{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Matrix Plots\n",
    "\n",
    "Matrix plots allow you to plot data as color-encoded matrices and can also be used to indicate clusters within the data (later in the machine learning section we will learn how to formally cluster data).\n",
    "\n",
    "Let's begin by exploring seaborn's heatmap and clutermap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flights = sns.load_dataset('flights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tips = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>January</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>February</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>March</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>April</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     month  passengers\n",
       "0  1949   January         112\n",
       "1  1949  February         118\n",
       "2  1949     March         132\n",
       "3  1949     April         129\n",
       "4  1949       May         121"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap\n",
    "\n",
    "In order for a heatmap to work properly, your data should already be in a matrix form, the sns.heatmap function basically just colors it in for you. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675734</td>\n",
       "      <td>0.598315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>0.675734</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>0.598315</td>\n",
       "      <td>0.489299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_bill       tip      size\n",
       "total_bill    1.000000  0.675734  0.598315\n",
       "tip           0.675734  1.000000  0.489299\n",
       "size          0.598315  0.489299  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matrix form for correlation data\n",
    "tips.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c31d470>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BNgJHA7uBdZm5o+f6HwPrgHu6p97YfaLNjEy2kooxwDbC6cDizDw+Io4DLuqem/J84LWZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c35c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tips.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c97a978>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cPX0wS5Zuw9/enqfoc6uQa7aZJNs48A8ze4XkyLbMzH4H4O7/lsvgsul4G8Kbi15kyLBz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c58c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tips.corr(),cmap='coolwarm',annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or for the flights data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1949</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>1960</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>January</th>\n",
       "      <td>112</td>\n",
       "      <td>115</td>\n",
       "      <td>145</td>\n",
       "      <td>171</td>\n",
       "      <td>196</td>\n",
       "      <td>204</td>\n",
       "      <td>242</td>\n",
       "      <td>284</td>\n",
       "      <td>315</td>\n",
       "      <td>340</td>\n",
       "      <td>360</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>118</td>\n",
       "      <td>126</td>\n",
       "      <td>150</td>\n",
       "      <td>180</td>\n",
       "      <td>196</td>\n",
       "      <td>188</td>\n",
       "      <td>233</td>\n",
       "      <td>277</td>\n",
       "      <td>301</td>\n",
       "      <td>318</td>\n",
       "      <td>342</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>132</td>\n",
       "      <td>141</td>\n",
       "      <td>178</td>\n",
       "      <td>193</td>\n",
       "      <td>236</td>\n",
       "      <td>235</td>\n",
       "      <td>267</td>\n",
       "      <td>317</td>\n",
       "      <td>356</td>\n",
       "      <td>362</td>\n",
       "      <td>406</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>129</td>\n",
       "      <td>135</td>\n",
       "      <td>163</td>\n",
       "      <td>181</td>\n",
       "      <td>235</td>\n",
       "      <td>227</td>\n",
       "      <td>269</td>\n",
       "      <td>313</td>\n",
       "      <td>348</td>\n",
       "      <td>348</td>\n",
       "      <td>396</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>121</td>\n",
       "      <td>125</td>\n",
       "      <td>172</td>\n",
       "      <td>183</td>\n",
       "      <td>229</td>\n",
       "      <td>234</td>\n",
       "      <td>270</td>\n",
       "      <td>318</td>\n",
       "      <td>355</td>\n",
       "      <td>363</td>\n",
       "      <td>420</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>135</td>\n",
       "      <td>149</td>\n",
       "      <td>178</td>\n",
       "      <td>218</td>\n",
       "      <td>243</td>\n",
       "      <td>264</td>\n",
       "      <td>315</td>\n",
       "      <td>374</td>\n",
       "      <td>422</td>\n",
       "      <td>435</td>\n",
       "      <td>472</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>148</td>\n",
       "      <td>170</td>\n",
       "      <td>199</td>\n",
       "      <td>230</td>\n",
       "      <td>264</td>\n",
       "      <td>302</td>\n",
       "      <td>364</td>\n",
       "      <td>413</td>\n",
       "      <td>465</td>\n",
       "      <td>491</td>\n",
       "      <td>548</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>148</td>\n",
       "      <td>170</td>\n",
       "      <td>199</td>\n",
       "      <td>242</td>\n",
       "      <td>272</td>\n",
       "      <td>293</td>\n",
       "      <td>347</td>\n",
       "      <td>405</td>\n",
       "      <td>467</td>\n",
       "      <td>505</td>\n",
       "      <td>559</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>136</td>\n",
       "      <td>158</td>\n",
       "      <td>184</td>\n",
       "      <td>209</td>\n",
       "      <td>237</td>\n",
       "      <td>259</td>\n",
       "      <td>312</td>\n",
       "      <td>355</td>\n",
       "      <td>404</td>\n",
       "      <td>404</td>\n",
       "      <td>463</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>119</td>\n",
       "      <td>133</td>\n",
       "      <td>162</td>\n",
       "      <td>191</td>\n",
       "      <td>211</td>\n",
       "      <td>229</td>\n",
       "      <td>274</td>\n",
       "      <td>306</td>\n",
       "      <td>347</td>\n",
       "      <td>359</td>\n",
       "      <td>407</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>104</td>\n",
       "      <td>114</td>\n",
       "      <td>146</td>\n",
       "      <td>172</td>\n",
       "      <td>180</td>\n",
       "      <td>203</td>\n",
       "      <td>237</td>\n",
       "      <td>271</td>\n",
       "      <td>305</td>\n",
       "      <td>310</td>\n",
       "      <td>362</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>118</td>\n",
       "      <td>140</td>\n",
       "      <td>166</td>\n",
       "      <td>194</td>\n",
       "      <td>201</td>\n",
       "      <td>229</td>\n",
       "      <td>278</td>\n",
       "      <td>306</td>\n",
       "      <td>336</td>\n",
       "      <td>337</td>\n",
       "      <td>405</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year       1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  \\\n",
       "month                                                                         \n",
       "January     112   115   145   171   196   204   242   284   315   340   360   \n",
       "February    118   126   150   180   196   188   233   277   301   318   342   \n",
       "March       132   141   178   193   236   235   267   317   356   362   406   \n",
       "April       129   135   163   181   235   227   269   313   348   348   396   \n",
       "May         121   125   172   183   229   234   270   318   355   363   420   \n",
       "June        135   149   178   218   243   264   315   374   422   435   472   \n",
       "July        148   170   199   230   264   302   364   413   465   491   548   \n",
       "August      148   170   199   242   272   293   347   405   467   505   559   \n",
       "September   136   158   184   209   237   259   312   355   404   404   463   \n",
       "October     119   133   162   191   211   229   274   306   347   359   407   \n",
       "November    104   114   146   172   180   203   237   271   305   310   362   \n",
       "December    118   140   166   194   201   229   278   306   336   337   405   \n",
       "\n",
       "year       1960  \n",
       "month            \n",
       "January     417  \n",
       "February    391  \n",
       "March       419  \n",
       "April       461  \n",
       "May         472  \n",
       "June        535  \n",
       "July        622  \n",
       "August      606  \n",
       "September   508  \n",
       "October     461  \n",
       "November    390  \n",
       "December    432  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.pivot_table(values='passengers',index='month',columns='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cd09320>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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riowF5tZtXwCs119s91DMzFYvhwCdknYH3gD8CHhJ3eNjgTnAPFJi6bm9T04oZmYVUNQk\nrzxOAoCkKcBRwHck7RwRU4E9gSnAbcApktYA1gK2Amb0F9sJxcysAkqeNvxF4II86H4vcFVEdEk6\nC5hOOjU2KSL6HYAckQlF0o3AkRFxf7PbYmZWhDLySURMrPtxl14evwi4qNF4DSWUPBf5FOBFeVMN\n6IqI9kZfyMzMBm8kXdg4CZgQEXeX2ZiCfU3SjRFxviQB50bEBEl3kFYi3pY0LW7viJgv6VRgHKk+\nyvci4qrmNd3MbGUVyCcNTxt+umLJBFLFxt5+Xhf4SUTsQqqFsqekPYBXRsTOwETgJEnrYmbWIoqa\nNlymfnsokg7Kd/8l6WrgamBZ9+MR8aMS2zYgktYBFtdVWqxPKD2P8N/zv48CawKbAdvnGQ810nHZ\nHLiztAabmQ3ASOihTMi3hcAzwE5123YptWUDNxkYJ6kN2BC4ixWVGLfvsW/P3su9wJQ8QDUR+Bnw\nUIltNTMbkMrXQ4mIQwAk7Z6vqvwPSR8os2GDcDrwfVKy+BlwOXClpJ1ZeamArp73I+IaSRMkTSXV\noP9lRJS3joaZ2Qi0qlNeHwHGAP8tqb6S4SjSQP0vSmzbgETErcBbemzeoZf9tqi7P6nu/hfKa52Z\n2dBU4ZTXqmZ5rQu8nXTJ/YS67cuAk8pqlJmZrazgxSFLsapTXheQrp7cNSJuGKY2mZlZDyPpOpRn\nJF0JbEDdjKkeV1mamdlqrNGE8iPgPNLCYD1nSJmZWckq0EFpOKEsioizS22JmZn1aSSd8rpe0qeB\n64H/lBSMiEdKaZWZma2kAvmk4YTysfzv5+u2dQFb9LLvamH5knLKyC5b9FxJccspLdy5dNmqdxqk\nskr1Lnq2nGM8e2Y5pWkBnp27pJS4/55bzuVWc58r5+8N4OFnZpUS996ZD5YStyjNXFKlUQ0llIh4\nZdkNMTOzvlUgnzS8fP1LgLOBXfNzpgBHR8S/S2ybmZllVRhDaXS14fNI5SC3IC2aeCsDKLpiZmZD\nU6s1fmuWRsdQtoiI+rW7vi3pY33ubWZmhapV4Er5RnsoXZJe0f2DpE2BjnKaVCxJN0raso/HHpa0\nxnC3ycxsoEZSD+XLwC2S/pJ/fhvwiXKaNKx8kaaZWUEa7aH8BTgf2JRUjOpqnl9jpJV9TdInAJTc\nWPdYu6QHJK2fHz9K0heb0kozsz5Uvh5Knd+SqhdeU7et9U/ordBXOeAasBy4FPgocC5wILDP8DXN\nzGzVKr/acL2IOKzMhhRpgOWAAS4GrpA0DXgqImaW3UYzs4GowKzhhhPKryQdTrr+pL6mfKsuvTIZ\n+H5OEBuSlozpqxwwEfGIpDmkGi+eDm1mNgiNJpT1gBOA+jUPWnnplcGUA74AOBM4YLgaaWbWsAp0\nURpNKPsCG0ZEOYsgFWww5YCBduCHEeGZX2bWcqpwpXyjCeUfwAuBSiSUgZJ0CrAL8J4mN8XMrFcV\nyCcNJ5Qu4B5JM4D/LAE7Uio2RsRJzW6DmVl/qnClfKMJ5ZRSW2FmZv0aMT2UiLip7IaYmVnfRtIY\nipmZNVEF8okTig3N8iXlVWzsXN5ZSty2UY2uODQwY8a0lxIXoL29nE+TrpLmNC5eVmIlz+XlrEu7\n5ug1S4lblCr0UMp5Z5mZ2WrHPRQzswqoQAfFCcXMrApqJZ32LJITiplZBRQ1hiKpjbTUlIBO4Chg\nCXBJ/nlGRByT9z2CVPuqAzglIq7tL3ZlxlAkHSfpibIrLEraRtJOZb6GmVkTvRfoiohxpOKJpwJn\nAJMiYjzQJmlvSRsBnwZ2BPYATpM0ur/AlUkopEUbLwf2K/l19gW2Lvk1zMwGpKgSwBFxNSsq7m4G\nPAtsFxHT8rbrgN1J6x9Oj4hlETEPeADYtr/YlTjlJWk88CCpANalwORcdfHIiLhf0pHARhHx35K+\nDLwfmAmsDXwJmAA8GRHnSxJwbkRMqFvDqx34OfAT4GBgiaTbI+Kvw/qLmpn1ochpwxHRKekS0mfl\nh0gJpNt8YF1gLDC3bvsC0srzfapKD+Vw4MKIeABYKmkHeqkHL2lb4F0RsT3pQL20j3jdz90v33YG\n5kTEE6TziGc4mZhZKymqh9ItIg4GtgQuBNaqe2gsMAeYR0osPbf3qeUTSq71/m7gs5KuI/2Cn+qx\nW/chfC3wvwARsZiVa5/03BdSud9vAb8D1i+w2WZmxSooo0g6UNIJ+cfFpDLof81nggD2BKYBtwHj\nJK0haT1gK2BGf7GrcMrrY6TeyfEAktYCHgbuIlVhvB/YDngMuJucbCSNAd6UYyymR8XGPLj0oYjY\nL/98j6QrSLMcyrvk2cxsEApcbfgXwMWSbiLlgM8A9wEX5s/Fe4GrIqJL0lnAdNIX8UkRsbSvoFCN\nhHIoKakAEBHPSbqKlED+R9IjwOP5sRmSrpN0K6m65FLSdLefAj+rr9gYER2Snsn7LgJ+FxGPSrod\n+Lake7woppm1iqKGUCJiEfCRXh7apZd9L2IAZdFbPqFExJt62dZ9yuub9dslvQR4NiLelqcXzwAe\njYjH6L1i49eBr/fY9lvgtwU138ysEFVYy6vlE8oAzQLeIukQ0qmrC3IyMTOrtArkk5GVUHI9+EOb\n3Q4zs9XRiEooZmYjVgW6KE4oZmYVMJJqypuZWRNVIaG0/IWNZmZWDe6hDFL7mHIWPe7qKKe8aeeY\nfq9HGrQxG6xTSlyAWns533dGlXQsli4ur+zt8mUl1eotyeYb9bvk05BsPHNsKXGfmPviUuIWpQJD\nKE4oZmZVUIVTXk4oZmYV4AsbzcysGK2fTzwob2ZmxSi9hyLpeGA3YDRpmeRjI+JvA3j+NsAL66qJ\nDaYNT0bExoN9vplZs7W1tf73/1ITiqTXAu+LiHfkn7cFJrNiWflG7As8RVqff7CqNUXGzKyn1s8n\npfdQ5gKvkHQoaXn4OyXtkHsdZ+V9ZpPW39oOOIm0qONGwPnA1dSV5CWV9D0FWAY8BBxFqjX/XlLF\nsZfmuHsDrwO+GBG/AdaUdBmwKXBHRBwjaV3Ssswb5HZ8JiLulvQv4B7gnoj4QknHxcxsQKowKF9q\nzssldd8HvAO4RdI9pA//C4BPRsRE4Drg+PyUTYD3ADsC/0WqZ3IJK0ryXgDsExETgCdIyQbgBRGx\nF/Bt4KiI+ABwJHBIfnwt4LiIGAe8SNJ7gUnAHyNi17zvuXnflwP7OZmYmQ1M2ae8XgXMj4jD8s/b\nkcrtjgHOkQRpbOWB/JQ/R8QyYJmku4FX1cV6CbAxqVBWDVgT+AOpp/J/ebc5pGpjAM/mfQAeqVvG\n/hZAwOuBCZI+Qpo/8cL8+MyI6LduspnZcFvteyjAtsDZuawkwIOkD/0HgINyD+V44Jr8+Jsk1SSt\nDWyd9+vM7ZwFPArsnXsopwJT8vNWNUbyckkb5fvjSOWD7wW+l9vwYeDSBmOZmQ2/2gBuTVJqDyUi\nfilpK+A2SfNJieGLpMTwY0mjSAnjMOBlpN7KdcCLgK9HxDPdJXlJCeCzwG8ltZHGZw4CNmugKbOA\nsyS9Arg5Iq6XdBtwkaQjgbHAV/O+Tihm1nKqcKV8raurNT4/JY0HjoyI/ZvdlkYsfOyhUg7csgUL\nywhLx/wF5cRd8FwpcQGWzi0ndseictbymvPk/FLiAsyZWc6xeHr2olLilunRmfNKifvE3HLiApx5\n4xlDzgb/uPLqhj9ztvjQ3k3JPr5S3sysAiowhNI6CSUibgJuanY7zMxaURUG5VsmoZiZWT8qMIbi\nhGJmVgFV6KFU4GJ+MzOrAvdQBqltzJqr3mkQyvof0tXZWU7c5eXETcHLCds2ur2UuOs8V061TYC2\nkk53tI8qJ+6yjvL+LlpkYuqwq8K0YScUM7MKcEIxM7NiVGAMxQnFzKwCPChvZmarjZbsoUjaHDid\nVKtkNHAHcEJEPG/9kLw+1xsi4pqej+XHNwOuiIgdy2uxmVnJWr+D0no9FElrAr8GvhkREyNiJ+B/\ngcv7eMpEUr2V/qym80LMbKSotdUavjVLK/ZQ9gL+lAtqARARP5J0lKRXAxcCawALSdUaTwDWknQz\n8BjwfVJFx8XAETnEhpJ+RaoEeW1EfEPSy0lVIdcEngM+QToevyGtTvzbiDi99N/WzKwBtQrUlG/F\nFm5BKprV0z+BvwKnRMTbgTNJ9VZOAy7Lp7y6K0FOAH4AfC8/dx3gQFJPZo9c2/504MxcD+W7wLfy\nvhsBuzuZmJkNTCsmlMeBV/ay/dWk3sStABFxTUT8kZXPLG4SEXfl+1NJRbq6SHXkF0REJ3AbsCWp\nYuMkSVOALwMb5uc9HBHLC/6dzMyGpq3W+K1ZTWzaK/ftamA3SW/u3iDpMGAmcC2wQ962v6RjWFHR\nEeBxSa/P93cB7iclnK0lrZ0Leu0AzCAV7Do+91COAq7Mz/N4i5m1nFqt1vCtWVpuDCUiFkp6L/D/\nJG1AauOdwH7AS4DzJJ0ELCKdxtqc1NP4G2nM5Oxcc76DVAkSYDbw0/z8KyLiPknHAj/IkwDWJFWD\nBCcUM2tFBeWJ/MX6h6TPzjWAU4B7gEtIX9BnRMQxed8jSOPLHaThhmv7i91yCQUgIh4G9u7loXnA\nbj22/R14bd3P43t53pv6eI09etn37Q0208xs2BTY8zgQmBURB0lan3RZxt+BSRExTdIPJO1NGl74\nNLAdsDYwXdLvI6LPRetaMqGYmVlpfsaKU/ztpFmx20XEtLztOuCdpN7K9IhYBsyT9ABpItTtfQV2\nQjEzq4BaezFD3hGxCEDSWFJiOYk067XbfGBdYCwwt277AmC9/mK34qC8mZn1VKs1fluFvMLIFGBy\nRFxB6o10GwvMIQ0xrNvL9j45oZiZVUBRs7wkbQRcDxwXEZPz5v+TtHO+vycwjXSJxThJa0haD9iK\nNEO2Tz7lZWa2ejkRWB/4sqSTSTNbPwt8X9Jo0iUVV0VEl6SzgOmkOWaTImJpf4GdUMzMqqCgCxYj\n4nPA53p5aJde9r0IuKjR2E4og9ReUgngWls55Wk7O8opT1tWXIC20eX8edZGlXOmd+2O8hZYaCto\nQHa4lFmmt6zYY9Yo571XlCrUQ3FCMTOrAicUMzMrgmvKm5lZMdxDMTOzIlRhDGXYRvokjZc0R9LL\n6radJumgYXjtiyW9s+zXMTMrTYEXNpZluKeOLAEuHubXNDOrPJcAfr4pQE3SMRHxP90bJX0e+Chp\nieSpEXGipNuAfSPiEUn7AuOAr5DmRG+Qn/qZiLg7L1p2M6lw1hTSejM7APdFxMfzvsdIOo60GNph\nEfEPSZ8C9ictO3BFRJwt6WLgRfk19oqI+rVszMysD8PdQ+kCjgY+J+lVedu6wIeAt0XEO4DXSNqL\nVDu+OxkcQirvOwn4Y0TsChwJnJsf35y0wNnOwGeAsyPiraRlA7rXork5InYDvg18R9JrgY+QygLv\nDOwjacu87w0RMc7JxMxahk95PV9EPAv8FzCZdDn/msCtuTwvpMv8twYuBz4oaWNgbETcQyrbe2gu\n23sB8ML8nNkR8XheZnlBRETePjfHh1QSGODPgIDXAZsBN+TbBqQywwDdzzczawm1traGb83SlFeO\niGtIH9qHAIuBt0pqy5UWdwbuj4h5pHX3v8eKcZd7ge/lsr0fBi7N2+uvna31cX+H/O/OwF359WdE\nxMSImECqVnZn3qd+5U0zs+arQE35Zk4b/hwwkbRE8s9IPYcaqaDL1XmfC0jFXg7JP58KXCTpSNJS\nyl/N2+sTSl/335arkHUCh0bEo5KmSJoOjAH+AjyBSwCbmQ1KravMRXdGsKXzZpdy4MpaG6tjXr9l\nDIYQd34pcQG6lpWzNlbHwudKibvkmYWlxAVYPKecNi98tpy4ZX6szH6qnOM8b0G/C+kOyQfP+eyQ\nuw1z7vl7w0d1/a3f2JRuii9sNDOrggpc2OiEYmZWAVW4Ut4JxcysCrw4pJmZFcE9FDMzK4YTipmZ\nFaLW+lU7nVAGqXPpklLiLlu4oJy4ixaVE3dBOdNOAbo6y7m+tGP+4lLiLpxV4rTh+eX8vS2cV9JU\n2RLnDc8qaarz/IXlTRsuQhUKbLV+yjMzs0pwD8XMrAo8hmJmZkWotbU3uwmr5IRiZlYBVRhDGbaE\nImk8aRE0XNWsAAAJP0lEQVTIu0ljN6OAMyPiymF47YcBRURrj7qZmVXYcPdQboiI/QEkrQPcJCki\n4s5VPG+ovAKmmVWbx1D6FhELJZ0LfEjSR4GdSD2XMyLi55LeSqqFUgMeBw4AXgOclUPMBg4FtgNO\nJNWrfzlwHmlZ/G1JPaDzcozzJL0SeIpUCXI5qeLjq/Prfikipkq6C7gfWNKd/MzMmq0KV8o3e9rw\n06Tyv5tHxE6kRPAlSeuRPuwPjogdgWtJVRwvAD6ZC2xdBxyf47wM2Af4JKkU8AHAu0llgrudExG7\nAP8EPgEcDszM294PnJP3ewHwNScTM2sptbbGb03S7EH5zYCfAB/LZX1rpDZtDmwUEfcDRMTFALkO\n/DmSAEYDD+Q4MyKiU9Ic4KGIWC7pWVaU/10aEbfl+7cAu+fX2in3hGpAu6QX5X3uL+sXNjMblAoM\nyg93KvvPEZG0LnAEMAeYknsdE0kD9w8BT0h6Vd73OEnvB+4DDsr7Hg9ck8P1VQK42xqSts33dyKV\nAL4XuCzH2hO4Engm7+MSwGbWUmq1WsO3ZhnuHsqE3BPpBNqBL0fE1ZK+K2kqsA7wy4hYIOko4GJJ\ny4EnSeMpjwA/ljQqxziMdLqrXm8D8IuBT0vaknTK6/j8+hdI+hOpnPA5EdElyQP4ZtZ6KrCWl0sA\nD9LiWU+UcuDKWsurY0FJceeUExfKW8tr6byS1oJ6cl4pccFredV79PFyyk6XuZbXkZedMORuw0A+\nc9Z88SYuAWxmZn2oQA+l9VtoZmaV4B6KmVkFeOkVMzMrRgUubHRCMTOrgCqsNuxZXmZmVggPypuZ\nWSGcUMzMrBBOKGZmVggnFDMzK4QTipmZFcIJxczMCuHrUIYo11P5ZkRMkLQd8APS6sZ/j4jP1u1X\nIxUK+1VEnC/phcClpJWOZwNHRMSsgcSV9P+AdwDdq+XtDXTkuBsC84CPR8Tsgba5t9gRMT8/tg/w\nwYg4oIi4pJIDlwLrkurcfCEibi0g7nLgMuCFpIqeH4+IJws+FlsBtwIbRsTSIuJKeowVNXluiYiT\nCjgWC4EzgO2BMcBXI+K3Bf3/+ySwB2ml7xeSahltUkDcGnAFqejdYuDAiHi6gLij6Oe9N4DYewIn\n56fcHhGfkrQmq3j/jWTuoQyBpGNJVSTH5E3nAZ+JiPHAXEn1VR+/Aaxf9/MkYFpE7AycDZzWYNx5\ndXG3B94VERPzbT5wNHBnjvtj4MsDaPOqYne/QU+hR92ZIcb9PPDHXD3zEOB/Cop7BPDXvO9PWFHh\ns6hjMRY4nfRBU0jcXAPo9rptJxURF/gYMCpXRn0/qfR1IW2OiG9FxIRcW+ix/FpFtPlgVvwt/ww4\nrqC4fb73Go0t6QXAt4G9clXZf+YCff2+/0Y6J5SheZBUerjbyyPiL/n+n4FxAJL2JX1b/l3dvluT\nyhgD3Ny9bwNxbwbG5R7Pa4DzJU2XdEh+fFzd61wH7DaANq8qdvc+R/N8Q4l7BulNC6mHUr++/KDj\nRsSZpOQHsCnwbMHH4nzgRGBRgXG3B14uaYqka3INnyLivotUtO6a3O7fFHwskPQB4JmIuKGguHeR\neq3kf+vXlx9K3P7ee43E3gl4e27fGbmW079zT2RV778RzQllCCLil8Cyuk0PSdop338vsI6k1wH7\nA19h5W/1/we8L9/fG1hrIHGBtYGzgANJpxuOlvR60htvbt53PivekEOJ/UlJ2+TnXznYY9FX3IiY\nFxFLJL2U9K3uhALb2yXpBuBTwC8LaPPRkl4v6SvANRFxFz16a0Ns8xPAqfnb/mmk0ydDbi/wYuBV\nEfEe0jfrSwpsc7cTgK8VGHc28E5JdwNfBC4q4FhsQz/vvQZjr006nrsAx5Iqvv6XpNewivffSOcx\nlGIdCpypVFFyGulUyEHAJsAUYHNgiaR/At8EzlKqGHkt8OgA4y4CzoqIxQCSbgTeQPpjHpufN5ZU\nYnmgbe4Ze0qOPaOBYzCouPlD7zLS+Mn0ItsbEbtKEuk4v7rXqI3H7j7OBwCPSToceCnwe9IHzFDb\n/Avyh1lE3Cxp44LaO4tcMjsipvbo+Qy1zTMkvRZ4NiL+UVDcN5J6Cd+KiAvy38cv8usN9VgM5L3X\nV+zZwG0RMTPHnprbPND334jiHkqx9gL2j4jdSd9g/hARx0fEjhExgfSt8IyI+D2wM3B+Hjd4iNSV\nbjguIOBmSTVJo0ld7dtznL3y895NegMMqM19xP7bAI7DgOJK2pp0jnz/fGyKinuCpAPz8xay8rfO\nocS+PSK2zOfkJwBPAbsX0WZST/ZzAJLeQP8fdoP6u8hx/1XQsej+u9iNFaeRhhr3HbnNz7Di2/5M\nVnxQD7W9A3nv9RX7b8A2kjbIieZtwN0M/P03oriHUqwHgCmSFgI3RsTv+tk3gB+lL848Bhw20LiS\nfgT8hXRueXJE3Jt7P5MlTSPNbNq/95ADj72KOIOJe0lu869IA6Bn5vPecyJinyHE7T4Ws/OxOIz0\n5emQPmIO9Vh00eO01xDa/E3gUkl7kWbsHVxQ3IeAH0i6JT/3qIKPxZakD9pVaSTuj3KbTwYulHQM\n6bPq8IKOxVIaf+/1F/tEUs+0C/hpRNwj6WEG9v4bUbzasJmZFcKnvMzMrBBOKGZmVggnFDMzK4QT\nipmZFcIJxczMCuGEYmZmhXBCMTOzQjihmJlZIXylvNkq5Kutp0bEhfnnKaSFEL8BbEBaM+ozEfF3\npcVAv09anHBD4LsRcXZeTPJtwCuAsyPi3Cb8Kmalcg/FbNV+SK7xIWlTcqIAjo2INwNHkgpBQVoe\n5OsR8VZgInBqXZwxEbGNk4mNVF56xawBku4nLYB4EGndri+RFgPsXsPrRaxY7XkPYNt8+0hEtOce\nypoRceJwt91suPiUl1ljJpMW+vsQ8B7SMvvbdT8oaZOIeFbSz0lLm/+G1Gv5SF2M+sJhZiOOT3mZ\nNWYyaYXeRyLiUeABSQcASNodmJr32w04OSJ+Q66PkldQNhvxnFDMGhARj5Fqk0zOmw4EDpd0B6nM\n8Ifz9q+QanD8lVQj5WHglcPcXLOm8BiKWQMkbQLcCGwTER3Nbo9ZK3IPxWwVJO1LqkN+gpOJWd/c\nQzEzs0K4h2JmZoVwQjEzs0I4oZiZWSGcUMzMrBBOKGZmVggnFDMzK8T/B6r/5xZpG4M/AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ccf48d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvflights = flights.pivot_table(values='passengers',index='month',columns='year')\n",
    "sns.heatmap(pvflights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d852780>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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35DEAfGLw3kXjPjbpcgA2X/x7ReMCjJl8OgA7DR5RNO6Nk0YC8N2VypYAPuO5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\nOKGYmVkRTihmZlbE/wPc2M7NTflEtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d864588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pvflights,cmap='magma',linecolor='white',linewidths=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clustermap\n",
    "\n",
    "The clustermap uses hierarchal clustering to produce a clustered version of the heatmap. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x11dbdf4a8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N4ineDBySmb+cbW3DliRJaoPPAZcDlwKPBN49ucZWZh61EY//v00JWmDYkiRJ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\nkiRJBRm2JEmSCvr/5HGLxYAXL6EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dbdf5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(pvflights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice now how the years and months are no longer in order, instead they are grouped by similarity in value (passenger count). That means we can begin to infer things from this plot, such as August and July being similar (makes sense, since they are both summer travel months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x11ef9d390>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NtzDJ4MYbNd3CJMtvvL3pFiZZ+sAHNN3CJCvuXtF0C5MsXLSw6RYmufCSTtMtbBDO/NrP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef8ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# More options to get the information a little clearer like normalization\n",
    "sns.clustermap(pvflights,cmap='coolwarm',standard_scale=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}