Python for Data Cleanup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Python for Data Science\n",
    "\n",
    "## Outline\n",
    "## Why Python?\n",
    "## Tutorial\n",
    "- ### Data Problem\n",
    "- ### Pandas - loading data, selecting and filtering \n",
    "- ### Data cleaning\n",
    "- ### Reshaping data using pandas\n",
    "\n",
    "## Questions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why Python? \n",
    "- Great language for beginners - open source, great community\n",
    "- Large ecosystem for writing web applications, deploying code in production\n",
    "- Data analytics and scientific computing - numpy, scipy, pandas, matplotlib\n",
    "- Advanced machine learning tools - sklearn, theano, tensorflow (keras)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Problem\n",
    "\n",
    "**Modeling Problem** - Can we predict a movies Gross from other parameters such as rating, genre, budget etc.?  \n",
    "\n",
    "**Dataset** - IMDB Movies Dataset\n",
    "\n",
    "**What** - Preparing a dataset for linear regression analysis\n",
    "\n",
    "## Pandas\n",
    "Pandas is a data analytics toolkit providing data structures for analyzing tabular and time-series data. It is built over numpy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Released</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>imdbVotes</th>\n",
       "      <th>Type</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39 Pounds of Love</td>\n",
       "      <td>2005-04-08</td>\n",
       "      <td>70 min</td>\n",
       "      <td>Documentary, Biography, Romance</td>\n",
       "      <td>6.8</td>\n",
       "      <td>234.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3:am</td>\n",
       "      <td>2005-01-25</td>\n",
       "      <td>21 min</td>\n",
       "      <td>Short, Thriller</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500 Years Later</td>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>106 min</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>7.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5th World</td>\n",
       "      <td>2005-01-20</td>\n",
       "      <td>75 min</td>\n",
       "      <td>Drama, Romance</td>\n",
       "      <td>6.4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>2005-03-12</td>\n",
       "      <td>14 min</td>\n",
       "      <td>War, Short</td>\n",
       "      <td>8.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title   Released  Runtime                            Genre  \\\n",
       "0  39 Pounds of Love 2005-04-08   70 min  Documentary, Biography, Romance   \n",
       "1               3:am 2005-01-25   21 min                  Short, Thriller   \n",
       "2    500 Years Later 2005-02-24  106 min                      Documentary   \n",
       "3          5th World 2005-01-20   75 min                   Drama, Romance   \n",
       "4                 90 2005-03-12   14 min                       War, Short   \n",
       "\n",
       "   imdbRating  imdbVotes   Type  Budget  Gross  \n",
       "0         6.8      234.0  movie     NaN    NaN  \n",
       "1         8.2       13.0  movie     NaN    NaN  \n",
       "2         7.0      161.0  movie     NaN    NaN  \n",
       "3         6.4       31.0  movie     NaN    NaN  \n",
       "4         8.3       18.0  movie     NaN    NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"data/movies.csv\", usecols=[\n",
    "    \"Title\", \n",
    "    \"Released\", \n",
    "    \"Runtime\", \n",
    "    \"Genre\",\n",
    "    \"Budget\",\n",
    "    \"Gross\",\n",
    "    \"imdbVotes\",\n",
    "    \"imdbRating\",\n",
    "    \"Type\"],\n",
    "    parse_dates=[\"Released\",],\n",
    ")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40789"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title                 object\n",
       "Released      datetime64[ns]\n",
       "Runtime               object\n",
       "Genre                 object\n",
       "imdbRating           float64\n",
       "imdbVotes            float64\n",
       "Type                  object\n",
       "Budget               float64\n",
       "Gross                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39 Pounds of Love\n",
       "1                 3:am\n",
       "2      500 Years Later\n",
       "3            5th World\n",
       "4                   90\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting a column\n",
    "data[\"Title\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39 Pounds of Love</td>\n",
       "      <td>70 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3:am</td>\n",
       "      <td>21 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500 Years Later</td>\n",
       "      <td>106 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5th World</td>\n",
       "      <td>75 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>14 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abel Raises Cain</td>\n",
       "      <td>82 min</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title  Runtime\n",
       "0  39 Pounds of Love   70 min\n",
       "1               3:am   21 min\n",
       "2    500 Years Later  106 min\n",
       "3          5th World   75 min\n",
       "4                 90   14 min\n",
       "5   Abel Raises Cain   82 min"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting data using ix\n",
    "data.ix[:5, [\"Title\", \"Runtime\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39 Pounds of Love</td>\n",
       "      <td>70 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3:am</td>\n",
       "      <td>21 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500 Years Later</td>\n",
       "      <td>106 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5th World</td>\n",
       "      <td>75 min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>14 min</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title  Runtime\n",
       "0  39 Pounds of Love   70 min\n",
       "1               3:am   21 min\n",
       "2    500 Years Later  106 min\n",
       "3          5th World   75 min\n",
       "4                 90   14 min"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:5, [0, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Released</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>imdbVotes</th>\n",
       "      <th>Type</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39 Pounds of Love</td>\n",
       "      <td>2005-04-08</td>\n",
       "      <td>70 min</td>\n",
       "      <td>Documentary, Biography, Romance</td>\n",
       "      <td>6.8</td>\n",
       "      <td>234.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500 Years Later</td>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>106 min</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>7.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abel Raises Cain</td>\n",
       "      <td>2005-01-23</td>\n",
       "      <td>82 min</td>\n",
       "      <td>Documentary, Biography</td>\n",
       "      <td>7.5</td>\n",
       "      <td>197.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title   Released  Runtime                            Genre  \\\n",
       "0  39 Pounds of Love 2005-04-08   70 min  Documentary, Biography, Romance   \n",
       "2    500 Years Later 2005-02-24  106 min                      Documentary   \n",
       "5   Abel Raises Cain 2005-01-23   82 min           Documentary, Biography   \n",
       "\n",
       "   imdbRating  imdbVotes   Type  Budget  Gross  \n",
       "0         6.8      234.0  movie     NaN    NaN  \n",
       "2         7.0      161.0  movie     NaN    NaN  \n",
       "5         7.5      197.0  movie     NaN    NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logical indexing\n",
    "data.ix[[True, False, True, False, False, True]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['movie', 'series', 'game'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# types of objects in the dataset\n",
    "data[\"Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets filter out the movies only\n",
    "movies = data[data[\"Type\"] == \"movie\"]\n",
    "len(movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3325"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gross is our predicted variables, remove NA values for Gross\n",
    "movies_for_model = movies[movies[\"Gross\"].notnull()]\n",
    "\n",
    "# select movies with Released Date > 2000\n",
    "movies_for_model = movies_for_model[movies_for_model[\"Released\"].notnull()]\n",
    "movies_2000 = movies_for_model[movies_for_model[\"Released\"].dt.year >= 2000]\n",
    "len(movies_for_model)\n",
    "len(movies_2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "### Converting columns to proper data types for analysis\n",
    "- Runtime\n",
    "- Release Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Released</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>imdbVotes</th>\n",
       "      <th>Type</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alone in the Dark</td>\n",
       "      <td>2005-01-28</td>\n",
       "      <td>96 min</td>\n",
       "      <td>Horror, Sci-Fi</td>\n",
       "      <td>2.3</td>\n",
       "      <td>37613.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>8178569.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Are We There Yet?</td>\n",
       "      <td>2005-01-21</td>\n",
       "      <td>95 min</td>\n",
       "      <td>Adventure, Comedy, Family</td>\n",
       "      <td>4.6</td>\n",
       "      <td>22611.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>98114471.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Be Cool</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>118 min</td>\n",
       "      <td>Comedy, Crime, Music</td>\n",
       "      <td>5.7</td>\n",
       "      <td>59443.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>75000000.0</td>\n",
       "      <td>94944017.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Beauty Shop</td>\n",
       "      <td>2005-03-30</td>\n",
       "      <td>105 min</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13197.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>38351350.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Because of Winn-Dixie</td>\n",
       "      <td>2005-02-18</td>\n",
       "      <td>106 min</td>\n",
       "      <td>Comedy, Drama, Family</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8427.0</td>\n",
       "      <td>movie</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>33508485.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Title   Released  Runtime                      Genre  \\\n",
       "12      Alone in the Dark 2005-01-28   96 min             Horror, Sci-Fi   \n",
       "20      Are We There Yet? 2005-01-21   95 min  Adventure, Comedy, Family   \n",
       "26                Be Cool 2005-03-04  118 min       Comedy, Crime, Music   \n",
       "27            Beauty Shop 2005-03-30  105 min                     Comedy   \n",
       "28  Because of Winn-Dixie 2005-02-18  106 min      Comedy, Drama, Family   \n",
       "\n",
       "    imdbRating  imdbVotes   Type      Budget       Gross  Runtime_numeric  \n",
       "12         2.3    37613.0  movie  20000000.0   8178569.0             96.0  \n",
       "20         4.6    22611.0  movie  20000000.0  98114471.0             95.0  \n",
       "26         5.7    59443.0  movie  75000000.0  94944017.0            118.0  \n",
       "27         5.5    13197.0  movie  25000000.0  38351350.0            105.0  \n",
       "28         6.4     8427.0  movie  15000000.0  33508485.0            106.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Runtime\n",
    "runtime = movies_2000[\"Runtime\"]\n",
    "runtime = runtime.str.extract(\"(\\d+) min\", expand=False)\n",
    "runtime = pd.to_numeric(runtime)\n",
    "\n",
    "movies_2000.loc[:, \"Runtime_numeric\"] = runtime\n",
    "movies_2000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movies_2000.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>imdbVotes</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alone in the Dark</td>\n",
       "      <td>Horror, Sci-Fi</td>\n",
       "      <td>2.3</td>\n",
       "      <td>37613.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>8178569.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Are We There Yet?</td>\n",
       "      <td>Adventure, Comedy, Family</td>\n",
       "      <td>4.6</td>\n",
       "      <td>22611.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>98114471.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Be Cool</td>\n",
       "      <td>Comedy, Crime, Music</td>\n",
       "      <td>5.7</td>\n",
       "      <td>59443.0</td>\n",
       "      <td>75000000.0</td>\n",
       "      <td>94944017.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Beauty Shop</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13197.0</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>38351350.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Because of Winn-Dixie</td>\n",
       "      <td>Comedy, Drama, Family</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8427.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>33508485.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Title                      Genre  imdbRating  imdbVotes  \\\n",
       "12      Alone in the Dark             Horror, Sci-Fi         2.3    37613.0   \n",
       "20      Are We There Yet?  Adventure, Comedy, Family         4.6    22611.0   \n",
       "26                Be Cool       Comedy, Crime, Music         5.7    59443.0   \n",
       "27            Beauty Shop                     Comedy         5.5    13197.0   \n",
       "28  Because of Winn-Dixie      Comedy, Drama, Family         6.4     8427.0   \n",
       "\n",
       "        Budget       Gross  Runtime_numeric  Released_Year  Released_Month  \n",
       "12  20000000.0   8178569.0             96.0           2005               1  \n",
       "20  20000000.0  98114471.0             95.0           2005               1  \n",
       "26  75000000.0  94944017.0            118.0           2005               3  \n",
       "27  25000000.0  38351350.0            105.0           2005               3  \n",
       "28  15000000.0  33508485.0            106.0           2005               2  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_2000.loc[:, \"Released_Year\"] = movies_2000[\"Released\"].dt.year\n",
    "movies_2000.loc[:, \"Released_Month\"] = movies_2000[\"Released\"].dt.month\n",
    "\n",
    "movies_1 = movies_2000.drop([\"Released\", \"Type\", \"Runtime\"], axis=1)\n",
    "movies_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with missing values\n",
    "- remove missing values\n",
    "- imputation\n",
    "    - replace missing values by mean or median\n",
    "    - replace missing values using an approximation from similar data or a probabilistic model of data\n",
    "    - represent missing values with an additional column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the movies_1 dataset: 3325\n",
      "\n",
      "Missing values in columns: \n",
      "Title                0                   \n",
      "Genre                3                   \n",
      "imdbRating           26                  \n",
      "imdbVotes            26                  \n",
      "Budget               0                   \n",
      "Gross                0                   \n",
      "Runtime_numeric      24                  \n",
      "Released_Year        0                   \n",
      "Released_Month       0                   \n"
     ]
    }
   ],
   "source": [
    "# number of missing values per column\n",
    "print \"Length of the movies_1 dataset: %s\\n\" % len(movies_1)\n",
    "\n",
    "print \"Missing values in columns: \"\n",
    "for col in movies_1:\n",
    "    print col.ljust(20), str(movies_1[col].isnull().sum()).ljust(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>imdbRating</th>\n",
       "      <th>imdbVotes</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Gross</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Alone in the Dark</td>\n",
       "      <td>Horror, Sci-Fi</td>\n",
       "      <td>2.3</td>\n",
       "      <td>37613.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>8178569.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Are We There Yet?</td>\n",
       "      <td>Adventure, Comedy, Family</td>\n",
       "      <td>4.6</td>\n",
       "      <td>22611.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>98114471.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Be Cool</td>\n",
       "      <td>Comedy, Crime, Music</td>\n",
       "      <td>5.7</td>\n",
       "      <td>59443.0</td>\n",
       "      <td>75000000.0</td>\n",
       "      <td>94944017.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Beauty Shop</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13197.0</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>38351350.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Because of Winn-Dixie</td>\n",
       "      <td>Comedy, Drama, Family</td>\n",
       "      <td>6.4</td>\n",
       "      <td>8427.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>33508485.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Title                      Genre  imdbRating  imdbVotes  \\\n",
       "12      Alone in the Dark             Horror, Sci-Fi         2.3    37613.0   \n",
       "20      Are We There Yet?  Adventure, Comedy, Family         4.6    22611.0   \n",
       "26                Be Cool       Comedy, Crime, Music         5.7    59443.0   \n",
       "27            Beauty Shop                     Comedy         5.5    13197.0   \n",
       "28  Because of Winn-Dixie      Comedy, Drama, Family         6.4     8427.0   \n",
       "\n",
       "        Budget       Gross  Runtime_numeric  Released_Year  Released_Month  \n",
       "12  20000000.0   8178569.0             96.0           2005               1  \n",
       "20  20000000.0  98114471.0             95.0           2005               1  \n",
       "26  75000000.0  94944017.0            118.0           2005               3  \n",
       "27  25000000.0  38351350.0            105.0           2005               3  \n",
       "28  15000000.0  33508485.0            106.0           2005               2  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will use median for rest of the variables\n",
    "movies_2 = movies_1\n",
    "for col in [\"imdbRating\", \"imdbVotes\", \"Budget\", \"Runtime_numeric\"]:\n",
    "    median = movies_2[col].median()\n",
    "    movies_2[col].fillna(median)\n",
    "\n",
    "movies_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to deal with missing values during testing or prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.00000000e+00,   3.00000000e+00,   2.00000000e+07,\n",
       "          1.02000000e+02],\n",
       "       [  1.00000000e+00,   3.62680000e+04,   4.00000000e+00,\n",
       "          3.00000000e+00]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import Imputer\n",
    "fit_data = movies_2[[\"imdbRating\", \"imdbVotes\", \"Budget\", \"Runtime_numeric\"]]\n",
    "imp = Imputer(missing_values=np.nan, strategy=\"median\", axis=0)\n",
    "imp = imp.fit(fit_data)\n",
    "imp.transform([[2, 3, np.nan, np.nan], [1, np.nan, 4, 3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* Put everything inside the global mpl namespace */\n",
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       "        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",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "\n",
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       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "    this.header = titletext[0];\n",
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       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
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       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
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       "        return fig.key_event(event, event['data']);\n",
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       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
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       "    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",
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       "        return false;\n",
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       "\n",
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       "        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",
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       "\n",
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       "        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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overriden (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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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": {
      "text/html": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115152950>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "movies_2.boxplot(\"Budget\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Outliers can be detected via finding observations which are +/- a few standard deviations away from mean. This method is a little problematic because the calculations are not robust\n",
    "- Robust statistics - remove/clip data from either end using ranking (or percentiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "        this.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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overriden (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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fbdbb90>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "high = movies_2[\"Budget\"].quantile(0.95)\n",
    "low = movies_2[\"Budget\"].quantile(0.01)\n",
    "\n",
    "movies_2 = movies_2[(movies_2[\"Budget\"] <= high) & (movies_2[\"Budget\"] >= low)]\n",
    "movies_2.boxplot(\"Budget\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to use categorical data (k-hot encoding)\n",
    "- We will use the genres column to demonstrate one-hot encoding but the technique is general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Biography</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>...</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>News</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Short</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Action  Adventure  Animation  Biography  Comedy  Crime  Documentary  \\\n",
       "12       0          0          0          0       0      0            0   \n",
       "20       0          1          0          0       1      0            0   \n",
       "26       0          0          0          0       1      1            0   \n",
       "27       0          0          0          0       1      0            0   \n",
       "28       0          0          0          0       1      0            0   \n",
       "\n",
       "    Drama  Family  Fantasy   ...     Musical  Mystery  News  Romance  Sci-Fi  \\\n",
       "12      0       0        0   ...           0        0     0        0       1   \n",
       "20      0       1        0   ...           0        0     0        0       0   \n",
       "26      0       0        0   ...           0        0     0        0       0   \n",
       "27      0       0        0   ...           0        0     0        0       0   \n",
       "28      1       1        0   ...           0        0     0        0       0   \n",
       "\n",
       "    Short  Sport  Thriller  War  Western  \n",
       "12      0      0         0    0        0  \n",
       "20      0      0         0    0        0  \n",
       "26      0      0         0    0        0  \n",
       "27      0      0         0    0        0  \n",
       "28      0      0         0    0        0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres = movies_2[\"Genre\"].str.get_dummies(\", \")\n",
    "genres_list = list(genres)\n",
    "movies_3 = pd.concat([movies_2, genres], axis=1)\n",
    "movies_3 = movies_3.drop(\"Genre\", axis=1)\n",
    "genres.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voila! We have a dataset ready for analysis\n",
    "- Last step, we will partition the data set into test and training size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "training_set_size = 0.6\n",
    "\n",
    "mask = np.random.rand(len(movies_3)) <= 0.6\n",
    "training_set = movies_3[mask]\n",
    "test_set = movies_3[~mask]\n",
    "\n",
    "len(movies_3), len(training_set), len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Special Treat: Split/Apply/Combine data using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Action</th>\n",
       "      <th>Adult</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Biography</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Family</th>\n",
       "      <th>...</th>\n",
       "      <th>News</th>\n",
       "      <th>Reality-TV</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Short</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Talk-Show</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Released_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>99</td>\n",
       "      <td>45</td>\n",
       "      <td>41</td>\n",
       "      <td>182</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>94</td>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "      <td>173</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>70</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Action  Adult  Adventure  Animation  Biography  Comedy  Crime  \\\n",
       "Released_Year                                                                  \n",
       "2014.0             82      0         51         19         31      99     45   \n",
       "2015.0             66      0         40         13         20      94     40   \n",
       "2016.0             43      0         30         12         10      42     15   \n",
       "2017.0              1      0          1          0          0       1      0   \n",
       "2018.0              0      0          0          0          0       0      0   \n",
       "\n",
       "               Documentary  Drama  Family   ...     News  Reality-TV  Romance  \\\n",
       "Released_Year                               ...                                 \n",
       "2014.0                  41    182      16   ...        3           0       38   \n",
       "2015.0                  27    173      16   ...        0           1       35   \n",
       "2016.0                  11     70      11   ...        1           0       17   \n",
       "2017.0                   0      2       0   ...        0           0        1   \n",
       "2018.0                   0      0       0   ...        0           0        0   \n",
       "\n",
       "               Sci-Fi  Short  Sport  Talk-Show  Thriller  War  Western  \n",
       "Released_Year                                                           \n",
       "2014.0             35     42      9          1        75    4        4  \n",
       "2015.0             29     39      7          0        81    3        2  \n",
       "2016.0             13     22      3          0        25    0        2  \n",
       "2017.0              1      0      0          0         0    0        0  \n",
       "2018.0              1      0      0          0         0    0        0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.loc[:, \"Released_Year\"] = pd.to_numeric(movies[\"Released\"].dt.year)\n",
    "\n",
    "genres = movies[\"Genre\"].str.get_dummies(\", \")\n",
    "movies_x = pd.concat([movies, genres], axis=1)\n",
    "\n",
    "genres_list = list(genres)\n",
    "g = movies_x.groupby(\"Released_Year\")\n",
    "genres_vs_year = g[genres_list].sum()\n",
    "genres_vs_year.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
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       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
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       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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       "\n",
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       "\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overriden (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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1154d8650>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres_vs_year[[\"Animation\", \"Comedy\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Melting & Pivoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Comedy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39 Pounds of Love</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3:am</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500 Years Later</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5th World</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title  Released_Year  Runtime_numeric  Animation  Comedy\n",
       "0  39 Pounds of Love         2005.0             70.0          0       0\n",
       "1               3:am         2005.0             21.0          0       0\n",
       "2    500 Years Later         2005.0            106.0          0       0\n",
       "3          5th World         2005.0             75.0          0       0\n",
       "4                 90         2005.0             14.0          0       0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime = movies_x[\"Runtime\"].copy()\n",
    "runtime = runtime.str.extract(\"(\\d+) min\", expand=False)\n",
    "runtime = pd.to_numeric(runtime)\n",
    "movies_x.loc[:, \"Runtime_numeric\"] = runtime\n",
    "movies_subset = movies_x[[\"Title\", \"Released_Year\", \"Runtime_numeric\", \"Animation\", \"Comedy\"]]\n",
    "movies_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39 Pounds of Love</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3:am</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500 Years Later</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5th World</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Title  Released_Year  Runtime_numeric      Genre  Value\n",
       "0  39 Pounds of Love         2005.0             70.0  Animation      0\n",
       "1               3:am         2005.0             21.0  Animation      0\n",
       "2    500 Years Later         2005.0            106.0  Animation      0\n",
       "3          5th World         2005.0             75.0  Animation      0\n",
       "4                 90         2005.0             14.0  Animation      0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_melted = pd.melt(movies_subset, id_vars=[\"Title\", \"Released_Year\", \"Runtime_numeric\"], \n",
    "        value_vars=[\"Animation\", \"Comedy\"], \n",
    "        var_name=\"Genre\",\n",
    "       value_name=\"Value\")\n",
    "movies_melted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Felix - Ein Hase auf Weltreise</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Pooh's Heffalump Movie</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Robots</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Valiant</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Baby Eat Baby</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Title  Released_Year  Runtime_numeric  \\\n",
       "80   Felix - Ein Hase auf Weltreise         2014.0             81.0   \n",
       "175          Pooh's Heffalump Movie         2005.0             68.0   \n",
       "184                          Robots         2005.0             91.0   \n",
       "228                         Valiant         2005.0             76.0   \n",
       "338                   Baby Eat Baby         2004.0             12.0   \n",
       "\n",
       "         Genre  Value  \n",
       "80   Animation      1  \n",
       "175  Animation      1  \n",
       "184  Animation      1  \n",
       "228  Animation      1  \n",
       "338  Animation      1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_melted = movies_melted[movies_melted[\"Value\"]==1]\n",
    "movies_melted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Runtime_numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1897.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1899.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1902.0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Released_Year   Genre  Runtime_numeric\n",
       "0         1897.0  Comedy              1.0\n",
       "1         1899.0  Comedy              1.0\n",
       "2         1900.0  Comedy              1.5\n",
       "3         1901.0  Comedy              1.2\n",
       "4         1902.0  Comedy              1.5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime_vs_year = movies_melted.groupby([\"Released_Year\", \"Genre\"], as_index=False)\n",
    "runtime_vs_year = runtime_vs_year[\"Runtime_numeric\"].mean()\n",
    "runtime_vs_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Genre</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Comedy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Released_Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1897.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Genre          Animation  Comedy\n",
       "Released_Year                   \n",
       "1897.0               NaN     1.0\n",
       "1899.0               NaN     1.0\n",
       "1900.0               NaN     1.5\n",
       "1901.0               NaN     1.2\n",
       "1902.0               NaN     1.5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime_vs_year_pivoted = runtime_vs_year.pivot(index=\"Released_Year\", columns=\"Genre\", values=\"Runtime_numeric\")\n",
    "runtime_vs_year_pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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",
       "        this.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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overriden (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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1179a4a90>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime_vs_year_pivoted.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thanks!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}