notebooks/analysis/analysis_missing_values/missing_visualization-missingno.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing data visualization with \"missingno\"\n",
    "\n",
    "#### Reference:\n",
    "\n",
    "- [github.missingno](https://github.com/ResidentMario/missingno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import missingno as msno\n",
    "import pandas as pd\n",
    "import pandas.util.testing as tm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EZRcooqtVG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.769466</td>\n",
       "      <td>-1.190229</td>\n",
       "      <td>-0.687346</td>\n",
       "      <td>-0.812779</td>\n",
       "      <td>-0.467430</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o0EcYmMdiQ</th>\n",
       "      <td>0.379452</td>\n",
       "      <td>-1.460923</td>\n",
       "      <td>0.377981</td>\n",
       "      <td>-1.011118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014586</td>\n",
       "      <td>-1.852610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v56NEiw9C1</th>\n",
       "      <td>0.394168</td>\n",
       "      <td>-0.439597</td>\n",
       "      <td>1.079458</td>\n",
       "      <td>1.383133</td>\n",
       "      <td>0.857546</td>\n",
       "      <td>0.739563</td>\n",
       "      <td>-0.418327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JHueQIgHsJ</th>\n",
       "      <td>1.824591</td>\n",
       "      <td>-0.683143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.141408</td>\n",
       "      <td>-0.131872</td>\n",
       "      <td>0.364894</td>\n",
       "      <td>-0.154622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BXzdxisllG</th>\n",
       "      <td>-0.071934</td>\n",
       "      <td>0.340618</td>\n",
       "      <td>-1.086039</td>\n",
       "      <td>0.615913</td>\n",
       "      <td>-0.376866</td>\n",
       "      <td>0.059936</td>\n",
       "      <td>-0.559154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E         F  \\\n",
       "EZRcooqtVG       NaN -1.769466 -1.190229 -0.687346 -0.812779 -0.467430   \n",
       "o0EcYmMdiQ  0.379452 -1.460923  0.377981 -1.011118       NaN -0.014586   \n",
       "v56NEiw9C1  0.394168 -0.439597  1.079458  1.383133  0.857546  0.739563   \n",
       "JHueQIgHsJ  1.824591 -0.683143       NaN  1.141408 -0.131872  0.364894   \n",
       "BXzdxisllG -0.071934  0.340618 -1.086039  0.615913 -0.376866  0.059936   \n",
       "\n",
       "                   G  \n",
       "EZRcooqtVG       NaN  \n",
       "o0EcYmMdiQ -1.852610  \n",
       "v56NEiw9C1 -0.418327  \n",
       "JHueQIgHsJ -0.154622  \n",
       "BXzdxisllG -0.559154  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set dimmensions \n",
    "tm.N, tm.K = 500, 7\n",
    "# it creates dataframe with missing values in it\n",
    "df= pd.util.testing.makeMissingDataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-44.518254</td>\n",
       "      <td>-89.383674</td>\n",
       "      <td>-127.867132</td>\n",
       "      <td>-84.515144</td>\n",
       "      <td>-9.698731</td>\n",
       "      <td>29.641838</td>\n",
       "      <td>107.412844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>1.747711</td>\n",
       "      <td>98.693657</td>\n",
       "      <td>-91.130662</td>\n",
       "      <td>98.029513</td>\n",
       "      <td>-202.359700</td>\n",
       "      <td>-18.102907</td>\n",
       "      <td>11.929990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>105.232105</td>\n",
       "      <td>-198.394303</td>\n",
       "      <td>3.878682</td>\n",
       "      <td>66.034593</td>\n",
       "      <td>-150.626077</td>\n",
       "      <td>26.468326</td>\n",
       "      <td>-86.388604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>137.525952</td>\n",
       "      <td>21.093120</td>\n",
       "      <td>-128.661998</td>\n",
       "      <td>128.863073</td>\n",
       "      <td>121.755776</td>\n",
       "      <td>-123.008141</td>\n",
       "      <td>65.904586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>48.449015</td>\n",
       "      <td>-36.069524</td>\n",
       "      <td>-51.088687</td>\n",
       "      <td>204.765833</td>\n",
       "      <td>114.301969</td>\n",
       "      <td>51.786837</td>\n",
       "      <td>-17.483624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A           B           C           D           E  \\\n",
       "2000-01-03  -44.518254  -89.383674 -127.867132  -84.515144   -9.698731   \n",
       "2000-01-04    1.747711   98.693657  -91.130662   98.029513 -202.359700   \n",
       "2000-01-05  105.232105 -198.394303    3.878682   66.034593 -150.626077   \n",
       "2000-01-06  137.525952   21.093120 -128.661998  128.863073  121.755776   \n",
       "2000-01-07   48.449015  -36.069524  -51.088687  204.765833  114.301969   \n",
       "\n",
       "                     F           G  \n",
       "2000-01-03   29.641838  107.412844  \n",
       "2000-01-04  -18.102907   11.929990  \n",
       "2000-01-05   26.468326  -86.388604  \n",
       "2000-01-06 -123.008141   65.904586  \n",
       "2000-01-07   51.786837  -17.483624  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# timeseries df\n",
    "st = pd.util.testing.makeTimeDataFrame() * 100\n",
    "st.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## missing data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ada2e10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10756a240>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matrix\n",
    "msno.matrix(st , freq='BQ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107599d68>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f568ba8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAAOuIxAAAAAAAd8RgAAAAAgI54DAAAAABARzwGAAAAAKAjHgMAAAAA0BGPAQAAAADoiMcAAAAAAHTEYwAAAAAAOuIxAAAAAAAd8RgAAAAAgI54DAAAAABARzwGAAAAAKAjHgMAAAAA0BGPAQAAAADoiMcAAAAAAHTEYwAAAAAAOqPicVXtUVUfrKobqurGqvpQVe058rl7VtWpVXV5Vd1cVZdU1YlV9cB7t3UAAAAAgG1bVf1NVbWqOnHZ9fVV9a6quraqbqqqs6rqgBWev1NVvaGqrqqqW6rqvKp6/Jh7L4zHVbVzkrOTPCLJM5M8I8nDkpyzKAAPj5+V5PFJXpXkPyd5V5LfSfKnYzYIAAAAADBFVfUrSR65wvVKsiHJ4UlekOToJDtk1mx3X7b83UmOS/LqJEckuSrJx6rqwEX3Xzdij8cl2SfJw1trlw6buzDJl5M8J8mbVnnuYzMLzYe11j4+XDunqnZN8pKq2rm1dvOIPQAAAAAATEZV/UCSNyf57STvXfbwkUkel+TQ1to5w/rzkmxK8rIkLxyuPTLJMUmOba2dMlw7N8nGJCcMr3O3xhxbcWSS85fCcZK01jYl+UySoxY89wHD9xuXXb9+uHeNuD8AAAAAwNSclGRja+19Kzx2ZJIrl8JxkrTWbkjykXxnsz0yye1Jzphbd0eS9yc5rKp2XG0DY+Lx/kkuWuH6xiT7LXjuWZm9Q/n1VbVfVe1SVYcmeVGSt7XWbhpxfwAAAACAyaiqxyX5tSTPu5slqzXbPatql7l1m1Y4/WFjZm/83Xe1fYyJx7smuW6F65uTrF/tia21b2X29unthg19I8nfJvlokuePuDcAAAAAwGRU1Q5J3p7kD1trX7qbZas12+Tb3XbRul1X28uYM4+TpK1wbeGRE1W1U2Zvif6hzD5o7/IkP5nZ4cx3JHnuyPsDAAAAAEzB7yb5viSvXWVNZVyzHbtuRWPi8XVZuUCvz8rVet5vJDk4yb6tta8M1z5VVTckeUdVva21dsGYjQIAAAAAbMuqas8kr0jy7CQ7LjuTeMfhQ/S+kdk7h++u2Sbf7rabk+y5yrrNKzz2b8YcW7Exs7MxltsvycULnntAkuvmwvGSzw3ff2zE/QEAAAAApmCfJDslOS2zALz0lSQvGf58QFZvtpe31r45/H1jkr2raucV1t2W5NLVNjMmHm9IclBV7bN0oar2SvLY4bHVfD3J+qpafvDyTw3f/2XE/QEAAAAApuCLSQ5Z4SuZBeVDMgu+G5LsVlVPWHpiVT04yVPync12Q5Idkjx1bt26JL+U5OOttVtX28yYYyvemdmH251ZVa/M7IyM1yS5IrODm5du+tAkX0lyQmvthOHye5K8OMlfV9VrMzvz+DFJXpXk75N8ZsT9AQAAAAC2ea2165N8cvn1qkqSr7bWPjn8fUOS85KcVlUvzewdycdndpbxSXOv98WqOiPJHw0fxLcps8+h2zvJ0xftZ+E7j1trNyU5NMklSf48yenDTQ6de/tzho1tP/+arbXLkhyUWTE/MclfJzkuyTuSPLG1dtei+wMAAAAA8G1DVz0iySeSnJzkw0nuTHJIa+2KZcufleSUzPrsXyXZI8nhrbUvLLrPmHcep7V2eZKjF6y5LCt8Sl9r7eIkTxtzHwAAAAAAvlNrbaXuujnJscPXas+9JbPTIV68pfcdc+YxAAAAAAATIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQGdUPK6qParqg1V1Q1XdWFUfqqo9x96kqn6sqj5QVddW1S1V9aWqetE93zYAAAAAwLalqg6rqrOr6utVdWtVfa2q/ndV7bds3aheW1Xrq+pdQ5e9qarOqqoDxu5nYTyuqp2TnJ3kEUmemeQZSR6W5JyqeuCI5z8myWeT7Jjk2UmenOSNSbYfu0kAAAAAgAnYNcnfJ3l+kp9PcnyS/ZOcX1UPTcb32qqqJBuSHJ7kBUmOTrLDsG73MZtZN2LNcUn2SfLw1tqlw40vTPLlJM9J8qa7e2JVbZfk1CR/21r7hbmHzhmzOQAAAACAqWitvS/J++avVdXnkvxzkl/M7E25Y3vtkUkel+TQ1to5w7rzkmxK8rIkL1y0nzHHVhyZ5PyljQz/iE1JPpPkqAXPPTjJflklMAMAAAAAcLf+dfh++/B9bK89MsmVS+F4WHdDko9kcddNMi4e75/kohWub8wsDK/mccP3narq/Kq6vaqurqq3VNX3jdkgAAAAAMCUVNX2VfWAqnpYkrcn+XqS9w8Pj+21q63bs6p2WbSPMfF41yTXrXB9c5L1C57774fvZyT5eJInJjkps7OP3zvi3gAAAAAAU/PZJLcmuSTJj2d29MTVw2Nje+1q65LFbXfUmcdJ0la4ViOetxSnT2utvXr48yeravskf1BV+7XWLh65BwAAAACAKXhGkgdndrbxS5J8oqoe11q7bHh8TK+tkevu1ph3Hl+XWaVebn1WLtfzls7j+MSy6x8fvh844v4AAAAAAJPRWvun1tpnhw/Q+9kkuyT5veHhsb128yrrksVtd1Q83pjZ+RjL7Zdk0buGNw7flxfupbp914j7AwAAAABMUmvt+iSXJtl3uDS216627vLW2jcX3XtMPN6Q5KCq2mfpQlXtleSxw2Or+T+Znc1x+LLrhw3fPz/i/gAAAAAAk1RVP5zkEUm+Mlwa22s3JNmtqp4wt+7BSZ6SxV03ybgzj9+Z5PlJzqyqV2b2LuLXJLkis0/6W7rxQ4d/wAmttROSpLX2r1X1uiSvqqobk5yd5DFJXp3k1NbapWM2CQAAAACwrauqDyf5QpILk9yY5EeT/HaSO5K8cVg2qtdmFojPS3JaVb00s2Mqjs/sVIiTxuxn4TuPW2s3JTk0s0/2+/MkpyfZlNkn/M2/tbmSbL/Ca56Q5GVJnpbkr5M8N8kbkhw3ZoMAAAAAABNxfpL/kuTUJH+V5MVJzk1yYGvtkmR8r22t3ZXkiMw+j+7kJB9OcmeSQ1prV4zZzJh3Hqe1dnmSoxesuSwrfFJfa60ledPwBQAAAADAClprr0/y+hHrFvbaYd3mJMcOX1tszJnHAAAAAABMjHgMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA64jEAAAAAAB3xGAAAAACAjngMAAAAAEBHPAYAAAAAoCMeAwAAAADQEY8BAAAAAOiIxwAAAAAAdMRjAAAAAAA6o+JxVe1RVR+sqhuq6saq+lBV7bmlN6uq46uqVdXfbflWAQAAAAC2XVX1i1X1F1X11aq6paq+VFWvq6oHLVu3vqreVVXXVtVNVXVWVR2wwuvtVFVvqKqrhtc7r6oeP3Y/C+NxVe2c5Owkj0jyzCTPSPKwJOdU1QPH3qiq9knyiiRXj30OAAAAAMCEvCTJnUlenuTwJH+S5LlJPlFV2yVJVVWSDcPjL0hydJIdMuu1uy97vXcnOS7Jq5MckeSqJB+rqgPHbGbdiDXHJdknycNba5cOG7wwyZeTPCfJm8bcKLN/6OlJHj7yvgAAAAAAU/KU1to1c38/t6o2Jzk1ycGZvcn3yCSPS3Joa+2cJKmq85JsSvKyJC8crj0yyTFJjm2tnTJcOzfJxiQnDK+zqjHHVhyZ5PylcJwkrbVNST6T5KgRz09VHZPk0UmOH7MeAAAAAGBqloXjJf93+L7b8P3IJFcuhePheTck+Ui+s9cemeT2JGfMrbsjyfuTHFZVOy7az5h4vH+Si1a4vjHJfoueXFXrk7w5yctaa5tH3A8AAAAAgJknDN//afi+Wq/ds6p2mVu3qbV28wrrHpBk30U3HhOPd01y3QrXNydZP+L5b0hySZL3jFgLAAAAAECSqtotsyMmzmqtfX64vFqvTb7dbBet23XR/ceePdxWuFaLnlRV/ynJryV5dGttpdcAAAAAAGCZ4R3EZya5I8mz5h/KuF47dt3dGhOPr8vKFXp9Vi7X896e2Sf6fa2qfmDuntsPf7+ltXbr2M0CAAAAAGzrqmqnJBuS7JPkCa21r809vDl332uTbzfbzUn2XGXdwiOGxxxbsTGz8zGW2y/JxQue+2NJfjOzDS99PTbJQcOfnzvi/gAAAAAAk1BVOyT5iyQ/meTJrbV/XLZktV57eWvtm3Pr9q6qnVdYd1uSSxftZUw83pDkoKraZ+4fsFdmEXjDgucessLXBZkd6HxIkg+OuD8AAAAAwDavqrZLcnqSn01yVGvt/BWWbUiyW1U9Ye55D07ylHxnr92QZIckT51bty7JLyX5+JgTIcYcW/HOJM9PcmZVvTKzc3n5y1UAABZRSURBVDJek+SKzI6lWLrxQ5N8JckJrbUTkqS19snlL1ZV1ydZt9JjAAAAAAAT9r8yi72vTXJTVR0099jXhuMrNiQ5L8lpVfXSzE54OD6zs4xPWlrcWvtiVZ2R5I+GdzNvyuwkiL2TPH3MZha+87i1dlOSQ5NckuTPMyvfm5IcOvcW6Ayb237MawIAAAAA0HnS8P0VmQXi+a9nJ0lr7a4kRyT5RJKTk3w4yZ1JDmmtXbHs9Z6V5JQkJyb5qyR7JDm8tfaFMZsZ887jtNYuT3L0gjWXZcQn9bXWDh5zTwAAAACAKWmt7TVy3eYkxw5fq627JcmLh68t5l3CAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IjHAAAAAAB0xGMAAAAAADriMQAAAAAAHfEYAAAAAICOeAwAAAAAQEc8BgAAAACgIx4DAAAAANARjwEAAAAA6IyKx1W1R1V9sKpuqKobq+pDVbXniOc9pqreUVX/XFU3V9XlVXV6Ve1977cOAAAAALBtqardq+qtVXXe0FRbVe21wrqdquoNVXVVVd0yrH/8Cuu2q6rjq+qyqvpWVV1QVUeP2cvCeFxVOyc5O8kjkjwzyTOSPCzJOVX1wAVP/+Uk+yd5S5InJfm9JI9O8vmq2mPMBgEAAAAAJmTfJE9Lcl2ST6+y7t1Jjkvy6iRHJLkqyceq6sBl616T5L8n+ePMGu35ST5QVU9etJF1IzZ7XJJ9kjy8tXZpklTVhUm+nOQ5Sd60ynNf31q7Zv5CVX0myaZ8+x8G8P/bu/dgW8u6DuDfH6AkXjElxzSvTQmamDmDUphUipewkjIRr6OVjpPmFfOWRzRvqTNqk6nZDFqQZmlliaWCkmSCYh7UxI53NBtQBBUUfv3xvluX+93n7AVn7708a38+M2f2Ps96n71+e2bttZ73+zzv8wIAAAAwOKO7fyxJqupRSe65+oCqumOS45M8srvfMLadnmRnkh1Jjh3bDkny5CQv7O6Xjt3fU1W3TfLCJO/YUyHzbFtxbJKzVoLjJOnuXUnOTHL/PXVcHRyPbZ9N8tUkPz7HcwMAAAAAbBvdfeUchx2b5DtJTp3p990kpyS5V1UdODbfK8k1k7xxVf83JrnDetsLzxMeH5bkY2u070xy6Bz9f0BV3S7JIUk+flX7AgAAAACQw5Ls6u5vrmrfmSEsvu3McZclOX+N45J18t15wuMbZthfY7ULkxw8R//vqaoDkvxZhpXHr78qfQEAAAAASLLnzHbl8ZWvX+vuXue4Nc2z53GSrP7hSVJz9p31qiR3S3Lf7l7rlwMAAAAAYM8q82W28x63pnlWHl+UtRPog7N2ur2mqvrjJL+TYRPn0+btBwAAAADAD7gwu89sVx5f+XpwVa0Oi1cft6Z5wuOdGfbGWO3QJOfN0T9V9YwkJyZ5fHefPE8fAAAAAADWtDPJrarqoFXthya5PN/f43hnkgOT3GaN45J18t15wuO3Jzmiqm690lBVt0xy5PjYHlXV7yc5KckzuvuVczwfAAAAAAC79/Yk10jymysN4/3mHpjktO6+bGz+lwxh8oNX9T8hyce6e9eenmSePY9fm+RxSd5WVc/MsEfG85J8PslrZoq7RZJPJ9nR3TvGtt9O8oqxyHdX1REzP/fi7p5r5TIAAAAAwHZRVceN3955/Hrvqvpqkq929+nd/ZGqOjXJK6rqGkl2JXlMkltlJiju7v+tqpcneXpVfSPJORkC5qOT3H+9OtYNj7v70qo6OsnLk5ycYTPlf0vyhO6+ZPZ3SrJ/fnA18zFj+zHjv1mnJ/nF9Z4fAAAAAGCbefOq///p+HU2U31Ekudn2PXhBknOTXJMd5+zqu8zklyS5PFJbpLkk0l+q7v/Yb0i5ll5nO7+XJIHrHPMZ7LqLn3d/fAkD5/nOQAAAAAASLp79Q3u1jrmW0meOP7b03FXZAiYT7qqdcyz5zEAAAAAANuM8BgAAAAAgAnhMQAAAAAAE8JjAAAAAAAmhMcAAAAAAEwIjwEAAAAAmBAeAwAAAAAwITwGAAAAAGBCeAwAAAAAwITwGAAAAACACeExAAAAAAATwmMAAAAAACaExwAAAAAATAiPAQAAAACYEB4DAAAAADAhPAYAAAAAYEJ4DAAAAADAhPAYAAAAAIAJ4TEAAAAAABPCYwAAAAAAJoTHAAAAAABMCI8BAAAAAJgQHgMAAAAAMCE8BgAAAABgQngMAAAAAMCE8BgAAAAAgAnhMQAAAAAAE8JjAAAAAAAmhMcAAAAAAEwIjwEAAAAAmBAeAwAAAAAwITwGAAAAAGBCeAwAAAAAwITwGAAAAACACeExAAAAAAATwmMAAAAAACaExwAAAAAATAiPAQAAAACYEB4DAAAAADAhPAYAAAAAYEJ4DAAAAADAhPAYAAAAAIAJ4TEAAAAAABPCYwAAAAAAJoTHAAAAAABMCI8BAAAAAJgQHgMAAAAAMCE8BgAAAABgQngMAAAAAMCE8BgAAAAAgAnhMQAAAAAAE8JjAAAAAAAmhMcAAAAAAEwIjwEAAAAAmBAeAwAAAAAwITwGAAAAAGBCeAwAAAAAwITwGAAAAACACeExAAAAAAATwmMAAAAAACaExwAAAAAATAiPAQAAAACYEB4DAAAAADAhPAYAAAAAYEJ4DAAAAADAhPAYAAAAAIAJ4TEAAAAAABPCYwAAAAAAJoTHAAAAAABMCI8BAAAAAJgQHgMAAAAAMCE8BgAAAABgQngMAAAAAMCE8BgAAAAAgAnhMQAAAAAAE8JjAAAAAAAmhMcAAAAAAEwIjwEAAAAAmBAeAwAAAAAwITwGAAAAAGBCeAwAAAAAwITwGAAAAACACeExAAAAAAATwmMAAAAAACaExwAAAAAATAiPAQAAAACYEB4DAAAAADAhPAYAAAAAYEJ4DAAAAADAhPAYAAAAAIAJ4TEAAAAAABPCYwAAAAAAJoTHAAAAAABMCI8BAAAAAJgQHgMAAAAAMCE8BgAAAABgYq7wuKpuXlVvqaqvV9XFVfXWqvqJOfv+SFW9pKouqKpvVdUHquqovSsbAAAAAGD57E0Wu9HWDY+r6qAk707y00keluQhSX4yyXuq6tpzPMfrkzw6ybOT3C/JBUneWVWHX92iAQAAAACWzQZksRvqgDmOeXSSWyf5qe4+P0mq6qNJPpXkd5O8bHcdq+qOSY5P8sjufsPYdnqSnUl2JDl2r6oHAAAAAFgeVzuL3QzzbFtxbJKzVopNku7eleTMJPefo+93kpw60/e7SU5Jcq+qOvAqVwwAAAAAsJz2JovdcPOEx4cl+dga7TuTHDpH313d/c01+l4zyW3neH4AAAAAgO1gb7LYDVfdvecDqi5P8rLuPnFV+0lJTuzu3W59UVWnJbledx+xqv2Xk7wryVHd/b6rWzwAAAAAwLLYmyx2M8yz8jhJ1kqYa45+tRd9AQAAAAC2mx+aPHWe8PiiJDdco/3g8bE9uXAPfVceBwAAAABg77LYDTdPeLwzw14bqx2a5Lw5+t6qqg5ao+/lSc6fdgEAAAAA2Jb2JovdcPOEx29PckRV3XqloapumeTI8bH1+l4jyW/O9D0gyQOTnNbdl13FegEAAAAAltXeZLEbbp4b5l07yblJvpXkmRn23Hhekusm+ZnuvmQ87hZJPp1kR3fvmOl/SpJ7JXlKkl1JHpPkfknu1t3nbPQvBAAAAACwL5o3i90q66487u5Lkxyd5L+TnJzkTRlC4KNXFVtJ9l/jZz4iyRuSnJTkn5LcPMkxgmMAAAAAgO+7Clnsllh35TEAAAAAANvPPHseAwAAAACwzQiPgW2jqmrRNcBWqqofqaoHVtVLqupIfwMAwDIbxz43XnQdsAjjPrmw4YTH24zggO1m5TVfVdX26WEbqarrJjktyR8kuXeG+xIctNCiANh0VXX9qnrdouuArVZV10vymSQPdt7LdlFV16qqp1XVPyd5XVXdcNE1sXwOWHQBbK5x5umJSc7q7nethGdVtX93X7HY6mBzja//k6rqdkkOqqrnJXl/d39rwaXBpqqq6yT5UJIvJPnDJB/q7m8vtirYGlW1f5JDkhyZ5PIkX+/u0xdbFWyNMTw7J8n/VdXB3X3RomuCrTC+9s9O8skkf2PRCNvBuFjkX5N0kq8k+cskBy6yJpaTG+YtsfHk6W+THDs2/UWSd3T3WxdXFWyNcQD5gSSXZPgg/YkkN0vy6939vkXWBpupqg5I8sYkN0ny8O7+zNi+X3dfucjaYLONk4YvSnLXJHcam69M8k9Jntbdn1hUbbDZxrHPR5L8T5KHdPcFCy4JtsT42j83w2v/hO6+wFWHLLuZxSJfTHJiknO7+/LFVsWysm3Fcusk/zt+f1aS30jylqp6f1X9alXdbPZgl/awLMYZ2HOTfDnJbyc5LsMKtAuTPGmBpcFWuEmSQ5OckuTzK42CY5bd+N5/RpI7JHlrksOT3CPJKzN8Bry5qu6+uAph88yEZ7uSPHQMz/YbH3POx9IaJw0/muRT+X5wvH93d1Vdo6qOWnCJsOHGxSJ/nuF891Hd/Z/dffnYvnLMo6rqsIUVyVIxkFhS40zrlUmeneGy5U8luXmSFya5cZK3JXlHVT2sqm6ZJGZmWQbjAPK/8v0B5K5xBvabST6c5HNVdUxV3bWqbrHIWmGT/GyS2yd573rbE40nVSYO2efNBGcXJnlMkhd090fH7SqeleSEJNdO8monUiybNcKzL43h2ZVVdWCSf6uqRy62Stg0D81wheG5K5Mm3X3F+No/J8ljxu9hmdwkyWFJ3pzkc8n3rjL87vj9UzKEy/9h3MNGEB4vqXGmtZJcnOSdSY5Pcrskz0xyRIbLGi5J8oYkp1bVC6rqBlV1rUXVDBtkMoAc26+T4TLm30ryNxlWp/1dVR2xmDJh0+yX5Dsr/1lrxdlM222S3G2L6oJNMROcnZ9hxeV54zhovyTp7ksy3Dzy95LcKMlLFlYsbI71wrODkrxjoRXC5jklw/v6k6rquTOTJh9KcmmSJ3f3ZQutEDbenTNcafWvK4tFVq4yrKoXJ3l+hm28PpXkjKq6/aIKZTkIj5dYD76Z5OQk+yc5rruv7O6LuvvFSb6d5KtJrpnkCRkub36mS9vYx+1uAPmBDPtBHZ9hpvZpSW6V5E+q6sYLqxY23nlJrkjyqGQYSK5eXTyzhcXLkvzO1pYHG+4HgrOVxtmtWsbvz0zy+iTHVNV9trxK2Dy7G/ucneTrSR7Q3V9eaIWwScabQj4/w5jmWeMNsj+YYaHUcd39xUXWB5tk/ww3BV4JjGe3KbpRhvuePD3Dlo2fT3J2Vf3YgmplCRyw/iHs67r7jKo6Ocljq+o13b2rqk5Ncsck907ypSS3SPLEJG+yLyb7su6+qKqen+ED9VnjB+ixSb6RYdXxF8YtWl5WVTdN8vgkh2SYSIFl8JUMgcGDqup93f22lVWYs+/vVXW7DJOHH1xUobBBTklyyyRPqapvdPeOtQ7q7kur6u+SPDXDFl6wFNYb+3T3F2aPH6803H9clQ/7vO6+uKpW3vufnCE4vmN3f2mBZcFm+niG4PjRSZ46ThqujPUfOXP11bur6v0ZFpZctLhy2dcJj7ePdyZ5cJITqurOSY7KcCOxs8fLHL6QYUUO7PPWG0CO+wCuvO4vyHByBUuhu79eVY/L8J7+7Kq6srv/YVVwfHCGv42bJ/nHBZUKG2JVcPZH430fnjt7zMwJ1WeTXJbk+gsoFTbNvOFZVV0nyeuSHFJVv7Le3viwrxj/Bp6X4T3+6RmuwFpzMhGWwAUZtiU6vqrOHBeLfC9Anrny8GYZViKfkaTHMZJ7XXGVCY+3ie7+q/FGGc/NEJj9WpIzDRhZVnsaQI77AB6c5Mgkn4hZWJZMd59bVccleUuSV1XVXZK8OsNeyL+Q5LgMq9KO6u7PLq5S2BirgrPnVFVmA+SZyZNjM9yZ/LStrhE223rhWVVdN8P2FvdN8kvOA1g24wT6i5IcmN1MJsIy6O6vjYtF3p/dLBZJcsMkz8lwz6tf7u7vrPWzYB7C421gZnbpdUkOT/LP3X3GgsuCTbe7AeS46uZFSX4pyc93t5XHLJ3u/pequnuGOy2fmOFS/WSYQPxikiO7+2OLqg822noBclX9eJJjknw4w5ZdsHTWGfu8NMlDMrz/f3ihhcImWeOz4IruPmmhRcEm6O6PzCwWeWVV/VySVyX5bpJ7JHlAkvskuUd3n7+4SlkGwuNtYOayhPdmWGF5eFVdu7svXVxVsDXWGEAekOHSnRMynDydt7jqYHN199lVdc8M+8EenuGy/rMy7P194SJrg82wxnt+d/eOMTh7ToYrTo7u7osXVyVsrj2MfQTHbAszfwNXJNlRVZePN4yHpTKzWOS1Sf4wyVOSVIZJ8s8l+QWLRdgIZbuT7aWqHpTkTUke2N1vXnQ9sFWq6npJnpHhA/XKJHdx8gSwnMb3/GdnuBnwC5L8aJKHJblbd39kkbXBVjH2Yburqutn2AP8ry0YYZlV1Y2S3CbJnTKEx/+e5PMWi7BRhMfbTFXdNMnfJ3lod39i0fXAVqqqGyR5bJK3ev0DLDfBGRj7wMwNUwG4moTH21BVHdjdly26DlgEA0iA7UNwBsY+AMDeER4DALC0BGcAAHD1CY8BAAAAAJjYb9EFAAAAAADww0d4DAAAAADAhPAYAAAAAIAJ4TEAAAAAABPCYwAAAAAAJoTHAAAAAABMCI8BAAAAAJj4f2iV3epqS5F0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(st)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap\n",
    "\n",
    "The missingno correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.\n",
    "\n",
    "Nullity correlation ranges from -1 (if one variable appears the other definitely does not) to 0 (variables appearing or not appearing have no effect on one another) to 1 (if one variable appears the other definitely also does).\n",
    "\n",
    "Variables that are always full or always empty have no meaningful correlation, and so are silently removed from the visualization—in this case for instance the datetime and injury number columns, which are completely filled, are not included.\n",
    "\n",
    "Entries marked <1 or >-1 are have a correlation that is close to being exactingly negative or positive, but is still not quite perfectly so. This points to a small number of records in the dataset which are erroneous. In this [example](https://github.com/ResidentMario/missingno), in this dataset the correlation between VEHICLE CODE TYPE 3 and CONTRIBUTING FACTOR VEHICLE 3 is <1, indicating that, contrary to our expectation, there are a few records which have one or the other, but not both. These cases will require special attention.\n",
    "\n",
    "The heatmap works great for picking out data completeness relationships between variable pairs, but its explanatory power is limited when it comes to larger relationships and it has no particular support for extremely large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f723518>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dendogram\n",
    "\n",
    "The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1fec5a20>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.dendrogram(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "analysis",
   "language": "python",
   "name": "analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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