plt/5. seaborn_matrix_plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matrix Plots with Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "flights = sns.load_dataset('flights')\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We need to convert the data for these plotting techniques to work.\n",
    "We can do that by creating a correlation or we can also create a pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc=tips.corr()\n",
    "tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb378510>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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NdKNbSYQde3YH/hnCYFRNDWW9euU7jKI2evRoGhoaWLduHfv27WPu3Llcepi/IO69915m\nzZrF3r17D5wbMmQICxcuBGDbtm00NzdTU1MTWOxBiLmnXfIh7cRrZiPM7JtmdqOZjchlULnSv0cZ\nW1t2Hjje2rKT/j3KDqlT2XsAlb0H8MCE25hz9TTOqjoNgBWb1/Lqxjep++r3qfvaD3hp/Qo27NgS\naPwi+w0cOJCNGw/uOt7Y2MjAgQMPqTN8+HAqKyuZN++QzRNYtmwZ48aNIxKJUFVVxciRI6msrCRM\nMtllOB/SSrxmdifwMNCXtt0z/93MZqR4zYEtk7csXpWsakGJWAkVZeVMefxH3PX7B5n2t1+mx8e6\nM7CsP1V9jueLc6Yz/t+mMfKvP84ZA0/Od7gih2Vm3Hfffdxyyy0fuVZbW0tjYyNLlizh/vvv58UX\nXyQajeYhytwp9B5vumO8VwPD3X0vgJl9D3gV+E5HL0jcMvnTP/pqQQy4bPtLM+U9ex84Lu/Zm21/\naW5XZycrNq8nGouxefd2Nu7YSkVZOSMqT2HF5rW8v+8DABave4PTTjiJZU0NQX4EEQCampoO6aVW\nVFTQ1HRw1/GePXsydOhQnn32WQCOO+446urqGDduHK+88go333zzgbqLFi1izZpw3aso9P9G0h1q\nWE/blsX7fQx4O+vR5NjqLeupKCvn+GP70q0kwvmn1PDC28sOqfPnhmWMqBwMQK/ux1DZp5xNu97l\nnZYdnFkxmIiVECkp4cyKwWzYrqEGyY/6+noGDRpEVVUVpaWlTJgwgbq6g7uO7969m/79+1NdXU11\ndTWLFy8+kHS7d+/O0UcfDcAFF1xAa2vrR27KFbuwzGr4AFhhZn+kbTujscALZvZTAHf/Zo7iy6qo\nx/jxwrncd/lUIiUlPP3GItZt38x1n7qE1e9s4IW3l/PS+hWMPnEIj0y6i1jMmf3ck+ze+x7PrHmF\nEZWn8Ktr78RxXlq3kkVrl+f7IxWlW269lZfr62lububc885jyg03cMXll+c7rKISjUaZMmUKCxYs\nIBKJUFtby8qVK5k5cyZLlizhqaee6vC15eXlLFiwgFgsRlNTE9dcc02AkQej0NdqsHSmkJjZtcmu\nu/vDya4XylBDmD0/dXa+Qwi9SGlpvkPoEtzdOtvGFbUXp51znviH+Z1+v0yl1eNNlVhFRApJoY/x\nJk28Zva4u3/JzF7nMDsmu/uwnEUmInKECn2oIVWPd2r86yrgtoTzBnw/JxGJiHRSvm6apStp4nX3\n/c/SnuzuGxKvmdnHcxaViEgnRDs/TJxTqYYavg58AzjJzBJv4fcEFuUyMBGRIxUr8NUQUg01PArM\nB74LTE843+LuO3IWlYhIZxRzj9fddwG7aHtyTUSkKDhFnHhFRIpSMfd4RUSKkXsk3yEkpcQrIqGT\nhYffckqJV0RCqLATb2HPuRAROQLulnZJxcwuMrM3zazBzKYf5vqPzWxpvKwxs+bDtZNIPV4RCaHs\n9CnNLALMpm1Fxkag3szq3H3l/jru/q2E+jcCw4OJTkSkgGSxxzsaaHD3te7+ITAXSLY99tXAY6ka\nVeIVkfDxkrRL4jZl8TI5oaWBwMaE48b4uY8wsxOBamBhqvA01CAioZPJrIbEbco6aQLwhLunXJVS\niVdEQihrf8w3AYlbMFfEzx3OBOCGdBrVUIOIhE4Wx3jrgUFmVm1mR9GWXOvaV4qv1tgb+J904lPi\nFZEQsgxKx9y9FZgCLKBtXfLH3X2Fmd1jZuMSqk4A5no6e6mhoQYRCaFsPjLs7vOAee3O3dnu+O5M\n2lTiFZHQ0SPDIiJBU+IVEQmW1uMVEQmaerwiIsHyAp+wpcQrIqHjrsQrIhIsDTWIiARLPV4RkcCp\nxysiEiwNNYiIBEtDDcDW1eVBvE2XFiktzXcIoRfdty/fIUialHhFRAKnoQYRkUBpkRwRkcAp8YqI\nBEpjvCIiAVPiFREJmsZ4RUSCpfV4RUSCpqEGEZFgqccrIhI0jfGKiAQrm9u754ISr4iEjsc83yEk\npcQrIuFT2HlXiVdEwsdj+Y4gOSVeEQkdV49XRCRYHs13BMkp8YpI6OjmmohI0DTGKyISLI3xiogE\nTLMaRESCph6viEiwYq2FnXkLe+00EZEj4RmUFMzsIjN708wazGx6B3W+ZGYrzWyFmT2aqk31eEUk\ndLI1xmtmEWA2MBZoBOrNrM7dVybUGQTcDnza3XeaWXmqdtXjFZHQcU+/pDAaaHD3te7+ITAXuLRd\nneuB2e6+s+29fWuqRpV4RSR8YukXM5tsZksSyuSElgYCGxOOG+PnEg0GBpvZIjNbbGYXpQpPQw0i\nEjqewURed58DzOnE23UDBgHnAhXAn83sdHdvTvYCEZFQ8dasNdUEVCYcV8TPJWoEXnL3fcA6M1tD\nWyKu76hRDTWISOh4LP2SQj0wyMyqzewoYAJQ167Of9LW28XM+tE29LA2WaPq8YpI6GRrVoO7t5rZ\nFGABEAFq3X2Fmd0DLHH3uvi1vzWzlUAUuM3dtydrV4lXRMIni6uTufs8YF67c3cmfO/AzfGSli49\n1DDmtJNZcO9U/vufbmLyRWMOW+fimqHMn3kj82beyH3XXRlwhMXrwgsvZPXq1bz11ltMmzatw3qX\nXXYZ7s7IkSMBKC0tpba2luXLl7N06VLOOeecoEIOlTtmzODTY8ZwyaXtZz51DVmcTpYTXbbHW2LG\n3RMvYdKPH2LLzt08ecfXWLhsNQ2btx2oc2J5H7528We4atYv2L1nL316HpPHiItHSUkJs2fPZuzY\nsTQ2NlJfX09dXR2rVq06pF6PHj2YOnUqixcvPnDu+uuvB2DYsGH079+f+fPnM2rUqIzuUguMHz+e\niRMnMv322/MdSl5k8eZaTnTZHu+w6go2bNvOxnd3si8a5ff1r3P+maceUueqMTU88sxL7N6zF4Ad\nLe/lI9SiM3r0aBoaGli3bh379u1j7ty5XHqYnte9997LrFmz2Lt374FzQ4YMYeHChQBs27aN5uZm\nampqAos9LEbV1FDWq1e+w8ibLN5cy4kum3iPKzuWzTt2HTjesnMXA8p6HlKnakA/qgf0Ze606/jN\n7ZMZc9rJQYdZlAYOHMjGjQfnnDc2NjJw4KFzzocPH05lZSXz5h0ydMayZcsYN24ckUiEqqoqRo4c\nSWVlJSKZKPTEm/ZQg5kNAP4ZOMHdLzazIcAn3f2XOYsuz7pFSjhxQF++/MNajut9LI/edh2fv/tn\ntLy/N/WLpUNmxn333cekSZM+cq22tpZTTz2VJUuWsGHDBl588UWi0QLfQEsKT4Fv/ZNJj/ch2qZN\nnBA/XgPc1FHlxMfwdq1+9cgjzJEtzbs5vs/BP8WO692Ld5pbDq2zcxcLl66mNRqj8d1m1r3zLlUD\n+gYdatFpamo6pJdaUVFBU9PBOec9e/Zk6NChPPvss6xbt45PfOIT1NXVMXLkSKLRKDfffDPDhw9n\n/PjxlJWVsWbNmnx8DClihd7jzSTx9nP3x4nvZuTurbTNWTssd5/j7jXuXtPr4yM6GWb2vb6+iary\nvlT0K6M0EuHzo07nT8tWH1Lnj6+tYvQp1QD07nE01QP6sXHbjnyEW1Tq6+sZNGgQVVVVlJaWMmHC\nBOrqDs453717N/3796e6uprq6moWL17MuHHjeOWVV+jevTtHH300ABdccAGtra0fuSknkkqhJ95M\nZjW8Z2Z9ia9gaWafAHYlf0nhisZizHz0aWpvupaIlfDEoldp2LSVqePO4/UNm1i4bDXPr2jg7NNO\nZv7MG4nGnFlPLKD5vffzHXrBi0ajTJkyhQULFhCJRKitrWXlypXMnDmTJUuW8NRTT3X42vLychYs\nWEAsFqOpqYlrrrkmwMjD45Zbb+Xl+nqam5s597zzmHLDDVxx+eX5DiswhT6rwdKdpmNmI4GfAkOB\nN4D+wJXuvizVawdd/78Le8AlBBoe/E6+Qwi96L59+Q6hSyjp1s0628YJf/u/0s45m/7wz51+v0yl\n3eN191fM7BzgFMCAN+OLQoiIFJYC3+wy7TFeM3sbuM7dV7j7G+6+z8yezmFsIiJHpNDHeDO5ubYP\n+KyZ/Xt8lR746ILAIiJ5F6bEu8fdrwJWAc+b2V9T8Jsoi0iXlMEOFPmQyawGA3D375vZq8AfgD45\niUpEpBMKfXv3TBJv4jJo/21mFwLXZj8kEZFOKvCbaykTr5l93N1XA01m1v5JCN1cE5GC4wX+lHk6\nPd6bgcnAjxLOJfbjz8tqRCIinZSvm2bpSnlzzd33b3X8AHCpu38WeIa2p9ZuzWFsIiJHpsBvrmUy\nq2GGu+82s7OBsbQtmvNATqISEemEWKunXfIhk8S7f9Tk88C/uvvvgKOS1BcRyY9oBiUPMpnV0GRm\n/0Zbb3eWmX2MLryQuogUrqIf403wJdrW473Q3Ztpm8N7W06iEhHphEJ/ci2TRXL2AL9NON4MbM5F\nUCIinRKC6WQiIkXFC3zrHyVeEQmdQl8IXYlXREInDE+uiYgUlUKf1aDEKyLhozFeEZFgaahBRCRg\nGmoQEQlYoW/Dq8QrIqGjHq+ISNAK/OaaFrkRkdDJ5loNZnaRmb1pZg1mNv0w1yeZ2TYzWxov16Vq\nUz1eEQmdbM1qMLMIMJu2VRkbgXozq3P3le2q/trdp6TbrhKviISOx5qz1dRooMHd1wKY2VzgUqB9\n4s2IhhpEJHSc99IuZjbZzJYklMkJTQ0ENiYcN8bPtXe5mS03syfMrDJVfOrxikgIvZd2TXefA8zp\nxJs9BTzm7h+Y2VeBh0mxCbASr4iEjmeQeFNoAhJ7sBXxcwffy317wuGDwPdTNarEKyKh4+zJVlP1\nwCAzq6Yt4U4AJiZWMLPj4xtDAIwDVqVqVIlXREJod1ZacfdWM5tC27ZnEaDW3VeY2T3AEnevA75p\nZuOAVmAHMClVu+Ze2BON88XMJsfHfiRH9DPOPf2MC5NmNXRscuoq0kn6GeeefsYFSIlXRCRgSrwi\nIgFT4u2YxsVyTz/j3NPPuADp5pqISMDU4xURCZgSr4hIwJR4JefMrMzMvhH//gQzeyLfMYWVmT1o\nZkPyHYckF4rEm/iLnaROlZlNTFYnod4bSa5PMrOfdXDtxfZtmNm5ZvZ0qvcNuTLgGwDuvsndr8hz\nPKHl7tcdZq1YKTChSLwk/GInUUW7Z6yzzd0/lcv2i9j3gL+Jr87/m4T/lCaZ2e/M7L/iK/zflec4\ni4qZHWNmvzezZWb2hpldZWbPmlmNmY1L2BHhTTNbF3/NSDN7zsxeMbMFZnZ8vj9HVxSWxJv4i/2D\neHnDzF43s6sS6oyJ1/lWvFf6vJm9Gi+ZJM3KwyULM/tLNj9UiEwH3nb3M4Hb2l0bDfwdcCZwpZnV\nBB1cEbsI2OTuZ7j7UOC/9l9w9zp3PzP+M18G/NDMSoF/Aa5w95FALfBP+Qi8qwvLIjnTgaHufqaZ\nXQ58DTgD6EfbVh1/jte51d2/AGBmRwNj3X2vmQ0CHgPS/aUfDQwF9sTb/727L8nuR+oy/rh/WT0z\n+y1wNqCfZXpeB35kZrOAp939eTM7pIKZfRt4391nm9lQ2v7d/jFeLwJsRgIXlsSb6GzaFiWOAu+Y\n2XPAKD66XFEp8DMzOxOIAoMzeA8li+xpP5FcE8vT5O5rzGwE8Dngu2b2h8TrZnYBcCXwmf2ngBXu\n/slgI5X2wjLUcCS+BbxDW8+4Bjgqg9cqWWSmBejZwbWxZtbHzLoD44FFwYVV3MzsBGCPuz8C/BAY\nkXDtRNo2abzS3d+Pn34T6G9mn4zXKTWz0wIOWwhP4k38xX4euMrMImbWn7b/7V/mo7/8vYDN7h4D\nrqHtz650KVlkIP7XwaL4TbUftLv8AvAfwFLgSQ3ZZOR04GUzWwrcAXwn4dokoC/wn/H7GvPc/UPg\nCmCWmS2j7WeuG8J5EIqhBnffbmb7f7HnA8tpu6HgwLfdfYuZbQei8X9wDwE/B540syuBZ8hkk6aD\nyeJk4FEli9TcvaMZJVsz2RZbDnL3BbQt0J3o3PjXJcDMw7xmKQeHHiRPtFaD5I2ZTQJqlHilq1Hi\nFREJWCiGGnLBzC4EZrU7vc7dv5iPeEQkPNTjFREJWFhmNYiIFA0lXhGRgCnxiogETIlXRCRg/x82\nA/vRs/puCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb378f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(tc, annot=True,cmap='gist_earth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp=flights.pivot_table(index='month',columns='year',values='passengers')\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fAf8TEdtTJEObxqq5bwY6ARixsWmxXu4JmlltRF+1rQ5q2dgABwFLI+I78Eyq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\nsvv/s1ZLzR4ZgpoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc44beb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(fp,cmap='coolwarm')\n",
    "plt.savefig('images/sns_13heatmap.png',dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pSFaJ7NLftIh4PiKeb6C980haVdLqZP8Yeb+u/Y2UYBwv6deS5knr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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc72f810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(fp,cmap='plasma',linecolor=\"white\",linewidth=1)\n",
    "plt.savefig('images/sns_13heatmap_lines.png',dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustermaps\n",
    "We get hierarchial clusters based on their similarities.\n",
    "\n",
    "The values on the x and y-axis may be out of order as they are clustered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8ADgiybfozTJ+dxq7vwZ4dpKvAvv0tb8VeGaSr9M7BD1xNvFXquoW4GzgjKrasnPvYm54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\nxaIkSZLa+hcp4yeLiN6yawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x96109b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(fp,cmap='coolwarm')\n",
    "plt.savefig('images/sns_14clustermap.png',dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Standarize the scales**: So that we can really see the similarities between the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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plWVRkiRNpCTrklyQZHWSbyd5RZJOulGSm7t43tlY3HUASZKkjtxWVQcCJLkr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\nI+HOZFQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfa23e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(fp,cmap='coolwarm',standard_scale=1)\n",
    "plt.savefig('images/sns_14clustermap_normalize.png',dpi=200)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}