{
 "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights = sns.load_dataset('flights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matrix form for correlation data\n",
    "tips.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f589fdd5a10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tips.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f586fac2b90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.pivot_table(values='passengers',index='month',columns='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5855917d90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvflights = flights.pivot_table(values='passengers',index='month',columns='year')\n",
    "sns.heatmap(pvflights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f584c1f3f90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f584c1b19d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f584c1053d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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)"
   ]
  }
 ],
 "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": "3.7.4"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}