{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<div id=\"toc\"> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "rnd.seed(42)\n",
    "\n",
    "# To plot figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Refresher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.labri.fr/perso/nrougier/teaching/matplotlib/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. More about that later. \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo1: Plotting a line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NQ+rx9A2DuGF4F40vEBG/oaA/A4l7M5gSG0/SvkwujzyLGddF0r65hpCJiH9R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\nKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24685a3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 2, 3, 4]\n",
    "plt.plot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have a set of points and you need to plot it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lVHuS7ExyLfA24CvTzjaQJbpfTFesPj4N/Ctw7TRDDS3Jc5Nce/LvcJIFuqsvvjztbAP6\nDPDu1Z/x5wHvBe4b6mDnWzHeDPzdKdt/QfcK7i1TSTOwJPuAG+jWe4+uzswAbqiq0dSCDavolkg+\nTTd5WAbeU1VfmGqqgVTVCrBycjvJo8DxqppML9WW2E132e/LgCfoXph+U1UdmmqqYd1G91neh4Dj\nwD3AgaEO5mebSFKDzqtlE0lSP5a3JDXI8pakBlnektQgy1uSGmR5S1KDLG9JapDlLUkNsrwlqUH/\nD4BQbi7somNnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443bda128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1, 3, 4, 6]\n",
    "y = [4, 3, 2, 9]\n",
    "\n",
    "plt.plot(x, y, 'bo')\n",
    "plt.show()\n",
    "\n",
    "# The 3rd argument is optional and default draws a 'line with blue color'. We have changed that to a 'dot'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 2: Plotting sine and cosine waves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KBUZf4vVEjEHxkq9tBP52FqZSKgXoZs+4tJrv5Z/2cTLtIt8+0Atvj9q53qJcfn+dgKwE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\nIiKiSApGREQU+RctUj7xN2wJkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443b8a5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.linspace(-np.pi, np.pi, 256, endpoint=True)\n",
    "C,S = np.cos(X), np.sin(X)\n",
    "\n",
    "plt.plot(X, C)\n",
    "plt.plot(X, S)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with shapes in a plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7ic+72LqoWbom99YJoJnd7rjYynjLt+YZ+Kro1tG83eFtShYqaTWH766jq/zT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\nLRhKKaXcogVDKaWUW7RgKKWUcsv/A4UQut9a1GFyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443a38978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, C, 'r--')\n",
    "plt.plot(X, S, 'g--')\n",
    "plt.show()\n",
    "\n",
    "# A few examples:\n",
    "# To get squares: '<color-initial>s'\n",
    "# To get triangles: '<color-initial>^'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Controlling the minute parameters related to the plots using the `setup()` command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BihXxxlJNShgiqeKrr0ovaUyYEPYrktRz6KHQuzds2QJjxsQdTbXoEyeSKm6+Gdatg2OO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\nQ0REEqKEISIiCVHCEBGRhChhiIhIQv4fAEd5/gImBzUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24439c9f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines = plt.plot(X, S)\n",
    "# use keyword args\n",
    "plt.setp(lines, color='r', linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using labels, titles and annotations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nHbjzTrfOrG4dOlT3eB0xomRbaQ40s1Jw8cWweDEMGQIbbZR1NZZHRxyR5vN8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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443948908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines = plt.plot(X, S)\n",
    "# use keyword args\n",
    "plt.setp(lines, color='r', linewidth=2.0)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('It is a Sine wave!')\n",
    "\n",
    "# Note: Annotation is not essential majority of the times,\n",
    "# there's always a trial&error invovled in getting it right\n",
    "plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 3: Working with subplots\n",
    "\n",
    "Below are the few ways in which a subplot can be created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IfI81ZCIiQTCQiYgEwUAmIhIEA5mISBAMZCIiQTCQiYgEwUAmIhIEA5mISBAMZCIiQTCQ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24438807b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AEQQZAIwgyABgBEEGACMIMgAYQZABwAiCDABGEGQAMIIgA4ARBBkAjCDIAGAEQQYAIwgy\nABhBkAHACIIMAEYQZAAwgiADgBEEGQCMIMgAYARBBgAjCDIAGEGQAcAIggwARhBkADCCIAOAEQQZ\nAIwgyABgBEEGACMIMgAYQZABwAiCDABGEGQAMIIgA4ARBBkAjCDIAGAEQQYAIwgyABhBkAHACIIM\nAEYQZAAwgiADgBEEGQCMIMgAYARBBgAjCDIAGEGQAcAIggwARjh+v3/qN3ac/yRdSdzh4DFX7Pf7\nF0z3TplrJNiU5zqiIAMAEodTFgBgBEEGACMIMgAYQZABwAiCDABGEGQAMIIgA4ARBBkAjCDIAGDE\n/wO850PqJT3DzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f244382add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(1,2,1)',ha='center',va='center',size=22,alpha=.5)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(1,2,2)',ha='center',va='center',size=22,alpha=.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443785128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(2,2,1)',ha='center',va='center',size=20,alpha=.5)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(2,2,2)',ha='center',va='center',size=20,alpha=.5)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(2,2,3)',ha='center',va='center',size=20,alpha=.5)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.text(0.5,0.5, 'subplot(2,2,4)',ha='center',va='center',size=20,alpha=.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 4: A generic multiplot example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BJ58czetLoqnvXTxUvPNp1KgwjXD+fPjiF6G6Orev7x7mdw8bFqYsSlHSfO/i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\nkRRS8RYRSSEVbxGRFFLxFhFJof8PJKVmNtYvA04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24438f6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A generic example is as follows:\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x, y, 'r--')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(y, x, 'g*-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 5: Adding legends to the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NOtsdjeMlpmTy9oKdjO7SmF4tynBJUgSGPweuPOsmXaNED45oQ/UAPx77dmOl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\nV1nhMcYY4yorPMYYY1z1/11eSacfC9dJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443b545c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, S, label=\"Sine\")\n",
    "plt.plot(X, C, label=\"Cosine\")\n",
    "plt.legend(loc=2); # upper left corner\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Sine and Cosine Waves')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Manipulating and Saving an image**\n",
    "\n",
    "We can manipulate the aspect ratio, size and DPI (Dots per inch) and save the give figure. We'll save the above sine-cosine plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sine_cosine\n"
     ]
    },
    {
     "data": {
      "image/png": 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vZBXqV/HhnaWHuZpevAYpGK4IAaFAjpTyaJ7b9gG3OhJqIIS4LIQ4KoQYJ4S4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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443798978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, S, label=\"Sine\")\n",
    "plt.plot(X, C, label=\"Cosine\")\n",
    "plt.legend(loc=2); # upper left corner\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Sine and Cosine Waves')\n",
    "save_fig(\"sine_cosine\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bar_chart.png\t\t   histogram.png  sine_cosine.png\r\n",
      "box_and_whiskers_plot.png  q_q_plot.png   stacked_bar_chart.png\r\n",
      "grouped_bar_chart.png\t   scatter.png\r\n"
     ]
    }
   ],
   "source": [
    "! ls ./images/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing image and displaying it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "img = mpimg.imread('./images/sine_cosine.png') #Reading and saving the image as a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2443ab6198>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CpKq8lGHTnrEdI1S0SCi1qxY2rYlUL6JVBSJeDaYWNq/LXKiQ0c1Ou2mRUOqabjBmlu0U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\ntEgopZRKSYuEUkqplLRIKKWUSun/A2n4+7XbMY4MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24437a9668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 6: A Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure bar_chart\n"
     ]
    },
    {
     "data": {
      "image/png": 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SWjJQkqSWDJQkqSUDJUlqyUBJkloyUJKklv4XkXrvVCVEL0MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443ac0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [3, 10, 7, 5, 3, 4.5, 6, 8.1]\n",
    "N = len(y)\n",
    "x = range(N)\n",
    "width = 1/1.5\n",
    "plt.bar(x, y, width, color=\"blue\")\n",
    "save_fig(\"bar_chart\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 7: A Stacked Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure stacked_bar_chart\n"
     ]
    },
    {
     "data": {
      "image/png": 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RZGW/7wpgRZI9gF9W1S/HGU6SNL2GjqBOA24BXgO8qP/zaeMKJUnS0GnmpwOnjzWJJEkj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24438647b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "data1 = np.array(range(n)) + np.random.rand(n)\n",
    "data2 = np.random.rand(n) + 1\n",
    "\n",
    "bar_locations = np.arange(n)\n",
    "plt.bar(bar_locations, data1)\n",
    "plt.bar(bar_locations, data2, bottom=data1, color='r')\n",
    "plt.title(\"Stacked Bar Charts\")\n",
    "save_fig(\"stacked_bar_chart\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 8: A Grouped Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure grouped_bar_chart\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24437ce320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "data1 = np.array(range(n)) + np.random.rand(n)\n",
    "data2 = (np.array(range(n)) + np.random.rand(n)) / 2\n",
    "\n",
    "bar_width = 0.4  # default: 0.8\n",
    "bar_locations = np.arange(n)\n",
    "\n",
    "plt.bar(bar_locations, data1, bar_width)\n",
    "plt.bar(\n",
    "\tbar_locations - bar_width, \n",
    "\tdata2, \n",
    "\tbar_width, \n",
    "\tcolor='r'\n",
    ")\n",
    "\n",
    "plt.title(\"Grouped Bar Charts\")\n",
    "save_fig(\"grouped_bar_chart\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 9: Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure scatter\n"
     ]
    },
    {
     "data": {
      "image/png": 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kST3QuUJX7TEKjnH0yde0E6WuiaDUqLP1CFTHGBstx66gK3mFxI0So9hy13K8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\nA8UwDMNkEjZQDMMwTCb5/wFzpvA4tfTOvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f244392f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1024\n",
    "X = np.random.normal(0,1,n)\n",
    "Y = np.random.normal(0,1,n)\n",
    "\n",
    "plt.scatter(X,Y)\n",
    "plt.title(\"Scatter Plot\")\n",
    "save_fig(\"scatter\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 10: Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243e9f5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import normal\n",
    "gaussian_numbers = normal(size=1000)\n",
    "plt.hist(gaussian_numbers)\n",
    "plt.title(\"Gaussian Histogram\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "save_fig(\"histogram\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 11: Q-Q plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure q_q_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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JqGn6GDiR0FCwVWX7VZISyYgoCaotYaJmgN+XWaf/wZLVfgCuAB4AdiZMVfSn\njEYkIpWJkqB6Al+7e0n8wlizwp1TGpVIipQQrk1fTuiyeTmhgZlqmkSyX5RRtF8C5d093ia2TiSr\nfAx0As4lTBb5PnALSk4itUWUBFXRRf6mwOoUxCKSEisJl/P2A2YTLuu9xm9nPhaR7FbpJb64lhsO\n3GBmq+JWNwQOAj5IQ2wiyXMHM54BLiK0xTgXuJnyT/sj7VdEMiKZM6gOsYcRBkp0iHvsTrhyck6a\n4hNJyoIFC/jTSSfxJ2Dz9u15/fXXecCd5iq0Fam1Kj2DKm25YWYPARe7+4q0RyWSpLVr13LnnXdy\nzTXXAHDLLbdwySWXsNFGG2U4MhGprij3oG4ldM8FwMy6m9loMxtgZg1TH5rUCzvuGGqNqvB4w4z9\nGzfmiiuuoPuqVcxetYrL//UvNmrcOOm6KBHJXlES1IOE+86Y2c7A04QRfBcA16c+NKnrDj7tNPj2\n28ivW0q4v/QHYDnhg/gU69s8/0pJSqRWi5KgSkfqApwGvOPuxwFnAX9LdWBS9zX54YdI25cQRuS1\nBh4l9GuaDagRmUjdFKVQtyFQFHveFXg+9vxzoEUqgxIpayahT9ObwBGEPk17ZTQiEUm3KGdQHwN9\nzOwPhAT1Ymz5joSrLiIpt5JwprQ/MJdwnXkaSk4i9UGUBHUFYTbzAmCsu8+MLT8JeDfFcUk9sLpZ\ns4TrnybUNdxKmGdrHilqIigitULS/9fd/TVgW6C5u58bt+pewtUXkUjemTABdtjhN8sXEP7qORnY\nEniD0OE2cTorh+qYRGq1SH+Munuxu/9UZtl8d/8+tWFJvfHNN78Wxa4tKuLmm26i3aabMmXTTbn1\n1lt5v6iIw1VkK1IvJRwkYWbPAD3cfXnseYXcXYOppMpef/11+vTpw6xZszj55JMZNmwYu+yyS6bD\nEpEMqmwU3w+s7/UUbUxwFZlZAXAIsC626Bt3b10T7y1J6NYNpkxJya46AUsIgyBGEeqYngFOfPLJ\nlOxfRGq3hAnK3XuV97wGXOju99fg+0kyUpicSggj8q4gFNteAVxFaNusLrYiAtHqoKS+S1Fymklo\nu/4WYTaIEUD7lOxZROoS8yT+UjWzTQhXYk4ltHt34AvgceB2d/8lZQGFS3ztCbOnzwMGuntBmW16\nA70BWrRoccC4ceNS9fYpVVhYSNOmdac9XqcuXSpsCpaMQuAa4E5ga9YPHy+7TwemTZ1ajXeqG+ra\n5yfVdHwk2KhzAAAX4klEQVQSy+bj06VLlxnu3rGy7SpNUGbWCHidUCv5ImF2GQPaAccC7wGd3H1d\nhTuJwMwOjr1HEXAGcDewr7t/Xt72HTt29OnTp6firVOuoKCAzp07ZzqM1Kni3HZOqGm6GPgKOA+4\niUqGjesSX937/KSYjk9i2Xx8zCypBJXMMPPehL5P+7v7n9x9gLv3j43a2x/Yk1DAmxLu/o67r3D3\nNe7+MGF2m+NStX+phq5dI79kPqGm6c/AVoSapvuoQk2TiNQ7ySSo04Ah7j6r7Ap3/xi4ETg91YHF\nvw0Vt5uXmjR5ctJJqohwltQOmArcBswADo/bpsJzJJ09iQjJJaj2wKsJ1k8mRVOjmdlWZnaMmTUx\ns0ZmlgMcyfp5/yTTJk+utED2tWnT2K9dOwYAx/75z8xZuJDL3NmozHbTpk5Vga2IVCiZBLU1oVyl\nIksIV29SYSNCb6klhAloLwJOdvdPUrR/SaMlS5bQq1cvOnXqxMqVK5k0aRITJ05k5513znRoIlIL\nJZOgGrK+aLY8JbFtqs3dl7j7ge6+ubtv5e6HuPsrqdh3vTdmDDRqVOXutRU+unWjpKSE+++/n9at\nWzN69Gj69+/P7NmzOeGEEzL9U4tILZZMHZQBo81sTQXrN05hPJIOY8ZAjx5p2fVHU6aQu802vL1s\nGUceeST5+fm0b6+qJhGpvmQS1MNJbPNIdQORNBo4MOW7LAQGA0OBrZctY9SoUZx99tmY2qyLSIpU\nmqBqeIojSYeFC1O2KweeItQ0fU2oL7gJ2KZnz5S9h4gIqPdb/ZCiWcG/BE4ETgG2IRSojYw9FxFJ\nNSWo+mDIkGq9vIhQ7Nae0E75dkJN02GlG1ShgFdEpDKaLLY+yMkJX3v2hOLiSC+dRmiXPIdw5jQU\n2GDQeNeuoTZKRCTFdAZVX+TkwLp1SXej/X7xYnqefTadgV9ateLZZ5/lCXd2LrutkpOIpIkSlGyg\npKSEkSNH0qZNG8aOHcuVV17JrFmzOP744zMdmojUM5ESlJn91cxGmtlTZvZM/CNdAdZ5Y8ZAq1bQ\noEH42q3bb4thN98c8vKgefP1yzbeuOrFtRXUKX344YccccQR/OMf/2Dvvffmww8/ZMiQIWy66aY1\nekhERCBCgjKzW4HRQCvgZ0IL+PiHRDVmDPTuDQsWhMtlCxaU3xSwsBBGjIAf4g5zUVHV33f27A2S\n1IoVK+jXrx8HHHAAn332GQ8//DBTp06lbdu2VX8PEZFqijJI4mzgb+4+IV3B1DsDB8KqVZl579mz\ncXcmTpxI3759+eabb+jduzc33ngj22yjgeMiknlRElQD4IN0BVIvpbCANqovgQtPOIHnn3+effbZ\nhwkTJnDIIYdkLB4RkbKi3IMaCaRnQrf6KkUFtFEUATcQ+jS99tpr3HHHHUyfPl3JSUSyTpQzqK2A\nM82sO/ARsDZ+pbtfnMrA6oUhQ8I9qBq6zFcA5BFqmk7dfHOGzp7NTjvtVCPvLSISVZQzqHaES3xF\nQBugQ9wjJQ0L652cHBg5Elq2DKPrWrYsf1aGpk2hTx9oFtcovXHjpN/me8INxC7AauC5nXdmwvLl\nSk4iktWSPoNy9y7pDKTeyslZP9NDZfLzI+26pKSE++67j/79+7Ny5UquvPxyBg4cqGHjIlIr1O9C\n3bI1SGPGJPe6vLz1zf8aNQq1SxXtJ37bBg2gSZOK65OaN08+hkp88MEHHH744eTm5rLvvvuqpklE\nap1Ic/GZWQvgAsLlPgdmA/nuvjgNsaVXaQ1S6f2fBQvC95D4jCYvL9QklSou3rB2KW4/uz/+ODz9\n9Pp17rCmor6PhDqnc8+tPIYEVqxYwdVXX82wYcNo1qwZjzzyCD169FCfJhGpdaIU6h4OfAacCfxC\nuJ2RA3xqZoemJ7w0Kq8GadWqypv7jRxZ+b5j+9lh0qTocRUVVanBoLvzxBNP0LZtW4YOHcr555/P\nvHnzOOuss5ScRKRWinIGdRswFsh19xIAM2sA3EPowHBYgtdmn4pqkCqrTUp2NvCFCzH3aDElG0MZ\nX3zxBRdeeCEvvPAC++67r2qaRKROiHIPal/g9tLkBBB7fgewX6oDS7uKapAqq01q2DDp/XuDKt7i\nS7I+as2aNQwZMoT27dvz+uuvc+edd/Lee+8pOYlInRDlN+gyYNdylu9KmJuvdhkyBMoOGNh008qb\n+5Xep0oktp9vTzwxelyNGyfVYHDq1Knss88+DBo0iBNOOIG5c+dyySWX0KiRWnyJSN0QJUGNAx4w\nsxwz2zX26AHcT7j0V7uUV4M0cmTlgxPy80NNUumZVMOGoXapnP18dsklG25bOgt5RZo1gwcfTBjD\n4sWLOeusszjqqKMoKiri+eef5/HHH2fHHXeMeABERLJblD+3/wUY8GDsdUYo2h0B9E99aDUgSg1S\nvPz85GuSomybQGmfpgEDBrBy5UoGDRrElVdeySabbFLtfYuIZKMohbpFQF8zGwDsFlv8ubtnaDru\n+uP//u//yM3N5d1336VLly7k5+fTpk2bTIclIpJWUYaZtzOz1u6+yt1nAr8DRprZADNLcuSARLFi\nxQouvfRSOnbsyPz58xk9ejRTpkxRchKReiHKPagHiY3WM7OdgaeAbQiFu9enPrT6y92ZMGECbdq0\nYdiwYfTu3Zu5c+eSk5OjmiYRqTeiJKg2wPux56cB77r7ccBZwN9SHVh99fnnn3Pcccdx+umns912\n2/H2228zYsQItt5660yHJiJSo6IkqIaEQREAXYHnY88/B1qkMqj6aM2aNVx//fXstddevPnmmwwd\nOpT33nuPgw8+ONOhiYhkRJRRfB8DfczsWUKCGhBbviOwNNWB1SevvvoqeXl5zJs3j9NPP50777xT\nw8ZFpN6LcgZ1BXA+oe/d2NhACYCTgHdTHFe9sHjxYnr06EHXrl1Zu3YtL7zwAuPHj1dyEhEh2jDz\n18xsW2ALd/8pbtW9gIaaR1BcXPxrTdOqVau46qqrGDBggGqaRETiRJoXx92LgZ/KLJufyoDquvia\npqOOOor8/Hxat26d6bBERLJO5DqouO+7m9lo1UElZ/ny5fTt23eDmqbJkycrOYmIVKA6dVBPk4Y6\nKDPbxsyeNLOVZrbAzM5M1b4zwd0ZP348bdq04T//+Q+5ubnMmzdPNU0iIpWIcomvbB3UO+5+nJl1\nAR5i/ai+6hpOGM7egtDi4zkz+9DdZ6Vo/zXmm2++4Y9//CMvvfQS++23H0899RQHHXRQpsMSEakV\noiSotNdBmdlmwKnAXu5eCLxhZk8TioFrzYS0a9as4ZZbbuG6666jSZMmDBs2jLy8PLXCEBGJwDzJ\nrq9m9jbwGvAs8DJwkLvPjLV7H+/uO1c7GLP9gDfdfdO4ZZcBnd39xLhlvYHeAC1atDhg3Lhx1X3r\nlJkxYwbDhg3jq6++4ogjjqBv3740b94802FlpcLCQpo2bZrpMLKWjk9iOj6JZfPx6dKlywx371jZ\ndlH+pL+CMP/e5cCoNNVBNQWWl1m2HNg8foG7jwRGAnTs2NE7d+6corevukWLFnHZZZfx3//+l912\n240XX3yRjTfemGyILVsVFBT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243eb95860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import numpy.random as random\n",
    "\n",
    "p = random.poisson(lam=10, size=4000)\n",
    "m = np.mean(p)\n",
    "s = np.std(p)\n",
    "n = random.normal(loc=m, scale=s, size=p.shape)\n",
    "\n",
    "a = m-4*s\n",
    "b = m+4*s\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(np.sort(n), np.sort(p), 'o', color='red')\n",
    "plt.plot([a,b], [a,b], 'k-')\n",
    "plt.xlim(a,b)\n",
    "plt.ylim(a,b)\n",
    "plt.xlabel('Normal Distribution')\n",
    "plt.ylabel('Poisson Distribution with $\\lambda=10$')\n",
    "plt.grid(True)\n",
    "save_fig(\"q_q_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 12: Box and Whiskers Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure box_and_whiskers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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VJmATwPDw8IzMhc2b7581025r46CrkKSZ0atZfNuByX/eHwFs28P6I4Dtk8NJkqQJvQqo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\nqZUMKElSKxlQkqRWMqAkSa30/wENxeCBkCzONQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443a8a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating data\n",
    "data1 = random.normal(100, 10, 200)\n",
    "data2 = random.normal(80, 30, 200)\n",
    "data3 = random.normal(90, 20, 200)\n",
    "data4 = random.normal(70, 25, 200)\n",
    "   \n",
    "data_to_plot = [data1, data2, data3, data4]\n",
    "\n",
    "plt.boxplot(data_to_plot)\n",
    "plt.title(\"Box-and-Whisker Plot\")\n",
    "save_fig('box_and_whiskers_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 13: Plotting Numeric vs Numeric Data with Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Loading the iris dataset from sklearn module\n",
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So the 4 columns that we have are \n",
    "data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5],\n",
       "       [ 4.9,  3. ],\n",
       "       [ 4.7,  3.2],\n",
       "       [ 4.6,  3.1],\n",
       "       [ 5. ,  3.6]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll be looking at SkLearn API extensively in the next session\n",
    "# For now just follow along\n",
    "# These are the first 2 columns and 5 rows that we are using\n",
    "data.data[:5, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We'll try to plot these two numeric columns against each other\n",
    "X = data.data[:, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XnQ/ptthHUqowOtRPeQL6JdZ+iTMXnQ/pNrVhZNJP4x/6JdZ+iTMXnQ8phSqM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\nf7ucXD6zHxh4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24372dc320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[:, 0], X[:, 1], 'bo')\n",
    "plt.xlabel(\"Sepal Length (cm)\")\n",
    "plt.ylabel(\"Sepal Width (cm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 14: Plotting Numeric vs Categorical data with Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2436dde630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Baseline\", \"System\"]\n",
    "data =   [3.75               , 4.75]\n",
    "error =  [0.3497             , 0.3108]\n",
    "\n",
    "xlocations = np.array(range(len(data)))+0.5\n",
    "width = 0.5\n",
    "plt.bar(xlocations, data, yerr=error, width=width)\n",
    "plt.title(\"Average Ratings on the Training Set\")\n",
    "plt.yticks(range(0, 8))\n",
    "plt.xticks(xlocations+ width/10, labels)\n",
    "plt.xlim(0, xlocations[-1]+width*2)\n",
    "plt.gca().get_xaxis().tick_bottom()\n",
    "plt.gca().get_yaxis().tick_left()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 18: Exploring Titanic\n",
    "\n",
    "* Import from Titanic training and testing CSV files (kaggle) to pandas dataframes\n",
    "    * Should result in two variables - train, test\n",
    "* Extract the age column as numpy array\n",
    "    * Should result in two variables - train_age, test_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from statsmodels import robust\n",
    "from math import sqrt\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"data/titanic_train.csv\")\n",
    "test = pd.read_csv(\"data/titanic_test.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 16: Correlation graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ATcCLQB9gtrs/XGv557eUy6N6hs8mKCLtm8Ec5jKr49PJ5n4HzAbuBYYDj5vZJ919S1sL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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2428af1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm as cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "cmap = cm.get_cmap('jet', 30)\n",
    "cax = ax1.imshow(train.corr(), interpolation=\"nearest\", cmap=cmap)\n",
    "ax1.grid(True)\n",
    "plt.title('Titanic Feature Correlation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 17: Facet plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_age = np.array(train.Age)\n",
    "test_age = np.array(test.Age)\n",
    "test_rows = len(test_age[~np.isnan(test_age)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For `train_age`, calculate\n",
    "    - the mean\n",
    "    - the median\n",
    "    - the mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 29.6991176471\n",
      "Median: 28.0\n",
      "Mode: 24.0\n"
     ]
    }
   ],
   "source": [
    "train_age_mean = np.nanmean(train_age)\n",
    "train_age_median = np.nanmedian(train_age)\n",
    "train_age_mode = stats.mode(train_age, axis=None)[0][0]\n",
    "\n",
    "print(\"Mean:\", train_age_mean)\n",
    "print(\"Median:\", train_age_median)\n",
    "print(\"Mode:\", train_age_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 19: Exploring a synthetic dataset\n",
    "\n",
    "* Create a `numpy` array by\n",
    "    - Sample 90 points from N(15, 5)\n",
    "    - Sample 10 points from N(30, 10)\n",
    "    - Combine the two\n",
    "    - Shuffle the list\n",
    "    - Call it `age2`\n",
    "* For the array, calculate\n",
    "    - the mean\n",
    "    - the median\n",
    "    - the mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age2_1 = np.random.normal(15, 5, 90)\n",
    "age2_2 = np.random.normal(30 ,10, 10)\n",
    "age2 = np.concatenate((age2_1, age2_2))\n",
    "np.random.shuffle(age2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 16.9701970128\n",
      "Median: 14.9236261466\n",
      "Mode: 1.08141888101\n"
     ]
    }
   ],
   "source": [
    "age2_mean = np.average(age2)\n",
    "age2_median = np.median(age2)\n",
    "age_2_mode = stats.mode(age2)[0][0]\n",
    "\n",
    "print(\"Mean:\", age2_mean)\n",
    "print(\"Median:\", age2_median)\n",
    "print(\"Mode:\", age_2_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 21: Titanic Embark Mode\n",
    "\n",
    "* From titanic `train` dataset, extract the `embark` column as numpy array\n",
    "    - name it `train_embark`\n",
    "* Create frequency table\n",
    "* Calculate mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_embarked = np.array(train.Embarked)\n",
    "test_embarked = np.array(test.Embarked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C  :  168\n",
      "Q  :  77\n",
      "S  :  646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = LabelEncoder()\n",
    "train.Embarked.fillna(\"S\", inplace=True)\n",
    "train_embarked = np.array(enc.fit_transform(train.Embarked))\n",
    "test_embarked = np.array(enc.transform(test.Embarked))\n",
    "unique, counts = np.unique(train_embarked, return_counts=True)\n",
    "[print(enc.classes_[i], \" : \" ,counts[i]) for i in range(3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_embarked_mode: S\n"
     ]
    }
   ],
   "source": [
    "train_embarked_mode = stats.mode(train_embarked)[0][0]\n",
    "print(\"train_embarked_mode:\", enc.classes_[train_embarked_mode])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean_RMSE     : 14.171\n",
      "Median_RMSE   : 14.341\n",
      "Mode_accuracy : 64.59%\n"
     ]
    }
   ],
   "source": [
    "mean_RMSE = sqrt(mean_squared_error(test_age[~np.isnan(test_age)], [train_age_mean]*test_rows))\n",
    "median_RMSE = sqrt(mean_squared_error(test_age[~np.isnan(test_age)], [train_age_median]*test_rows))\n",
    "mode_accuracy = accuracy_score(test_embarked, [train_embarked_mode]*len(test_embarked))\n",
    "\n",
    "print(\"{:<13} : {:.3f}\".format(\"Mean_RMSE\", mean_RMSE))\n",
    "print(\"{:<13} : {:.3f}\".format(\"Median_RMSE\", median_RMSE))\n",
    "print(\"{:<13} : {:.2f}%\".format(\"Mode_accuracy\", mode_accuracy*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 22: Exploring with histograms and boxplots\n",
    "\n",
    "* For `train_age`, `age2` and `train_embark`\n",
    "    - Plot histograms\n",
    "    - Plot boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2428190828>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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w1roLHwcoFosdBtMJ/jmAIAjSQRD8syAITgZBkAb+OYMp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2428518d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "\n",
    "plt.subplot(231)\n",
    "plt.title(\"train_age\")\n",
    "sns.distplot(train_age[~np.isnan(train_age)], kde=False,)\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.title(\"train_age\")\n",
    "sns.boxplot(train_age[~np.isnan(train_age)])\n",
    "sns.swarmplot(train_age[~np.isnan(train_age)], color=\"brown\", alpha=.5)\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.title(\"age2\")\n",
    "sns.distplot(age2, kde=False)\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.title(\"age2\")\n",
    "sns.boxplot(age2)\n",
    "sns.swarmplot(age2, color=\"brown\", alpha=.5)\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.title(\"train_embarked\")\n",
    "sns.distplot(train_embarked, kde=False)\n",
    "\n",
    "plt.subplot(236)\n",
    "plt.title(\"train_embarked\")\n",
    "sns.boxplot(train_embarked)\n",
    "sns.stripplot(train_embarked, color=\"brown\", alpha=.5, jitter=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 23: Dispersion\n",
    "\n",
    "\n",
    "* For `train_age`, `age2` and `train_embark`, calculate\n",
    "    - Standard deviation\n",
    "    - Interquartile range (IQR)\n",
    "    - Range\n",
    "    - Mean absolute difference\n",
    "    - (optional) Median absolute deviation (MAD)\n",
    "    - Variance\n",
    "    - Entropy\n",
    "    \n",
    "https://en.wikipedia.org/wiki/Qualitative_variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======\n",
      "Std: 14.5163211508\n",
      "IQR: 17.875\n",
      "MAD: 13.3434199666\n",
      "Range: 79.58\n",
      "Variance: 210.723579754\n",
      "Entropy: 6.44007801855\n",
      "=======\n",
      "Std: 7.26722239942\n",
      "IQR: 7.15769284111\n",
      "MAD: 5.43971991053\n",
      "Range: 41.6502966347\n",
      "Variance: 52.8125214026\n",
      "Entropy: 4.52030647757\n"
     ]
    }
   ],
   "source": [
    "for dist in [train_age, age2]:\n",
    "    print(\"=======\")\n",
    "    print(\"Std:\", np.nanstd(dist))\n",
    "    i75, i25 = np.nanpercentile(dist, [75, 25])\n",
    "    print(\"IQR:\", i75 - i25)\n",
    "    mini = np.nanmin(dist)\n",
    "    maxi = np.nanmax(dist)\n",
    "    print(\"MAD:\", robust.mad(dist[~np.isnan(dist)]))\n",
    "    print(\"Range:\", maxi - mini)\n",
    "    print(\"Variance:\", np.nanvar(dist))\n",
    "    print(\"Entropy:\", stats.entropy(dist[~np.isnan(dist)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 24\n",
    "\n",
    "**Task**:\n",
    "\n",
    "* Create a `numpy` array of range(20, 80)\n",
    "    - name it `age3`\n",
    "    - calculate mean: `m`\n",
    "* Create a function that accepts `age3` and another number as an argument and returns `RMS`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "age3 = np.arange(20,80)\n",
    "age3_mean = np.mean(age3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rms(array, number):\n",
    "    return np.sqrt(np.mean(np.square(array - number)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.325318659888097"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rms(age3, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_dataset = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys for the iris dataset are: dict_keys(['target_names', 'target', 'DESCR', 'data', 'feature_names'])\n"
     ]
    }
   ],
   "source": [
    "print (\"Keys for the iris dataset are: {}\".format(iris_dataset.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll explore them one by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (iris_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The targets of the dataset are ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print (\"The targets of the dataset are {}\".format(iris_dataset.target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The feature names or columns of the dataset are ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(\"The feature names or columns of the dataset are {}\".format(iris_dataset.feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print (\"Type of data: {}\".format(type(iris_dataset.data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We'll print a few rows from the dataset: \n",
      "\n",
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]]\n"
     ]
    }
   ],
   "source": [
    "print(\"We'll print a few rows from the dataset: \\n\")\n",
    "print(iris_dataset.data[:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the dataset is (150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of the dataset is {}\".format(iris_dataset.data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: \n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Target: \\n {}\".format(iris_dataset.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris_dataset.data, iris_dataset.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knnmodel = KNeighborsClassifier(n_neighbors=2) # n_neighbors: parameter here to set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=2, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 0, 1, 0, 0, 2, 0, 2, 2, 2, 0, 1, 1, 1, 1, 0, 2, 0, 0, 0, 2, 1,\n",
       "       1, 1, 2, 2, 0, 0, 1, 1, 0, 2, 2, 1, 2, 1, 2])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_transform = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_transform = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo 29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93915375248221555"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgemodel.score(X_test, y_test) # Provides us a test accuracy of 0.94"
   ]
  }
 ],
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