{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Seaborn Exercises\n",
    "\n",
    "Time to practice your new seaborn skills! Try to recreate the plots below (don't worry about color schemes, just the plot itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "We will be working with a famous titanic data set for these exercises. Later on in the Machine Learning section of the course, we will revisit this data, and use it to predict survival rates of passengers. For now, we'll just focus on the visualization of the data with seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who adult_male deck  embark_town alive  alone  \n",
       "0    man       True  NaN  Southampton    no  False  \n",
       "1  woman      False    C    Cherbourg   yes  False  \n",
       "2  woman      False  NaN  Southampton   yes   True  \n",
       "3  woman      False    C  Southampton   yes  False  \n",
       "4    man       True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.**\n",
    "\n",
    "** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x11d0389e8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K4njHJIhgC3K5WJeI9Dm5CX+n5MLEJIhgC3K5WJeIkgkTk0IivcsJtiCXi3WJKJlwjEkh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\nIiIiofx/wXPk8jWAjFAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d038cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fc5ca90>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MJZ8lPrQFwOylNPqWZenxxx/X0NCQsrKy9KMf/Ui33nprKn/FgpLoyp6WlLJLPo9duaJR\njyepD23hyQHAtaQ0+t3d3ZqYmNDLL7+sEydOaO/evTp06FAqf8Wik8pLPifzBGLCJ3rxkZbAzKU0\n+oFAQHfffbck6c4779S7776byps3XjJPIDdasgFO5SksPtISn/XZx2EoFNLoNf7NzfQXAimNfigU\nUl5e3r9v3OHQ1NSU7Hb+SOhGSfajI1P1wE82wKk8hcVHWuKzPvs4/NzZs9LAwLTjeCGQ4ug7nU6F\nw+HY98kE/8zUVNz9l222j++oOEKRiDKV+FOvkjkulbd1eWJCoUjkhq7r3Oioxp95RorzoA5HIhr9\n1rdm/cD/5NVUqp7Sg5FIwrVL0oejo4pkZyd80gqOj2sqiSfA/ybeK8V0kG7zpfJxmO5s1if/350C\nr7/+un7/+99r7969GhgY0KFDh/T8889f8/hAIJCqXw0ARnG5XDP6uZRG/9N/vSNJe/fu1cqVK1N1\n8wCAWUpp9AEACxunwQDAIEQfAAxC9AHAIEQfAAwyLxdcS7dr9Jw4cUJPPvmk/H6/zp49q9bWVtnt\ndpWWlqq9vV2S1NnZqY6ODmVmZsrr9aqmpmZ+F52EyclJ7dixQ++//76i0ai8Xq9uu+22tJhvampK\nbW1tOn36tOx2u3bv3q2srKy0mO3TRkZGtGnTJr344ovKyMhIq/k2btwop9MpSbrlllvk9XrTar7n\nn39eb775pqLRqDwej9atW5ea+ax58Prrr1utra2WZVnWwMCA1dzcPB/LSIlf/vKXVl1dnfXggw9a\nlmVZXq/X6u/vtyzLsh577DHrjTfesP71r39ZdXV1VjQatYLBoFVXV2dNTEzM57KTcvToUevHP/6x\nZVmWNTo6atXU1KTNfG+88Ya1Y8cOy7Is6+2337aam5vTZrZPRKNR6/vf/7711a9+1RoeHk6r+SKR\niLVhw4b/2JZO87399tuW1+u1LMuywuGw9fOf/zxl883L6Z10ukZPUVGRDh48GPv+1KlTqqiokCRV\nV1err69PJ0+elMvlksPhkNPpVHFxcey9DAvZfffdp61bt0qSrl69qoyMDA0ODqbFfPfcc49++MMf\nSpLOnTungoKCtJntE0888YTq6+t18803y7KstJrvvffe0+XLl9XU1KSHH35YJ06cSKv5/vSnP+n2\n22/X9773PTU3N6umpiZl881L9K91jZ7FaP369crIyIh9b33qbQ+5ubkKhUIKh8P/MW9OTo6CweAN\nXedMLF26VDk5OQqFQtq6dau2bduWVvPZ7Xa1trZqz549qqurS6vZjh07pmXLlqmqqio216f/G1vs\n82VnZ6upqUkvvPCCHn/8cT366KNpdf9dunRJ7777rp5++unYfKm6/+blnP5MrtGzWHx6jnA4rPz8\nfDmdToVCoWnbF4MPPvhAjzzyiBoaGnT//ffrJz/5SWxfOsy3b98+jYyMaPPmzYpEIrHti322Y8eO\nyWazqbe3V0NDQ2ppadGlS5di+xf7fMXFxSoqKop9XVhYqMHBwdj+xT5fYWGhSkpK5HA4tHLlSi1Z\nskTnz5+P7Z/NfPNS2i984Qv6wx/+IEkaGBjQ7bffPh/LmBN33HGH+vv7JUk9PT1yuVwqLy9XIBDQ\nxMSEgsGghoeHVVpaOs8rTezChQtqamrSD37wA23YsEGStGbNmrSY75VXXoldF2rJkiWy2+0qKyvT\n8ePHJS3u2STpyJEj8vv98vv9Wr16tfbv36+77747Le47STp69Kj27dsnSTp//rxCoZCqqqrS5v5z\nuVz64x//KOnj+a5cuaLKysqUzDcvr/TXr1+v3t5ebdmyRdLH1+hJFy0tLdq1a5ei0ahKSkpUW1sr\nm82mxsZGeTweWZYln8+nrKys+V5qQs8995zGxsZ06NAhHTx4UDabTTt37tSePXsW/Xz33nuvtm/f\nroaGBk1OTqqtrU2rVq1SW1vbop/tWtLpsbl582Zt375dHo9Hdrtd+/btU2FhYdrcfzU1Nfrzn/+s\nzZs3x/7acfny5SmZj2vvAIBB0uNEOgAgKUQfAAxC9AHAIEQfAAxC9AHAIEQfAAxC9GGk7du3q7a2\nVr/97W/neynADcXf6cNIa9as0TvvvCOHY17enwjMGx7xME5zc7Mkye12684779Rf/vIXjY6O6qab\nbtIvfvELLVu2TJWVlSorK9PIyIh+/etf64UXXtBrr72mqakpfelLX9Kjjz46z1MAM8PpHRjnmWee\nkSQ9/fTTunjxojo6OvTaa69pxYoV+s1vfiNJ+uijj+T1etXV1aW+vj6dOnVKR48eVVdXlz788MPY\nccBiwyt9GOvWW29VS0uLOjs7dfr0aQ0MDGjFihWx/Z///OclSX19fXrnnXe0ceNGWZalSCSi5cuX\nz9eygVkh+jDWqVOn5PP59O1vf1u1tbWy2+3/cU32Ty5cNTU1pYceekgPP/ywpI8/D+LTn6EALCac\n3oGRLMtSf3+/vvjFL+rBBx/UqlWr1Nvb+18/zKeyslKvvvqqLl++rMnJSTU3N+t3v/vdPKwamD1e\n6cNINptNX/va1/TII4/ogQcekMPh0OrVq/XPf/4ztv8TX/7ylzU0NKRvfOMbmpqaUnV1tb7+9a/P\n19KBWeFPNgHAIJzeAQCDEH0AMAjRBwCDEH0AMAjRBwCDEH0AMAjRBwCDEH0AMMj/A/OVtY18YzY3\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ff50668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f23da90>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cXAyVSoWCggIolUrExMSgqKhIZAlE5CN9IsOwMXuYv8sIWm+Xnei2toQG/759+6BQKPDn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\nMv8P9qlbLeIn4+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12139aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f215320>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nUy6LMzL5j7YDeKIhSjLcXJNTiqpp/Ka/mbimkmt18mDVBtDh5OQAFsWUauM8\nk5kuI0g+KYyE/BTa3XP2Ffx29Ml5fuweY5CfaAi9rxW16VX0iSGkletRbn5EBHKXiMkZffYBwpEE\nbxzooWvAR6bLyk2NZdx/czVvHuolHEmQl2UjHlfpG/Lz7rEBItEEFSVuNM3o+5+cMp73Skf52te+\n9rWlFuJCDA0NUVKytKmI/niUszMmaOVaHaxwZKfm6AI4zVa6A5OoukZIjRNTVVRdIzrDhRNVEwxN\nz9rtD05R5sji4ZpGcqefFJ7rOEq7bwxV15mIBunyT3Bish9/PEpC1+gJTOKPR9g/2kVC14iqCfqC\nHhRJNgSetxfV0OEbS23bFTMfq27APF9XScFvRzElUzNnbEurN8NId3ottwS96ySM9iSDwqO9EIsg\nV61fdHEF4A3EGJoxyc5hNzMwGiCe0PAHY3T2TTHuCTExFUHTdDy+KBNTEQ6dGiIQihONq4xOhrBa\nFFQ1/Zu6bm0BxflX1sjTC+lOYfFfBHeUrUbVVY6O95Fnc/KRyg1kmMy80HWcnsAkq7MKeXe403BM\nh3+M+8rXJSt4dWjMr+DNQWO//ZapYX7Td4Ye/yR1WYW0zLi5QLIXz2xaZqWVAmwtqGI8EiCqJbi5\nuJZthSvJttrZN9KJy2zjQxXrsYvMnveNXLYK7vojtONvgsmMvPkepLJVqJKC3noQFHNyyPq+Fw3H\nab0tiFvs0rD9ulJkWaJ7wEtulp0pfyRVqQsQiiQMwV2AvhG/IbcfoLTAiSRJ+IJRVlXmsGH11VUE\nKRT/RSBLMveWr+fecqMV9wer0m13+4NTtE0HgCGZuTMzA2cgOIXTZCUwI/c/qiZSqZlHJ/rIsWYQ\nnuG6KbS78USDhsyeMkeW4UkDoDYzn2yrHQmJ1dMtHG4pWcUtJat+l8sWAPKarchrtqa2dV1D72uB\ncLJlg77vRbDYIRZO7SMVLL+JTpcLJkVmR0MZOxqSNRRvHeplZDztljWbZJwZZjy+9O8wL8tY6AVQ\nXuzmmlX5NHdM4PFGGBgJUFZ09VTviuDuB0DVNY6O9/HWYCueaPJL9dHKjWRP99YptLsMgVqAVu8I\n95TXkzGdd1/jzmdylgKPaSorHNnT53DzyVXX85HKDdimz7Uuu4Q/WLWNxrwKZEnCJMncXFzLc51H\n+WXvaV7uPcU3j/0Gf+zq8kcuNnosgnbmXbTTe9CjxliOPtKTDu6eJ7sQzqf2Zhcib3tgkSRdHoSj\nCU62jdGuZiZBAAAgAElEQVTcMUE8oc67jzcQ5WjLCB29Uwb//LZrS6gsTca2bFaF2opsGtcVke1O\nxmAKcjK4+8Yqbt1SjsWcnKBRVuhkfW0ev9rdya79PTQ1j/CzV1tTfX/OE0+oNHdMcLJtjHBkblbe\n5Yyw+D8A/3RmN6en++z8vPsEn15zIz9oO8DUtNWnSDLZFhtjMxp5mWWZF7qOp0YpusxWnGYr/hnF\nQtmWDJ667m7CiTh2k5lmzxD/3XUMdfqYLKudV/uaOTKe7AOjoRNKxAnO6Nrpi0doGu/l5pIZueeC\ni0aPRUj8+Btw3qV28FeYHn0KyZ7070oZbpJ5WGnlImUVoAem+8J4RlB/8R2kR78iAryXgGA4zo9e\nbk65aw6ftvPovWtS1bcAg6MBnnutNeWTr6vI5r6bqwGwWU18dGcdx1pGeOtQH80dEzR3THDDxhI2\nrC7Eakk65cLRBKqajJT1jwR4eXcn3QNegyzHz46yqipZxZtQNX7yylnGPcnf/P7jgzx23xqcGVdG\nzYaw+N8nvYHJlNKHpLvmha7jKaUPMBjyUptZkLLUJSQK7G7D/NxjE/3sLF2TmtJllhU+WrWBuKYy\nEvYRTsR4tb85pfQh2Ytn18BZgzxt3rk+fxHE/eDobYfTSh/AN4529kBycPp4fzJls+GO9N8zXMke\nPMEZDcEmB9Hbjy6e0FcxZ9rHDT76iakwHX1TeHwRJr3J31xT84ghENvW48HjjTA2GcIfjKX2mcmR\n0yMoSjJVMxxNcPTMiMHP3z3gZXYfVZNJZtIbweOLTAeJ07/5YDjO6XNzY3KXK8Lif5+o2twCEFWf\nu3baM0hkugCr2p1HhsnCQNDYLfDgSHoA+7aCKmyKmb889CL+eBSrbCLHNneyljarxNymmCmwKYxO\nP12UZGTSmF/xwS5OgK7OdSXorYdJvP1TQEcqrka+51PQfSrp8gn5YaB97om0+V0SgvfH7KArwNHm\nYYan/faVpe55W12/9FY7E94IkgTXrSmck56pahr//vwpguEEiiKRm2mfc466qpyUe0eRJaIxlR+8\nmIzbFeY55uw/n6yXK0Lxv08qXblUu/NT6ZImSeZD5dfww/aDKZeLy2yly58eotLuG2Nn6WpkSUq1\nfM2zORmcUVT1znA7nf7xlOsnqiWYioYNToWGvHKcZmtq2hfA7WVr2JRfwYmJfiRJ4tqcUixiqPcH\nRl7ViHboV3B+cI41A31Gkzt9qAPt9R8Y/Pz6UAdY7RCdtgBdOUg11y2i1Fcv9dW5HG0eIRpL3kgz\nbKaU0gfoHvDRsLaQbsnL+W7KmU4LE95knEvXk9Z+w9pCg9XvzLAw5U/+1lRVZ9IbRpbgvO4uL3Zx\n746VXFOXj8cXIR5X2X2kP3X8yHiQDJsplSFktSisq8lbsPfhUiM0xPtEkiT+17pbODDajTcWoiGv\nnBJHFpXuXA6MdmGSZBKaxos9xnm645EATpOVYCJKnbsAt8XG+IwYAMDkrEBiRI3z5PrbaJkaptDu\nZlN+BZIkUZdZQH9wirXZxdRN99qfXdEreG+0lv2o7/4coiHk9TuQb3wIyebE9NhX0Zr3g66BxY72\n5o8Mx+n+yTnnkrc/BJFgsr/P2m1IYg7yJcHttPLx++tp7pjApEgkVI13jxmD61P+KHariUg0QVmR\nG7tNwRswTqsLhGKYTTK6rrNmZS6DY8bEioSq89GdtQyMBnA7LayuymH3kT5OtY1htZgozJ37eW5Y\nXYDFrBBPaKxZmYPbeeXEdITi/wBYFBM7imsMa9nWDO5eUQ8klfyv+k6numTKSJyY6E9Z7i3eEW4u\nNgZfHSYLDXnlvDOcdhvUZ5dQl1VIXZZxZF9jfoVw5/yO6FNjqK8+w3kzUWt6DSm3BKl+O1KGG6Xx\nzuR+4QDau8+nrXlAXrMNbWbuvs2BvGozknWuu0Dwu+NyWNhyTTEAXn+UAyeHUj59SYKOvrQLtXfI\nx4bVxlkIFrNMa3e6Vfmpc+NcU5fHxFT6My3Od1BZmkllabKn1Zn2cZrOJJ8QYvEY/mAMSUp9XVBk\niVVVOWS7r8xe/ULxX2K8sTCv9jVTZHej6Ro5Vgdljmx+3X9m1n4hVk1b7vk2Fx+v28JUNESbdwR/\nPMrqrCIeq9nEW4NtHJ/oI9/m4t7ydWQLS/KSoA93pn/F02hdp9H62yDgQV69Bbl+O/poD+StAO8Y\nOLNQGu9Crm1AyipEO7MXJBkdUH/1XeT67cirNqF1HEM7uRssdpRNdyMVlKOPD6AefgXCgdR+ggsz\n5glx6NQw0ViCdTV51FXmkOmy8tAddRw5PYKu6+Rl2zl0ylj4GAwlKCt0Mu4Jk+W2kptl50y7cX61\nIkvkZtkIhuKUFDi5/fpKzrSPc7ZzEuesHv3naawvYmIqjCRJNNQXXrFKHy5S8Xu9Xv7mb/6G3t5e\n/uEf/oG//uu/5ktf+hKZmaLj42z+8fTbhkEoWwqraMyr4NX+ZkNgts07mooJBAMTHBvv41d9p1Mx\ngBMT/RTaXbzSl7xhnGWEDt8Y//u6e8Tc1kuAVLQSgwkHycKs6Y6ram8Lun8K7cBLSZcPQDiAVJB8\n0pJXbUKqWk/imS9BOJBU/j1n0L2jaO++yPnITKL7NMrjT6P+99+kir7UnjNgtiKvvGbRrvdKIxpL\n8NyrrUSiyafm7gEfHzUrVJZmUlrgovTWZDGVNxDl8Olhwz28f8RHePq44fEQRfMEYk+2jaeCsV0D\nXk61jbHveNqFZLMYM+MkCdbX5pF1BSv7mVxUOudXv/pV1q9fz9TUFA6Hg4KCAj7/+c8vtGxXHKNh\nv0HpAzSN9dIbmGR9TglOsxWrbKIhr9yQew9wYLTLMOszoWscHO027DMY8jIUMuYWCz4YUlY+yh1/\nmCy8MluR6jallP55tJZ9aaUPoMbRWg+hHn0d9fCv0doOp5R56pjmfRiGe8TC6MfemLtfquunYD56\nhvwppX+es12TnD43zv7jg4x5kvGwTKeVO2+owplhxmySqavITin980z5otSUZ6HIElZLsohrZgaO\nrkNzh/GJIBJTqavMTlX63nlDJW6nlZbOCd49NpCq9A1HEjSdGebQqaFU6uiVwEVZ/P39/fz+7/8+\nP/nJT7BYLHz2s5/l/vvvv6gXmJiY4MEHH+T73/8+iqLwpS99CVmWqa2t5emnn/6dhL/ccJltmGXF\nMAErnIjz3ZY9qe1HqzdxTW4pR8f7DI3Vsq0ZhoKv82sz2zOYJJlMi/AjXyrktdcjr70eAN03QeLc\nEcMTgOTKQZ8aNRyjHX0dznc/tcy1/iRnDvrsDp7ztGmW3Llz1gRpMp1zC6EGRvwpBX3w1BAP3l5L\nSb4Ts0nmxoYyqldkEY2pnOv1GJ4ApgJRpqZbNKgxlax5zu1ypLN8IGnhN9YXUVmSicthobzYxSvv\ndNHanQzsHzw5xF3bq9h3fADfdCD5yJkRnvjQWsNgl8uVi7L4FUXB7/enXAzd3d3I8nsfmkgkePrp\np7HZkj+Qb33rWzz55JP86Ec/QtM0du3a9R5nuLzRdZ1O3zgdvjF0XcduMvORymtRpt+nbGsGE7OU\n+esDLZhlhS0FlanirUK7iyd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v5Lste1JPFPXZxdxSXMd3W/akXEkPVW3k9rI17+etX3L0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\n6Kk1bwetsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f2395f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f207ef0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d84cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d72da58>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YY7hw4UKRYhARuRPyiuE0sDdNEkuXLqVx48ZER0eTmprK1q1bqVixIvv37yc0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\nISUJERFx6P8AZLY+O+sfZSgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d8229b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "# REPLICATE EXERCISE PLOT IMAGE BELOW\n",
    "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n",
    "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11d81c240>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kqqqqAACZmZnYvHmzfX8AIhvi+HBcXM28F/jPf/6DN998E3369IGbmxsGDRoE\nlUqF7373u/jwww/h4eGBAQMGICgoCNHR0SgoKMC2bdtgNBoxePBgbNmyhSeBqdfi+HBcDKheqqqq\nCgUFBYiKigIALFu2DL/61a8QGhpq176I5IDjwzHwHFQvNWTIEJw5cwYzZ86EQqHAT37yEw4+ov/h\n+HAM3IMiIiJZ4iQJIiKSJQYUERHJEgOKiIhkiQFFRESyxIAiIiJZYkAREZEs/T9ZtOJ0ZIoq9gAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6fcc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!\n",
    "\n",
    "### That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}