shoe2_variable_cleanup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NCBI Hackathon - Fair Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shoe #2 - Variable Cleanup "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The variable name: LasikGLCLCLSEScl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survey Question: How much of the time do you expect to use glasses or contact lenses to see things up close after healing from LASIK surgery? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. All of the time\n",
    "2. Most of the time\n",
    "3. Some of the time\n",
    "4. Hardly ever\n",
    "5. Never"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and of course we have 'NA' in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The library you need\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.SubjectIDNumLasik</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.AgeYrs</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.GenderTyp</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.RaceUSACat</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.EthnUSACat</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.LASIKPROWLCohort</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.GeneralNotesTxt</th>\n",
       "      <th>PROWL_LASIK_PostOpV1.0.Study ID</th>\n",
       "      <th>PROWL_LASIK_PostOpV1.0.Dataset</th>\n",
       "      <th>PROWL_LASIK_PostOpV1.0.Main.GUID</th>\n",
       "      <th>...</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikACMPTASKScl</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikMASTTASKScl</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikGLCLCLSEScl</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikMINUTESTimDur</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikQULENGTHScl</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikCOMPPROBScl</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikINTCOMPScl</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikCOMMENTSTxt</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikEDUCATIONTyp</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.General.LasikHHINCOMERng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1856</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204</td>\n",
       "      <td>EYEGENE-DATA0000291</td>\n",
       "      <td>NEI_INVHR969PA3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>824</td>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>Brazilian/Italian</td>\n",
       "      <td>204</td>\n",
       "      <td>EYEGENE-DATA0000291</td>\n",
       "      <td>NEI_INVBP664MC0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1074</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PRWL2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204</td>\n",
       "      <td>EYEGENE-DATA0000291</td>\n",
       "      <td>NEI_INVJA185WLP</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1581</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204</td>\n",
       "      <td>EYEGENE-DATA0000291</td>\n",
       "      <td>NEI_INVJM238FBA</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Some of the questions I was not sure if you me...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1268</td>\n",
       "      <td>48</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PRWL2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204</td>\n",
       "      <td>EYEGENE-DATA0000291</td>\n",
       "      <td>NEI_INVEV925NNY</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 261 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PROWL_Demo_2V1.0.Main.SubjectIDNumLasik  PROWL_Demo_2V1.0.Main.AgeYrs  \\\n",
       "0                                     1856                            24   \n",
       "1                                      824                            29   \n",
       "2                                     1074                            21   \n",
       "3                                     1581                            38   \n",
       "4                                     1268                            48   \n",
       "\n",
       "  PROWL_Demo_2V1.0.Main.GenderTyp PROWL_Demo_2V1.0.Main.RaceUSACat  \\\n",
       "0                            Male                            White   \n",
       "1                            Male                          Unknown   \n",
       "2                            Male                            White   \n",
       "3                            Male                            White   \n",
       "4                          Female                            White   \n",
       "\n",
       "  PROWL_Demo_2V1.0.Main.EthnUSACat PROWL_Demo_2V1.0.Main.LASIKPROWLCohort  \\\n",
       "0           Not Hispanic or Latino                                  PROWL   \n",
       "1               Hispanic or Latino                                  PROWL   \n",
       "2           Not Hispanic or Latino                                  PRWL2   \n",
       "3           Not Hispanic or Latino                                  PROWL   \n",
       "4           Not Hispanic or Latino                                  PRWL2   \n",
       "\n",
       "  PROWL_Demo_2V1.0.Main.GeneralNotesTxt  PROWL_LASIK_PostOpV1.0.Study ID  \\\n",
       "0                                   NaN                              204   \n",
       "1                     Brazilian/Italian                              204   \n",
       "2                                   NaN                              204   \n",
       "3                                   NaN                              204   \n",
       "4                                   NaN                              204   \n",
       "\n",
       "  PROWL_LASIK_PostOpV1.0.Dataset PROWL_LASIK_PostOpV1.0.Main.GUID  ...  \\\n",
       "0            EYEGENE-DATA0000291                  NEI_INVHR969PA3  ...   \n",
       "1            EYEGENE-DATA0000291                  NEI_INVBP664MC0  ...   \n",
       "2            EYEGENE-DATA0000291                  NEI_INVJA185WLP  ...   \n",
       "3            EYEGENE-DATA0000291                  NEI_INVJM238FBA  ...   \n",
       "4            EYEGENE-DATA0000291                  NEI_INVEV925NNY  ...   \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikACMPTASKScl  \\\n",
       "0                                                3.0             \n",
       "1                                                3.0             \n",
       "2                                                4.0             \n",
       "3                                                3.0             \n",
       "4                                                4.0             \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikMASTTASKScl  \\\n",
       "0                                                4.0             \n",
       "1                                                3.0             \n",
       "2                                                4.0             \n",
       "3                                                3.0             \n",
       "4                                                4.0             \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikGLCLCLSEScl  \\\n",
       "0                                                5.0             \n",
       "1                                                4.0             \n",
       "2                                                5.0             \n",
       "3                                                4.0             \n",
       "4                                                3.0             \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.General.LasikMINUTESTimDur  \\\n",
       "0                                               35.0   \n",
       "1                                               45.0   \n",
       "2                                               25.0   \n",
       "3                                               45.0   \n",
       "4                                               20.0   \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.General.LasikQULENGTHScl  \\\n",
       "0                                              3.0   \n",
       "1                                              4.0   \n",
       "2                                              3.0   \n",
       "3                                              4.0   \n",
       "4                                              3.0   \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.General.LasikCOMPPROBScl  \\\n",
       "0                                              2.0   \n",
       "1                                              1.0   \n",
       "2                                              1.0   \n",
       "3                                              1.0   \n",
       "4                                              1.0   \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.General.LasikINTCOMPScl  \\\n",
       "0                                             3.0   \n",
       "1                                             1.0   \n",
       "2                                             1.0   \n",
       "3                                             1.0   \n",
       "4                                             3.0   \n",
       "\n",
       "     PROWL_LASIK_PreOp2V1.0.General.LasikCOMMENTSTxt  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3  Some of the questions I was not sure if you me...   \n",
       "4                                                NaN   \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.General.LasikEDUCATIONTyp  \\\n",
       "0                                               4.0   \n",
       "1                                               4.0   \n",
       "2                                               4.0   \n",
       "3                                               6.0   \n",
       "4                                               4.0   \n",
       "\n",
       "   PROWL_LASIK_PreOp2V1.0.General.LasikHHINCOMERng  \n",
       "0                                              1.0  \n",
       "1                                              3.0  \n",
       "2                                              6.0  \n",
       "3                                              6.0  \n",
       "4                                              4.0  \n",
       "\n",
       "[5 rows x 261 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get a sense of the original data set\n",
    "df = pd.read_csv('query_result_PROWL_Dem.csv').iloc[:,4:]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1301, 261)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the column names you need\n",
    "col_needed = ['PROWL_Demo_2V1.0.Main.SubjectIDNumLasik', 'PROWL_Demo_2V1.0.Main.AgeYrs', 'PROWL_Demo_2V1.0.Main.GenderTyp',\n",
    "             'PROWL_Demo_2V1.0.Main.RaceUSACat', 'PROWL_Demo_2V1.0.Main.EthnUSACat','PROWL_Demo_2V1.0.Main.LASIKPROWLCohort',\n",
    "             'PROWL_LASIK_PreOp2V1.0.Form Administration.VisitNoLasik', 'PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikGLCLCLSEScl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a new dataframe based on the question that you are interested\n",
    "new_df = df[col_needed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nan_rows = new_df[new_df['PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikGLCLCLSEScl'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nan_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "532"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_df['PROWL_Demo_2V1.0.Main.SubjectIDNumLasik'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.SubjectIDNumLasik</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.AgeYrs</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.GenderTyp</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.RaceUSACat</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.EthnUSACat</th>\n",
       "      <th>PROWL_Demo_2V1.0.Main.LASIKPROWLCohort</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.Form Administration.VisitNoLasik</th>\n",
       "      <th>PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikGLCLCLSEScl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1856</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1856</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>1856</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PROWL_Demo_2V1.0.Main.SubjectIDNumLasik  PROWL_Demo_2V1.0.Main.AgeYrs  \\\n",
       "0                                       1856                            24   \n",
       "166                                     1856                            24   \n",
       "470                                     1856                            24   \n",
       "\n",
       "    PROWL_Demo_2V1.0.Main.GenderTyp PROWL_Demo_2V1.0.Main.RaceUSACat  \\\n",
       "0                              Male                            White   \n",
       "166                            Male                            White   \n",
       "470                            Male                            White   \n",
       "\n",
       "    PROWL_Demo_2V1.0.Main.EthnUSACat PROWL_Demo_2V1.0.Main.LASIKPROWLCohort  \\\n",
       "0             Not Hispanic or Latino                                  PROWL   \n",
       "166           Not Hispanic or Latino                                  PROWL   \n",
       "470           Not Hispanic or Latino                                  PROWL   \n",
       "\n",
       "     PROWL_LASIK_PreOp2V1.0.Form Administration.VisitNoLasik  \\\n",
       "0                                                    0         \n",
       "166                                                  0         \n",
       "470                                                  0         \n",
       "\n",
       "     PROWL_LASIK_PreOp2V1.0.Lasik Questionnaire.LasikGLCLCLSEScl  \n",
       "0                                                  5.0            \n",
       "166                                                5.0            \n",
       "470                                                5.0            "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.loc[new_df['PROWL_Demo_2V1.0.Main.SubjectIDNumLasik'] == 1856, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the duplicate rows\n",
    "new_df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 8)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.rename(columns=lambda x: x.split('.')[-1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubjectIDNumLasik</th>\n",
       "      <th>AgeYrs</th>\n",
       "      <th>GenderTyp</th>\n",
       "      <th>RaceUSACat</th>\n",
       "      <th>EthnUSACat</th>\n",
       "      <th>LASIKPROWLCohort</th>\n",
       "      <th>VisitNoLasik</th>\n",
       "      <th>LasikGLCLCLSEScl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1856</td>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>824</td>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1074</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PRWL2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1581</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PROWL</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1268</td>\n",
       "      <td>48</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Hispanic or Latino</td>\n",
       "      <td>PRWL2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SubjectIDNumLasik  AgeYrs GenderTyp RaceUSACat              EthnUSACat  \\\n",
       "0               1856      24      Male      White  Not Hispanic or Latino   \n",
       "1                824      29      Male    Unknown      Hispanic or Latino   \n",
       "2               1074      21      Male      White  Not Hispanic or Latino   \n",
       "3               1581      38      Male      White  Not Hispanic or Latino   \n",
       "4               1268      48    Female      White  Not Hispanic or Latino   \n",
       "\n",
       "  LASIKPROWLCohort  VisitNoLasik  LasikGLCLCLSEScl  \n",
       "0            PROWL             0               5.0  \n",
       "1            PROWL             0               4.0  \n",
       "2            PRWL2             0               5.0  \n",
       "3            PROWL             0               4.0  \n",
       "4            PRWL2             0               3.0  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the dataframe to a new csv file\n",
    "new_df.to_csv('new_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-1.1138511755542908,\n",
       " 1.121028918246873,\n",
       " -1.110455106740067,\n",
       " 1.1004978622257175)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "gender = ['male', 'female']\n",
    "number = [327, 205]\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(number, labels=gender, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_df['answer']=new_df['LasikGLCLCLSEScl'].map({1:'All of the time',2:'Most of the time', 3:'Some of the time', 4:'Hardly ever',\n",
    "                                            #5:' Never'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['LasikGLCLCLSEScl'] = new_df['LasikGLCLCLSEScl'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['LasikGLCLCLSEScl'].isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"601\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(y =\"AgeYrs\", x=\"LasikGLCLCLSEScl\", data=new_df, kind=\"swarm\", hue = 'GenderTyp', split = True, alpha = 0.75)\n",
    "g.set_xticklabels(rotation=30)\n",
    "plt.subplots_adjust(bottom=0.2)\n",
    "plt.xlabel('', alpha=0.8)\n",
    "g.set_xticklabels(['NA', 'All of the time','Most of the time', 'Some of the time', 'Hardly ever', ' Never'])\n",
    "#plt.title('How much of the time do you expect to use glasses or contact lenses to see things up close after healing from LASIK surgery?') \n",
    "plt.savefig('a.pdf')"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}