Lec 51 - Box and Violin Plots.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The normal imports\n",
      "import numpy as np\n",
      "from numpy.random import randn\n",
      "import pandas as pd\n",
      "\n",
      "# Import the stats library from numpy\n",
      "from scipy import stats\n",
      "\n",
      "# These are the plotting modules adn libraries we'll use:\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "\n",
      "# Command so that plots appear in the iPython Notebook\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now we'll learn about box and violin plots\n",
      "url = 'http:https://en.wikipedia.org/wiki/Box_plot#mediaviewer/File:Boxplot_vs_PDF.svg'\n",
      "\n",
      "# Let's create two distributions\n",
      "data1 = randn(100)\n",
      "data2 = randn(100) + 2 # Off set the mean"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now we can create a box plot\n",
      "sns.boxplot([data1,data2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x191d3b38>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x191e14e0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Notice how the previous plot had outlier points, we can include those with the \"whiskers\"\n",
      "sns.boxplot([data1,data2],whis=np.inf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x19549240>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAECCAYAAADelD2uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19549e48>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# WE can also set horizontal by setting vertical to false\n",
      "sns.boxplot([data1,data2],whis=np.inf, vert = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x195ae128>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAD9CAYAAAB6DlaSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAC2BJREFUeJzt3VuMXedZx+F/PJ4kKpjtSjblNHUuoJ9AULhAKqqDk6qA\nWgkEqcqNAyGllQrlIhykhLY0NykFq0II1IAApfSAQkWqIFQhIKhKcxiJCpULEBdfmpZ4TEBoWsWT\nAySxx8PFjFXH9Rxqrz1rv57nkUaabW99683sWb9Za+2M1zVra2sBYLbtG3sAALYn1gAFiDVAAWIN\nUIBYAxQg1gAF7B9ysbNnV9eeeeZ/h1xyKl796lfFnMOpMGeFGRNzDq3KnIcPH7hmu+cMemS9f//c\nkMtNjTmHVWHOCjMm5hxalTl3wmUQgALEGqAAsQYoQKwBChBrgALEGqAAsQYoQKwBChj0Nxhhr7r/\n/k/k1KmTV7zO/PxczpxZ3fI5KyunkySTycEr2tbCwpEcP37bFa3B7hFrGMCpUyfzxJd69k+unfq2\nzq68lCT56plnrmCNl4cah10i1jCQ/ZNrc/BHv3Pq2zn92NNJckXbOr8GdbhmDVCAWAMUINYABYg1\nQAFiDVCAWAMUINYABYg1QAFiDVCAWAMUINYABYg1QAFiDVCAWAMUINYABYg1QAFiDVCAWAMUINYA\nBYj1HrS4+GgWFx8dewwoZ8x9xw1z96DHH38kSXL06LGRJ4Faxtx3HFkDFCDWAAWINUABYg1QgFgD\nFCDWAAWINUABYg1QgFgDFCDWAAWINUABYg1QgFgDFCDWAAWINUABYg1QgFgDFCDWAAVseVuv1tp8\nko8mOZLkuiQf7L1/ZjcGA+BrtjuyvjXJcu/9WJK3JPnI9EcC4GLb3TD3gSSf3vh8X5Kz0x2H3bCy\ncjorKys5ceKeQdabn5/LmTOrg6w1LdOecWnpZM7NzfbX4ELnXlrN0tLJy/4eqPCaJ8PPubR0MpPJ\nZLD1vhFbxrr3/kKStNYOZD3c79+NoQB4pe2OrNNaW0jyYJJ7e++fmv5ITNtkcjCTycHcddcHBlnv\n8OEDWV5+bpC1pmXaM544cU++/JX/mNr6Q9t33Vxee+jIZX8PVHjNk+HnHOps9HJs9wbja5I8lOQ9\nvfeHd2ckAC623ZH1+5JMktzdWrt748/e2nt/cbpjAXCh7a5Z35Hkjl2aBYBN+KUYgALEGqAAsQYo\nQKwBChBrgALEGqAAsQYoQKwBChBrgALEGqAAsQYoQKwBChBrgALEGqAAsQYoQKwBChBrgALEGqCA\nbe9uztXnxhtvGnsEKGnMfUes96CjR4+NPQKUNOa+4zIIQAFiDVCAWAMUINYABYg1QAFiDVCAWAMU\nINYABYg1QAFiDVCAWAMUINYABYg1QAFiDVCAWAMUINYABYg1QAFiDVCAWAMU4B6MMJCzKy/n9GNP\n78J2XkqSK9rW2ZWXk0NDTcRuEGsYwMLCkUHWmZ+fy5kzq1s+Z2X+dJJkMjl4+Rs6NNzM7A6xhgEc\nP37bIOscPnwgy8vPDbIWVxfXrAEKEGuAAsQaoACxBihArAEKEGuAAsQaoACxBihArAEKEGuAAsQa\noACxBihArAEKEGuAAsQaoACxBihArAEKEGuAAsQaoAD3YNyD7r//Ezl16uRg6+3oJq8rA9zkdQsL\nC0cGuw8izCKx3oNOnTqZ/sUnM3f9dMJ5Kasvrsd6+dmzU1sbrmZivUfNXX8w33zDzbu2veef+lyS\nTGWb59eGq5lr1gAFiDVAAWINUIBYAxQg1gAFiDVAAWINUIBYAxQg1gAFiDVAAWINUIBYAxQg1gAF\niDVAAWINUIBYAxQg1gAFiDVAAWINUIBYAxQws7FeXHw0i4uPjj0GMEX2852b2bubP/74I0mSo0eP\njTwJMC32852b2SNrAL5GrAEKEGuAAsQaoACxBihArAEKEGuAAsQaoACxBihArAEKEGuAAsQaoACx\nBihArAEKEGuAAsQaoACxBihgx7Furb2htfbwNIcB4NJ2dFuv1tqdSX4uyfPTHQeAS9npkfWTSd6W\n5JopzgLAJnZ0ZN17f7C1dsOUZ3mFlZXTWVlZyYkT9wy+9vz8XM6cWR183aFNa86lpZM5tzo3+Lpj\nOXf2xSwtndzye2Wvv+ZDG2rOpaWTmUwmA0x09fMGI0ABOzqyHsNkcjCTycHcddcHBl/78OEDWV5+\nbvB1hzatOU+cuCdPnvrK4OuOZd/+6/PahUNbfq/s9dd8aEPNOY0z56vVN3pkvTaVKQDY0o6PrHvv\nTyV54/RGAWAzrlkDFCDWAAWINUABYg1QgFgDFCDWAAWINUABYg1QgFgDFCDWAAWINUABYg1QgFgD\nFCDWAAWINUABYg1QgFgDFCDWAAXM7A1zb7zxprFHAKbMfr5zMxvro0ePjT0CMGX2851zGQSgALEG\nKECsAQoQa4ACxBqgALEGKECsAQoQa4ACxBqgALEGKECsAQoQa4ACxBqgALEGKECsAQoQa4ACxBqg\nALEGKECsAQoQa4ACZvaGuUzX6oun8/xTn9vV7SWZyjbX1z40+LowS8R6D1pYODLoevPzczlzZnXL\n56ysrH+rTSYHB932ukOD/zfBrBHrPej48dsGXe/w4QNZXn5u0DWBV3LNGqAAsQYoQKwBChBrgALE\nGqAAsQYoQKwBChBrgALEGqCAa9bW1saeAYBtOLIGKECsAQoQa4ACxBqgALEGKECsAQoY9OYDrbVv\nSnJ/koNJXk7yC733/xpyG0NorU2S/EWSA0muTfLrvfd/GneqzbXWbkny9t77rWPPcl5rbV+SP0ry\n+iQvJXlX7/1L4061udbaG5L8bu/9TWPPcimttfkkH01yJMl1ST7Ye//MuFN9vdbaXJI/S/K6JGtJ\nfqn3/u/jTnVprbVvTfKFJG/uvT8x9jyX0lr7lyQrGw+/3Ht/52bPHfrI+l1J/rn3flPWY3jnwOsP\n5deS/GPv/eYktye5d9RpttBa+4MkH0pyzdizXORnklzbe39jkt9M8nsjz7Op1tqdWQ/MdWPPsoVb\nkyz33o8leUuSj4w8z2Z+Msm53vuNSX4ryW+PPM8lbfzw+5MkL4w9y2Zaa9cnSe/9TRsfm4Y6GTjW\nvffzYUnWjxCeGXL9Af1+kj/d+Hw+yf+NOMt2FpP8cmYv1keT/H2S9N4/n+SHxx1nS08meVtm72t4\noQeS3L3x+b4kZ0ecZVO9979J8u6NhzdkdvfxDyf54yT/PfYgW/jBJK9qrf1Da+2zG2d/m7rsyyCt\ntXcm+dWL/vj23vsXWmufTfL9SX7ictcfyjZzfluSTya5Y/cne6Ut5vyr1trNI4y0nW9J8uwFj1db\na/t67+fGGmgzvfcHW2s3jD3HVnrvLyRJa+1A1sP9/nEn2lzvfbW19rEktyR5+8jjfJ3W2u1ZP0t5\nqLX23szuD+kXkny4935fa+17kvxda+11m+1Dlx3r3vt9Se7b5O/e3FprSf42yXdf7jaGsNmcrbUf\nSPKXSX6j9/7Yrg92ka2+njPq2axf8z9vJkNdSWttIcmDSe7tvX9q7Hm20nu/vbV2V5LPt9a+t/c+\nS2en70iy1lr7sSQ/lOTjrbWf7r3/z8hzXeyJrJ/1pff+xdbaV5N8e5KnL/Xkod9gfG+S/+y9fzLr\nPzVm8lSutfZ9WT96+dne+7+NPU9Ri0l+KskDrbUfSfKvI89TWmvtNUkeSvKe3vvDY8+zmdbazyf5\nrt7772T98uG5jY+ZsfGeWZKktfZwknfPYKiT9R8qr0/yK62178j62eqml20GjXXWjww/3lr7xSRz\nG8PMog9l/f8C+cP1E4Cc7r3fMu5IW1rb+Jglf53kx1trixuPZ/W1vtCsfQ0v9L4kkyR3t9bOX7t+\na+/9xRFnupRPJ/lYa+2RrL/fc0fv/aWRZ6rqviR/3lp7dOPxO7Y6O/Wv7gEU4JdiAAoQa4ACxBqg\nALEGKECsAQoQa4ACxBqgALEGKOD/AXSzg4KcAUfkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1ad95400>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# While box plots are great, they can sometimes not give the full picture\n",
      "\n",
      "# Violin/Viola plots can combine the simplicity of a box plot with the information of a kde plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's create an example where a box plot doesn't give the whole picture\n",
      "\n",
      "# Normal Distribution\n",
      "data1 = stats.norm(0,5).rvs(100)\n",
      "\n",
      "# Two gamma distributions concatenated together (Second one is inverted)\n",
      "data2 = np.concatenate([stats.gamma(5).rvs(50)-1,\n",
      "                        -1*stats.gamma(5).rvs(50)])\n",
      "\n",
      "# Box plot them\n",
      "sns.boxplot([data1,data2],whis=np.inf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1fe246a0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAECCAYAAADuGCyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x200a70f0>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# From the above plots, you may think that the distributions are fairly similar\n",
      "# But lets check out what a violin plot reveals\n",
      "sns.violinplot([data1,data2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x203835c0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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74GsB0LTL44SiKGZVVQ2ZAhgOh7nr7tvZsnkj7rLR2LzG9HmJtrO6S3CXjaFq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\nQuQICXQhhMgREuhCCJEjJNCFECJH/H9SdYblKHJrLgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x201cc518>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Wow, quite revealing!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also change the bandwidth of the kernel used for the density fit of the violin plots if desired\n",
      "sns.violinplot(data2,bw=0.01)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x24fdee80>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BbOjERBJFERYW5jE/fxM3b97E17/+VVy8+CjAOIz6rbCO3Q3Nqu3Z+0gpEC4+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\nZBu+6avCAEhwzcyjR5PIh2b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Wd1PTs7jv+ffh3nufC8e5B+VyeZ/eliAUJOgEsQnCMMQj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x251070f0>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Much like a rug plot, we can also include the individual points, or sticks\n",
      "sns.violinplot(data1,inner=\"stick\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x25a33668>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x25a33320>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Next up: Multiple Regression Plots!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}