Lecture 51 - Box and Violin Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The normal imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "\n",
    "# Import the stats library from numpy\n",
    "from scipy import stats\n",
    "\n",
    "# Import plotting modules\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Make plots appear in iPython notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'http://en.wikipedia.org/wiki/Box_plot#mediaviewer/File:Boxplot_vs_PDF.svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1 = randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2 = randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109ac24a8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD9CAYAAACLBQ0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b6a5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109c38f60>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAECCAYAAADelD2uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlqRk/h9NFrHTBLyyIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cbaba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=[data1,data2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109e9f4e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAECCAYAAADelD2uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMdySlIzhlqRkDLckJWO4JSkZwy1JyfwfFmxhPXtSjcoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ef8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=[data1,data2],whis=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jspang/anaconda/lib/python3.4/site-packages/seaborn/categorical.py:1613: UserWarning: The boxplot API has been changed. Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a100d30>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD9CAYAAACLBQ0fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a10c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data1,vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a463518>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAECCAYAAADuGCyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJBVh0CWpCIMuSUUYdEkq4n8BTUtyq7B4MdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a39e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normal Dist\n",
    "data1 = stats.norm(0,5).rvs(100)\n",
    "\n",
    "# Two gamma dist. Concatenated together\n",
    "data2 = np.concatenate([stats.gamma(5).rvs(50)-1,\n",
    "                        -1*stats.gamma(5).rvs(50)])\n",
    "\n",
    "# Box plot both data1 and data2\n",
    "sns.boxplot(data=[data1,data2],whis=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a52ec18>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAECCAYAAADuGCyPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbEICXQghbEICXQghbEICXQghbOL/A0Wi787RY9FiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a398390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=[data1,data2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a11aa90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD9CAYAAACVzD2sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a605278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data2,bw=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a385390>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD9CAYAAACVzD2sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAH/JJREFUeJzt3Xl0VFWiLvCvhqRSU5LKPJABgtnMECYBCYbbCooKODUq\nrQ3etofb2na/vq/7PbufvvWeq9dd1/v09r2rxQmnxmsroKCISosDEZnDJCQnhDEEMo+VVKaq8/6o\nJAYEUkmqsk9Vfb+1WCaVSvySVH05tc/e++hUVQUREWmXXnYAIiK6NhY1EZHGsaiJiDSORU1EpHEs\naiIijWNRExFpnHE4n9zd7VYbGtr8lWXEORwWML88zC9PMGcHgj9/YqJdN5j7D+uI2mg0DOfTpWN+\nuZhfnmDODgR//sHi0AcRkcaxqImINI5FTUSkcSxqIiKNY1ETEWkci5qISONY1EREGseiJiLSOBY1\nEZHGsaiJiDSORU1EpHEsaiIijWNRExFpHIuaiEjjWNRERBrHoiYi0jgWNRGRxrGoiYg0jkVNRKRx\nw7q4LZGv3B4PzlxowLGTVaiobkJzawecbR1QVSAuxoL4GAsyU2MxfVw6YmxRsuMSaQqLmgKqodmF\nz/eVYf/x82hr77rifVraOnD2YgOKSiqw6YtjuC4zAfOnZWNqbip0ukFdrJkoJLGoKSDqm9rwya5S\n7DtWDo9H7bu9pfhd2Mf/EM7STVDd7bCPvw8A4CovhNtVB4M5HieQjxPnajE63YE7F05CVqpD1rdB\npAksavIrt9uDL/efxMffKOjq9gD4roRtucsBeG9T3R2Xfl5HA1R3B9wdDX33P43leHZdIW6Ylo07\nF05EhNEw0t8OkSawqMlvzlU24q2tB1FZ1wLAW9DmjPy+EvbV5fffeegMzlTUY9WymUhy2Pyem0jr\nOOuDhs2jqti+5wSeW1fYV9KAt3CHw1Ve2Pd2WdFHeOaNr3C0rHJYX5MoGLGoaViane1Y8+4ufLCj\nGK3ndvj1a/cvendHAzq73Fi7aS++OXzWr/8fIq1jUdOQna6oxzNvfoXSc7UAhn8EPRBn6SaoKvDK\nn/+AT75RoKrqwJ9EFAI4Rk2Dpqoqdh4+i43bj/bN6HCWboLqufL0O7/9f/vGrT34eKcCV0cXlhdM\n5BQ+CnksahoUt9uDjduPYudlww+DOVnoL1/uPwWDXo87FoxnWVNI49AH+azV1Ynn1+/CzsNn4Szd\nBAB9/x1pvScat+8tw5bCEg6DUEjjETX5pKquBS9u3IO6pjYA3x1ByziSBi4dD/9szwmYIgxYNDdX\nShaiQOMRNQ2o9GwNnn2rsK+ktaT3yHrD2y/jfz/9fyWnIQoMFjVd0+6j57Bm/W60d3TDVV54ydxm\nLeg9snZ3NKC8/CwOllRITkTkfxz6oCtSVRVbvy7Btt0n+m4L9PQ7f3jzoyKYoyIwLjtJdhQiv+ER\nNX1Pd7cbf/2oCNt2n4CrvFDaCcOh8HhUrN20D+ermmRHIfIbFjVdoq29C2s27MaBYu8QwmD36ZBJ\n7fbmrD+2EWs27EJdY6vkRET+waKmPo0tLvz57a9RVl4nO8rQqN/tzOds68Tz63fD2RYcf2SIroVF\nTQCAytoWPPdWISprWzR3wnCoahtb8dJ7e9DZ1S07CtGwsKgJZy404N//62s0trQDCI6ThgPp/WNz\n7KvX8caWIrg9HsmJiIaORR3mlLM1+Ms736C+7HPZUfyq94+N6u7At2WV2PDZUa5epKDFog5jR05c\nxIsbdqOz2x0SR9HX8s3hs9i2q1R2DKIhYVGHqUPKBby6eT/cntA/yuwdBtm6U8GuI9zLmoIPizoM\nFZVU4PUP96Pt3I6QOXF4Lf1fLbyz7TC+PcmrxFBwYVGHmaKSCry55QBU1VtgoT7kcTlVBV77YD9O\nV9TLjkLkMxZ1GPn2ZCX+uqUI4X5Orbvbgxc37kFlbcvAdybSABZ1mDhxrhavbt4PT09LtxT/TXIi\nuVwdXXh+/S40NLtkRyEaEIs6DJy92ICX3tsDt5tziftrcrZjzfpdaHV1yo5CdE0s6hBX29iKlzbu\nQWeXW3YUTaqqd+LFjXvQ0cnVi6RdLOoQ1urqxAsbdqOmdLvsKJp29mIDXvtgP19xkGaxqENUd7cb\nr2zai5qG1rCb2TEUxaer8fYnh7h6kTSJFw4IQaqq4p1tR3DqPKeg+aql+F3sKE9FtC0KS2+cIDsO\n0SVY1CHoq6LT2HusPCwWs/iPB+6OBmzfW4ZoqwkFM3NkByLqw6GPEKOcrcGmL74FEJ4LWvzh/S+O\noaiY114k7WBRh5C6pja8tnl/2C9o8Yd1W4tQVl4rOwYRABZ1yOjuduO1zfvg6uiSHSWo9Q4XuT0q\nXn5/L1cvkiawqEPEpi+PobyqCc7STWgpfld2nKDVf6iovaMbazbsQpOzXWIiIhZ1SCgqqUDhwTMA\n0HMhWs4H9pfGlna89B4XxJBcLOogV93gxNufHJIdI6SVFL6BdVsP9u2TQjTSWNRBrKvbjaf/9Cc0\nnfpSdpSQpro7cOTERWzZUSw7CoUpFnUQe+fjQ2htruYUvBGyfW8Z9n57TnYMCkNc8BKkjp+qwtbC\nEtkxwoarvBBuVx3+ptchKc6O7DSH7EgURnhEHYSane1Yt/Wg7Bhhxd3RANXdAbdHxSub9nImCI0o\nFnWQUVUV//XJIdRyRzxpWlo78Mr7e7l1LI0YFnWQ2VF0GsWnOS4t27nKRryxeZ/sGBQmWNRB5EJN\nMzZ/dVx2DIJ3t70v9p7EriNnZUehMMCiDhJd3W68ueUAN7fXDO/vYf1nR3GuslFyFgp1LOog8cFX\nx3GR+05ojtvtwdpN++Bs65AdhUIYizoIHD9VhR1Fp2XHoKtobHFx5SIFFIta45pb2/EWp+Jplqu8\nEK7yQhSfrsZnu0/IjkMhikWtYaqq4u1PDsHp6pQdha6i/8UZtu4swYlz3MOa/I9FrWFfHTiF46eq\nAYCX1QoCqgq8/sF+LoYhv2NRa1R5VeMlU/E4bzo4OF2d3tk5Hs7OIf9hUWtQe2c3Xv/gADwenpwK\nRmXldfjkm1LZMSiEsKg1RlVVbPj7EdQ2tsqOQsOwbVcpSk5Xy45BIYJFrTG7j57DvuPnZccgP3hz\nywE0trhkx6AQwKLWkPNVTVj/2VHZMchPWtu78PqH+7malIaNRa0Rbe1deHXzPj6pQ8zpigZsKeSV\nYWh4WNQa4FFVvPXxQdQ1tcmOQgHw+b6TOHriouwYFMRY1Brw8dcl+LasEgDnS4eqdVsPorrBKTsG\nBSkWtWQHSyqwrd/SY86XDj2u8kK0d3Zj7aZ96Ojslh2HghCLWqLzVU1Y9zH38Qh1vX98K2tb8Pan\nh6By8yYaJBa1JPVNbXhh4250d/PkYTg5WHIBX+w7KTsGBRkWtQStrk68sGE3Wlq5h3G4cZUX4oMd\nx3HsZJXsKBREWNQjrKvbjVc27UVVPU8shSN3RwNUFXjjw/24UNMsOw4FCRb1COp2e/D6B/tx6ny9\n7CgkWUeXGy+9t4evqsgnLOoR4vZ48OaWA/i25yUvp+FRQ7MLL7+/hzNBaEAs6hHg8ahYt/UgDpd+\nt+iB0/AIAEr2fohXN+9DN1ek0jWwqAOsu9uNNz7cj6LiCtlRSIPcHQ0oOVODtz7mNRfp6ljUAdTR\n2Y2X3tuDQz1H0hzuoKspKq7A+m1HWNZ0RUbZAUKVs60DL27cg3OVjX23cbiDruWbI2fh9nhw3+Jp\n0Ot1suOQhvCIOgAqqpvw+z8+dUlJE/liz7flWLe1iJfyokvwiNrPDpdexLqPitDeWgub7DAUlA4U\nV6CtvQs/vmMGzKYI2XFIA3hE7Sfd3W68//m3eHXzPnR2u2XHoSBXfLoaz71VyEuyEQAWtV9U1Tvx\n7FuF+PLAKQA8aUj+UVXnxLN/3YFiXnsx7LGoh8Ht9mD7nhN45o0vUVH93XJgnjQkf2lt78Jzzz2D\nDZ8dQWcXF8aEK45RD9GZCw14Z9th7tdAAefuaEDhwTNQztTgnpumQGQnyo5EI4xFPUjVDU5sLSzB\nQeWC7CgUZqobWvH8+l2YMCYZywomICXeLjsSjRAWtY+q6p34ct9J7D56josSSKrjp6pQfLoKk3JS\nUDBzDHJGxUOn47zrUMaivgaPR0XpuRoUFp3u20yJSAtUFThaVomjZZVITbAjT6Rham4aUhJ4lB2K\nWNSXUVUVFdXNKCqpwP7j59HkbJcdieiaLta24GKtgq07FSTEWjAmPR6j0+OQmRKLpDgrIiP4NA92\n/A0CaHK249T5OpSercWxU1UsZwpatY1tqG1sw95j5X23xUWbkRBrRazdjNhoM6ItJqSlxkDt9sBm\nNsFqjoTVHMll6xoWVkWtqiqanO24WNuC89VNqG1qw4kzNahrapMdjShg6ptdqG92DXg/qzkS0VYT\n7BaTt9TtUXBEmxEfY0VCrAWx0WYY9JzRK0NIFnVbexdqGpyoaWjt+ed9u6quBR1dXDVI1MtVXghz\nRj4AoLZ0O1oz8nERLVe8r0GvQ1KcDSkJdqTG25GeFINRyTGIsUXxZGaABW1Ruz0e1DW2oaquBZX1\nTlTXO1FT70R1QytaXZ3fu3//ByQRefVfnDXQQi23R+0ZD2/BwX63W82RyEyJRWZKLLJSHchKjYXN\nYgpQ4vCk+aJWVRUNzS5cqG3GhZpmXKxtQWVtC6rqWuD2fDdNbqAi5mpBosCoLd2OVlf+JUvd42Is\nyO4p7axUB0YlxyDCaJCYMrhpqqhbXZ2orGvxFnJNMy7Uet/25ZpyLGIiOa703KtvakN9UxuKSrxX\nNtLrdEhNtCMzxVvao5JikJZo54wUH434T6mr2436ZhdqG1pR3W/suKrOiZY2XpGZKBR5eqa99t8T\nR6cDEmKtSE2wIyUhGkkOKxIdNiQ6rLBERXDcux+/FLXHo6LR6YKrvQuuji60ujrhbOuE09WJltZ2\nNDm9/+qbXCxjIgLgXbTTe8L/yInKSz4WGWFAfIwFjmgzoq1RiLFFwW7tmUoYFQlnRxfa2zoRZTLC\nao4M+VL3S1G/unkfjpZVDnxHIiIfdHa5+05cDmTG+HQ8dPuMEUglj18mRdY3cx4yEckRDhdX8MsR\n9Q9vnoJjJ6vg6uhCW7v3n7OtA862TjS3tl8yO4OIaLDsFhPsVhNsPaso4xxWwKPCbIrA+NFJsuMF\nnF+KOjstDtlpcVf8mKqqaHV1osnZjoZmF+qb21DX5F3mWlPvRG1jK4ucKMzZLJFIjrMh0WFDgsOK\n+GgL4mLMiLWbYbeavrciMjHRjpqagYdFQkXAZ33odDrYLCbYLCakJ8V87+Mej4q6plZU1jlRWeed\nI32xthmVtS0scKIQY9DrkJoY7V3VmBSNtMRopMTbuUBmANInMer1up4pOTZMHpvSd7vb40FNQysu\n1DT3zauuqGlGgw97FhCRNiTH25Cd6kBmqgOZKbFIS7DDyIUvgya9qK/GoNcjJd6OlHg7po9L77u9\nvbO776i7qs6JqvoWVNY54TI5JKYlCl+Gnuee3WJCVtp3qxEzU2JhNkVIThcaNFvUVxMVaUR2mgPZ\naZcWc1f3Qu/eHz3j3v03Y2pytvc9mIho+GLtURiVFOMdwlg+C5kpsdycKYCCrqivJsJoQEqC/YpX\nuOjo7EZNw42orveOg1+sbUFFdRO3NyXq5/KDGZslEg67dy/r+FgrEh3Wnle5NkTxSHlEhUxRX4sp\n0ujdXyD50pOZVpsJB4+dx8nz9Th1vg6nL9Sjq9sjKSVR4NkskYixRiEu1oJIowHFLaOwcP44WM2R\nsFtnwW4xIdpqQowtimPJGhIWRX01FnMkcrMSkZuVCMC7GupEeS2OlVXicOlFOK+wXSqR1ul0OoxK\nikZmSiyS4+1Ijrd5r/DSr3z7preF+Iq+UBHWRX25yAgDJo5JxsQxybj7B5NRcqYGe4+V40jpRV55\nnDQt1h6FqblpmJiTjOxUB0yRfGqHEv42r8Jg0GNiTjIm5iSjodmFHUWnsPPwWZ+2XCUaCXqdDnnj\n0pCfNxrZaQ6eyAthLGofOKLNWFYwETfPycXn+8rw5f6THMsmaQx6HRZMH4OFs3IQY4uSHYdGAIt6\nECxREbg9fzzy80Zj69cl2H30nOxIFGamiTTcsWA8EmKtsqPQCGJRD0GMLQr33zINsydl4J1PD6Oq\n3ik7EoU4u8WElUvywmIDIvo+Xvt9GHJGxeN3qwqweG4uODxIgeKIT8X/WF3Akg5jPKIeJqNBjyXz\nxyE3KxFvbjmAJme77EgUQpYXTETBzDt4ojDM8YjaT8ZmxOP3qwowYUwyl6vTsEVGGPDIXbOxcFYO\nS5pY1P5kNUfikTtn4/a7V8uOQkHMZonErx+Yj0k5KQPfmcICi9rP9Hodlt44AVmZ2TDoeSREgxNj\ni8Lj98+/4t7tFL5Y1AHy5B/+iJ/dMweREdwvgXzjsJvxq/tvQFKcTXYU0hgWdQCJrEQ8umIeLFHc\naYyuLdpqwmP338D50XRFLOoAy0p14PEH5sNuMfEkI12R2WTEL+6di/gYi+wopFEs6hGQEm/Hoyvm\nISH3B7KjkMYYzXH42d1zkJYYLTsKaRiLeoSkJNjxyx/O5aWJ6BKPPfrfMDo9TnYM0jgW9QhKT4rB\nL384F5ERBg6DEJYVTMCksZyCRwNjUY+wjJRYPLxsFqyZC2RHIYnmTM7Ewpk5smNQkGBRSzB+dBLu\nu2Wq7BgkgcHkQM6oONx78xSuOCSfsagluX5SJpbcIGTHoBGWMv5mrF46C0YDn3rkOz5aJFo0Nxd5\n49Jkx6ARYtDr8PCyWbBbTbKjUJBhUUuk0+nwwC3TMIrLhcPCPTdNQXYaTyLT4LGoJYuMMOInd86G\nzRwJAJwNEoIMJgeun5yJeVOzZEehIMWi1gBHtBmrl82CTgeYM/JlxyE/y5l+G+75wWTZMSiIsag1\nYmxGPO7IHy87BvmZKcKA1ctmcnMuGhYWtYYsnD0Wk3KSZccgP3rg1jwkObgbHg0Pi1pD9DodVi7J\nQ1y0WXYU8oMF00djmuCsHho+FrXGWKIisWrpTOi5GCKoZabEYlnBRNkxKESwqDUoK9WB2/PHyY5B\nQxRlMmLV0plc1EJ+w0eSRi2cPRYiO1F2DBqCH92ax72lya9Y1Bql1+nwoyV5nF8dZBbOzMHk61Jl\nx6AQw6LWsGhrFFYuyQPA+dXBYHS6A3cs4BRL8j8WtcZNGJOMBdNHy45BA7BGRWDVHTNh4Lg0BQAf\nVUFg6Y0TkJJglx2DrsBgcsBgcuCh22cg1s5plRQYLOogEGE04Me3z+DRmgaZM/Jx9/2PYNzoJNlR\nKITxmR8k0hKjsbxgguwYdJlJOcm4ec51smNQiGNRB5H8vNEYzyM3zUiOt2HlkulcnEQBx6IOIjqd\nDg/cOg3Wnil7JJFOj988tACWKF5VngKPRR1koq1RWHlrHudVS/ar3/0rMlP5O6CRwaIOQhNzkrF4\n2UOyY4StxXNzudkSjSgWdZBaeuMEZKbGyo4RNgwmB3QGEyaNTcEtvCgxjTAWdZCKMBrw2Mr50IEn\nskaCOSMf1817EA/expOHNPJY1EEsPSkGGRlZHK8eATZLJH569/WIijTKjkJhiEUd5J78wx8wf9F9\nsmOENJ3RhJ/edT3iorkjHsnBog5yOp0OKxZNRXIcL/cUKI/99mlkcYYHScSiDgGmSCMeXjYLEUb+\nOv1tecFEzvAg6fjMDhEpCXasWDQVOoMJ/LX6x4Lpo1Ewc4zsGETgmZEQMmtiBs7d9xvsKDotO0rQ\n6j0xO+W6FNy5cBJ0nOFBGsBDrxCzvGAirsuIlx0jaJkz8jEmPQ4P3jYDej1LmrSBRR1iDAY9Vi2d\nCUe0uW+vZPJdSrwdj9w1G5ERBtlRiPqwqEOQzWLCI3fORsyYAl7Cy2d6RFnj8Yt758ASxU2vSFtY\n1CEqPSkGDy+bxTFWHyVNW4n/9cQfeZUW0iQWdQgbPzoJKxZNkR1D84xGPX529/W83BlpFos6xM2d\nkoWU1FGyY2iWTqfDw8tmYXR6nOwoRFfFog4DTz/1FOZNzZIdQ5MeuGUaJo5Jlh2D6JpY1GFAp9Ph\n3pumYMb4dNlRNGXZjRMwe1KG7BhEA2JRhwm9XoeVt+Zh0tgU2VE0YeHMHPzD7LGyYxD5hEUdRgwG\nPVbfMQMTc5JhHx++O+7NnDAKS3lFdwoiLOowYzQa8PDSmZg0NiUsF8SI7ETcf8s0bv5PQYV7fYQh\no9GA1UtnQq8DjpyolB0n4Hr/GI1K9s4tNxp4fELBhY/YMGXsWWo+a2LoT90zZ+QjPsaCn/EKLRSk\n+KgNYwa9Hg/cmgezKQKflofuEIg1KgI/v3cOoq1RsqMQDQmPqMOcXqfDXf8wCXff9xPZUQLmp3fP\nQZKDV8Ch4MWiJuh0OiyeJ3Df4qkIlXNsBpMDOh1gsdqQnRa6rxYoPHDog/rMnZIFu9WE//zPr2VH\nGTZzRj5WLJqKuVOWyo5CNGw8oqZLTMpJwRO//5+wmYN7q89b5gnMncJl8xQaWNT0PVmpDvzmR/lI\ndFi9QwgGk+xIvtF5H86RURbcMi9Xchgi/+HQB11RQqwVv1mZj7VWE06er5cdxyc6owmTx6Zg9W9f\n5D7cFFJ4RE1XZTVH4p/unYtZE4JjrnWEwYAf3zEDBj0f1hRaeERN12Q0GrBySR4S42zYWF4oO873\n9K46tEYnYcyoOEQYea1DCj0sahqQTqfD4rm5SHL8Guu2HkS32yM7Uh9zRj6S42341S+fgs0SJGPp\nRIPE14jks7xx6Xj8gfmItpr6TjDKPtGYFGfDoyvmsaQppPGImgYlMyUW//zQjVgbbcbZi42w5S6X\nksNgciDRYcWjK+ZxaTiFPB5R06DF2KLwq/tuuOTyXjqDqW963EgYPW0JHrvvBsTYWNIU+ljUNCRG\nowErFk3F/bdMg8Gghy13OXTGwA4/9A236Ax4/AGWNIUPFjUNy5zJmfjnBxcgJd4e8IsQ2HKXY/LY\nFKx54XVYooJ75STRYHCMmoYtLTEav31wATZnxOPrQ2cC9v9ZcoPAzXNyoddzMQuFFx5Rk19ERhhw\n781T8OiKeUiK824pOtwj7N7Pt1tMGDsmB4vnCZY0hSUeUZNfXZeZgN+vKsDn+8rw990GdHa5YTA5\n4Pb4Pve69/6WzHzMmZyF2+aPg926OICpibSNRU1+ZzTosWhOLuZPy0Zh0Wl8eSACbe1dPR/1vojT\nGUxQ3e19n9NbzgaTA+aMfIisRCwrmID0pBgJ3wGRtrCoKWAsUZFYPE+gYGYOjp+uxrGTlTgWtRJt\n7V2w5S5HS/Hf+u5rzshHUpwNM8anY/r4dF6RhagfFjUFnCnSiDyRhjyRBo9HRUOLCy2tHdAZF6C5\nuR3xMRbExVh44Vmiq+Azg0aUXq9DfIwF8TEWJCbaUVPTIjsSkeZx1gcRkcaxqImINI5FTUSkcSxq\nIiKNY1ETEWkci5qISONY1EREGseiJiLSOBY1EZHGsaiJiDSORU1EpHEsaiIijWNRExFpHIuaiEjj\nWNRERBrHoiYi0jgWNRGRxrGoiYg0jkVNRKRxLGoiIo3TqaoqOwMREV0Dj6iJiDSORU1EpHEsaiIi\njWNRExFpHIuaiEjjWNRERBpnHOonCiHuBHCPoigr+73/DIDynrs8pSjKjuFHDIwr5J8D4N8BdAPY\npijK/5GZzxdCCB2A8wBKe27apSjKExIjDUgIoQfwPIApADoA/ERRlJNyUw2OEKIIQFPPu6cURflH\nmXl8JYS4HsC/KIqyUAgxFsDrADwAvgXwS0VRNDtX97LseQA+BHCi58NrFEV5V166axNCRAB4FUAW\nABOApwEUYxA//yEVtRDizwAWATjY7+bpAH6nKMp7Q/maI+kq+dcAuEtRlNNCiI+EENMURTkkJ6HP\ncgAcUBRlqewgg7AcQKSiKPN6nnz/r+e2oCCEiAIARVEWys4yGEKI3wH4EQBnz03PAnhCUZQdQog1\nAJYB2CQr37VcIfsMAM8qivKsvFSDshJAjaIoDwohHAAOw9s9Pv/8hzr0sRPALwDo+t02A8DDQogd\nQoh/E0IYhvi1R8Il+YUQ0QBMiqKc7vn4pwBukpRtMGYASBdCfN7zxyVXdiAf3ADgEwBQFGUPgJly\n4wzaVAAWIcSnQojtPX9sgkEZgLvw3XN2er9XvB9D24/3y7PPAHCbEOIrIcQrQgibvGg+WQ/gyZ63\n9QC6MMif/zWPqIUQ/wjg15fdvEpRlHeFEAWX3f53AO8rinJGCPECgJ8D+MuA30IADSJ/NIDmfu+3\nABgT4HiDcpXv5Z8A/ElRlI1CiBsArAMwe8TDDc7lP2u3EEKvKIpHVqBBagXwjKIoa4UQ1wH4WAiR\nq/X8iqK8J4TI7ndT/4MsJ4CYkU3kuytk3wPgJUVRDgohngDwFID/LiWcDxRFaQUAIYQd3tL+I4B/\n63eXAX/+1yxqRVHWAljrY55XFUXpHbfbDOBuHz8vYAaRvxmAvd/70QAaAxJqiK70vQghzPCOqUNR\nlJ1CiDQZ2Qbp8p91MJU04D0fUAYAiqKcEELUAUgFUCE11eD1/5nbobHH+wDe79c1mwD8h8wwvhBC\nZAB4D8BfFEV5Wwjxr/0+PODP3y+zPnpOah0WQqT33HQTgP3++NojQVGUZgCdQogxPd/LIgCaPRHa\nz5PoOcoWQkwFcE5uHJ/sBLAE6DuBe0RunEFbDe+4Onr+MEYDuCg10dAcFELc2PP2rQiOx3uvT4QQ\ns3re/gE03jVCiGQA2+A9h/d6z82D+vkPedYHALXnHxRFUXtemm8UQrTDexbz5WF87ZHQl7/HzwG8\nBcAA4FNFUfZJSTU4/wJgnRBiCbxH1qvkxvHJ+wBuFkLs7Hl/tcwwQ7AWwGtCiN4n1uoge0XQ+5j/\nLYCXhRCRAI4D2CAvks96s/8cwF+EEF3w/pH8qbxIPnkC3qGNJ4UQvWPVjwP4D19//tw9j4hI47jg\nhYhI41jUREQax6ImItI4FjURkcaxqImINI5FTUSkcSxqIiKNY1ETEWnc/wdRLlOq3sf41gAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a10e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data1,inner='stick')"
   ]
  },
  {
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}