03-Matrix Plots.ipynb

{
 "cells": [
  {
   "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": "iVBORw0KGgoAAAANSUhEUgAAAVsAAAECCAYAAABQTnTSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAVsAAAECCAYAAABQTnTSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEMCAYAAADj8ECOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEMCAYAAADj8ECOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAJcCAYAAADOwGHJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAJfCAYAAABIVBNnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}