loans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loans - Predicting loan default risk of Czech bank customers\n",
    "\n",
    "This notebook demonstrates the application of our relational learning algorithm to predict if a customer of a bank will default on his loan. We train the predictor on customer metadata, transaction history, as well as other successful and unsuccessful loans.\n",
    "\n",
    "Summary:\n",
    "\n",
    "- Prediction type: __Binary classification__\n",
    "- Domain: __Finance__\n",
    "- Prediction target: __Loan default__\n",
    "- Source data: __8 tables, 78.8 MB__\n",
    "- Population size: __682__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "This notebook features a textbook example of predictive analytics applied to the financial sector. A loan is the lending of money to companies or individuals. Banks grant loans in exchange for the promise of repayment. Loan default is defined as the failure to meet this legal obligation, for example, when a home buyer fails to make a mortgage payment. A bank needs to estimate the risk it carries when granting loans to potentially non-performing customers.\n",
    "\n",
    "The analysis is based on the [financial](https://relational.fit.cvut.cz/dataset/Financial) dataset from the [the CTU Prague Relational Learning Repository](https://arxiv.org/abs/1511.03086) (Motl and Schulte, 2015) (Now residing at [relational-data.org](https://relational-data.org/dataset/Financial).)\n",
    "."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get started with the analysis and set-up your session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q \"getml==1.5.0\" \"matplotlib==3.9.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "\n",
    "import getml\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching ./getML --allow-push-notifications=true --allow-remote-ips=true --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false --token=token in /home/user/.getML/getml-1.5.0-x64-linux...\n",
      "Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912214744.log.\n",
      "\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Connected to project <span style=\"color: #008000; text-decoration-color: #008000\">'loans'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Connected to project \u001b[32m'loans'\u001b[0m.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getml.engine.launch(allow_remote_ips=True, token=\"token\")\n",
    "getml.engine.set_project(\"loans\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Loading data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 Download from source\n",
    "\n",
    "Downloading the raw data from the CTU Prague Relational Learning Repository into a prediction ready format takes time. To get to the getML model building as fast as possible, we prepared the data for you and excluded the code from this notebook. It will be made available in a future version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_train, population_test, order, trans, meta = getml.datasets.load_loans(\n",
    "    roles=True, units=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Prepare data for getML\n",
    "\n",
    "The `getml.datasets.load_loans` method took care of the entire data lifting:\n",
    "* Downloads csv's from our servers in python\n",
    "* Converts csv's to getML [DataFrames](https://getml.com/latest/reference/data/data_frame#dataframe)\n",
    "* Sets [roles](https://getml.com/latest/user_guide/concepts/annotating_data#roles) to columns inside getML DataFrames\n",
    "\n",
    "The only thing left is to set [units](https://getml.com/latest/user_guide/concepts/annotating_data#annotating-units) to columns that the relational learning algorithm is allowed to compare to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Data visualization__\n",
    "\n",
    "To simplify the notebook, original data model (image below) is condensed into 4 tables, by resolving the trivial one-to-one and many-to-one joins:\n",
    "\n",
    "- A population table `population_{train, test}`, consiting of `loan` and `account` tables\n",
    "- Three peripheral tables: `order`, `trans`, and `meta`.\n",
    "- Whereas `meta` is made up of `card`, `client`, `disp` and `district`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"assets/loans-schema.png\", width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Population table\n",
    "\n",
    "- Information on the loan itself (duration, amount, date, ...)\n",
    "- Geo-information about the branch where the loans was granted (A**)\n",
    "- Column `status` contains binary target. Levels [A, C] := _loan paid back_ and [B, D] := _loan default_;\n",
    "  we recoded status to our binary target: `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_train.set_role(\"date_loan\", \"time_stamp\")\n",
    "population_test.set_role(\"date_loan\", \"time_stamp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  table {\n",
       "    font-family: Helvetica, sans-serif;\n",
       "  }\n",
       "\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right !important;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.sub-header {\n",
       "    font-weight: normal;\n",
       "    font-style: italic;\n",
       "  }\n",
       "  .join_key,\n",
       "  .numerical,\n",
       "  .target,\n",
       "  .unused_float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "\n",
       "  .char-align {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  span.left {\n",
       "    text-align: right;\n",
       "    width: 3em;\n",
       "  }\n",
       "  span.right {\n",
       "    float: right;\n",
       "    text-align: left;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th>name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"time_stamp\">                  date_loan</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\">account_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">default</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">frequency         </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\"> duration</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\"> payments</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">   amount</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\">     loan_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\"> district_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_string\">date_account </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_string\">status       </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">role</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">                 time_stamp</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">  join_key</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\"> target</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical       </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">unused_string</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">unused_string</th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">unit</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">time stamp, comparison only</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">          </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">       </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">                  </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">         </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">         </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    money</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">             </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">             </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1996-04-29</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">19</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2523</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">30276</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4961</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">21</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1995-04-07</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">B</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-10-14</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">37</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">60</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5308</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">318480</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4967</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">20</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1997-08-18</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">D</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-04-19</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">38</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK TYDNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">48</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2307</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">110736</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4968</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">19</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1997-08-08</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">C</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1997-08-10</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">97</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">8573</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">102876</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4986</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">74</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1996-05-05</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">A</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1996-11-06</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">132</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK PO OBRATU</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">7370</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">88440</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4996</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">40</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1996-05-11</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">A</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>218</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1995-12-04</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11042</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">36</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6032</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">217152</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">7243</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1995-01-29</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">A</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>219</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1996-08-20</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11054</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK TYDNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">60</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2482</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">148920</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">7246</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">59</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1996-02-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">C</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>220</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1994-01-31</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11111</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">36</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3004</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">108144</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">7259</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1993-05-20</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">A</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>221</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-11-22</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11317</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">60</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5291</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">317460</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">7292</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">50</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1997-07-11</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">C</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>222</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1996-12-27</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11362</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">POPLATEK MESICNE</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">24</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5392</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">129408</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">7308</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">67</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1995-10-14</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">A</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    223 rows x 11 columns<br />\n",
       "    memory usage: 0.02 MB<br />\n",
       "    name: population_test<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name                     date_loan   account_id   default   frequency            ...      amount        loan_id    district_id\n",
       "role                    time_stamp     join_key    target   categorical          ...   numerical   unused_float   unused_float\n",
       "unit   time stamp, comparison only                                               ...       money                              \n",
       "   0                    1996-04-29           19         1   POPLATEK MESICNE     ...       30276           4961             21\n",
       "   1                    1998-10-14           37         1   POPLATEK MESICNE     ...      318480           4967             20\n",
       "   2                    1998-04-19           38         0   POPLATEK TYDNE       ...      110736           4968             19\n",
       "   3                    1997-08-10           97         0   POPLATEK MESICNE     ...      102876           4986             74\n",
       "   4                    1996-11-06          132         0   POPLATEK PO OBRATU   ...       88440           4996             40\n",
       "                               ...          ...       ...   ...                              ...            ...            ...\n",
       " 218                    1995-12-04        11042         0   POPLATEK MESICNE     ...      217152           7243             72\n",
       " 219                    1996-08-20        11054         0   POPLATEK TYDNE       ...      148920           7246             59\n",
       " 220                    1994-01-31        11111         0   POPLATEK MESICNE     ...      108144           7259              1\n",
       " 221                    1998-11-22        11317         0   POPLATEK MESICNE     ...      317460           7292             50\n",
       " 222                    1996-12-27        11362         0   POPLATEK MESICNE     ...      129408           7308             67\n",
       "\n",
       "name   date_account    status       \n",
       "role   unused_string   unused_string\n",
       "unit                                \n",
       "   0   1995-04-07      B            \n",
       "   1   1997-08-18      D            \n",
       "   2   1997-08-08      C            \n",
       "   3   1996-05-05      A            \n",
       "   4   1996-05-11      A            \n",
       "       ...             ...          \n",
       " 218   1995-01-29      A            \n",
       " 219   1996-02-01      C            \n",
       " 220   1993-05-20      A            \n",
       " 221   1997-07-11      C            \n",
       " 222   1995-10-14      A            \n",
       "\n",
       "\n",
       "223 rows x 11 columns\n",
       "memory usage: 0.02 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Peripheral tables\n",
    "\n",
    "- `meta`\n",
    "  - Meta info about the client (card_type, gender, ...)\n",
    "  - Geo-information about the client\n",
    "- `order`\n",
    "  - Permanent orders related to a loan (amount, balance, ...)\n",
    "- `trans`\n",
    "  - Transactions related to a given loan (amount, ...)\n",
    "\n",
    "While the contents of `meta` and `order` are omitted for brevity, here are contents of `trans`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  table {\n",
       "    font-family: Helvetica, sans-serif;\n",
       "  }\n",
       "\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right !important;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.sub-header {\n",
       "    font-weight: normal;\n",
       "    font-style: italic;\n",
       "  }\n",
       "  .join_key,\n",
       "  .numerical,\n",
       "  .target,\n",
       "  .unused_float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "\n",
       "  .char-align {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  span.left {\n",
       "    text-align: right;\n",
       "    width: 3em;\n",
       "  }\n",
       "  span.right {\n",
       "    float: right;\n",
       "    text-align: left;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th>   name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"time_stamp\">                       date</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\">account_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">type       </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">k_symbol   </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">bank       </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">operation    </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">   amount</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">  balance</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\">    trans_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\">     account</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">   role</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">                 time_stamp</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">  join_key</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical  </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">   unit</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">time stamp, comparison only</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">          </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">             </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">         </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    money</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1995-03-24</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">VKLAD</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1000</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1000</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1995-04-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">AB</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PREVOD Z UCTU</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3679</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4679</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">41403269</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1995-05-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">AB</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PREVOD Z UCTU</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3679</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">20977</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">41403269</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1995-06-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">AB</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PREVOD Z UCTU</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3679</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">26835</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">7</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">41403269</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1995-07-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">AB</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PREVOD Z UCTU</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3679</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">30415</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">8</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">41403269</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1056315</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-08-31</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">10451</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">UROK</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">62</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">17300</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3682983</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1056316</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-09-30</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">10451</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">UROK</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">49</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">13442</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3682984</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1056317</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-10-31</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">10451</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">UROK</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">34</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">10118</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3682985</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1056318</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-11-30</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">10451</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">UROK</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">26</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">8398</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3682986</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1056319</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">1998-12-31</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">10451</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">PRIJEM</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">UROK</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">42</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">13695</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3682987</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    1056320 rows x 10 columns<br />\n",
       "    memory usage: 63.38 MB<br />\n",
       "    name: trans<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "   name                          date   account_id   type          k_symbol      ...   operation          amount     balance\n",
       "   role                    time_stamp     join_key   categorical   categorical   ...   categorical     numerical   numerical\n",
       "   unit   time stamp, comparison only                                            ...                                   money\n",
       "      0                    1995-03-24            1   PRIJEM        NULL          ...   VKLAD                1000        1000\n",
       "      1                    1995-04-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679        4679\n",
       "      2                    1995-05-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679       20977\n",
       "      3                    1995-06-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679       26835\n",
       "      4                    1995-07-13            1   PRIJEM        NULL          ...   PREVOD Z UCTU        3679       30415\n",
       "                                  ...          ...   ...           ...                 ...                   ...         ...\n",
       "1056315                    1998-08-31        10451   PRIJEM        UROK          ...   NULL                   62       17300\n",
       "1056316                    1998-09-30        10451   PRIJEM        UROK          ...   NULL                   49       13442\n",
       "1056317                    1998-10-31        10451   PRIJEM        UROK          ...   NULL                   34       10118\n",
       "1056318                    1998-11-30        10451   PRIJEM        UROK          ...   NULL                   26        8398\n",
       "1056319                    1998-12-31        10451   PRIJEM        UROK          ...   NULL                   42       13695\n",
       "\n",
       "   name       trans_id        account\n",
       "   role   unused_float   unused_float\n",
       "   unit                              \n",
       "      0              1           nan \n",
       "      1              5      41403269 \n",
       "      2              6      41403269 \n",
       "      3              7      41403269 \n",
       "      4              8      41403269 \n",
       "                   ...            ...\n",
       "1056315        3682983           nan \n",
       "1056316        3682984           nan \n",
       "1056317        3682985           nan \n",
       "1056318        3682986           nan \n",
       "1056319        3682987           nan \n",
       "\n",
       "\n",
       "1056320 rows x 10 columns\n",
       "memory usage: 63.38 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Define relational model\n",
    "\n",
    "To start with relational learning, we need to specify an abstract data model. Here, we use the [high-level star schema API](https://getml.com/latest/reference/data/star_schema) that allows us to define the abstract data model and construct a [container](https://getml.com/latest/reference/data/container) with the concrete data at one-go. While a simple `StarSchema` indeed works in many cases, it is not sufficient for more complex data models like schoflake schemas, where you would have to define the data model and construct the container in separate steps, by utilzing getML's [full-fledged data model](https://getml.com/latest/reference/data/data_model) and [container](https://getml.com/latest/reference/data/container) APIs respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\n",
       "            <div style='margin-top: 15px; margin-bottom: 5px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\n",
       "            <div style=\"height:320px;width:660px;position:relative;\"><svg height=\"310\" width=\"650\"><rect y=\"0\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"73.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">trans</text><rect x=\"51\" y=\"10\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"10\" x2=\"67.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"10\" x2=\"83.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"26.0\" x2=\"99\" y2=\"26.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"42.0\" x2=\"99\" y2=\"42.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"110\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"183.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">order</text><rect x=\"51\" y=\"120\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"120\" x2=\"67.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"120\" x2=\"83.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"136.0\" x2=\"99\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"152.0\" x2=\"99\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"220\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"293.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">meta</text><rect x=\"51\" y=\"230\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"230\" x2=\"67.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"230\" x2=\"83.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"246.0\" x2=\"99\" y2=\"246.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"262.0\" x2=\"99\" y2=\"262.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"220\" x=\"500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"293.8\" x=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">population</text><rect x=\"551\" y=\"230\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"567.0\" y1=\"230\" x2=\"567.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"583.0\" y1=\"230\" x2=\"583.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"246.0\" x2=\"599\" y2=\"246.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"262.0\" x2=\"599\" y2=\"262.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"150\" y1=\"43.0\" x2=\"573.0\" y2=\"43.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"573.0\" y1=\"41.0\" x2=\"573.0\" y2=\"210\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"573.0, 220 567.0, 210 579.0, 210 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"10.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"40.0\" x=\"324.0\" font-size=\"7pt\" >account_id = account_id</tspan><tspan y=\"50.0\" x=\"324.0\" font-size=\"7pt\" >date &lt;= date_loan</tspan></text><line x1=\"150\" y1=\"153.0\" x2=\"573.0\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"573.0\" y1=\"151.0\" x2=\"573.0\" y2=\"210\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"573.0, 220 567.0, 210 579.0, 210 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"120.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"155.0\" x=\"324.0\" font-size=\"7pt\" >account_id = account_id</tspan></text><line x1=\"150\" y1=\"263.0\" x2=\"490\" y2=\"263.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 263.0 490, 257.0 490, 269.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"230.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"265.0\" x=\"324.0\" font-size=\"7pt\" >account_id = account_id</tspan></text></svg></div>\n",
       "            </div>\n",
       "\n",
       "            <div style='margin-top: 15px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\n",
       "            <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">data frames</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">staging table              </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">population</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">POPULATION__STAGING_TABLE_1</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">meta</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">META__STAGING_TABLE_2</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">order</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">ORDER__STAGING_TABLE_3</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">trans</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">TRANS__STAGING_TABLE_4</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "            </div>\n",
       "            \n",
       "<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\n",
       "<div style='margin-top: 15px;'>\n",
       "<div style='float: left; margin-right: 50px;'>\n",
       "<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\n",
       "    <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">subset</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name            </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\">rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type     </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">population_train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">459</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">population_test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">223</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div style='float: left;'>\n",
       "<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\n",
       "    <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\">   rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type     </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">trans</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">1056320</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">order</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">6471</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">meta</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">5369</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "data model\n",
       "\n",
       "  population:\n",
       "    columns:\n",
       "    - frequency: categorical\n",
       "    - account_id: join_key\n",
       "    - duration: numerical\n",
       "    - payments: numerical\n",
       "    - amount: numerical\n",
       "    - ...\n",
       "\n",
       "    joins:\n",
       "    - right: 'trans'\n",
       "      on: (population.account_id, trans.account_id)\n",
       "      time_stamps: (population.date_loan, trans.date)\n",
       "      relationship: 'many-to-many'\n",
       "      lagged_targets: False\n",
       "    - right: 'order'\n",
       "      on: (population.account_id, order.account_id)\n",
       "      relationship: 'many-to-many'\n",
       "      lagged_targets: False\n",
       "    - right: 'meta'\n",
       "      on: (population.account_id, meta.account_id)\n",
       "      relationship: 'many-to-many'\n",
       "      lagged_targets: False\n",
       "\n",
       "  trans:\n",
       "    columns:\n",
       "    - type: categorical\n",
       "    - k_symbol: categorical\n",
       "    - bank: categorical\n",
       "    - operation: categorical\n",
       "    - account_id: join_key\n",
       "    - ...\n",
       "\n",
       "  order:\n",
       "    columns:\n",
       "    - bank_to: categorical\n",
       "    - k_symbol: categorical\n",
       "    - account_id: join_key\n",
       "    - amount: numerical\n",
       "    - account_to: unused_float\n",
       "    - ...\n",
       "\n",
       "  meta:\n",
       "    columns:\n",
       "    - type_disp: categorical\n",
       "    - type_card: categorical\n",
       "    - gender: categorical\n",
       "    - A3: categorical\n",
       "    - account_id: join_key\n",
       "    - ...\n",
       "\n",
       "\n",
       "container\n",
       "\n",
       "  population\n",
       "      subset   name               rows   type     \n",
       "  0   train    population_train    459   DataFrame\n",
       "  1   test     population_test     223   DataFrame\n",
       "\n",
       "  peripheral\n",
       "      name       rows   type     \n",
       "  0   trans   1056320   DataFrame\n",
       "  1   order      6471   DataFrame\n",
       "  2   meta       5369   DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "star_schema = getml.data.StarSchema(\n",
    "    train=population_train, test=population_test, alias=\"population\"\n",
    ")\n",
    "\n",
    "star_schema.join(\n",
    "    trans,\n",
    "    on=\"account_id\",\n",
    "    time_stamps=(\"date_loan\", \"date\"),\n",
    ")\n",
    "\n",
    "star_schema.join(\n",
    "    order,\n",
    "    on=\"account_id\",\n",
    ")\n",
    "\n",
    "star_schema.join(\n",
    "    meta,\n",
    "    on=\"account_id\",\n",
    ")\n",
    "\n",
    "star_schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  table {\n",
       "    font-family: Helvetica, sans-serif;\n",
       "  }\n",
       "\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right !important;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.sub-header {\n",
       "    font-weight: normal;\n",
       "    font-style: italic;\n",
       "  }\n",
       "  .join_key,\n",
       "  .numerical,\n",
       "  .target,\n",
       "  .unused_float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "\n",
       "  .char-align {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  span.left {\n",
       "    text-align: right;\n",
       "    width: 3em;\n",
       "  }\n",
       "  span.right {\n",
       "    float: right;\n",
       "    text-align: left;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th>name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\">account_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">type_disp  </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">type_card  </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">gender     </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">A3             </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">       A4</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">       A5</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">       A6</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">       A7</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">       A8</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">       A9</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A10</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A11</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A12</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A13</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A14</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A15</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">      A16</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\">     disp_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\">   client_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\">     card_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\"> district_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_string\">issued       </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_string\">birth_date   </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_string\">A2             </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">role</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">  join_key</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical    </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">unused_string</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">unused_string</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">unused_string  </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">south Bohemia</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">70699</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">60</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">13</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">65</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.3</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">8968</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.8</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.35</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">131</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1740</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1910</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">18</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1970-12-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Pisek</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">M</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Prague</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1204953</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">100</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12541</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.2</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.43</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">167</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">85677</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">99107</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1945-02-04</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Hl.m. Praha</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">DISPONENT</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Prague</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1204953</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">100</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12541</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.2</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.43</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">167</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">85677</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">99107</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1940-10-09</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Hl.m. Praha</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">M</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">central Bohemia</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">95616</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">65</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">51</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.4</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">9307</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.8</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.43</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">118</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2616</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3040</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1956-12-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Kolin</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">DISPONENT</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">central Bohemia</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">95616</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">65</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">51</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.4</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">9307</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.8</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.43</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">118</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2616</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3040</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1960-07-03</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Kolin</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5364</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11349</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Prague</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1204953</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">100</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12541</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.2</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.43</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">167</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">85677</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">99107</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13647</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13955</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1945-10-30</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Hl.m. Praha</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5365</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11349</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">DISPONENT</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">M</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Prague</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1204953</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">100</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">12541</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.2</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.43</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">167</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">85677</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">99107</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13648</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13956</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1943-04-06</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Hl.m. Praha</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5366</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11359</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">classic</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">M</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">south Moravia</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">117897</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">139</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">28</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">53</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.8</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">8814</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.7</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.74</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">107</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2112</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2059</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13660</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13968</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1247</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">61</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1995-06-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1968-04-13</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Trebic</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5367</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11362</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">north Moravia</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">106054</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">38</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">25</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">63</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.1</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">8110</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.7</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.55</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">109</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3244</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3079</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13663</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13971</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">67</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1962-10-19</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Bruntal</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5368</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">11382</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">OWNER</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">north Moravia</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">323870</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">100</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">10673</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 1ch\"\n",
       "                >.7</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 2ch\"\n",
       "                >.44</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">100</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">18782</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">18347</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13690</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">13998</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">nan</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">74</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1953-08-12</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">Ostrava - mesto</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    5369 rows x 25 columns<br />\n",
       "    memory usage: 1.10 MB<br />\n",
       "    name: meta<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name   account_id   type_disp     type_card     gender        ...        card_id    district_id   issued       \n",
       "role     join_key   categorical   categorical   categorical   ...   unused_float   unused_float   unused_string\n",
       "   0            1   OWNER         NULL          F             ...           nan              18   NULL         \n",
       "   1            2   OWNER         NULL          M             ...           nan               1   NULL         \n",
       "   2            2   DISPONENT     NULL          F             ...           nan               1   NULL         \n",
       "   3            3   OWNER         NULL          M             ...           nan               5   NULL         \n",
       "   4            3   DISPONENT     NULL          F             ...           nan               5   NULL         \n",
       "              ...   ...           ...           ...                          ...            ...   ...          \n",
       "5364        11349   OWNER         NULL          F             ...           nan               1   NULL         \n",
       "5365        11349   DISPONENT     NULL          M             ...           nan               1   NULL         \n",
       "5366        11359   OWNER         classic       M             ...          1247              61   1995-06-13   \n",
       "5367        11362   OWNER         NULL          F             ...           nan              67   NULL         \n",
       "5368        11382   OWNER         NULL          F             ...           nan              74   NULL         \n",
       "\n",
       "name   birth_date      A2             \n",
       "role   unused_string   unused_string  \n",
       "   0   1970-12-13      Pisek          \n",
       "   1   1945-02-04      Hl.m. Praha    \n",
       "   2   1940-10-09      Hl.m. Praha    \n",
       "   3   1956-12-01      Kolin          \n",
       "   4   1960-07-03      Kolin          \n",
       "       ...             ...            \n",
       "5364   1945-10-30      Hl.m. Praha    \n",
       "5365   1943-04-06      Hl.m. Praha    \n",
       "5366   1968-04-13      Trebic         \n",
       "5367   1962-10-19      Bruntal        \n",
       "5368   1953-08-12      Ostrava - mesto\n",
       "\n",
       "\n",
       "5369 rows x 25 columns\n",
       "memory usage: 1.10 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Predictive modeling\n",
    "\n",
    "We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 getML Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- #### 2.1.1  -->\n",
    "__Set-up of feature learners, selectors & predictor__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = getml.preprocessors.Mapping(min_freq=100)\n",
    "\n",
    "fast_prop = getml.feature_learning.FastProp(\n",
    "    aggregation=getml.feature_learning.FastProp.agg_sets.All,\n",
    "    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n",
    "    num_threads=1,\n",
    ")\n",
    "\n",
    "feature_selector = getml.predictors.XGBoostClassifier(n_jobs=1)\n",
    "\n",
    "# the population is really small, so we set gamma to mitigate overfitting\n",
    "predictor = getml.predictors.XGBoostClassifier(\n",
    "    gamma=2,\n",
    "    n_jobs=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Build the pipeline__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = getml.pipeline.Pipeline(\n",
    "    data_model=star_schema.data_model,\n",
    "    preprocessors=[mapping],\n",
    "    feature_learners=[fast_prop],\n",
    "    feature_selectors=[feature_selector],\n",
    "    predictors=predictor,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K⠧ Staging...                                            0% • --:--"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Trying 808 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as feature selector... ━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:00:01.070034.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['meta', 'order', 'trans'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['container-CE93xg'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['meta', 'order', 'trans'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['container-CE93xg'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(star_schema.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"datetime\">date time          </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">set used</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">target </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">accuracy</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">    auc</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">cross entropy</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 21:48:14</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">default</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9978</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0671</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 21:48:15</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">default</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9686</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9465</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.13814</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    date time             set used   target    accuracy       auc   cross entropy\n",
       "0   2024-09-12 21:48:14   train      default     0.9978    1.             0.0671 \n",
       "1   2024-09-12 21:48:15   test       default     0.9686    0.9465         0.13814"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.score(star_schema.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Studying features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Visualizing the learned features__\n",
    "\n",
    "The feature with the highest importance is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_21\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_21\" AS\n",
       "SELECT Q1( t2.\"balance\" ) AS \"feature_1_21\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRANS__STAGING_TABLE_4\" t2\n",
       "ON t1.\"account_id\" = t2.\"account_id\"\n",
       "WHERE t2.\"date\" <= t1.\"date_loan\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_21\";\\n\\nCREATE TABLE \"FEATURE_1_21\" AS\\nSELECT Q1( t2.\"balance\" ) AS \"feature_1_21\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRANS__STAGING_TABLE_4\" t2\\nON t1.\"account_id\" = t2.\"account_id\"\\nWHERE t2.\"date\" <= t1.\"date_loan\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_importances = pipe.features.sort(by=\"importances\")\n",
    "by_importances[0].sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature correlations__\n",
    "\n",
    "We want to analyze how the features are correlated with the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, correlations = pipe.features[:50].correlations()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax.bar(names, correlations)\n",
    "\n",
    "ax.set_title(\"feature correlations\")\n",
    "ax.set_xlabel(\"feature\")\n",
    "ax.set_ylabel(\"correlation\")\n",
    "ax.tick_params(axis=\"x\", rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature importances__\n",
    "\n",
    "Feature importances are calculated by analyzing the improvement in predictive accuracy on each node of the trees in the XGBoost predictor. They are then normalized, so that all importances add up to 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe.features[:50].importances()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax.bar(names, importances)\n",
    "\n",
    "ax.set_title(\"feature importances\")\n",
    "ax.set_xlabel(\"feature\")\n",
    "ax.set_ylabel(\"importance\")\n",
    "ax.tick_params(axis=\"x\", rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Column importances__\n",
    "\n",
    "Because getML uses relational learning, we can apply the principles we used to calculate the feature importances to individual columns as well.\n",
    "\n",
    "As we can see, a lot of the predictive power stems from the account balance. This is unsurprising: People with less money on their bank accounts are more likely to default on their loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe.columns.importances()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "ax.bar(names, importances)\n",
    "\n",
    "ax.set_title(\"column importances\")\n",
    "ax.set_xlabel(\"column\")\n",
    "ax.set_ylabel(\"importance\")\n",
    "ax.tick_params(axis=\"x\", rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important feature looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_21\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_21\" AS\n",
       "SELECT Q1( t2.\"balance\" ) AS \"feature_1_21\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRANS__STAGING_TABLE_4\" t2\n",
       "ON t1.\"account_id\" = t2.\"account_id\"\n",
       "WHERE t2.\"date\" <= t1.\"date_loan\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_21\";\\n\\nCREATE TABLE \"FEATURE_1_21\" AS\\nSELECT Q1( t2.\"balance\" ) AS \"feature_1_21\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRANS__STAGING_TABLE_4\" t2\\nON t1.\"account_id\" = t2.\"account_id\"\\nWHERE t2.\"date\" <= t1.\"date_loan\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.features.to_sql()[pipe.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5 Productionization\n",
    "\n",
    "It is possible to productionize the pipeline by transpiling the features into production-ready SQL code. Please also refer to getML's `sqlite3` and `spark` modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder named loans_pipeline containing\n",
    "# the SQL code.\n",
    "pipe.features.to_sql().save(\"loans_pipeline\", remove=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder named baseball_pipeline_spark containing\n",
    "# the SQL code.\n",
    "pipe.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\n",
    "    \"loans_pipeline_spark\", remove=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "getml.engine.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By applying getML to the PKDD'99 Financial dataset, we were able to show the power and relevance of Relational Learning on a real-world data set. Within a training time below 1 minute, we outperformed almost all approaches based on manually generated features. This makes getML the prime choice when dealing with complex relational data schemes. This result holds independent of the problem domain since no expertise in the financial sector was used in this analysis.\n",
    "\n",
    "The present analysis could be improved in two directions. By performing an extensive hyperparameter optimization, the out of sample AUC could be further improved. On the other hand, the hyperparameters could be tuned to produce less complex features that result in worse performance (in terms of AUC) but are better interpretable by humans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Schulte, Oliver, et al. \"A hierarchy of independence assumptions for multi-relational Bayes net classifiers.\" 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2013."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "enterprise",
   "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": "3.11.9"
  },
  "toc-autonumbering": false,
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}