{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "donn= [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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(donn  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(1, np.shape(y)[0]+1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "fig= plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y, color='lightblue', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.,  3.,  5.,  9., 16., 20., 22.,  9.,  8.,  4.]),\n",
       " array([ 2.8 ,  4.86,  6.92,  8.98, 11.04, 13.1 , 15.16, 17.22, 19.28,\n",
       "        21.34, 23.4 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAACmRJREFUeJzt3UGIXId9x/Hfv3F6SXKw8dqY1KraYEp8qVKECbgUh5Dgxgc7h0B9KDoElIMNCeQickkuBfeQ5FQCCjbWIXEJJK4NNm2MCLiFEioHE8uowSGoqWMhyfgQ91Rs/3vYMai25J3dHe1o//p8QMzM2zd6f56evjze7put7g4A+98frHsAAFZD0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxjihr3c2M0339wHDx7cy00C7HsvvPDC6929sdV6exr0gwcP5tSpU3u5SYB9r6r+a5n1XHIBGELQAYYQdIAhBB1gCEEHGELQAYYQdIAhBB1gCEEHGGJP7xQF3u/gsWfWst2zj9y3lu1y9ThDBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxhC0AGG2DLoVXV7Vf2sqs5U1ctV9dXF8puq6rmqemXxeOPVHxeAK1nmDP2tJF/v7k8m+XSSh6rqziTHkpzs7juSnFy8BmBNtgx6d5/r7l8snr+Z5EySjye5P8mJxWonkjxwtYYEYGvbuoZeVQeTfCrJz5Pc2t3nks3oJ7ll1cMBsLylfwVdVX00yY+TfK27f19Vy77vaJKjSXLgwIGdzAhX3bp+DRys0lJn6FX14WzG/Afd/ZPF4vNVddvi67cluXC593b38e4+3N2HNzY2VjEzAJexzE+5VJJHk5zp7u9c8qWnkxxZPD+S5KnVjwfAspa55HJ3kr9N8lJVvbhY9o0kjyT5UVV9Oclvk3zp6owIwDK2DHp3/1uSK10w/+xqxwFgp9wpCjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBCCDjCEoAMMIegAQwg6wBA3rHsAuNTBY8+sewTYt5yhAwwh6ABDCDrAEIIOMISgAwyxZdCr6rGqulBVpy9Z9q2q+l1Vvbj484WrOyYAW1nmDP3xJPdeZvl3u/vQ4s+zqx0LgO3aMujd/XySN/ZgFgB2YTfX0B+uql8uLsncuLKJANiRnQb9e0k+keRQknNJvn2lFavqaFWdqqpTFy9e3OHmANjKjoLe3ee7++3ufifJ95Pc9QHrHu/uw919eGNjY6dzArCFHQW9qm675OUXk5y+0roA7I0tP5yrqp5Ick+Sm6vq1STfTHJPVR1K0knOJvnKVZwRgCVsGfTufvAyix+9CrMAsAvuFAUYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhhB0gCEEHWAIQQcYQtABhtgy6FX1WFVdqKrTlyy7qaqeq6pXFo83Xt0xAdjKMmfojye59z3LjiU52d13JDm5eA3AGm0Z9O5+Pskb71l8f5ITi+cnkjyw4rkA2KadXkO/tbvPJcni8ZbVjQTATlz1b4pW1dGqOlVVpy5evHi1Nwdw3dpp0M9X1W1Jsni8cKUVu/t4dx/u7sMbGxs73BwAW9lp0J9OcmTx/EiSp1YzDgA7tcyPLT6R5N+T/FlVvVpVX07ySJLPVdUrST63eA3AGt2w1Qrd/eAVvvTZFc8CwC64UxRgCEEHGELQAYYQdIAhtvymKNefg8eeWfcI7IF1/juffeS+tW17MmfoAEMIOsAQgg4whKADDCHoAEMIOsAQgg4whKADDOHGImDPreumpuk3NDlDBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYAhBBxhC0AGGEHSAIQQdYIgbdvPmqjqb5M0kbyd5q7sPr2IoALZvV0Ff+Ex3v76CvweAXXDJBWCI3Qa9k/y0ql6oqqOrGAiAndntJZe7u/u1qrolyXNV9Z/d/fylKyxCfzRJDhw4sMvNXV8OHntm3SMA+8iuztC7+7XF44UkTya56zLrHO/uw919eGNjYzebA+AD7DjoVfWRqvrYu8+TfD7J6VUNBsD27OaSy61Jnqyqd/+eH3b3P69kKgC2bcdB7+7fJPnzFc4CwC74sUWAIQQdYAhBBxhC0AGGWMVnueyJdd5kc/aR+9a2bWB1pnfEGTrAEIIOMISgAwwh6ABDCDrAEIIOMISgAwwh6ABDCDrAEPvmTtF18qvggP3AGTrAEIIOMISgAwwh6ABDCDrAEIIOMISgAwwh6ABDCDrAEIIOMISgAwwh6ABDCDrAEIIOMISgAwwh6ABDCDrAEIIOMISgAwwh6ABDCDrAEIIOMISgAwyxq6BX1b1V9auq+nVVHVvVUABs346DXlUfSvIPSf46yZ1JHqyqO1c1GADbs5sz9LuS/Lq7f9Pd/5vkH5Pcv5qxANiu3QT940n++5LXry6WAbAGN+zivXWZZf2+laqOJjm6ePk/VfWrXWxz3W5O8vq6h9gH7Ket2UfLGbOf6u939fY/Xmal3QT91SS3X/L6j5K89t6Vuvt4kuO72M41o6pOdffhdc9xrbOftmYfLcd+2p7dXHL5jyR3VNWfVNUfJvmbJE+vZiwAtmvHZ+jd/VZVPZzkX5J8KMlj3f3yyiYDYFt2c8kl3f1skmdXNMt+MOLS0R6wn7ZmHy3HftqG6n7f9zEB2Ifc+g8whKAvoarOVtVLVfViVZ1a9zzXiqp6rKouVNXpS5bdVFXPVdUri8cb1znjteAK++lbVfW7xTH1YlV9YZ0zXguq6vaq+llVnamql6vqq4vljqklCfryPtPdh/wI1f/zeJJ737PsWJKT3X1HkpOL19e7x/P+/ZQk310cU4cW34+63r2V5Ovd/ckkn07y0OLjRBxTSxJ0dqy7n0/yxnsW35/kxOL5iSQP7OlQ16Ar7Cfeo7vPdfcvFs/fTHImm3efO6aWJOjL6SQ/raoXFne+cmW3dve5ZPM/aJJb1jzPtezhqvrl4pKMywiXqKqDST6V5OdxTC1N0Jdzd3f/RTY/WfKhqvqrdQ/Evve9JJ9IcijJuSTfXu84146q+miSHyf5Wnf/ft3z7CeCvoTufm3xeCHJk9n8pEku73xV3ZYki8cLa57nmtTd57v77e5+J8n345hKklTVh7MZ8x90908Wix1TSxL0LVTVR6rqY+8+T/L5JKc/+F3XtaeTHFk8P5LkqTXOcs16N1ALX4xjKlVVSR5Ncqa7v3PJlxxTS3Jj0Raq6k+zeVaebN5Z+8Pu/rs1jnTNqKonktyTzU/EO5/km0n+KcmPkhxI8tskX+ru6/obglfYT/dk83JLJzmb5CvvXie+XlXVXyb51yQvJXlnsfgb2byO7phagqADDOGSC8AQgg4whKADDCHoAEMIOsAQgg4whKADDCHoAEP8H8IJKtNnWIm7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "#ax = fig.add_subplot(111)\n",
    "plt.hist(y, bins=10)\n",
    "#ax.plot(x, y, color='lightblue', linewidth=3)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4, 5, 5, 5, 6]), array([ 0,  5, 10, 15, 20, 25]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(x, bins=[0,5,10,15,20,25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  2.,  0.,  3.,  2.,  3.,  6.,  3.,  5., 11., 10., 10., 12.,\n",
       "        10.,  4.,  5.,  4.,  4.,  2.,  2.]),\n",
       " array([ 2.8 ,  3.83,  4.86,  5.89,  6.92,  7.95,  8.98, 10.01, 11.04,\n",
       "        12.07, 13.1 , 14.13, 15.16, 16.19, 17.22, 18.25, 19.28, 20.31,\n",
       "        21.34, 22.37, 23.4 ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 41,
     "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": [
    "fig = plt.figure()\n",
    "plt.hist(y, bins=20)\n"
   ]
  },
  {
   "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
}