d008-Function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f03f7d46cc0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TkcR50Frz/Df7eWpJGlf37ciLUwfK8IiHk5+uABrHvF+dNoir+3bkqSVpPPN1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\nbkaKWwgh3IwUtxBCuJn/D8Su/Rbf76FzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f03f7d3ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(-3, 3, 1000)\n",
    "plt.plot(xs, f(xs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z\n",
      "Y\n",
      "X\n",
      "W\n",
      "V\n",
      "U\n",
      "T\n",
      "S\n",
      "R\n",
      "Q\n",
      "P\n",
      "O\n",
      "N\n",
      "M\n",
      "L\n",
      "K\n",
      "J\n",
      "I\n",
      "H\n",
      "G\n",
      "F\n",
      "E\n",
      "D\n",
      "C\n",
      "B\n",
      "A\n"
     ]
    }
   ],
   "source": [
    "for i in range(ord('Z'),ord('A')-1,-1):\n",
    "    print(chr(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 121\n",
      "12 144\n",
      "13 169\n",
      "14 196\n",
      "15 225\n",
      "16 256\n",
      "17 289\n",
      "18 324\n",
      "19 361\n",
      "20 400\n",
      "21 441\n",
      "22 484\n",
      "23 529\n",
      "24 576\n",
      "25 625\n",
      "26 676\n",
      "27 729\n",
      "28 784\n",
      "29 841\n",
      "30 900\n"
     ]
    }
   ],
   "source": [
    "for i in range(11,31):\n",
    "        print(i, f(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "('abcdefghijklmnopqrstuvwxyz' * 10).join([chr(i) for i in range(ord('a'), ord('z')+1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0\n",
      "2 1\n",
      "3 3\n",
      "4 6\n",
      "5 10\n",
      "6 15\n",
      "7 21\n",
      "8 28\n",
      "9 36\n",
      "10 45\n",
      "11 55\n",
      "12 66\n",
      "13 78\n",
      "14 91\n",
      "15 105\n",
      "16 120\n",
      "17 136\n",
      "18 153\n",
      "19 171\n",
      "20 190\n",
      "21 210\n",
      "22 231\n",
      "23 253\n",
      "24 276\n",
      "25 300\n",
      "26 325\n",
      "27 351\n",
      "28 378\n",
      "29 406\n",
      "30 435\n",
      "31 465\n",
      "32 496\n",
      "33 528\n",
      "34 561\n",
      "35 595\n",
      "36 630\n",
      "37 666\n",
      "38 703\n",
      "39 741\n",
      "40 780\n",
      "41 820\n",
      "42 861\n",
      "43 903\n",
      "44 946\n",
      "45 990\n",
      "46 1035\n",
      "47 1081\n",
      "48 1128\n",
      "49 1176\n",
      "50 1225\n",
      "51 1275\n",
      "52 1326\n",
      "53 1378\n",
      "54 1431\n",
      "55 1485\n",
      "56 1540\n",
      "57 1596\n",
      "58 1653\n",
      "59 1711\n",
      "60 1770\n",
      "61 1830\n",
      "62 1891\n",
      "63 1953\n",
      "64 2016\n",
      "65 2080\n",
      "66 2145\n",
      "67 2211\n",
      "68 2278\n",
      "69 2346\n",
      "70 2415\n",
      "71 2485\n",
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      "100 4950\n",
      "5050\n"
     ]
    }
   ],
   "source": [
    "s = 0\n",
    "for i in range(1,101):\n",
    "    print(i, s)\n",
    "    s = s+i\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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