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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "code",
  5.    "execution_count": 1,
  6.    "metadata": {
  7.     "collapsed": false
  8.    },
  9.    "outputs": [
  10.     {
  11.      "name": "stdout",
  12.      "output_type": "stream",
  13.      "text": [
  14.       "Hello World!\n"
  15.      ]
  16.     }
  17.    ],
  18.    "source": [
  19.     "print('Hello World!')"
  20.    ]
  21.   },
  22.   {
  23.    "cell_type": "code",
  24.    "execution_count": 2,
  25.    "metadata": {
  26.     "collapsed": false
  27.    },
  28.    "outputs": [
  29.     {
  30.      "name": "stdout",
  31.      "output_type": "stream",
  32.      "text": [
  33.       "Count at:  0\n",
  34.       "Count at:  1\n",
  35.       "Count at:  2\n",
  36.       "Count at:  3\n",
  37.       "Count at:  4\n",
  38.       "Count at:  5\n",
  39.       "Count at:  6\n",
  40.       "Count at:  7\n",
  41.       "Count at:  8\n",
  42.       "Count at:  9\n"
  43.      ]
  44.     }
  45.    ],
  46.    "source": [
  47.     "for i in range(10):\n",
  48.     "    print('Count at: ', i)"
  49.    ]
  50.   },
  51.   {
  52.    "cell_type": "code",
  53.    "execution_count": 4,
  54.    "metadata": {
  55.     "collapsed": false
  56.    },
  57.    "outputs": [
  58.     {
  59.      "name": "stdout",
  60.      "output_type": "stream",
  61.      "text": [
  62.       "[1 2 3]\n"
  63.      ]
  64.     }
  65.    ],
  66.    "source": [
  67.     "import numpy as np\n",
  68.     "a=np.array([1,2,3])\n",
  69.     "print(a)"
  70.    ]
  71.   },
  72.   {
  73.    "cell_type": "code",
  74.    "execution_count": 5,
  75.    "metadata": {
  76.     "collapsed": true
  77.    },
  78.    "outputs": [],
  79.    "source": [
  80.     "import matplotlib.pyplot as plt "
  81.    ]
  82.   },
  83.   {
  84.    "cell_type": "code",
  85.    "execution_count": 10,
  86.    "metadata": {
  87.     "collapsed": false
  88.    },
  89.    "outputs": [
  90.     {
  91.      "data": {
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0pVSrWoVnr+nOT1Jbqd2CiERMwV+BZG/Yw33pC8hav4dLOjfliau60rSemqqJ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\n",
  93.       "text/plain": [
  94.        "<matplotlib.figure.Figure at 0x7f270b8>"
  95.       ]
  96.      },
  97.      "metadata": {},
  98.      "output_type": "display_data"
  99.     }
  100.    ],
  101.    "source": [
  102.     "plt.plot([1,2,3], [1,2,3])\n",
  103.     "plt.show()"
  104.    ]
  105.   },
  106.   {
  107.    "cell_type": "code",
  108.    "execution_count": null,
  109.    "metadata": {
  110.     "collapsed": false
  111.    },
  112.    "outputs": [],
  113.    "source": [
  114.     "\n"
  115.    ]
  116.   },
  117.   {
  118.    "cell_type": "code",
  119.    "execution_count": null,
  120.    "metadata": {
  121.     "collapsed": true
  122.    },
  123.    "outputs": [],
  124.    "source": []
  125.   }
  126.  ],
  127.  "metadata": {
  128.   "kernelspec": {
  129.    "display_name": "Python 3",
  130.    "language": "python",
  131.    "name": "python3"
  132.   },
  133.   "language_info": {
  134.    "codemirror_mode": {
  135.     "name": "ipython",
  136.     "version": 3
  137.    },
  138.    "file_extension": ".py",
  139.    "mimetype": "text/x-python",
  140.    "name": "python",
  141.    "nbconvert_exporter": "python",
  142.    "pygments_lexer": "ipython3",
  143.    "version": "3.6.0"
  144.   }
  145.  },
  146.  "nbformat": 4,
  147.  "nbformat_minor": 2
  148. }
RAW Paste Data
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