Lecture 51 - Box and Violin Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now we'll learn about box and violin plots\n",
    "url = 'http://en.wikipedia.org/wiki/Box_plot#mediaviewer/File:Boxplot_vs_PDF.svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1 = randn(100)\n",
    "data2 = randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x740dee3160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAECCAYAAAAxVlaQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x740df4aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=[data1,data2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x740df6b160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAECCAYAAAAxVlaQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEm+AgsQboCDxBihIvAEKEm+Agv4Xgx0U/1Ad1VMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x740dfcc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=[data1,data2],whis=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\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",
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x740dfef0b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWUAAAD9CAYAAABkx2YhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x740dff5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data1,vert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x740e226c50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAECCAYAAAAB2kexAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x741374b2b0>"
      ]
     },
     "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x74137ea2e8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAECCAYAAAAB2kexAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLERCXQghLOT/A+3Wy/QbLhFJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x741376fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=[data1,data2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x741397ba90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAD9CAYAAAB6DlaSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7413930438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data2,bw=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x74139d51d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAD9CAYAAAB6DlaSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlUW9ehLvDvSIAQowGDJzwAxhvPA57nMbFdu7YTp02a\n5mZoe5us3tzXIXmv7X1vdch9brqa2yRNm+ElbXozOnFiO4OTOIOdBNt4HsHmgBnFPIlBQggknfcH\nQ/DEZGDrSN9vLS+DkMQngT629tnnHEXTNBARkXczyA5AREQ9Y1kTEekAy5qISAdY1kREOsCyJiLS\nAZY1EZEOBNzsHbhcbs1qbRqILFJERYVAr/n1nB1gftmYX67Y2HClL9e/6ZF1QIDxZu9CKj3n13N2\ngPllY3594TQIEZEOsKyJiHSAZU1EpAMsayIiHWBZExHpAMuaiEgHWNZERDrAsiYi0gGWNRGRDrCs\niYh0gGVNRKQDLGsiIh1gWRMR6QDLmohIB1jWREQ6wLImItIBljURkQ6wrImIdIBlTUSkAzd9wlwi\n6p96WzMKSmtRXFGPKqsdVVY7bE1OtLo9cLncMAcHIiYyBNERIZg4bjimJY1AWIhJdmyShGVNNERs\nTU5kF1Ujq6AKOYVVqG1wdHt9Z6sbdY3NyEUtTlwshqIASfExWLcgGWJCLBSlTyfHJp1jWRMNEk3T\nUFrVgMy8CmTmVqCg1Nqn2zssaTCPXQaHJQ0A4HbU4LK2FZctNUgeG4PNK6Zg/KiowYhOXohlTTSA\nWlpdyLHUIDO3Apm55ahrbL7u9WzZe2E0x8A8dtkN78vttF7xv+Z2dt4uB8vw5Otp2Lg0BWsXJMPA\nUbbPY1kT3QRN01BZa8Olgipk5Vcip6gaLrfniut0FKzbUQOjOabtdm5nZwn36ft1uZ2mAe+88SLy\nS7bj+xtnI9QcdPMPiLwWy5qoj+ptzcgpqkZRRR3Oq2WwNl4592zL3ouwSVs7P+8o2P4WdHfcTisu\n5lXgydfT8PCdSxAZFjyg90/eg2VN1IMGezNyLTXIKapGjqUGlbW2616vYwStuZ1DnBCostrx17eO\n4OE7FyMilIXti1jWRFdpam5BTlE1sgurkVNUjYoblHOHjg2BgzFy7g1b9t62DyZtbSvs7y5BeCiX\n+PkaljX5PU3TUFxZj4u5FbiYX4nCMis07frX7bphsKOkZRR0V11H8hU1Nry45xj+/c4lCAgwSkxF\nA41lTX5J0zQUldfhTFYpzmWX3nDNc0chd96uy+hZdklfjy17L7IsMfiPrM/x+GO/41psH8KyJr/S\naHfieKYFxzKKUFFz7fRGx5rmjoL2xkLuTscfk+oqKw6dLcCy2QmyI9EAYVmTXyiuqMdXp/Jw6lIx\n3J62OY6OYgb0W87d2f1FBuLjIpEwJlp2FBoALGvyafkltfjkiIqsgqrOy7xlrnmweTQNr+47jf91\n30qYgvhS1zv+BMknFVfU44OvL15R0h18vaQBQHM54bCkoTC7Br+xpOHxx34nOxLdJJY1+ZS6Rgc+\nTLuEE5nFnZd1LG0Lm7S17WODHxwZWPN07ohTVVmCzLwKTE0cITsV3QSWNfkEl8uNAydz8Wl6Nlpd\nbbt7X28nFc3thGIwy4opzZsfn8WvHljFXdJ1jGVNuqcWVmHXZ+dRZbV3XmbL3ittJxVv1NjkxAdf\nX8Kdt86UHYX6iWVNumV3tOC9LzNxLMNyzddk7PLt7Q7sew3zp8YjMT5GdhTqBz+YvCNfdC67FDv+\nceCKou66FI+u5XZa8dZn5+G+6qiApA8cWZOu2JqceOeLCziTVXrN1zjl0bPy6kYcPJmLtQuSZUeh\nPmJZk25cyCnDzv3nYHO0yI6ia58cUZE6OR6xseGyo1AfsKzJ6zmcrdj9RQaOZ147N01947Ckweao\nwY4/HsM//99TsuNQH3DOmrxadmEVHn/5II5nWjrnpDk33X8da69rq8uQlV8pOw71AcuavFJLqxu7\nD2Tgb2+nd57H0JuPdqdHr7x/Eh7PDY4FS16HZU1ex1JRhyde+QpfncrjKHoQFZRYcSyjSHYM6iXO\nWZPX8Hg0fHH8Mj46lAVP+9H/OYoeXB9+fQmzU8YgmAd68nocWZNXqKlvwjM7D+PDtEvwaFrniFpz\nceeWwWRztOCzozmyY1Av8M8pSaVpGk5eLMauzy+gLvcgAFx5+FKNO3AMtoMnc7Fk5nhER4bIjkLd\n4MiapGlqbsWr+07jtY/OwNnigttp5bSHBG63Bx+kXZIdg3rAkTVJUVBqxWsfnb7i4Eskz+lLJVgx\nJwETRvOsMt6KI2saUpqm4cDxy3j6jUMsai+z+0AmtBud1p2kY1nTkLE7WvDSnuPY+doLsBd9LTsO\nXaWwzIoz6rXHXCHvwGkQGhLFFfV4ac9xWBsdnJf2Yu9/dRHTJ45EYIBRdhS6CkfWNOhOXizGk6+n\nwdrogMOSxuV4Xsza4MCXp/Jkx6DrYFnToPF4NOw9mIlX952Gq/0Yym6nlcvxvNxn6dlosDfLjkFX\nYVnToHC2uPD3907g4Mlc2VGoj5ytbuxLy5Idg67CsqYBV9fowNNvHkLG5fLOy3iMD305eqEIlvI6\n2TGoC5Y1DajKWhuefP0QSiobrricGxX1590DF7iUz4uwrGnAWMrr8NTraSi7+ClH0j4gv8SK01kl\nsmNQO5Y1DYjLlmr8Zedh2Jtbudu4D3nvy4twtrhkxyCwrGkAZBdW4fl3jqKl1S07Cg2welszPjmi\nyo5BYFnTTcoqqMQL7x5DQ/5XnZdxHbVv+fJkHkqrGnq+Ig0qljX1W3ZhFV7cfRwut+fKaQ+uo/Yp\nHk3Drs/Pc2OjZCxr6pf8ktrOoubGRN+XV1yLE5nFsmP4NZY19Zmloq5tjtrVNkfNjYn+Yc/BDNia\nOMUlC8ua+qSy1obn3k5HM1cI+J2m5la8+0WG7Bh+i2VNvVZva8azb6fD3tzKqQ8/dTqrBBe67JlK\nQ4dlTb3S1NyK53alw9roAMCpD3/21qfn0NTcKjuG32FZU49cLjde2nMcZdWNsqOQF2i0O7H7wAXZ\nMfwOy5q65dE0vP7xWWSk7+XUB3X6+pM3eVaZIcaypm7tS7uE01kl3IWcruB2WvHW/nOoa58Wo8HH\nsqYbOny2AJ8fuyw7Bnkph7MVr310Bh7uLDMkWNZ0XZfyK7Hr8/OyY5CXyymqxufHcmTH8Assa7pG\nSWU9/vHeCXDARD1xWNLw0aEs/OGPf5AdxeexrOkK9bZmvPDuMR5Bj3rF7bRC04Dc/DzOXw8yljV1\nam5x4YV3jqLe1syVH9Qnmqbhn++f7DwxMg08ljUBANweD/77g5MoaT8UJld+UF/ll1qx6zMenW+w\nsKwJmqZh9xcZuJhXKTsK6ZjDkoajF4rw5ck82VF8Esua8MXxyzh0tkB2DNK5jndje7/MREYujx8y\n0FjWfu7kxWJ88PUlAOA8NQ2Y/37/FIrK62TH8Cksaz+mFlbh9Y/PdH7OeWoaKPX5X+L5d46i0mqT\nHcVnsKz9VFF5HV7acxweDzcG0cBzO62wO1rw3NvpqLc1y47jE1jWfqiiphHP70rvXEvN6Q8aLLUN\nDjy7K51nmBkALGs/U9vQhL+1n0CgA6c/aDCVVzfi2V3pPAb2TWJZ+5G6Rgf+uvMI35bSkCupbMDz\n76Sj2cnC7i+WtZ+otzXjmZ1HUFPfJDsK+anCsjo8u+soR9j9xLL2A21FfRjVdXbZUcjPZR1/H397\n+wjsjhbZUXSHZe3jquvseOr1NFRZWdQkn9tpRXFFPf761hE02Dkd1xcsax9WWtWAp944hNoGHg2N\nvEvu6X14+o3DqOW0XK+xrH1UTlE1/vLmYTTauWSKvI/baW171/fGIZTzRMy9wrL2QennC/Hs2+lw\ncMs7ebl6WzOeeuMQ8oprZEfxeixrH+J2e7D3YCZ27j/H8+KRbjicrXj8T3/EWZ4tvVssax9hbXDg\nmZ2HcfBkruwoRH3mbq7Fy++fxMETuTwe9g0EyA5AN+9CThle//gspz1I9/Z+mYmK2kZsXzsDAUaO\nJbtiWetYg70Zr3x4Coc+3Qnz2GWy4xANiPTzRaisteOBLXMRFmKSHcdr8E+XDnk0DcczivDoEx/i\n1KUSHtuDfE5ucQ2eeOVrHhO7C46sdSavuAa7D2TAUlEvOwrRoLI2OvDUG4dwx9rpWDRjvOw40rGs\ndSK/pBb707NxKZ/nSST/4XZ7sHP/OeRaarB93QwEB/lvZfnvI9cBj0dDZl4FvjqVh5yiatlxiKQ5\ncbEY+aW1uHfzXIwbOUx2HClY1l6otqEJJy8W48i5QlgbHHBY0rgBkfxedV0TnnwtDesWJuOWRZNk\nxxlyLGsv0WBvRsblcpzOKr1mFM0NiERtPJqG/enZOJdThn+7awnCg4NkRxoyLGtJNE1DaVUDLuVX\nIjO3AnkltbIjEelGeXUj/s8zn2DRzPHYtGwyQs2+X9os6yGiaRpq6pqQY6lGTlHbvwYeZImo3zQA\nR84V4kxWKdYvEVgyczwCA4yyYw0alvUgcbncKK5sQEGZFfkltcgrrmE5Ew0Ch7MVew5k4MDxy1i/\neBLmTxvnk3s/sqwHgNvjQWWtDZbyehSVW1FUXofiinq4PTzGAdFQqbc1461Pz+OjQyqWzUnAkpnj\nfWoPSJZ1HzlbXCivaURJZQNKKutRXFmPksp6tLo8ndfh6g0ieRqbnPjoUBb2p2djxsSRmDd1LFIS\nYmE06Hu0zbK+gZZWF6qsdlTU2FBW04jy6gaUVTf26vRYXL1BJJ/b7cEZtRRn1FKEmoMwJTEO05JG\nQkyIhdkUKDten/ltWWuaBrujBY3FLcgtqEZNfRNq6uyoqrOjymqHlafCIvIZdkcLTmQW40RmMRQF\nGB0bgaT4GIwbOQyjYyMwIibc6+e5faasm52tcDhdcLa40NzSiqbmVjicrbA7WmF3OGF3tKDB7mz7\nZ2tGva0ZLren5zsmIp+iaWifxmzovMxgUBATGYLhw0KRMDoKtyyaBEVRJKa8lk+U9eGzBXj7s/Oy\nYxCRTnk8Gqqsbe+qL+VXInVKPIYPC5Ud6wrePe7vpeq6nueRiYh6yxtPi+cTI+tVc5MQHmr6ZhrE\n2TYF0tTc9s/W1DYNwqV0RNSTYeHBiAwNlh3jGj5R1hFhwVg9b2K319E0DQ5nKxrsTtTbmlHf6EBd\nYzOaWlwoq6xHTX0TauubWOhEfiIowIj4EZEYExeB0bGRiIsOQ2xUKCJCTV43Xw34SFn3hqIoCAkO\nQkhwEEbGhHdeHhsbjqqqRgBtO7fUNThQZbWj0mpDRY0NFbU2lFU3wtbEvQ+J9MxgUJA8djhSEmKR\nFB+D+LhIGL18BUhXflPWvWE0GBAzLBQxw0KRkhB3xdfsjhaUVjWgrLptK3JJVQPKqhq4ooTIixkU\nBVOTRiB1SjwmJ8Tp+uQF+k0+xELNQUgeNxzJ44Z3Xub2eFBRY0NxRT0sFXWwVNSjuKIORlOUxKRE\nFB1hxvI5iZg3Nd5ndjlnWd8Eo8GA0bERGB0bgfnTxgJoK/Dy6mUoLKtDQZkVBSW1qKi1SU5K5B/i\nR0TiloXJmD5xFAwG75t3vhks6wFmNBgwJi4SY+IisXhm20k+7Y4WFJRakVtcg8uWahSV10PzwqVB\nRHo1ang4Ni5NwfSJI71y4+BAYFkPgVBzEKYmjcDUpBEA2va2vFxcg6z8SlzMq0RNfZPkhET6FBIc\niE3LJmPRjPE+N5K+GstagmBTIKYljcS0pJHQNA2VtTaczynHuexSWCrqZccj0oU1C5OxZm6SX5wl\nBmBZS6coCkbEhGNdTDjWLUxGldXWdsCZi8Wo5Yib6BoxkSG4a/0sLE5N6Fx26w9Y1l4mNioMG5em\nYP0SAbWgCl+fzscpC1eXEAHAklkTsGXFFJh0vASvv/zvEeuEQVEwOSEOkxPiULl6Kj47moMTmcXc\nMEl+yWwKxN0bZmF68ijZUaRhWetAXFQY7t4wG7cumoRPjqg4kVksOxLRkEkYE4X7Ns/FsHCz7ChS\nsax1ZPiwUHx/4xwsm52APQczkF/CM9KQb1s+JwFbV07V1W7hg4VlrUPjR0Xhf9y1FBcLq/DKeyfB\nXW7I1wQYDbhr/SzMnRIvO4rXYFnrlKIoWDkvCWNiwvHYjiNwyQ5ENEDCQ0340bb5GD+KG9a74nsL\nnRsWbsYTOx7Dbaun+fxOAeT74kdE4pF7lrOor4Mjax+gKApWpCZiwugovPz+SZ7sl3RpSuII3Lc5\n1S+X5fUGR9Y+ZPyoKDxyz3IkxkfLjkLUJ7FxY/DDbfNY1N1gWfuYsBATfnLHos6jABJ5u41LBP7w\n+9/CaGAddYfPjg8KCDDie+tnYf1iITsKUbfuWDsdty4WPnukvIHE9xw+SlEUbFgiEBIciN0HMmTH\nIbqCMTgK925KxZzJY2RH0Q2OrH3citRE3L1hNjhwIW9hNCh4+OFfsKj7iCNrPzB/2lgYDApe3Xda\ndhTyewp+dNsCTL7qHKfUM46s/cTcKfG4e8Ns2THIjwUYDZiYmMii7ieOrP3I/Glj4dE0vPnJWdlR\nyM8YDQp+uG0+Jidskh1Ftziy9jMLp4/D1pVTZccgP2JQFNz/7bkcUd8klrUfWjUvCesWJsuOQX7i\nnk1z/Po41AOFZe2nvrU0pfPs60YTj8NAg+P2NdMxJ4WrPgYC56z9lKIo2L52OhpszcjAMtlxyAfd\nsmgSls9JkB3DZ3Bk7ceMBgPu3ZyK8aOGyY5CPiYmdjQ2LuEetAOJZe3nggID8K+3LcDwYSGcDqEB\nkRQfjR2/+y13IR9gLGtCWIgJP96+EDHJq2VHIZ2LjjDjgS3zEMDTcA04PqMEoO2kvD/aNp/nuqN+\nCzRH48e3L0RYiEl2FJ/EVyZ1SoyPwfc3tu3lyCkR6qsHH/opRg4Plx3DZ3E1CF1hTsoY1NQ14cM0\n2UlIT4KDAjFLjJYdw6exrOkaaxdMRHWdHUcvFMmOQl7OaIpCwpgoGEwTZUfxeSxruoaiKPjOuhmo\nbWhCdmG17DjkxaInrsZ9m+diWDjX6g82zlnTdRmNBjywZR7nIKlbd946E8PCzbJj+AWWNd2Q2RSI\nB29fiIhQbt2nay2YNpbz1EOIZU3dioow48HtCxFojuYKEeoUHDYct62ZLjuGX2FZU4/GxEXiZz99\nFKHjl8uOQl7iFz97FMFB3OQ1lFjW1CtiQizu2ThHdgzyAsvnJCAxPkZ2DL/DsqZemzN5DG5fMw0A\nd5rxV1ERZmxaNll2DL/E9zHUJ8vnJMLW1IL9soOQFHfdOgsmTn9IwZE19dmGJQIrUhNlx6AhtmDa\nWIgJsbJj+C2WNfWZoijYtmoqFs0YB4BTIv7AHByIb6+YIjuGX2NZU7+07eU4E/OnjoV5LPde83Vb\nVkzh0fQk4+QT9ZvBoOCuDbNgMCj4/LLsNDRYxo+KwoLp42TH8HscWdNNMSgKvnvrTMTGdTkpqsJf\nK1+hKMB31s2AgWd9kY6vKrppBkXBjt//FqvmJgEAlAC+XfYVS2dNQPyISNkxCJwGoQFiUBRsWTkF\nYSFBeNvCg2H7ArMpEBuWpMiOQe1Y1jRgFEXB2gXJCAt5GDv3n4OmabIj0U3YuDQFoeYg2TGoHadB\naMAtnD4OD25fwJ0ndGxkTDiWzBovOwZ1wbKmQZEyIQ4/v3sZoiLajnXMtdj6ctvqaTAaWA/ehD8N\nGjQjh4fjkXuWI3nccK7F1pFpSSO4p6IXYlnToAoLMeGhOxZi5dy23dONpigu7fNiBkXBt1dOlR2D\nroOvGhp0RoMB21ZNw72bUxGZuJJL+7zY0tkTMCI6THYMug6WNQ2ZOSlj8Oi/rEBoRCznsL2Q2RSI\n9YuF7Bh0AyxrGlJx0WF4Ysd/Ys2m78uOQle5dfEkLtXzYixrGnJBgUZ895aZ+Ok9yxBs4vI+bzB8\nWAiWzU6QHYO6wVcKSTN/+jhEhpjw2r4zyC2ukR3Hr21ZORUBRo7dvBl/OiRVdEQI/u27i/Ht5ZNh\nDI7iXLYEE8fGYPrEkbJjUA84sibpDAYFaxYkQ0z433jlw1OoqLXJjuRXtq6aCoVH1fN6HFmT14gf\nEYlH/mUFls9J+GaEzTXZg2r+tLEYO2KY7BjUC3wlkFcJCjTi9jXT8fOfP4rIsGCuyR5EpqAAbOaZ\nynWDZU1eKWVCHH55/0pEDx8lO4rPum3tNESEBcuOQb3EsiavFRIchD/938dw3+ZUmIMDZcfxKUaD\nAeuX8ljVesKyJq83O2UMfnX/KgyLbluxwBUj/Wc0RUExmjB+/AQEBhhlx6E+4GoQ0oXIsGA8seMx\nHMuwYPeBADhbXLIj6ZJ57DJMTojDj29fIDsK9RFH1qQbiqJg4fRx+NX9q5DCQ3j2S0CAAXesnc6l\nejrEsibdiYow48HtC3H3hlkwm76Zy+b0SM/WLxaIGRYqOwb1A8uadElRFMyfNg6//sEqzE4ZDQA8\nwUEPjAYDVrefgZ70h2VNuhYRGoz7Ns/FQ3csRGzUNyNGxcj12VdLTEiAkcf/0C3+5MgnpEyIwy/v\nW4nNyyfDFGhE2KStUIwmTo20W5GaiF/+z1/JjkE3gatByGcEBBixdkEyFkwbh0+OqDisbIOmaWi8\n9JbsaFIZDQZs4p6KuseyJp8THmrCHetmYNXcJHx6NBtf5JgAre1r/jg9EmoOQlAg11TrHcuafNbw\nqFB8b8Ns3LLwGRw4mYvjGUUIm7QVDkua7GiDTzHAaIpCQIABiQk8qYAvYFmTzxseFYrvrJuBTctS\nkH6+COnnQ1Fltfv0fLYSYMKsZbfhwdsXwmDgmmpfwLImvxESHIQ18ydi9bwk5JfU4njmOJzLLkNT\nc6vsaAPOoBhw76ZUFrUPYVmT31EUBYnxMUiMj8Eda2cgx1KN89llyMyrgOOq0bbeRt+K0YQAczQm\nxsfw5Lc+hmVNfs1oNCBlQhxSJsRB0zSUVS9EVn4lsouqcdlSA1y1o423b6AMF1vxgy3zMD2Zh5b1\nNSxronaKomB0bARGx0Zg9fyJcLk9KCyz4rKlBjlF1cgvqb1iA6W3jboVBbh3UyqL2kexrIluIMBo\nQFJ8DJLiY3DroklwudwoLK9DTpFATlE1Cowr4HJ7Oku7YyccV6tjSHMqRhOgAPd8aw5mp4wZ0u9N\nQ4dlTdRLAQHGzvJev1ggItKMY2cLkVWQiKz8KpRP2goAsGXvHdJc0VNvx90bZmOWGD2k35eGFsua\nqJ9MQQGYnBCHyQlxwCrA2uDApfwKZE4cCbWgCq0uN4DBHXGHhMfikXuWY0RM+IDfN3kXljXRAImK\nMGPxzAlYPHMCWl1uXLbUIONyOTIj7oK1wQFb9t7OKRO3x9OvOe+O4lcUYNGM8di6ciNMQXwZ+wP+\nlIkGQWCAsXPUvV3TUFbdiEt5k5FVUIXc4hq4PVqv76trwYdN2opZk0Zh49IUjqb9DMuaaJB1XWWy\nZkEyWlpdyCupRUGpFfkltbBU1MPuaLnmdh0lbR67DONHRWFG8kjMSB6FuOiwoX4I5AVY1kRDLCgw\noHNtdweHsxVVVjsam5xwuTxwuz0wBy9EdGQIosLNPBATsayJvIHZFIhxI4fJjkFejCcfICLSAZY1\nEZEOsKyJiHSAZU1EpAMsayIiHWBZExHpAMuaiEgHWNZERDrAsiYi0gGWNRGRDrCsiYh0gGVNRKQD\nLGsiIh1gWRMR6QDLmohIB1jWREQ6wLImItIBljURkQ6wrImIdIBlTUSkA4qmabIzEBFRDziyJiLS\nAZY1EZEOsKyJiHSAZU1EpAMsayIiHWBZExHpQEB/biSEiADwGoAIAIEAfq6q6jEhxEIATwFoBfCZ\nqqq/H7Ckg0AIsQ3AdlVV727/fCuAJwAUtV/lN6qqpsnK153rZF8A4Gno5LnvIIQoBpDd/mm6qqr/\nITNPbwghFADPApgJoBnAD1VVzZObqm+EEKcA1Ld/mq+q6g9k5umN9t/xx1VVXSWESALwTwAeABmq\nqv5EarheuCr/LAAf4pvf/edUVd3V3e37VdYAfg7gc1VV/yKEmATgTQCpAJ4DsE1V1QIhxD4hxExV\nVc/183sMKiHEUwBuAXC2y8WpAB5VVXWPnFS9c4Psz0Mnz32H9hfcKVVVt8jO0kdbAZhUVV3c/gL8\nc/tluiCEMAGAqqqrZWfpLSHEowDuAWBrv+jPAH6tqmqaEOI5IcQWVVXfk5ewe9fJnwrgv1RVfbK3\n99HfaZA/A3ih/eNAAA4hRDiAIFVVC9ov3w9gbT/vfygcBvDQVZelAnhACPG1EOIJIYS3ThNdkV2H\nz32HVADxQogDQogP2//w68FSAJ8AgKqqxwDMlRunz2YCCBVC7BdCfN7+B8fbXQawrcvnqV3e9X4M\n7/99vyY/gG8JIb4SQrwkhAjt6Q56HFkLIR4A8DMAGgCl/f/7VVU9JYQYCeBVAP+OtimRhi43bQSQ\n0NtHMli6yb9LCLHiqqt/CmBv++j0eQAPou3trhR9yO6Vz31XN3gsPwGwQ1XVd4UQS9A2tTZfXspe\ni8A3UwgA4BJCGFRV9cgK1EdNAP6kqurfhRDJAD4WQkzy5vyqqu4RQozvcpHS5eNGAJFDHKlPrpP/\nGIAXVVU9I4T4NYDfAni0u/vosaxVVf0HgH9cfbkQYjqANwD8QlXVQ+2ju4guVwkHUNfjoxhkN8p/\nAy+rqtrxInwPwG2Dk6p3+pC9AV743Hd1vccihDADcLV//bAQYpSMbP3QgLbnuIOeihpomye9DACq\nquYIIWoAjAJQIjVV33R9vr3u970X9nbpmj0A/tLTDfr1Nl8IMQXA2wC+p6rqpwCgqmojAKcQIqF9\nA8ytALxy41w3zgshRrd/vAbAKZlhekvHz/1vAPwUAIQQMwFY5MbptcMANgJA+0b1C3Lj9NkDAP4L\nANp/38MBlElN1HenhRDL2z/eAH38vne1XwjRMX3Wq67p7wbGHQBMAJ5uL4c6VVW3oW0e9Q20/RH4\nVFXVE/28f1l+AGCPEKIJwEUAL0rO0xcPQn/P/eMAXhNCfAttq1jukxun1/YAWCeEONz++f0yw/TD\n3wG8LIRuSyvbAAAAYUlEQVRIQ9sI9QGdvTMAgEcAvCiECARwCcA7kvP01UMAnhFCtAAoB/CvPd2A\nR90jItIBb13tQEREXbCsiYh0gGVNRKQDLGsiIh1gWRMR6QDLmohIB1jWREQ6wLImItKB/w90Q0eP\nzoTGGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x74139f72e8>"
      ]
     },
     "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}