzcsG9pMO0FDZu3Oi7hCAwJ7uPPmJGLriXdNjQFUqlku8SgsCc7PbtY0YuuJd02NAVYrGY7xKCwJzsEglm5IJ7SYcNXWFyctJ3CUFgTnbHjzMjF9xLOmzoCocOHfJdQhCYk9077zAjF9xLOmzoCrdu3fJdQhCYk93588zIBfeSDi/ORS2lXS7O1Rprt/7FuWgOL87VBLzYvhvmZMc3uHDDvaTDCZ1aCif0tV2XE3oYVm1CF5HviEheRN6b97VdIvJTEUnVb3eutOAQcFpww5zsOKG74V7SsU7oInIcQAnA/zTG/Hr9a38I4J4x5lsi8haAncaYb9gW44RONpzQ13ZdTuhhWLUJ3RhzFcC9j335FIC3639+G8BvqysMUCKR8F1CEJiT3YULzMgF95LOcn8puscYkwOA+u3u1SupdfX19fkuIQjMye4HP2BGLriXdJr+KhcReU1ErovI9Vwuh5mZGeRyOUxNTaFQKCCdTqNcLmN8fBy1Wu3xm8I2njuLx+Oo1WoYHx9HuVxGOp1GoVDA1NQUGveXyWRQKpWQTCZRrVYxNja24D4at4lEApVKBalUCsViEdlsFvl8Hvl8HtlsFsViEalUCpVK5fFkMP8+stksxsbGUK1WkUwmUSqVkMlkgj4mAKt+TOl0ekXHdOFCAh0dFQwPp9DdXcTgYBYDA3kMDOQxOJhFd3cRw8MpdHRUHk+6jeekG7cjI2OIRqs4cyaJzs4ShoYy6O+fwdGjORw7NoXe3gJOnkwjFivj3LlxRCKRx2/c3LiP0dE4IpEazp0bRyxWxsmTafT2FnDs2BSOHs2hv38GQ0MZdHaWcOZMEtFoFSMjY0+t5+PH9NprP593TINNOqaa5ZhiLb/33n///bZ/PJXLZbhyepWLiPQA+PG859DfB/B5Y0xORDoB/JMx5lO2+wn9OfRisYiOjg7fZbS8leTULs+hd3cX8eGHjYz4HPpi+Jib0+zXof8IwPn6n88DeGeZ9xOU+/fv+y4hCMzJ7uBBZuSCe0nH5WWL3wfwMwCfEpFJEbkI4FsAviAiKQBfqH++7kWjUd8lBIE52RUKzMgF95LOBts3GGN+Z5H/9PIq10JERCvAU/8VZmdnfZcQBOZkt3MnM3LBvaTDhq6wY8cO3yUEgTnZffABM3LBvaTDhq4wPT3tu4QgMCe7559nRi64l3TY0BX279/vu4QgMCe7K1eYkQvuJR02dIWJiQnfJayJvXt7ICLL/vjqV7+67L/bLl55pRX20rMr+ndeycfevT1OFbbLY2618PK59AR/J/cAPk+y4TGv7dqtflJTK+EbXDQBL+XphpeGtWNGbviY0+GETk/ghN4ua3NCDwUn9CbgtOCG06cdM3LDx5wOJ3R6Aif0dlmbE3ooOKE3QeOSm7S0xiVkaXHMyA0fczqc0BWq1So2bLBe/iZ4K53Qo9EqZmeXm1N7TKsLM2qPY/742i69p10eczac0Jvg9u3bvksIwqlTzMmGGbnhY06HP/oU9u3b57uEIFy9ypxsmNGzTieSdXZ2IpfLrdqqe/Z045e/zKza/bUaTugKMzMzvksIwuHDzMmGGVUw93TP0h+HD/+z0/e5fkxPf7g2h+cJG7rCtm3bfJcQhMlJ5mTDjNwwJx02dIVHjx75LiEIW7cyJxtm5IY56bChK9RqNd8lBGHTJuZkw4zcMCcdNnSFLVu2+C4hCPk8c7JhRm6Ykw4busK9e/d8lxCEvj7mZMOM3DAnHTZ0ha6uLt8lBOHaNeZkw4zcMCcdNnSFO3fu+C4hCCdOMCcbZuSGOenw1H+FWq2GSGT9/wxc6an/kUgNtdpyc2qP0+AXZtQex7yctVe2l56+bogXBeOp/01w8+ZN3yUE4fJl5mTDjNwwJx1O6PQEXj63XdZuz2PmhE4AeLF9V3zzBjtm5IY56XBCpydwQm+XtdvzmDmhEwAgHo/7LiEIo6PMyYYZuWFOOpzQFfgqFzd8lYsdX+XCV7locEJvgmQy6buEILz6KnOyYUZumJMOG7rCgQMHfJcQhHffZU42zMgNc9JhQ1e4e/eu7xKC8OKLzMmGGblhTjps6Aq7du3yXUIQJiaYkw0zcsOcdNjQFR48eOC7hCDs3s2cbJiRG+akw4au0A6vcFkNDx8yJxtm5IY56TAthY0bN/ouIQgffcScbJiRG+akw4auUCqVfJcQhH37mJMNM3LDnHTY0BVisZjvEoKQSDAnG2bkhjnprKihi0hGRBIiclNEwj0F1NHk5KTvEoJw/DhzsmFGbpiTzopO/ReRDIAXjDEzLt8f+qn/1WoVGzZs8F1G06301P9otIrZ2eXm1B6nwS/MqD2OeTlrr2wvPX1dnvpPAIBbt275LiEI588zJxtm5IY56ax0Qr8DoIC5H7V/YYz59lLfH/qE3i54+dx2Wbs9j5kT+uKOGWOeB/BbAL4mIsefUshrInJdRK7ncjnMzMwgl8thamoKhUIB6XQa5XIZ4+PjqNVqjy9R23gziXg8jlqthvHxcZTLZaTTaRQKBUxNTaFxf5lMBqVSCclkEtVqFWNjYwvuo3GbSCRQqVSQSqVQLBaRzWaRz+eRz+eRzWZRLBaRSqVQqVSQSCSeuI8bN25gbGwM1WoVyWQSpVIJmUymacf0G79xFGfPnsXmzZtx6dIliAjefPPNBbcXL17E9u3bcfr0afT09OCll17CkSNHcOTIEbz00kvo6enB6dOnsX37dly8ePGp93Hp0iVs3rwZZ8+eRVdXF4aGhtDfP4OjR3M4dmwKvb0FnDyZRixWxrlz44hEao8va9p4A4LR0TgikRrOnRvHN75xDSdPptHbW8CxY1M4ejSH/v4ZDA1l0NlZwpkzSUSjVYyMjC24j8bthQsJdHRUMDycQnd3EYODWQwM5DEwkMfgYBbd3UUMD6fQ0VHBhQuJp97HyMgYotEqzpxJorOzhKGhjOWYIkseUyxWXtVj+oM/uDrvmAabdExL/zvFYjGP/05vOB3T7/3ez5THZPt3OtPUHgGgKT3C1apdPldEvgmgZIz574t9Dyd0HX+TcntObjzmdlibE/piC2wVkV9p/BnAEID3lnt/IeBb0Lnh24bZMSM3zEln2RO6iBwE8MP6pxsAfM8Y8/tL/R1O6Dqc0NthXZ9rt+cxc0J/CmPMB8aY/1j/+LStma8HjefMaGmN50tpcczIDXPS4VvQKVQqFTz77LNrtl6oE3pHRwXF4nJzCvOYtRZm1B7HvJy1V7aXnr4uJ3QCAGSzWd8lBOHll5mTDTNyw5x02NAV9uzZ47uEIMTjzMmGGblhTjps6Ar379/3XUIQDh5kTjbMyA1z0gmmoe/d2wMR8fKxd28PACAajfoNIRCFAnOyYUZuVj+nZ733kWYK5kpT09MfwtcvcKanxcu6RLTaKljPfSSYCb0VzM7O+i4hCDt3MicbZuSGOemwoSvs2LHDdwlB+OAD5mTDjNwwJx02dIXp6WnfJQTh+eeZkw0zcsOcdNjQFfbv3++7hCBcucKcbJiRG+akw4auMDEx4buEILzyCnOyYUZumJNOMKf++37TBR+nC4d66n+Ya/OY22PtMPsIT/1vAl4+1w0veWrHjNwwJx1O6G6rc0Jf92vzmNtj7TD7CCf0VfXsgrdsW6uPUHGqsmNGbpiTDid0t9U9rd1u6/pcm8fcHmtzQqe6xpvl0tKYkx0zcsOcdDihu60OwCAarWJ2di0vfxPmFLOynMI8Zq2FGbXHMS9n7dV/zHFCp7pTp277LiEIzMmOGblhTjps6ApXr+7zXUIQmJMdM3LDnHTY0BUOH57xXUIQmJMdM3LDnHTY0BUmJ7f5LiEIzMmOGblhTjps6Apbtz7yXUIQmJMdM3LDnHTY0BU2bar5LiEIzMmOGblhTjps6Ar5/BbfJQSBOdkxIzfMSYcNXaGv757vEoLAnOyYkRvmpMOGrnDtWpfvEoLAnOyYkRvmpMOGrnDixB3fJQSBOdkxIzfMSYen/rutDsAgEqmhVlvLn4FhnhK+spzCPGathRm1xzEvZ+3Vf8zx1H+qu3z5pu8SgsCc7JiRG+akwwndbXVPa7fbuj7X5jG3x9qc0KmOF9t3w5zsmJEb5qTDCd1tdU9rt9u6PtfmMbfH2pzQqW50NO67hCAwJztm5IY56XBCd1sdfJWLO77KxY6vcuGrXFR/kxP66nv11aTvEoLAnOyYkRvmpMOGrvDuuwd8lxAE5mTHjNwwJx02dIUXX7zru4QgMCc7ZuSGOemsqKGLyAkReV9EbovIW6tVVKuamNjlu4QgMCc7ZuSGOeksu6GLyDMA/gzAbwHoB/A7ItK/WoW1ot27H/guIQjMyY4ZuWFOOiuZ0D8L4LYx5gNjzEMAfw3g1OqU1ZoePuQzVC6Ykx0zcsOcdFaS1n8A8H/nfT5Z/9q69dFHG32XEATmZMeM3DAnnQ0r+LvylK898SJLEXkNwGv1T0si8v7qLrlWBOPjiAFY47ch93XMy1935TmFd8xaT2a0/o95OWs35zHn75jnzqdZlm6Xb1pJQ58E8Ml5n+8D8MSvpI0x3wbw7RWs0zJE5LrLi/vbHXOyY0ZumJPOSp5y+RcAvSJyQEQ2ATgL4EerUxYREWkte0I3xlRF5DKAfwDwDIDvGGNurVplRESkspKnXGCM+QmAn6xSLSFYF08drQHmZMeM3DAnhTW9OBcRETUPX+RJRLROsKE7EJGMiCRE5KaILO/6v+uQiHxHRPIi8t68r+0SkZ+KSKp+u9Nnja1gkZy+KSJT9T11U0S+5LPGViAinxSRfxSRX4jILRF5vf517ilHbOjuBo0xA3wJ1QLfBXDiY197C8AVY0wvgCv1z9vdd/FkTgDwJ/U9NVD/fVS7qwL4XWPMrwH4TQBfq19OhHvKERs6LZsx5iqAex/78ikAb9f//DaA317TolrQIjnRxxhjcsaYeP3P/wbgF5g7+5x7yhEbuhsD4H+LyI36ma+0uD3GmBww9wAFsNtzPa3ssoj8vP6UDJ9GmEdEegAcAXAN3FPO2NDdHDPGPI+5K0t+TUSO+y6IgvfnAJ4DMAAgB+CP/JbTOkRkG4C/BfCGMabou56QsKE7MMbcrd/mAfwQc1eapKebFpFOAKjf5j3X05KMMdPGmP9njKkB+EtwTwEARGQj5pr5/zLG/F39y9xTjtjQLURkq4j8SuPPAIYAvLf032prPwJwvv7n8wDe8VhLy2o0qLphcE9B5q5c9VcAfmGM+eN5/4l7yhFPLLIQkYOYm8qBuTNrv2eM+X2PJbUMEfk+gM8DiAGYBvBfAfw9gL8BsB9AFsB/Msa09S8EF8np85h7usUAyAAYaTxP3K5E5HMA/g+ABIBa/cv/BXPPo3NPOWBDJyJaJ/iUCxHROsGGTkS0TrChExGtE2zoRETrBBs6EdE6wYZORLROsKETEa0TbOhEROvE/weMv4cUI/VRYQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.hist(data,color = 'blue',edgecolor = \"black\")\n",
+    "plt.grid(linestyle=':')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +75,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-