{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "a = np.array([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.hist(a)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1d04e686a0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "a = np.array([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.subplot(1, 2, 2)\n",
    "plt.plot(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}