Notebook3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version:        1.10.4 (need at least 1.7.1)\n",
      "SciPy version:        0.17.0 (need at least 0.12.0)\n",
      "Pandas version:       0.18.0 (need at least 0.11.0)\n",
      "Mapltolib version:     1.5.1 (need at least 1.2.1)\n",
      "Scikit-Learn version: 0.17.1 (need at least 0.13.1)\n",
      "requests version:      2.9.1 (need at least 1.2.3)\n",
      "NetworkX version:       1.11 (need at least 1.7)\n"
     ]
    }
   ],
   "source": [
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "print (\"Numpy version:        %6.6s (need at least 1.7.1)\" % np.__version__)\n",
    "\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "print (\"SciPy version:        %6.6s (need at least 0.12.0)\" % sp.__version__)\n",
    "\n",
    "# Pandas makes working with data tables easier\n",
    "import pandas as pd\n",
    "print (\"Pandas version:       %6.6s (need at least 0.11.0)\" % pd.__version__)\n",
    "\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "print (\"Mapltolib version:    %6.6s (need at least 1.2.1)\" % matplotlib.__version__)\n",
    "\n",
    "# SciKit Learn implements several Machine Learning algorithms\n",
    "import sklearn\n",
    "print (\"Scikit-Learn version: %6.6s (need at least 0.13.1)\" % sklearn.__version__)\n",
    "\n",
    "# Requests is a library for getting data from the Web\n",
    "import requests\n",
    "print (\"requests version:     %6.6s (need at least 1.2.3)\" % requests.__version__)\n",
    "\n",
    "# Networkx is a library for working with networks\n",
    "import networkx as nx\n",
    "print (\"NetworkX version:     %6.6s (need at least 1.7)\" % nx.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x25a052d2cc0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YqBKHvW6W2IN1eOABKyLyHtWr68G4vftTBvdCQwlv3pwpxYszZMkSMzBeGNlo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25a0525dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "x = np.linspace(0, 10, 30)  #array of 30 points from 0 to 10\n",
    "y = np.sin(x)\n",
    "z = y + np.random.normal(size=30) * .2\n",
    "plt.plot(x, y, 'ro-', label='A sine wave')\n",
    "plt.plot(x, z, 'b-', label='Noisy sine')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.xlabel(\"X axis\")\n",
    "plt.ylabel(\"Y axis\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   2.,\n",
       "          0.,   0.,   0.,   0.,   5.,   0.,   0.,   0.,   0.,   0.,   2.,\n",
       "          0.,   3.,   0.,   3.,   0.,   5.,   0.,   0.,   6.,   0.,   9.,\n",
       "          0.,   9.,   0.,  13.,   0.,  18.,   0.,  11.,   0.,  15.,   0.,\n",
       "          0.,  15.,   0.,  19.,   0.,  17.,   0.,  29.,   0.,  38.,   0.,\n",
       "         40.,   0.,  38.,   0.,   0.,  25.,   0.,  48.,   0.,  41.,   0.,\n",
       "         48.,   0.,  54.,   0.,  53.,   0.,  50.,   0.,   0.,  52.,   0.,\n",
       "         57.,   0.,  52.,   0.,  47.,   0.,  53.,   0.,  53.,   0.,  59.,\n",
       "          0.,   0.,  52.,   0.,  42.,   0.,  42.,   0.,  44.,   0.,  35.,\n",
       "          0.,  36.,   0.,  25.,   0.,   0.,  27.,   0.,  32.,   0.,  22.,\n",
       "          0.,  27.,   0.,  18.,   0.,  19.,   0.,  16.,   0.,   0.,  16.,\n",
       "          0.,   8.,   0.,  10.,   0.,   3.,   0.,  11.,   0.,   2.,   0.,\n",
       "          4.,   0.,   0.,   2.,   0.,   4.,   0.,   2.,   0.,   6.,   0.,\n",
       "          2.,   0.,   0.,   0.,   2.,   0.,   1.]),\n",
       " array([ 213.        ,  213.46666667,  213.93333333,  214.4       ,\n",
       "         214.86666667,  215.33333333,  215.8       ,  216.26666667,\n",
       "         216.73333333,  217.2       ,  217.66666667,  218.13333333,\n",
       "         218.6       ,  219.06666667,  219.53333333,  220.        ,\n",
       "         220.46666667,  220.93333333,  221.4       ,  221.86666667,\n",
       "         222.33333333,  222.8       ,  223.26666667,  223.73333333,\n",
       "         224.2       ,  224.66666667,  225.13333333,  225.6       ,\n",
       "         226.06666667,  226.53333333,  227.        ,  227.46666667,\n",
       "         227.93333333,  228.4       ,  228.86666667,  229.33333333,\n",
       "         229.8       ,  230.26666667,  230.73333333,  231.2       ,\n",
       "         231.66666667,  232.13333333,  232.6       ,  233.06666667,\n",
       "         233.53333333,  234.        ,  234.46666667,  234.93333333,\n",
       "         235.4       ,  235.86666667,  236.33333333,  236.8       ,\n",
       "         237.26666667,  237.73333333,  238.2       ,  238.66666667,\n",
       "         239.13333333,  239.6       ,  240.06666667,  240.53333333,\n",
       "         241.        ,  241.46666667,  241.93333333,  242.4       ,\n",
       "         242.86666667,  243.33333333,  243.8       ,  244.26666667,\n",
       "         244.73333333,  245.2       ,  245.66666667,  246.13333333,\n",
       "         246.6       ,  247.06666667,  247.53333333,  248.        ,\n",
       "         248.46666667,  248.93333333,  249.4       ,  249.86666667,\n",
       "         250.33333333,  250.8       ,  251.26666667,  251.73333333,\n",
       "         252.2       ,  252.66666667,  253.13333333,  253.6       ,\n",
       "         254.06666667,  254.53333333,  255.        ,  255.46666667,\n",
       "         255.93333333,  256.4       ,  256.86666667,  257.33333333,\n",
       "         257.8       ,  258.26666667,  258.73333333,  259.2       ,\n",
       "         259.66666667,  260.13333333,  260.6       ,  261.06666667,\n",
       "         261.53333333,  262.        ,  262.46666667,  262.93333333,\n",
       "         263.4       ,  263.86666667,  264.33333333,  264.8       ,\n",
       "         265.26666667,  265.73333333,  266.2       ,  266.66666667,\n",
       "         267.13333333,  267.6       ,  268.06666667,  268.53333333,\n",
       "         269.        ,  269.46666667,  269.93333333,  270.4       ,\n",
       "         270.86666667,  271.33333333,  271.8       ,  272.26666667,\n",
       "         272.73333333,  273.2       ,  273.66666667,  274.13333333,\n",
       "         274.6       ,  275.06666667,  275.53333333,  276.        ,\n",
       "         276.46666667,  276.93333333,  277.4       ,  277.86666667,\n",
       "         278.33333333,  278.8       ,  279.26666667,  279.73333333,\n",
       "         280.2       ,  280.66666667,  281.13333333,  281.6       ,\n",
       "         282.06666667,  282.53333333,  283.        ]),\n",
       " <a list of 150 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25a069ab6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.binomial(500, .5, size=1500), bins=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}