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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Profile report</title>
<meta name="description" content="Profile report generated by pandas-profiling. See GitHub.">
<meta name="author" content="pandas-profiling">
<script src="http://code.jquery.com/jquery-latest.js"></script>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css"
integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" crossorigin="anonymous">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap-theme.min.css"
integrity="sha384-fLW2N01lMqjakBkx3l/M9EahuwpSfeNvV63J5ezn3uZzapT0u7EYsXMjQV+0En5r" crossorigin="anonymous">
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" crossorigin="anonymous"></script>
<script>
$(function () {
$('[data-toggle="tooltip"]').tooltip()
})
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// prevent default anchor click behavior
e.preventDefault();
// store hash
var hash = this.hash;
// animate
$('html, body').animate({
scrollTop: $(hash).offset().top
}, 300, function(){
// when done, add hash to url
// (default click behaviour)
window.location.hash = hash;
});
});
</script>
</head>
<body>
<meta charset="UTF-8">
<style>
.variablerow {
border: 1px solid #e1e1e8;
border-top: hidden;
padding-top: 2em;
padding-bottom: 2em;
padding-left: 1em;
padding-right: 1em;
}
.headerrow {
border: 1px solid #e1e1e8;
background-color: #f5f5f5;
padding: 2em;
}
.namecol {
margin-top: -1em;
overflow-x: auto;
}
.dl-horizontal dt {
text-align: left;
padding-right: 1em;
white-space: normal;
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.dl-horizontal dd {
margin-left: 0;
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.ignore {
opacity: 0.4;
}
.container.pandas-profiling {
max-width:975px;
}
.col-md-12 {
padding-left: 2em;
}
.indent {
margin-left: 1em;
}
.center-img {
margin-left: auto !important;
margin-right: auto !important;
display: block;
}
/* Table example_values */
table.example_values {
border: 0;
}
.example_values th {
border: 0;
padding: 0 ;
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font-weight: 600;
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.example_values tr, .example_values td{
border: 0;
padding: 0;
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/* STATS */
table.stats {
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.stats th {
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font-weight: 600;
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/* Sample table */
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margin-bottom: 2em;
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.sample td, .sample th{
padding: 0.5em;
white-space: nowrap;
border: none;
}
.sample thead {
border-top: 0;
border-bottom: 2px solid #ddd;
}
.sample td {
width:100%;
}
/* There is no good solution available to make the divs equal height and then center ... */
.histogram {
margin-top: 3em;
}
/* Freq table */
table.freq {
margin-bottom: 2em;
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}
table.freq th, table.freq tr, table.freq td {
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.freq thead {
font-weight: 600;
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overflow: hidden;
text-overflow: ellipsis;
}
td.fillremaining{
width:auto;
max-width: none;
}
td.number, th.number {
text-align:right ;
}
/* Freq mini */
.freq.mini td{
width: 50%;
padding: 1px;
font-size: 12px;
}
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width:100%;
}
.freq.mini th {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
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font-weight: 400;
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padding-right: 0.5em;
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color: #a94442;
}
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}
/* Bars in tables */
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.extrapadding{
padding: 2em;
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.pp-anchor{
}
</style>
<div class="container pandas-profiling">
<div class="row headerrow highlight">
<h1>Overview</h1>
</div>
<div class="row variablerow">
<div class="col-md-6 namecol">
<p class="h4">Dataset info</p>
<table class="stats" style="margin-left: 1em;">
<tbody>
<tr>
<th>Number of variables</th>
<td>28 </td>
</tr>
<tr>
<th>Number of observations</th>
<td>10000 </td>
</tr>
<tr>
<th>Total Missing (%)</th>
<td>7.1% </td>
</tr>
<tr>
<th>Total size in memory</th>
<td>2.1 MiB </td>
</tr>
<tr>
<th>Average record size in memory</th>
<td>224.0 B </td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-6 namecol">
<p class="h4">Variables types</p>
<table class="stats" style="margin-left: 1em;">
<tbody>
<tr>
<th>Numeric</th>
<td>8 </td>
</tr>
<tr>
<th>Categorical</th>
<td>5 </td>
</tr>
<tr>
<th>Boolean</th>
<td>1 </td>
</tr>
<tr>
<th>Date</th>
<td>2 </td>
</tr>
<tr>
<th>Text (Unique)</th>
<td>1 </td>
</tr>
<tr>
<th>Rejected</th>
<td>11 </td>
</tr>
<tr>
<th>Unsupported</th>
<td>0 </td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-12" style="padding-left: 1em;">
<p class="h4">Warnings</p>
<ul class="list-unstyled"><li><a href="#pp_var_advance_rate"><code>advance_rate</code></a> is highly correlated with <a href="#pp_var_advance_rate_original"><code>advance_rate_original</code></a> (ρ = 0.97174) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_credit_amount"><code>credit_amount</code></a> is highly skewed (γ1 = 34.246) <span class="label label-info">Skewed</span></li><li><a href="#pp_var_credit_amount"><code>credit_amount</code></a> has 9934 / 99.3% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_debit_amount"><code>debit_amount</code></a> has constant value 0 <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_debtor_bank_assignment"><code>debtor_bank_assignment</code></a> has 9544 / 95.4% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_debtor_c3_assignment"><code>debtor_c3_assignment</code></a> has 9748 / 97.5% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_debtor_name"><code>debtor_name</code></a> has a high cardinality: 5700 distinct values <span class="label label-warning">Warning</span></li><li><a href="#pp_var_debtor_risk"><code>debtor_risk</code></a> is highly correlated with <a href="#pp_var_debtor_implicit_risk"><code>debtor_implicit_risk</code></a> (ρ = 0.98709) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_debtor_tax_number"><code>debtor_tax_number</code></a> has a high cardinality: 5706 distinct values <span class="label label-warning">Warning</span></li><li><a href="#pp_var_discount_rate_max"><code>discount_rate_max</code></a> is highly correlated with <a href="#pp_var_rate_2"><code>rate_2</code></a> (ρ = 0.99448) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_discount_rate_min"><code>discount_rate_min</code></a> is highly correlated with <a href="#pp_var_discount_rate_max"><code>discount_rate_max</code></a> (ρ = 0.98669) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_document_percentage_of_notes"><code>document_percentage_of_notes</code></a> has 9238 / 92.4% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_invoice_number"><code>invoice_number</code></a> has a high cardinality: 9951 distinct values <span class="label label-warning">Warning</span></li><li><a href="#pp_var_issue_date"><code>issue_date</code></a> has 10000 / 100.0% missing values <span class="label label-default">Missing</span></li><li><a href="#pp_var_issue_date"><code>issue_date</code></a> has constant value <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_issuer_name"><code>issuer_name</code></a> has a high cardinality: 297 distinct values <span class="label label-warning">Warning</span></li><li><a href="#pp_var_issuer_tax_number"><code>issuer_tax_number</code></a> has a high cardinality: 297 distinct values <span class="label label-warning">Warning</span></li><li><a href="#pp_var_net_amount"><code>net_amount</code></a> is highly correlated with <a href="#pp_var_total_amount"><code>total_amount</code></a> (ρ = 1) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_notes"><code>notes</code></a> has 9934 / 99.3% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_rate_1"><code>rate_1</code></a> is highly correlated with <a href="#pp_var_debtor_risk"><code>debtor_risk</code></a> (ρ = 0.99985) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_rate_2"><code>rate_2</code></a> is highly correlated with <a href="#pp_var_rate_1"><code>rate_1</code></a> (ρ = 0.98612) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_reception_date"><code>reception_date</code></a> has 10000 / 100.0% missing values <span class="label label-default">Missing</span></li><li><a href="#pp_var_reception_date"><code>reception_date</code></a> has constant value <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_total_amount"><code>total_amount</code></a> is highly skewed (γ1 = 41.177) <span class="label label-info">Skewed</span></li><li><a href="#pp_var_updated_at"><code>updated_at</code></a> has constant value 2019-06-17 16:02:35.751813 <span class="label label-primary">Rejected</span></li> </ul>
</div>
</div>
<div class="row headerrow highlight">
<h1>Variables</h1>
</div>
<div class="row variablerow ignore">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_advance_rate"><s>advance_rate</s><br/>
<small>Highly correlated</small>
</p>
</div><div class="col-md-3">
<p><em>This variable is highly correlated with <a href="#pp_var_advance_rate_original"><code>advance_rate_original</code></a> and should be ignored for analysis</em></p>
</div>
<div class="col-md-6">
<table class="stats ">
<tr>
<th>Correlation</th>
<td>0.97174</td>
</tr>
</table>
</div>
</div><div class="row variablerow">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_advance_rate_original">advance_rate_original<br/>
<small>Numeric</small>
</p>
</div><div class="col-md-6">
<div class="row">
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Distinct count</th>
<td>31</td>
</tr>
<tr>
<th>Unique (%)</th>
<td>0.3%</td>
</tr>
<tr class="ignore">
<th>Missing (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Missing (n)</th>
<td>0</td>
</tr>
<tr class="ignore">
<th>Infinite (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Infinite (n)</th>
<td>0</td>
</tr>
</table>
</div>
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Mean</th>
<td>0.99606</td>
</tr>
<tr>
<th>Minimum</th>
<td>0</td>
</tr>
<tr>
<th>Maximum</th>
<td>1</td>
</tr>
<tr class="ignore">
<th>Zeros (%)</th>
<td>0.0%</td>
</tr>
</table>
</div>
</div>
</div>
<div class="col-md-3 collapse in" id="minihistogram-658727100291494195">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAAQdJREFUeJzt1sEJwkAURVEjKSlF2FPW9pQi7GlsQC4oyEQ5Zz/wNpc/yxhjXICXrrMHwJmtswfwv7b9ePvN4377wpLPuSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBDW2QP4Ddt%2BzJ4wxTLGGLNHwFn5YkEQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUB4ApSlDPjvQYA4AAAAAElFTkSuQmCC">
</div>
<div class="col-md-12 text-right">
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<ul class="nav nav-tabs" role="tablist">
<li role="presentation" class="active"><a href="#quantiles-658727100291494195"
aria-controls="quantiles-658727100291494195" role="tab"
data-toggle="tab">Statistics</a></li>
<li role="presentation"><a href="#histogram-658727100291494195" aria-controls="histogram-658727100291494195"
role="tab" data-toggle="tab">Histogram</a></li>
<li role="presentation"><a href="#common-658727100291494195" aria-controls="common-658727100291494195"
role="tab" data-toggle="tab">Common Values</a></li>
<li role="presentation"><a href="#extreme-658727100291494195" aria-controls="extreme-658727100291494195"
role="tab" data-toggle="tab">Extreme Values</a></li>
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<div role="tabpanel" class="tab-pane active row" id="quantiles-658727100291494195">
<div class="col-md-4 col-md-offset-1">
<p class="h4">Quantile statistics</p>
<table class="stats indent">
<tr>
<th>Minimum</th>
<td>0</td>
</tr>
<tr>
<th>5-th percentile</th>
<td>0.99</td>
</tr>
<tr>
<th>Q1</th>
<td>1</td>
</tr>
<tr>
<th>Median</th>
<td>1</td>
</tr>
<tr>
<th>Q3</th>
<td>1</td>
</tr>
<tr>
<th>95-th percentile</th>
<td>1</td>
</tr>
<tr>
<th>Maximum</th>
<td>1</td>
</tr>
<tr>
<th>Range</th>
<td>1</td>
</tr>
<tr>
<th>Interquartile range</th>
<td>0</td>
</tr>
</table>
</div>
<div class="col-md-4 col-md-offset-2">
<p class="h4">Descriptive statistics</p>
<table class="stats indent">
<tr>
<th>Standard deviation</th>
<td>0.02648</td>
</tr>
<tr>
<th>Coef of variation</th>
<td>0.026584</td>
</tr>
<tr>
<th>Kurtosis</th>
<td>543.29</td>
</tr>
<tr>
<th>Mean</th>
<td>0.99606</td>
</tr>
<tr>
<th>MAD</th>
<td>0.0072777</td>
</tr>
<tr class="">
<th>Skewness</th>
<td>-18.212</td>
</tr>
<tr>
<th>Sum</th>
<td>9960.6</td>
</tr>
<tr>
<th>Variance</th>
<td>0.00070116</td>
</tr>
<tr>
<th>Memory size</th>
<td>78.2 KiB</td>
</tr>
</table>
</div>
</div>
<div role="tabpanel" class="tab-pane col-md-8 col-md-offset-2" id="histogram-658727100291494195">
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7779ff/zjH7Vw4ULt3LlT48eP1/Dhw/X%2B%2B%2B8bOcfq1at166236qabbtJDDz2kL7/8UpK0a9cujRkzRjfffLPuvPNO5eXleb1uw4YNGjJkiHr27Km0tDR98sknnrlz585pzpw5GjBggPr27aspU6ZwBw4AADSIZQWrpKRE69at07Bhw/Tkk0%2BqR48eWrlypR544AE9/fTTWr9%2B/S9a/4033lBeXp5effVV7dixQ507d9aGDRt08uRJpaena9y4cdq9e7dmz56tuXPnekrU9u3btWrVKi1evFi7du3SoEGDNGnSJFVVVUmSli5dqv379ys7O1vvvfeegoKCNGvWrF%2BcBwAAuHzYTC%2B4Y8cO5eTk6IMPPlBUVJTuvfdejR07Vh06dPAc061bN6Wnp%2Bvhhx/%2B2ef5z//8Tz3xxBPq3LmzJGnOnDmSpFdeeUWdOnXSmDFjJEn9%2BvXToEGDlJubq%2B7duysnJ0ejR49WUlKSJGnChAl69dVXlZ%2Bfr6FDh2rTpk1atGiRrrrqKklSZmamhg8frpMnT6pt27Y/e78AAODyYfwO1m9%2B8xuVl5dryZIlys/P19SpU73KlSR1795drVu3/tnnOHnypI4dO6bKykoNHz5cffr0UUZGhkpKSuR0OpWQkOB1fHx8vPbt2ydJ9eaDgoIUFxenwsJCHT16VBUVFerWrZtn/rrrrlNYWJicTufP3i8AALi8GL%2BDtW3bNnXs2FHnz5%2BXzfbt8pWVlWrVqpXXce%2B%2B%2B%2B7PPsfXX38tSdq6davWr18vt9utzMxMPf300zpz5ky9O01RUVFyuVySpNLSUtntdq95u90ul8ul0tJSSVJkZKTXfGRkpOf1vjh16pSKi4u9xmy2cMXGxvq8hi9CQoK9vsIcsrUO2VqLfK1DttZrTtkaL1jBwcEaPny4MjIyNHToUEnSm2%2B%2BqdzcXK1du1bXXHPNLz6H2%2B2WJD3yyCOeMjV58mRNnDhR/fr1%2B97jg4KCfnS9XzJ/qezsbK1cudJrLD09XRkZGT6v0RCRkS0tWRdkayWytRb5WodsrdOcsjVesJ555hl17dpVPXv29IzdfffdOnjwoBYuXKiXXnrpF5%2BjTZs2krzvNLVv3151dXW6cOGC507URaWlpXI4HJIkh8NR725UWVmZunTpoujoaEmSy%2BVSeHi4Z768vNwz54vU1FQNHDjQa8xmC5fLVeXzGr4ICQlWZGRLlZdXq7a2zujalzuytQ7ZWot8rUO21rMiW4cjwuh6vjJesD766CPl5%2BerZcv/30LbtGmjp59%2BWrfffruRc7Rr107R0dE6cOCA5/1Sx48f1xVXXKHk5GRt3rzZ6/h9%2B/Z53tSemJgop9OpUaNGSZJqa2u1f/9%2BjRkzRtdcc43sdrucTqfnfWMHDx7U%2BfPnlZiY6PP%2BYmNj6z0OLC6uUE2NNX8ga2vrLFv7cke21iFba5GvdcjWOs0pW%2BMPO91ut86fP19vvLKyUrW1tUbOYbPZlJqaqjVr1ujo0aP65ptvtGrVKo0YMUKjRo3S8ePHlZOTo3PnzqmgoEAFBQUaO3asJCktLU15eXnau3evqqurtXr1arVo0UIpKSkKCQnR2LFjtWbNGp04cUIul0vLli3THXfc4blrBgAA8FOM38G67bbb9NRTTykzM1NXX3216urqVFRUpP/4j/9QSkqKsfM8/vjjKisr03333acLFy5oyJAhmj17tiIiIrR27VotXrxYCxcu1NVXX63nn39ecXFxkqQBAwZo2rRpmjlzpoqLi9WtWzdlZWUpLCxMkpSRkaEzZ84oNTVVNTU16t%2B/v%2BbOnWts3wAAoPkLcl98x7gh33zzjdLT07V3716vN4b36tVLL7300i/6eIZAVlxcYXxNmy1YDkeEXK6qZnNL1V%2BQrXXI1lrka51AzHbY8l1NvQWf7XlmqCXZxsQ0Te8wfgfryiuv1MaNG/Xpp5/q6NGjCgkJ0T/90z/p%2BuuvN30qAAAAv2S8YF0UFxfneSwHAABwOTFesAoLC7VgwQIdPHhQZ8%2BerTd/4MAB06cEAADwK8YL1pw5cxQeHq4pU6Z4fZYUAADA5cJ4wTpy5Ih27txZ70fjAAAAXC6Mfw5W%2B/btTS8JAAAQUIwXrKlTp%2BrZZ59VZWWl6aUBAAACgvFHhKtXr9axY8eUl5enqKgoBQd7d7idO3eaPiUAAIBfMV6wLv0hxwAAAJcb4wVr8uTJppcEAAAIKMbfgyVJ//jHP/TUU0/pgQcekCTV1dXp3XffteJUAAAAfsd4wXrnnXf0wAMPqKysTHv37pUkff3115o7d65yc3NNnw4AAMDvGC9Ya9eu1ZIlS7RmzRrPD3tu3769VqxYoVdffdX06QAAAPyO8YL1xRdfaPDgwZLkKViS1KdPHx07dsz06QAAAPyO8YLlcDhUUlJSb/zw4cMKCwszfToAAAC/Y7xg9e3bV7/97W916NAhSVJpaal27typzMxMpaSkmD4dAACA3zFesJ566ilVV1frzjvv1Llz59SvXz898sgj6tChg2bOnGn6dAAAAH7H%2BOdgRUVF6fXXX9enn36qzz//XGFhYerUqZM6depk%2BlQAAAB%2ByXjBuiguLk5xcXFWLQ8AAOC3jBesuLg4r%2B8evNSBAwdMnxIAAMCvGC9Yc%2BfO9SpYtbW1Onz4sD788ENNmjTJ9OkAAAD8jvGClZaW9r3jf/vb35Sdna1Ro0aZPiUAAIBfseRnEX6f3r17q6CgoLFOBwAA0GQarWB98MEHstkse089AACA3zDeeG699dZ6Y%2BfOnVNlZeUPPj4EAABoTowXrNTU1HrfRRgaGqrOnTtr4MCBpk8HAADgd4wXrClTppheEgAAIKAYL1grV6706bigoCClp6ebPj0AAECTM16w/vSnP%2Bn06dM6d%2B6cWrZsKZvNpoqKCoWFhalVq1Zex1KwAABAc2S8YE2fPl1/%2Bctf9MQTT6hdu3aSpC%2B//FLPP/%2B8hg0bpmHDhpk%2BJQAAgF8xXrBeeOEFvfnmm7Lb7Z6xa665RvPmzVNqaioFCwAANHvGPwfrxIkTCgkJqTd%2BxRVX6PTp06ZPBwAA4HeMF6wuXbroqaeektPpVHl5uSorK/Xpp5/q3//939W1a1fTpwMAAPA7xh8Rzp8/X9OnT9e9997r%2BTwst9ut9u3b66WXXjJ9OgAAAL9jvGAlJCRo69at2rdvn06cOCG326127dopMTFRwcGN9pN5AAAAmoxlPxzQbreroqJC/fr1s%2BoUAAAAfsn4LaWSkhJNmDBBd9xxhyZOnChJKi4u1l133aWvv/7a9OkAAAD8jvGC9dxzz6murk45OTmeR4KtW7dWQkKCnnvuOdOnAwAA8DvGHxF%2B%2BOGH2rx5s9q2bet5k3tYWJhmzZqlESNGmD4dAACA3zF%2BB%2BvChQuKjY2tNx4WFqbKykrTpwMAAPA7xgtW586d9f7779cbz87OVqdOnUyfDgAAwO8Yf0Q4fvx4PfHEE9q6datqa2s1f/58OZ1OffLJJ1q%2BfLnp0wEAAPgd43ewhg4dqrVr16qmpkbXXnutPvroI3Xo0EEbN27U4MGDTZ8OAADA7xi/g1VYWKh%2B/frx%2BVcAAOCyZfwO1oMPPqja2lrTywIAAAQM4wXrzjvv1IYNG%2BR2u00vDQAAEBCMPyI8ffq0PvjgA7388stq3769WrRo4TW/ceNG06cEAADwK8YLlsPhUHJysullAQAAAoaxgpWRkaEVK1Zo4cKFnrEVK1YoIyPD1CkAAAACgrH3YBUUFNQbe%2BWVV0wtDwAAEDCMv8n9u3ijOwAAuBxZWrAu/rBnqz377LPq2rWr59e7du3SmDFjdPPNN%2BvOO%2B9UXl6e1/EbNmzQkCFD1LNnT6WlpemTTz7xzJ07d05z5szRgAED1LdvX02ZMkUlJSWNch0AAKB5sLRgNYYDBw54FaiTJ08qPT1d48aN0%2B7duzV79mzNnTvXU6K2b9%2BuVatWafHixdq1a5cGDRqkSZMmqaqqSpK0dOlS7d%2B/X9nZ2XrvvfcUFBSkWbNmNcm1AQCAwBTQBauurk5z587Vv/7rv3rGtmzZok6dOmnMmDEKDQ1Vv379NGjQIOXm5kqScnJyNHr0aCUlJSksLEwTJkxQcHCw8vPzVVtbq02bNunxxx/XVVddJbvdrszMTBUUFOjkyZNNdZkAACDAGPsuwgsXLmj69Ok/ObZ06VJTp9TGjRvVokUL3XPPPVqxYoUkyel0KiEhweu4%2BPh4bd261TM/fPhwz1xQUJDi4uJUWFio%2BPh4VVRUqFu3bp756667TmFhYXI6nWrbtq2xvQMAgObLWMHq2bOnTp069ZNjppw%2BfVovvviiXnvtNa/x0tLSekUoKipKLpfLM2%2B3273m7Xa7XC6XSktLJUmRkZFe85GRkZ7X%2B%2BLUqVMqLi72GrPZwhUbG%2BvzGr4ICQn2%2BgpzyNY6ZGst8rUO2VqvOWVrrGC9/vrrppbyycKFC3XvvfeqS5cuOnbs2I8e63a7f/QN9790/lLZ2dlauXKl11h6erplnwkWGdnSknVBtlYiW2uRr3XI1jrNKVvjn%2BTeGHbv3q2PPvpICxYsqDfncDg8d6IuKi0tlcPh8MxfejeqrKxMXbp0UXR0tCTJ5XIpPDzcM19eXu6Z80VqaqoGDhzoNWazhcvlqvJ5DV%2BEhAQrMrKlysurVVtbZ3Ttyx3ZWodsrUW%2B1iFb61mRrcMRYXQ9XwVkwXrrrbf0zTffKCUlRdL//7ytPn36aPz48dqyZYvX8fv27VNSUpIkKTExUU6nU6NGjZIk1dbWav/%2B/RozZoyuueYa2e12OZ1OdejQQZJ08OBBnT9/XomJiT7vLzY2tt7jwOLiCtXUWPMHsra2zrK1L3dkax2ytRb5WodsrdOcsg3Ih50zZ87Ue%2B%2B9p82bN2vz5s3KysqSJG3evFkjRozQ8ePHlZOTo3PnzqmgoEAFBQUaO3asJCktLU15eXnau3evqqurtXr1arVo0UIpKSkKCQnR2LFjtWbNGp04cUIul0vLli3THXfcoTZt2jTlJQMAgAASkHew7Ha71xvVa2pqJEnt2rWTJK1du1aLFy/WwoULdfXVV%2Bv5559XXFycJGnAgAGaNm2aZs6cqeLiYnXr1k1ZWVkKCwuT9O3PVDxz5oxSU1NVU1Oj/v37a%2B7cuY18hQAAIJAFufl5No2iuLjC%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%2B%2BOADvf322/r888%2B1aNEinT17Vo899pj69u2rHTt2aPny5Vq3bp22bdsmSdq/f79mzZqlGTNm6K9//avGjx%2BvKVOm6MSJE5Kk119/Xe%2B8846ysrKUn5%2Bv6667TpMnT5bb7W7KywUAAAEkIAtWeXm5EhISNH36dEVERCgmJkYjR47Unj17lJ%2Bfr5qaGk2aNEnh4eFKSEjQfffdp5ycHElSbm6ukpOTlZycrNDQUN11112Ki4vT22%2B/LUnKycnRQw89pM6dOysiIkKZmZk6fPiwPv7446a8ZAAAEEBsTb2BnyMyMlILFy70Gvvqq68UGxsrp9Oprl27KiQkxDMXHx%2BvN998U9K3jw%2BTk5O9XhsfH6/CwkKdPXtWRUVFSkhI8MxFRETo2muvVWFhoW666Saf9nfq1CkVFxd7jdls4YqNjW3Qdf6UkJBgr68wh2ytQ7arej65AAANvklEQVTWIl/rkK31mlO2AVmwLlVYWKg33nhDq1ev1tatWxUZGek1HxUVpdLSUtXV1am0tFR2u91r3m63q6ioSGVlZXK73d8773K5fN5Pdna2Vq5c6TWWnp6ujIyMBl6ZbyIjW1qyLsjWSmRrLfK1DtlapzllG/AF6%2B9//7smTZqk6dOnq3///tq6dWu9Y9xut4KCgn50nR%2Bb9%2BX135WamqqBAwd6jdls4XK5qnxewxchIcGKjGyp8vJq1dbWGV37cke21iFba5GvdcjWelZk63BEGF3PVwFdsP77v/9bM2bM0Jw5czRy5EhJUnR0tI4cOeJ1XFlZmaKiohQcHCyHw1HvblRpaakcDofnmEvny8rKFB0d7fO%2BYmNj6z0OLC6uUE2NNX8ga2vrLFv7cke21iFba5GvdcjWOs0p24B92PmPf/xDTz75pF544QVPuZKkxMREffbZZ6qpqfGMFRYWKikpyTO/b98%2Br7UuzoeGhqpLly5yOp2eufLycn3xxRfq3r27xVcEAACai4AsWDU1NZo9e7ZmzJihW2%2B91WsuOTlZrVq10urVq1VdXa2PP/5Yubm5SktLkySNHTtW//M//6P8/HydO3dOubm5OnLkiO6%2B%2B25JUlpaml577TUdOnRIlZWVWrJkieLj43XjjTc2%2BnUCAIDAFJCPCPfu3atDhw5pwYIFWrBggdfctm3btGbNGs2fP18bNmxQTEyMpk6dqpSUFEnSDTfcoCVLlmjZsmU6duyYrr/%2Beq1du1YxMTGSpHHjxun06dOaOHGiqqqq1KNHj3pvWAcAAPgxQW4%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%2B//FITJ05Unz59dPvtt2vRokWqra1t6m0BAIAAYWvqDfijjIwMJSYmatmyZSopKdFvfvMbtWnTRhMmTGjqrQEAmlCv325r6i0gQHAH6xKFhYX67LPPNGPGDLVu3VodO3bUww8/rJycnKbeGgAACBDcwbqE0%2BlUhw4dZLfbPWMJCQk6fPiwKisr1apVq59c49SpUyouLvYas9nCFRsba3SvISHBXl9hDtlah2ytFWj53rFkR1NvAX4kUH7f%2BoKCdYnS0lJFRkZ6jV0sWy6Xy6eClZ2drZUrV3qNTZ48WVOmTDG3UX1b5F59dZ1SU1ONl7fLHdlah2ytFWj57nlmaFNvwWenTp1SdnZ2wGQbSE6dOqUXX3yxWWXbfKqihdxutyQpKCjIp%2BNTU1O1adMmr39SU1ON76u4uFgrV66sd7cMvxzZWodsrUW%2B1iFb6zTHbLmDdYno6Gi5XC6vsbKyMgUFBcnhcPi0RmxsbLNp4AAAoOG4g3WJxMREffXVVyopKfGMFRYW6vrrr1dEREQT7gwAAAQKCtYlEhIS1L17dy1dulSVlZU6dOiQ1q9fr7S0tKbeGgAACBAh8%2BbNm9fUm/A3t912m7Zu3arnnntO77//vkaOHKkJEyb4/B6sxhQREaFbbrmFu2sWIFvrkK21yNc6ZGud5pZtkPviO7gBAABgBI8IAQAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYPm5L7/8UhMnTlSfPn10%2B%2B23a9GiRaqtrf3eYzds2KAhQ4aoZ8%2BeSktL0yeffNLIuw0sDcn2j3/8o4YMGaIePXpoxIgR2r59eyPvNrA0JNuLTp48qR49eujFF19spF0Grobke%2BjQIT3wwANKSkpScnKy1q9f38i7DSy%2BZltXV6cVK1Zo4MCBnv8uvPvuu02w48CyY8cO9e/fX1OnTv3R4%2Brq6rR06VINGjRIvXv31oQJE3T06NFG2qUhbvi1kSNHumfPnu0uLy93HzlyxD148GD3unXr6h335z//2d2rVy/33r173dXV1e6XX37Z3b9/f3dlZWUT7DowNCTbnj17uvfs2eO%2BcOGCe9OmTe5u3bq5v/jiiybYdWDwNdvvmjx5svvmm292r1ixopF2Gbh8zbe6utqdkpLiXrNmjfvMmTPuvXv3uocPH%2B4uKipqgl0HBl%2Bz/cMf/uD%2B1a9%2B5T548KD7woUL7j//%2Bc/uhIQE94EDB5pg14EhKyvLPXjwYPe4cePcmZmZP3rshg0b3Lfffru7qKjIXVlZ6V6wYIH7rrvuctfV1TXSbn857mD5scLCQn322WeaMWOGWrdurY4dO%2Brhhx9WTk5OvWNzcnI0evRoJSUlKSwsTBMmTFBwcLDy8/Mbf%2BMBoCHZnjlzRtOmTVPPnj1ls9k0atQoRUREaO/evU2wc//XkGwvKigo0P/93/8pJSWl8TYaoBqS79atW9WqVSs9%2BuijatmypZKSkvTOO%2B%2Boc%2BfOTbBz/9eQbJ1Op3r16qUuXbrIZrPp17/%2BtaKiovTZZ581wc4DQ2hoqHJzc9WxY8efPDYnJ0cPPfSQOnfurIiICGVmZurw4cP6%2BOOPG2GnZlCw/JjT6VSHDh1kt9s9YwkJCTp8%2BLAqKyvrHZuQkOD5dVBQkOLi4lRYWNho%2Bw0kDcn27rvv1j//8z97fl1eXq6qqiq1bdu20fYbSBqSrSSdPXtWv//97zV37lxdccUVjbnVgNSQfP/%2B97/rhhtu0KxZs9SrVy8NHTpUW7ZsaewtB4yGZJuSkqK//e1v2r9/v86fP6/t27erurpat9xyS2NvO2A8%2BOCDat269U8ed/bsWRUVFXn9nRYREaFrr702oP5Oo2D5sdLSUkVGRnqNXfyD73K56h373f8oXDz20uPwrYZk%2B11ut1uzZ89WUlIS/yH9AQ3NdtWqVbrpppvUr1%2B/RtlfoGtIvl9//bX%2B8pe/qH///tq5c6cee%2BwxPfHEEzpw4ECj7TeQNCTbwYMHKzU1VaNGjdKNN96o6dOn69lnn9VVV13VaPttrsrKyuR2uwP%2B7zRbU28ADeN2uyV9e4fKl2N9OQ7f%2BqlsL1y4oJkzZ6qoqEivvfZaY24t4P1QtkVFRcrNzdVbb73VFNtqNn4oX7fbrW7dumnEiBGSpJEjR2rjxo3atm2b4uPjG32fgeiHss3Ly1NeXp5ycnLUtWtX7d69W9OnT1f79u3VvXv3pthqsxdof6dxB8uPRUdH12vrZWVlCgoKksPh8Bp3OBzfe%2Bylx%2BFbDclW%2BvaW9aOPPqqvvvpKb7zxhtq0adNYWw04Dcl23rx5mjx5smJiYhpziwGtIfnGxMTUeyTTvn17nTp1yvJ9BqKGZPuHP/xBqamp6t69u0JDQ5WSkqK%2BffvyPwsGREVFKTg4%2BHv/XURHRzfRrhqOguXHEhMT9dVXX6mkpMQzVlhYqOuvv14RERH1jnU6nZ5f19bWav/%2B/UpKSmq0/QaShmTrdrs1depU2Ww2bdiwgdL6E3zN9vjx4/rf//1frVixQn369FGfPn30zjvvaN26dRo1alRTbD0gNOT3bnx8vA4ePOi5CyN9m3uHDh0abb%2BBpCHZ1tXV1fv4hp/6KBL4JjQ0VF26dPH6O628vFxffPFFQN0dpGD5sYSEBHXv3l1Lly5VZWWlDh06pPXr1ystLU2SNHToUO3Zs0eSlJaWpry8PO3du1fV1dVavXq1WrRowXdl/YCGZPv222%2BrqKhIL7zwgkJDQ5ty2wHB12zbtWungoICbd682fPPwIEDNW7cOGVlZTXxVfivhvzeHTlypEpLS7VmzRqdPXtWW7ZskdPp1N13392Ul%2BC3GpLtwIEDlZubq08//VQ1NTXatWuXdu3apUGDBjXlJQSskydPaujQofryyy8lfft32muvvaZDhw6psrJSS5YsUXx8vG688cYm3qnveA%2BWn1uxYoXmzZungQMHqnXr1ho5cqTnO9oOHz6sM2fOSJIGDBigadOmaebMmSouLla3bt2UlZWlsLCwpty%2BX/M12//6r//S8ePH672p/Z577tGCBQsafd%2BBwJdsQ0JC1K5dO6/XtWzZUq1ateKR4U/w9feu3W5XVlaW5s%2Bfr5deeknt27fXSy%2B9pGuvvbYpt%2B/XfM320UcfVU1NjdLT01VSUqIOHTpo/vz5fLPGj7hYjmpqaiTJ84HNhYWFunDhgg4fPqzz589LksaNG6fTp09r4sSJqqqqUo8ePbRy5cqm2fjPFOT%2B7r1jAAAA/GI8IgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw/4fZGpsHX50DiMAAAAASUVORK5CYII%3D"/>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="common-658727100291494195">
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">1.0</td>
<td class="number">9238</td>
<td class="number">92.4%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.99</td>
<td class="number">292</td>
<td class="number">2.9%</td>
<td>
<div class="bar" style="width:4%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.98</td>
<td class="number">137</td>
<td class="number">1.4%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.97</td>
<td class="number">64</td>
<td class="number">0.6%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.94</td>
<td class="number">39</td>
<td class="number">0.4%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.95</td>
<td class="number">33</td>
<td class="number">0.3%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.96</td>
<td class="number">25</td>
<td class="number">0.2%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.84</td>
<td class="number">18</td>
<td class="number">0.2%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.92</td>
<td class="number">16</td>
<td class="number">0.2%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.89</td>
<td class="number">16</td>
<td class="number">0.2%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="other">
<td class="fillremaining">Other values (21)</td>
<td class="number">122</td>
<td class="number">1.2%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr>
</table>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="extreme-658727100291494195">
<p class="h4">Minimum 5 values</p>
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">0.0</td>
<td class="number">2</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.15</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:50%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.49</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:50%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.65</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:50%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.68</td>
<td class="number">2</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr>
</table>
<p class="h4">Maximum 5 values</p>
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">0.96</td>
<td class="number">25</td>
<td class="number">0.2%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.97</td>
<td class="number">64</td>
<td class="number">0.6%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.98</td>
<td class="number">137</td>
<td class="number">1.4%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.99</td>
<td class="number">292</td>
<td class="number">2.9%</td>
<td>
<div class="bar" style="width:4%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.0</td>
<td class="number">9238</td>
<td class="number">92.4%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr>
</table>
</div>
</div>
</div>
</div><div class="row variablerow">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_credit_amount">credit_amount<br/>
<small>Numeric</small>
</p>
</div><div class="col-md-6">
<div class="row">
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Distinct count</th>
<td>65</td>
</tr>
<tr>
<th>Unique (%)</th>
<td>0.7%</td>
</tr>
<tr class="ignore">
<th>Missing (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Missing (n)</th>
<td>0</td>
</tr>
<tr class="ignore">
<th>Infinite (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Infinite (n)</th>
<td>0</td>
</tr>
</table>
</div>
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Mean</th>
<td>294.21</td>
</tr>
<tr>
<th>Minimum</th>
<td>0</td>
</tr>
<tr>
<th>Maximum</th>
<td>326470</td>
</tr>
<tr class="alert">
<th>Zeros (%)</th>
<td>99.3%</td>
</tr>
</table>
</div>
</div>
</div>
<div class="col-md-3 collapse in" id="minihistogram-2364398549250928231">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAAPtJREFUeJzt1bEJAlEQRVFXLGmLsCdje7IIexpzkQsbyN/gnHzgJZfZZmYuwE/X1QPgzG6rB3zbH6/DN%2B/n/Q9LwAeBJBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAjbzMzqEXBWPggEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAiED9obC49m6JdFAAAAAElFTkSuQmCC">
</div>
<div class="col-md-12 text-right">
<a role="button" data-toggle="collapse" data-target="#descriptives-2364398549250928231,#minihistogram-2364398549250928231"
aria-expanded="false" aria-controls="collapseExample">
Toggle details
</a>
</div>
<div class="row collapse col-md-12" id="descriptives-2364398549250928231">
<ul class="nav nav-tabs" role="tablist">
<li role="presentation" class="active"><a href="#quantiles-2364398549250928231"
aria-controls="quantiles-2364398549250928231" role="tab"
data-toggle="tab">Statistics</a></li>
<li role="presentation"><a href="#histogram-2364398549250928231" aria-controls="histogram-2364398549250928231"
role="tab" data-toggle="tab">Histogram</a></li>
<li role="presentation"><a href="#common-2364398549250928231" aria-controls="common-2364398549250928231"
role="tab" data-toggle="tab">Common Values</a></li>
<li role="presentation"><a href="#extreme-2364398549250928231" aria-controls="extreme-2364398549250928231"
role="tab" data-toggle="tab">Extreme Values</a></li>
</ul>
<div class="tab-content">
<div role="tabpanel" class="tab-pane active row" id="quantiles-2364398549250928231">
<div class="col-md-4 col-md-offset-1">
<p class="h4">Quantile statistics</p>
<table class="stats indent">
<tr>
<th>Minimum</th>
<td>0</td>
</tr>
<tr>
<th>5-th percentile</th>
<td>0</td>
</tr>
<tr>
<th>Q1</th>
<td>0</td>
</tr>
<tr>
<th>Median</th>
<td>0</td>
</tr>
<tr>
<th>Q3</th>
<td>0</td>
</tr>
<tr>
<th>95-th percentile</th>
<td>0</td>
</tr>
<tr>
<th>Maximum</th>
<td>326470</td>
</tr>
<tr>
<th>Range</th>
<td>326470</td>
</tr>
<tr>
<th>Interquartile range</th>
<td>0</td>
</tr>
</table>
</div>
<div class="col-md-4 col-md-offset-2">
<p class="h4">Descriptive statistics</p>
<table class="stats indent">
<tr>
<th>Standard deviation</th>
<td>6577.1</td>
</tr>
<tr>
<th>Coef of variation</th>
<td>22.355</td>
</tr>
<tr>
<th>Kurtosis</th>
<td>1362.8</td>
</tr>
<tr>
<th>Mean</th>
<td>294.21</td>
</tr>
<tr>
<th>MAD</th>
<td>584.53</td>
</tr>
<tr class="alert">
<th>Skewness</th>
<td>34.246</td>
</tr>
<tr>
<th>Sum</th>
<td>2942100</td>
</tr>
<tr>
<th>Variance</th>
<td>43258000</td>
</tr>
<tr>
<th>Memory size</th>
<td>78.2 KiB</td>
</tr>
</table>
</div>
</div>
<div role="tabpanel" class="tab-pane col-md-8 col-md-offset-2" id="histogram-2364398549250928231">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xt0VOWh/vFnkikEkISkElCgLSiQG%2BEmt4hcwgGqiAICMdZLC4UWIjlBqIIHihxpKQpqEctFPAY9HsVQCi0W8XgwASld1ioSBpAfoCjXBDKTGyGQ5P39QZlhDDflHZIh389aLJf73Xu/ez%2BZmfXMnpvDGGMEAAAAa0Jq%2BgAAAACuNxQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGCZs6YPoK7Izy%2B2vs%2BQEIeiohqpoKBUVVXG%2Bv6DCVmcRQ4%2BZOFDFj5k4VNXsmjatHGNzMsVrCAWEuKQw%2BFQSIijpg%2BlxpHFWeTgQxY%2BZOFDFj5kEVgULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBltbZglZaWqnPnzmrfvr0SEhKUmpqq7du3e8fLy8s1c%2BZM9enTRz179tSkSZNUUFDgHf/66681btw49ejRQ/3799e8efNUWVlZE6cCAADqmFpZsI4dO6ahQ4fK6Tz7Sz5r167VgAEDNGHCBJWWlkqSFixYoJ07d2rlypXasGGDHA6Hpk%2Bf7t1Henq6mjdvrvfff1%2BZmZnauHGjMjMza%2BJ0AABAHVMrC5bb7dbhw4fVo0cP77KxY8cqJCRE2dnZqqys1OrVqzVx4kTddNNNioiIUEZGhnJycnTs2DHl5ubq888/19SpU9W4cWP98Ic/1M9%2B9jNlZWXV4FkBAIC6olYWrJiYGN14442Ki4vzLnM4HIqJiVFubq4OHDig4uJixcfHe8fbtGmjsLAwuVwuuVwutWjRQhEREd7xuLg4ffHFFyopKbmm5wIAAOoeZ00fwMV4PB6/giRJERERcrvd8ng8kqTw8HC/8fDwcO/4N8fO7cvtduuGG24I4JFLeXl5ys/P91vmdDZUdHS01XlCQ0P8/luXkcVZ5OBDFj5k4UMWPmQRWLW2YF2IMUYOx8V/9ftS48YYSbrk9rasXLlSixYt8luWlpam9PR063Pd9h/vWt9noHz8mx8HfI7w8AYBnyMYkIMPWfiQhQ9Z%2BJBFYNTaghUZGSm32%2B23rLCwUG3btlVUVJSks1ejGjZs6B0vKipSVFSUKioqLritw%2BFQZGRkwI89JSVFycnJfsuczoZyu0utzhNszzpsn//5QkNDFB7eQEVFZaqsrArYPLUdOfiQhQ9Z%2BJCFT13JIjKyUY3MW2sLVkJCgnbs2OH9/8rKSu3cuVMjR45Uq1atFBER4X2vlSTt2bNHp0%2BfVkJCgvLy8nT48GEVFBR4y1hubq5uvfVWNWoU%2BKCjo6OrvRyYn1%2Bsiorr9wZ8Ja7F%2BVdWVtX5nCVyOB9Z%2BJCFD1n4kEVg1NpLIKmpqfr4448lnX15b/HixapXr5769eun0NBQjR49WkuWLNGRI0fkdrv13HPPaeDAgd43xycmJmrBggUqKSnRvn379Oqrryo1NbWGzwoAANQFtfIK1h/%2B8ActXrzY%2B76pYcOGqaKiQunp6QoLC5N09nuuTp48qZSUFFVUVCgpKUmzZs3y7mPhwoV66qmnlJycrMaNG2vYsGF64IEHauR8AABA3eIw51oMAio/v9j6Pp3OEA2cv9n6fgNlfcbtAdu30xmiyMhGcrtL6/SlbnLwIQsfsvAhC5%2B6kkXTpo1rZN5a%2BxIhAABAsKJgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWBa0BWvXrl16%2BOGHddttt%2Bn222/X1KlTVVBQIEnasmWLRo4cqS5dumjIkCFas2aN37aZmZkaPHiwunbtqtTUVG3fvt07Vl5erpkzZ6pPnz7q2bOnJk2a5N0vAADAlQjKglVZWalx48apQ4cO%2BvDDD/XnP/9Zx48f11NPPaVjx44pLS1N999/v7Zu3aoZM2Zo1qxZ3hL1/vvv66WXXtIzzzyjLVu2aMCAAZowYYJKS0slSQsWLNDOnTu1cuVKbdiwQQ6HQ9OnT6/J0wUAAEEmKAtWXl6e8vPzNWLECIWFhen73/%2B%2BBg8erF27dmndunVq3bq1Ro4cqfr166tXr14aMGCAVq1aJUnKysrSiBEj1LFjR4WFhWns2LEKCQlRdna2KisrtXr1ak2cOFE33XSTIiIilJGRoZycHB07dqyGzxoAAASLoCxYzZo1U2xsrN566y2VlpbqxIkT2rBhg/r16yeXy6W4uDi/9WNjY7Vjxw5JqjbucDgUExOj3NxcHThwQMXFxYqPj/eOt2nTRmFhYXK5XNfm5AAAQNBz1vQBfBchISFauHChxowZo9dee02S1K1bN02ZMkUTJ05Us2bN/NZv0qSJ3G63JMnj8SgiIsJvPCIiQm63Wx6PR5IUHh7uNx4eHu7d/kqcu8J2PqezoaKjo694H1ciNDS4%2BrHTGbjjPZdFsGViGzn4kIUPWfiQhQ9ZBFZQFqzTp09rwoQJGjx4sH75y1/q5MmTmj17tqZOnXrB9Y0xcjgcF93f1Y5/08qVK7Vo0SK/ZWlpaUpPT7/ifVyPIiMbBXyO8PAGAZ8jGJCDD1n4kIUPWfiQRWAEZcHaunWrDh48qMcee0yhoaFq3Lix0tPTde%2B99%2BqOO%2B7wXok6x%2BPxKDIyUpIUGRlZ7WpUYWGh2rZtq6ioKEmS2%2B1Ww4YNveNFRUXesSuRkpKi5ORkv2VOZ0O53aXf6jwvJ9ieddg%2B//OFhoYoPLyBiorKVFlZFbB5ajty8CELH7LwIQufupLFtXhyfyFBWbAqKytVVVUlY4x3WUVFhSQpKSlJf/rTn/zW37Fjhzp27ChJSkhIkMvl0vDhw7372rlzp0aOHKlWrVopIiJCLpdLLVq0kCTt2bNHp0%2BfVkJCwhUfX3R0dLWXA/Pzi1VRcf3egK/EtTj/ysqqOp%2BzRA7nIwsfsvAhCx%2ByCIzgugTyL507d1bDhg314osvqqysTG63W0uWLFG3bt1077336tChQ8rKylJ5eblycnKUk5Oj0aNHS5JSU1O1Zs0abdu2TWVlZVq8eLHq1aunfv36KTQ0VKNHj9aSJUt05MgRud1uPffccxo4cKBuvPHGGj5rAAAQLILyClZkZKReeeUVzZs3T3369FG9evXUvXt3LViwQN///ve1dOlSPfPMM5o7d65atmypZ599VjExMZKkPn366LHHHtO0adOUn5%2Bv%2BPh4LVu2TGFhYZKk9PR0nTx5UikpKaqoqFBSUpJmzZpVk6cLAACCjMOc/zobAiY/v9j6Pp3OEA2cv9n6fgNlfcbtAdu30xmiyMhGcrtL6/SlbnLwIQsfsvAhC5%2B6kkXTpo1rZN6gfIkQAACgNqNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgmfWCVVxcbHuXAAAAQcV6werdu7eeeOIJffzxx7Z3DQAAEBSsF6zZs2frxIkT%2BulPf6o777xTr776qgoKCmxPAwAAUGtZL1jDhg3T8uXLtWnTJv3kJz/Re%2B%2B9p379%2Bmny5MnaunWr7ekAAABqnYC9yT0qKkoPPvig3nzzTc2dO1cffvihxowZo7vuukvvvfdeoKYFAACocc5A7bigoECrV6/WH//4R3311Ve6/fbblZKSory8PP3617/WoUOH9LOf/SxQ0wMAANQY6wVr8%2BbNysrK0saNG9WkSRPdd999Gj16tFq0aOFdJz4%2BXmlpadYL1uLFi/XGG2%2BopKREnTp10tNPP61WrVppy5Ytev7557V//37ddNNNGjdunIYNG%2BbdLjMzU2%2B%2B%2BaaOHz%2Budu3aafr06UpMTJQklZeXa86cOcrJydHp06fVrVs3zZ49W1FRUVaPHQAAXD%2Bsv0Q4fvx4FRUVaf78%2BcrOztbkyZP9ypUkJSYmqnHjxlbnfeONN7RmzRqtWLFCmzdv1i233KLMzEwdO3ZMaWlpuv/%2B%2B7V161bNmDFDs2bN0vbt2yVJ77//vl566SU988wz2rJliwYMGKAJEyaotLRUkrRgwQLt3LlTK1eu1IYNG%2BRwODR9%2BnSrxw4AAK4v1gvWu%2B%2B%2Bq8zMTCUnJ8vpPHuBrKSkpNp6f/3rX63O%2B1//9V%2BaPHmybrnlFjVu3FgzZ87UzJkztW7dOrVu3VojR45U/fr11atXLw0YMECrVq2SJGVlZWnEiBHq2LGjwsLCNHbsWIWEhCg7O1uVlZVavXq1Jk6cqJtuukkRERHKyMhQTk6Ojh07ZvX4AQDA9cN6wQoJCdFdd92ljRs3epe9/fbbuuuuu/T111/bnk6SdOzYMR08eFAlJSW666671KNHD6Wnp6ugoEAul0txcXF%2B68fGxmrHjh2SVG3c4XAoJiZGubm5OnDggIqLixUfH%2B8db9OmjcLCwuRyuQJyLgAAIPhZfw/Wb37zG7Vv315du3b1Lrvnnnu0Z88ezZ07V3/4wx9sT6mjR49KktavX69XX31VxhhlZGTo17/%2BtU6ePKlmzZr5rd%2BkSRO53W5JksfjUUREhN94RESE3G63PB6PJCk8PNxvPDw83Lv9heTl5Sk/P99vmdPZUNHR0d/tBC8iNDS4funI6Qzc8Z7LItgysY0cfMjChyx8yMKHLALLesH69NNPlZ2drQYNGniX3Xjjjfr1r3%2Bt/v37255OkmSMkST9/Oc/95apRx99VOPGjVOvXr0uuL7D4bjk/q5mfOXKlVq0aJHfsrS0NKWnp1/yPK53kZGNAj5HeHiDy69UB5CDD1n4kIUPWfiQRWBYL1jGGJ0%2BfdqvYEln34dVWVlpezpJZwuc5H%2Bl6eabb1ZVVZXOnDnjvRJ1jsfjUWRkpCQpMjKy2tWowsJCtW3b1vtJQbfbrYYNG3rHi4qKLvkpwpSUFCUnJ/stczobyu0u/Q5nd3HB9qzD9vmfLzQ0ROHhDVRUVKbKyqqAzVPbkYMPWfiQhQ9Z%2BNSVLK7Fk/sLsV6w7rjjDj3xxBPKyMhQy5YtVVVVpb179%2Br5559Xv379bE8nSWrevLmioqK0a9cu7/ulDh06pO9973vq27ev1q5d67f%2Bjh071LFjR0lSQkKCXC6Xhg8fLkmqrKzUzp07NXLkSLVq1UoRERFyuVzeT0Lu2bNHp0%2BfVkJCwkWPJzo6utrLgfn5xaqouH5vwFfiWpx/ZWVVnc9ZIofzkYUPWfiQhQ9ZBIb1SyBPPvmkPB6Phg0bpm7duqlHjx76yU9%2BIkmaNWuW7ekkSU6nUykpKVqyZIkOHDigEydO6KWXXtLQoUM1fPhwHTp0SFlZWSovL1dOTo5ycnI0evRoSVJqaqrWrFmjbdu2qaysTIsXL1a9evXUr18/hYaGavTo0VqyZImOHDkit9ut5557TgMHDvReNQMAAPgm61ewvv/97%2Butt97S7t27deDAAYWGhupHP/qRbr31VttT%2BZk4caIKCws1atQonTlzRoMHD9aMGTPUqFEjLV26VM8884zmzp2rli1b6tlnn1VMTIwkqU%2BfPnrsscc0bdo05efnKz4%2BXsuWLVNYWJgkKT09XSdPnlRKSooqKiqUlJQUsKIIAACuDw5z7h3iCKj8/GLr%2B3Q6QzRw/mbr%2Bw2U9Rm3B2zfTmeIIiMbye0urdOXusnBhyx8yMKHLHzqShZNm9r9YvMrZf0KVm5urubMmaM9e/bo1KlT1cZ37dple0oAAIBaxXrBmjlzpho2bKhJkyb5ffIOAACgrrBesL788kt9%2BOGHuuGGG2zvGgAAIChY/xThzTffbHuXAAAAQcV6wZo8ebJ%2B%2B9vfXvAHngEAAOoC6y8RLl68WAcPHtSaNWvUpEkThYT4d7gPP/zQ9pQAAAC1ivWC9c2fiAEAAKhrrBesRx991PYuAQAAgkpAfi34k08%2B0RNPPKGHHnpIklRVVaW//vWvgZgKAACg1rFesN555x099NBDKiws1LZt2yRJR48e1axZs7Rq1Srb0wEAANQ61gvW0qVLNX/%2BfC1ZskQOh0PS2a9uWLhwoVasWGF7OgAAgFrHesH66quvNGjQIEnyFixJ6tGjhw4ePGh7OgAAgFrHesGKjIxUQUFBteVffPGFwsLCbE8HAABQ61gvWD179tR//Md/aN%2B%2BfZIkj8ejDz/8UBkZGerXr5/t6QAAAGod6wXriSeeUFlZmYYMGaLy8nL16tVLP//5z9WiRQtNmzbN9nQAAAC1jvXvwWrSpIlef/117d69W/v371dYWJhat26t1q1b254KAACgVrJesM6JiYlRTExMoHYPAABQa1kvWDExMX6fHvymXbt22Z4SAACgVrFesGbNmuVXsCorK/XFF19o06ZNmjBhgu3pAAAAah3rBSs1NfWCyz/66COtXLlSw4cPtz0lAABArRKQ3yK8kG7duiknJ%2BdaTQcAAFBjrlnB2rhxo5zOgL2nHgAAoNaw3nh69%2B5dbVl5eblKSkou%2BvIhAADA9cR6wUpJSan2KcL69evrlltuUXJysu3pAAAAah3rBWvSpEm2dwkAABBUrBesRYsWXdF6DodDaWlptqcHAACocdYL1p/%2B9CcdP35c5eXlatCggZxOp4qLixUWFqYbbrjBb10KFgAAuB5ZL1hTpkzR//3f/%2BlXv/qVmjdvLkn6%2Buuv9eyzz%2BrOO%2B/UnXfeaXtKAACAWsV6wfr973%2Bvt99%2BWxEREd5lrVq10lNPPaWUlBQKFgAAuO5Z/x6sI0eOKDQ0tNry733vezp%2B/Ljt6QAAAGod6wWrbdu2euKJJ%2BRyuVRUVKSSkhLt3r1bTz75pNq3b297OgAAgFrH%2BkuETz/9tKZMmaL77rvP%2B31YxhjdfPPN%2BsMf/mB7OgAAgFrHesGKi4vT%2BvXrtWPHDh05ckTGGDVv3lwJCQkKCblmv8wDAABQYwL244AREREqLi5Wr169AjUFAABArWT9klJBQYHGjh2rgQMHaty4cZKk/Px83X333Tp69Kjt6QAAAGod6wXrd7/7naqqqpSVleV9SbBx48aKi4vT7373O9vTAQAA1DrWXyLctGmT1q5dq2bNmnnf5B4WFqbp06dr6NChtqcDAACodaxfwTpz5oyio6OrLQ8LC1NJSYnt6QAAAGod6wXrlltu0XvvvVdt%2BcqVK9W6dWvb0wEAANQ61l8iHDNmjH71q19p/fr1qqys1NNPPy2Xy6Xt27frhRdesD0dAABArWP9CtaPf/xjLV26VBUVFfrBD36gTz/9VC1atNBbb72lQYMG2Z4OAACg1rF%2BBSs3N1e9evXi%2B68AAECdZf0K1sMPP6zKykrbuwUAAAga1gvWkCFDlJmZKWOM7V0DAAAEBesvER4/flwbN27Uyy%2B/rJtvvln16tXzG3/rrbdsTwkAAFCrWC9YkZGR6tu3r%2B3dAgAABA1rBSs9PV0LFy7U3LlzvcsWLlyo9PR0W1MAAAAEBWvvwcrJyam27JVXXrG1ewAAgKBh/U3u5%2BON7gAAoC4KaME692PPAAAAdUlAC9a18tvf/lbt27f3/v%2BWLVs0cuRIdenSRUOGDNGaNWv81s/MzNTgwYPVtWtXpaamavv27d6x8vJyzZw5U3369FHPnj01adIkFRQUXLNzAQAAwS/oC9auXbv8CtSxY8eUlpam%2B%2B%2B/X1u3btWMGTM0a9Ysb4l6//339dJLL%2BmZZ57Rli1bNGDAAE2YMEGlpaWSpAULFmjnzp1auXKlNmzYIIfDoenTp9fIuQEAgOBk7VOEZ86c0ZQpUy67bMGCBbamVFVVlWbNmqVHHnlECxculCStW7dOrVu31siRIyVJvXr10oABA7Rq1SolJiYqKytLI0aMUMeOHSVJY8eO1YoVK5Sdna0f//jHWr16tebNm6ebbrpJkpSRkaG77rpLx44dU7NmzawdOwAAuH5Zu4LVtWtX5eXl%2Bf270DKb3nrrLdWrV0/33nuvd5nL5VJcXJzferGxsdqxY8cFxx0Oh2JiYpSbm6sDBw6ouLhY8fHx3vE2bdooLCxMLpfL6rEDAIDrl7UrWK%2B//rqtXV2R48eP68UXX9Rrr73mt9zj8VS70tSkSRO53W7veEREhN94RESE3G63PB6PJCk8PNxvPDw83Lv9lcjLy1N%2Bfr7fMqezoaKjo694H1ciNDS4XuF1OgN3vOeyCLZMbCMHH7LwIQsfsvAhi8Cy/k3u18rcuXN13333qW3btjp48OAl1zXGXPITjVc7/k0rV67UokWL/JalpaXV%2BS9djYxsFPA5wsMbBHyOYEAOPmThQxY%2BZOFDFoERlAVr69at%2BvTTTzVnzpxqY5GRkd4rUed4PB5FRkZ6x795NaqwsFBt27ZVVFSUJMntdqthw4be8aKiIu/YlUhJSVFycrLfMqezodzu0ivex5UItmcdts//fKGhIQoPb6CiojJVVlYFbJ7ajhx8yMKHLHzIwqeuZHEtntxfSFAWrD//%2Bc86ceKE%2BvXrJ8n3haY9evTQmDFjtG7dOr/1d%2BzY4X1Te0JCglwul4YPHy5Jqqys1M6dOzVy5Ei1atVKERERcrlcatGihSRpz549On36tBISEq74%2BKKjo6u9HJifX6yKiuv3BnwlrsX5V1ZW1fmcJXI4H1n4kIUPWfiQRWAE1yWQf5k2bZo2bNigtWvXau3atVq2bJkkae3atRo6dKgOHTqkrKwslZeXKycnRzk5ORo9erQkKTU1VWvWrNG2bdtUVlamxYsXq169eurXr59CQ0M1evRoLVmyREeOHJHb7dZzzz2ngQMH6sYbb6zJUwYAAEEkKK9gRURE%2BL1RvaKiQpLUvHlzSdLSpUv1zDPPaO7cuWrZsqWeffZZxcTESJL69Omjxx57TNOmTVN%2Bfr7i4%2BO1bNkyhYWFSTr7o9UnT55USkqKKioqlJSUpFmzZl3jMwQAAMHMYfjBwGsiP7/Y%2Bj6dzhANnL/Z%2Bn4DZX3G7QHbt9MZosjIRnK7S%2Bv0pW5y8CELH7LwIQufupJF06aNa2TeoHyJEAAAoDajYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFgWtAXr0KFDSktLU48ePdSzZ089/vjjKioqkiS5XC49%2BOCD6tq1qwYNGqTly5f7bbtu3ToNHTpUnTt31ogRI7Rp0ybvWFVVlRYsWKABAwaoW7duGjt2rA4cOHBNzw0AAAS3oC1YEydOVHh4uDZu3Ki//OUv2r9/v%2BbNm6dTp07pl7/8pXr27KnNmzfrhRde0PLly/Xuu%2B9Kknbu3Knp06dr6tSp%2Bvvf/64xY8Zo0qRJOnLkiCTp9ddf1zvvvKNly5YpOztbbdq00aOPPipjTE2eLgAACCJBWbCKiooUFxenKVOmqFGjRmratKmGDRumjz/%2BWNnZ2aqoqNCECRPUsGFDxcXFadSoUcrKypIkrVq1Sn379lXfvn1Vv3593X333YqJidFf/vIXSVJWVpZ%2B%2BtOf6pZbblGjRo2UkZGhL774Qp999llNnjIAAAgiQVmwwsPDNXfuXN14443eZYcPH1Z0dLRcLpfat2%2Bv0NBQ71hsbKx27Ngh6ezLh3FxcX77i42NVW5urk6dOqW9e/f6jTdq1Eg/%2BMEPlJubG%2BCzAgAA1wtnTR%2BADbm5uXrjjTe0ePFirV%2B/XuHh4X7jTZo0kcfjUVVVlTwejyIiIvzGIyIitHfvXhUWFsoYc8Fxt9t9xceTl5en/Px8v2VOZ0NFR0d/yzO7tNDQ4OrHTmfgjvdcFsGWiW3k4EMWPmThQxY%2BZBFYQV%2Bw/vnPf2rChAmaMmWKkpJQJcLCAAATcUlEQVSStH79%2BmrrGGPkcDguuZ9LjV/J9udbuXKlFi1a5LcsLS1N6enpV7yP61FkZKOAzxEe3iDgcwQDcvAhCx%2By8CELH7IIjKAuWB988IGmTp2qmTNnatiwYZKkqKgoffnll37rFRYWqkmTJgoJCVFkZGS1q1Eej0eRkZHedb45XlhYqKioqCs%2BrpSUFCUnJ/stczobyu0u/RZnd3nB9qzD9vmfLzQ0ROHhDVRUVKbKyqqAzVPbkYMPWfiQhQ9Z%2BNSVLK7Fk/sLCdqC9cknn%2Bjxxx/X73//e/Xu3du7PCEhQW%2B%2B%2BaYqKirkdJ49vdzcXHXs2NE7fu79WOfk5uZqyJAhql%2B/vtq2bSuXy6Xu3btLOvuG%2Bq%2B%2B%2BkqJiYlXfGzR0dHVXg7Mzy9WRcX1ewO%2BEtfi/Csrq%2Bp8zhI5nI8sfMjChyx8yCIwgusSyL9UVFRoxowZmjp1ql%2B5kqS%2Bffvqhhtu0OLFi1VWVqbPPvtMq1atUmpqqiRp9OjR%2Btvf/qbs7GyVl5dr1apV%2BvLLL3XPPfdIklJTU/Xaa69p3759Kikp0fz58xUbG6sOHTpc8/MEAADBKSivYG3btk379u3TnDlzNGfOHL%2Bxd999V0uWLNHTTz%2BtzMxMNW3aVJMnT1a/fv0kSe3atdP8%2BfP13HPP6eDBg7r11lu1dOlSNW3aVJJ0//336/jx4xo3bpxKS0vVuXPnau%2BnAgAAuBSH4Rs0r4n8/GLr%2B3Q6QzRw/mbr%2Bw2U9Rm3B2zfTmeIIiMbye0urdOXusnBhyx8yMKHLHzqShZNmzaukXmD8iVCAACA2oyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyC9S19/fXXGjdunHr06KH%2B/ftr3rx5qqysrOnDAgAAtQgF61tKT09X8%2BbN9f777yszM1MbN25UZmZmTR8WAACoRShY30Jubq4%2B//xzTZ06VY0bN9YPf/hD/exnP1NWVlZNHxoAAKhFKFjfgsvlUosWLRQREeFdFhcXpy%2B%2B%2BEIlJSU1eGQAAKA2oWB9Cx6PR%2BHh4X7LzpUtt9tdE4cEAABqIWdNH0CwM8ZIkhwOh3dZXl6e8vPz/dZzOhsqOjra6tyhocHVj53OwB3vuSyCLRPbyMGHLHzIwocsfMgisChY30JUVFS1K1WFhYVyOByKjIz0Llu5cqUWLVrkt96jjz6qSZMmWT2evLw8PdL8/yklJcV6eQs2eXl5WrFieZ3Pghx8yMKHLHzIwocsAova%2Bi0kJCTo8OHDKigo8C7Lzc3VrbfeqkaNGnmXpaSkaPXq1X7/UlJSrB9Pfn6%2BFi1aVO1qWV1EFmeRgw9Z%2BJCFD1n4kEVgcQXrW4iLi1NiYqIWLFig6dOn69ixY3r11Vc1ZswYv/Wio6N5NgAAQB3GFaxvaeHChTpx4oSSk5M1fvx4DRs2TA888EBNHxYAAKhFuIL1LTVv3lxLliyp6cMAAAC1WOhTTz31VE0fBL67Ro0aqXv37n7vAauryOIscvAhCx%2By8CELH7IIHIc59z0DAAAAsIL3YAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2Bdgc2bNyspKUmTJ0/2W/73v/9d7du3V4cOHfz%2Bbd%2B%2BXZJUVVWlBQsWaMCAAerWrZvGjh2rAwcOeLd3u93693//dyUlJal3796aPn26ysrKvOMul0sPPvigunbtqkGDBmn58uXX5oQBAMBVoWBdxssvv6w5c%2Bbohz/8YbWx4uJi/ehHP1Jubq7fv8TEREnS66%2B/rnfeeUfLli1Tdna22rRpo0cffVTnvjx/xowZOnXqlNatW6fVq1frwIEDevbZZyVJp06d0i9/%2BUv17NlTmzdv1gsvvKDly5fr3XffrXYcX3/9tcaNG6cePXqof//%2BmjdvniorKwOYil39%2B/dXQkKCX0l9%2BumnJUlbtmzRyJEj1aVLFw0ZMkRr1qzx2zYzM1ODBw9W165dlZqa6i23klReXq6ZM2eqT58%2B6tmzpyZNmqSCggLveG3I7WLlXZLWrVunoUOHqnPnzhoxYoQ2bdrkN05Bv34cOnRIaWlp6tGjh3r27KnHH39cRUVF3vHL/S0vdVspLy9XcnKyunfvrvj4eMXGxupPf/rTVR/z5Z5A2nCxXKqqqhQTE1PtceOVV17xbnupTILxye/u3bv1yCOPqGvXrkpKSlJGRoby8/Ml1ezj5OXmrtMMLmnFihWmqKjIPPHEEyYjI8NvLCsry4waNeqi2w4ZMsSsWLHC%2B/8lJSUmPj7efPrppyY/P9/ExMSYXbt2ecc3bdpkOnXqZE6fPm3Wr19vevbsaSoqKrzj8%2BfPN2PGjKk2z7Bhw8yMGTNMUVGR%2BfLLL82gQYPM8uXLr%2Ba0r6kuXbqYf/7zn9WWHz161HTs2NFkZWWZU6dOmb/97W8mMTHRfPbZZ8YYY/73f//X3HbbbWbbtm2mrKzMvPzyyyYpKcmUlJQYY4z5zW9%2BY0aMGGEOHz5sPB6PmTRpkhk/frx3/zWd27Jly8ygQYPM/fffX%2B225XK5TEJCgsnOzjanTp0yf/nLX0xiYqI5fPiwd52JEyea8ePHmxMnTphjx46Z1NRUM3v2bGOMMWVlZaZ3797mxRdfNKWlpcblcpkePXqY9evXX7Pzw5W75557zLRp00xJSYnJy8sz9913n3nyySeNMZf/W17utjJt2jTTrl078%2BKLL5pjx46ZHj16%2BN2PvqvMzEzTv39/s3fvXlNSUmLmzJlj7r77blNVVXV1YZznYrl4PB7Trl07c/To0Qtud7lMLnfste2%2BVV5ebnr16mUWLVpkysvLTX5%2BvnnggQfMxIkTa/Rx8nJz13UUrCt0oYL1yiuvmEGDBpkHH3zQ3Hbbbebuu%2B82a9euNcacvRO2b9/e/OMf//Db5s477zSvvfaa2bRpk4mPj/d7MDp%2B/Lhp166d2b17t5k/f7555JFH/LZ95513TPfu3f2Wbd%2B%2B3cTGxhqPx%2BNd9uabb5rBgwfbOO2Aq6ioMO3btzd79%2B6tNrZ8%2BXIzbNgwv2WTJ082M2fONMYYM378ePPb3/7WO1ZVVWV69%2B5t1q1bZyoqKkzXrl3N%2B%2B%2B/7x3ft2%2Bfad%2B%2BvTl69GityO1S5X327NkmLS3Nb9no0aPN0qVLjTHGakFHzSosLDTTpk0z%2Bfn53mWvv/66GTRokDHGXPZveanbSkVFhenUqZPf/Wjfvn2mXbt2ZsaMGVd13Jd6AmnDpXL56quvTLt27cypU6cuuO3l7j/X6smvLR6Px7z99tvmzJkz3mUrVqwwAwcOrNHHycvNXdfxEuFVaNiwoZo1a6YpU6boww8/1Pjx4zV9%2BnRt3bpVhYWFMsYoIiLCb5uIiAi53W55PB7dcMMNcjgcfmOSvOPh4eF%2B2zZp0kQej0dVVVXeZS6XSy1atPCbJy4uTl988YVKSkoCcdpWFRUVyRijF198UX369NEdd9yhWbNmqbS0VC6XS3FxcX7rx8bGaseOHZJUbdzhcCgmJka5ubk6cOCAiouLFR8f7x1v06aNwsLC5HK5akVuDz/8sBo3bnzBsYude25uriRp165dCg0NVfv27b3jcXFxOnnypPbv3y%2BXy6X27dsrNDTUb/tz2aH2CA8P19y5c3XjjTd6lx0%2BfFjR0dGSdNm/5aVuKwcOHNDJkyf9xtu0aaP69evL5XJ952M%2BdeqU9u7d67ffRo0a6Qc/%2BIH3Nnq1LpVLYWGhQkNDNX36dCUlJSk5OVnPP/%2B8zpw5I%2BnSmVzu2GvjfSsiIkKjRo2S0%2BmUJB04cEBr1qzRXXfdVaOPk5ebu66jYF2F%2B%2B%2B/X6%2B99po6deqk%2BvXra%2BjQofq3f/s3rV69%2BqLbGGP8StWFXGz8QtteqIidX9Rqu9OnTys%2BPl5dunTRhg0b9Oqrr%2BqTTz7RU089ddGSee68PB7PJQuspGrbh4eHX7TA1qbcLnVu58ZtFHTUPrm5uXrjjTc0YcIESRe%2Bj5//t/wu94Pz70ffxeWeQAbC%2BbkYYxQbG6v%2B/fsrOztbzz//vNasWaOXXnpJ0qXvP9fqyW8gHDp0SAkJCRo8eLASExOVnp5eo4%2BTl5u7rqNgWdayZUvl5eWpSZMmCgkJqXZDKywsVFRUlKKiolRcXOz3ZsFzN/Zz4xfa9tx%2BL8X86030lytytUGzZs20evVqPfzww2rQoIFuvfVWTZ06Ve%2B884732ej5LldQr2Y8GHK7kmP7NgUdtcs///lPjR07VlOmTFFSUtJF17uaJ2pXuv13Eaj9fjOXDh066I9//KOGDh2qevXqqWPHjho/fvxl37x/tZnU9H2rRYsWys3N1bvvvqv9%2B/frV7/61Xc6nkA%2BTvI440PBugpvv/22NmzY4Lds3759atWqlerXr6%2B2bdv6XYYvKirSV199pcTERMXGxsoYo927d3vHc3NzFR4ertatWyshIUGff/65Kioq/MY7duzoN9/FipjD4VBkZKTN071mWrZsqcrKSoWEhHhL5zkej8d7XpGRkRc898jISEVFRUmqfjWqqKjokgW2tuR2oXM7/9wDWdBRMz744AONHz9eTz75pB5%2B%2BGHv8sv9LS91Wzl3P/jm/ejc/eS7utwTSJsulss3tWzZUsePH5cx5pKZ1OSTXxscDod%2B9KMfafLkyVq3bp2cTmeNPU5GRkZecu66jkfaq1BWVqb//M//1I4dO3TmzBn99a9/1ebNm5WamipJSk1N1WuvvaZ9%2B/appKRE8%2BfPV2xsrDp06KCoqCgNHjxYL7zwggoKCnT06FG99NJLGjlypJxOp/r27asbbrhBixcvVllZmT777DOtWrXKu%2B9zEhISdPjwYb%2BP1ebm5urWW29Vo0aNrmke38Xu3bu9X01xzr59%2B1SvXj317du32mv5O3bs8JbMhIQEvwJbWVmpnTt3qmPHjmrVqpUiIiL8xvfs2aPTp08rISGh1ueWkJBQ7dzPL9i2Cjpqh08%2B%2BUSPP/64fv/732vYsGF%2BY5f7W17qttKqVStJ8hvfs2ePzpw5o4SEhO98vJd7AmnLxXL5%2B9//rmXLlvmtu3//frVo0UIOh%2BOSmVyrJ782bdmyRYMHD/Z7CfJcmUtKSqqxx8kOHTpccu467xq9mT5oJSQkmISEBBMTE2NiYmK8/2/M2U9jLFq0yPTv39906tTJDB8%2B3GRnZ3u3raqqMgsXLjT9%2B/c33bt3N7/4xS/MkSNHvONFRUXmscceM7169TJ33HGHmT17tikvL/eOf/755%2BbBBx80Xbp0MYMHDzZvvPHGBY9x1KhR5sknnzTFxcVm7969Jjk52fz3f/93gBKx6%2BjRo6ZTp07m5ZdfNuXl5Wb//v3m7rvvNnPmzDHHjx83nTt3Nm%2B//bY5deqUyc7ONomJid5P9%2BTk5JiuXbuaTz/91Jw8edK8%2BOKLpm/fvqasrMwYY8yzzz5rhg8fbg4fPmwKCgrML37xCzNp0iTv3LUltwt9ivDzzz83HTp0MB988IE5deqUycrKMp07dzZ5eXnedTIyMszPf/5zc%2BLECXPkyBFz3333md/97nfGmLMf6%2B7fv79ZuHChOXnypNm2bZu57bbbzAcffHAtTw1X4MyZM%2BbOO%2B80b7311gXHL/e3vNxt5dzXNCxatMgcPXrUxMXFmXbt2vl9Su67%2BJ//%2BR/Tr18/s3fvXlNcXGxmzpxp7rvvvqva5/kulUtubq6Jj483f/7zn83p06dNbm6u6d27t3n11VeNMZfP5HLHXtvuWx6Px/Ts2dPMmzfPnDx50pw4ccKMHTvWPPDAAzX6OHm5ues6CtZ14MiRI%2BYXv/iF6datm0lOTjYLFy60%2Bl00gfbRRx%2BZUaNGmU6dOpn%2B/fubefPmeYvmRx99ZEaOHGk6d%2B5shg4dajZs2OC37RtvvGEGDx5sunTpYh566CHz%2Beefe8fKy8vN7NmzzR133GF69eplpkyZYoqKirzjNZ3bpcq7McZs2LDBDB061HTu3NmMGjXKfPTRR37b2yroqFn/%2BMc/TLt27bx///P/HTx40Bhz%2Bb/lpW4r5eXl5qGHHjLdu3c3cXFxpmPHjmb16tVXfdyXewJ5tS6Xy3vvvWeGDh1qOnXqZAYOHGiWLVtmKisrvdtfKpNr9eTXpl27dpkHH3zQJCYmmp49e5qMjAzv94DV5OPk5eauyxzG/OsdawAAALCC92ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGX/H/g28vmsBSxgAAAAAElFTkSuQmCC"/>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="common-2364398549250928231">
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">0.0</td>
<td class="number">9934</td>
<td class="number">99.3%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">6000.0</td>
<td class="number">2</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">10000.0</td>
<td class="number">2</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">29850.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">4384.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">17547.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">2578.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">242827.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">40234.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">2000.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="other">
<td class="fillremaining">Other values (55)</td>
<td class="number">55</td>
<td class="number">0.5%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr>
</table>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="extreme-2364398549250928231">
<p class="h4">Minimum 5 values</p>
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">0.0</td>
<td class="number">9934</td>
<td class="number">99.3%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">2000.0</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>