soep-notebooks-python.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"top\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebooks for SOEP data analysis\n",
    "## Python Notebook\n",
    "In this Jupyter Notebook an exemplary analysis of SOEP data is conducted with Python 3.7.3 and common data science libraries.\n",
    "\n",
    "---\n",
    "\n",
    "**Authors:**\n",
    "\n",
    "Heinz-Alexander Fütterer (ZBW) and [Andreas Franken (DIW)](https://www.diw.de/de/diw_01.c.556354.de/ueber_uns/menschen_am_diw_berlin/franken_andreas.html)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index\n",
    "\n",
    "* [Introduction](#introduction)\n",
    "* [Goal](#goal)\n",
    "* [Set up environment](#environment)\n",
    "* [Dataset: `ppathl.dta` (Individual Tracking File)](#dataset-ppathl)\n",
    "* [Dataset: `pgen.dta` (Generated Individual Data)](#dataset-pgen)\n",
    "* [Dataset: `hgen.dta` (Generated Household Data)](#dataset-hgen)\n",
    "* [Version and Hardware Information](#version-hardware-info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"introduction\"></a>\n",
    "[Return to top](#top)\n",
    "## Introduction\n",
    "The datasets we use in this notebook are part of the bilingual Stata based distribution of the SOEP data in version 34.\n",
    "Researchers will find the datasets in `STATA_DEEN_v34.zip`. We assume the datasets to be extracted to a directory called `data/`.\n",
    "\n",
    "Citation:\n",
    "> Liebig, Stefan; Schupp, Jürgen; Goebel, Jan; Richter, David; Schröder, Carsten et. al. (2019): Sozio-oekonomisches Panel (SOEP), Daten der Jahre 1984-2017. Version: v34. SOEP - Sozio-oekonomisches Panel. Dataset. http://doi.org/10.5684/soep.v34\n",
    "\n",
    "We will make use of three datasets: `hgen.dta`, `pgen.dta` and `ppathl.dta`.\n",
    "\n",
    "See also:\n",
    "- <https://www.diw.de/sixcms/detail.php?id=diw_01.c.616136.de>\n",
    "- <https://paneldata.org/soep-core>\n",
    "- <https://paneldata.org/soep-core/data/hgen>\n",
    "- <https://paneldata.org/soep-core/data/pgen>\n",
    "- <https://paneldata.org/soep-core/data/ppathl>\n",
    "- <http://companion.soep.de/>\n",
    "- <https://www.da-ra.de/dara/search/search_show?res_id=672526&lang=en&mdlang=de&detail=true>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"goal\"></a>\n",
    "[Return to top](#top)\n",
    "## Goal\n",
    "This notebook demonstrates how to:\n",
    "-   read (multiple) SOEP data from Stata files on disk\n",
    "-   load external / install external libraries for data analysis\n",
    "-   preprocess data, e.g.\n",
    "    -   drop rows based on certain conditions\n",
    "    -   declare values as NaN\n",
    "    -   merge tables based on shared columns as keys\n",
    "-   generate descriptive statistics of (multiple) columns of a table\n",
    "-   generate crosstables\n",
    "-   generate histograms\n",
    "-   generate box plots\n",
    "-   calculate correlations betweeen columns\n",
    "-   generate new columns based on existing columns\n",
    "-   calculate a statistical model, e.g. linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Return to top](#top)\n",
    "<a id=\"environment\"></a>\n",
    "## Set up environment\n",
    "We import the libraries for the coming data analysis steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from linearmodels import PooledOLS\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the base directory to read Stata files from. This directory is assumed to be the extracted version of `STATA_DEEN_v34.zip`. For demonstration purposes it contains only the three datasets we are interested in for this notebook. We can verify the integrity of the files with `md5sum`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "510427c28ed0d7113d989a3651191af2  data/hgen.dta\n",
      "096d87642640a076f4514b7163e716b2  data/pgen.dta\n",
      "33fef93b16c406d2a82350772d6070cc  data/ppathl.dta\n"
     ]
    }
   ],
   "source": [
    "DATASETS_DIRECTORY = Path('data/')\n",
    "! md5sum $DATASETS_DIRECTORY/*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin to read Stata files from disk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"dataset-ppathl\"></a>\n",
    "[Return to top](#top)\n",
    "## Dataset: `ppathl.dta` (Individual Tracking File)\n",
    "From <https://paneldata.org/soep-core/data/ppathl>:\n",
    "\n",
    "> Person core information and respondent status for each year in long format. It contains one record for each individual and year a person has been a member of a respondent household. It is keyed on PID, the Cross-Wave Person Identifier, and SYEAR, the survey year identifier. It contains the Household ID, and never changing individual characteristics, individual weights, as well as the response status, for that individual at each wave, see http://www.diw.de/documents/dokumentenarchiv/17/diw_01.c.432038.de/diw_ssp0150_ppfad.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxr-xr-x 1 afuetterer afuetterer 123M Mär  6 12:42 data/ppathl.dta\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lh $DATASETS_DIRECTORY/ppathl.dta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `ppathl.dta` has a size of 123 MB and contains 1039257 rows with 48 columns, e.g. 1039257 observations with 48 variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1039257, 48)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = DATASETS_DIRECTORY / \"ppathl.dta\"\n",
    "ppathl = pd.read_stata(filename, convert_categoricals=False)\n",
    "ppathl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pid', 'syear', 'cid', 'sex', 'gebjahr', 'eintritt', 'erstbefr',\n",
       "       'austritt', 'psample', 'letztbef', 'hid', 'sampreg', 'netto', 'nett1',\n",
       "       'casemat', 'pop', 'todjahr', 'todinfo', 'immiyear', 'germborn',\n",
       "       'corigin', 'gebmonat', 'gebmoval', 'loc1989', 'migback', 'miginfo',\n",
       "       'germborninfo', 'corigininfo', 'immiyearinfo', 'sexor', 'sexorinfo',\n",
       "       'locinfo', 'birthregion', 'arefback', 'arefinfo', 'prgroup', 'pbleib',\n",
       "       'phrf', 'phrf0', 'phrf1', 'phrfe', 'pbleibe', 'phrfe0', 'phrfe1',\n",
       "       'persnr', 'parid', 'partner', 'piyear'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppathl.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"variable-netto\"></a>\n",
    "### Variable: `netto` (Current survey status)\n",
    "\n",
    "Value labels we are interested in:\n",
    "\n",
    "- [10] Interviewee With Succesful Interview (_P)\n",
    "- [12] Individual Questionnaire And Person Biography\n",
    "- [13] Individual Questionnaire And Youth Biography\n",
    "- [14] Individual Questionnaire And Other Questionnaires\n",
    "- [15] Individual Questionnaire And Experiments, Test\n",
    "- [16] Individual Questionnaire, First Time Surveyed, Age 17\n",
    "- [17] Youth Biography First Time Surveyed, Age 17\n",
    "- [18] Individual Questionnaire And Child under age 17\n",
    "- [19] Individual Questionnaire Without Household Interview\n",
    "\n",
    "see: <https://paneldata.org/soep-core/data/ppathl/netto>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(657601, 48)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select a subset of rows by a condition on one column\n",
    "# only keep rows describing \"adults with 'realized' interviews\"\n",
    "ppathl = ppathl[ppathl[\"netto\"].between(1, 19)]\n",
    "ppathl.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"variable-pop\"></a>\n",
    "### Variable: `pop` (Sample Membership)\n",
    "\n",
    "Value labels we are interested in:\n",
    "\n",
    "- [1] Private HH, German HH-Head\n",
    "- [2] Private HH, Foreign HH-Head\n",
    "- [3] Institutional. HH, Collective accommodation, German HH-Head\t\n",
    "- [4] Institutional. HH, Collective accommodation, Foreign HH-Head\n",
    "\n",
    "see: <https://paneldata.org/soep-core/data/ppathl/pop>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(656960, 48)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select a subset of rows by a condition on one column\n",
    "ppathl = ppathl.loc[(ppathl['pop'] > 0) & (ppathl['pop'] < 5)]\n",
    "ppathl.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"hist-syear\"></a>\n",
    "### Histogram: Survey Year\n",
    "\n",
    "Generate a histogram of the variable `syear` (Survey Year). The histogram shows the number of participants in the survey per year.\n",
    "\n",
    "\n",
    "see: <https://paneldata.org/soep-core/data/ppathl/syear>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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0jTkXzc3NVdWr9yi+53L3HPn34ly+CNi9UG9ILuuqfEgH5V0dw8zMepF6J6jpwJi8PAa4o1B+hqR+kg4CXs7ddDOBIyRtnwdHHAHMzOuWSToojwA8o92+OjqGmZn1IrUcZv5T4BBgJ0kLSaPxJgDTJJ0DPAmcmKvPIA0xn0caZn4WQEQskXQV8GCu9+WIqAy8+DTrhpn/Ov/QxTHMzKwXqVmCiohTOll1WAd124DzO9nPjcCNHZQ/BOzTQfmLHR3DzMx6F88kYWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWZmpeQEZWa2AZavXN3oEDZ5Dbvlu5lZbzZoYH+Gjr2zYcefP+GYhh27XtyCMjOzUnKCMjOzUnKCMrMN1p3rMMOHD2/Ysa138jUoM9tgjbwO0xeuwfR1bkGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpNeR7UJI+C3wCaAMeAc4CdgVuAXYEmoHTI2KFpCZgKjASeBE4KSLm5/1cCpwDrAYujIiZuXwUMBHoD0yKiAn1e3ZmZtYT6t6CkjQYuBDYPyL2ISWRk4GvA9dExF7AUlLiIf9emsuvyfWQNCJv905gFHCdpP6S+gPfA44CRgCn5LpmZtaLNKqLbwCwuaQBwBbAM8ChwG15/RTguLw8Oj8mrz9MUr9cfktEtEbEE8A84ID8My8iHo+IFaRW2eg6PCczM+tBde/ii4hFkq4GngLeAO4idem9FBGrcrWFwOC8PBhYkLddJellUjfgYGBOYdfFbRa0Kz9wfXG1trbS0tKyQc+pWsuXL6/5MWrJ8TdWGePv6fn1uque56P9+W/0c2+0epz7uicoSduTWjR7Ai8BPyN10TVUU1NTzV9wLS0tvfpF7fgbq7fHXwv1PB8+/39tY85Fc3NzVfUa0cX3EeCJiHg+IlYCPwc+AGyXu/wAhgCL8vIiYHeAvH5b0mCJteXttums3MzMepFGJKingIMkbZGvJR0GPAb8Fjg+1xkD3JGXp+fH5PX3RkRbLj9ZUpOkPYFhwAPAg8AwSXtKehNpIMX0OjwvMzPrQXVPUBFxP2mww8OkIeabAT8EvgBcLGke6RrTDXmTG4Adc/nFwNi8n0eBaaTk9hvg/IhYna9jXQDMBFqAabmumZn1Ig35HlREjAPGtSt+nDQCr33d5cAJnexnPDC+g/IZwIyNj9TMzBrFM0mYmVkpOUGZmVkpVZWgJL2r1oGYmZkVVXsN6ro8J95k4McR8XLtQjIzM6uyBRURHwI+Tvp+UbOkn0g6vKaRmZlZn1b1NaiI+AtwBWk4+MHAtZL+JOnvaxWcmZn1XdVeg9pX0jWk7xUdCnw0Iobn5WtqGJ+ZmfVR1V6D+g4wCbgsIt6oFEbE05KuqElkZmbWp1WboI4B3oiI1QCSNgMGRcTrEXFzzaIzM7M+q9prUHcDmxceb5HLzMzMaqLaBDUoIl6tPMjLW9QmJDMzs+oT1GuS9qs8kDSSdLNBMzOzmqj2GtQ/AT+T9DTQD9gFOKlmUZmZWZ9XVYKKiAcl7Q1oXVGsrF1YZmbW13Vnstj3AfsC+wGnSDqjNiGZWbWWr1zd6BDMaqaqFpSkm4G3A3OByn9EGzC1RnGZWRUGDezP0LF3Nuz48ycc07BjL1+5mkED+9fteMOHD6/bsSyp9hrU/sCIfKt1M7OG68vJua+otovvj6SBEWZmZnVRbQtqJ+AxSQ8ArZXCiPhYTaIyM7M+r9oEdWUtgzAzM2uv2vtB/Q6YDwzMyw8CD9cwLjMz6+Oqvd3GucBtwA9y0WDgl7UKyszMrNpBEucDHwCWwdqbF76lVkGZmZlVm6BaI2JF5YGkAaTvQZmZmdVEtQnqd5IuAzaXdDjwM+DfaheWmZn1ddUmqLHA88AjwCeBGYDvpGtmZjVT7WSxa4B/zT9mZmY1V+1cfE/QwTWniHjbhhxU0nbAJGCfvN+zgQBuBYaShrSfGBFLJfUDJgJHA68DZ0bEw3k/Y1jXkvtKREzJ5SOByaS7AM8ALvI0TWZmvUu1XXz7k2Yzfx/wIeBa4EcbcdyJwG8iYm/g3UALqRvxnogYBtyTHwMcBQzLP+cB1wNI2gEYBxwIHACMk7R93uZ64NzCdqM2IlYzM2uAarv4XmxX9G1JzcC/dPeAkrYF/g44M+97BbBC0mjgkFxtCjAb+AIwGpiaW0BzJG0naddcd1ZELMn7nQWMkjQb2CYi5uTyqcBxwK+7G6uZmTVOtV18+xUebkZqUVU7TVJ7e5IGXNwk6d1AM3ARsHNEPJPrPAvsnJcHAwsK2y/MZV2VL+ygvEutra20tLR0+8l0x/Lly2t+jFpy/I3VUfy+BYQ1Sj3+l6pNMt8sLK8iXyPaiGPuB3wmIu6XNJF13XkARESbpLpeM2pqaqr5P3tLS0uvfkNx/I3V2+O3TcvGvBabm5urqldtF9+HNziSv7UQWBgR9+fHt5ES1HOSdo2IZ3IX3uK8fhGwe2H7IblsEeu6BCvls3P5kA7qm5lZL1JtF9/FXa2PiG9Ve8CIeFbSAkmKiAAOAx7LP2OACfn3HXmT6cAFkm4hDYh4OSexmcBXCwMjjgAujYglkpZJOgi4HzgD+E618ZmZWTl054667yMlC4CPAg8Af9nA434G+LGkNwGPA2eRrm1Nk3QO8CTruhBnkIaYzyMNMz8LICeiq0gzqwN8uTJgAvg064aZ/xoPkDAz63WqTVBDgP0i4hUASVcCd0bEaRty0IiYS0p67R3WQd020mS1He3nRuDGDsofIn3HyszMeqlqvwe1M7Ci8HgF60bZmZmZ9bhqW1BTgQck/SI/Po70XSUzM7OaqPaOuuNJ136W5p+zIuKrtQzMzMz6tmq7+AC2AJZFxERgoaQ9axSTmZlZ1bd8H0eadujSXDSQjZuLz6zHLF+5uqp6tfiSa7XHNrPuq/Ya1P8F3gs8DBART0vaumZRmXXDoIH9GTr2zoYc+09X1W8eYs8iYX1NtQlqRXH6IUlb1jAms16jkckRYP6EYxp2bLNaq/Ya1DRJPwC2k3QucDe+eaGZmdVQtXPxXS3pcGAZIOBfImJWTSMzM7M+bb0JSlJ/4O48YayTkpmZ1cV6u/giYjWwJt9o0MzMrC6qHSTxKvBIvmvta5XCiLiwJlGZmVmfV22C+nn+MTMzq4suE5Skt0bEUxHheffMzKyu1ncN6peVBUm31zgWMzOztdaXoPoVlt9Wy0DMzMyK1peg2jpZNjMzq6n1DZJ4t6RlpJbU5nmZ/LgtIrapaXRmZtZndZmgIqJ/vQIxMzMr6s79oMzMzOrGCcrMzErJCaqHVHPjulrdz8c3zTOzTVG1M0nYejTyvkC+J5CZbYrcgjIzs1JygjIzs1JygjIzs1Jq2DWofCPEh4BFEXGspD2BW4AdgWbg9IhYIakJmAqMBF4EToqI+XkflwLnAKuBCyNiZi4fBUwE+gOTImJCXZ+cmZlttEa2oC4CWgqPvw5cExF7AUtJiYf8e2kuvybXQ9II4GTgncAo4DpJ/XPi+x5wFDACOCXXNTOzXqQhCUrSEOAYYFJ+3A84FLgtV5kCHJeXR+fH5PWH5fqjgVsiojUingDmAQfkn3kR8XhErCC1ykbX/lmZmVlPalQX37eBzwNb58c7Ai9FxKr8eCEwOC8PBhYARMQqSS/n+oOBOYV9FrdZ0K78wPUF1NraSktLy/qqdapW33Gq1sbEXq3ly5d3eJy3Dn0bW27eVPPjm1l51OM9p+4JStKxwOKIaJZ0SL2P35mmpqaGJ5mNUY/YW1paOj1Oo74DBv4emFkjbMx7TnNzc1X1GtHF9wHgY5Lmk7rfDiUNaNhOUiVhDgEW5eVFwO4Aef22pMESa8vbbdNZuZmZ9SJ1T1ARcWlEDImIoaRBDvdGxMeB3wLH52pjgDvy8vT8mLz+3ohoy+UnS2rKIwCHAQ8ADwLDJO0p6U35GNPr8NTMzKwHlel7UF8ALpY0j3SN6YZcfgOwYy6/GBgLEBGPAtOAx4DfAOdHxOp8HesCYCZplOC0XNfMzHqRhs7FFxGzgdl5+XHSCLz2dZYDJ3Sy/XhgfAflM4AZPRiqmZnVWZlaUGZmZms5QZmZWSk5QZmZWSk5QZmZWSk5QZmZWSk5QW0C6nXL994804aZ9T6+5fsmoJG3mwdPNWRmteEWlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZlZITlJmZldKAeh9Q0u7AVGBnoA34YURMlLQDcCswFJgPnBgRSyX1AyYCRwOvA2dGxMN5X2OAK/KuvxIRU3L5SGAysDkwA7goItrq8gTNzKxHNKIFtQq4JCJGAAcB50saAYwF7omIYcA9+THAUcCw/HMecD1ATmjjgAOBA4BxkrbP21wPnFvYblQdnpeZmfWguieoiHim0gKKiFeAFmAwMBqYkqtNAY7Ly6OBqRHRFhFzgO0k7QocCcyKiCURsRSYBYzK67aJiDm51TS1sC8zM+sl6t7FVyRpKPBe4H5g54h4Jq96ltQFCCl5LShstjCXdVW+sIPyLrW2ttLS0tL9J5ENHz58g7c1M+ttNub9sloNS1CStgJuB/4pIpZJWrsuItok1fWaUVNTk5OMmVmVNub9srm5uap6DRnFJ2kgKTn9OCJ+noufy91z5N+Lc/kiYPfC5kNyWVflQzooNzOzXqTuCSqPyrsBaImIbxVWTQfG5OUxwB2F8jMk9ZN0EPBy7gqcCRwhafs8OOIIYGZet0zSQflYZxT2ZWZmvUQjuvg+AJwOPCJpbi67DJgATJN0DvAkcGJeN4M0xHweaZj5WQARsUTSVcCDud6XI2JJXv4064aZ/zr/mJlZL1L3BBUR/wH062T1YR3UbwPO72RfNwI3dlD+ELDPRoRpZmYN5pkkzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslAY0OoBakTQKmAj0ByZFxIQGh2RmZt2wSbagJPUHvgccBYwATpE0orFRmZlZd2ySCQo4AJgXEY9HxArgFmB0g2MyM7Nu6NfW1tboGHqcpOOBURHxifz4dODAiLigs22am5ufB56sU4hmZn3ZHiNHjnzz+iptsteguquak2VmZvWzqXbxLQJ2LzweksvMzKyX2FRbUA8CwyTtSUpMJwOnNjYkMzPrjk2yBRURq4ALgJlACzAtIh5tbFRmZtYdm+QgCTMz6/02yRaUmZn1fk5QZmZWSpvqIIm6kXQjcCywOCL2yWXvBr4PbAXMBz4eEcskDQQmAfuRzv3UiPha3mY+8AqwGlgVEfuXMP43AT8A9gfWABdFxOy8zUhgMrA5MCOvq3n/cQ/GPxvYFXgj7/qIiFhc49h3B6YCOwNtwA8jYqKkHYBbgaE5/hMjYqmkfqTpu44GXgfOjIiH877GAFfkXX8lIqbUMvYaxL8aeCTv+qmI+FgJ498buIn0/3t5RFxd2Ffdp1br4fjn04D3n/VxC2rjTQZGtSubBIyNiHcBvwD+OZefADTl8pHAJyUNLWz34Yh4T51fHJOpPv5zAXL54cA3JVVeQ9fn9cPyT/t91srkDo61IfFDSmTvyT81TU7ZKuCSiBgBHAScn6fkGgvcExHDgHvyY0hTd1XO73mkc05+QxoHHEiaRWWcpO17S/zZG4VzX/PktIHxLwEuBK4u7qSBU6v1SPwFjXj/6ZIT1EaKiPtIf/iidwD35eVZwD/k5TZgS0kDSC2NFcCyesTZmW7GPwK4N2+3GHgJ2F/SrsA2ETEnt5qmAsfVOvYcx0bHX4cwOxSVpU8fAAAGUElEQVQRz1RaEBHxCmnE6WDStFyVFtAU1p3L0aRWd1tEzAG2y+f+SGBWRCyJiKWk51zzDwg9GH9DdDf+iFgcEQ8CK9vtqiFTq/Vg/KXlBFUbj7LuBXoC6740fBvwGvAM8BRwdURU3lzbgLskNUs6r57BdqCz+P8H+JikAfk7ZiPzusHAwsL2C3NZo3Q3/oqbJM2V9MXcHVU3uSX9XuB+YOeIeCavepbUhQPpnC4obFY5z52V181Gxg8wSNJDkuZIqsuHm6Iq4+9Mbzn/XSnT+89aTlC1cTbwaUnNwNaklhKkT1qrgd2APYFLJL0tr/tgROxH6iY4X9Lf1Tnmos7iv5H0z/cQ8G3gP0nPp2w2JP6P566/D+Wf0+sVrKStgNuBf4qIv2pR5xZpqb8L0kPx75G7lk4Fvi3p7T0facd8/oFyvf+s5QRVAxHxp4g4IiJGAj8F/jevOhX4TUSszF1Mvyd3MUXEovx7Mem6yQH1jzzpLP6IWBURn8391KOB7YA/k2brGFLYRUOnltqA+Ivn/xXgJ9Tp/OeBM7cDP46In+fi5ypdX/l35XpYZ1N4NWxqrx6Kv3j+Hwdmk1oDNdfN+DvTW85/p8r0/lPkBFUDkt6Sf29GGln1/bzqKeDQvG5L0oXNP0naUtLWhfIjgD/WO+6KzuKXtEWOD0mHk0b7PJa7E5ZJOih3jZ0B3NGY6Lsff+7y2ymXDySNCqz5+c/n6gagJSK+VVg1HRiTl8ew7lxOB86Q1E/SQcDL+dzPBI6QtH0eHHFELusV8ee4m/I+dwI+ADxWwvg7s3ZqtTxS9OS8j5rqqfjL9v5T5JkkNpKknwKHADsBz5FGU20FnJ+r/By4NCLaclP8JtLF+n7ATRHxjdzN94tcfwDwk4gYX8L4h5Le+NaQPiGeExFP5v3sz7ph5r8GPhP1GWa+0fHnf8r7gIGkYcJ3AxdHRE27LyV9EPh30vDqNbn4MtJ1hGnAW0m3gDkxIpbkN6TvkgZAvA6cFREP5X2dnbcFGB8RN9Uy9p6MX9L7ScP/15A+NH87Im4oYfy7kLqHt8n1XwVGRPoKw9GkbuP+wI31+P/tqfhJ/zsNef9ZHycoMzMrJXfxmZlZKTlBmZlZKTlBmZlZKTlBmZlZKTlBmZlZKXk2c7MeIuly0pexV5OG8X4yIu5vYDxvAR4ADoqIZ3PZ94CFkWfRNyszt6DMeoCk/0P6gu9+EbEv8BH+en629W3f4x8W86wAE8izV0vajzSNU2ezWVclf9HW7x1Wc25BmfWMXYEXIqIVICJeqKzI99rZPyJeyF9ovjoiDpF0JfB24G3AU3kC23Mi4tG83Wzgc6RZqr8D7EP6MvGVEXGHpPuACyNibq7/H8D5EfE/hbh+CIyR9GHgq8AFEbEyJ8T/B3wQGARcGxGTJG0D/JI0DdQA4LKI+JWkvUgzFPw3aRqiw2ngdFbWN/hTkFnPuAvYXdKfJV0n6eAqtxsBfCQiTiHdZO5EWDuH2q55pojLgXsj4gDgw8A38uwXNwBn5vrvAAa1S05ExBrgH0nztUWk25NAuh/T4rzP95EmCH0r6YaNx+WJQz8CXFPY3d7ANRExojJ3m1ktOUGZ9YCIeJV0+47zgOeBWyWdWcWm0yOichffacDxeflE0u1ZIM2NNlbSXNJEqoNI09j8DDg2zx94NmmqqY5im0uaW+26QvERwFl5n/eTWkzDSFNwTZD0B9Yl3Z3yNv9bmVrJrB7cxWfWQ/LcfbOB2ZIeIU3UOZl059PKh8FB7TZ7rbD9IkkvStoXOAn4VF7VD/iHiIj2x5Q0i3TvqxNJCbIza1g3X1tln5+OiHva7e8TwLaka2mrJC0sxPwaZnXkFpRZD1AyrFD0HtJEnQDzWZc8/oGu3Qp8Htg2Iv6Qy2YCn8mTrSKpeCuKScC1wIOR7qZbrZmke2YNKMS/OSk5Lc7J6XAae+NJ6+OcoMx6xlbAFEmP5e6xEcCVed2XgImSHmL9N3i8jXS7hmmFsqtIgyP+IOnR/BiAiGgGlpFmye+OHwB/AeZK+iNwPalH5Wbg/bkFeHKuY9YQns3crBeTtBupW3HvPCDCbJPhFpRZLyXpDNIAh8udnGxT5BaUmZmVkltQZmZWSk5QZmZWSk5QZmZWSk5QZmZWSk5QZmZWSv8fGOVD0FWlCaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppathl.hist(\"syear\")\n",
    "plt.title(\"Number of responses per survey year\")\n",
    "plt.xlabel(\"Survey Year\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"crosstab-syear-sex\"></a>\n",
    "### Crosstable between two columns: Survey year and gender\n",
    "\n",
    "The crosstable shows the gender distribution of the participants in the survey per year.\n",
    "\n",
    "Variables used:\n",
    "- `syear` (Survey Year), see: <https://paneldata.org/soep-core/data/ppathl/syear>\n",
    "- `sex` (Gender), see: <https://paneldata.org/soep-core/data/ppathl/sex> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>6007</td>\n",
       "      <td>6238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>5458</td>\n",
       "      <td>5632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>5267</td>\n",
       "      <td>5379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>5208</td>\n",
       "      <td>5308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>4955</td>\n",
       "      <td>5068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>4781</td>\n",
       "      <td>4929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>6806</td>\n",
       "      <td>7166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6663</td>\n",
       "      <td>7006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>6494</td>\n",
       "      <td>6903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>6393</td>\n",
       "      <td>6786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>6476</td>\n",
       "      <td>6941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>6640</td>\n",
       "      <td>7128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>6511</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>6420</td>\n",
       "      <td>6863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>7090</td>\n",
       "      <td>7580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>6806</td>\n",
       "      <td>7279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>11814</td>\n",
       "      <td>12762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>10722</td>\n",
       "      <td>11629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>11563</td>\n",
       "      <td>12329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>10872</td>\n",
       "      <td>11720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>10563</td>\n",
       "      <td>11449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>10083</td>\n",
       "      <td>11008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>10801</td>\n",
       "      <td>11838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>10115</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>9501</td>\n",
       "      <td>10428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>10040</td>\n",
       "      <td>10980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>12401</td>\n",
       "      <td>14697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>13276</td>\n",
       "      <td>15944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>12951</td>\n",
       "      <td>15524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>14395</td>\n",
       "      <td>17104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>12855</td>\n",
       "      <td>15090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>12740</td>\n",
       "      <td>14923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>14272</td>\n",
       "      <td>15260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>16047</td>\n",
       "      <td>16983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     male  female\n",
       "syear               \n",
       "1984    6007    6238\n",
       "1985    5458    5632\n",
       "1986    5267    5379\n",
       "1987    5208    5308\n",
       "1988    4955    5068\n",
       "1989    4781    4929\n",
       "1990    6806    7166\n",
       "1991    6663    7006\n",
       "1992    6494    6903\n",
       "1993    6393    6786\n",
       "1994    6476    6941\n",
       "1995    6640    7128\n",
       "1996    6511    7000\n",
       "1997    6420    6863\n",
       "1998    7090    7580\n",
       "1999    6806    7279\n",
       "2000   11814   12762\n",
       "2001   10722   11629\n",
       "2002   11563   12329\n",
       "2003   10872   11720\n",
       "2004   10563   11449\n",
       "2005   10083   11008\n",
       "2006   10801   11838\n",
       "2007   10115   11100\n",
       "2008    9501   10428\n",
       "2009   10040   10980\n",
       "2010   12401   14697\n",
       "2011   13276   15944\n",
       "2012   12951   15524\n",
       "2013   14395   17104\n",
       "2014   12855   15090\n",
       "2015   12740   14923\n",
       "2016   14272   15260\n",
       "2017   16047   16983"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(ppathl[\"syear\"], ppathl[\"sex\"])\n",
    "crosstab.rename(columns={1: \"male\", 2: \"female\"}, inplace=True)\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"crosstab-syear-psample\"></a>\n",
    "### Crosstable between two columns: Survey year and Sample Member\n",
    "\n",
    "The crosstable shows the Sample member distribution of the participants in the survey per year.\n",
    "\n",
    "Variables used:\n",
    "- `syear` (Survey Year), see: <https://paneldata.org/soep-core/data/ppathl/syear>\n",
    "- `psample` (Sample Member), see: <https://paneldata.org/soep-core/data/ppathl/psample>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>psample</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>9076</td>\n",
       "      <td>3169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>8372</td>\n",
       "      <td>2718</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>8009</td>\n",
       "      <td>2637</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>7868</td>\n",
       "      <td>2648</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>7481</td>\n",
       "      <td>2542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>7201</td>\n",
       "      <td>2509</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>7036</td>\n",
       "      <td>2483</td>\n",
       "      <td>4453</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6974</td>\n",
       "      <td>2493</td>\n",
       "      <td>4202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>6821</td>\n",
       "      <td>2484</td>\n",
       "      <td>4092</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>6747</td>\n",
       "      <td>2459</td>\n",
       "      <td>3973</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>6637</td>\n",
       "      <td>2364</td>\n",
       "      <td>3945</td>\n",
       "      <td>471</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>6567</td>\n",
       "      <td>2231</td>\n",
       "      <td>3892</td>\n",
       "      <td>1078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>6454</td>\n",
       "      <td>2152</td>\n",
       "      <td>3882</td>\n",
       "      <td>1023</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>6378</td>\n",
       "      <td>2089</td>\n",
       "      <td>3844</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>6184</td>\n",
       "      <td>1961</td>\n",
       "      <td>3730</td>\n",
       "      <td>885</td>\n",
       "      <td>1910</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>6045</td>\n",
       "      <td>1864</td>\n",
       "      <td>3709</td>\n",
       "      <td>838</td>\n",
       "      <td>1629</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>5852</td>\n",
       "      <td>1771</td>\n",
       "      <td>3687</td>\n",
       "      <td>837</td>\n",
       "      <td>1549</td>\n",
       "      <td>10880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>5713</td>\n",
       "      <td>1711</td>\n",
       "      <td>3576</td>\n",
       "      <td>789</td>\n",
       "      <td>1464</td>\n",
       "      <td>9098</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>5577</td>\n",
       "      <td>1598</td>\n",
       "      <td>3466</td>\n",
       "      <td>780</td>\n",
       "      <td>1373</td>\n",
       "      <td>8427</td>\n",
       "      <td>2671</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>5480</td>\n",
       "      <td>1519</td>\n",
       "      <td>3453</td>\n",
       "      <td>789</td>\n",
       "      <td>1332</td>\n",
       "      <td>8006</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>5341</td>\n",
       "      <td>1468</td>\n",
       "      <td>3435</td>\n",
       "      <td>758</td>\n",
       "      <td>1300</td>\n",
       "      <td>7724</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>5150</td>\n",
       "      <td>1422</td>\n",
       "      <td>3304</td>\n",
       "      <td>734</td>\n",
       "      <td>1240</td>\n",
       "      <td>7371</td>\n",
       "      <td>1870</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>4897</td>\n",
       "      <td>1301</td>\n",
       "      <td>3159</td>\n",
       "      <td>684</td>\n",
       "      <td>1198</td>\n",
       "      <td>6986</td>\n",
       "      <td>1798</td>\n",
       "      <td>2616</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>4752</td>\n",
       "      <td>1203</td>\n",
       "      <td>3063</td>\n",
       "      <td>658</td>\n",
       "      <td>1144</td>\n",
       "      <td>6640</td>\n",
       "      <td>1682</td>\n",
       "      <td>2073</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4509</td>\n",
       "      <td>1110</td>\n",
       "      <td>2889</td>\n",
       "      <td>602</td>\n",
       "      <td>1071</td>\n",
       "      <td>6274</td>\n",
       "      <td>1573</td>\n",
       "      <td>1901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4225</td>\n",
       "      <td>971</td>\n",
       "      <td>2765</td>\n",
       "      <td>565</td>\n",
       "      <td>1024</td>\n",
       "      <td>5816</td>\n",
       "      <td>1487</td>\n",
       "      <td>1735</td>\n",
       "      <td>2432</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3935</td>\n",
       "      <td>854</td>\n",
       "      <td>2556</td>\n",
       "      <td>488</td>\n",
       "      <td>971</td>\n",
       "      <td>5308</td>\n",
       "      <td>1437</td>\n",
       "      <td>1584</td>\n",
       "      <td>1968</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3770</td>\n",
       "      <td>4227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>3765</td>\n",
       "      <td>770</td>\n",
       "      <td>2387</td>\n",
       "      <td>457</td>\n",
       "      <td>960</td>\n",
       "      <td>4979</td>\n",
       "      <td>1357</td>\n",
       "      <td>1474</td>\n",
       "      <td>0</td>\n",
       "      <td>5161</td>\n",
       "      <td>...</td>\n",
       "      <td>3038</td>\n",
       "      <td>3385</td>\n",
       "      <td>1487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>3526</td>\n",
       "      <td>672</td>\n",
       "      <td>2261</td>\n",
       "      <td>434</td>\n",
       "      <td>160</td>\n",
       "      <td>4600</td>\n",
       "      <td>1279</td>\n",
       "      <td>1389</td>\n",
       "      <td>0</td>\n",
       "      <td>4223</td>\n",
       "      <td>...</td>\n",
       "      <td>2713</td>\n",
       "      <td>3368</td>\n",
       "      <td>1377</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3328</td>\n",
       "      <td>598</td>\n",
       "      <td>2108</td>\n",
       "      <td>398</td>\n",
       "      <td>134</td>\n",
       "      <td>4322</td>\n",
       "      <td>1257</td>\n",
       "      <td>1331</td>\n",
       "      <td>0</td>\n",
       "      <td>3800</td>\n",
       "      <td>...</td>\n",
       "      <td>2506</td>\n",
       "      <td>3301</td>\n",
       "      <td>1337</td>\n",
       "      <td>4964</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3184</td>\n",
       "      <td>571</td>\n",
       "      <td>2004</td>\n",
       "      <td>365</td>\n",
       "      <td>128</td>\n",
       "      <td>4044</td>\n",
       "      <td>1166</td>\n",
       "      <td>1256</td>\n",
       "      <td>0</td>\n",
       "      <td>3495</td>\n",
       "      <td>...</td>\n",
       "      <td>2307</td>\n",
       "      <td>2564</td>\n",
       "      <td>1078</td>\n",
       "      <td>3822</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2980</td>\n",
       "      <td>517</td>\n",
       "      <td>1851</td>\n",
       "      <td>337</td>\n",
       "      <td>110</td>\n",
       "      <td>3772</td>\n",
       "      <td>1088</td>\n",
       "      <td>1162</td>\n",
       "      <td>0</td>\n",
       "      <td>3279</td>\n",
       "      <td>...</td>\n",
       "      <td>2209</td>\n",
       "      <td>2601</td>\n",
       "      <td>1106</td>\n",
       "      <td>3126</td>\n",
       "      <td>1711</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2747</td>\n",
       "      <td>438</td>\n",
       "      <td>1745</td>\n",
       "      <td>291</td>\n",
       "      <td>102</td>\n",
       "      <td>3451</td>\n",
       "      <td>1039</td>\n",
       "      <td>1068</td>\n",
       "      <td>0</td>\n",
       "      <td>3096</td>\n",
       "      <td>...</td>\n",
       "      <td>2091</td>\n",
       "      <td>2423</td>\n",
       "      <td>1024</td>\n",
       "      <td>2775</td>\n",
       "      <td>1104</td>\n",
       "      <td>2205</td>\n",
       "      <td>2239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2560</td>\n",
       "      <td>373</td>\n",
       "      <td>1618</td>\n",
       "      <td>275</td>\n",
       "      <td>104</td>\n",
       "      <td>3218</td>\n",
       "      <td>976</td>\n",
       "      <td>992</td>\n",
       "      <td>0</td>\n",
       "      <td>2941</td>\n",
       "      <td>...</td>\n",
       "      <td>1988</td>\n",
       "      <td>2419</td>\n",
       "      <td>1032</td>\n",
       "      <td>2533</td>\n",
       "      <td>941</td>\n",
       "      <td>1495</td>\n",
       "      <td>1938</td>\n",
       "      <td>2252</td>\n",
       "      <td>3770</td>\n",
       "      <td>33030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>191371</td>\n",
       "      <td>57670</td>\n",
       "      <td>89049</td>\n",
       "      <td>15508</td>\n",
       "      <td>18903</td>\n",
       "      <td>110916</td>\n",
       "      <td>24679</td>\n",
       "      <td>18581</td>\n",
       "      <td>4400</td>\n",
       "      <td>25995</td>\n",
       "      <td>...</td>\n",
       "      <td>20622</td>\n",
       "      <td>24288</td>\n",
       "      <td>8441</td>\n",
       "      <td>17220</td>\n",
       "      <td>3756</td>\n",
       "      <td>3700</td>\n",
       "      <td>4177</td>\n",
       "      <td>2252</td>\n",
       "      <td>3770</td>\n",
       "      <td>656960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "psample       1      2      3      4      5       6      7      8     9  \\\n",
       "syear                                                                     \n",
       "1984       9076   3169      0      0      0       0      0      0     0   \n",
       "1985       8372   2718      0      0      0       0      0      0     0   \n",
       "1986       8009   2637      0      0      0       0      0      0     0   \n",
       "1987       7868   2648      0      0      0       0      0      0     0   \n",
       "1988       7481   2542      0      0      0       0      0      0     0   \n",
       "1989       7201   2509      0      0      0       0      0      0     0   \n",
       "1990       7036   2483   4453      0      0       0      0      0     0   \n",
       "1991       6974   2493   4202      0      0       0      0      0     0   \n",
       "1992       6821   2484   4092      0      0       0      0      0     0   \n",
       "1993       6747   2459   3973      0      0       0      0      0     0   \n",
       "1994       6637   2364   3945    471      0       0      0      0     0   \n",
       "1995       6567   2231   3892   1078      0       0      0      0     0   \n",
       "1996       6454   2152   3882   1023      0       0      0      0     0   \n",
       "1997       6378   2089   3844    972      0       0      0      0     0   \n",
       "1998       6184   1961   3730    885   1910       0      0      0     0   \n",
       "1999       6045   1864   3709    838   1629       0      0      0     0   \n",
       "2000       5852   1771   3687    837   1549   10880      0      0     0   \n",
       "2001       5713   1711   3576    789   1464    9098      0      0     0   \n",
       "2002       5577   1598   3466    780   1373    8427   2671      0     0   \n",
       "2003       5480   1519   3453    789   1332    8006   2013      0     0   \n",
       "2004       5341   1468   3435    758   1300    7724   1986      0     0   \n",
       "2005       5150   1422   3304    734   1240    7371   1870      0     0   \n",
       "2006       4897   1301   3159    684   1198    6986   1798   2616     0   \n",
       "2007       4752   1203   3063    658   1144    6640   1682   2073     0   \n",
       "2008       4509   1110   2889    602   1071    6274   1573   1901     0   \n",
       "2009       4225    971   2765    565   1024    5816   1487   1735  2432   \n",
       "2010       3935    854   2556    488    971    5308   1437   1584  1968   \n",
       "2011       3765    770   2387    457    960    4979   1357   1474     0   \n",
       "2012       3526    672   2261    434    160    4600   1279   1389     0   \n",
       "2013       3328    598   2108    398    134    4322   1257   1331     0   \n",
       "2014       3184    571   2004    365    128    4044   1166   1256     0   \n",
       "2015       2980    517   1851    337    110    3772   1088   1162     0   \n",
       "2016       2747    438   1745    291    102    3451   1039   1068     0   \n",
       "2017       2560    373   1618    275    104    3218    976    992     0   \n",
       "All      191371  57670  89049  15508  18903  110916  24679  18581  4400   \n",
       "\n",
       "psample     10  ...     12     13    14     15    16    17    18    19    20  \\\n",
       "syear           ...                                                            \n",
       "1984         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1985         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1986         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1987         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1988         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1989         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1990         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1991         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1992         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1993         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1994         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1995         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1996         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1997         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1998         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "1999         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2000         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2001         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2002         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2003         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2004         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2005         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2006         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2007         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2008         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2009         0  ...      0      0     0      0     0     0     0     0     0   \n",
       "2010         0  ...   3770   4227     0      0     0     0     0     0     0   \n",
       "2011      5161  ...   3038   3385  1487      0     0     0     0     0     0   \n",
       "2012      4223  ...   2713   3368  1377      0     0     0     0     0     0   \n",
       "2013      3800  ...   2506   3301  1337   4964     0     0     0     0     0   \n",
       "2014      3495  ...   2307   2564  1078   3822     0     0     0     0     0   \n",
       "2015      3279  ...   2209   2601  1106   3126  1711     0     0     0     0   \n",
       "2016      3096  ...   2091   2423  1024   2775  1104  2205  2239     0     0   \n",
       "2017      2941  ...   1988   2419  1032   2533   941  1495  1938  2252  3770   \n",
       "All      25995  ...  20622  24288  8441  17220  3756  3700  4177  2252  3770   \n",
       "\n",
       "psample     All  \n",
       "syear            \n",
       "1984      12245  \n",
       "1985      11090  \n",
       "1986      10646  \n",
       "1987      10516  \n",
       "1988      10023  \n",
       "1989       9710  \n",
       "1990      13972  \n",
       "1991      13669  \n",
       "1992      13397  \n",
       "1993      13179  \n",
       "1994      13417  \n",
       "1995      13768  \n",
       "1996      13511  \n",
       "1997      13283  \n",
       "1998      14670  \n",
       "1999      14085  \n",
       "2000      24576  \n",
       "2001      22351  \n",
       "2002      23892  \n",
       "2003      22592  \n",
       "2004      22012  \n",
       "2005      21091  \n",
       "2006      22639  \n",
       "2007      21215  \n",
       "2008      19929  \n",
       "2009      21020  \n",
       "2010      27098  \n",
       "2011      29220  \n",
       "2012      28475  \n",
       "2013      31499  \n",
       "2014      27945  \n",
       "2015      27663  \n",
       "2016      29532  \n",
       "2017      33030  \n",
       "All      656960  \n",
       "\n",
       "[35 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(ppathl[\"syear\"], ppathl[\"psample\"], margins=True)\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"dataset-pgen\"></a>\n",
    "[Return to top](#top)\n",
    "## Dataset: `pgen.dta` (Generated Individual Data)\n",
    "\n",
    "From <https://paneldata.org/soep-core/data/pgen>:\n",
    "\n",
    "> Contains individual-level generated data to ensure complete and consitent information over time. It is keyed on PID (Person ID) and SYEAR (Survey Year), see http://www.diw.de/documents/dokumentenarchiv/17/diw_01.c.432040.de/diw_ssp0149_pgen.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxr-xr-x 1 afuetterer afuetterer 133M Mär  6 12:37 data/pgen.dta\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lh $DATASETS_DIRECTORY/pgen.dta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(657963, 84)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = DATASETS_DIRECTORY / \"pgen.dta\"\n",
    "pgen = pd.read_stata(filename, convert_categoricals=False)\n",
    "pgen.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pid', 'hid', 'cid', 'sample1', 'syear', 'pgnation', 'pgstatus_asyl',\n",
       "       'pgstatus_refu', 'pgpartz', 'pgpartnr', 'pgfamstd', 'pglabgro',\n",
       "       'pgimpgro', 'pglabnet', 'pgimpnet', 'pgsndjob', 'pgimpsnd', 'pgstib',\n",
       "       'pgemplst', 'pglfs', 'pgjobch', 'pgisco88', 'pgisco08', 'pgisei88',\n",
       "       'pgkldb92', 'pgkldb2010', 'pgmps92', 'pgsiops88', 'pgegp88', 'pgerljob',\n",
       "       'pgausb', 'pgerwzeit', 'pgtatzeit', 'pgvebzeit', 'pguebstd', 'pgoeffd',\n",
       "       'pgnace', 'pgnace2', 'pgbetr', 'pgallbet', 'pgjobend', 'pgexpft',\n",
       "       'pgexppt', 'pgexpue', 'pgisced97', 'pgisced11', 'pgcasmin', 'pgbilzeit',\n",
       "       'pgpsbil', 'pgpbbil01', 'pgpbbil02', 'pgpbbil03', 'pgpsbilo',\n",
       "       'pgpbbilo', 'pgpsbila', 'pgpbbila', 'pgfield', 'pgdegree', 'pgtraina',\n",
       "       'pgtrainb', 'pgtrainc', 'pgtraind', 'pgfdt_f', 'pgbilztch', 'pgbilztev',\n",
       "       'pgpicoriga', 'pgpicorigb', 'pgpicorigc', 'pgsndtyp1', 'pgsndtyp2',\n",
       "       'pghonor1', 'pghonor2', 'pghonor3', 'pgimpsnd3', 'pgimpsnd2',\n",
       "       'pgsndtyp3', 'pgsndjob2', 'pgsndjob3', 'pgimpsnd1', 'pgsndjob1',\n",
       "       'pgmonth', 'pgpiyear', 'pgmode', 'inputdataset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pgen.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"variable-pgtatzeit\"></a>\n",
    "### Variable: `pgtatzeit` (Actual Work Time Per Week)\n",
    "\n",
    "see: <https://paneldata.org/soep-core/data/pgen/pgtatzeit>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set values to NaN if condition applies\n",
    "\n",
    "Set the negative values for `pgtatzeit` to NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set to NaN\n",
    "pgen.loc[pgen[\"pgtatzeit\"] < 0, \"pgtatzeit\"] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"hist-pgtatzeit\"></a>\n",
    "### Histogram: Actual Work Time Per Week\n",
    "Generate a histogram of the variable `pgtatzeit` (Actual Work Time Per Week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "pgen.hist(\"pgtatzeit\", bins=15, ax=ax)\n",
    "plt.title(\"Actual Work Time Per Week\")\n",
    "plt.xlabel(\"Actual work time per week in hours\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.tight_layout()\n",
    "\n",
    "# Write plot to disk as .png\n",
    "# fig.savefig('pgtatzeit.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"describe-pgtatzeit\"></a>\n",
    "### Describe: Actual Work Time Per Week\n",
    "Calculate summary statistics of one column in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    366282.000000\n",
       "mean         38.033905\n",
       "std          13.102446\n",
       "min           0.100000\n",
       "25%          35.000000\n",
       "50%          40.000000\n",
       "75%          45.000000\n",
       "max          80.000000\n",
       "Name: pgtatzeit, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pgen[\"pgtatzeit\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"describe-pgtatzeit-syear\"></a>\n",
    "### Describe: Actual Work Time Per Week grouped by Survey Year\n",
    "\n",
    "Calculate summary statistics of one column in the dataframe grouped by another column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>6557.0</td>\n",
       "      <td>39.947231</td>\n",
       "      <td>11.896158</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>6005.0</td>\n",
       "      <td>39.559200</td>\n",
       "      <td>12.489210</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>5818.0</td>\n",
       "      <td>39.862667</td>\n",
       "      <td>11.961640</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>5907.0</td>\n",
       "      <td>39.604877</td>\n",
       "      <td>11.480971</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>5612.0</td>\n",
       "      <td>38.888630</td>\n",
       "      <td>12.154338</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>5521.0</td>\n",
       "      <td>39.702408</td>\n",
       "      <td>11.167662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>8607.0</td>\n",
       "      <td>41.332378</td>\n",
       "      <td>11.091295</td>\n",
       "      <td>0.2</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>8039.0</td>\n",
       "      <td>39.533417</td>\n",
       "      <td>11.240531</td>\n",
       "      <td>0.2</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>7650.0</td>\n",
       "      <td>39.936325</td>\n",
       "      <td>10.844688</td>\n",
       "      <td>0.6</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>7406.0</td>\n",
       "      <td>39.938114</td>\n",
       "      <td>11.197069</td>\n",
       "      <td>0.1</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>7367.0</td>\n",
       "      <td>39.768673</td>\n",
       "      <td>11.263668</td>\n",
       "      <td>0.5</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>7746.0</td>\n",
       "      <td>39.462238</td>\n",
       "      <td>11.895111</td>\n",
       "      <td>0.4</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>7467.0</td>\n",
       "      <td>39.461781</td>\n",
       "      <td>11.619616</td>\n",
       "      <td>0.5</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>7330.0</td>\n",
       "      <td>39.901066</td>\n",
       "      <td>11.923630</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>7949.0</td>\n",
       "      <td>39.618065</td>\n",
       "      <td>12.098083</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>8040.0</td>\n",
       "      <td>39.002613</td>\n",
       "      <td>12.610220</td>\n",
       "      <td>0.5</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>13488.0</td>\n",
       "      <td>38.964859</td>\n",
       "      <td>12.948785</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>12456.0</td>\n",
       "      <td>38.613453</td>\n",
       "      <td>13.071692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>13480.0</td>\n",
       "      <td>38.735222</td>\n",
       "      <td>13.393473</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>12539.0</td>\n",
       "      <td>38.218460</td>\n",
       "      <td>13.394451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>12009.0</td>\n",
       "      <td>38.013432</td>\n",
       "      <td>13.355294</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>11374.0</td>\n",
       "      <td>38.116394</td>\n",
       "      <td>13.430831</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>12012.0</td>\n",
       "      <td>38.314751</td>\n",
       "      <td>13.606622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>11449.0</td>\n",
       "      <td>38.257710</td>\n",
       "      <td>13.615576</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>10861.0</td>\n",
       "      <td>38.091354</td>\n",
       "      <td>13.467873</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>11432.0</td>\n",
       "      <td>37.969391</td>\n",
       "      <td>13.727776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>15167.0</td>\n",
       "      <td>36.801510</td>\n",
       "      <td>14.270864</td>\n",
       "      <td>0.4</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>16784.0</td>\n",
       "      <td>37.058300</td>\n",
       "      <td>14.067155</td>\n",
       "      <td>0.5</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>16481.0</td>\n",
       "      <td>36.640736</td>\n",
       "      <td>13.885223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>18744.0</td>\n",
       "      <td>36.176109</td>\n",
       "      <td>13.855709</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>16597.0</td>\n",
       "      <td>35.886395</td>\n",
       "      <td>13.658179</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>16260.0</td>\n",
       "      <td>36.046112</td>\n",
       "      <td>13.453383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>14957.0</td>\n",
       "      <td>35.909332</td>\n",
       "      <td>13.366645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>17171.0</td>\n",
       "      <td>35.775677</td>\n",
       "      <td>13.278804</td>\n",
       "      <td>0.6</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count       mean        std  min        25%   50%   75%   max\n",
       "syear                                                                 \n",
       "1984    6557.0  39.947231  11.896158  1.0  40.000000  40.0  44.0  80.0\n",
       "1985    6005.0  39.559200  12.489210  1.0  40.000000  40.0  45.0  80.0\n",
       "1986    5818.0  39.862667  11.961640  1.0  39.000000  40.0  45.0  80.0\n",
       "1987    5907.0  39.604877  11.480971  1.0  39.000000  40.0  44.0  80.0\n",
       "1988    5612.0  38.888630  12.154338  1.0  38.000000  40.0  44.0  80.0\n",
       "1989    5521.0  39.702408  11.167662  1.0  38.000000  40.0  44.0  80.0\n",
       "1990    8607.0  41.332378  11.091295  0.2  38.000000  40.0  48.0  80.0\n",
       "1991    8039.0  39.533417  11.240531  0.2  37.500000  40.0  44.0  80.0\n",
       "1992    7650.0  39.936325  10.844688  0.6  37.500000  40.0  45.0  80.0\n",
       "1993    7406.0  39.938114  11.197069  0.1  37.500000  40.0  45.0  80.0\n",
       "1994    7367.0  39.768673  11.263668  0.5  37.500000  40.0  45.0  80.0\n",
       "1995    7746.0  39.462238  11.895111  0.4  37.500000  40.0  45.0  80.0\n",
       "1996    7467.0  39.461781  11.619616  0.5  37.500000  40.0  45.0  80.0\n",
       "1997    7330.0  39.901066  11.923630  1.0  37.500000  40.0  45.0  80.0\n",
       "1998    7949.0  39.618065  12.098083  2.0  37.000000  40.0  45.0  80.0\n",
       "1999    8040.0  39.002613  12.610220  0.5  37.000000  40.0  45.0  80.0\n",
       "2000   13488.0  38.964859  12.948785  1.0  36.000000  40.0  45.0  80.0\n",
       "2001   12456.0  38.613453  13.071692  1.0  35.000000  40.0  45.0  80.0\n",
       "2002   13480.0  38.735222  13.393473  1.0  35.000000  40.0  45.0  80.0\n",
       "2003   12539.0  38.218460  13.394451  1.0  35.000000  40.0  45.0  80.0\n",
       "2004   12009.0  38.013432  13.355294  1.0  34.000000  40.0  45.0  80.0\n",
       "2005   11374.0  38.116394  13.430831  1.0  33.000000  40.0  45.0  80.0\n",
       "2006   12012.0  38.314751  13.606622  1.0  32.000000  40.0  45.0  80.0\n",
       "2007   11449.0  38.257710  13.615576  1.0  32.000000  40.0  45.0  80.0\n",
       "2008   10861.0  38.091354  13.467873  1.0  32.000000  40.0  45.0  80.0\n",
       "2009   11432.0  37.969391  13.727776  1.0  30.000000  40.0  45.0  80.0\n",
       "2010   15167.0  36.801510  14.270864  0.4  30.000000  40.0  45.0  80.0\n",
       "2011   16784.0  37.058300  14.067155  0.5  30.000000  40.0  45.0  80.0\n",
       "2012   16481.0  36.640736  13.885223  1.0  30.000000  40.0  45.0  80.0\n",
       "2013   18744.0  36.176109  13.855709  1.0  28.799999  40.0  45.0  80.0\n",
       "2014   16597.0  35.886395  13.658179  1.0  28.000000  40.0  44.0  80.0\n",
       "2015   16260.0  36.046112  13.453383  1.0  30.000000  40.0  44.0  80.0\n",
       "2016   14957.0  35.909332  13.366645  1.0  30.000000  40.0  44.0  80.0\n",
       "2017   17171.0  35.775677  13.278804  0.6  30.000000  40.0  43.0  80.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pgen.groupby(\"syear\")[\"pgtatzeit\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"merge-datasets\"></a>\n",
    "## Merge Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge two datasets based on shared columns\n",
    "\n",
    "Two datasets can be merged on columns, that exist in both datasets. We will merge `ppathl` (Individual Tracking File) and `pgen` (Generated Individual Data) on `pid` (Never Changing Person ID), `syear` (Survey Year) and `hid` (ID Household), which exist in both datasets.\n",
    "\n",
    "see: \n",
    "- <https://paneldata.org/soep-core/data/ppathl/pid>\n",
    "- <https://paneldata.org/soep-core/data/ppathl/syear>\n",
    "- <https://paneldata.org/soep-core/data/ppathl/hid>\n",
    "- <https://paneldata.org/soep-core/data/pgen/pid>\n",
    "- <https://paneldata.org/soep-core/data/pgen/syear>\n",
    "- <https://paneldata.org/soep-core/data/pgen/hid>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(656960, 129)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_on = [\"pid\", \"syear\", \"hid\"]\n",
    "ppathl_pgen = pd.merge(ppathl, pgen, on=merge_on)\n",
    "# dimensions of merged dataset\n",
    "ppathl_pgen.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set values to NaN if condition applies\n",
    "Set the negative values for `sex` to NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set to NaN\n",
    "ppathl_pgen.loc[ppathl_pgen[\"sex\"] < 0, \"sex\"] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pid', 'syear', 'cid_x', 'sex', 'gebjahr', 'eintritt', 'erstbefr',\n",
       "       'austritt', 'psample', 'letztbef',\n",
       "       ...\n",
       "       'pgimpsnd2', 'pgsndtyp3', 'pgsndjob2', 'pgsndjob3', 'pgimpsnd1',\n",
       "       'pgsndjob1', 'pgmonth', 'pgpiyear', 'pgmode', 'inputdataset'],\n",
       "      dtype='object', length=129)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppathl_pgen.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"boxplot-pgtatzeit-sex\"></a>\n",
    "### Boxplot: \"Actual Work Time Per Week\" and \"Gender\"\n",
    "\n",
    "The boxplot shows the distibrution of Actual Work Time Per Week based on gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppathl_pgen.boxplot(\n",
    "    column=\"pgtatzeit\",\n",
    "    by=\"sex\"\n",
    ")\n",
    "plt.title(\"Actual Work Time Per Week grouped by Gender\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Actual work time per week in hours\")\n",
    "plt.suptitle(\"\")\n",
    "plt.xticks([1, 2], ['male', 'female'])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"boxplot-pgtatzeit-syear\"></a>\n",
    "### Boxplot: \"Actual Work Time Per Week\" and \"Survey Year\"\n",
    "\n",
    "The boxplot shows the distibrution of Actual Work Time Per Week based on Survey Year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppathl_pgen.boxplot(\n",
    "    column=\"pgtatzeit\",\n",
    "    by=\"syear\"\n",
    ")\n",
    "plt.title(\"Actual Work Time Per Week grouped by Survey Year\")\n",
    "plt.xlabel(\"Survey Year\")\n",
    "plt.ylabel(\"Actual work time per week in hours\")\n",
    "plt.suptitle(\"\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"boxplot-pgtatzeit-syear\"></a>\n",
    "### Boxplot: \"Actual Work Time Per Week\" and \"Survey Year\" grouped by \"Gender\"\n",
    "\n",
    "The boxplot shows the distibrution of Actual Work Time Per Week based on Survey Year grouped by \"Gender\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax = sns.boxplot(\n",
    "    x=\"syear\",\n",
    "    y=\"pgtatzeit\", \n",
    "    hue=\"sex\",\n",
    "    data=ppathl_pgen\n",
    ")\n",
    "plt.title(\"Actual Work Time Per Week grouped by Survey Year\")\n",
    "plt.xlabel(\"Survey Year\")\n",
    "plt.ylabel(\"Actual work time per week in hours\")\n",
    "\n",
    "handles, _ = ax.get_legend_handles_labels()\n",
    "plt.legend(handles, ['male', 'female'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between two columns\n",
    "Calculate the correlation between the variables `pgtatzeit` and `sex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pgtatzeit</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pgtatzeit</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.404648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.404648</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pgtatzeit       sex\n",
       "pgtatzeit   1.000000 -0.404648\n",
       "sex        -0.404648  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppathl_pgen[[\"pgtatzeit\", \"sex\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"dataset-hgen\"></a>\n",
    "[Return to top](#top)\n",
    "## Dataset: `hgen.dta` (Generated Household Data)\n",
    "\n",
    "From <https://paneldata.org/soep-core/data/hgen>:\n",
    "\n",
    "> Contains household-level generated data to ensure complete and consitent information over time. It is keyed on HID (Household ID) and SYEAR (Survey Year), see http://www.diw.de/documents/dokumentenarchiv/17/diw_01.c.432036.de/diw_ssp0151_hgen.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxr-xr-x 1 afuetterer afuetterer 39M Mär  6 12:36 data/hgen.dta\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lh $DATASETS_DIRECTORY/hgen.dta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356864, 68)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = DATASETS_DIRECTORY / \"hgen.dta\"\n",
    "hgen = pd.read_stata(filename, convert_categoricals=False)\n",
    "hgen.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hid', 'cid', 'syear', 'iyear', 'hgacquis', 'hgcnstyrmax',\n",
       "       'hgcnstyrmin', 'hgcondit', 'hgelectr', 'hgelectrinfo', 'hgeqpair',\n",
       "       'hgeqpalm', 'hgeqpbas', 'hgeqpfhea', 'hgeqpgar', 'hgeqphea',\n",
       "       'hgeqpinsul', 'hgeqpiwc', 'hgeqpkit', 'hgeqplif', 'hgeqpmglass',\n",
       "       'hgeqpnobar', 'hgeqpnrj', 'hgeqppark', 'hgeqpshw', 'hgeqpsol',\n",
       "       'hgeqptel', 'hgeqpter', 'hgeqpwat', 'hgf2rent', 'hgfelectr', 'hgfheat',\n",
       "       'hgfhinc', 'hgfrent', 'hgfroom', 'hgfsize', 'hgutil', 'hgutilinfo',\n",
       "       'hgfutil', 'hggas', 'hggasinfo', 'hgheat', 'hgheatinfo', 'hghinc',\n",
       "       'hghmode', 'hghmonth', 'hgi1hinc', 'hgi2hinc', 'hgi3hinc', 'hgi4hinc',\n",
       "       'hgi5hinc', 'hgmoveyr', 'hgnorent', 'hgnuts1', 'hgosubs', 'hgowner',\n",
       "       'hgreduc', 'hgrent', 'hgrentinfo', 'hgreval', 'hgroom', 'hgrsubs',\n",
       "       'hgseval', 'hgsize', 'hgsubsid', 'hgtyp1hh', 'hgtyp2hh',\n",
       "       'inputdataset'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hgen.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge two datasets based on shared columns\n",
    "\n",
    "We will merge the result of the last merge `ppathl_pgen` and `hgen` (Generated Individual Data) on `hid` (ID Household) and `syear` (Survey Year), which exist in both datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(656960, 195)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_on = [\"hid\", \"syear\"]\n",
    "ppathl_pgen_hgen = pd.merge(ppathl_pgen, hgen, on=merge_on)\n",
    "# dimensions of merged dataset\n",
    "ppathl_pgen_hgen.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"variable-hgtyp1hh\"></a>\n",
    "### Variable: `hgtyp1hh` (Household Typology, One Digit)\n",
    "\n",
    "see: <https://paneldata.org/soep-core/data/hgen/hgtyp1hh>\n",
    "\n",
    "Value labels:\n",
    "\n",
    "- \t[1] 1-Pers.-HH\n",
    "-\t[2] Couple Without Children\n",
    "-\t[3] Single Parent\n",
    "-\t[4] Couple With Children LE 16\n",
    "-\t[5] Couple With Children GT 16\n",
    "-\t[6] Couple With Children LE And GT 16\n",
    "-\t[7] Multiple Generation-HH\n",
    "-\t[8] Other Combination\n",
    "-\t[9] No Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "hgtyp1hh_labels = {\n",
    "    1: \"1-Pers.-HH\",\n",
    "    2: \"Couple Without Children\",\n",
    "    3: \"Single Parent\",\n",
    "    4: \"Couple With Children LE 16\",\n",
    "    5: \"Couple With Children GT 16\",\n",
    "    6: \"Couple With Children LE And GT 16\",\n",
    "    7: \"Multiple Generation-HH\",\n",
    "    8: \"Other Combination\",\n",
    "    9: \"Answer\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hgtyp1hh\n",
       "1-Pers.-HH                            85024\n",
       "Couple Without Children              189577\n",
       "Single Parent                         43756\n",
       "Couple With Children LE 16           151164\n",
       "Couple With Children GT 16           103382\n",
       "Couple With Children LE And GT 16     57845\n",
       "Multiple Generation-HH                14349\n",
       "Other Combination                     11863\n",
       "Name: pid, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hgtyp1hh_counts = ppathl_pgen_hgen.groupby(\"hgtyp1hh\")[\"pid\"].count()\n",
    "hgtyp1hh_counts.rename(hgtyp1hh_labels, inplace=True)\n",
    "hgtyp1hh_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new variable: `kindhh`\n",
    "\n",
    "If `hgtyp1hh` (Household Typology, One Digit) is 1, 2 or 3 the housegold has no children. We create a new variable `kindhh` that is 0 if `hgtyp1hh` is 3 or less and 1 otherwise.\n",
    "\n",
    "Value labels:\n",
    "\n",
    "- \t[0] Household has no children\n",
    "-\t[1] Household has at least one child"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppathl_pgen_hgen['kindhh'] = np.where(ppathl_pgen_hgen['hgtyp1hh'] <= 3, 0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modify existing variable `sex`\n",
    "\n",
    "If `sex` (Gender) is 1 or 2 for \"male\" or \"female\". To train a statistical model that needs 0 or 1 as values, we substact the column by 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppathl_pgen_hgen[\"sex\"] = ppathl_pgen_hgen[\"sex\"].astype(np.int64)\n",
    "ppathl_pgen_hgen[\"sex\"] = ppathl_pgen_hgen[\"sex\"] - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"crosstab-hgtyp1hh-kindhh\"></a>\n",
    "### Crosstable between two columns: hgtyp1hh and kindhh\n",
    "\n",
    "The crosstable shows that the new variable `kindhh` is 0 for households without children and 1 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>kindhh</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1-Pers.-HH</th>\n",
       "      <td>85024</td>\n",
       "      <td>0</td>\n",
       "      <td>85024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Couple Without Children</th>\n",
       "      <td>189577</td>\n",
       "      <td>0</td>\n",
       "      <td>189577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Parent</th>\n",
       "      <td>43756</td>\n",
       "      <td>0</td>\n",
       "      <td>43756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Couple With Children LE 16</th>\n",
       "      <td>0</td>\n",
       "      <td>151164</td>\n",
       "      <td>151164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Couple With Children GT 16</th>\n",
       "      <td>0</td>\n",
       "      <td>103382</td>\n",
       "      <td>103382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Couple With Children LE And GT 16</th>\n",
       "      <td>0</td>\n",
       "      <td>57845</td>\n",
       "      <td>57845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Generation-HH</th>\n",
       "      <td>0</td>\n",
       "      <td>14349</td>\n",
       "      <td>14349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Combination</th>\n",
       "      <td>0</td>\n",
       "      <td>11863</td>\n",
       "      <td>11863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>318357</td>\n",
       "      <td>338603</td>\n",
       "      <td>656960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "kindhh                                  0       1     All\n",
       "1-Pers.-HH                          85024       0   85024\n",
       "Couple Without Children            189577       0  189577\n",
       "Single Parent                       43756       0   43756\n",
       "Couple With Children LE 16              0  151164  151164\n",
       "Couple With Children GT 16              0  103382  103382\n",
       "Couple With Children LE And GT 16       0   57845   57845\n",
       "Multiple Generation-HH                  0   14349   14349\n",
       "Other Combination                       0   11863   11863\n",
       "All                                318357  338603  656960"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crosstab = pd.crosstab(ppathl_pgen_hgen[\"hgtyp1hh\"], ppathl_pgen_hgen[\"kindhh\"], margins=True)\n",
    "crosstab.index = list(hgtyp1hh_labels.values())[:8] + [\"All\"]\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for statistical modelling\n",
    "\n",
    "To estimate model parameters for a Pooled OLS we need to prepare the data. We select only the columns of the merged datasets that we want to take into consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ppathl_pgen_hgen[[\"pid\", \"syear\", \"pgtatzeit\", \"sex\", \"kindhh\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we set the index for a row to be the combination of `pid` and `syear`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index([\"pid\", \"syear\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"pooled-ols-1\"></a>\n",
    "## Pooled coefficient estimator for panel data\n",
    "\n",
    "see: <https://bashtage.github.io/linearmodels/doc/panel/models.html#linearmodels.panel.model.PooledOLS>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PooledOLS is used to analyze if there is a connection between `pgtatzeit` and `sex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/afuetterer/.local/share/virtualenvs/soep-notebooks-jfsdctRl/lib/python3.7/site-packages/linearmodels/utility.py:476: MissingValueWarning: \n",
      "Inputs contain missing values. Dropping rows with missing observations.\n",
      "  warnings.warn(missing_value_warning_msg, MissingValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PooledOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>pgtatzeit</td>    <th>  R-squared:         </th>     <td>0.1637</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PooledOLS</td>    <th>  R-squared (Between):</th>    <td>0.1666</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>365918</td>      <th>  R-squared (Within):</th>     <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, May 08 2019</td> <th>  R-squared (Overall):</th>    <td>0.1637</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:31:09</td>     <th>  Log-likelihood     </th>   <td>-1.428e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>      <td>Unadjusted</td>    <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>7.165e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>61817</td>      <th>  P-value            </th>     <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>5.9194</td>      <th>  Distribution:      </th>   <td>F(1,365916)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>1.0000</td>      <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>34.000</td>      <th>  F-statistic (robust):</th>  <td>7.165e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>     <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>            <td>34</td>        <th>  Distribution:      </th>   <td>F(1,365916)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>              <td>1.076e+04</td>    <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>5521.0</td>      <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>              <td>1.873e+04</td>    <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "      <td></td>      <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>42.953</td>    <td>0.0270</td>   <td>1590.0</td>  <td>0.0000</td>   <td>42.900</td>   <td>43.006</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex</th>        <td>-10.634</td>   <td>0.0397</td>   <td>-267.67</td> <td>0.0000</td>   <td>-10.712</td>  <td>-10.556</td>\n",
       "</tr>\n",
       "</table><br/>id: 0x7fcb152b97f0"
      ],
      "text/plain": [
       "                          PooledOLS Estimation Summary                          \n",
       "================================================================================\n",
       "Dep. Variable:              pgtatzeit   R-squared:                        0.1637\n",
       "Estimator:                  PooledOLS   R-squared (Between):              0.1666\n",
       "No. Observations:              365918   R-squared (Within):               0.0000\n",
       "Date:                Wed, May 08 2019   R-squared (Overall):              0.1637\n",
       "Time:                        15:31:09   Log-likelihood                -1.428e+06\n",
       "Cov. Estimator:            Unadjusted                                           \n",
       "                                        F-statistic:                   7.165e+04\n",
       "Entities:                       61817   P-value                           0.0000\n",
       "Avg Obs:                       5.9194   Distribution:                F(1,365916)\n",
       "Min Obs:                       1.0000                                           \n",
       "Max Obs:                       34.000   F-statistic (robust):          7.165e+04\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                      34   Distribution:                F(1,365916)\n",
       "Avg Obs:                    1.076e+04                                           \n",
       "Min Obs:                       5521.0                                           \n",
       "Max Obs:                    1.873e+04                                           \n",
       "                                                                                \n",
       "                             Parameter Estimates                              \n",
       "==============================================================================\n",
       "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      42.953     0.0270     1590.0     0.0000      42.900      43.006\n",
       "sex           -10.634     0.0397    -267.67     0.0000     -10.712     -10.556\n",
       "==============================================================================\n",
       "PanelResults, id: 0x7fcb152b97f0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = PooledOLS.from_formula(\n",
    "    'pgtatzeit ~ sex + 1',\n",
    "    data=data\n",
    ")\n",
    "model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**:\n",
    "\n",
    "On average the gender variable is responsible for a difference of 10 hours, i.e. on average women work 10 hours less than men."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PooledOLS is used to analyze if there is a connection between `pgtatzeit` and `sex` and `kindhh`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>PooledOLS Estimation Summary</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>pgtatzeit</td>    <th>  R-squared:         </th>     <td>0.1688</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Estimator:</th>            <td>PooledOLS</td>    <th>  R-squared (Between):</th>    <td>0.1705</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>365918</td>      <th>  R-squared (Within):</th>     <td>0.0033</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, May 08 2019</td> <th>  R-squared (Overall):</th>    <td>0.1688</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:31:10</td>     <th>  Log-likelihood     </th>   <td>-1.427e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cov. Estimator:</th>      <td>Unadjusted</td>    <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  F-statistic:       </th>    <td>3.717e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Entities:</th>               <td>61817</td>      <th>  P-value            </th>     <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>               <td>5.9194</td>      <th>  Distribution:      </th>   <td>F(2,365915)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>1.0000</td>      <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>               <td>34.000</td>      <th>  F-statistic (robust):</th>  <td>3.717e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>  P-value            </th>     <td>0.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time periods:</th>            <td>34</td>        <th>  Distribution:      </th>   <td>F(2,365915)</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Avg Obs:</th>              <td>1.076e+04</td>    <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Min Obs:</th>               <td>5521.0</td>      <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Max Obs:</th>              <td>1.873e+04</td>    <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td></td>         <th>                     </th>        <td></td>      \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Parameter Estimates</caption>\n",
       "<tr>\n",
       "      <td></td>      <th>Parameter</th> <th>Std. Err.</th> <th>T-stat</th>  <th>P-value</th> <th>Lower CI</th> <th>Upper CI</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>44.184</td>    <td>0.0374</td>   <td>1181.1</td>  <td>0.0000</td>   <td>44.111</td>   <td>44.257</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex</th>        <td>-10.840</td>   <td>0.0398</td>   <td>-272.07</td> <td>0.0000</td>   <td>-10.919</td>  <td>-10.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>kindhh</th>     <td>-1.9172</td>   <td>0.0404</td>   <td>-47.422</td> <td>0.0000</td>   <td>-1.9964</td>  <td>-1.8379</td>\n",
       "</tr>\n",
       "</table><br/>id: 0x7fcb416296d8"
      ],
      "text/plain": [
       "                          PooledOLS Estimation Summary                          \n",
       "================================================================================\n",
       "Dep. Variable:              pgtatzeit   R-squared:                        0.1688\n",
       "Estimator:                  PooledOLS   R-squared (Between):              0.1705\n",
       "No. Observations:              365918   R-squared (Within):               0.0033\n",
       "Date:                Wed, May 08 2019   R-squared (Overall):              0.1688\n",
       "Time:                        15:31:10   Log-likelihood                -1.427e+06\n",
       "Cov. Estimator:            Unadjusted                                           \n",
       "                                        F-statistic:                   3.717e+04\n",
       "Entities:                       61817   P-value                           0.0000\n",
       "Avg Obs:                       5.9194   Distribution:                F(2,365915)\n",
       "Min Obs:                       1.0000                                           \n",
       "Max Obs:                       34.000   F-statistic (robust):          3.717e+04\n",
       "                                        P-value                           0.0000\n",
       "Time periods:                      34   Distribution:                F(2,365915)\n",
       "Avg Obs:                    1.076e+04                                           \n",
       "Min Obs:                       5521.0                                           \n",
       "Max Obs:                    1.873e+04                                           \n",
       "                                                                                \n",
       "                             Parameter Estimates                              \n",
       "==============================================================================\n",
       "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      44.184     0.0374     1181.1     0.0000      44.111      44.257\n",
       "sex           -10.840     0.0398    -272.07     0.0000     -10.919     -10.762\n",
       "kindhh        -1.9172     0.0404    -47.422     0.0000     -1.9964     -1.8379\n",
       "==============================================================================\n",
       "PanelResults, id: 0x7fcb416296d8"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = PooledOLS.from_formula(\n",
    "    'pgtatzeit ~ sex + kindhh + 1',\n",
    "    data=data\n",
    ")\n",
    "model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**:\n",
    "\n",
    "On average the gender variable `sex` is responsible for a difference of 10 hours, i.e. on average women work 10 hours less than men.\n",
    "In addition the variable `kindhh` is responsible for a diffrence of 1,9 hours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"version-hardware-info\"></a>\n",
    "[Return to top](#top)\n",
    "## Version and Hardware Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.7.3\n",
      "IPython 7.5.0\n",
      "\n",
      "ipywidgets 7.4.2\n",
      "matplotlib 3.0.3\n",
      "numpy 1.16.3\n",
      "pandas 0.24.2\n",
      "linearmodels 4.12\n",
      "\n",
      "compiler   : GCC 7.3.0\n",
      "system     : Linux\n",
      "release    : 4.15.0-48-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -m -p ipywidgets,matplotlib,numpy,pandas,linearmodels"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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