Lec 53 - Heatmaps and Clustered Matrices.ipynb

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     "cell_type": "code",
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     "input": [
      "# The normal imports\n",
      "import numpy as np\n",
      "from numpy.random import randn\n",
      "import pandas as pd\n",
      "\n",
      "# Import the stats library from numpy\n",
      "from scipy import stats\n",
      "\n",
      "# These are the plotting modules adn libraries we'll use:\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "\n",
      "# Command so that plots appear in the iPython Notebook\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Again seaborn comes with a great dataset to play and learn with\n",
      "flight_dframe = sns.load_dataset('flights')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Preview\n",
      "flight_dframe.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's pivot this dataframe do its easier to manage\n",
      "flight_dframe = flight_dframe.pivot(\"month\",\"year\",\"passengers\")\n",
      "\n",
      "#Show\n",
      "flight_dframe"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>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>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>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",
        "    <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>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>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>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>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>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>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>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>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",
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        "</div>"
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       "text": [
        "year       1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  \\\n",
        "month                                                                         \n",
        "April       129   135   163   181   235   227   269   313   348   348   396   \n",
        "August      148   170   199   242   272   293   347   405   467   505   559   \n",
        "December    118   140   166   194   201   229   278   306   336   337   405   \n",
        "February    118   126   150   180   196   188   233   277   301   318   342   \n",
        "January     112   115   145   171   196   204   242   284   315   340   360   \n",
        "July        148   170   199   230   264   302   364   413   465   491   548   \n",
        "June        135   149   178   218   243   264   315   374   422   435   472   \n",
        "March       132   141   178   193   236   235   267   317   356   362   406   \n",
        "May         121   125   172   183   229   234   270   318   355   363   420   \n",
        "November    104   114   146   172   180   203   237   271   305   310   362   \n",
        "October     119   133   162   191   211   229   274   306   347   359   407   \n",
        "September   136   158   184   209   237   259   312   355   404   404   463   \n",
        "\n",
        "year       1960  \n",
        "month            \n",
        "April       461  \n",
        "August      606  \n",
        "December    432  \n",
        "February    391  \n",
        "January     417  \n",
        "July        622  \n",
        "June        535  \n",
        "March       419  \n",
        "May         472  \n",
        "November    390  \n",
        "October     461  \n",
        "September   508  "
       ]
      }
     ],
     "prompt_number": 15
    },
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This dataset is now in a clear format to be dispalyed as a heatmap\n",
      "sns.heatmap(flight_dframe)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "<matplotlib.axes._subplots.AxesSubplot at 0x18ada518>"
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cwPM5xahGA6sDtwJ9zGxxoL27vwWsCexvZiMIyaVDfM0kd//U3X8Bprt79mKK\nqTE+xCnpgecBA9YgJKGngScJvZGVss1M+6lFRIpUkyn8Vq4mtvQbuvuDhB12P2AGsIGZtTGzDKGX\n4O4+DXgF+Afh0BbA28Cl7t4L2ItQzAoKGxPZMP7tDrwR3/9Nd+8V490EjGtCPBGRFlMNZXtbcswk\ndyd9NLAZMA24gzDmUQOMdvf74jpDgEcISQfgXOA6MzsYaA+ckRObRu5vZmb9CJUW93f3z8zsKTN7\nltg7Aj7L8zoRkfJLlCPMbE7CD/ROQFvgHMIP9WHATGA8cIS715rZQcDBhPHsc9z9oYZit0gyya1+\nGB9/Cyybs8qleV4zFvhTzuPJwE551luinvud49396mnTxcDFdRbnXVdEpJwS9jj6Al+7+97xpKQ3\ngNeAAe4+yswGAzuY2fPAkUAXYB7gWTN7wt1/qi+wroAXEZl93AncFe/XEI7WdHb3UXHZI8CWwK/A\nGHf/Gfg5njW7FvByfYGVTEREKlymTZrhbXefDmBm7QiJ5VR+f4TmW8LlFe0JJzHVXV6vyj95WURk\ndpewCLyZLU04k3W4u99GGCvJag9MIYxnt8tZ3g74pqG4SiYiIhUu1dlcZrYo8DjQ392HxcWvmVmP\neL83MAp4EehmZm3j9XqrEgbn66XDXCIis48BhMNVp5vZ6XHZUcDlZjYX8BZwVzyb63LCtX81hAH6\negffQWV7i6ENJyKFKPpUrK/Gji54f7PIRt1UtldERP6oGuqZKJkU4YevP2t8pSaYZ+ElAZj+6XtJ\n4863VJgKbcpbryeNC/Cn1dYB4Ovnn00ad+ENuwLwv6efShp38U03A+CThx5JGnfpbXoD8PZ1tyeN\nC7DqAbsBpSuvO+LUa5LG7XXOIQA8cWLa0roAW1wYyuvecuCgpHH7Dj0WgAt3PDNp3BPvTRRPyURE\nRIqlGvAiIlI89UxERKRYGjNpBjNbjnBF5oLAnIS5Y0509+/yrLs0sHaciThfrGWBf7r7dqVrsYhI\niVVBMqmoixbNbB7gPuCCOD18V+AF4LZ6XrIZocCViEirpbK9TbcN8Iy7v5Rd4O7DzewwM1uRUIFx\nTuB7YE/gJGAeMxtDmEL+csIEZTOAg2KIZczsYUJP5353Py/2aK4hzIb5A2Ga5TmAB4CJwMPuPrDk\nn1ZEpJWoqJ4JsBzwfp7lHxJmqzw3lve9DFgbOB+4JR7mGkKYh78ncBUwiHBh4XzAzsDGwDZmthbh\nMNrlsTC82GsBAAALcElEQVTWJcAFcd1FgS2USESkoiScm6tUKq1n8hmwfp7lKxKKWI0FcPcHAGLB\nq6zF3T1bLXE0IUEAvOjuM+L6LwErE8r2DjCzEwlXp2anCfgglv8VEakYmZpK+93/R5XWwvuALcxs\nvewCMzsQ+Bp4iJhozGwPMzuCcEirTVz1czNbM97vwaxa7mvHycrmIJTvHQ+8QxjU7wX8DcheaZY7\ne6aISGWoghrwFdUzcffpZrYdcKmZdSS07w1gd2Bh4BozOxWYTqgDvyxwipm9QhgjuSLWkv8ZOCCG\nnUpIUh2Am9z9HTM7HhhsZnMTxk3+HtfVfFsiIs1QUckEwN3fB3bI89Q0YPM6y14HVsl53IM/2jjP\ne3wAbF3IuiIi5ZbJVNpBpD+quGQiIiJ1VMF1JkomIiIVTlfAi4hI8TTRo4iIFKsaeiaqtNh82nAi\nUoiiM8G0CW8WvL9pv+LqqrQoIiJ56Gyu1q1UFRGnvjOukTWbZoFV1gJg4kvPJY0LsNB64Wzq/41I\nXBGxV6iI+NG9eSeEbrZOO24LwIRb70kad8U9dwbg5UtuTBoXYN3j9gXgyZOuThp38wsOBeDWg9JW\nLdxzyLEliZsbu1QVEY/qdWzSuJeNSLMNqqE4VuWnOxERqXjqmYiIVLoqGIBXMhERqXCZmjaNr1Rm\nSiYiIhWuGsZMSppMzOwkQjXEOQkz8h7v7q824fVrAB3cfXQRbfiPu6/Z+JoiItJcJUsmZrYasJ27\nbxIfrw3cCKzThDB9gP8R6pOIiMyeZvMxk6mEkrn7A4+5+xtmtn6sOXIZ4UKeScD+QGfgWEIBrEWB\nwcD9QD9ghpm9CswLnEOoYfIecAhhGvrt4usWj3F3IBS/Ot7d7wf+ZGb3AIsAr7n7kWa2AKEE8IKx\nrX939/Fm9hHwNvCWu6c9R1BEpJmq4Qr4kp0a7O6fAdsDmwDPmdnbhB3/tcDhsTDVw0B/wtXkCwG9\ngY2A4wk1SW4ABsWa8EOAnWJZ3s8IiaYWmN/dtwEuBA5z950JNd33i02ZHzjY3bsCi8R6KQOAJ919\nU0JSGhzXXQrYQ4lERCpKpqbwW5mU8jDXCsBUdz8gPu4CPAq0JRSmgjCW8m58yUh3/xX43szGA8vH\n5RkzWxhYDLgzvm4e4AlgAvBaXG8qoVcBMIXQWwF4290nxvtjASP0XHqZ2W5xeYf4d6K7f5Pg44uI\npFMFA/ClTGNrAVea2Zzx8X+Bb+LffWLPZADwQHx+XQAzmxdYNa43M7ZxEvApsH183QXAk/F1jc1Z\ns5KZdYgVGLsB4whley+NsfYChsV1VbZXRCpOJpMp+FYuJeuZuPu/zWxV4CUz+46QFI4nJIXhsSb7\nTEJ53SWB9mb2BKGXcJa7T47leAcSehxHAQ+bWQ2hF7IvoWxvNpnU8vvEkr3/NeFw2SKE3s/jZvYy\ncJ2ZHQy0B86o8xoRkcoxu8/N5e7nAefleapX7gMzWwp40d2PrPP6hwnjKllP1IlzY866jwGPxfuv\nA3+J91fL067JwE55li/RwMcRESmLahiAr5SLFuv2KkREJGt275kUyt1HAiPL3Q4REWmeikgmIiJS\nv9l+OhUREUmgCsZMVLa3+bThRKQQRWeCn6ZNKnh/M1f7jmXJPEomIiJStMo/RUBERCqekomIiBRN\nyURERIqmZCIiIkVTMhERkaIpmYiISNF00WIRzGwD4AJ37xXLEl8N/EKYPv9Qd/8prlcDPATc6+7X\nmFkHYDjwJ+B74CB3/7ipsc3sMkLxsW/jy7aP69wMLByX75tTz6W5sWuBHd19Wnz9TkAfd++boL01\nsb3tgLmAY939+UTbYiZwa9zOP8Vt8XnC7bAK8DywSPb/dZHt/Y4wq3a2xs9Ydx+QaFt8DwwCusTt\nfLq7P1rstgAOB7aKYToAi7r74gna+ytwW87/u73c/csEcecgzXevN3B6fMlL7v53M5uHRr57rZl6\nJs1kZv0J1R/bxkVDgWPcvRuhEuThOaufQ/jHm72oZwAwJq57EXB5M2N3BrZ0917x9i1wGPCGu3cn\nfGlOTRB705wd6GWEmaAzRcbMtvcY4IlYQbMfcGXCbXEg4Yveg/Al759wO7QHLgFmJGzvCsArOcvq\nJpJiYu8NzBErju5IqBlU9LZw9wuy7wN8Et8nRXv3IRS26wHcDpyQKG7R3z0zaxdfu427bwR8Fgv4\nNfjda+2UTJpvArAzs3aqS+X8on4O6AFgZn0Iv7IezXntajmPf1u3KbFjsa+VgCFm9qyZZcsUb5IT\n+1Fg84SxAcYQvjS5V9kWE/NSQilnCJU3f0jVXnfPJj6AToTibEXHjcuvAU5O2V5Cr2FJM3vazB4y\ns5UTxt6SsNN7kLCzvC9RXOI22RmY7O5P5iwuJu4PQMd4fwFC7yRF3KK/e4TS4v8BBpnZKOB/7v41\njX/3WjUlk2Zy93sIXd+s982se7y/HTCfma0B7EHoDmeY9Q/0dUKXm/h33ibGnheYj/Crqi+wNeEX\n05qEYl9T47rfEr6IqWLj7nc0Y1vUG9Pdp7r7DDNbDLiJsINO2d6ZZvYUcARwb6K4ZwAPufu4uP5v\nibXIuJ8D57n7poQkeHPCbbEQsIK7bwtcSCgYlyJu1knAWQnb+2+gq5m9CRwHXJ8obrHfvfkI27IX\noafbGzjazFaike9ea6dkks5+wMlm9iTwJTCR0OVfEniaUBnyWDPbEjgfWNbMRhJ+MX/SxNiTCMd7\nL3f3Ge7+XXyPtYFphH/UEMYhpiSMXagmxYxf8ieBk919dOr2uvtmQHfg7kRx+wIHmNkIYDFiUbYE\ncV8G7o9tHgM0VqytKbEnEcbtcPdRQN1eT3PjYmarAVPc/f2E7b0YGOTuqxPGZFL9vyv2uzcxxn/J\n3b9y9+nAKGAdmv7da1WUTNLZFujr7psTuuePufuJ7r5hPJ48DLjE3R8ndJWHxOPB7wGN7UD/EJuw\nM3jWzGrMbE6gK/AK4TDUX+LrehP+oaeKXaiCY8Yd0Z3AHh6qZaaMfbKZZY/hT+f3vzibHdfdV8oZ\nJ/iCcAip2LivEnqwRwPEgd+P84dsepuBZ4n/LmLsjxLFhXA45+G8kZq/LeYj7JwhlN5unydec9qb\n4rv3KrCGmXW0UH58Q+BNmv7da1WUTIqXHVR/F3jSzMbGZcMbeM07wEAzG0MYdD6hnvXqje3u78T3\nGAuMAIa5+9vAYGB1MxtNGIA+649hmx0797X5ZghtTszzCGcXXW5mI8zs3wnbex2wZ+xB3Er4lZly\nO+S+vti4bwEXAN1jey8m/NtItS2GAJm47tXAoQm3xcpAQ72S5myLAYTe3yjgHsK/5RTtTfHd+5pw\nOPYxwtl8d8c2F/rda5U0a7CIiBRNPRMRESmakomIiBRNyURERIqmZCIiIkVTMhERkaIpmYiISNGU\nTEREpGhKJiIiUjTVMxFphJkNB0a7+5D4eARwIqG0QEfCPFBHuvvrcXLPy4H5gUUIU+j808zOJEy7\nsTTwT3e/uuU/iUjpqGci0rjrgb0AzKwTofjRIKC/u3cBDgH+Fdc9ADjb3dcHNgXOzYkzl7uvrkQi\nrZGmUxFpRKyN8S5hQsN9CFPOnwK8lbPaQsBahCnIewNrEmap/au7t4k9k7bu/rsp9kVaC/VMRBrh\n7rXAjcCewK6EGaBnuPufszdgY3f/hjAD8g6EWWRPZlatk1rqVGUUaU2UTEQKM4ww0+7HHmqG/9fM\n+gKY2RbAM3G9zYEz3P0BoGd8vobfV6YUaXWUTEQK4O6fEmqADIuL+gIHmtkbhHGRv8blZxLqaIwB\nVgHeBpaj/mn7RVoFjZmIFMDMliD0PlZ395/L3ByRiqOeiUgjzKwPoXb4SUokIvmpZyIiIkVTz0RE\nRIqmZCIiIkVTMhERkaIpmYiISNGUTEREpGhKJiIiUrT/BxAH55QMh00SAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x18c4b320>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We also have the option to annotate each cell\n",
      "sns.heatmap(flight_dframe,annot=True,fmt='d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1b51ae80>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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b1tC8QhXqlJI785NSIHVLl3tpGiA1PY2ZuzZjo7Omf+NWOepGxccyZefvNClX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CamcvPMDTmq8SRtbW9DxWBvzu08A4BBr0ebLJsV3+jRgqgbUcRHxRV5LP4X+V8c5sqr9Z0uiuJN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\nvlHVpTEvB4l7AIgWX6mJm52EETfw2MOVF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       "text": [
        "<matplotlib.figure.Figure at 0x1b5ed6a0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# seaborn will automatically try to pick the best color scheme for your dataset, whether is be diverging or converging colormap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can choose our own 'center' for our colormap\n",
      "sns.heatmap(flight_dframe,center=flight_dframe.loc['January',1955])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1bca9a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JN3f/2hT7Ip2FeiYirXD3RuAa4ADge4QZoOe6+0a5BRjq7p8TZkDe\nkzCL7EksrHXSSJOqjCKdiZKJSHFGE2bafc9DzfDXzexAADPbGXg0brcTcKq73w1sH5+v5euVKUU6\nHSUTkSK4+weEGiCj46oDgR+b2YuEcZHvx/W/J9TReAIYArwKrErz0/aLdAoaMxEpgpkNJPQ+1nH3\n+WVujkjFUc9EpBVmti+hdviJSiQihalnIiIimalnIiIimSmZiIhIZkomIiKSmZKJiIhkpmQiIiKZ\nKZmIiEhm/w9WlaD2AVPfqwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1bcb46d8>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# heatmap() can be used on an axes for a subplot to create more informative figures\n",
      "f, (axis1,axis2) = plt.subplots(2,1)\n",
      "\n",
      "yearly_flights = flight_dframe.sum()\n",
      "\n",
      "# Since yearly_flights is a weird format, we'll have to grab the values we want with a Series, then put them in a dframe\n",
      "\n",
      "years = pd.Series(yearly_flights.index.values)\n",
      "years = pd.DataFrame(years)\n",
      "\n",
      "flights = pd.Series(yearly_flights.values) \n",
      "flights = pd.DataFrame(flights)\n",
      "\n",
      "# Make the dframe and name columns\n",
      "year_dframe = pd.concat((years,flights),axis=1)\n",
      "year_dframe.columns = ['Year','Flights']\n",
      "\n",
      "\n",
      "\n",
      "# Create the bar plot on top\n",
      "sns.barplot('Year',y='Flights',data=year_dframe, ax = axis1)\n",
      "\n",
      "# Create the heatmap on bottom\n",
      "sns.heatmap(flight_dframe,cmap='Blues',ax=axis2,cbar_kws={\"orientation\": \"horizontal\"})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x28bfcb38>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F4luUd+fVd77Ip/PqastE59UX3p+SS7yNey8K5D93W97Ve3nP3Xb4LaNyiTfk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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2877e048>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Finally we'll learn about using a clustermap\n",
      "\n",
      "# Clustermap will reformat the heatmap so similar rows are next to each other\n",
      "sns.clustermap(flight_dframe)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "<seaborn.matrix.ClusterGrid at 0x20af8438>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5c7q6/8HBQZrNZlffo9ufQZKkSWwIuBlYLyKmZebjwF7A2cCaEXEY8DZgaeBB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\nShHxOaAfeAnw0cy8ICJuAa4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S8raUM73Qokwdz4Wgbax2A8hzwIKIWJqnzfsfyzwLXBoR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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x20f9bba8>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's uncluster the columns\n",
      "sns.clustermap(flight_dframe,col_cluster=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "<seaborn.matrix.ClusterGrid at 0x220f1cc0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqSJBXKIihJklQoi6Ak\nSSpKRFwdEWu/xLo/RsQrep2pLhZBSZJUmibQN8S6YlgEJUlSiRoRcRBARKwTEVd1rBsXEb+LiGWq\n9YdExMdrSdllFkFJkqR/Ngs4F9i9er4nMLW2NF1kEZQkSWNeRLwyIsZXT/v451PAc54mbgLfAD4Q\nEa8H/pyZD/cgZs+NH34TjQb9/f00Go25xgcbkySpQFOBUyNiOrAC8BPg1dW6jebcODPvi4gngE8A\nZ/YqZK9ZBMeISZMm1R1BkqSR7ETglGr5fOC7wPcjYhvgJga/SWRKtc+ePUlYA4ugJEka8zLzOmCT\nOYY3HWS7NTuejgPOyswxeyexRVCSJGkOEfEFYBtg57qzdJNFUJIkaQ6ZeUzdGXrBu4YlSZIKZRGU\nJEkqlEVQkiSpUBZBSZKkQlkEJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQkSSqURVCS\nJKlQFkFJkqRC9TWbzaHWD7my0WjQaDQWaiAtdH3V45CfpSRJY0Df8JuokzOCkiRJhRpfdwD1zrMP\n3FN3hH9YcpW1AHjq7ttrTtI24XWvB+DRm39Vc5K25TbaDIAHr7ii5iRtr9lhBwD++IOLa07StsZ/\nvAuAu865oOYkLWvvvRsAN35xar1BOmxy5L4AXPLRU+sN0mHnL/8/AL6x9xdrTtK2/zlHAnDaHsfV\nnKTtkPOOBuDYnT9Vc5K2T11yLAD7bH5wzUnazr7+9LojjErOCEqSJBXKIihJklQoi6AkSVKhLIKS\nJEmFsghKkiQVyiIoSZJUKIugJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqS\npKJExNURsXbdOUYCi6AkSSpNE+irO8RIML7uAJIkSTVoRMTVmXlGRKwDnJaZEyPiVuBqYH1ahXGX\nzHwqIo4D3gqMA07KzAtqS74QOSMoSZLUthTwnczcFngQ2DEidgTWyMytgO2AT0TE0jVmXGicEZQk\nSWNeRLwSGMjMF2idFm52rJ7zNPGvq8f7gX5gNeDNEXFVNT4eWB24tXuJe8MiOEJNnjyZgYGBBT5O\no9FY8DCSJI1+U4FTI2I6sALwE+DV1bqN5ti2OcfzmcBVmXlQRIwHjgF+38WsPWMRHKEGBgYscZIk\nLTwnAqdUy+cD3wW+HxHbADcxd/mbrZmZP4qIbasS+Urgh5n5TNcT94BFUJIkjXmZeR2wyRzDmw6y\n3Zody0d3LH+se+nq480ikiRJhbIISpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqSJBXKIihJklQo\ni6AkSVKhLIKSJEmFsghKkiQVyiIoSZJUKIugJElSoSyCkiRJhbIISpIkFaqv2WwOtX7IlY1Gg0aj\nsVADqWUh/tv2VY9DfpaSJI0BfcNvok7j6w7QK5MnT2ZgYKDuGPNs5syZdUeQJEljXDFFcGBgYFTN\nXnYj67MP3LPQjzm/llxlLQCenHlrzUnall5nfQAeufGXNSdpW36TLQD4v6uurDlJ26snvg2Aey+8\npOYkbavvujMAd3/nhzUnaXnd+98DwIwTz645SdvGH9sHgJ9NOr3mJG3bTz4YgO8ccFLNSdreP+UI\nYGRmOn7XRr1BOhx1YQOAj0w8ot4gHU6+auR8ZqOJ1whKkiQVyiIoSZJUKIugJElSoSyCkiRJhbII\nSpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqSJBXKIihJklQoi6AkSVKhLIKSJEmFGl93AEmSpF6I\niEnA24BFgVnAf2fmzS9j/zcAy2TmNQuQ4beZ+cb53X9hswhKkqQxLyLWA96ZmVtWzzcAzgY2fBmH\n2Q34P2C+i+BIYxGUJEkleBJYLSL2By7PzFsiYtOIeCNwMtAHPArsD2wEHAH0AysBpwEXA/sCAxFx\nM7AE8DngReAe4CBgL+Cd1X6vro67C/AGWrOPFwP/EhE/BFYEfp2Zh0fE0sBZwLJV1g9n5m0RcS9w\nJ3BHZh7RjX8UrxGUJEljXmY+CLwL2BL4ZUTcSau0fR04NDMnApcBRwJNYHlgR+AtwH8DzwPfBE7K\nzBuBKcC7M3Nb4EFaJbEJvDIzdwKOBw7JzPcABwL7VVFeCRyYmW8FVoyIdwLHAD/LzO1oFcrTqm1X\nAfboVgkEZwQlSVIBImIt4MnM/GD1/M3AT4DFgNMiAlrXDt5V7TItM18E/hoRtwGvrcb7ImIF4FXA\n+dV+iwM/Be4Gfl1t9ySt2TyAJ2jNEgLcmZmPVMvXAUFrxnBiRLyvGl+menwkMx9fCG//JVkER6j+\n/n4ajcYCH2dhHEOSpDFgfeDAiHhXZj4P/A54HHga2Dsz74+IrYHlqu03BoiIJYB1q+13oHU29VHg\nAeBdmfl0ROxaHWsNWrOCQ/nXiFiGVjncCjiD1mnkb2fmeRHxGmCPattZC/62h2YRHKEmTZpUdwRJ\nksaMzPzfiFgXuDEinqFV6P6bVqE7JyLG0ypeHwReA0yIiJ/Smp37bGY+FhE3ASfQmun7CHBZRCxC\na/ZvH/65CDb551I4e/lhWqeYV6Q163hFRMwAzoqIA4EJwGfm2KdrLIKSJKkImfkF4AuDrJrY+SQi\nVgFuyMzD59j/MlrXEc720zmOc3bHtpcDl1fLvwH+vVpeb5BcjwHvHmR85SHezkLhzSKSJEn/bM7Z\nvDHLGUFJkqQOmTkNmFZ3jl5wRlCSJKlQFkFJkqRCWQQlSZIKZRGUJEkqlEVQkiSpUBZBSZKkQlkE\nJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQkSSpUX7PZHGr9kCsbjQaNRmOhBuqW0ZR1\nIeurHof8LCVJGgP6ht9EnZwRlCRJKtT4ugOodwYeeajuCP/Qv/zKADxz7101J2l75eprA/D47TfX\nnKRtmddvBMBfrp1ec5K2FbfcGoA/X3N1vUE6rLTVtgDcf+mP6w1SWXWnHQG448zv1Zykbb0PvQ+A\nm798Ts1J2jb66N4AXPPZM2tO0rbVZz4EwDWNKTUnaduqcQAA3zngpJqTtL1/yhEAnPH+yTUnaTvo\nO5PqjjAqOSMoSZJUKIugJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqSJBXK\nIihJklQoi6AkSVKhLIKSJEmFsghKkiQVanzdASRJkrotIrYFvg/cDvQBiwJfyczze/Dav83MN3b7\ndeaHM4KSJKkETeDKzJyYmdsCOwBHRcQG9caqlzOCkiSpBH2dTzLz2Yg4A9gtIt4HbAWMA07KzAsi\nYjPgy7QmzR4E9gT+FTi5OtajwP7ARsDRwACwKnA6sB2wAXByZp4OjIuIc4DVgIeq/V6stn1d9Rqf\nzMxpEXEbkMBzmblHt/4xZnNGUJIkleovwHuBNTNzK1oF7hMRsTRwBrBfZm4OXAKsC3wdODQzJwKX\nAUfSmml8DfAe4BDgk8BewI7AQdXrvAL4fDUTeT9wAPAh4OHM3AbYFfhqte2SwP/XixIIzghKkqRy\nrQ6cC3wgIq6qxsYDawArZWYCZOY3ASJiXeC0iIDWNYZ3VfvclpkvRsSTwD2Z+UJEPAH0V+v/MvtY\nwHXA9rRmFbeqZh6hNWu4XLU8e9uuswiOIpMnT2ZgYOBl7dNoNLoTRpKkUSwiJtCalTsTuCozD4qI\n8cAxwD3AQxHxusy8OyI+DvwOmAl8IDMfiIitgdnFrTnMyy0fEa/NzN8D2wC30CqJD2TmcVWWjwGP\nVdvPWohvdUgWwVFkYGDAYidJ0vxpAttVM38v0upAn87MCyPixIiYDrwS+GFmPhMRBwHfiIhZtK7r\n+wpwH/CtqjDOAj5I67Rwc47XmXP5CeDYiFgVuBv4Bq3rEadExNXABOCrmdmMiOFK5UJlEZQkSWNe\nZk4DVnqJdR8bZGwGsPUcwzcDE+cYuxuYVu0zk9Z1hmTmE8B61fKmg7zsi8A+g7zua4d6HwubN4tI\nkiQVyiIoSZJUKIugJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqSJBXKIihJ\nklQoi6AkSVKhLIKSJEmFsghKkiQVyiIoSZJUqL5msznU+iFXNhoNGo3GQg3ULaMp60uZz/fQVz0O\n+VlKkjQG9A2/iTo5IyhJklSo8XUHUO88++Dv647wD0u+5rUAPP3HrDlJ21JrBACP3vyrmpO0LbfR\nZgD85bprak7StuJbtgLg3gsvqTlJ2+q77gzAvReNjEyr79LKk2efX3OSttjnvQDccPzUeoN02PSo\nfQH42aTT6w3SYfvJBwNwxZGn1ZykbYcvHgLAuR86qeYkbXueeQQA39znhJqTtO139sfrjjAqOSMo\nSZJUKIugJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJUqEsgpIkSYWyCEqSJBXKIihJklQoi6Ak\nSVKhLIKSJEmFsghKkiQVyiIoSZLGvIjYNiJmRcT75hi/NSK+uQDH/dOCp6uPRVCSJJViJrD77CcR\n8UZgCaC5AMdckH1rN35Bdu7v76fRaCykKN01c+bMrh5/8uTJDAwMdPU1uv0eJEkaw5rALcDaETEh\nM58C9gLOBVaLiMOA9wBLAo8A7wb2BPYH+oDPAGsCBwPjgIszswEsFhHnAqsBjwK7ZeYLvXxjC2KB\niuCkSZMWVo6u63ZhHRgY6PprjJbSLUnSCPYDWoVvKrAJcDywBrAcsH1mNiPiJ9W6JvBYZu4aESsC\npwNvzMy/R8QXImJJ4JXA0Zl5X0RcBbwJuLHXb2p+LVARlCRJGiX6qsfzgNMi4vfANdXYLOA54LyI\neAZYBVi0WpfV42uB2zLz7wCZeQxARDyWmfdV2/yJ1qnmUcMiOAq9nNPQziJKktSWmX+oZvI+DEwC\n1gKWBnbNzM0jYglgBu3iOKt6vAdYJyJekZnPRcT3gI9S8jWCqkcvTkNLkjTGNGmXtu8Be2Xm3RGx\nFvA88ExETKd1feDNwMod+5GZD0fE8cC0iGjSukbwoWp5ztcZNSyCkiRpzMvMacC0avlU4NRq+XLg\n8nk8xtnA2XOMrdyxvMfCytsrfn2MJElSoSyCkiRJhfLUsCRJ0igWEQcAnweW7xhuZua44fa1CEqS\nJI1unwAmAndk5su6WcUiKEmSNLr9OTNvn58dLYKSJEmjUETsUy3eGxEXARcBL1Zjzcw8Z7hjWAQl\nSZJGp4m0vrfwWeCvwFZzrLcISpIkjUWZuS9AROyQmVd0rouI/5iXY1gEJUmSRqGI2B1YDPhsRHy6\nY9WiwDHAD4Y7hkVQkiRpdJoAbAEsRes08Wwv0CqCw7IISpIkjUKZ+XXg6xGxfWb+bH6OYRGUJEka\n3R6LiAuAZYG+aqyZmdsNt6NFUJIkaXQ7BzgduJ3WXcR0PA7JIihJkjS6PZuZp87PjhbBgiz5mtfW\nHWEuS60RdUeYy3IbbVZ3hLms+JY5vxqqfqvvunPdEeay+i4jK1Ps8966I8xl06P2rTvCXLaffHDd\nEeaywxcPqTvCXPY884i6I8xlv7M/XncEtVweER8GfgIMzB7MzPuG29EiKEmSNLrtTetU8EfnGF9z\nuB0tggX521/urzvCPyy+4qoA/PXPw/6flZ5ZYqXVAHjslhtrTtK27AabAPDIjb+sOUnb8ptsAcD9\nl/645iRtq+60IwD3XnRJzUlaZs9M5tTza07SFvu2ZidvOH5qvUE6zJ6d/Nmk0+sN0mH27ORPPv61\nmpO0veOEQwH4xt5frDlJ2/7nHAnAsTt/quYkbZ+65Ni6I9QmM9eY330tgpIkSaNYRKwInAq8jVa3\n+zlwcGb+ebh9F+lyNkmSJHXXGcANwGuB1YHrgLPmZUdnBCVJkka312bmuzuefzEi9p6XHZ0RlCRJ\nGt1mRcRqs59ExOrAc/OyozOCkiRJo9ungF9GxK9o/bLI5sCB87KjM4KSJEmj2w3AFGA1WtcIXghs\nNC87OiMoSZI0ul0G3ApcQmtGsEn7N4eHZBGUJEka3ZqZuf/87GgRlCRJGt0ujIgDgCuBF2YP+hNz\nkiRJY9/SwCTgkTnG/Yk5SZKkMW43YMXM/NvL3dG7hiVJkka3e4Bl52dHZwRHkf7+fhqNBjNnzqw7\niiRJo0olcLaiAAAafUlEQVREbEvrN3j3yMzvdYzfCtyUmfvVlW0huSMibqP9RdLNzNxuuJ0sgqPI\npEmTAGg0GvUGkSRpdJoJ7A58DyAi3ggsQevrVkazzw8yNk/vySIoSZJK0ARuAdaOiAmZ+RSwF3Au\nsFpEHAa8B1iS1k0X7wamAudm5mURsS5wQmbuXEv6IWTm1fO7r9cISpKkkvyAVuED2AT4Ja0+tByw\nfWZuTmuibBNav9axT7Xt/sCZvY3afRZBSZJUgtm/tHEesHtEbA1cU43NonVt3XkRcSawCjC+mmlb\nLyKWB94O/Ki3kbvPU8Oj2OTJkxkYGBhyG68nlCSpLTP/EBFLAh+m9d17a9H6Hr5dM3PziFgCmEG7\nOH4L+B/g8sx8sY7M3WQRHMUGBgYsepIkzZsm7RsovgfslZl3R8RawPPAMxExndb1gTcDK1fbTgWO\nBd7Y27i9YRGUJEljXmZOA6ZVy6cCp1bLlwOXD7HrOGB6Zt7V9ZA18BpBSZKkQUTEe2iVxE/XnaVb\nnBGUJEkaRGb+EPhh3Tm6yRlBSZKkQlkEJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQk\nSSqURVCSJKlQFkFJkqRCWQQlSZIKZRGUJEkqlEVQkiSpUOPrDqDeWXzFVeuOMJclVlqt7ghzWXaD\nTeqOMJflN9mi7ghzWXWnHeuOMJfVd9m57gj/JPZ9b90R5rLpUfvWHWEu208+uO4Ic3nHCYfWHWEu\n+59zZN0R5vKpS46tO4IWkDOCkiRJhXJGsCB/fegPdUf4hyVWXhOAZx+4p+YkbUuushYAj99+c81J\n2pZ5/UYAPDLjupqTtC2/8VsAuPeiS2pO0jZ7JvCBy35Sc5KWVf79HQDk2efXnKQt9mnNTs444eya\nk7Rt/PF9ALjqk2fUnKRt4ucOAkZmpu8f/JWak7T95+n/BcDxuzbqDdLhqAsbdUcYlZwRlCRJKpRF\nUJIkqVAWQUmSpEJZBCVJkgplEZQkSSqURVCSJKlQFkFJkqRCWQRHof7+fhqNBjNnzqw7iiRJGsX8\nQulRaNKkSQA0Go16g0iSpFHNGUFJkqRCWQQlSZIKZRGUJEkqlNcISpKkokTEkcB/AWtm5t/ncZ/z\ngL2BKcB5mXl5FyP2jDOCkiSpNHsB5wG7z+sOmblHZj4PNKu/McEZQUmSVIyI2Bb4HXAG8G3g7Ii4\nGvg18CZgFq2CuB5wPPB34OvAscA61WH6ehq6i5wRlCRJJfkQcFZm3gX8PSI2pTXD97PM3Bb4IfCJ\namyxzNw6M79dW9ouc0ZQkiQVISKWAXYEVoiIw4EJwOHV6p9Wj9cCO1XL2duEvWcRHCMmT57MwMDA\nXON+6bQkSf+wF3BmZh4FEBGLA38EHgY2A64BtgB+W20/q4aMPWURHCMGBgYsfZIkDe2DtMogAJn5\nt4i4gNbp4sMi4ljgKeADwIb8800hL7U8qlkEJUlSETJzw0HGDouI9YBDM/OxjlXTqr/Z2722Wtyv\nuyl7y5tFJEmSCuWMoCRJKlpmTqw7Q12cEZQkSSqURVCSJKlQFkFJkqRCWQQlSZIKZRGUJEkqlEVQ\nkiSpUBZBSZKkQhXzPYL9/f1d/Qm2mTNndu3YkiRJ3VBMEZw0aVJXj+/v/EqSpNHGU8OSJEmFsghK\nkiQVyiIoSZJUqL5msznU+iFXqq3RaPT8OsHO1xzi9fuqRz9LSdJY1zf8JupUzM0iY1HnndDetSxJ\nkl4ui+Ao1nkn9LzMRv7t4Qe7mOblWXyF1wDw7AP31JykbclV1gLgiTt+U3OStn9Zb0MAHr7+FzUn\naVth87cC8H8/v7LmJG2v3u5tANx/6Y9rTtKy6k47AnDnWd+rOUnbuh98HwA3f/mcmpO0bfTRvQG4\n6pNn1JykbeLnDgLgp0edVnOStrcffwgA537opJqTtO155hEAHL9ro94gHY66sFF3hFHJawQlSZIK\nZRGUJEkqlEVQkiSpUBZBSZKkQlkEJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQkSSqU\nRVCSJKlQFkFJkqRCja87gCRJUi9ExJrAl4BlgUWBW4CjMvOZQbZdFdggMy95iWOtAfxPZr6ze4m7\nzxlBSZI05kXE4sBFwOTMnJiZbwV+BZz3Eru8DdiyV/nq4oygJEkqwU7A1Zl54+yBzDwnIg6JiNcB\nZ9GaJfwr8H5gErB4RFwLPAicArwIDAAHVIdYLSIuozXDeHFmfqGaSTwDWBz4G3Agrb71I+AR4LLM\nPKHr73YeOSMoSZJKsCbw+0HG/wjMAD6fmVsAJwMbAMcB51anhqcAh2XmtsDXgJOAJrAk8B5gC2Cn\niFif1qnnUzJzInAiMLnadiXg7SOpBIIzgmNGf38/jUZjrvHBxiRJKtCDwKaDjL8O6AeuA8jMHwFE\nxL4d27w6M2+tlq+hVe4AbsjMgWr7G4G1gTcAx0TEUUAf8Fy17R8y84WF9m4WEovgGDFp0qS6I0iS\nNJJdBHwiIjaZfXo4Ij4EPAxcSqskXhkRe9A61fsUMK7a96GIeGNm/hbYBshqfIOIWIzWKePNgdOB\nmcCXMvO6iHgDsFm17ayuv8P5YBGUJEljXmY+GxHvBL4cEcvR6kC3ALsDKwBnRMQngWeBvYA1aBXH\nm2hdE3hqRPQBzwMfrA77JK2CuQzwrcycGRH/DZwWEf20rhP8cLVtswdv82WzCEqSpCJk5u+BXQZZ\n9RSw/RxjvwHW6Xi+zSD7bTHIa/wBeMe8bDsSeLOIJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJ\nUqEsgpIkSYWyCEqSJBXKIihJklQoi6AkSVKhLIKSJEmFsghKkiQVyiIoSZJUKIugJElSoSyCkiRJ\nheprNptDrR9ypdoajQaNRqPuGIPpqx79LCVJY13f8JuokzOCkiRJhRpfdwD1zt8f/3PdEf5hsWVW\nAmDg0T/VnKStf7lXAfD0H2bWnKRtqTXXAeCxW26sOUnbshtsAsBfrp1ec5K2FbfcGoA/Xf3zmpO0\nvGrb7QD44w8urjlJ2xr/8S4Abv/6d2tO0vb6A3cH4MYvTq03SIdNjtwXgGsaU+oN0mGrxgEA/PSo\n02pO0vb24w8BYOq+J9ScpG3fqR+vO8Ko5IygJElSoSyCkiRJhbIISpIkFcoiKEmSVCiLoCRJUqEs\ngpIkSYWyCEqSJBXKIihJklQoi6AkSVKhLIKSJEmFsghKkiQVyiIoSZJUKIugJEkqSkRcHRFRd46R\nwCIoSZJK06z+ije+7gCSJEk1WCEivgT0A68GPpmZF0XErcDVwPq0yuIuwEbAQZm5B0BE/CkzXxUR\nbwBOBMYBywOHZOZ1EXEvcCdwB/BOYNPMfDwiDgFemZkn9PSdDsEZQUmSVKINgBMzcwfgQOCwanwp\n4DuZuS3wILAjc88ezn6+HvCxzNweOB7YrxpfBdgjM48AzgV2r8b3BKYu9HeyAJwRHCUmT57MwMDA\ny96v0Wgs/DCSJI0yEfFKYCAzX6iGfgFMiogP0ip2nZ3o19Xj/bRmDOfUVz0+BHwqIv5Gq0A+WY0/\nkpmPV8vfAL4bEdOBP2fmwwv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iIiJGVBrBiIiIiBGVRjAiIiJiRKURjIiIiBhRaQQjIiIi\nRlQawYiIiIgRlUYwIiIiYkSlEYyIiIgYUXOaDhAR8VQi6Rpgk+019eMNwAXApZSXzXoU+IDtbZIW\nAKuBZwLPpbws45WSLqG8lNYLgSttXzX4/0lERCaCERGduho4A0DS4cDBwCpgue1jgPcA36nbng18\nwvargcXAJ9u+ztNtz08TGBFNykvMRUR0QNIY5TVTlwBnAmPAR4Hb2zY7CHg5sBNYBhwFHA2cavtp\ndSK4n+2JAUaPiNhLJoIRER2wPQ18HTgdeAewFtht+5WtN+A1th8ErgXeBmwHJihNI8A0sHvQ2SMi\n9pRGMCKic2uBc4G7bd8N/F7SOICkNwC/qNstAS62/SPghPr5fZhpCCMiGpVGMCKiQ7b/BNxFaQgB\nxoFzJN1GWQd4ar39EmCzpEngpcAU8GLKRDDrciKicVkjGBHRIUmHUqZ+823/s+E4ERGzlolgREQH\nJJ0CbAMuTBMYEU91mQhGREREjKhMBCMiIiJGVBrBiIiIiBGVRjAiIiJiRKURjIiIiBhRaQQjIiIi\nRlQawYiIiIgR9R9qs+C3hjVPvgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x220f1d68>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Since the number of flights increase every year, we should set a standard scale\n",
      "sns.clustermap(flight_dframe,standard_scale=1) # standardize by columns (year)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "<seaborn.matrix.ClusterGrid at 0x227967f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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nx32u5iRtTzjsJQD8+ryv15ykbf3nPxeAm885t+YkLRvv9QIA8vRzak7SFgfs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\nR2HVU/P9AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x20f9bb70>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Or scale the rows\n",
      "sns.clustermap(flight_dframe,standard_scale=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<seaborn.matrix.ClusterGrid at 0x2095c2e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CcPev/1/NSdrWeM7zAPjzjy+pOUnLOjvsCMArNn19zUnaLrj+bADmrD+35iRt\n82+dB8BHX/XBmpO0HX7+MQC8c/vDak7S9plLjwfgqg+fVnOStq2OPACAcw/5dM1J2vY84V11R5iU\nPEdQkiSpUBZBSZKkQlkEJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQkSSqURVCSJKlQ\nFkFJkqRCWQQlSZIKZRGUJEkqlEVQkiSpUBZBSZJUlIh4X0TcHhErdfl9NomIbbr5Ho+VRVCSJJVm\nX+AcYK8uv88ewHO6/B6PyYy6A0iSJPVKRGwH/AY4ETgLODMiLgMOysybIuItwJMy85iIOBrYDbgD\nWAU4Gtge+HNmnhgRGwEnZOb2EfFhYDta3eqb1bH3A5oR8bPMvK6HH3OZOSIoSZJKciBwambeBNwX\nEZsDwx3rhwEiYlPgZcCLaJXBdTrXL8Xrgb2BbYC7M/N24HTg+IlaAsERwZ4bHByk2Wz27P0ajUbP\n3kuSpIksItYEdgbWioh3ALOAt4/YrK963Aj4SWYO0xrVW1qZ6+t4vg/wMeDJwA8eZZsJxyLYY81m\n03ImSVI99gVOycz3A0TEysDvgF8C6wI3AS8E/gj8CnhHRPQBM4HnV8do0h4dfEF1nJnAnpm5d7X9\nryLia8BiJvjs64QOJ0mSNI7eBHxlyYvM/Det8/l+DHwxIi6i1Y2GM/MG4ELgGuBbwP3AIuDrwC4R\ncSmtcjicmYuAuyLimupYF2fmH4CfAW+PiLm9+oDLyxFBSZJUhMx83lKWva16+tHO5RGxFvD3zNyi\n+pqZG4DbMvOPwOZLOc6xwLEjll1Iq0xOWBZBSZKkR7oT2Cwi9qd1gcjJVQmcUiyCkiRJI1QXiRxQ\nd45uswhKkiRNYhHxZuDDwBM7Fg9n5vSx9rUISpIkTW5H0vqi619XI5nLzCIoSZI0uf01M3+1Ijta\nBCVJkiahiHhj9fTWiDgfOB94sFo2nJlfHusYFkFJkqTJaXtaVzT/E/gXrdvbdbIISpIkTUWZuR9A\nRPxnZv5P57qIeM2yHMMiKEmSNAlFxF7ASsAxEfGBjlWPA46gddeUUVkEJUmSJqdZwFbAarSmiZd4\ngFYRHJNFUJIkaRLKzJOAkyLipZn5oxU5hkVQkiRpcrsrIs4DngD0VcuGM3OHsXa0CEqSJE1uXwa+\nBPyK1lXEdDyOyiJYkJmzZtcd4RFWWXfDuiM8woZ7vKruCI8Qb9yz7giPsMZznld3hEdYZ4cd647w\nMBdcf3Z/IarcAAAazUlEQVTdER5h/q3z6o7wCIeff0zdER7hM5ceX3eER9jqyIl329s9T3hX3RHU\n8s/M/PyK7GgR7KL+/n4ajcbDlg0NDdUTRpIkTVUXR8ShwEVAc8nCzPzDWDtaBLtoYGDgEctGFsNe\nWrRwQW3vPdKS0cl//+22mpO0rbz2UwH43Xnn15ykbcnoZJ5xbs1J2mK/1ujk3TdeX3OStjU23hSA\n2y9ZoXOlx926O74UgH23OKjmJG1nXXsSAHPWn1tzkrYlo5P/9+VH15yk7QPfPxaA9+30vpqTtH38\nhx8H4IrGyTUnadum8WYAvnLAcTUnafvv095Td4Q6vYHWVPC7Rywfc9rNIihJkjSJZeYGK7qvRVCS\nJGkSi4i1gc8DO9Lqdj8G3pKZfx1r32ldziZJkqTuOhH4CfB0YH3gauDUZdnREUFJkqTJ7emZuXvH\n649HxBuWZUdHBCVJkia3xRHxtCUvImJ9YNGy7OiIoCRJ0uR2NHBVRFxL684iWwLL9LUFjghKkiRN\nbj8BTgaeRuscwe8AL1iWHR0RlCRJmtwuBOYDF9AaERymfc/hUVkEJUmSJrfhzFyhexBaBCVJkia3\n70TEm4FLgAeWLPQWc5IkSVPf6sAAcOeI5d5iTpIkaYrbA1g7M/+9vDt61bAkSdLkdgvwhBXZ0RFB\nSZJUlIi4DDgoM2+qO8s4+nVE3ED7i6SHM3OHsXayCEqSpNIs89erTBIfXsqy4WXZ0SIoSZJK1IiI\nyzLzxIjYCDghM7ePiPnAZcAcWmXqVZm5MCI+CrwEmA4cn5nn1ZZ8hMy8bEX39RxBSZKkttWAszNz\nO+BPwM4RsTOwQWZuA+wAHBkRq9eYcdw4Ithj/f39NBqNnr1fL99LkqSJKiJWBZqZ+QDtu28sMXKa\n+BfV421AP61bt70wIi6tls+gdSu3+d1L3BsWwR4bGBioO4IkSSU6A/h8RFwOrAVcBKxTrRt5X96R\n59cNAZdm5sERMQM4AvhtF7P2jFPDkiSpBMcBnwCuBc4FvgbsUo3yPZ9Hv7hiODO/B9xblcifAIsz\n894eZO46RwQlSdKUl5lXA5uNWLz5UrbbsOP54R3P39O9dPVxRFCSJKlQFkFJkqRCWQQlSZIKZRGU\nJEkqlEVQkiSpUBZBSZKkQlkEJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQkSSqURVCS\nJKlQfcPDw6OtH3XlZNVoNGg0Gl3fZ4Loqx6n5N9SkqQOfWNvok6OCEqSJBVqRt0B1DuLFi6oO8JD\nZs6aDcC/7/hTzUnaVl7rKQDc8rVv15yk7Rl77Q7Ard+5oOYkbevv9goA/v6rn9ecpG3N/3gBAH/8\nwUU1J2lZb+eXAXDAVm+tOUnbaVd9EYA568+tOUnb/FvnAXD8a46tOUnbYd88GoCjdz6i5iRtx/7g\nIwBcO3h6zUnathjYH4CvHnh8zUna9jnlsLojTEpFFsH+/v7lnuYdGhrqThhJkqSaFFkEBwYGlnuf\nSXp+oCRJ0qPyHEFJkqRCWQQlSZIKZRGUJEkqlEVQkiSpUBZBSZKkQlkEJUmSCmURlCRJKpRFUJIk\nqVAWQUmSpEJZBCVJkgplEZQkSSpUkfcaliRJZYmIDYD5wM86Fv84M49dyraXAq/JzLt6FK82FkFJ\nklSKX2Xm9su4bV9Xk0wQFkFJklSsiPgo8BJgOnB8Zp5Xrfp0RDwF+BewH7AJ8DHgPuAk4FggMnNR\nRAwCNwJfqdatB6wDfDczj46IM4AnALOB64EbMvOLEbEm8MPMfFFPPuxSeI6gJEkqxXMi4tKOn9cD\nG2TmNsAOwJERsXq17Zczcwfg+8DhwDCwUmZum5lnjTjucPX4VODqzHwZsAXwlo71l2Tm1sAngDdU\ny18PjDxWTzkiWLPBwUGazWbXjt9oNLp2bEmSJplfd04NR8T7gBdW5wRCqxdtUD2/rHq8Bnh59Twf\n5bhLppHvAjaLiO2BhcBKHdskQGb+LiL+EREb0yqCu67wpxkHFsGaNZtNy5okSfW4Ebg0Mw+OiBnA\nEcAt1boXA5cD29KazgVY3LFvE1g3Im4Fnlcdaz/g7sx8S0Q8EzioY/vhjucnAx8Abqv7ghSnhiVJ\nUik6yxiZ+T3g3oi4HPgJsDgz761W71ONFM4FBpey/8eBC2lNHd9VrbsEeFlE/BAYAK6LiHWXsu+3\ngR2BU8frg60oRwQlSdKUl5m/B7ZayvL3LGXZ0q4snlf9LNnmdOD0pWz3vKUs23/E6xnA7zPzh6NE\n7glHBCVJknokIrYCrqU9ylgrRwQlSZJ6JDOvAubUnWMJRwQlSZIKZRGUJEkqlEVQkiSpUBZBSZKk\nQlkEJUmSCmURlCRJKpRFUJIkqVAWQUmSpEJZBCVJkgplEZQkSSqURVCSJKlQfcPDw6OtH3VlSRqN\nBo1GY9Ict0Nf9ejfUpI01fWNvYk6zag7wFQ0ODhIs9lcpm2Hhoa6nEaSJGnpLIJd0Gw2l3mUr8uj\ngQ+zaOGCnr3XWGbOmg3AfXf/reYkbSutsTYAvz33/JqTtD19z1cB8JuvfrPmJG3P2uc1ANx94/U1\nJ2lbY+NNAfjLZT+uOUnLk7fbAYA3b/32mpO0nXzl5wGYs/7cmpO0zb91HgCf2uNDNSdpe/d5RwEw\n8J/vrzlJ2+D/fAyAqz9yWs1J2l58xAEAfOMtn645Sdtrv/SuuiNMSp4jKEmSVCiLoCRJUqEsgpIk\nSYWyCEqSJBXKIihJklQoi6AkSVKhLIKSJEmFsghKkiQVyiIoSZJUKIugJElSoSyCkiRJhbIISpIk\nFcoiKEmSVCiLoCRJKkpEXBYRUXeOicAiKEmSSjNc/RRvRt0BJEmSarBWRHwS6AfWAY7KzPMjYj5w\nGTCHVll8FfAC4ODM3BsgIv6SmU+OiE2A44DpwBOBQzLz6oi4FbgR+DWwK7B5Zv49Ig4BVs3MT/T0\nk47CIliz/v5+Go1G147fzWNLkjSJbQocl5nzIuLFwDHA+cBqwNmZeWhEnAXsDPxlxL5LRhOfA7wn\nM2+IiL2B/YGrgfWA51XlbyGwF3ACsA+we7c/2PKwCNZsYGCg7giSJE15EbEq0MzMB6pF/wsMRMSb\naBW7zk70i+rxNlojhiP1VY+3A0dHxL9pFch7quV3Zubfq+enAV+LiMuBv2bmHePygcaJ5whKkqQS\nnAG8JCKmAWsDnwK+nJlvoDUV3NmJRp4/2KQ1fUxErA88oVr+GeCDmbkf8MuOYyxesmNm/gG4GzgS\nOGXcPs04cURQkiSV4Djgs9Xzc4Eh4JMR8U7gGtrlbqRh4Drg7oi4hta5f7+t1p0FnBsRt1XbrNOx\nT6eTq/feZxw+x7iyCEqSpCkvM68GNhux+Gsdz4+pttuwY5/DO9bvtpRjforWyOLI5euOWDQdODUz\nJ9yVyhZBSZKkLomIjwBzgVfUnWVpLIKSJEldkplH1J1hNF4sIkmSVCiLoCRJUqEsgpIkSYWyCEqS\nJBXKIihJklQoi6AkSVKhLIKSJEmFsghKkiQVyiIoSZJUKIugJElSoSyCkiRJheobHh4ebf2oK0vS\naDRoNBrjvm0P9FWP/i0lSVN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8VYFMZY67RaTT49NIV5s/IWk+rR13a7ou7QC0qhvYaKb5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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2095cf98>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Finally we can also normalize the rows by their Z-score.\n",
      "# This subtracts the mean and devides by the STD of each column, then teh rows have amean of 0 and a variance of 1\n",
      "sns.clustermap(flight_dframe,z_score=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "<seaborn.matrix.ClusterGrid at 0x20314160>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fRMSjgPnAUG2JauaMoCRJKsEQcBXw8IhYlJmrgBcDnwd2i4jXAs8BtgJuB54N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\nNBqNumN0hcHBwbojSJKkaWz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       "text": [
        "<matplotlib.figure.Figure at 0x20314be0>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Above we can see which values are greater than the mean and which are below very clearly"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# CONGRATULATIONS!! We've developed quite a toolbox to hammer out some great data anaysis projects!\n",
      "\n",
      "# Up next: Projects to apply what we've learned to real datasets!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}