{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simpson paradoxes over time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2021 Allen B. Downey\n",
    "\n",
    "License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)\n",
    "\n",
    "[Click here to run this notebook on Colab](https://colab.research.google.com/github/AllenDowney/ProbablyOverthinkingIt2/blob/master/simpson_wages.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simpson import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gss = pd.read_hdf('gss_simpson', 'gss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Would you say that most of the time people try to be helpful, or that they are mostly just looking out for themselves?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.005825</td>\n",
       "      <td>3.914851e-16</td>\n",
       "      <td>0.000716</td>\n",
       "      <td>[-0.007227578565834578, -0.004422785059639855]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>-0.019158</td>\n",
       "      <td>4.795507e-01</td>\n",
       "      <td>0.027097</td>\n",
       "      <td>[-0.07226678623878108, 0.03395058250983201]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>0.031164</td>\n",
       "      <td>4.159768e-04</td>\n",
       "      <td>0.008829</td>\n",
       "      <td>[0.013859514124449107, 0.048468582435378126]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>0.011832</td>\n",
       "      <td>1.382779e-02</td>\n",
       "      <td>0.004807</td>\n",
       "      <td>[0.002411631450232177, 0.021252823199767908]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>0.007490</td>\n",
       "      <td>1.368502e-02</td>\n",
       "      <td>0.003038</td>\n",
       "      <td>[0.0015356085968546039, 0.013444454041372303]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>0.008493</td>\n",
       "      <td>4.814896e-04</td>\n",
       "      <td>0.002433</td>\n",
       "      <td>[0.0037247556387850593, 0.01326223285510425]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>0.005320</td>\n",
       "      <td>5.022354e-03</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>[0.0016034244262409045, 0.00903609532788564]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>0.008550</td>\n",
       "      <td>4.216908e-07</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>[0.005237326124043439, 0.011862170189889918]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>0.014967</td>\n",
       "      <td>7.996334e-10</td>\n",
       "      <td>0.002436</td>\n",
       "      <td>[0.010193019613194458, 0.019740344684094562]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>0.020643</td>\n",
       "      <td>6.542301e-07</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>[0.012509708919919588, 0.02877671732266779]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>0.038891</td>\n",
       "      <td>1.298280e-05</td>\n",
       "      <td>0.008919</td>\n",
       "      <td>[0.021409694870457442, 0.056371943729181244]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>0.024598</td>\n",
       "      <td>3.586891e-01</td>\n",
       "      <td>0.026799</td>\n",
       "      <td>[-0.02792767994438156, 0.07712432640161354]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param        pvalue    stderr  \\\n",
       "all    -0.005825  3.914851e-16  0.000716   \n",
       "1890.0 -0.019158  4.795507e-01  0.027097   \n",
       "1900.0  0.031164  4.159768e-04  0.008829   \n",
       "1910.0  0.011832  1.382779e-02  0.004807   \n",
       "1920.0  0.007490  1.368502e-02  0.003038   \n",
       "1930.0  0.008493  4.814896e-04  0.002433   \n",
       "1940.0  0.005320  5.022354e-03  0.001896   \n",
       "1950.0  0.008550  4.216908e-07  0.001690   \n",
       "1960.0  0.014967  7.996334e-10  0.002436   \n",
       "1970.0  0.020643  6.542301e-07  0.004150   \n",
       "1980.0  0.038891  1.298280e-05  0.008919   \n",
       "1990.0  0.024598  3.586891e-01  0.026799   \n",
       "\n",
       "                                              conf_inf  \n",
       "all     [-0.007227578565834578, -0.004422785059639855]  \n",
       "1890.0     [-0.07226678623878108, 0.03395058250983201]  \n",
       "1900.0    [0.013859514124449107, 0.048468582435378126]  \n",
       "1910.0    [0.002411631450232177, 0.021252823199767908]  \n",
       "1920.0   [0.0015356085968546039, 0.013444454041372303]  \n",
       "1930.0    [0.0037247556387850593, 0.01326223285510425]  \n",
       "1940.0    [0.0016034244262409045, 0.00903609532788564]  \n",
       "1950.0    [0.005237326124043439, 0.011862170189889918]  \n",
       "1960.0    [0.010193019613194458, 0.019740344684094562]  \n",
       "1970.0     [0.012509708919919588, 0.02877671732266779]  \n",
       "1980.0    [0.021409694870457442, 0.056371943729181244]  \n",
       "1990.0     [-0.02792767994438156, 0.07712432640161354]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'helpful'\n",
    "gvarname = 'cohort10'\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>48.497852</td>\n",
       "      <td>50.960117</td>\n",
       "      <td>57.409565</td>\n",
       "      <td>52.086140</td>\n",
       "      <td>47.216496</td>\n",
       "      <td>41.009464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>57.632401</td>\n",
       "      <td>51.335876</td>\n",
       "      <td>60.000004</td>\n",
       "      <td>56.108601</td>\n",
       "      <td>52.406418</td>\n",
       "      <td>39.981785</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>64.885498</td>\n",
       "      <td>55.223881</td>\n",
       "      <td>59.216587</td>\n",
       "      <td>56.091953</td>\n",
       "      <td>55.348045</td>\n",
       "      <td>51.553673</td>\n",
       "      <td>43.193714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>59.183674</td>\n",
       "      <td>56.451614</td>\n",
       "      <td>54.637867</td>\n",
       "      <td>51.861362</td>\n",
       "      <td>55.472015</td>\n",
       "      <td>47.003609</td>\n",
       "      <td>37.755936</td>\n",
       "      <td>41.666664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>62.121212</td>\n",
       "      <td>52.153111</td>\n",
       "      <td>63.157890</td>\n",
       "      <td>56.696426</td>\n",
       "      <td>54.852318</td>\n",
       "      <td>49.291496</td>\n",
       "      <td>36.099140</td>\n",
       "      <td>36.578171</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>60.000004</td>\n",
       "      <td>62.804878</td>\n",
       "      <td>58.999996</td>\n",
       "      <td>55.128204</td>\n",
       "      <td>50.326801</td>\n",
       "      <td>44.560837</td>\n",
       "      <td>40.547066</td>\n",
       "      <td>31.774811</td>\n",
       "      <td>31.182796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>53.571426</td>\n",
       "      <td>66.666672</td>\n",
       "      <td>52.777779</td>\n",
       "      <td>53.696495</td>\n",
       "      <td>58.823532</td>\n",
       "      <td>46.531792</td>\n",
       "      <td>42.618385</td>\n",
       "      <td>26.712328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>54.838711</td>\n",
       "      <td>62.195122</td>\n",
       "      <td>61.676643</td>\n",
       "      <td>56.159420</td>\n",
       "      <td>46.827793</td>\n",
       "      <td>46.162529</td>\n",
       "      <td>36.280136</td>\n",
       "      <td>31.559404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.241379</td>\n",
       "      <td>62.359547</td>\n",
       "      <td>55.688625</td>\n",
       "      <td>48.262550</td>\n",
       "      <td>45.890411</td>\n",
       "      <td>40.564373</td>\n",
       "      <td>43.516876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.413788</td>\n",
       "      <td>65.517242</td>\n",
       "      <td>56.375839</td>\n",
       "      <td>52.846973</td>\n",
       "      <td>50.085182</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>40.031643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    48.497852  50.960117  57.409565  52.086140  47.216496  41.009464   \n",
       "1977.0    57.632401  51.335876  60.000004  56.108601  52.406418  39.981785   \n",
       "1982.0    64.885498  55.223881  59.216587  56.091953  55.348045  51.553673   \n",
       "1987.0    59.183674  56.451614  54.637867  51.861362  55.472015  47.003609   \n",
       "1992.0    62.121212  52.153111  63.157890  56.696426  54.852318  49.291496   \n",
       "1997.0    60.000004  62.804878  58.999996  55.128204  50.326801  44.560837   \n",
       "2002.0          NaN  53.571426  66.666672  52.777779  53.696495  58.823532   \n",
       "2007.0          NaN  54.838711  62.195122  61.676643  56.159420  46.827793   \n",
       "2012.0          NaN        NaN  67.241379  62.359547  55.688625  48.262550   \n",
       "2017.0          NaN        NaN  72.413788  65.517242  56.375839  52.846973   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    42.857143        NaN        NaN  \n",
       "1982.0    43.193714        NaN        NaN  \n",
       "1987.0    37.755936  41.666664        NaN  \n",
       "1992.0    36.099140  36.578171        NaN  \n",
       "1997.0    40.547066  31.774811  31.182796  \n",
       "2002.0    46.531792  42.618385  26.712328  \n",
       "2007.0    46.162529  36.280136  31.559404  \n",
       "2012.0    45.890411  40.564373  43.516876  \n",
       "2017.0    50.085182  47.500000  40.031643  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Would you say that most of the time people try to be helpful, \n",
    "or that they are mostly just looking out for themselves?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"helpful\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('helpful_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trust\n",
    "\n",
    "Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.013451</td>\n",
       "      <td>1.635942e-72</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>[-0.014914877801887255, -0.011987170663713648]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>-0.037958</td>\n",
       "      <td>1.868996e-01</td>\n",
       "      <td>0.028760</td>\n",
       "      <td>[-0.09432758189038531, 0.01841119793414462]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>0.022180</td>\n",
       "      <td>9.635582e-03</td>\n",
       "      <td>0.008568</td>\n",
       "      <td>[0.005386576791562049, 0.03897303255181153]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>-0.023103</td>\n",
       "      <td>1.879545e-06</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>[-0.032604235603353626, -0.013602205002939325]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>-0.013333</td>\n",
       "      <td>8.273245e-06</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>[-0.01919505039781757, -0.00747118388268748]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>-0.011764</td>\n",
       "      <td>1.236946e-06</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>[-0.01651906007167977, -0.007009893302926963]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>-0.003639</td>\n",
       "      <td>5.438850e-02</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>[-0.00734610246048668, 6.853840616605992e-05]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>0.005298</td>\n",
       "      <td>2.247567e-03</td>\n",
       "      <td>0.001734</td>\n",
       "      <td>[0.001899573656206997, 0.008697010111858335]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>0.013464</td>\n",
       "      <td>2.803729e-07</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>[0.008326414353880133, 0.018602447712284018]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>0.007495</td>\n",
       "      <td>9.796910e-02</td>\n",
       "      <td>0.004530</td>\n",
       "      <td>[-0.0013823338034487887, 0.01637302796972214]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>0.015568</td>\n",
       "      <td>1.134109e-01</td>\n",
       "      <td>0.009834</td>\n",
       "      <td>[-0.0037067684953792385, 0.034843461403460764]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>0.167110</td>\n",
       "      <td>1.713328e-05</td>\n",
       "      <td>0.038869</td>\n",
       "      <td>[0.09092820648936911, 0.24329234288528695]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param        pvalue    stderr  \\\n",
       "all    -0.013451  1.635942e-72  0.000747   \n",
       "1890.0 -0.037958  1.868996e-01  0.028760   \n",
       "1900.0  0.022180  9.635582e-03  0.008568   \n",
       "1910.0 -0.023103  1.879545e-06  0.004848   \n",
       "1920.0 -0.013333  8.273245e-06  0.002991   \n",
       "1930.0 -0.011764  1.236946e-06  0.002426   \n",
       "1940.0 -0.003639  5.438850e-02  0.001892   \n",
       "1950.0  0.005298  2.247567e-03  0.001734   \n",
       "1960.0  0.013464  2.803729e-07  0.002621   \n",
       "1970.0  0.007495  9.796910e-02  0.004530   \n",
       "1980.0  0.015568  1.134109e-01  0.009834   \n",
       "1990.0  0.167110  1.713328e-05  0.038869   \n",
       "\n",
       "                                              conf_inf  \n",
       "all     [-0.014914877801887255, -0.011987170663713648]  \n",
       "1890.0     [-0.09432758189038531, 0.01841119793414462]  \n",
       "1900.0     [0.005386576791562049, 0.03897303255181153]  \n",
       "1910.0  [-0.032604235603353626, -0.013602205002939325]  \n",
       "1920.0    [-0.01919505039781757, -0.00747118388268748]  \n",
       "1930.0   [-0.01651906007167977, -0.007009893302926963]  \n",
       "1940.0   [-0.00734610246048668, 6.853840616605992e-05]  \n",
       "1950.0    [0.001899573656206997, 0.008697010111858335]  \n",
       "1960.0    [0.008326414353880133, 0.018602447712284018]  \n",
       "1970.0   [-0.0013823338034487887, 0.01637302796972214]  \n",
       "1980.0  [-0.0037067684953792385, 0.034843461403460764]  \n",
       "1990.0      [0.09092820648936911, 0.24329234288528695]  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'trust'\n",
    "gvarname = 'cohort10'\n",
    "yvalue = 1\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>40.212765</td>\n",
       "      <td>47.360703</td>\n",
       "      <td>50.582748</td>\n",
       "      <td>47.169811</td>\n",
       "      <td>41.521069</td>\n",
       "      <td>34.428795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>39.130436</td>\n",
       "      <td>44.612476</td>\n",
       "      <td>47.164593</td>\n",
       "      <td>50.977444</td>\n",
       "      <td>45.802338</td>\n",
       "      <td>35.009140</td>\n",
       "      <td>18.681318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>33.962265</td>\n",
       "      <td>38.524590</td>\n",
       "      <td>47.440273</td>\n",
       "      <td>48.242809</td>\n",
       "      <td>46.578365</td>\n",
       "      <td>41.242939</td>\n",
       "      <td>36.678200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>48.223351</td>\n",
       "      <td>40.755470</td>\n",
       "      <td>40.455120</td>\n",
       "      <td>40.966923</td>\n",
       "      <td>45.295586</td>\n",
       "      <td>36.978416</td>\n",
       "      <td>25.856443</td>\n",
       "      <td>41.666664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>57.575756</td>\n",
       "      <td>34.761906</td>\n",
       "      <td>39.259258</td>\n",
       "      <td>35.555557</td>\n",
       "      <td>37.447403</td>\n",
       "      <td>39.235413</td>\n",
       "      <td>26.881721</td>\n",
       "      <td>29.585800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>51.724136</td>\n",
       "      <td>35.227272</td>\n",
       "      <td>35.391922</td>\n",
       "      <td>43.265991</td>\n",
       "      <td>46.107178</td>\n",
       "      <td>38.027107</td>\n",
       "      <td>31.737848</td>\n",
       "      <td>21.975090</td>\n",
       "      <td>30.107529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.571430</td>\n",
       "      <td>51.428574</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>44.787643</td>\n",
       "      <td>45.454548</td>\n",
       "      <td>33.141212</td>\n",
       "      <td>27.576601</td>\n",
       "      <td>18.620689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.333336</td>\n",
       "      <td>42.458099</td>\n",
       "      <td>34.025974</td>\n",
       "      <td>44.897957</td>\n",
       "      <td>36.851685</td>\n",
       "      <td>38.974361</td>\n",
       "      <td>24.684874</td>\n",
       "      <td>20.310766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.206894</td>\n",
       "      <td>38.333332</td>\n",
       "      <td>32.238804</td>\n",
       "      <td>40.926640</td>\n",
       "      <td>33.901196</td>\n",
       "      <td>25.131809</td>\n",
       "      <td>25.800711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.333332</td>\n",
       "      <td>47.260273</td>\n",
       "      <td>40.863789</td>\n",
       "      <td>41.312057</td>\n",
       "      <td>31.217838</td>\n",
       "      <td>31.780365</td>\n",
       "      <td>25.236591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    40.212765  47.360703  50.582748  47.169811  41.521069  34.428795   \n",
       "1977.0    39.130436  44.612476  47.164593  50.977444  45.802338  35.009140   \n",
       "1982.0    33.962265  38.524590  47.440273  48.242809  46.578365  41.242939   \n",
       "1987.0    48.223351  40.755470  40.455120  40.966923  45.295586  36.978416   \n",
       "1992.0    57.575756  34.761906  39.259258  35.555557  37.447403  39.235413   \n",
       "1997.0    51.724136  35.227272  35.391922  43.265991  46.107178  38.027107   \n",
       "2002.0          NaN  28.571430  51.428574  37.500000  44.787643  45.454548   \n",
       "2007.0          NaN  33.333336  42.458099  34.025974  44.897957  36.851685   \n",
       "2012.0          NaN        NaN  36.206894  38.333332  32.238804  40.926640   \n",
       "2017.0          NaN        NaN  43.333332  47.260273  40.863789  41.312057   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    18.681318        NaN        NaN  \n",
       "1982.0    36.678200        NaN        NaN  \n",
       "1987.0    25.856443  41.666664        NaN  \n",
       "1992.0    26.881721  29.585800        NaN  \n",
       "1997.0    31.737848  21.975090  30.107529  \n",
       "2002.0    33.141212  27.576601  18.620689  \n",
       "2007.0    38.974361  24.684874  20.310766  \n",
       "2012.0    33.901196  25.131809  25.800711  \n",
       "2017.0    31.217838  31.780365  25.236591  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Generally speaking, would you say that most people can be trusted \n",
    "or that you can't be too careful in dealing with people?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"can be trusted\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('trust_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you think most people would try to take advantage of you if they got a chance, or would they try to be fair?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.010977</td>\n",
       "      <td>3.439721e-52</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>[-0.01239260710371703, -0.009562006898250907]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>-0.018565</td>\n",
       "      <td>5.166361e-01</td>\n",
       "      <td>0.028627</td>\n",
       "      <td>[-0.07467265652757318, 0.0375416620240953]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>0.042908</td>\n",
       "      <td>7.818749e-06</td>\n",
       "      <td>0.009599</td>\n",
       "      <td>[0.024094383010655227, 0.06172130979577424]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>0.000983</td>\n",
       "      <td>8.432464e-01</td>\n",
       "      <td>0.004970</td>\n",
       "      <td>[-0.008758492007722286, 0.010724110809917716]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>0.001674</td>\n",
       "      <td>5.914467e-01</td>\n",
       "      <td>0.003119</td>\n",
       "      <td>[-0.004439529767318436, 0.007788093250046219]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>-0.000050</td>\n",
       "      <td>9.841519e-01</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>[-0.004940447210043803, 0.0048413103346909405]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>0.001983</td>\n",
       "      <td>3.065279e-01</td>\n",
       "      <td>0.001939</td>\n",
       "      <td>[-0.0018175354212342462, 0.005782619241239932]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>0.004863</td>\n",
       "      <td>4.109611e-03</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>[0.0015414540814034845, 0.008183928716427216]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>0.012788</td>\n",
       "      <td>1.092845e-07</td>\n",
       "      <td>0.002408</td>\n",
       "      <td>[0.008068221249952951, 0.017507371025538898]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>0.023897</td>\n",
       "      <td>4.903755e-09</td>\n",
       "      <td>0.004085</td>\n",
       "      <td>[0.015891241129705608, 0.0319029245169848]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>0.018530</td>\n",
       "      <td>3.112648e-02</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>[0.0016805751400463982, 0.035379496305145836]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>0.008230</td>\n",
       "      <td>7.544276e-01</td>\n",
       "      <td>0.026309</td>\n",
       "      <td>[-0.04333584393368995, 0.05979540170621026]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param        pvalue    stderr  \\\n",
       "all    -0.010977  3.439721e-52  0.000722   \n",
       "1890.0 -0.018565  5.166361e-01  0.028627   \n",
       "1900.0  0.042908  7.818749e-06  0.009599   \n",
       "1910.0  0.000983  8.432464e-01  0.004970   \n",
       "1920.0  0.001674  5.914467e-01  0.003119   \n",
       "1930.0 -0.000050  9.841519e-01  0.002495   \n",
       "1940.0  0.001983  3.065279e-01  0.001939   \n",
       "1950.0  0.004863  4.109611e-03  0.001695   \n",
       "1960.0  0.012788  1.092845e-07  0.002408   \n",
       "1970.0  0.023897  4.903755e-09  0.004085   \n",
       "1980.0  0.018530  3.112648e-02  0.008597   \n",
       "1990.0  0.008230  7.544276e-01  0.026309   \n",
       "\n",
       "                                              conf_inf  \n",
       "all      [-0.01239260710371703, -0.009562006898250907]  \n",
       "1890.0      [-0.07467265652757318, 0.0375416620240953]  \n",
       "1900.0     [0.024094383010655227, 0.06172130979577424]  \n",
       "1910.0   [-0.008758492007722286, 0.010724110809917716]  \n",
       "1920.0   [-0.004439529767318436, 0.007788093250046219]  \n",
       "1930.0  [-0.004940447210043803, 0.0048413103346909405]  \n",
       "1940.0  [-0.0018175354212342462, 0.005782619241239932]  \n",
       "1950.0   [0.0015414540814034845, 0.008183928716427216]  \n",
       "1960.0    [0.008068221249952951, 0.017507371025538898]  \n",
       "1970.0      [0.015891241129705608, 0.0319029245169848]  \n",
       "1980.0   [0.0016805751400463982, 0.035379496305145836]  \n",
       "1990.0     [-0.04333584393368995, 0.05979540170621026]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'fair'\n",
    "gvarname = 'cohort10'\n",
    "yvalue = 2\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>57.974136</td>\n",
       "      <td>63.328426</td>\n",
       "      <td>66.511627</td>\n",
       "      <td>61.943321</td>\n",
       "      <td>57.394005</td>\n",
       "      <td>48.985958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>68.965515</td>\n",
       "      <td>67.366409</td>\n",
       "      <td>65.138885</td>\n",
       "      <td>67.119156</td>\n",
       "      <td>61.456100</td>\n",
       "      <td>53.173874</td>\n",
       "      <td>45.054947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>71.111115</td>\n",
       "      <td>60.060059</td>\n",
       "      <td>61.484921</td>\n",
       "      <td>63.448273</td>\n",
       "      <td>63.032372</td>\n",
       "      <td>56.276447</td>\n",
       "      <td>44.094486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>70.256409</td>\n",
       "      <td>67.813766</td>\n",
       "      <td>64.948448</td>\n",
       "      <td>62.901157</td>\n",
       "      <td>63.636364</td>\n",
       "      <td>57.894737</td>\n",
       "      <td>43.811073</td>\n",
       "      <td>47.222221</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>83.582092</td>\n",
       "      <td>63.809525</td>\n",
       "      <td>67.910454</td>\n",
       "      <td>56.787331</td>\n",
       "      <td>59.661495</td>\n",
       "      <td>57.777779</td>\n",
       "      <td>44.637054</td>\n",
       "      <td>41.317364</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>60.000004</td>\n",
       "      <td>62.849873</td>\n",
       "      <td>60.443623</td>\n",
       "      <td>60.812294</td>\n",
       "      <td>51.876022</td>\n",
       "      <td>50.040688</td>\n",
       "      <td>36.786060</td>\n",
       "      <td>44.086021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>67.857140</td>\n",
       "      <td>64.761909</td>\n",
       "      <td>54.166668</td>\n",
       "      <td>55.294121</td>\n",
       "      <td>60.687958</td>\n",
       "      <td>44.956772</td>\n",
       "      <td>47.075211</td>\n",
       "      <td>26.027397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>73.333336</td>\n",
       "      <td>59.393944</td>\n",
       "      <td>65.137611</td>\n",
       "      <td>63.423424</td>\n",
       "      <td>50.050251</td>\n",
       "      <td>52.203388</td>\n",
       "      <td>41.637833</td>\n",
       "      <td>41.022446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.206894</td>\n",
       "      <td>70.056496</td>\n",
       "      <td>61.261261</td>\n",
       "      <td>58.994198</td>\n",
       "      <td>51.360542</td>\n",
       "      <td>50.088493</td>\n",
       "      <td>43.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.862068</td>\n",
       "      <td>68.055557</td>\n",
       "      <td>61.666668</td>\n",
       "      <td>62.300884</td>\n",
       "      <td>54.017097</td>\n",
       "      <td>52.921532</td>\n",
       "      <td>44.145569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    57.974136  63.328426  66.511627  61.943321  57.394005  48.985958   \n",
       "1977.0    68.965515  67.366409  65.138885  67.119156  61.456100  53.173874   \n",
       "1982.0    71.111115  60.060059  61.484921  63.448273  63.032372  56.276447   \n",
       "1987.0    70.256409  67.813766  64.948448  62.901157  63.636364  57.894737   \n",
       "1992.0    83.582092  63.809525  67.910454  56.787331  59.661495  57.777779   \n",
       "1997.0    68.000000  60.000004  62.849873  60.443623  60.812294  51.876022   \n",
       "2002.0          NaN  67.857140  64.761909  54.166668  55.294121  60.687958   \n",
       "2007.0          NaN  73.333336  59.393944  65.137611  63.423424  50.050251   \n",
       "2012.0          NaN        NaN  86.206894  70.056496  61.261261  58.994198   \n",
       "2017.0          NaN        NaN  75.862068  68.055557  61.666668  62.300884   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    45.054947        NaN        NaN  \n",
       "1982.0    44.094486        NaN        NaN  \n",
       "1987.0    43.811073  47.222221        NaN  \n",
       "1992.0    44.637054  41.317364        NaN  \n",
       "1997.0    50.040688  36.786060  44.086021  \n",
       "2002.0    44.956772  47.075211  26.027397  \n",
       "2007.0    52.203388  41.637833  41.022446  \n",
       "2012.0    51.360542  50.088493  43.392857  \n",
       "2017.0    54.017097  52.921532  44.145569  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Do you think most people would try to take advantage of you if they got a chance, \n",
    "or would they try to be fair?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"would try to be fair\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('fair_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there any area right around here--that is, within a mile--where you would be afraid to walk alone at night?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.012328</td>\n",
       "      <td>4.249652e-57</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>[0.010810771933626414, 0.013845309443939616]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>-0.079679</td>\n",
       "      <td>1.055168e-02</td>\n",
       "      <td>0.031158</td>\n",
       "      <td>[-0.14074793363139834, -0.018609118609463675]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>-0.017515</td>\n",
       "      <td>7.369016e-02</td>\n",
       "      <td>0.009793</td>\n",
       "      <td>[-0.03670801021651464, 0.0016787834759727192]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>-0.014597</td>\n",
       "      <td>4.414805e-03</td>\n",
       "      <td>0.005127</td>\n",
       "      <td>[-0.02464697120628914, -0.004547698369112861]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>-0.001810</td>\n",
       "      <td>5.747878e-01</td>\n",
       "      <td>0.003226</td>\n",
       "      <td>[-0.008132916187274662, 0.0045131452271464645]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>0.002928</td>\n",
       "      <td>2.531914e-01</td>\n",
       "      <td>0.002562</td>\n",
       "      <td>[-0.002094232307007154, 0.007949716746511858]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>0.011611</td>\n",
       "      <td>2.149857e-08</td>\n",
       "      <td>0.002074</td>\n",
       "      <td>[0.007547059949823356, 0.0156755764124481]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>0.012591</td>\n",
       "      <td>9.727969e-12</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>[0.008967804533088923, 0.016215118555470188]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>0.014720</td>\n",
       "      <td>3.355490e-09</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>[0.009840690623795986, 0.01959849816753189]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>0.013566</td>\n",
       "      <td>1.053552e-03</td>\n",
       "      <td>0.004141</td>\n",
       "      <td>[0.005449140161843367, 0.021682084487936574]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>0.015112</td>\n",
       "      <td>7.910421e-02</td>\n",
       "      <td>0.008607</td>\n",
       "      <td>[-0.0017562266905255773, 0.03198090496813623]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>-0.000324</td>\n",
       "      <td>9.914506e-01</td>\n",
       "      <td>0.030207</td>\n",
       "      <td>[-0.05952924823264222, 0.05888188432984788]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param        pvalue    stderr  \\\n",
       "all     0.012328  4.249652e-57  0.000774   \n",
       "1890.0 -0.079679  1.055168e-02  0.031158   \n",
       "1900.0 -0.017515  7.369016e-02  0.009793   \n",
       "1910.0 -0.014597  4.414805e-03  0.005127   \n",
       "1920.0 -0.001810  5.747878e-01  0.003226   \n",
       "1930.0  0.002928  2.531914e-01  0.002562   \n",
       "1940.0  0.011611  2.149857e-08  0.002074   \n",
       "1950.0  0.012591  9.727969e-12  0.001849   \n",
       "1960.0  0.014720  3.355490e-09  0.002489   \n",
       "1970.0  0.013566  1.053552e-03  0.004141   \n",
       "1980.0  0.015112  7.910421e-02  0.008607   \n",
       "1990.0 -0.000324  9.914506e-01  0.030207   \n",
       "\n",
       "                                              conf_inf  \n",
       "all       [0.010810771933626414, 0.013845309443939616]  \n",
       "1890.0   [-0.14074793363139834, -0.018609118609463675]  \n",
       "1900.0   [-0.03670801021651464, 0.0016787834759727192]  \n",
       "1910.0   [-0.02464697120628914, -0.004547698369112861]  \n",
       "1920.0  [-0.008132916187274662, 0.0045131452271464645]  \n",
       "1930.0   [-0.002094232307007154, 0.007949716746511858]  \n",
       "1940.0      [0.007547059949823356, 0.0156755764124481]  \n",
       "1950.0    [0.008967804533088923, 0.016215118555470188]  \n",
       "1960.0     [0.009840690623795986, 0.01959849816753189]  \n",
       "1970.0    [0.005449140161843367, 0.021682084487936574]  \n",
       "1980.0   [-0.0017562266905255773, 0.03198090496813623]  \n",
       "1990.0     [-0.05952924823264222, 0.05888188432984788]  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'fear'\n",
    "gvarname = 'cohort10'\n",
    "yvalue = 2\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>51.677853</td>\n",
       "      <td>61.085976</td>\n",
       "      <td>59.852673</td>\n",
       "      <td>60.680527</td>\n",
       "      <td>58.717110</td>\n",
       "      <td>63.829784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>51.273884</td>\n",
       "      <td>51.993065</td>\n",
       "      <td>63.144333</td>\n",
       "      <td>60.000004</td>\n",
       "      <td>60.177975</td>\n",
       "      <td>59.051304</td>\n",
       "      <td>55.421684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>40.776699</td>\n",
       "      <td>45.708157</td>\n",
       "      <td>53.369270</td>\n",
       "      <td>60.029499</td>\n",
       "      <td>61.599998</td>\n",
       "      <td>58.072918</td>\n",
       "      <td>64.106583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>49.006622</td>\n",
       "      <td>57.731956</td>\n",
       "      <td>52.334946</td>\n",
       "      <td>56.752140</td>\n",
       "      <td>68.474579</td>\n",
       "      <td>64.077667</td>\n",
       "      <td>60.310883</td>\n",
       "      <td>73.033707</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>36.363636</td>\n",
       "      <td>47.679325</td>\n",
       "      <td>55.710304</td>\n",
       "      <td>49.047619</td>\n",
       "      <td>60.108547</td>\n",
       "      <td>62.918919</td>\n",
       "      <td>57.044674</td>\n",
       "      <td>59.210526</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>52.173912</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>52.676056</td>\n",
       "      <td>61.648743</td>\n",
       "      <td>63.492065</td>\n",
       "      <td>63.255440</td>\n",
       "      <td>59.950249</td>\n",
       "      <td>61.773117</td>\n",
       "      <td>59.574467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60.869564</td>\n",
       "      <td>48.453609</td>\n",
       "      <td>68.131866</td>\n",
       "      <td>72.222221</td>\n",
       "      <td>71.356789</td>\n",
       "      <td>70.277077</td>\n",
       "      <td>72.799995</td>\n",
       "      <td>62.264149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>56.666668</td>\n",
       "      <td>67.096779</td>\n",
       "      <td>61.721069</td>\n",
       "      <td>69.485901</td>\n",
       "      <td>65.392776</td>\n",
       "      <td>70.370369</td>\n",
       "      <td>66.543213</td>\n",
       "      <td>63.846153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.627457</td>\n",
       "      <td>63.815792</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>74.842766</td>\n",
       "      <td>64.991020</td>\n",
       "      <td>71.212120</td>\n",
       "      <td>66.489365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>64.963501</td>\n",
       "      <td>71.176468</td>\n",
       "      <td>70.175438</td>\n",
       "      <td>70.625999</td>\n",
       "      <td>67.687073</td>\n",
       "      <td>66.153847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    51.677853  61.085976  59.852673  60.680527  58.717110  63.829784   \n",
       "1977.0    51.273884  51.993065  63.144333  60.000004  60.177975  59.051304   \n",
       "1982.0    40.776699  45.708157  53.369270  60.029499  61.599998  58.072918   \n",
       "1987.0    49.006622  57.731956  52.334946  56.752140  68.474579  64.077667   \n",
       "1992.0    36.363636  47.679325  55.710304  49.047619  60.108547  62.918919   \n",
       "1997.0    52.173912  42.000000  52.676056  61.648743  63.492065  63.255440   \n",
       "2002.0          NaN  60.869564  48.453609  68.131866  72.222221  71.356789   \n",
       "2007.0          NaN  56.666668  67.096779  61.721069  69.485901  65.392776   \n",
       "2012.0          NaN        NaN  68.627457  63.815792  67.000000  74.842766   \n",
       "2017.0          NaN        NaN  75.000000  64.963501  71.176468  70.175438   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    55.421684        NaN        NaN  \n",
       "1982.0    64.106583        NaN        NaN  \n",
       "1987.0    60.310883  73.033707        NaN  \n",
       "1992.0    57.044674  59.210526        NaN  \n",
       "1997.0    59.950249  61.773117  59.574467  \n",
       "2002.0    70.277077  72.799995  62.264149  \n",
       "2007.0    70.370369  66.543213  63.846153  \n",
       "2012.0    64.991020  71.212120  66.489365  \n",
       "2017.0    70.625999  67.687073  66.153847  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Is there any area right around here--that is, within a mile--\n",
    "where you would be afraid to walk alone at night?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"no\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('fear_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## happy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.002285</td>\n",
       "      <td>0.013764</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>[-0.0041032155360231585, -0.00046696535868165833]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>0.038240</td>\n",
       "      <td>0.260519</td>\n",
       "      <td>0.033987</td>\n",
       "      <td>[-0.0283719266073775, 0.10485287547877495]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>0.013303</td>\n",
       "      <td>0.269695</td>\n",
       "      <td>0.012052</td>\n",
       "      <td>[-0.010318885219879297, 0.036923936886392394]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>-0.000571</td>\n",
       "      <td>0.926663</td>\n",
       "      <td>0.006205</td>\n",
       "      <td>[-0.01273346612146039, 0.011591134383984458]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>0.012295</td>\n",
       "      <td>0.002059</td>\n",
       "      <td>0.003990</td>\n",
       "      <td>[0.004475333777179293, 0.02011549252262864]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>0.005512</td>\n",
       "      <td>0.089231</td>\n",
       "      <td>0.003244</td>\n",
       "      <td>[-0.0008449362671818783, 0.011869753403289293]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>-0.005121</td>\n",
       "      <td>0.039920</td>\n",
       "      <td>0.002493</td>\n",
       "      <td>[-0.01000690901132986, -0.00023582305915941133]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>-0.002493</td>\n",
       "      <td>0.244426</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>[-0.0066919040760102634, 0.0017050661328273297]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>-0.011806</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.003093</td>\n",
       "      <td>[-0.017867459185350452, -0.005745054358809668]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>-0.016417</td>\n",
       "      <td>0.001657</td>\n",
       "      <td>0.005219</td>\n",
       "      <td>[-0.02664549930962453, -0.006187730687237497]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>0.041103</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>[0.021601700203285976, 0.06060335667134924]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>-0.100883</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>0.034212</td>\n",
       "      <td>[-0.16793823019606974, -0.03382823087112466]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param    pvalue    stderr  \\\n",
       "all    -0.002285  0.013764  0.000928   \n",
       "1890.0  0.038240  0.260519  0.033987   \n",
       "1900.0  0.013303  0.269695  0.012052   \n",
       "1910.0 -0.000571  0.926663  0.006205   \n",
       "1920.0  0.012295  0.002059  0.003990   \n",
       "1930.0  0.005512  0.089231  0.003244   \n",
       "1940.0 -0.005121  0.039920  0.002493   \n",
       "1950.0 -0.002493  0.244426  0.002142   \n",
       "1960.0 -0.011806  0.000135  0.003093   \n",
       "1970.0 -0.016417  0.001657  0.005219   \n",
       "1980.0  0.041103  0.000036  0.009950   \n",
       "1990.0 -0.100883  0.003191  0.034212   \n",
       "\n",
       "                                                 conf_inf  \n",
       "all     [-0.0041032155360231585, -0.00046696535868165833]  \n",
       "1890.0         [-0.0283719266073775, 0.10485287547877495]  \n",
       "1900.0      [-0.010318885219879297, 0.036923936886392394]  \n",
       "1910.0       [-0.01273346612146039, 0.011591134383984458]  \n",
       "1920.0        [0.004475333777179293, 0.02011549252262864]  \n",
       "1930.0     [-0.0008449362671818783, 0.011869753403289293]  \n",
       "1940.0    [-0.01000690901132986, -0.00023582305915941133]  \n",
       "1950.0    [-0.0066919040760102634, 0.0017050661328273297]  \n",
       "1960.0     [-0.017867459185350452, -0.005745054358809668]  \n",
       "1970.0      [-0.02664549930962453, -0.006187730687237497]  \n",
       "1980.0        [0.021601700203285976, 0.06060335667134924]  \n",
       "1990.0       [-0.16793823019606974, -0.03382823087112466]  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'happy'\n",
    "gvarname = 'cohort10'\n",
    "yvalue = [1,2]\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "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>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>86.173637</td>\n",
       "      <td>87.082405</td>\n",
       "      <td>84.995583</td>\n",
       "      <td>86.947792</td>\n",
       "      <td>87.952759</td>\n",
       "      <td>82.339447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>90.645882</td>\n",
       "      <td>87.686066</td>\n",
       "      <td>87.328094</td>\n",
       "      <td>91.337715</td>\n",
       "      <td>90.622406</td>\n",
       "      <td>88.950279</td>\n",
       "      <td>91.208794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>92.605629</td>\n",
       "      <td>89.863846</td>\n",
       "      <td>87.577003</td>\n",
       "      <td>85.963005</td>\n",
       "      <td>88.153305</td>\n",
       "      <td>89.083824</td>\n",
       "      <td>86.375320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>86.075951</td>\n",
       "      <td>90.327873</td>\n",
       "      <td>89.550682</td>\n",
       "      <td>89.155373</td>\n",
       "      <td>91.178497</td>\n",
       "      <td>91.607567</td>\n",
       "      <td>90.210251</td>\n",
       "      <td>93.162392</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>84.810127</td>\n",
       "      <td>89.017342</td>\n",
       "      <td>92.733559</td>\n",
       "      <td>89.743591</td>\n",
       "      <td>88.369965</td>\n",
       "      <td>87.874565</td>\n",
       "      <td>89.048340</td>\n",
       "      <td>92.518250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>91.666672</td>\n",
       "      <td>82.857147</td>\n",
       "      <td>91.265594</td>\n",
       "      <td>89.621490</td>\n",
       "      <td>91.793755</td>\n",
       "      <td>87.942886</td>\n",
       "      <td>92.041710</td>\n",
       "      <td>88.952026</td>\n",
       "      <td>88.721809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>92.682930</td>\n",
       "      <td>91.558441</td>\n",
       "      <td>95.061729</td>\n",
       "      <td>89.005241</td>\n",
       "      <td>89.721260</td>\n",
       "      <td>88.724586</td>\n",
       "      <td>91.319443</td>\n",
       "      <td>81.048386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>87.903229</td>\n",
       "      <td>91.033134</td>\n",
       "      <td>87.165176</td>\n",
       "      <td>85.088333</td>\n",
       "      <td>86.595581</td>\n",
       "      <td>89.257500</td>\n",
       "      <td>83.249580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.518074</td>\n",
       "      <td>88.047806</td>\n",
       "      <td>86.609070</td>\n",
       "      <td>85.258965</td>\n",
       "      <td>86.091125</td>\n",
       "      <td>87.236534</td>\n",
       "      <td>91.058823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.818184</td>\n",
       "      <td>87.962959</td>\n",
       "      <td>85.050507</td>\n",
       "      <td>88.141808</td>\n",
       "      <td>85.066376</td>\n",
       "      <td>86.766396</td>\n",
       "      <td>88.865097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    86.173637  87.082405  84.995583  86.947792  87.952759  82.339447   \n",
       "1977.0    90.645882  87.686066  87.328094  91.337715  90.622406  88.950279   \n",
       "1982.0    92.605629  89.863846  87.577003  85.963005  88.153305  89.083824   \n",
       "1987.0    86.075951  90.327873  89.550682  89.155373  91.178497  91.607567   \n",
       "1992.0    84.810127  89.017342  92.733559  89.743591  88.369965  87.874565   \n",
       "1997.0    91.666672  82.857147  91.265594  89.621490  91.793755  87.942886   \n",
       "2002.0          NaN  92.682930  91.558441  95.061729  89.005241  89.721260   \n",
       "2007.0          NaN  85.000000  87.903229  91.033134  87.165176  85.088333   \n",
       "2012.0          NaN        NaN  79.518074  88.047806  86.609070  85.258965   \n",
       "2017.0          NaN        NaN  81.818184  87.962959  85.050507  88.141808   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    91.208794        NaN        NaN  \n",
       "1982.0    86.375320        NaN        NaN  \n",
       "1987.0    90.210251  93.162392        NaN  \n",
       "1992.0    89.048340  92.518250        NaN  \n",
       "1997.0    92.041710  88.952026  88.721809  \n",
       "2002.0    88.724586  91.319443  81.048386  \n",
       "2007.0    86.595581  89.257500  83.249580  \n",
       "2012.0    86.091125  87.236534  91.058823  \n",
       "2017.0    85.066376  86.766396  88.865097  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Taken all together, how would you say things are these days--\n",
    "would you say that you are very happy, pretty happy, or not too happy?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"very happy\" or \"pretty happy\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('happy_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pornlaw\n",
    "\n",
    "https://gss.norc.org/Documents/quex/GSS2018%20Ballot%202%20-%20English.pdf\n",
    "\n",
    "\n",
    "Which of these statements comes closest to your feelings about pornography laws?     \n",
    "\n",
    "1. There should be laws against the distribution of pornography, whatever the age, or\n",
    "\n",
    "2. There should be laws against the distribution of pornography to persons under 18, or\n",
    "\n",
    "3. There should be no laws forbidding the distribution of pornography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.011273</td>\n",
       "      <td>1.277596e-46</td>\n",
       "      <td>0.000786</td>\n",
       "      <td>[-0.012814157706280808, -0.009732028169030328]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>0.049571</td>\n",
       "      <td>2.488427e-01</td>\n",
       "      <td>0.042987</td>\n",
       "      <td>[-0.034681924587223564, 0.13382425168956025]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>0.001062</td>\n",
       "      <td>9.185716e-01</td>\n",
       "      <td>0.010386</td>\n",
       "      <td>[-0.01929529389481996, 0.021418981267806593]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>0.016735</td>\n",
       "      <td>2.862712e-03</td>\n",
       "      <td>0.005612</td>\n",
       "      <td>[0.005736044776578969, 0.02773381307212317]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>0.018362</td>\n",
       "      <td>4.235711e-08</td>\n",
       "      <td>0.003350</td>\n",
       "      <td>[0.011795533184439917, 0.02492836323164188]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>0.012158</td>\n",
       "      <td>1.860490e-06</td>\n",
       "      <td>0.002550</td>\n",
       "      <td>[0.0071603454218177615, 0.01715590025094741]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>0.022156</td>\n",
       "      <td>9.168343e-26</td>\n",
       "      <td>0.002111</td>\n",
       "      <td>[0.018017742705881714, 0.0262934470123472]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>0.021974</td>\n",
       "      <td>1.920759e-31</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>[0.018282171319694028, 0.025666350513799785]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>0.018347</td>\n",
       "      <td>3.580736e-12</td>\n",
       "      <td>0.002639</td>\n",
       "      <td>[0.013175175338581077, 0.023519117216250517]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>0.026695</td>\n",
       "      <td>2.131507e-08</td>\n",
       "      <td>0.004766</td>\n",
       "      <td>[0.01735377334527781, 0.03603692475501992]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>-0.000554</td>\n",
       "      <td>9.576488e-01</td>\n",
       "      <td>0.010435</td>\n",
       "      <td>[-0.02100545474681647, 0.01989721556037316]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>0.055484</td>\n",
       "      <td>1.008570e-01</td>\n",
       "      <td>0.033817</td>\n",
       "      <td>[-0.01079611059586183, 0.12176403010848791]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param        pvalue    stderr  \\\n",
       "all    -0.011273  1.277596e-46  0.000786   \n",
       "1890.0  0.049571  2.488427e-01  0.042987   \n",
       "1900.0  0.001062  9.185716e-01  0.010386   \n",
       "1910.0  0.016735  2.862712e-03  0.005612   \n",
       "1920.0  0.018362  4.235711e-08  0.003350   \n",
       "1930.0  0.012158  1.860490e-06  0.002550   \n",
       "1940.0  0.022156  9.168343e-26  0.002111   \n",
       "1950.0  0.021974  1.920759e-31  0.001884   \n",
       "1960.0  0.018347  3.580736e-12  0.002639   \n",
       "1970.0  0.026695  2.131507e-08  0.004766   \n",
       "1980.0 -0.000554  9.576488e-01  0.010435   \n",
       "1990.0  0.055484  1.008570e-01  0.033817   \n",
       "\n",
       "                                              conf_inf  \n",
       "all     [-0.012814157706280808, -0.009732028169030328]  \n",
       "1890.0    [-0.034681924587223564, 0.13382425168956025]  \n",
       "1900.0    [-0.01929529389481996, 0.021418981267806593]  \n",
       "1910.0     [0.005736044776578969, 0.02773381307212317]  \n",
       "1920.0     [0.011795533184439917, 0.02492836323164188]  \n",
       "1930.0    [0.0071603454218177615, 0.01715590025094741]  \n",
       "1940.0      [0.018017742705881714, 0.0262934470123472]  \n",
       "1950.0    [0.018282171319694028, 0.025666350513799785]  \n",
       "1960.0    [0.013175175338581077, 0.023519117216250517]  \n",
       "1970.0      [0.01735377334527781, 0.03603692475501992]  \n",
       "1980.0     [-0.02100545474681647, 0.01989721556037316]  \n",
       "1990.0     [-0.01079611059586183, 0.12176403010848791]  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'pornlaw'\n",
    "gvarname = 'cohort10'\n",
    "yvalue = 1\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>64.827583</td>\n",
       "      <td>62.004662</td>\n",
       "      <td>46.812748</td>\n",
       "      <td>41.398865</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.796610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>70.873787</td>\n",
       "      <td>63.498096</td>\n",
       "      <td>50.983150</td>\n",
       "      <td>45.921452</td>\n",
       "      <td>27.586206</td>\n",
       "      <td>21.735161</td>\n",
       "      <td>14.285715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>66.417908</td>\n",
       "      <td>63.384617</td>\n",
       "      <td>63.317757</td>\n",
       "      <td>54.588234</td>\n",
       "      <td>31.911263</td>\n",
       "      <td>28.672318</td>\n",
       "      <td>21.522310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>60.215057</td>\n",
       "      <td>62.857143</td>\n",
       "      <td>59.143967</td>\n",
       "      <td>49.159122</td>\n",
       "      <td>37.625416</td>\n",
       "      <td>30.652174</td>\n",
       "      <td>25.693312</td>\n",
       "      <td>28.767124</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>80.645164</td>\n",
       "      <td>75.961533</td>\n",
       "      <td>59.547741</td>\n",
       "      <td>51.793724</td>\n",
       "      <td>41.043724</td>\n",
       "      <td>28.658537</td>\n",
       "      <td>26.457882</td>\n",
       "      <td>16.566265</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>72.327042</td>\n",
       "      <td>63.250000</td>\n",
       "      <td>55.452866</td>\n",
       "      <td>40.264027</td>\n",
       "      <td>35.748791</td>\n",
       "      <td>29.159937</td>\n",
       "      <td>18.216682</td>\n",
       "      <td>19.354837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>64.285713</td>\n",
       "      <td>65.048546</td>\n",
       "      <td>52.857143</td>\n",
       "      <td>47.011955</td>\n",
       "      <td>34.803921</td>\n",
       "      <td>33.815029</td>\n",
       "      <td>22.005571</td>\n",
       "      <td>19.444445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>67.741936</td>\n",
       "      <td>58.641975</td>\n",
       "      <td>54.407291</td>\n",
       "      <td>42.286751</td>\n",
       "      <td>39.676113</td>\n",
       "      <td>31.151241</td>\n",
       "      <td>28.041714</td>\n",
       "      <td>21.766169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.413788</td>\n",
       "      <td>58.192093</td>\n",
       "      <td>46.483181</td>\n",
       "      <td>33.657585</td>\n",
       "      <td>34.582623</td>\n",
       "      <td>30.948120</td>\n",
       "      <td>18.360071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.428570</td>\n",
       "      <td>52.739727</td>\n",
       "      <td>43.581081</td>\n",
       "      <td>41.415932</td>\n",
       "      <td>36.597939</td>\n",
       "      <td>26.333332</td>\n",
       "      <td>21.135647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    64.827583  62.004662  46.812748  41.398865  23.000000  17.796610   \n",
       "1977.0    70.873787  63.498096  50.983150  45.921452  27.586206  21.735161   \n",
       "1982.0    66.417908  63.384617  63.317757  54.588234  31.911263  28.672318   \n",
       "1987.0    60.215057  62.857143  59.143967  49.159122  37.625416  30.652174   \n",
       "1992.0    80.645164  75.961533  59.547741  51.793724  41.043724  28.658537   \n",
       "1997.0    64.000000  72.327042  63.250000  55.452866  40.264027  35.748791   \n",
       "2002.0          NaN  64.285713  65.048546  52.857143  47.011955  34.803921   \n",
       "2007.0          NaN  67.741936  58.641975  54.407291  42.286751  39.676113   \n",
       "2012.0          NaN        NaN  72.413788  58.192093  46.483181  33.657585   \n",
       "2017.0          NaN        NaN  46.428570  52.739727  43.581081  41.415932   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    14.285715        NaN        NaN  \n",
       "1982.0    21.522310        NaN        NaN  \n",
       "1987.0    25.693312  28.767124        NaN  \n",
       "1992.0    26.457882  16.566265        NaN  \n",
       "1997.0    29.159937  18.216682  19.354837  \n",
       "2002.0    33.815029  22.005571  19.444445  \n",
       "2007.0    31.151241  28.041714  21.766169  \n",
       "2012.0    34.582623  30.948120  18.360071  \n",
       "2017.0    36.597939  26.333332  21.135647  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Do you think there should be laws against the distribution of pornography, \n",
    "whatever the age?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent who agree')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('pornlaw_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you think the use of marijuana should be made legal or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.007628</td>\n",
       "      <td>4.894648e-25</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>[0.006181028622331223, 0.009074056999469483]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890.0</th>\n",
       "      <td>-0.033302</td>\n",
       "      <td>3.176265e-01</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>[-0.09861428262082172, 0.03201100393840778]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900.0</th>\n",
       "      <td>-0.047445</td>\n",
       "      <td>5.688114e-06</td>\n",
       "      <td>0.010456</td>\n",
       "      <td>[-0.06793839227718237, -0.026952103556243365]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910.0</th>\n",
       "      <td>-0.006878</td>\n",
       "      <td>1.893425e-01</td>\n",
       "      <td>0.005240</td>\n",
       "      <td>[-0.017148161334355302, 0.0033926322764510396]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920.0</th>\n",
       "      <td>-0.007816</td>\n",
       "      <td>1.734340e-02</td>\n",
       "      <td>0.003285</td>\n",
       "      <td>[-0.014254712258633652, -0.0013776770265430211]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930.0</th>\n",
       "      <td>-0.004494</td>\n",
       "      <td>8.514546e-02</td>\n",
       "      <td>0.002610</td>\n",
       "      <td>[-0.009609505230442256, 0.0006222233728750397]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>-0.004128</td>\n",
       "      <td>4.140376e-02</td>\n",
       "      <td>0.002024</td>\n",
       "      <td>[-0.008094322536882637, -0.00016089734706081164]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950.0</th>\n",
       "      <td>-0.006393</td>\n",
       "      <td>2.262750e-04</td>\n",
       "      <td>0.001734</td>\n",
       "      <td>[-0.009791087561745196, -0.002995314647718323]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960.0</th>\n",
       "      <td>-0.018968</td>\n",
       "      <td>8.145024e-15</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>[-0.023755710800939682, -0.014180582957056752]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970.0</th>\n",
       "      <td>-0.023100</td>\n",
       "      <td>1.227564e-08</td>\n",
       "      <td>0.004056</td>\n",
       "      <td>[-0.03104873869197835, -0.015151177999504961]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>-0.011127</td>\n",
       "      <td>1.871949e-01</td>\n",
       "      <td>0.008437</td>\n",
       "      <td>[-0.02766331627836619, 0.005408337065099423]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>-0.027890</td>\n",
       "      <td>2.716327e-01</td>\n",
       "      <td>0.025370</td>\n",
       "      <td>[-0.07761469315900867, 0.02183503775561164]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           param        pvalue    stderr  \\\n",
       "all     0.007628  4.894648e-25  0.000738   \n",
       "1890.0 -0.033302  3.176265e-01  0.033323   \n",
       "1900.0 -0.047445  5.688114e-06  0.010456   \n",
       "1910.0 -0.006878  1.893425e-01  0.005240   \n",
       "1920.0 -0.007816  1.734340e-02  0.003285   \n",
       "1930.0 -0.004494  8.514546e-02  0.002610   \n",
       "1940.0 -0.004128  4.140376e-02  0.002024   \n",
       "1950.0 -0.006393  2.262750e-04  0.001734   \n",
       "1960.0 -0.018968  8.145024e-15  0.002443   \n",
       "1970.0 -0.023100  1.227564e-08  0.004056   \n",
       "1980.0 -0.011127  1.871949e-01  0.008437   \n",
       "1990.0 -0.027890  2.716327e-01  0.025370   \n",
       "\n",
       "                                                conf_inf  \n",
       "all         [0.006181028622331223, 0.009074056999469483]  \n",
       "1890.0       [-0.09861428262082172, 0.03201100393840778]  \n",
       "1900.0     [-0.06793839227718237, -0.026952103556243365]  \n",
       "1910.0    [-0.017148161334355302, 0.0033926322764510396]  \n",
       "1920.0   [-0.014254712258633652, -0.0013776770265430211]  \n",
       "1930.0    [-0.009609505230442256, 0.0006222233728750397]  \n",
       "1940.0  [-0.008094322536882637, -0.00016089734706081164]  \n",
       "1950.0    [-0.009791087561745196, -0.002995314647718323]  \n",
       "1960.0    [-0.023755710800939682, -0.014180582957056752]  \n",
       "1970.0     [-0.03104873869197835, -0.015151177999504961]  \n",
       "1980.0      [-0.02766331627836619, 0.005408337065099423]  \n",
       "1990.0       [-0.07761469315900867, 0.02183503775561164]  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'fair'\n",
    "gvarname = 'cohort10'\n",
    "yvalue = 1\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>cohort10</th>\n",
       "      <th>1900.0</th>\n",
       "      <th>1910.0</th>\n",
       "      <th>1920.0</th>\n",
       "      <th>1930.0</th>\n",
       "      <th>1940.0</th>\n",
       "      <th>1950.0</th>\n",
       "      <th>1960.0</th>\n",
       "      <th>1970.0</th>\n",
       "      <th>1980.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>34.267242</td>\n",
       "      <td>30.633286</td>\n",
       "      <td>29.767441</td>\n",
       "      <td>32.388664</td>\n",
       "      <td>36.608067</td>\n",
       "      <td>44.305771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>26.018806</td>\n",
       "      <td>28.625954</td>\n",
       "      <td>30.555555</td>\n",
       "      <td>29.713425</td>\n",
       "      <td>31.691650</td>\n",
       "      <td>41.490341</td>\n",
       "      <td>51.648354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>22.222223</td>\n",
       "      <td>36.336334</td>\n",
       "      <td>33.178654</td>\n",
       "      <td>31.724136</td>\n",
       "      <td>31.516186</td>\n",
       "      <td>38.645981</td>\n",
       "      <td>54.068245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>23.076923</td>\n",
       "      <td>27.327934</td>\n",
       "      <td>32.603092</td>\n",
       "      <td>34.659821</td>\n",
       "      <td>31.693077</td>\n",
       "      <td>37.646198</td>\n",
       "      <td>51.465797</td>\n",
       "      <td>40.277779</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>8.955224</td>\n",
       "      <td>26.666668</td>\n",
       "      <td>25.373135</td>\n",
       "      <td>33.936653</td>\n",
       "      <td>32.440056</td>\n",
       "      <td>35.959595</td>\n",
       "      <td>49.079090</td>\n",
       "      <td>53.592812</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>31.176472</td>\n",
       "      <td>26.972012</td>\n",
       "      <td>32.717190</td>\n",
       "      <td>33.150383</td>\n",
       "      <td>41.517128</td>\n",
       "      <td>39.137508</td>\n",
       "      <td>53.823818</td>\n",
       "      <td>40.860214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>27.619049</td>\n",
       "      <td>34.722221</td>\n",
       "      <td>36.862747</td>\n",
       "      <td>28.992630</td>\n",
       "      <td>40.345821</td>\n",
       "      <td>46.239555</td>\n",
       "      <td>63.698631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.333334</td>\n",
       "      <td>33.333336</td>\n",
       "      <td>27.217125</td>\n",
       "      <td>27.927929</td>\n",
       "      <td>40.703518</td>\n",
       "      <td>38.079094</td>\n",
       "      <td>45.905418</td>\n",
       "      <td>50.872822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.068965</td>\n",
       "      <td>24.293785</td>\n",
       "      <td>30.630630</td>\n",
       "      <td>37.717602</td>\n",
       "      <td>40.986397</td>\n",
       "      <td>41.061947</td>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.241379</td>\n",
       "      <td>23.611111</td>\n",
       "      <td>33.333336</td>\n",
       "      <td>32.389381</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>41.903172</td>\n",
       "      <td>48.101269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort10     1900.0     1910.0     1920.0     1930.0     1940.0     1950.0  \\\n",
       "year5                                                                        \n",
       "1972.0    34.267242  30.633286  29.767441  32.388664  36.608067  44.305771   \n",
       "1977.0    26.018806  28.625954  30.555555  29.713425  31.691650  41.490341   \n",
       "1982.0    22.222223  36.336334  33.178654  31.724136  31.516186  38.645981   \n",
       "1987.0    23.076923  27.327934  32.603092  34.659821  31.693077  37.646198   \n",
       "1992.0     8.955224  26.666668  25.373135  33.936653  32.440056  35.959595   \n",
       "1997.0    24.000000  31.176472  26.972012  32.717190  33.150383  41.517128   \n",
       "2002.0          NaN  10.714286  27.619049  34.722221  36.862747  28.992630   \n",
       "2007.0          NaN  23.333334  33.333336  27.217125  27.927929  40.703518   \n",
       "2012.0          NaN        NaN  12.068965  24.293785  30.630630  37.717602   \n",
       "2017.0          NaN        NaN  17.241379  23.611111  33.333336  32.389381   \n",
       "\n",
       "cohort10     1960.0     1970.0     1980.0  \n",
       "year5                                      \n",
       "1972.0          NaN        NaN        NaN  \n",
       "1977.0    51.648354        NaN        NaN  \n",
       "1982.0    54.068245        NaN        NaN  \n",
       "1987.0    51.465797  40.277779        NaN  \n",
       "1992.0    49.079090  53.592812        NaN  \n",
       "1997.0    39.137508  53.823818  40.860214  \n",
       "2002.0    40.345821  46.239555  63.698631  \n",
       "2007.0    38.079094  45.905418  50.872822  \n",
       "2012.0    40.986397  41.061947  47.500000  \n",
       "2017.0    40.000000  41.903172  48.101269  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.drop([1890, 1990], axis=1, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Do you think the use of marijuana should be made legal or not?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"No legal\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Birth decade', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('grass_vs_year_by_cohort10.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if she is married and does not want any more children?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.001087</td>\n",
       "      <td>1.125147e-01</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>[-0.0002553661356612109, 0.002428518308773977]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor</th>\n",
       "      <td>-0.007197</td>\n",
       "      <td>1.408424e-04</td>\n",
       "      <td>0.001891</td>\n",
       "      <td>[-0.010902896392419013, -0.003491560224995832]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>-0.003035</td>\n",
       "      <td>2.664773e-01</td>\n",
       "      <td>0.002731</td>\n",
       "      <td>[-0.008388718933076215, 0.0023183130679895665]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school</th>\n",
       "      <td>-0.004824</td>\n",
       "      <td>5.901322e-07</td>\n",
       "      <td>0.000966</td>\n",
       "      <td>[-0.006717657373921055, -0.0029311627462750072]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Junior college</th>\n",
       "      <td>-0.005357</td>\n",
       "      <td>1.051011e-01</td>\n",
       "      <td>0.003305</td>\n",
       "      <td>[-0.011835141107864013, 0.0011216886119192592]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than high school</th>\n",
       "      <td>-0.005804</td>\n",
       "      <td>6.186059e-04</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>[-0.009126510318211626, -0.0024808930384135324]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          param        pvalue    stderr  \\\n",
       "all                    0.001087  1.125147e-01  0.000685   \n",
       "Bachelor              -0.007197  1.408424e-04  0.001891   \n",
       "Graduate              -0.003035  2.664773e-01  0.002731   \n",
       "High school           -0.004824  5.901322e-07  0.000966   \n",
       "Junior college        -0.005357  1.051011e-01  0.003305   \n",
       "Less than high school -0.005804  6.186059e-04  0.001695   \n",
       "\n",
       "                                                              conf_inf  \n",
       "all                     [-0.0002553661356612109, 0.002428518308773977]  \n",
       "Bachelor                [-0.010902896392419013, -0.003491560224995832]  \n",
       "Graduate                [-0.008388718933076215, 0.0023183130679895665]  \n",
       "High school            [-0.006717657373921055, -0.0029311627462750072]  \n",
       "Junior college          [-0.011835141107864013, 0.0011216886119192592]  \n",
       "Less than high school  [-0.009126510318211626, -0.0024808930384135324]  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'abnomore'\n",
    "gvarname = 'degree5'\n",
    "yvalue = 1\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.003790</td>\n",
       "      <td>2.351366e-03</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>[-0.006231733743745992, -0.0013479066579696942]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor</th>\n",
       "      <td>-0.017306</td>\n",
       "      <td>1.992308e-06</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>[-0.02444058458187502, -0.010171453047968702]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>-0.015681</td>\n",
       "      <td>4.433129e-03</td>\n",
       "      <td>0.005511</td>\n",
       "      <td>[-0.02648156251583954, -0.004880278427213492]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school</th>\n",
       "      <td>-0.010132</td>\n",
       "      <td>4.905516e-09</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>[-0.013526278301109883, -0.006737554537518539]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Junior college</th>\n",
       "      <td>-0.025549</td>\n",
       "      <td>7.390727e-05</td>\n",
       "      <td>0.006446</td>\n",
       "      <td>[-0.03818363759624023, -0.012914460628362566]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than high school</th>\n",
       "      <td>-0.012809</td>\n",
       "      <td>8.673247e-06</td>\n",
       "      <td>0.002880</td>\n",
       "      <td>[-0.018453559931449825, -0.007164752017908858]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          param        pvalue    stderr  \\\n",
       "all                   -0.003790  2.351366e-03  0.001246   \n",
       "Bachelor              -0.017306  1.992308e-06  0.003640   \n",
       "Graduate              -0.015681  4.433129e-03  0.005511   \n",
       "High school           -0.010132  4.905516e-09  0.001732   \n",
       "Junior college        -0.025549  7.390727e-05  0.006446   \n",
       "Less than high school -0.012809  8.673247e-06  0.002880   \n",
       "\n",
       "                                                              conf_inf  \n",
       "all                    [-0.006231733743745992, -0.0013479066579696942]  \n",
       "Bachelor                 [-0.02444058458187502, -0.010171453047968702]  \n",
       "Graduate                 [-0.02648156251583954, -0.004880278427213492]  \n",
       "High school             [-0.013526278301109883, -0.006737554537518539]  \n",
       "Junior college           [-0.03818363759624023, -0.012914460628362566]  \n",
       "Less than high school   [-0.018453559931449825, -0.007164752017908858]  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'abnomore'\n",
    "gvarname = 'degree5'\n",
    "yvalue = 1\n",
    "\n",
    "pre2002 = gss['year'] <= 2002\n",
    "run_subgroups(gss[pre2002].copy(), xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>degree5</th>\n",
       "      <th>Graduate</th>\n",
       "      <th>Bachelor</th>\n",
       "      <th>Junior college</th>\n",
       "      <th>High school</th>\n",
       "      <th>Less than high school</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>66.521744</td>\n",
       "      <td>64.489792</td>\n",
       "      <td>65.909096</td>\n",
       "      <td>48.397213</td>\n",
       "      <td>33.031456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>71.713150</td>\n",
       "      <td>59.868420</td>\n",
       "      <td>51.369865</td>\n",
       "      <td>47.039143</td>\n",
       "      <td>35.184166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>65.161285</td>\n",
       "      <td>60.095387</td>\n",
       "      <td>49.814125</td>\n",
       "      <td>41.382488</td>\n",
       "      <td>29.060326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>66.535431</td>\n",
       "      <td>57.009346</td>\n",
       "      <td>39.170506</td>\n",
       "      <td>40.535145</td>\n",
       "      <td>28.598484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>58.483757</td>\n",
       "      <td>56.616413</td>\n",
       "      <td>50.239235</td>\n",
       "      <td>46.364494</td>\n",
       "      <td>27.201260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>62.796829</td>\n",
       "      <td>53.030300</td>\n",
       "      <td>42.239185</td>\n",
       "      <td>40.330349</td>\n",
       "      <td>27.013630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>60.975613</td>\n",
       "      <td>46.540878</td>\n",
       "      <td>40.909092</td>\n",
       "      <td>40.782700</td>\n",
       "      <td>27.966103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>63.120567</td>\n",
       "      <td>48.463688</td>\n",
       "      <td>42.553192</td>\n",
       "      <td>40.812798</td>\n",
       "      <td>29.347824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>65.862068</td>\n",
       "      <td>61.657036</td>\n",
       "      <td>50.678738</td>\n",
       "      <td>41.760563</td>\n",
       "      <td>29.756100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>66.230370</td>\n",
       "      <td>56.739815</td>\n",
       "      <td>48.547718</td>\n",
       "      <td>44.581860</td>\n",
       "      <td>31.435644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "degree5   Graduate   Bachelor  Junior college  High school  \\\n",
       "year5                                                        \n",
       "1972.0   66.521744  64.489792       65.909096    48.397213   \n",
       "1977.0   71.713150  59.868420       51.369865    47.039143   \n",
       "1982.0   65.161285  60.095387       49.814125    41.382488   \n",
       "1987.0   66.535431  57.009346       39.170506    40.535145   \n",
       "1992.0   58.483757  56.616413       50.239235    46.364494   \n",
       "1997.0   62.796829  53.030300       42.239185    40.330349   \n",
       "2002.0   60.975613  46.540878       40.909092    40.782700   \n",
       "2007.0   63.120567  48.463688       42.553192    40.812798   \n",
       "2012.0   65.862068  61.657036       50.678738    41.760563   \n",
       "2017.0   66.230370  56.739815       48.547718    44.581860   \n",
       "\n",
       "degree5  Less than high school  \n",
       "year5                           \n",
       "1972.0               33.031456  \n",
       "1977.0               35.184166  \n",
       "1982.0               29.060326  \n",
       "1987.0               28.598484  \n",
       "1992.0               27.201260  \n",
       "1997.0               27.013630  \n",
       "2002.0               27.966103  \n",
       "2007.0               29.347824  \n",
       "2012.0               29.756100  \n",
       "2017.0               31.435644  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.sort_values(by=2017, axis=1, ascending=False, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rUfa2ls+F+lJdgHqJvYAKGr/vEOx7C/XCu8XY103ADVLKhSGb/QJV9b8I5QF+GlU11laZftR1M6Fi215EvYTdEZj0BapF7OfAmyjx+cuQ9N+ixNBtxnYfo8IJdhxhub9CtXx8DiXyRqHu5YB4qEX9mXyH8kaNAc4w/nyqUffTO8AWVOvVu6WU/2zHnp2oQPOFhi3bUc9Tq0gpn0bd95ehYg4Xo1ordnTc4eW8RcfXO5Jy/CgP5yjUc/oI6tp0xjVubX/7UOfYD3xg5H/E2F9gnw8AL6Gu4bfGujdp/2MlfD+lqOf1HNT5+R1wa6T5Q8r5GiXqX0U9v+0d3+cor9SiDtbBYb5zO7D1SK5lRxz0HjM8mmegPpRWoloJv4pyMrRlY6R5Xkadt6Wod/4zqI/PAHehPs620Vyb9CfUuXwf9VFZb5Sj0XQKge5BNAZCiPXAy1LKe7rbFs2xjVD9kaVIKSPui1DTNRztayGEWA4skVL+/GjsT6PRaMLpbVXAXYYQoh9wEapbgSe61xqNRnOsIITIQfUr+AXqnXstMNqYazQaTbegBWAzJag4i+vCuiXQaDSaI8EPzEGFkJhQ1Y5nSCk77NJGo9FougpdBazRaDQajUbTx+htjUA0Go1Go9FoNEfIMS0AhRC/F0Ks7Yb9XmH02t6tZRzGPlsMLN6F+1kkhHg4ZNkphPi3UIPBBwdmP8QypRDix51qqCp3pxCitU5+ew2d9RwYI11c0QkmHep+U4zrO/0Q871jNO7QaHotkbyDwreJME+3PM+ansMxLQA1rSOEmG78oaaEJd2E6uOrqzkP+H8hy1eiBpGfiuofbU9rmbqJ4zD63OvFPIAaukvTC+muD9lI6Mm29TF67HtKCNFfCPGEUEMY1gshlgohJnecU9PVaAHYxxBCtDkclpSyOjD6SVcipawwOsgNMBTYIKVcI6Xcb4x32yOQUpa20wlsj6G16yqEMAkhzFLKOilleXfYpdH0FcSRD/152HTFe6oTj2cUqrPtn6D6S9wJvB02eoqmG+g1F0AIMVMIsVgIUSmEqBBCfCiEOGiA+gjKmSuEWC+EaBRCbBZC3BJ6IwohcoUQXxjpm4QQZ4a7yoUQ9xppDYar/a9CCPsh2nGrUIN81wshioQQTwshElrZbpZhZ6MQ4nMhxOCw9OuEEFuFEE3G/JqwdCmE+KkQ4j9CiHpUz/yfG8mlRvrzxrYtqoCFEDYhxN+FECXG/r81eqUPpAc8iacYX3UuIcQyIcS4Do49WAUshFiE8jyeaJS1qI088UKIl4QQBwxbtgshbg7bLEkIscA4p9uFEJeGlZEphHjNuIcqhRDvCiGGdWBreNXKdSHXo9S4D1ttTS+EGGgc04XGPdUghFghhBglhBghhPjasPUroUZPCeQbIoT4rxBiv5G+XAhxdljZO4Xyvjwr1CgcLwsjbMC4Z9eiOrrNF614aSJ4DoYa1ynwHHTYP54Q4jghxEdCiDIhRI1xXJPDtpFCiGs7uE7HCSG+N/a9ApgUwb6dxv1bZ9yvB3XeK4RIFEK8YFz7BiHEJ0KIwrBtTjCulUuo5/IxoYY1C6SfaDwHdUKFLCwVajjKtuzaKYT4jVAekBohxF4hxO1h22QLId4UQtQa03+EEFlG2hWoTp8LjXMnRSvVdkKIGCGERwgxKWTdXiHEhpDlGcY5jzKW230HhdxPpwgh1hrbfR64VyO1zdg20nu6zXNl3OvvhOUxCSF2CzVyTWv7DbyjzjDuqQah/keyhBAnCSFWGcf4jlBDz4WW+1shxB4hhFsIsUYI8aOQ9MCzfZEQ4jMhRANqhJcOn6027DzLuJcahBDlQoiFouV/ir2De6jdKl/RwfN8JMcjOnimpZQfSil/IaVcKqXcjOoEOwk1WpKmO5FS9ooJNarCbGAY6oviX6ixUq3t5Pk9sDZk+RrUSCI/Rg2pMws1TufPjHQTqif/T1EjKkxG9dzuAa4IKee3qN7/BwJnokYOuTsk/QqgroPjuRk1ZutAVPXcauClsDI8qBFCpqC+nL5EjbYQaL19rrHNz1Ajb/zcWJ4VUo5EjY5xNWqYoiGoKlgJFADpQLyx7fPAOyF5/2Gcr7NQQ089hfqS62+kTzfK+Q74AWq80g9RI5uIdo59EWqoJFAvgmdRI5KkA0lt5HkI1dP+ROOcTQfODzvOvagq7KGoYfiaUOPzghqZYLNxjKMMW58GdgHOdmzdCdxm/J6AGobqEiAH1ZfbLYCljbwDDbs2GfdJHkp8rzXmP0CNI7sMNeZnIN9oYB5qyKihwK+NY8kLs6sGNZLDUNRzcYVh39eoeyYXNXbq7zn052AN6n4ba5S1jLDnoJXjPRk1Iki+cawPo4bzSjmE6xSNul8XACNQ/edtMPJNb2ffj6KGHTzdyLfAOD/Ph2zzX9RQgCca5/ZtVLiBw0gfibq/f2Gcz0nAN8C/jXSLcTwPoJ6jPNTQe/kd3D/lqGd0KOoZlcBkI12gRnD5GlWNNwE1WsgyI81h7G8j6vlID9jbyr6WAncYv4ehRolpoPl5vQf4+DDeQZ+gnrtRqGHIPjTSD8W2SO/p9s7VZNT93T8kz+lGOalt7Hc6ze+oaTSPKrIE9Z6fZJzzHcBDIfluMe6fi1HP0R9Rw02OCXu2d9L8HGXRwbPVho0zjeP6E+qdPAo1opAzkvMS/p5q5b3V4fN8JMdDB8902LHGo0bKermt86Gnozd1uwGHbbj6o/ABU9vZ5ve0/OPbDVwWts3NwHrj9+nGg5gZkn6CcYNf0c5+5qEGRw8sX0EHArCVMmaihjgyhZQhgSkh2+QYx3yqsbwEeDasnOeBr0KWJSEvNmPddGN9Sit53wk5v03AnJB0M2qooj+FlXN6yDZTjHVZ7RzrIgwBaCw/DCzq4Py8DTzXTroE/hKybEENSXepsXwlajg0EXY85cBP2il3J80v0vNQw6vFRnhNBxp2XRey7mxj3XmHcr+gRMFvwuxaGLZN4J4Zf4TPwWnGfZYdkj6VDp6DVmwWqD+PSw/hOgXGko0J2eZS2hGAKE+CG7gkbF0VhgBECSKJGjYysE28cT2vNpZfBJ4JK3uMka8f6mNFAicdwjnYCbwatm5L4FoCM4xzPTAkfTCq78DAc97i+rWzr/toFmfXoIYQ+wK4yFi3BPh1O/nbegcND9nmEtR7wXQoth3CPd3muTKW12KIXGP5dQyB3sY+pnPwO+pnxrpx7TwjRcBdYWUtAv5p/B5olPGLsG3afbbasHEJ8Nrh3kMh27QlADt8no/keOjgmQ5ZHwd8j3qX2w7nntFT5069qQp4iBDiFSHENiFEDarjZhOtj0XbWv5U1FiLTxgu/zqhWtnei/qaB/VFv0+q8R0D/A/1Mg4t68dCVW/tN8r4W6R2hJRxshDiY8OdXwv8B7CivqID+FFfrgBIKXcB+1BfiaA8LUvCiv4qJD3A4XQ4OwSICi1fqti8b1opf3XI733GvN9h7LM9HgN+YlTZPCCEOKmVbYJ2SCm9qDE1A3aMR33B1oZc+2ogkebr3xEfozyGO4QQLwshLhdCxEaQL/T8lBjzNWHrooUQTgAhRLRQYQXrhaqurEN5KcLvsdauqxflKW2VCJ+DfKBISrk7JOtSwp6DVsruZ1RTbRZCVKM8UP1asbu965QPrJZShraA/6a9/Rp2W0O3M/KHnuN8w/7QbaqNbQL383jg0rDzErj/h0gpK1AfSR8KFT5wqxBiQAe2tTheg320PN59UsqdIXZtp+VzHimLgClCVfFOR3mZFwHTjXvrOELG8I3wHeSWUm4Ksz0KSDgUww7hnm7vXIGqhZhrlJkE/AhVpdgRkTyD/Yxy44AMDvHdGuGz1RpjUd7ISO2Hg89LexzK83y4x9PeMx3gaiAFVXPTGeM5a46Q3jQSyELUV9l1xtyL6lG/zUYNYQTE7jxUdUtrCNTXTJsIIY4HXgP+gKomqAJ+iKoKiQihhoZ6F/UyuwvlhRqHGkQ80uMJ0Jq94evqD7FMUOci0vI9raR16seFlPJ947ydAZwCvCuEWCClnNuGHQFbAnaYUMLowlaKr4jQhlqh4htPRHlu/h/wZyHEcVLKfe1kbe38tHfOHkB5Y25Dfem7UN6p8Hujtevqlu03oon0OTgcXgDSUM/FTpQ36VMOtru963Q4+44kT3vbhJ7/p1EfdOEUAUgp5woh/o66Pj8E7hFCnCOl/LCd8js63rbeOe2+i1phMWBDCb2TgL+jPKFPoDzzHowPykN4B3nbsOlQn+9I7+n2zhXAS8B9QsUij0WN3vRRBPs/6HmTUoavCz+mQ323RvJsHS4dnZf2OJRn6nCPJxL7BgE7tPjrOfQKAShUcG4+8FMp5efGunEcgv1SyhIhRBHqS/7FNjbbAGQKITJC/tAn0PJGnoL6mro7xL6cyI8mWKYVuCXwZx0elGtgQr3Mvza2yUZ9mQYCuzegXPnPhuSZihLG7dFkzM3tbLPV2G4qsN3YvxkVh/NKB+V3CVIN0fcS8JIQ4n3gVSHEvAhfKMtRYz2XySNo6Wx83X4GfCaE+B0qXu1s4MnDLbMVpgIvSinfADCCwYegYhiPiAifg/Wo52CAlDLQJc9EOv7DmQrcKKV817A7DdWtz6GwHrhcCBEtpQz8GR3fQZ6tqD+g42m+V6NRsYDbQso1oe7fL41t4lAxac8Z2ywHCqWUW9vbmZRyFSoW9z7jPrwcFft6OATO9cCAF1Cohl4ZND/HTbT/rAbsqhNCLEdVo8eijicK5WW7BPg6RPRE+g7qiIhso5PuaSllhRDiP6iQjrGoKv5O7TVASlkjhNhn2PxZSFK779YIn63WWIH6qH3qcOyNgMN6no/geNriflTcqKaH0CsEICrwugy4RgixB8hE3UzhX6cd8XvgIaFaTb6HejmOQ8X8/QVVxbcJeEGoFlUO4P+M/QS+/DajHqZLUNVJp6OExaGwBfXw3Wy8zI5HxVWE4wX+LoS4CRXM/TdUI5VPjPT7gQVCiO9RX8EzUS/68zrY/y7jeM4SQiwEGsKq3JBS1gshHgPuFUKUoYKkb0F5eI56f1NCiD+i/tDWoe7b84Dth/A1+TLK+/BfIcRdqNiWAagqpMellFsisOFs1J/Wlyiv4Q9Qf7Qb2st3GGwGzhVC/BclbH4HHFIr8w74Pe0/B5+gAvtfFELcgnoO/kbHz9tmVBXqUlQM6V9p/tiIlFdQjRWeNa55BqrBQJsYwucZlCArRVWP3UWIMJFSbjHO5xNCiECc4T2oQP/AB819wLdCiMdRXrNaVFjILCnldUK1fr0OFcNUhIrVG4UKTzhcPkGJyZeFEDeivDUPoe71gPjYCeQYH727gdp27vtFqEYsHxjCyGdcj0tR91GASN9BHRGpbZ15Tz8FfIC6bzu983eD+4E/CiG2oOLWLkU1IhnfQb7f0/6z1Rr3AAuFEFtR96JAxe09ITuna5fDfZ7h8I6nLa5H/XfPOcR8mi6iV8QASin9wAU0t+B6BNUS95BcyVLKp1FfjpehXrqLUV/LO0L2cy6qGuU7VJXWPSix1GhssxD1cvg7Ku5hBurP5lDsWI3q+uRW1NfZ1ShxEo7b2P+LqJgNE6rxQKAK4y1Ui7BbjHJuAm4wbGxv/0WoF/A9qNiXh9vY9Feo1tbPoapPRwEzpZTFkR1ppxI4F6tQsTmxqBZpEWG8SE9EeYgWoF6IL6BiACsjLKYKOIfmF+ptqAYEiyO1I0JuRXkWF6MC+b81fncKh/AcmFD33YuoFoodPW9Xoqocv0eFSTyLEgiHYlsdyqM6DCWCHkDdhx1xGyrm7U2aW1p/GbbNXNRz/bYxd6Lu5wZj36tR98hAVOOJVagWjYGYMReqRegClKB5AfVhcd+hHGMoxrN8DipmapFh+37gnMBzDryB+vP91NiuvQ/Oz1HCd1F76w7hHdQRkdrWmff0IlSr00VSym0dbHu4zEe95/+KupfOBWZLKVe2l6mjZ6uNPO8Z5Z+B8gZ+gfq4bDfmNlKO4Hk+rONph/4cYqy8pmsRze8YTWsIIUajxM8EKeX33WyORqPR9GmEEA6UB/bnUsqXu9sejaa3ogVgGEKIc1GBsFtQnoD/Q7nkx0p9sjQajaZbEKrz4UAjo0uAQVLKQw0x0Gg0Br0lBvBoEouq0hmAqhpchAqU1uJPo9Fouo9sVNXjXmCuFn8azZGhPYAajUaj0Wg0fYxe0QhEo9FoNBqNRtN5aAGo0Wg0Go1G08fQAlCj0Wg0Go2mj6EFoEaj0Wg0Gk0fQwtAjUaj0Wg0mj6GFoAajUaj0Wg0fQwtADUajUaj0Wj6GFoAajQajUaj0fQxtADUaDQajUaj6WNoAajRaDQajUbTx9ACUKPRaDQajaaPoQWgRqPRaDQaTR9DC0CNRqPRaDSaPoYWgBqNRqPRaDR9DC0ANRqNRqPRaPoYWgBqNBqNRqPR9DG0ANRoNBqNRqPpY2gBqNFoNBqNRtPHsHS3AZGQkpIiBw4c2N1maDQajUZz2Hz//fdlUsrUw8jXz2KxPA2MQDtuNJHjB9Z6vd6rx48ffyA8sVcIwIEDB7Js2bLuNkOj0Wg0msNGCLHrcPJZLJan09PT81NTUytNJpPsbLs0xyZ+v1+UlpYW7N+//2ngh+Hp+ktCo9FoNJqezYjU1NQaLf40h4LJZJKpqanVKM/xwelH2R6NRqPRaDSHhkmLP83hYNw3rWo9LQA1Go1Go9F0iNPpHBu6PH/+/OQ5c+ZkA/z1r39Nffjhh5Pbyx+6/ZHw0ksvJXz//ff2SLYNt1nTTK+IAdRoNBqNRtNz+eUvf1l6tPb11ltvJXi93urx48c3Hq19ejweoqKijtbujgraA6jRaDQajeaIuPXWWzPuuuuuNIAvvvjCmZubWzBmzJi86667LmvYsGGFge32798fNW3atGE5OTkj5s2blxVY/5///CduzJgxeQUFBflnnHHG4OrqahPADTfckDlkyJDC3NzcgmuvvTbr448/jv7kk08SfvOb32Tl5eUVrFu3zhZqx8aNG61jxozJGzFiRP5NN92UEZr229/+Nm3EiBH5ubm5Bbfccksw7fbbb+8/aNCgwhNOOGHYrFmzBgWOY+LEicN/9rOfZR533HHD//SnP6UtXrzYedxxxw0vLCzMnzp16rBdu3ZFAaxbt842bdq0YYWFhfnjx48fvmLFioi8k92N9gBqNBqNRqPpELfbbcrLyysILFdXV5tnzJhRHb7d1VdfPejRRx/dOWPGjPobbrghMzRt/fr1zlWrVq13OBz+oUOHjrjttttKoqOj5Z///Of+X3755ea4uDj/r3/96/S777477fbbbz/w3nvvJW7fvn2tyWSirKzMnJKS4jv11FOrzj777Oq5c+dWhu/7hhtuyL766qtLf/azn5X/5S9/CXa585///Cdu69at9tWrV2+QUnLqqacOff/992Oio6P9CxcuTFyzZs16j8cjxowZUzB27FhXIF9VVZX5f//73ya32y2OP/744e++++7WjIwM71NPPZV42223ZS5YsGDn1VdfnfPkk0/uGjlypPuzzz6Lvv7667O//fbbzZ135rsGLQA1Go1Go9F0iM1m82/cuHF9YHn+/PnJy5Ytiw7dpqyszFxfX2+aMWNGPcDll19e8fHHHycE0qdOnVqTnJzsAxg6dGjjtm3bbBUVFeZt27bZJ06cmAfg8XjE+PHj65KSknw2m81/4YUX5px11lnVF1xwwUFiM5zly5fHvP/++9sArrvuuvK77747C+CDDz6I+/LLL+MKCgoKAFwul2njxo322tpa0xlnnFEVExMjATljxoyq0PIuuuiiCoDVq1fbtmzZ4jj55JNzAfx+P6mpqZ7q6mrTihUrYs4///whgTxNTU3iEE5rt6EFYA+muKqBVXurqKhvIinayuisBPonOLrbLI1Go9FoWkXK9hsrW63W4AZms1l6PB4hpWTq1Kk1Cxcu3BG+/cqVKze8/fbbca+99lriY4891i8Sz1prLaallNx8883Ft99+e1no+j/84Q/92isrNjbWb+QXQ4cObVi5cuXG0PSKigpTbGysN1QY9xZ0DGAPpbiqgY/Xl9DQ5CMlxkZDk4+P15dQXNXQ3aZpNBqNRtMqqampvujoaP+nn34aDfDSSy8ldZRn+vTp9cuWLYtZu3atDaC2tta0evVqW3V1tamiosJ8wQUXVD/++ON7NmzY4ASIiYnx1dTUtKpfxo0bV/fUU08lATz11FPBVslnnHFGzUsvvZQSiC3csWNHVFFRkWX69Ol1H374YbzL5RLV1dWmTz75JKG1ckeNGtVYUVFh+eSTT6IB3G63WLZsmT0pKcmflZXV9OyzzyaC8gx+8803vcJTowVgD2XV3ipi7RZi7VGYhCDWHkWs3cKqvVXdbZpGo9FoNG3yxBNP7Lz++utzxowZkyelJDY21tfe9hkZGd4nnnhi54UXXjg4Nze3YPz48Xlr1qyxV1VVmWfOnDksNze3YNq0acP/9Kc/7QG45JJLKubPn5+en59/UCOQRx99dPeTTz7Zb8SIEfnV1dXmwPrzzjuv5vzzz6847rjj8nJzcwvOPffcIVVVVeaTTjrJNXPmzOqCgoLCM888c8ioUaPq4+PjD7LXbrfL1157bdsdd9yRNXz48ILCwsKCL774Igbg1Vdf3f7cc8+lDB8+vGDYsGGFb7zxRkKnnMguRnTkru0JTJgwQfa1oeBeWbqLlBgbJtEcSuCXkrI6NxdPyulGyzQajUZzOAghvpdSTjjUfKtWrdo5evToso637BlUV1eb4uPj/QB33nlnenFxcdRzzz23p7vtaouAvbW1tabJkycPf/zxx3dNnTrV1XHO3sGqVatSRo8ePTB8vY4B7KEkRVupd3uJtTf3O1Tv9pIUbe1GqzQajUajaZ9//etf8Q8++GB/n88nMjMz3a+88srO7rapPS699NKcLVu2ONxut7jwwgvLjyXx1x5aAPZQRmcl8PH6EgCibRbq3V5qG70cP7jdjtY1Go1Go+lWrrnmmsprrrnmoC5aeiqtNT7pC+gYwB5K/wQHMwrScFjNlNW5cVjNzChI062ANRqNRqPRHDHaA9iD6Z/g0IJPo9FoNBpNp9NlHkAhxHAhxMqQqUYIcbMQIkkI8bEQYosxT+wqGzQajUaj0Wg0B9NlAlBKuUlKOUZKOQYYD7iAN4E7gE+llMOAT41ljUaj0Wg0Gs1R4mjFAJ4CbJNS7gJ+BLxgrH8BOOco2aDRaDQajaaTyMzMHFlcXGwBcDqdY7vbHs2hcbQE4IXAq8bvNCllMYAxb3cYFo1Go9FoNBpN59LlAlAIYQV+CCw4xHzXCiGWCSGWlZaWdo1xGo1Go9FoOuTUU08dUlhYmD906NDCBx54IKW77dEcOUejFfAZwHIpZYmxXCKE6C+lLBZC9AcOtJZJSvkk8CSokUCOgp0ajUaj0fR4nnrqqeHh6/Lz8yumTp1a6na7TS+++OKw8PRRo0aVTZo0qby2ttby2muvDQlNu+aaazZ1tM+XX355Z1pamq+urk6MHTu24NJLL+01/fxpWudoVAFfRHP1L8DbwOXG78uB/x4FGzQajUaj0Rwm9913X9rw4cMLxo8fn79///6odevW2bvbJs2R0aUeQCGEE5gBXBey+l7gX0KIq4DdwPldaYNGo9FoNMcS7XnsbDabv7302NhYbyQev1Deeeed2C+++CJ22bJlG2NjY/0TJ04c3tDQoAeS6OV0qQCUUrqA5LB15ahWwRqNRqPRaHo4VVVV5vj4eF9sbKx/xYoV9lWrVkV3t02aI0creI1Go9FoNG0ye/bsaq/XK3JzcwvuvPPOjNGjR9d3t02aI0cPBafRaDQajaZNHA6H/PLLL7eEry8qKloT+O1yuVYcXas0R4r2AGo0Go1Go9H0MbQA1Gg0Go1Go+lj9KkqYJ9fctrfviAtzk5GgoOMBAeZCc2/M+IdOKzm7jZTo9FoNBqNpkvpUwKwweMjv38c+6oa+GpLGQdqG/GHdTGd6IwKEYcOMgyB2D9eLafG2jCbRPccgEaj0Wg0Gk0n0KcEYIzNwsMXjwsue3x+Smoa2VfVyL6qBoqqGthnTHsqXHy7vZzaRm+LMiwmQXq8PSgQ+4f8zjAEY6w96mgfmkaj0Wg0Gk3E9CkBGE6U2URWopOsRGeb29Q0eigOEYjF1Q3sq2qkqKqB/+2sYH91I94wN2Ks3UJGfLP3MFQg9o+3kx5vJ8qswy81Go1Go9F0D31aAEZCnD2KuPQohqfHtpru80tKa93sq272HgYE4r6qBlbtraaivqlFHiEgLdYeFIihnsTAcoIzCiF0VbNGo9Foup89e/ZYbrjhhgErVqyIiY+P90ZFRclbb711/5w5c6oOp7xbb701IyYmxvfHP/6x5FDzbtq0yfr555/HzJs3r+Jw9q1RaAF4hJiNKuH0eDvjshNb3aahyddCIBZVNVJc1cC+6gbW7avho/UlNHn9LfI4oswtBGJgnpmo5tqLqNFoNJqjgd/vZ9asWUMvvvji8oULF+4A2Lx5s3XBggUJodt5PB6ioro+BGrLli22119/PUkLwCOjTwnA4qoGVu2toqK+iaRoK6OzEuif4Ojy/TqsZoakxjAkNabVdCkl5fVNLTyI+wyBWFTZwIbiWsrq3C3yBLyIAUGYERSHdjITnGQmOoix9anLe8h01/2g0Wg0vYmFCxfGRkVFyV/+8pelgXW5ublNv/71rw/Mnz8/+f333493u90ml8tl+vDDD7fOnDlzaHV1tdnr9Yq77rpr36WXXloF8Ktf/Sr99ddfT8nIyGhKTk72jB071gUwceLE4Q888MCeE0880VVcXGyZMGFCflFR0ZpNmzZZL7744kGBcYf/8Y9/7J4xY0b9r3/968zt27fb8/LyCi666KKyX//61wd++tOfZi1ZsiS2qalJXHPNNQduv/32sm45Wb2IPqMQiqsa+Hh9CbF2CykxNurdXj5eX8KMgrRu/9MXQpASYyMlxsaorIRWt2n0+CiubqSoUonEvQFvYmUDK/dU8f7aYjy+lrGIcXYLmYlO5TlMUGIx1JOYEm3D1EdbNPfk+0Gj0XQvPfnj8PZ/rxqweX9t24Hrh0Fueqzr/h+P3tNW+po1axyjRo1ytZW+fPnymNWrV69LS0vzeTwe3n333a1JSUn+4uJiy6RJk/IuvvjiqiVLljjffPPNpDVr1qz3eDyMGTOmICAA2yIjI8O7ePHizU6nU65Zs8Z20UUXDV67du2Ge+65p+jBBx9M+/zzz7cCPPDAAynx8fG+tWvXbmhoaBDHHXdc3qxZs2ry8vKa2iu/r9NnBOCqvVXE2i3BFrqB+aq9VT3mwW4Pe5SZQSnRDEppfQxuv19SWudmb2VDixbNRZUN7K10sXTHwS2arRYTGSGtmAMCMcv4nR5vx2Y5NvtF7O33g0aj6Rr0x2HHXHbZZdnfffddTFRUlLz22msPTJs2rSYtLc0H4Pf7xc0335z17bffxphMJg4cOGDdu3ev5fPPP48588wzq2JjY/0Ap512WlVH+2lqahJXXXVVzvr16x0mk4ldu3bZWtvuk08+idu4caPz7bffTgSora01r1+/3q4FYPv0GQFYUd+EJaqG1RWbqfNWEWNJIMuZS0N9XHeb1imYTIK0ODtpcXbG57Qei1jT6AmKwiJDJAZ+f7mllAO1bmSIE1EISI2xBauXs8JiETMSHMQ7emeXNxX1TaTEtHyXRNssB1W1azSaI6cne9TC6ekfh+156rqKkSNHNvz3v/8N/rG89NJLuwNVtQBOpzMYxP7EE08klZeXW9asWbPBZrPJzMzMkYEq3LYaNlosFunz+QBwuVzBje655560fv36ed54440dfr8fh8MxvrX8Ukrx4IMP7p49e3ZNZxxvX6HPCECTpZrlZUtIcMQRG5WI29/A8rKvGJM8pbtNO2oEWjTnpbcuepu8fvZXN7K3ymVUNTdSVOWiqKqB9ftq+LiVxiqxNktI/GFzLGKWIRhTYnpmNXNStJV6t7dFn431bi9J0dZutEqjOfboDR61ereXXeUudpXX89bKIurdPkpr3UTbzNwwfWif/zicNWtW7W9/+1tx3333pf7qV78qBairq2u1FWJ1dbU5JSXFY7PZ5MKFC2P37dtnBTj55JPrrrzyyoF33313scfjER9//HHC5ZdfXgowYMAA93fffRf9gx/8wPXyyy8nhpaVlZXVZDabefjhh5MDIjE+Pt5XV1cXrJ6aMWNG9WOPPZZ69tln19psNrl69WrbwIEDPXFxcX40bdJnBKDZsRfpdyB9NjAJpM+G9DswO/YCo7rbvB6B1WIiO9lJdnLr4SV+v2qsEvAcBqqaA9XO3++qpLrBc1CZmYbXMCux2XuYlagaqqTH2btlZJXRWQl8vF71PhBts1Dv9lLb6OX4wclH3RaN5limp3jUaho97CpzsbO8nl3l9ew0BN/OcheltS3FXYzNQlqcjaRoZV9f/zg0mUwsXLhw209/+tMB8+fPT09KSvI6nU7f73//+70B716Aq6++uuKMM84YOmLEiPzCwkLXoEGDGgGmTp3qOvfccytGjBhRmJmZ6Z44cWJdIM8dd9xRcsEFFwx+7bXXkqdNmxb04t18880HZs+ePeStt95KnDp1aq3D4fADTJw4scFiscjhw4cXXHzxxWW/+c1vDuzcudM2cuTIfCmlSEpK8rz33nvbjtb56a0IKWXHW3UzEyZMkMuWLTuiMhZsWoDwx7K73EVto5dYu4XsZCfSVMv5w8/vJEs1dW6vUa3sMuIPVYOVvZVKNIZ/RQdGVlEC0Rmsas5KVEKxf7wDq6VrurvpTdVSGk1v5ZWlu1RNQEj1n19KyurcXDwpp9P2I6WkyuUxBJ7roHl4f6xpcTZykqMZmOw05tHkJDuxW0x8va2cWLulxcdhZ3gshRDfSyknHGq+VatW7Rw9erRu1ao5LFatWpUyevTogeHr+4wHMNGeSKO3kXE5ScF1dU112C2tx8tpDp1wQXVq/sEvzEaPL+hB3BsmFL/eVsb+msaD4hBDu7sJCMOsYOtmBw7r4TVU6Z/g0IJPo+liOjPcQkpJWV3TQR68XeX17CyrpyakoZsQkBHvICfZyemF6c1CL8VJdpITp7Xtv79om4VVe6soq3OTFG3l+MHJ+l2hOeboMwJwRMoIFu1ZBIAzyonL46LOU8eE9EP+GNO0QqRxPvao9vtEDI1DDHgNVTWzixV7KnlvTfFBQ++lxFhbVi0HqpyT1FyPzazRdB+HGm7h90sO1LoPrqotU/P6Jl9wW5OArEQnOclOfjQmk5xkJwMNkZeV6MQepT8ONZq26DMCMD06nekDprO2bC3lDeUk2hOZkD6B9Oj07jbtmKCz4nw6ikP0+SUlNY0hXkRXMA5xY3Etn2w4cFBDlXhHVIhAbPYkqnGgVUtmPeyeRtM19E9wMKMgrYVH7biBSfikZMnWsuaq2jI131VRT6On+Rm2mATZSUrkTRyUpDx5KarKNjOh60JENJpjnT4jAAHSvT7S6xvAVQc+OyT4Os6kiYij1a2K2SSCYyYfN/DgdL9fUlbvDvEcNgvFXeX1fL21rIUHAVSH2QNTosPigdQ8JcaqxaFGcxhUN3jYW+liT4V6/vZWNrCnQsXk7alooMnXLPKsFhM5SeqZmzYsxRB4ypvXP96ORQ97qdF0On1HAFYXwab3wB4HMf2gqU4tDz8T4jO727peT0/pVsVkEvSLtdMv1s7YVsZmDgSKB8ShEobqT2nVnireXb2P0BrmaKs5GDcUHjDeL7ZndnGj0RwNaho97A0Vd8Y88FyFdzwfbTUzIMnJsH6xnFqQFmx0MTA5mvQ4u36WNJqjTN8RgEXLlfizGX3gBeZFy7UA7AR6S7cqQggSo60kRlsZkRl/UHqT109RVYOqliprjj/auL+Wj9eXtBhuzx5lIifJ+BNLiSY7yRn8U8tIcHRL9zYaTWdR5/Ye5MHbGyLywrt8clrNwdCKiQMTgyEWgXmCU4daaDQ9ib4jAF1lyvMXijUG6g50jz3HGK3F+fTGlnNWi6l5yL3hLdN8fsm+qoaQriWUQNxZXs8Xm0txh8QeWs0mspIcLbwcgXlmooMoXaWl6Wbq3d4wUWeIPaMBVpWrpcBzRAUEnoNx2YlBcTcgSc0TtcA7pjGbzeOHDRvWIKXEbDbLf/zjH7tnzJhRf6jlzJ49e+DZZ59dPXfu3MpI8zidzrEul2vFoe5L0z59RwA6U1S1ry1kFIymOrVe0ykc6y3nzCbBgCQnA5KcTB3W8r7x+yUltY3BloqhXVR8u70cV0jcodkkyEp0tBpzOCDJccyOv6w5uriavMEulva04sEL7xfPZjGRlehgQJKTMQMSDvLgJUfreNi+jM1m82/cuHE9wBtvvBF35513Zs2YMWNTd9sVjtfrxWLpO9LmSOg7ZylznIr5A+X5a6qDxhoYPrV77dIcE5hMgv7xquPqyUNaVntLKSmtc7do6Rho+bhid2WLWKlA32WtxRxmJzkPu89DzbGHq8nLvqoG9oTE3e2tbGBvhZqXhwk8qyHwshKdjMiMV2IvROTpBk+aSKmurjbHx8d7jd+mmTNnDq2urjZ7vV5x11137bv00kurAB5++OHk+fPnpwkhyM/Pb3jrrbd2AHzxxRcx8+fPTystLY26++679wa8gb/97W/T3nzzzaSmpiZx1llnVf3tb3/bF7pfv9/P9ddfn/XZZ5/FCyHk7bffXnzNNddUvvPOO7F33313/379+nnWr1/v3LZt27qjfEp6JX1HAMZnqgYfRctVta8zRYk/Hf+n6WKEaG6YctzApBZpUkoqg6MXGN1gGALxg7X7D/LS9Iu1kRJjIznGSnK0laTo0N9WkmOa18XaLPoPvZdS2+hhf3UjxdWNzfMaNT63Wm5o0ekxqLCDQFdHp2XEB6trA9W0KdG60dIxwVs/HcCB9a33k3W49Ctwcc4je9rbxO12m/Ly8grcbrcoKyuLeu+99zYDOJ1O/7vvvrs1KSnJX1xcbJk0aVLexRdfXLV8+XL7Aw880P+bb77Z2L9/f29JSUnw67WkpCRq2bJlG1euXGk/99xzh86dO7fyP//5T9zWrVvtq1ev3iCl5NRTTx36/vvvx5xxxhnBIeNefPHFhDVr1jg2bNiwrri42DJx4sT80047rQ5g9erV0StWrFiXl5fXdLD1mtboOwIQlNhb+TKkjYDs48GZ1HEejaYLEUKQZIi3ca20Wq5u8LA7JOZwT0UD5fVuYzQENbxVndvbSskQZVZlJxuCMLCflBhbyG8lGJOircTZtWDsaqSU1DR4Ka5paCnuqtVyYF1r1zQlxkb/eDs5yU6OH5xEeryD/vH2YAxeaowWeJquI7QK+JNPPomeO3fuoM2bN6/z+/3i5ptvzvr2229jTCYTBw4csO7du9fy4Ycfxs2aNauyf//+XoC0tLRgHMwPf/jDKrPZzPjx4xvLy8ujAD744IO4L7/8Mq6goKAAwOVymTZu3GgPFYCLFy+O/clPflJhsVgYMGCAd9KkSXVfffWVMz4+3j9q1Kh6Lf4Ojb4lAOvL4csHwGf0TdevAHJOgOzJah6X0b32aTRhxDuiGJkVz8isg1ssB2j0+Kiob6KivomyOnfI7yYq6tVyeX3kgjEp2kZy0JvYlqfRpgVjGAFvbnF1Q6veu8CyK6wfSiGUZzc93sHQ1BimDUuhf7w9KPDS4+ykxdlbdHgcOuxiaa2bjHiHFn99hQ48dUeDU089tb6ystJSXFxseeONN+LLy8sta9as2WCz2WRmZubIhoYGk5QSIYRsLb/dbg+ul8bYn1JKbr755uLbb7+9zTGPpWy1OEB5Ig//iPomfUsARifDHbth33LY9bWaVr0G/3tapScOhJwpzaIwabB6O2s0PRh7lDnYOXYkhArG8vomyuvcrf7es8dFeV37gjHRebBXMTnaisUsqHJ58EtJotNKQf840hPsRJlNWM0mLGZBlNlElMlElEVgMZmIMoseKyj9fkmFq4niKlX9ur8mVOA1Cz532Cg0ZpMgLdZGeryd/PQ4fjC8H/3j7fSPd5Aeb6d/vJ3UWNshtQqPdNhFjaarWLFihd3v95OWluatrq42p6SkeGw2m1y4cGHsvn37rAAzZ86s+fGPfzz0zjvvLElPT/eVlJSYQ72A4Zxxxhk1v//97zOuvfbaivj4eP+OHTuirFarzMzMDL6ATjrppNqnnnoq9Wc/+1n5gQMHLN99913M/Pnz96xevVrf+IdB3xKAAFF2JfByTlDLPi+UrGkWhJveV9XEADHpkDO5WRSm5oNJd9+h6d0cjmCsdDVRXqeEYUW9u/l3XRPl9W4lGCtdVNQ1UduGYIwEi0kJQ4tZtBCKob8tZhNWsyEaLSaiwvJEhQrM8DxGenC9SWC1mIK/fX7J/prGlh68mgZKqt0tRq4AJYDT4pSIG5mVwGmFylvXP96uWsTH20mJsXV6f5CdNeyiRnMoBGIAQXniHnvssZ0Wi4Wrr7664owzzhg6YsSI/MLCQtegQYMaASZMmND4i1/8onjatGl5JpNJjhgxwvXGG2/sbKv88847r2bdunX24447Lg+UR+/ll1/eESoAL7vssqqvv/46Jj8/v1AIIf/whz/szc7O9q5evbqLj/7YRLTnUu0pTJgwQS5btuzo7ExKKN0Eu79uFoU1RSrNnmBUFxuisP9oMEe1W5xG09dYuKqIslo3Ugrqm7x4/ZLaRg9mk2BEZjwen8Tj8+P1+WnySbw+P56w383bqLnHL/F4/Xj97W/XFJrHSPf6/S068I4Eq9kU9NC1qI4NLtu7rVHFK0t3kRJjwxTiLfVLSVmdm4sn5Rx1ezSRI4T4Xko54VDzrVq1aufo0aPbrBrVaNpj1apVKaNHjx4Yvr7veQA7Qgjol6emCVcqQVi1WwnBgCjc/L7aNsoJWccZHsLJkDkBrJ3bOEuj6W3UNnrJTHS2KlB+NKZ7Wt1LKfH6ZeviMmSdEJAeZyepB/d511OGXdRoNL0bLQA7QghIzFHTmIvUuroDzd7B3V/Dor8AEkxRqr/BbMNDmD0J7G0H72s0xyI9UaAIIYLVvr2d3jLsokaj6dloAXg4xPSDwnPUBNBQBXuWNovCbx6BJX8HBKSPMMSg0dI4fDg6jeYYQwuUruVYGXZRo9F0L1oAdgaOBMg9XU0ATS4oWtYsCJe/CEsfV2nJQ41GKIYoTMjWLY01xxRaoHQ9vW7Yxeoi1Qm/q0x1wp85TnfCr9F0M1oAdgVWJww6UU0APg8Ur4JdS2DXN7D+v0oUAsRlGY1KDFGYkqsFoabX0+sEiqbrqC5Sw3Da41QNSFOdWh5+phaBGk03ogXg0cAcBVkT1DTlJvD74cB62P2NEoU7voQ1C9S2zuTm6uKcEyBtJJj1ZdJoNL2UouVK/Nni1HJgXrRcC0CNphvRyqI7MJlUbGD6CJh4jWppXLHdaFRiiMKN76htrbGqMUnOCZB9gqo6sdi6136NRqOJFFfZwbHP1hjVmE7Ta3A6nWNdLteKQ8338ssvx69bt87x5z//eX9X2BXOxIkThz/wwAN7TjzxRFdmZubIZcuWbQgMR6dpiRaAPQEhIHmImsZdptbV7AtpafwNfPpHtd5iV93NBDyEAyaCNbr7bNdojgFCh1ZLirYyOitBV2F3Fs4UVe0b8PyBWnamdJ9NmqPGJZdcUg1UR7q9x+MhKkr3r3s06P19IhyrxGXAyB/D2f8HN3wDv9wBF74Cx10NnnpY/AC8dA7cmw1PnQIf/RY2fQANld1tuUbTqwgMrdbQ5CMlxkZDk4+P15dQXNXQ3aYdG2SOg8YacNeA9Kt5Y41ar+lVvPPOO7E/+MEPhgaW58yZkz1//vxkgMzMzJG33HJLRkFBQX5ubm7BihUr7ADz589PnjNnTjbA5s2brZMnT87Nzc0tmDx5cu6WLVusALNnzx549dVXZ02aNCn3hhtuyArdp9fr5dprr83Kzc0tyM3NLbjnnnv6Afz3v/+Nzc/PL8jNzS04//zzBzY0NLQbPP/oo48mjRw5Mj8vL6/g4osvzvF6lVPwb3/7W8rAgQNHTJw4cfiFF16YE7B13759ltNPP33IiBEj8keMGJH/0UcfHXOeFu0B7C04kyDvLDWBeoHu/a7ZS7j0cfh6PiAgrbDZQ5h9AsSmdavpGk1PRg+t1sXEZ6oGH0XLVbWvMwWGT9Xxf4fJb5f8dsDWyq2dOuLA0MShrrun3L3nSMtJSUnxrl+/fsO9996beu+996a9/vrru0LT582bl33xxReX//znPy//+9//nnz99dcP+OSTT7YBbNu2zb5kyZLNFktLWfLggw+m7tq1y7Zu3br1UVFRlJSUmF0ul7juuusGffTRR5tGjRrlPvfccwfef//9qXfddVercQXLly+3//vf/05atmzZRpvNJi+99NLsxx9/PPnss8+ueeCBB/ovX758fUJCgv+EE07ILSwsbAC47rrrBtx6660lp59+et2WLVusp59++rDt27evO9Jz1JPQArC3Yo+DoaeqCcDTCEXfG4JwCax4Gb57UqWFdj2Tc4Lqekaj0QBQUd9ESkzLuNpom4WyOnc3WXQMEp+pBV8f4OKLL64EmDhxouvtt99ODE9fsWJF9Pvvv78N4Prrr6/4wx/+EPT2nXfeeZXh4g/gs88+i5s3b15poFo4LS3N98033ziysrLco0aNcgNcccUV5Y888kg/oFUB+MEHH8SuXbvWOXr06HyAxsZGU79+/byLFy+OnjRpUm1aWpoP4Nxzz63cvHmzHWDJkiVxW7ZsCX4B1tXVmSsrK02JiYn+1vbRG9EC8Fghyg4Dp6iJ242uZ1YbXc983bLrmfgBhnfQGLEkZVjndD2j+/rS9EJ64sglGk1bdIan7nCJioqSfn+z/nG73S3+OOx2uwSwWCzS6/Ue0p9KTExMq8JKSokQQoavOxSklOL8888vf+SRR4pC17/44osJ7eRh2bJlG2JiYg5tZ72ILo0BFEIkCCH+LYTYKITYIISYLIRIEkJ8LITYYswP+krQdALmKMgaD1NuhItfg1/uhHlL4MwHVHc02z6Hd26GR46D+4fC65fBt48r0ej3Hfr+An19eV2qxZ/XpZarizrOq9F0I6OzEqht9FLb6MEvJbWNHmobvYzOSuhu0zSaHsWQIUPcW7dudTQ0NIjy8nLzV199FddxrmbGjh1b//TTTycCPPHEE0kTJkyo6yjPqaeeWvP444+nejweAEpKSsxjxoxpLCoqsq5du9YG8OKLLyZPmzattq0yZs6cWfPOO+8kFhUVWQJlbN682Tpt2rT6pUuXxpaWlpo9Hg///e9/g3pk6tSpNffdd1+w+frXX399zMWDdLUH8B/AB1LKHwshrIATuBP4VEp5rxDiDuAO4FddbEfvpDM9am12PbOkudp4w9tqW1s8ZB9vdFA9BfqPAUsH3hDd15eml6JHLtFo2sfj8WC1WuXQoUM9s2bNqszPzy8cNGhQY2FhoetQynnsscd2X3755QP/8Y9/pCcnJ3tffPHFnR3lueWWW0o3b95sy8vLK7RYLPLyyy8vvfPOO0sff/zxneeff/4Qn8/H6NGjXbfddltpW2WMHz++8Te/+U3RKaeckuv3+4mKipLz58/ffcopp9Tfcsstxccdd1x+v379PLm5uQ3x8fE+gCeffHLP1VdfnZ2bm1vg8/nEpEmTak844YTdh3K8PR1xqK7UiAsWIg5YBQyWITsRQmwCpkspi4UQ/YFFUsrh7ZU1YcIEuWzZsk6xq9HbiN1i75SyupTQ3vOtMarbhMaaru09v2pPcz+Eu76Bsk1qvcUBA45rjiHMnKBGOwll2XPK8ydCnMrSr4K+J8ztGns1Go2mFyGE+F5KOeFQ861atWrn6NGjy7rCpkj45ptvHNdee+3ANWvWbOguG7qK6upqU3x8vN/j8XD66acPveKKK8rmzJlT1d12dSarVq1KGT169MDw9V3pARwMlALPCSFGA98DNwFpUspiAEME9mstsxDiWuBagOzszmm04PK4+OFbP+SkrJO4bvR19HO2uuueQXd41BIGqGnUT9RyXakhCA0P4aJ7AQmmKOWNDLQyzp6k+/rSaDSaY5C//vWvqU888US/+++/v9tiD7uS22+/PePLL7+Mc7vd4qSTTqq59NJLq7rbpqNFV3oAJwDfAlOklEuFEP8AaoCfSykTQrarlFK2GwfYWR7Aanc1D614iDc2v4HFZOGi/Iu4svBKEuwJHeY96vREj1pDFez5rrnaeN8K8HuUjanDldhLK1RVxiZT13ssNRqNphfRWz2Amt5Nd3gA9wJ7pZRLjeV/o+L9SoQQ/UOqgI/aeEDxtnh+c/xvuLzwch5d+SjPr32eBZsWcEXhFVxWcBnOqE7tWunI6IkeNUcC5J6mJoAmFxQta/YQ7vkOdi5WabEZqsrYFqdiCXXXMxqNRqPR9Bi6TABKKfcLIfYIIYZLKTcBpwDrjely4F5j/t+usqEtBsQO4C/T/sKVI67koRUP8fDKh3ll4ytcM/IafjL8J1jNPaD7h8xxKgYQwmIAp3avXaFYnTDoRDUBeJuUV3D31yqGcMtHsHaBSosfYHQ7E+h6Jrdzup7RaDQajUZzyHRZFTCAEGIM8DRgBbYDc1Fdz/wLyAZ2A+dLKSvaK6czG4G0xurS1cxfPp+l+5fSP7o/14++nllDZmExdXM3ib29Xz2/Dw6sV2Jw1xIVT1hXotKcyYYgNEYsSRsJZt0tpUajOXbRVcCa7qCtKuAuFYCdRVcLwADf7PuG+cvns7Z8LYPiB/GzMT/j1JxTMYku7S6x7xDsesYYvm7311C5U6VZY2DApGYPYcY41bm1BlDj1a7aW0VFfRNJ0VZGZyXobko0ml6GFoCa7qAtAaiVTQiTMybzylmv8Pfpf8eEiV988QsufOdClhQtOeSexzWtIAQkD4Fxl8G5j8FNq+DWDTD7GRh1AdQWw2d/gufOgHsHwLMz4dM/wpZPVPV3H6W4qoGP15fQ0OQjJcZGQ5OPj9eXUFzV0N2maTSaPoLT6Rwbujx//vzkOXPmZINqKfzwww8nt5c/dPvOYNOmTdZhw4YVdkZZs2fPHvjcc8/1uUEpdJ1bGEIITsk5hekDpvPujnd5dOWjzPtkHuPTxnPzuJsZ029Md5t4bBGXASN/rCYAVwXs/taII/wavvo7yAdVS+P0kco7GKg6ju4bXcys2ltFrN0SHKosMF+1t0p7ATUaTbfzy1/+ss1OmDU9F+0BbAOzycwPh/yQt895m/838f+xs3onl71/GT/79GdsqtjU3eYduziTIO9MOO1PcM1ncMduuOwtOPF21aJ42bPwr8vg/iHw8HHw9o2w6nWoOqY6aG9BRX0T0baW32rRNgsV9U3dZJFGo9E0c+utt2bcddddaQBffPGFMzc3t2DMmDF51113XVaol27//v1R06ZNG5aTkzNi3rx5Wa2VdcMNN2QOGTKkMDc3t+Daa6/NAtizZ49lxowZQ4YPH14wfPjwgo8//jgawOfzceGFF+YMHTq0cMqUKcPq6uoEqGHbRo8enZebm1swY8aMIaWlpeb21vdVtAewA6xmKxfnX8w5Q8/hlY2v8OzaZzl/4fnMHDSTn435GdlxunuTLsUWA0N+oCY4uKXxurdg+QsqLdjS2GhYcoy0NE6KtlLv9gY9fwD1bi9J0T2gtbpGozmq7Lvz1wPcW7Z0ap9ltmHDXBl/vqfdjp7dbrcpLy+vILBcXV1tnjFjRnX4dldfffWgRx99dOeMGTPqb7jhhhatFtevX+9ctWrVeofD4R86dOiI2267rWTo0KGeQHpJSYn5vffeS9y+fftak8lEWVmZGWDevHnZ06ZNq73rrru2eb1eqqurzWVlZebdu3fb//nPf24/4YQTdp155pmDX3zxxcQbbrih4oorrhj0t7/9bfdZZ51Vd/PNN2f86le/ynj22Wf3tLX+yM9g70QLwAhxRjm5euTVnJ97Ps+ve56XN7zMRzs/4txh5zJv1DzSotO628S+gcWqRh7JngRTbwlpaWxUGW9fBGv+pbZ1phhjGvfulsajsxL4eL1qPR1ts1Dv9lLb6OX4we2G3Gg0Gk2nYbPZ/Bs3blwfWJ4/f37ysmXLokO3KSsrM9fX15tmzJhRD3D55ZdXfPzxxwmB9KlTp9YkJyf7AIYOHdq4bds2W6gATEpK8tlsNv+FF16Yc9ZZZ1VfcMEF1QBff/117L///e8dABaLheTkZF9ZWZk5MzPTfcIJJzQAjB071rVz505beXm5uba21nzWWWfVAVxzzTXl559//uC21nfR6eoVdPhvKISIBhqklH4hRC6QB7wvpfR0kPWYJN4Wz03jbuKS/Et4cvWTLNi8gIXbFnJR3kVcNeKqnjmqyLGMyaxiA9NHwqTrQloaL2nufmbjO2rbXtrSuH+CgxkFaazaW0VZnZukaCvHD07W8X8aTR+kI09dd9JRY0mr1RrcwGw2S4/H06KKJioqipUrV254++2341577bXExx57rN+33367OdLyGhoadFjbIRCJO+RLYJoQIhH4FFgGXABc0pWG9XRSHCncOelO5hTM4bFVj/HCuhdYsHkBlxdezpyCOURHRXdciKbzCbQ0Th4C4+aoddVFzWMa7/5GtTQGMFshc7zyEg44HgZMVDGIPZD+CQ4t+DQaTY8mNTXVFx0d7f/000+jTznllPqXXnrpkF6o1dXVprq6OtMFF1xQPX369Lrc3NyRAFOmTKm9//77U++6664DXq+XmpqaNoVecnKyLy4uzvfBBx/EzJw5s+6ZZ55Jnjx5cl1b64/0mHszkQhAIaV0CSGuAh6SUv5VCLGiqw3rLWTFZnHP1HuYWziXh1c+zKMrH+XVDa9y9ciruSDvAmxmW3ebqInPbL2l8ZYPYcdiWPIPkH9TaSnDVfXygElKFCYPOSbiCDUajeZo8MQTT+ycN29ejtPp9E+ZMqU2NjbWF2neqqoq89lnnz3U7XYLgD/96U97AB577LHdV1xxRU5ubm6KyWTi4Ycf3jVgwIA2ayGfe+65Hddff33OjTfeaMrOzna/+uqrO9tb31fpsCNoQ+zdAPwNuEpKuU4IsUZKOfJoGAhHryPozmBN6Rrmr5jPt8XfkuZM4/rR1/OjoT/q/lFFNC2pLlJD7dnjwBQFJWuhZD146qF4FTQasc3OFCUGsw1BmDEGLFrUazTHNF00ClNf6Ai6urraFB8f7we4884704uLi6Oee+65Hltt3RdoqyPoSFTJzcD/A940xN9g4PPONe/YYWTqSJ467SmWFi/lH8v/we+/+T3Pr3uen479KaflnKZHFekpFC1X4s8Wp5azjoPU4WBxQt5ZULYZ9nwLu5eq+aZ31XZmG2SMbRaEAyZB9FFojNHbhwXUaHoLoR+HMf3UOOyb3oPhZ+pnLgL+9a9/xT/44IP9fT6fyMzMdL/yyis7u9smTetEPBScECJaSlnfxfa0Sm/yAIYipeTzPZ/z0IqH2Fq1lfykfH4+9udMzZyK0NWK3cuy59TLPVSQSz/UHYAJcw/evu4A7Fmqqo73LIV9K8Fv1EAkD2sWhNnHQ/LQzq02Dv1DssaoP6TGGv2HpNF0BesXgtfV/HEI4K5RH4cFs46o6L7gAdT0PA7bAyiEmAw8A8QA2UKI0cB1UsobOt3KYwwhBCdnn8xJWSfx3o73eGTlI9zw6Q2M6zeOm8bdxLi0cd1tYt/FmaKEVOhLvqlOrW+NmH6QP0tNAJ4Goz9CQxBufBdW/FOlOZLCqo3HHllr43BvZWBetFwLQI0mUvw+JeTcteoDyl2rlhtrjPXGut1L1fbeBnAkwnFXqw+vugPda79G08lEUgX8d+B04G0AKeUqIcSJXWnUsYbZZGbWkFnMHDiTN7a8wROrn+DyDy5nWuY0bhx3I3lJed1tYt8jc5zyqkGYV21qZPmjHM39CwL4/VC+tWW18eb3VZrZCv3HtPQSHsowdq4yJUBD0X9Imr6ClOqDq4V4q2lluVbF7rYQdrXNaU0RNPgUZvVsW+xgjQaL0fK+vY9DjaaXElHLBCnlnrAqy4hb9WiaiTJHcWHehfxwyA95deOrzaOKDJzJz8b+jJy4nO42se8Qn6mqUIuWKyHlTFHi73A9aiYTpOaqKdD9TH1Zy2rjpU/A1w+ptKQhRvczk4xq42GqjNY4VG+lRtMTkBK8jeCug6ZaNXfXhoiymjDx1o5Xzu/teH9R0c2eclus+h2fqZbt8WpdaJrNmOxxzWlRDqjZ1zLkImBPpB+HGk0vIRIBuEcIcQIghRBW4EZgQ9eadWzjjHJy1cirOH/4+Ty/9nn+ueGffLzrY84Zeg7zRs8jPTq9u03sG8Rndm0VanSKalCSd5Za9jRC8cpmQbj5A1j5skpzJBpdzxhT5jj1ZwRH7q3UaCLF72v2lrnrjHlNyO8QMdfWsru2eZ2MwFdgsoQIMWNKGNAsykIFWvhy6G9TJw3r2tkfhxpNDyWSbmBSgH8ApwIC+Ai4SUpZ3vXmKXprI5BIKWso4+k1T/OvTf9CILgw70KuGnkVSfae2SmxppOQEsq3GdXGhigsMzq9N0VB/9HNXsKEHKjarVsBa1oSrB4NiLZw8dbWcm3r4s3bENl+TRb1MWKLNeYxIfNYQ5TFtL6NPQ5s8c3izWLvM31t9uZGIE6nc6zL5erSPoDvuOOO9HvvvXc/wKZNm6xnn332sC1btqzrzH20V+7NN9+cMX369Npzzjmntq38t956a0ZMTIzvj3/8Y0ln2PPOO+/EPvjgg2mff/751iMta+LEicMfeOCBPSeeeKIrdP2RdAPjl1L26VE/upoURwp3TLyDOQVzeHTlo/xzwz/59+Z/B0cVibHGdLeJmq5ACEgZqqaxl6p1roqW1cbfPQXfPKzSEgdB9mQVd+hzqz//PvLH2aeQEhoqlfeprqR5Xn/g4HWuctV6PRKiog8WanFZrYi3tpZjm8WcxabvPU2nM3/+/P4BAdgd/P3vf9/XXfvuDiIRgEuFECuBZ4EPZKT9xmgOmYyYDP409U/MHTGXR1Y+wmOrHuPVja9yReEVXJh3oR5eri/gTILhZ6gJwOtWHVMHROGWj2DVKyotLlONaTxwCgycBkmD9Z9yT8ZdFyboSo3lkjBhd6C5i6FQzDaISYOYVEjIhqwJ4EwO8bCFzMPFmzW686pINRqDdevW2ebNm5ddUVFhsdvt/qeffnrX2LFjG5999tnEv/zlLxkmk0nGxsb6li1btmnZsmX2uXPnDvJ4PMLv9/PGG29sGzlypDtQ1g033JDpdrtNeXl5Bbm5uQ33339/kc/n48ILL8xZtmxZTFpaWtOHH364NSYmRj744IMpzz33XKrH4xEDBw50//vf/94RGxvrnz179sDY2FjfqlWroktLS6PuvvvuvXPnzq0Mt7utcmfPnj3w7LPPrp47d27l66+/Hn/HHXdkJSUleUeOHOnatWuXLeCl27Bhg2PixInD9+3bZ503b17Jb37zmxYt8rxeLxdccMHA1atXRwsh5CWXXFL2u9/97sDatWtt1157bU55ebnFbDbLBQsWbAeor683z5w5c/CmTZscI0eOdL311ls7TCYT//3vf2PvuOOOAT6fj9GjR7tefPHFXQ6HQ7a1/lCvXyRVwAJV/XslMBF4HXheStnmAM2dzbFeBdwW68rW8dDKh1hStIR4WzxzCuZwUd5FxFpju9s0TXchJZRugl1fwc4lsPMr5RkCiEk3xOBUyJkKKcO0IOxqvO5m0VYf5p0LFXR1B9QoM+EIE0SnqlbeMWnGZPyOTm25zh6vr2cvpzOqgD99ccOAiqI6Z2falZQZ4zplTn67o3W0VgU8efLk3CeffHLXyJEj3Z999ln0nXfemfntt99uzs3NLfjwww+3DBo0yFNWVmZOSUnxXX755QOOP/74+uuvv76isbFReL1eYmJiZFv72LRpk7WwsHDkl19+uf6EE05oOPPMMwefffbZVTfccEPF/v37zenp6T6AG2+8MSMtLc3761//+sDs2bMHulwu0zvvvLN95cqV9nPPPXfo7t2714buo71yAwLwggsuqBo8ePDIRYsWbczLy2uaNWvWoLq6OvPnn3++9dZbb834/PPP477++utNVVVV5vz8/BElJSWrbDZb8FgWL17s/NWvfpX59ddfbwEInINRo0bl3XbbbfvnzJlT5XK5hM/nE1988UX0RRddNGTlypXrBg4c6Bk/fnzefffdt3fatGn1gwcPHvnRRx9tGjVqlPvcc88dOHbsWNdtt91W2tr6u+6660CnVwEbHr+PgY+FED8A/gncIIRYBdwhpfymozI0h0dhSiGPn/o4a0rX8MTqJ3hoxUM8v+55Lsu/jEsKLiHOGtdxIZpjCyGgX56ajrvaiCPcqoTgzq9g1xJY+4baNrqfEoQ5hihMzdMCIhL8PtWC+yBBF+alqyuBxqrWy3AkNgu3rAlhgi5E7DmTtGdO0yuprq42rVixIub8888fEljX1NQkACZMmFB3ySWXDJw9e3blJZdcUgkwefLk+gceeKD/3r17rRdeeGFlqPevLTIzM90nnHBCA8DYsWNdO3futAF8//33jrvuuiuztrbWXF9fbz7ppJOqA3l++MMfVpnNZsaPH99YXl4edSjlBli5cqV9wIAB7ry8vCaACy+8sOLpp59ODaSfdtppVQ6HQzocDm9SUpJn7969liFDhgTd9nl5ee49e/bYLr/88gGzZs2qPvfcc2sqKytNJSUl1jlz5lQBOJ1OCUiAkSNH1gfyFxYWurZt22aNi4vzZWVluUeNGuUGuOKKK8ofeeSRfjNmzKhtbT1wyP2CRdIRdDJwKXAZUAL8HNUn4BhgATDoUHeqOTRGpo7k4VMeZl35Op5c9SSPrnqUF9e/yCX5l3BZwWXE2+K720RNdyGE8vSlDFMjmEgJFdubxeDOr2Ddm2pbZ4qKHxw41RCE+W13PXOsIyVU74WyTcqjGpgqtquGNq3F1VljmsVbvzwYdGKYoAvx3FmsR/+YNH2Cjjx1Rwufz0dsbKx348aN68PTXnnlld2fffZZ9Ntvvx0/ZsyYwpUrV66bN29exbRp0+rffPPN+DPOOCP30Ucf3fnDH/6wzcYWAFarNehVM5vNsqGhwQRw7bXXDvr3v/+9dfLkyQ3z589P/uKLL4LVYna7PZinrRrOtsrtKF+AUG+f2WzG6/W2+LJOTU31rV27dv2bb74Z9+ijj/Z7/fXXk5544ondh1JeWzZ0ZhReJDGA3wAvAedIKfeGrF8mhHi80yzRdEhhciH/OPkfbKrYxBOrn+CJ1U/w0vqXuDj/YuYUzCHRntjdJmq6GyEgeYiaxl+uhE7lTkMMGoJww9tqW0dis3cwZwqkjTj2BKHfp46/dBOUblStrEs3QtmWlh0DO5OVhzT3dIjtH1Ylm6q8qTbdGEujCZCUlOTPyspqevbZZxOvvPLKSr/fz9KlSx2TJ09uWLdune3kk0+uP/nkk+s//PDDhO3bt1srKip8+fn57sLCwgPbt2+3rVy50hEuAC0Wi3S73SJUELWGy+UyZWdne9xut3jttdeS+vfv30rQ7OEzevToxj179tg2bdpkHT58eNPrr79+SF1yFBcXW2w2m/+KK66oys3NdV955ZWDkpKS/Onp6U0vvfRSwmWXXVbV0NAgwoVjKGPGjGksKiqyrl271jZixAj3iy++mDxt2rTattYfznFGIgCHt9XwQ0p53+HsVHNkDE8azv9N/z+2VG7hqdVP8cyaZ3h5w8tcOPxC5hTOIcWhOwjWGAgBSYPUFGhpXLW7WQzu+go2vqPW2+OVEAw0LEkf1XuqJ71u1aVOuEevfKtqMR0gNgNSh6tzkTocUoar+aGMzKLR9EEaGxtNaWlpowLL119/fcmrr766/Zprrsm57777+nu9XnHuuedWTJ48ueGWW27J2rlzp01KKaZOnVpz/PHHN/z6179OX7BgQbLFYpGpqamev/zlLwe1uL3kkktK8/PzC0aMGOG6//77i9qy5Y477tg3ceLE/MzMzKb8/HxXXV1dp76oYmJi5P/93//tmjlz5rCkpCTv2LFjWwngbZudO3dGXXXVVQP9fr8A+OMf/7gX4J///OeOa665Jufuu+/OiIqKkgsWLNjWVhlOp1M+/vjjO88///whgcYet912W6nD4Wh1/eEcZ4eNQHoCfbURSKRsr9rOU2ue4r0d72E1WTl/+PnMLZxLqjO148waTfVeJQgDDUsqjHeSLU51OzNwimpU0n80mCMaPKjraKpX3rvSTSFibyNU7AjpdFhAYk6zuEsdrrx7KcOUyNVouone3A9gX6O6utoUHx/v9/v9zJkzJ3vYsGGNv/vd73rl+JttNQLRAvAYYlfNLp5c/STvbn8XszDz49wfM3fEXD2yiObQqClujh/c+RWUb1HrrbFqPONAK+OMMWBuNcb6yGmoaq6uDXjzyjYp72UAk0UNqZeaawg8Q+wlDwVrpzaS1Gg6BS0Aew9/+MMf+r366qspHo9HFBYWul5++eVdsbGxEXa62bPQArAPsadmD0+vfZq3t76NEILzhp3HVSOuon9M/+42TdMbqS1pFoS7lihRBqpj4exJzXGEGeMOrfGDlKovvPD4vNLNUBfSF6zFbjR0MTx5AcGXNLjrBKhG0wVoAajpDg5bAAoh5reyuhpYJqX8b+eY1z5aAB4eRXVFPLPmGd7cqlqB/mjIj7h65NVkxWZ1s2WaXk1dqRKCAVF4wGgEaHHAgInNrYwzx6sRI6SEmqKW3ryAR68hpI9Wa2yIN8+Ypw5XnR73llhEjaYdtADUdAdHIgCfBPJQXb4AzAbWAQOA7VLKmzvV0lbQAvDI2F+/n2fWPMMbW97AL/3MGjKLa0ZeQ3ZcdnebpjkWqC+H3V8bVcZLoGQtIJXnLmkIVO1q2eLWkdQs7gJTynCIy9D9FGqOaY5AAG4fOXJkpclk6vlVdpoehd/vF2vWrEkcPXr04PC0SATgZ8BpUkqvsWwBPgJmAGuklAVdYHMLtADsHErqS3h+3fMs2LwAj9/DWYPO4ppR1zAoXnflqOlEXBWw8V3VurhqN8RnwYDjIft43eJW06c5AgH4dnp6ekFqamq1FoGaSPH7/aK0tDR+//7960ePHv3D8PRImvRlAtGoal+M3xlSSp8QosOevDU9h7ToNH418VdcNfIqnl/7PK9vep13tr/DzEEzuW7UdQxJGNJxIRpNR3gawNsII85TnSc31UFjDSQO1OJPozkMvF7v1fv37396//79I4BjrLNOTRfiB9Z6vd6rW0uMxAN4FfAbYBEggBOBPwOvAr+XUt7emda2hvYAdg3lDeW8uP5FXt34Ko3eRmbkzODaUdcyPGl4d5um6c2sXwhel+pGJoC7BixOKJjVfXZpNN3M4XoANZquIKJWwEKI/sBElAD8Tkp5UAeOXYkWgF1LZWMlL61/iVc2vkK9p55Tsk/hulHXkZ+c392maXojy55TI2mIEEeF9KvxcyfM7T67NJpuRgtATU8iUleyCSgFKoChQogTu84kzdEm0Z7IjeNu5MPZHzJv9Dy+K/6On7zzE37+6c9ZW7a2u83T9DacKS0bfYBadurqX41Go+kpRFIFfB9wAarlb6ATRCmlPCigsKvQHsCjS01TDa9seIWX1r9ETVMNUzOnMm/0PEanju5u0zS9geoi2PQe2ONaxgAOPxPiM7vbOo2m29AeQE1PIhIBuAkYJaXstgYfWgB2D3VNdby26TVeWPcCVe4qTsg4gXmj5zG239juNk3T06kugqLl4CpTnr/McVr8afo8WgBqehKRCMD3gfOllHXtbtiFaAHYvbg8Ll7f9DrPr3ueisYKJqVP4rrR13Fc+nHdbZpGo9H0GrQA1PQkIhGAbwCjgU+BoBdQSnlj15rWTF8VgPvr97O2bC2VjZUk2hMZkTKiW8f1bfA2sGDTAp5b9xxlDWWMTxvPvNHzmJQ+CaE78NVoNJp20QJQ05OIRABe3tp6KeULXWJRK/RFAbi/fj+L9iwiJioGZ5QTl8dFnaeO6QOmd6sIBGj0NvLGljd4ds2zHGg4wJjUMcwbPY8TMk7QQlCj0WjaQAtATU8iom5gupu+KAA/2fUJjd5GYqwxwXV1TXXYLXZOzTm1Gy1rxu1z89aWt3h67dPsr9/PyJSRzBs9j2mZ07QQ1Gg0mjC0ANT0JNrsBkYI8S9jvkYIsTp8Onom9k0qGytxRjlbrHNGOalsrOwmiw7GZrZxQd4FvHfue/xu8u+oaKzgp5/+lJ+88xM+3PkhPr+vu03UaDQajUbTCu0NBXeTMT/7aBiiaUmiPRGXx9XCA+jyuEi0J3ajVa0TZY7ix7k/5kdDf8S729/lmTXPcNsXtzEofhBXjbiKMwefSZQpqrvN1Gg03URPi2fWaDTteACllMXGz1lAtZRyV+h0dMzru4xIGUGdp466pjr80k9dUx11njpGpIzobtPaJMoUxTlDz+GtH73F/Sfdj9Vk5TdLfsOsN2fx+sbXcfv00NEaTV8jEM/c6G0k2ZFMo7eRRXsWsb9+f3ebptH0aSJpBPIn4EJgOfAs8KE8yoGDfTEGEHr/V7OUki/3fsmTa55kdelqUh2pXF54Oefnnn9Q9bamfXr7vaDpu/SGeOajhY4B1PQkIh0LWACnAXOBCcC/gGeklNs6yLcTqAV8gFdKOUEIkQS8DgwEdgI/kVK2G9jWVwXgsYKUkv/t/x9PrnmSpcVLibfFc0n+JVycdzHxtvjuNq/H05NbhGs0HbFg0wKSHcmYQsaG9ks/5Q3lnD/8/G607OijBaCmJxHRWMCGx2+/MXmBRODfQoi/RpD9B1LKMSE3/R3Ap1LKYai+Be84dLM1PZH99fv5ZNcnLNi0gE92fRKs4hFCMLH/RJ4+7Wn+eeY/GZs6lkdXPsrpb5zO377/G2UNZd1sec9mbdlaYqJiiLHGYBImYqwxxETF6HGaNb2CQDxzKD01nlmj6Ut0KACFEDcKIb4H/gosAUZKKa8HxgOzD2OfPwICfQi+AJxzGGVoehiRxvmMTh3NQ6c8xL9n/ZtpmdN4bu1zzHxjJn9Z+hcdE9QGvaFFuEbTFr0xnlmj6QtEEgP4R1R170ENP4QQ+VLKDe3k3QFUAhJ4Qkr5pBCiSkqZELJNpZTyoE9BIcS1wLUA2dnZ43ft0u1OejKHG+ezs3onz6x9hne2vQMCZg2exVUjryInLudomN0r0DFUmt6OjmFV6CpgTU8i4o6ghRD9AHtgWUq5O4I8GVLKfUbej4GfA29HIgBD0TGAPZ8jjfMprivmuXXP8Z8t/8Hj93B6zulcPepqchNzu9LsXoGOAdRojg20ANT0JCKpAp4lhNgC7AC+QDXceD+SwqWU+4z5AeBNYCJQIoTob5TdHzhwWJZrehRHGufTP6Y/d066kw9mf8DlhZfzxd4vmP32bH7+2c9ZU7qmK0zuNaRHpzN9wHTsFjvlDeXYLXYt/jQajUZzRERSBbwKOBn4REo5VgjxA+AiKeW1HeSLBkxSylrj98fAH4FTgHIp5b1CiDuAJCnlL9srS3sAez6d7aWqdlfzysZXeHnDy1S7qzm+//FcM/Iajks/Tg8zp9FoeiXaA6jpSUQiAJcZ3besAsZKKf1CiO+klBM7yDcY5fUDNeLIK1LKe4QQyahuZLKB3cD5UsqK9srSArB30BVxPvWeehZsWsAL61+grKGM0amjuXbUtXq8YY1G0+vQAlDTk4hEAH6Caqn7FyAFVWV7nJTyhC63zkALQI3b5+bNLW/y3Nrn2Fe/j7ykPK4eeTWnZp+K2WTubvM0Gs0xRFc1WtECUNOTiEQARgMNqHjBS4B44GUpZXnXm6foLAHYtH8/jWvW4KuowJyUhH3kSKzpOo6qN+Hxe4LjDe+s2cnAuIFcNfIqzhp8lh5vWNN3qC6CouXgKgNnCmSOg/jM7rbqmKArG11pAajpSUQsAI2q31wgD3hfSuk5GgZC5wjApv37qfvsM0wxsZicTvwuF/66WmJOPlmLwF6Iz+/jk92f8NTqp9hUuYmM6AzmjpjLucPOxWa2dbd5Gk3XUV0Em94DexxYY6CpDhprYPiZWgQeAY3eRrZUbuHNrW+yu2Y3Ja4S7GY7Px37007rdkkLQE1PwhLBNl8C04QQiaiRO5YBF6C8gb2GxjVrMMXEYo5RfakF5o1r1mgB2Asxm8ycPvB0Tss5jcVFi3ly9ZPcs/Qenlj9BJcXXM5Phv9EjzesOTYpWq7Eny1OLQfmRcu1AIyQanc1Gys2srFiIxsqNrCpYhM7qnfgkz4AHBYHWTFZwf5InVFOyhuOWqWXRnNUiEQACimlSwhxFfCQlPKvQogVXW1YZ+OrqMCcnNJincnpxFeuhyHrzQghODHrRKZlTuN/+//HU2ue4sHvH+TptU/r8YY1xyauMojp13KdNQbqdI9a4Ugp2V+/PyjyNlRsYGPFRorri4Pb9HP2Iz8pn1OyTyEvKY/yhnKcFiexttjgNnroOs2xSEQCUAgxGeXxu+oQ8vUozElJ+F0uZFMT5sREhBD4XS7MSUndbZqmEwiMNzyx/0RWl67mqTVP8ejKR3l+7fNckHcBcwrmkOJI6bggjaan40xR1b4Bzx+oZWffvr+9fi+7anYpkVeuvHsbKzdS7a4GQCAYGD+QMaljuDDvQvKS8shLyiPJ3vI/IBADKIRoEQM4IV3X3GqOLSKJATwRuA1YIqW8z+je5WYp5Y1Hw0DovBjA2g8/5MB9f8XkcBA1YACW/unEzZpF3EknYYqO7iRrNT2FTRWbeGbNM3y460OiTFGcN+w85hbOpX9M/+42TaM5fHQMIA3eBrZUbglW426s2Mjmys24fW4ArCYrwxKHBUVeXlIeuYm5EYeF6FbAmr5AxEPBdSed1Qq4cccOKl94EfemjXj2FeMtKVEJJhO23FwcY8fgHDMGx5gxRGVn637mjhF21ezimTXPsHDbQgBmDdHjDWt6OX2oFXBVYxUbKzeysTwkXq9mB37pByDWGhsUeflJ+QxPGs6g+EE9slcALQA1PYk+JQDD8VVV0bB6NQ0rV6pp1Wr89fUAmBMTcRhi0DFmDI6RIzA5j26jAt1tTedSXFfM8+ue540tb+Dxezgt5zSuHnk1w5OGd7dpGk2fR0pJcX1xME4vMO2v3x/cJs2ZFhR5+Un55CXnkRGd0Ws+1rUA1PQk+rQADEf6fLi3bmsWhCtX0rRjh0o0m7ENzw16CB1jxhA1YECXvXh0tzVdR1lDGS+tf4nXN71OvaeeIfFDmJY1jamZUxnXbxxR5p7nOdBojiW8fi87qne0EHobKzZS01QDNMfrhXr1WovX62zcbje1tbWkpHRNPKUWgJqeRCQxgFOklEs6WteVdOdIIN7KShpXr8ZlCMLGVavxu1wAmJOTcYwebQjC0ThGjsTkcHTKfms+/hh/Q2OwuxoAX10dJoeduBkzOmUffZ1qdzULty3ky71fsqxkGR6/B6fFyeSMyUzLVIIwLTqtu83UaHotUkpKXCVsr97OjuodbK3aysbyjWyp2tIiXi83MbeFV29YwrCj0o3Tli1b2Lp1K2VlZZSWllJbW0tMTAy/+MUvumR/WgBqehKRCMDlUspxHa3rSnrSUHDS58O9ZYvyEK4wvIS7dqlEsxn78OFKEI41vIRZWYflJax8/XXMySkIk6l5334/vvIyEi+4oLMOR2Pg8rhYWryUr4q+4suiL4PVTsMThzMtaxrTMqcxKnUUFlOvawCviYQ+FFPXFTT5mthds5sdNTvYXrWdHTU72FGtpgZvQ3C7WGusEnkhjTMGxQ/qsueqvr6e4uJiSktLKSsro6ysjPLycm6++WYsFgsffPABy5cvJzU1lZSUFFJSUkhNTWX48OFdUrujBaCmJ9GmADS6fjkBuBn4W0hSHHCulHJ0l1tn0JMEYGt4KyuNKuNVar5mDTLgJUxJMbyEo3GOGYN9xIiIvITaA9h9SCnZVrWNxUWLWVy0mBUlK/BKL7HWWKZkTGFa1jSmZEwh2ZF81GzS8aBdiG5VGzHV7uqgsNtRvSPo2dtbtzfYKAMgPTqdwfGDGRQ/iEFxg9Q8fhApjpROF1Z+v5+KioqgF6+srIxTTz2V2NhYlixZwieffAKAw+EICr1TTz0Vh8OBx+PBYrEctRhCLQA1PYn2BOBJwHRgHvB4SFItsFBKuaXLrTPo6QIwHOn1NnsJV67EtXIlnl27VaLForyEY8c2xxJmHhzErGMAew61TbV8W/wti/cqQVjWoDoPH5E8Ihg7WJhciNlk7pL963uhi1m/ELyulv3quWvA4oSCWd1nVzfhl36K64uVJ696RwtvXkVjRXC7KFMUOXE5QXEXEHwD4wZ2SfWtx+MJevEGDBhAQkICmzdv5vXXX8fvbxafcXFxXHDBBWRkZFBVVUV1dTUpKSlE94CuvrQA1PQkIqkCzpFS7jpK9rRKbxOAreGtqGj2EAa8hA2qasScmtKicYm9sBCT3a69Pj0Qv/SzqWKT8g7uXczqstX4pZ9EWyJTMqcwLXMaUzKndOroI9ob3MUse06NrCGawy2QfjWyxoS53WdXF9PobWRXza4Wnrwd1TvYWbMzGJ8HEG+LP8ibNzh+MBkxGV3y0dPY2IiUEofDQVVVFe+99x6lpaVUVVUFt5k1axbjxo2joqLioCpcm63njgWuBaCmJxGJAMxFdQQ9kJARQKSUJ3epZSEcCwIwHOn14t68Odi4pGHFSjx79qhEiwV7fn5z45JRow47llDTtVQ1VvH1vq9ZXLSYJUVLqHRXYhImRqWMCsYO5iXlHdG10/GgXcwx7AGUUlLRWHGQJ29H9Q721e1Dot7/AkFGTEaz0AuZurLlrdfrZfny5S1i9Orq6jjppJOYPn06LpeLF198MRibF5gnJSVhsfS+eFwtADU9iUgE4CpUFfD3gC+wXkr5fdea1syxKABbw1te3uwhXLGShrVrkY2NAJji43EUFmIvLMQ+YgSOEYVYMnpP/1d9AZ/fx7rydUHv4LrydQCkOlKZmjmVaVnTOL7/8cRaYzsoqSXaA9jFHAMxgF6/l311+w7y5u2o2REcCg3AbrYzMH5gc1xegvLq5cTlYLfYu8S2AwcOUF5eHozTKysrIysri9NPPx0pJX/+858xmUykpqYGRd6gQYPIyMjoEnu6Ey0ANT2JSATg91LK8UfJnlbpKwIwHOnx0Lh5M41r19G4di0N69bi3rwFvF5AdVZtHzEC+4hCHCNGYC8sxJKWpkVhD6GsoYwlRUtYXLSYr4u+ptZTi0VYGJs2lmmZyjs4JGFIh9dLxwAeBXpBK2ApJeWN5eyu2c2uml3srt0drMLdVbMLj98T3DbZnnyQJ29w/GDSo9MxhVZ1dwJNTU1UVFQEp/LychwOB6eddhoADz30EBUVKnYwOjqa1NRUcnNzmTx5MqBa6jqdzj7x3tICUNOTiEQA/h44ALwJBANDpJQVbeXpbPqqAGwNv9uNe9MmGtaupXHdOhrXrsO9dSv4lHPWnJKiPIUhwtCSmtrNVmu8fi+rSlcFG5JsrtwMQP/o/so7mDmNSf0ntRk8r+NB+wZSSirdleyu2R0UeKGCr95TH9zWLMxkxWY1i7yQ1radGYMKqoPkUJHX1NTEKaecAsALL7zAzp07g9tGR0czcOBAfvzjHwOwc+dObDYbiYmJ2O1d42XsLWgBqOlJRCIAd7SyWkopB3eNSQejBWD7+BsaaNy4sYWnsGnbdjCurSUtTQnCwgLlKRwxAktS1/aor2mf/fX7+aroKxbvXcy3xd/i8rqIMkUxIW1CMHYwJy6nT3hF+iLV7mp21exq4cnbXbOb3TW7qfXUBrczCRMZ0RnkxOWQHZet5rFq3j+mf6eOd9vY2BgUeFVVVUyZMgUhBO+++y7h79+EhARuvPFGhBBs2bKFpqYmkpOTSUxM7NGNMLobLQA1PQk9FNwxir++3hCFa2kwhGFwWDvAktEfR+GIZk9hYSHmhITuM7gP0+RrYvmB5Szeu5ivir5ie/V2AAbEDlBVxVnTmJA2octitDRdQ01TTbP3rmY3u2p3sadmD7tqd7WIyws0wMiOzQ6KvJy4HAbEDiArJqtThyYMFXm5ublYrVaWLVvG559/jsvouzTAbbfdRnR0NJs3b+bAgQMkJSUFJ6vV2mk29SW0ANT0JCLxADqBW4FsKeW1QohhwHAp5TtHw0DoXAFYVlbGsmXLcDqdLab09PRjvnrCV1dH47r1RtWx8hQG+ycEorKygg1M7CNGYC8owBwX106Jmq5gb+1e5R0sWsx3xd/R6GvEbrYzsf9EJvefTHZcNv2c/UhzppFgS9Bewm6krqmuWdiFefMq3ZXB7QSC9Oh0JfBilTcv4MnLis3Cau48QdXQ0EBFRQVJSUk4HA62b9/O559/TkVFRQuRd80115CRkcHWrVvZsGFDC4GXmJjYp0VeV4VcaAGo6UlEIgBfR7UAniOlHCGEcADfSCnHHAX7gM4VgFu3bmXBggU0NTW1WD9nzhwGDRrEunXrWLhwIdHR0S0E4kknnURCQgLl5eWUlpa2SLPb7ZhMnRtYfbTwVVfTuH69iik0PIWeoqJgujUnJ9jy2D6iEHtBIeaY7u9Qta/Q6G1kWckyFu9dzJd7v2Rv3d4W6VaTVYnB6DT6OfuR7kwP/k5zqnmKI0UPYXcEuDyuVuPxdtXsatExMkA/Z78W1bQBwZcVm9VpHlwpJS6XC7PZjN1up7y8nC+++CLo2Wsw+he94IILyMvLY/fu3SxatIjExESSk5ODIi85ORmzuWs6L+/NdGWjKy0ANT2JSATgMinlBCHECinlWGPdqt4+FJzX68XlcgWn/v3743A4KCoqYvXq1cH1DQ0NuFwuLrvsMpKTk/nmm2/46KOPDirvpptuIiEhgdWrV7N+/XocDkcLkThy5EgsFkvw5Wy323us58ZbWak8hWvX0rhOVSF7i4tVohBYBw1q0fLYnp+Pydn1A7f3daSUlDaUsr9+PyWuEkrqSzjgOsB+1/7g7xJXSYvWoKDiyFIcKaQ504KiMC26+Xe6M51+0f2wmftW7JZf+qn31FPbVEttUy11njoqGivYVbOLPbV7goKvtKG0Rb5UR+pB8XjZcdkMiB2Aw9LxMI+R4PV68fl82Gw2Ghoa+PLLL6msrAxOHo+HmTNnMmnSJMrLy/nnP/8Z9NwFBN6AAQN6xOgXvY2u7HZJC0BNTyISt0CT4fWTAEKIIYS0Bu6tWCwW4uLiiAur4szMzCQzs+3uH8aMGcPAgQNbiEeXyxV80TY1NVFVVcW+fftwuVz4jNa5o0aNAmDRokV89913CCFwOp04HA5iYmKYM2cOQgg2btxIZWVlC/HocDhIOoqNNiyJicRMnULM1CnBdd6yMhrXrTNaH6/H9e1Sat5eqBJNJmxDBmMPxBQWFmDPy4tozGNN5Agh6OfsRz9nvza3kVJS5a4KCsQSl5oOuA5QUl/C9urtfFv8LXWeuoPyJtgSWgjEoEcxZF1MVEyP+HCRUtLgbWgh3kLnrf2ua6qj1mMsN9VR76kPdoQcTrI9mZy4HKZkTgnG4wUEX2cMcyalxOv1EhUVhd/v56uvvmoh8GpqajjhhBOYMWMGZrOZ77//nsTERBITExk0aFBwDpCcnMxNN910xDZpFL6KCszJKS3WmZxOfOVl3WSRRtM1ROIBnAH8BigAPgKmAFdIKRd1uXUGvbURiJQSj8eDy+UiwWhgsXv37qA4DEw+n4+LLroIgH/9619s2LChRTmxsbHceuutACxYsIC9e/ficDiw2+3Y7fbg4OYAGzdupKmpCbvdHtzG6XR2iSfAU3KgRTxh49p1+MrLVaLJhG3IEOUhDEz5WhT2FOo99S1EYkAgBn+7Sg6q3gRwWBzKk2h4EIPi0FjXz9mPJHtSu33NSSlx+9zUeeqoaaqhrulgcVbrMeZNtcHfoYKu3lOPT/ra3AeARViIscYQExVDrDWWWGssMVExxFhjiLPGtZqWYEtgQOwAYqwx7ZYdCVLKoFgOjHYRKvKGDx/O7NmzAfjrX/+KxWIJirzExEQGDhxITk7OQWVpuhbtAdT0FSJqBSyESAaOBwTwrZTyqH4K9VYBeDhIKXG73S0EopSS4cOHA7B06VKKi4tpbGykoaGBxsZG4uPjufjiiwF44okn2L9/f4sys7OzmTtXjWn6/PPP43K5WgjErKwsjjvuOADWr1+PyWQKpjkcDhwOB1FRHbdElFLi3b9feQrXrVPicN16LQp7KU2+pqAYDBWIoV7FUlfpQULMYrLQz6E8hvG2eBo8DQcJOq/f2+6+BYIYawyxUbFBodaqaAvZJtYa2+K33Xz0wiy2bNlCcXFxC4GXmJjIFVdcAcDjjz9ORUVFUNwlJCQwYMAACgsLAfD5fDoer4egYwA1fYVIPIDnAp9JKauN5QRgupTyrS63zqAvCcAjJRC3GBCHDQ0N2O12hg0bBsBHH31EVVVVCwE5cOBAfvSjHwFw77334na3rOEfPXo055xzDgCPPfYYUVFRLQTkkCFDyMvLQ0rJhg0bgqIxkC4qKnCvX9+iCrmlKByMvSAwzF2hqj7WMYW9Ap/fR0VjxUFVzgGBWOWuItoSHRRqQdFmeNxCvW+hv51Rzk4fseJIKCoqoqioqIXAk1Jyww03APDKK6+wZcsWYmNjgyIvPT2d448/HlDdr9hsNu3F6yXoVsCavkAkAnBleIvf0AYhRwMtAI8elZWVQWEYEImJiYkMHjwYv9/Pf/7znxbisbGxkfHjx3PyySfT0NDAX//614PKnD59OieddBL19fW8/vrrOOx2YpqaiCstJXr/fuLLypHbtuErMxzLJhNRAwfiCO2SRotCTScS8LQHRNmBAwfYu3cvtbW1VFVVUVlZSXV1NT//+c8xmUy88847fP/990RFRbWopj3ttNMQQlBfX4/Vao3IU67pu2gBqOlJRCIAV0spR4WtWyOlHNmlloWgBWDvwO/3c+DAgaBADEzZ2dkMGDCAmpoa3nrrrRbeSbfbzdlnn824ceMoWr2a9x9+hMTKChIrKkmsqMDR2KgKN5kwZ2dTnhCPNycHBg/BPGwojsREhgwZQlxcHE1NTTQ2NkZcZa05dpBS0tDQQH19fTB0or6+noKCApxOJ5s3b+bbb78Nrne5XPj9fm6++Wbi4+NZvHgxn332GUALL94ZZ5yBzWajtrYWIQTR0dHai3eMIqXEV1mJ98CB4ITFQoJR+9EZaAGo6UlE0gp4mRDi/4BHUC2Bf47qF1CjaYHJZCK9nWqSuLg45syZ02JdoJW0EIKU4cOZ/svbg8KxpqGBsv37yYuKwrGvmMpl/8OxZi22lasAdTPWxMVRPH483uMnsT86mjdXrsQbFYXFYglWU8+ePZu0tDT27NkT7KIntIo6Ozsbq9VKU1MTfr8fq9Xaa/t1PFbw+/24XC6sVitWq5Wamho2btzYQsDV19dz2mmnkZGRwdq1a/nPf/5zUDlpaWk4nU58Ph8ej4eEhAQyMjKCDaMCnR2PHz+eUaNGER0djcVy8GsxNja2y49Z0zVIKfHX1bUQdp6SAy2WvQcO4CktBU/LLpSsgwZ1qgDUaHoSkQjAnwO/BV43lj9CtQrWaI6Y0MD30FjF1kg15p6SAzSsW0v96tXY16zFu3YtJZ9/jgDOFQJfejrurExc6enUpPbD4lUNDkpLS/n+++/xhL3kb7zxRqxWK9999x2ffvopQFB42Gw2rrzySpxOJ2vWrGHr1q3YbLZgms1mY8KECZhMJioqKmhsbGyRFhUVpT1GKEFXV1d3kIDLycmhf//+lJeX8/bbbwfTAv1lnnfeeYwcOZLKykref/99gGAfm9HR0cEPiMzMTE4//XSio6ODnbgHfgPk5+eTn5/fpn1OHV7QK/E3NOAtLcVbUoLnwAG8B0pbiroDJXgPlCKN+ykUU2wsln79sPRLxXncBPU7tR+WtDQs/VKJ6tcPc2pqK3vVaI4N2hWAQggz8F8p5alHyR6NpkOi0voRlXYycSefHFznOXAg2Oq40WiBHP2/ZaQCB557jqpBg0grLOS6wgKseXnIQYNwm0w0NDQEvTuDBw/GbDbjdruDU1NTU7A6uaamht27dwfT/H4/QLAF9ddff83337d0jkdFRXHnnXcC8Omnn7Jz584WAjEmJoZTTjkFgG3btuFyuYJpNpsNu90e7EKoNUK7B2lsbAx2IOzz+fD7/VgslmD+vXv3Br2cgW1iY2MZMGAAoLoq8Xg8wbw+n4+0tDTy8vIAeO+991qk+f1+hgwZwtixY/F4PLzyyistyvb7/UyYMIHjjjuO2tpa/v73vx9k/6mnnkr//v2xWCyYTKagxy4g4jIyMgAl8H7xi1/gdDpb9c4mJSUFG1z0BfbX72dt2VoqGytJtCcyImUE6dFH3kihpyA9HiXsDhxoRdg1iz1/Tc1BeYXNFhRxjsIRWH7QzxB6/YLCzpKaikl3kq3p47QrAKWUPiGESwgRH2gFrNH0RKL69SOqXz9if/CD4LpwUehaupSahUbn1UJgHTgQe2EhlYMGYs3OJjE7m355eZgTWh9fd8qUKUyZojrHllLi8/lwu93BbSdNmkRubm4LARkQiaA8nFarFbfbTW1tLW63G7vdHhSAX3/9Ndu3b2+xz5SUFH76058C8OKLL1JcXNxCgGVlZXHllVcC8Mwzz1BW1rKHpqFDh3LJJZcAqg/JmrA/zIKCgqAA/Pjjj2kMxFwajBkzJigA169fDyivrdlsDgo2UNX/fr8fk8lEVFQU9aKeA+IAX9V8RfWuavIT8jnrrLNaeOcCnZwDxMfHc/nll7d2aQHVcXtMzJH3zXcssL9+P4v2LCImKoZkRzIuj4tFexYxfcD0Hi8Cpc+Hr6LCEHCGsCspwVvaUugFewkIxWLBkqoEnG3QYKInHR8UdlFpzSLPFBurve4aTQRE0gjkX6g+AD8G6gPrpZQ3dq1pzehGIJrOwlta2qKPwsYNG5qHuTMwxcVhzc7Gmp1NVPYArNk5WHPUsjklpcv+XOrr64MNYwLeR7PZHKwWX7p0KRUVFZhMJkwmE2azmYSEBMaNGwfAmjVraGxsDAo0s9lMbGxssDPhPXv24Pf7g+LNbDbjcDiCo+HU1dUF1we2OZxYyFCB4oxy4vK4qPPU9QqB0hv4ZNcnNHobW3RWXddUh91i59Sco19ZI6XEX12Nt7wcb2kZ3vIyfGVleMvK8ZapZW9ZGb6ycrzl5eAL68BbCMwpyUSlhnrq+mFJUx91gWVzYiKil8fm6kYgmp5EJAKw1c9yKeULXWJRK2gBqOlK/I2NePbupWn3Hpp278KzezdNu3bTtHs3nn37WvxhCacT64ABSiDmZBOVna0EYvYALOnpvf4PqjPoaQLlWGPBpgUkO5Jb9JPol37KG8o5f/j5nbIPKSX+2lpDxJXiC4o7Yzko7gxRFxZXC0BUFJbkZDWlpGBOScaSknqQsLOkpCBaaXhzLKIFoKYn0eFTJ6V8wRgLOFtKueko2KTRHFVMdju2oUOxDR16UJr0ePDs20eTIQo9e9TcvW0bdYsWIUP++ITVSlRAHIZ5D6MyMvrMn1xlYyXJjuQW65xRTsobWqnW0xwyifZEXB5XC4Ht8rhItCe2m09Kib++3vDOGR66UO9cWUuBJ5uaDi7EbMaSnGyIuRRsublYUlKwpCRjTknBkqx+W1JSMMXHB73l4R0r20aM6JSOlTUazeHT4T+SEGIW8ABgBQYJIcYAf5RS/rCLbdNouh0RFYU1JwdrTg5Ma5kmfT68+/fTtGeP4TFs9h7Wf/tty5aHFgtRmRlYB2S39B7m5BCVlYXJ6I7kWOBwBYomMkakjGDRnkWAEtYNtVU0HdjPGFsuNZs+CvPWlTULvvJyZFiMJ6D62ExKUkIuORnboEHN3jpDzJmTk7GkpmKOjz9kL3fo0Grm5BT8Lhd1n33WKUOraTSawycSl8TvgYnAIgAp5UohxKAutEnTS+mq4ZN6KsJsJiozk6jMTKLDWqBKKfGWlrasTja8h9UrV+KvqwspSGDpn25UJYdWLWdjHTCg142AEi5QAjGAE9J1zVdH+Bsa8FVW4quqwldVhTfkt6+yKvh7XPkB3BVliOo6zI3KU1drTICKq0tMVNWvqSk4sscFPXVK0KVgSVWCz5yYiOjCcYgb16xR4s9oxBOYN65Zc0y/HzSank4kAtArpawOC3xvP3BQ0+fQX/ktEUIEWyY7J7QUPlJKfFVVeHbtauk93LWb2k8+wVdR0WJ7S2oqUTnZWAdkE5WViTkuHlNsDOaYGEwxMZiiYzDFRAeXhd3era0g06PTmT5gOmvL1lLeUE6iPZEJ6RP6VAOQQOfDSriFirhKvK0IusA2Mmwc7lBMsbGYExIwJyRgT00jethwLImJzVWvAUGXkoIlKanHhBz4KiowJ6e0WGdyOvGVl7WRQ6PRHA0ieUOsFUJcDJiFEMOAG4Gvu9YsTW9Df+VHjhACS2IilsREHGPGHJTuq61VHsOA93DPbjy7dlO/ZIkanqojzGZMMTGYo6OVQIwJEYjRYcsh68wxodvHYnI6DrtRS3p0eq8SfO31qyd9Pnw1NSGCrbLl74O8ddX4qqrA6ID8IEwmzPHxQTEXlZGBvaBALScmBNdbEhODv83x8YheOryhOSkJv8sVfCcAajkpqRut0mg0kY4E8mvADbwCfAj8qSuN0vQ+9Fd+52GOjcVRWIijsPCgNNnUhK++Hn9dXXDy1dXhr6vHXx+2XFeHv14t+yqr8OzZq9Lr65EuV8eGCIEpKCKjMQfFYweC0lhvjolGOJ3g9yO9PvD7kD6/Mfep9eFzr7f19aFznx/p87Zc9recB/Ylfd520/H7qG+spbhkG4nVjaQ2evB7PBRJH9UeM6K6Fl9NDbTVW0JUFJaEZtFmGzykWbQFBVx8CzFnios74tbivSncwj5yJHXGOMsmpxO/y4W/rhbnxOO62TKNpm/TpgAUQtiBecBQYA0wWUrZxietpq/TG7/ye9OfaABhtWKxWiHxyBpUSK9X/RHX1uILEY9tCcjgcl0dnpL9Ien1bYujnojJBGazEmDG3Ct9JPh8YDYjLWZMdiv+KDP1WSlkTD0xTMy1FHemaOdRr27vbeEW1vR0Yk4+WT1r5WWYk5JwTjyuR9qq0fQl2vMAvgB4gMXAGUA+cPNRsEnTC+ltX/m97U+0sxEWC+a4OMxxcRxJxaL0+/G7GpoFpCEom/buxb15E77KKkwxMUQNyMKSmIgwmcFsUo0OTMbcbG65HDYPbNNWOiYzwtLGenOY4GtFrH3w/B+I89sgJqSxTZ2LGpObcVf89gjOTtfQG8MtrOnpPdY2jaav0p4ALJBSjgQQQjwDfHd0TNL0RnrbV35v/BPtiQiTCXNMNOaYaDCGhWvav5+m3btwjBnb4mMgetq0Hnlu41ySxniBPWRdo00QV90zPZs63EKj0XQG7QnAYA+3Ukrv4VZzCCHMwDKgSEp5thAiCXgdGAjsBH4ipaw8rMI1PYre9JWv/0S7jt4mrrOyC1m/dwXECWxmG26fm6baaoZmj+1u01qlN4ZbaDSankd7kcijhRA1xlQLjAr8FkLUtJMvnJuADSHLdwCfSimHAZ8ayxrNUSXwJxqK/hPtHHwVFQf1XWhyOg/q3qankD5hGnm2bGyNPmrd1dgafeTZskmfMK3jzN2AfeRI/HW1+OrqkH6/EaNZi33kyO42TaPR9CLaFIBSSrOUMs6YYqWUlpDfcZEULoTIAs4Cng5Z/SNUfCHG/JzDtF2jOWz0n2jX0dvEtTU9nYwzzmHkgAlMsg1n5IAJZJxxTo/0VkJzuIXJYcdXXobJYe8zsasajabz6OqeQv8O/BKIDVmXJqUsBpBSFgsh+rWWUQhxLXAtQHZ2dhebqelr9LaYxd5Eb2sQBL0rfAF6n70ajabncWSdUbWD+P/t3XmMnHd9x/H3d+57Z+z1HsEOa0KCSBNyYFGqotZqC4UepCqCUnpEAglVoohWrVrSVgKJf4BWVVXRqkIqNC0VRykoKRVQelhpmkDIgcEkToITk4R4d2Pvrnd37uPXP55nrj0cb7yzM7PP5yWNnmd+88zMzz/Z3s/+judn9kvAonPuoZfyfufcJ51zx5xzxw4dOrTLtROBpSw8cJ3xnzeHeOA6Yyn74u+RF6ceKhGR0TfIHsCfBN5qZr8AJICcmX0GWDCzWb/3bxa4jK0NRHbXfHGeE8+eIBPNcDB5kFK9xIlnT3D8yPGx2sFiVKmHSkRktA2sB9A5d4dz7rBzbg54J/DfzrnfBO4Gbvcvux24a1B1ENnOqfOnyEQzZGIZQhYiE8uQiWY4df7UsKsmIiIycAMLgJfwUeCNZvYk8Eb/ucieWq4sk4r2r1RNRVMsV3RHIhER2f8GvQgEAOfcCeCEf34B+Nm9+F6R7RQSBUr1EplY915qpXqJQuLKtlgTEREZB8PoARQZuhsmb2C9vs56bZ2Wa7FeW2e9vs4NkzcMu2oiIiIDpwAogTSTnuH4keMkIgkulC+QiCS0AERERAJjT4aARUbRTHpGgU9ERAJJPYAiIiIiAaMAKCIiIhIwCoAiIiIiAaMAKCIiIhIwCoAiIiIiAaMAKCIiIhIwCoAiIiIiAaMAKCIiIhIwCoAiIiIiAaMAKCIiIhIwCoAiIiIiAaMAKCIiIhIwCoAiIiIiARMZdgVke2vLFRbPrlJeq5PMRpmay5EtJIZdLRERERlz6gEcUWvLFZ4+eZ5GrUVqIkaj1uLpk+dZW64Mu2oiIiIy5hQAR9Ti2VXiyQixZAQzI5aMEE9GWDy7OuyqiYiIyJhTABxR5bU60US4ryyaCFNeqw+pRiIiIrJfKACOqGQ2Sr3S7CurV5oks9Eh1UhERET2CwXAETU1l6NablArN3DOUSs3qJYbTM3lhl01ERERGXMKgCMqW0hw9KZJIrEQpYs1IrEQR2+a1CpgERERuWK6DcwIyxYSCnwiIiKy69QDKCIiIhIwCoAiIiIiAaMAKCIiIhIwgZoDqK3VRERERALUA6it1UREREQ8gQmA2lpNRERExBOYAKit1UREREQ8gQmA2lpNRERExBOYRSBTczmeeuQF7v/yGVITMVLZGLFUhFfeOkW1VCeeUhAUERGRYAhMAMwWEhx+dYEfPLjA2lKFC8+t02o6nnxgAYBULkZhJkVhJk1h1j/OpEnnY5jZkGsvIiIisnsCEwABDs5meNsfHQOg1XKsni+zMl9iab7I8nyJ5XNFnvj2ArVyo/OeaCJMYTpFYTbdCYgHZtPkJhOEwoEZQRcREZF9JFABsFcoZOSnUuSnUsy9ZrJT7pyjtFrrBMLl+RLL80WeO73M49+c774/bExMpTgw0x8O89MpovHwVl8pIiIiMhICGwC3Y2akJ+KkJ+IcflWh77VaudEJhMvzRZbOlbjwfJGnTp7HtVznuuyBRM8wcndYOZmJ7fUfR0REdkibBkgQKADuQCwZYfpojumjub7yZr3Fygslls+1w6F3fP6JFRr1Vue6RCbqBcLZdN+wcraQwEKaZygiMmztTQPiyQipiRj1SpOnT57n6E2TCoGyrygA7oJwNMTBqzIcvCrTV+5ajrWlSk+voXd86uEXqBS79x+MxEKd4eMDPQtQJqaShCOaZygisld6Nw0AOsfFs6sKgLKvKAAOkIWM3GSS3GSSl99wsO+18lqtGwz9nsNzZ1Z48tsLfe+fOJQkP52iMJ0iP9M9ajhZRGT3ldfqpCb6/3+NJsKULtaGVCORwVAAHJJkNkYyG+Oqa/N95fVqk5WFEkvnip1ew5WFEs88eoFWozvPMJGOku8NhdMpCjMpcoeShLU6WUTksjQbLYorVdZXqhSXq8w/dZHSao16pUk0EebG44e1aYDsSwqAIyYaD3Po6iyHrs72lbdajrUL5U4gXF4osTJf4plTFzh937nOdb29hu1Q2O5BTGSiuqehiARGo970wt2y9+ieVyiuVFlbrlJe3dyzF4qY90v2VIpauUG13OCq6ya3+AaR8aUAOCZCIWPiUIqJQym4sf+1arnBynyJlYViJxguL5R49tElmo3uIpR4KtIJhF449OYdThzSXEMRGS/1apP15Uqn5259ud2L55WtL1eprG/e6z2eipDOx8kU4kwezpAuJMgU4mTycdKFOJlCgmq5zgs/XKO8VicSC3HVdVoAIvvPwAKgmSWAe4C4/z1fdM59yMwOAJ8H5oCzwDucc8uDqkcQxLdZnez1GlZYWfB7DeeL/nDyEqfv797T0JurmOgMJfeGw2RWvYYisrdq5YYf6Cqbeu7az6ulxqb3JdJRP8TFmZ7LecGukPDK8nHS+TixxIv/2IsnI+QOJAfxRxMZGeace/GrXsoHe6kh7ZxbN7MocC/wAeBXgSXn3EfN7INAwTn3x5f6rGPHjrkHH3xwIPUMqlq54fUWbgiHKwvlTb2GvYtQvPO012sYVa+hiFw+5xzVUqNvGLav584/r1eam96bzMXI+D13vb11nfN8nEhstG/Cb2YPOeeODbseIjDAHkDnJct1/2nUfzjgNuC4X34ncAK4ZACU3RdLRpieyzE9t7nXcH2p0jeUvLJQ5NnHljjdsxOKGWQnk31zDNs9h+o1FAme9mKK4kqV4sVad2HFSpXV82XWLlSoFOu0mhs6HQzSuRjpQoIDs2mOXH+ATN4blu3tudM0FZHdNdA5gGYWBh4CXgn8jXPuW2Y27Zw7B+CcO2dmU9u8973AewGuvvrqQVZTeoR6b13zY/23rqmVG6wslroLUfzjc6eXada3mGvo74RyYCZNfiZFbjJJSDe8Fhkrzjkq63WKF7sLKTaGvNLFKuW1zfPtwpEQyWyUUDhE9mCCqbks0USEcCTE0ddMcujqLKmJmO5cIDIEAxsC7vsSszzwZeD9wL3OuXzPa8vOucI2bwU0BDzq2je8bofC5QV/H+WFUt8Ku3AkRH46SX7a2xqvHQwL06mRH7oRGSW7tVVZvdbsBrp2mFupdXruihe9R+8tqNqS2ai3mCIfJ5Xv9tSl895Wmpl8nHg6wlPfeYFGrdW5oTJ4v0xGYiGuuWXL3//3LQ0ByyjZk1XAzrkVMzsBvBlYMLNZv/dvFljcizrI4LRveG1ho15rEo6GOPLqAlNzOaKx8KadUF54do2nHlmk87uH+fsn+3smt7fJOzCTJpHRvbdEel3OVmWtlqO8VuvMseuEuQ29d1stpIjEw36YizH7yglvb/QNAS+Vi132kKxurCwymga5CvgQUPfDXxL4OeBjwN3A7cBH/eNdg6qD7J1L/VCavWaC2Wsm+q5v1JusLJS7N7ueL7I0X+JHT/QPJ2+5f/J0iuwB7Z8sweOc47nTS9QrDSrFOtWidyxdrPL4N88BRnGlSmm1hmv199pZyEjlYqTzcfJTKV52bZ50odtj1w550UR4V+fwJrNR6pVmXw+gbqwsMnyD7AGcBe705wGGgC84575iZvcDXzCz9wDPAG8fYB1kj+x0/8xINMzk4QyTh/v3T24vQvF2QvGC4fJ8iTMPL1ItdnsrIrFQZ9FJYaZ7zE+ltDpZxk6z2aK8Wqe06vXOlS76x9We84tesNu0iALv30MsGeHgyzIcmE11h2YnvFWz6XycZDY2lDm4U3M5nj55HvB6/uqVpm6sLDICBrkK+LvALVuUXwB+dlDfK8OxW8M8vYtQ5jbc8NrbP9kfSvb3T54/c3HT/sm5yURPMPTD4WyaeLL/r/tuzaMS2U692uwJdTWKF6uULtYorVb95955eb3u3SNhg0QmSnoiRmoiTmEmRXoiRmmtRjQWJp2PE09FSaSjNButkZ1Tly0kOHrTJItnVyldrJHMRnVjZZERoJ1AZFfsxTBPd//k/jVD7f2Tu+HQW4DyzPcv9PWWpHIxb47hTJpkLkZ5rUb+UIpUPkaj1to0j0pkK845qsUGRT/EdXvoat2wt+qFva3uZxcKGamJGKlcjOzBBNOvyJHOeSGvHfZSudi28+x6p1uMS49atpDQvyuREbMnq4CvlFYBj77tfigNM1C1mi1Wz1f6FqC0A2Jtww/mUMSIxsJEE2FyB5PEUxES6SjxVIR4Oto5T6SixNMRv+clQiwR0VzEfaDVcjRqTeqVZqeXrugPufaet49brYqNxMN+kIuRboe49vlEjFTOC3iJdPSK/86o93o8aRWwjBIFQNk14/JDyTnHw//xDK1Gi9JqjVqlSb3aoF5pUlmvE0tGqJbqVEveBPtGrbXtZ5lBPNUbFP1w6D/vBMkNATKRimqu4mVwLUej3qJRb9KotWjUvGO91uycbyqrt3rKu9fUe65tbLi2d/ebjRLpqB/kur1z7VCX9oNdaiJ2WVuMSbApAMoo0f9YsmvGZZjHzMhPJWnUWkweyXbKt7s3WaPepFpqeCsuS3WqxW449MrrVNrH9ToXF8vedaXGlvO62iKxkN+z2BsUu72LveXtHsjOELvzgqxrH1veEbzQ1C6n93U2PPffv/Gz6FzvP29t+C7vhc3f7bzvcK3u617P2hZhrN6iuTGU1XvDmnfeuyJ8JyKxEJFYmEgsRDQW7pzHkhFSue5rkVi4s2d2PBEmlooQjoQJheGaW6c4dCSrHShEZF9SAJRA2snKxEg0TGQiTHoivqPvcC1HrdKgUmx4PYo9AbJd1g6O1ZK3y0r7vPESg8+oC0dDm0JZJOodE5ko0U5wCxPxr430XBvtCW6byvzPCUdDO7qNyZlHFre8UXFxpcrM0YlLvFNEZHwpAEog7cXKRAuZ34sXBZI7em+j5vU6Vkrde71VS3Vq5ab/2V5PphngH80M/GPv633lvc9DhkH33Lb+TPNf3/IzNj3f8P0h6wa2SGgk50vqRsUiEkQKgBJYozxk3e7hSue9Xsf2/Mpmw430/MpxpBsVi0gQaXKLyIhrr7Bu1FqkJrq3rFlbrgy7attaW65w5pFFTt3zI848sjjSdZ2ay1EtN6iVGzjnqJUbVMsNpuZyw66aiMjAKACKjLjeXVbMjFgyQjwZYfHs6rCrtqVxC6zt6QCRWIjSxRqRWEj3gxSRfU9DwCIjbtzmqO10W8BRMMrTAUREBkE9gCIjrj1Hrdcoz1Err9WJJsJ9ZdFEmPJafUg1EhGRjRQARUbcuM1RG7fAKiISRAqAIiNu3OaojVtgFREJIs0BFBkD4zRHbS/usSgiIldGAVBEdt04BVYRkSDSELCIiIhIwCgAioiIiASMAqCIiIhIwCgAioiIiASMAqCIiIhIwCgAioiIiASMAqCIiIhIwJhzbth1eFFm9gLwQ2ASOD/k6uxnat/BUvsOltp3sNS+V+7lzrlDw66ECIxJAGwzswedc8eGXY/9Su07WGrfwVL7DpbaV2R/0RCwiIiISMAoAIqIiIgEzLgFwE8OuwL7nNp3sNS+g6X2HSy1r8g+MlZzAEVERETkyo1bD6CIiIiIXCEFQBEREZGAGXoANLNPmdmimZ3qKbvJzO43s++Z2b+ZWc4v/w0z+07Po2VmN/uvvda//gdm9tdmZkP6I42MHbZt1Mzu9MsfM7M7et6jtt3CDts3Zmaf9stPmtnxnveofbdgZkfM7H/8v4/fN7MP+OUHzOwbZvakfyz0vOcOvx0fN7Of7ylXG2+w0/Y1s4P+9etm9okNn6X2FRk3zrmhPoCfAm4FTvWUfRv4af/83cBHtnjfjcBTPc8fAH4CMOCrwFuG/Wcb9mMnbQu8C/icf54CzgJzattda9/3AZ/2z6eAh4CQ2veS7TsL3OqfZ4EngOuBjwMf9Ms/CHzMP78eOAnEgaPAGSCsNt619k0DbwB+B/jEhs9S++qhx5g9ht4D6Jy7B1jaUPwq4B7//BvA27Z4668DnwUws1kg55y73znngH8EfmUgFR4jO2xbB6TNLAIkgRqwqrbd3g7b93rgv/z3LQIrwDG17/acc+eccw/752vAY8DLgNuAO/3L7qTbXrfh/RJTdc49DfwAeJ3aeGs7bV/nXNE5dy9Q6f0cta/IeBp6ANzGKeCt/vnbgSNbXPNr+AEQ7z+t53pee84vk822a9svAkXgHPAM8BfOuSXUtju1XfueBG4zs4iZHQVe67+m9r0MZjYH3AJ8C5h2zp0DL8Tg9aiC127P9ryt3ZZq4xdxme27HbWvyBga1QD4buB9ZvYQ3tBErfdFM/txoOSca8+92mq+ie5vs7Xt2vZ1QBO4Cm/47A/M7BWobXdqu/b9FN4PxgeBvwLuAxqofV+UmWWAfwV+zzm3eqlLtyhzlygXdtS+237EFmVqX5ERFxl2BbbinDsNvAnAzK4DfnHDJe+k2/sH3g/Wwz3PDwPPD7KO4+oSbfsu4GvOuTqwaGb/BxwD/he17WXbrn2dcw3g99vXmdl9wJPAMmrfbZlZFC+c/LNz7kt+8YKZzTrnzvnDj4t++XP0jxa021L/P2xjh+27HbWvyBgayR5AM5vyjyHgz4C/63kthDe09rl2mT9MsWZmr/dXn/02cNeeVnpMXKJtnwF+xjxp4PXAabXtzmzXvmaW8tsVM3sj0HDOPar23Z7fHn8PPOac+8uel+4GbvfPb6fbXncD7zSzuD/Mfi3wgNp4ay+hfbek9hUZT0PfCcTMPgscByaBBeBDQAZv1STAl4A7/MnF+LfP+Khz7vUbPucY8A94Cxi+CrzfDfsPN2Q7aVt/GOjTeIsVDG/F6p/7n6O23cIO23cO+DrQAn4EvMc590P/c9S+WzCzN+D1QH8Pr90A/gRvntoXgKvxfnF5uz9fFTP7U7xh+AbekOZX/XK18QYvsX3PAjkghreQ6U3OuUfVviLjZ+gBUERERET21kgOAYuIiIjI4CgAioiIiASMAqCIiIhIwCgAioiIiASMAqCIiIhIwCgAiuxj/n0d7zWzt/SUvcPMvjbMeomIyHDpNjAi+5yZ3QD8C95er2HgO8CbnXNnXsJnhZ1zzd2toYiI7DUFQJEAMLOPA0Ug7R9fDtyItx3kh51zd/k3q/4n/xqA33XO3efffP1DwDngZufc9XtbexER2W0KgCIB4G9D9zBQA74CfN859xkzywMP4PUOOqDlnKuY2bXAZ51zx/wA+O/ADc65p4dRfxER2V2RYVdARAbPOVc0s88D68A7gF82sz/0X07gbfv1PPAJM7sZaALX9XzEAwp/IiL7hwKgSHC0/IcBb3POPd77opl9GG9P45vwFohVel4u7lEdRURkD2gVsEjwfB14v5kZgJnd4pdPAOeccy3gt/AWjIiIyD6kACgSPB8BosB3zeyU/xzgb4HbzeybeMO/6vUTEdmntAhEREREJGDUAygiIiISMAqAIiIiIgGjACgiIiISMAqAIiIiIgGjACgiIiISMAqAIiIiIgGjACgiIiISMP8PTHsLFPq0INsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Do you think it should be possible for a pregnant woman to obtain \n",
    "a legal abortion if she is married and does not want any more children?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying yes')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Highest degree', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('abnomore_vs_year_by_degree.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose an admitted Communist wanted to make a speech in your community. Should he be allowed to speak, or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.014292</td>\n",
       "      <td>5.678844e-79</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>[0.012803253681013845, 0.0157808503460148]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor</th>\n",
       "      <td>0.002997</td>\n",
       "      <td>2.818237e-01</td>\n",
       "      <td>0.002785</td>\n",
       "      <td>[-0.0024609126971154053, 0.008454834831469148]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>0.007417</td>\n",
       "      <td>8.523145e-02</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>[-0.0010293443646586095, 0.01586340838257216]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school</th>\n",
       "      <td>0.004062</td>\n",
       "      <td>1.179069e-04</td>\n",
       "      <td>0.001055</td>\n",
       "      <td>[0.0019943791836820293, 0.0061297568942805525]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Junior college</th>\n",
       "      <td>-0.004073</td>\n",
       "      <td>3.115858e-01</td>\n",
       "      <td>0.004025</td>\n",
       "      <td>[-0.011960826181515005, 0.0038156611943626385]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than high school</th>\n",
       "      <td>0.006822</td>\n",
       "      <td>3.189803e-05</td>\n",
       "      <td>0.001640</td>\n",
       "      <td>[0.0036074539209238364, 0.01003659248511412]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          param        pvalue    stderr  \\\n",
       "all                    0.014292  5.678844e-79  0.000760   \n",
       "Bachelor               0.002997  2.818237e-01  0.002785   \n",
       "Graduate               0.007417  8.523145e-02  0.004309   \n",
       "High school            0.004062  1.179069e-04  0.001055   \n",
       "Junior college        -0.004073  3.115858e-01  0.004025   \n",
       "Less than high school  0.006822  3.189803e-05  0.001640   \n",
       "\n",
       "                                                             conf_inf  \n",
       "all                        [0.012803253681013845, 0.0157808503460148]  \n",
       "Bachelor               [-0.0024609126971154053, 0.008454834831469148]  \n",
       "Graduate                [-0.0010293443646586095, 0.01586340838257216]  \n",
       "High school            [0.0019943791836820293, 0.0061297568942805525]  \n",
       "Junior college         [-0.011960826181515005, 0.0038156611943626385]  \n",
       "Less than high school    [0.0036074539209238364, 0.01003659248511412]  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'spkcom'\n",
    "gvarname = 'degree5'\n",
    "yvalue = 1\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>degree5</th>\n",
       "      <th>Graduate</th>\n",
       "      <th>Bachelor</th>\n",
       "      <th>Junior college</th>\n",
       "      <th>High school</th>\n",
       "      <th>Less than high school</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>83.419685</td>\n",
       "      <td>87.431694</td>\n",
       "      <td>82.352943</td>\n",
       "      <td>66.939888</td>\n",
       "      <td>39.827374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>82.631577</td>\n",
       "      <td>83.514099</td>\n",
       "      <td>82.692307</td>\n",
       "      <td>60.303982</td>\n",
       "      <td>37.187721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>95.121948</td>\n",
       "      <td>80.652168</td>\n",
       "      <td>69.430054</td>\n",
       "      <td>58.449860</td>\n",
       "      <td>39.844357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>88.846153</td>\n",
       "      <td>87.722420</td>\n",
       "      <td>69.469032</td>\n",
       "      <td>62.519623</td>\n",
       "      <td>44.380402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>87.328766</td>\n",
       "      <td>84.853424</td>\n",
       "      <td>79.166672</td>\n",
       "      <td>69.074074</td>\n",
       "      <td>48.183254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>91.948051</td>\n",
       "      <td>83.914513</td>\n",
       "      <td>71.820450</td>\n",
       "      <td>64.413055</td>\n",
       "      <td>40.149628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>89.696968</td>\n",
       "      <td>88.379204</td>\n",
       "      <td>71.333328</td>\n",
       "      <td>69.547737</td>\n",
       "      <td>45.147678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>87.954544</td>\n",
       "      <td>84.856758</td>\n",
       "      <td>71.818176</td>\n",
       "      <td>65.567451</td>\n",
       "      <td>43.650795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>87.162163</td>\n",
       "      <td>87.269371</td>\n",
       "      <td>69.361702</td>\n",
       "      <td>63.488697</td>\n",
       "      <td>42.821159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>91.304352</td>\n",
       "      <td>85.648148</td>\n",
       "      <td>76.543213</td>\n",
       "      <td>67.063728</td>\n",
       "      <td>48.195877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "degree5   Graduate   Bachelor  Junior college  High school  \\\n",
       "year5                                                        \n",
       "1972.0   83.419685  87.431694       82.352943    66.939888   \n",
       "1977.0   82.631577  83.514099       82.692307    60.303982   \n",
       "1982.0   95.121948  80.652168       69.430054    58.449860   \n",
       "1987.0   88.846153  87.722420       69.469032    62.519623   \n",
       "1992.0   87.328766  84.853424       79.166672    69.074074   \n",
       "1997.0   91.948051  83.914513       71.820450    64.413055   \n",
       "2002.0   89.696968  88.379204       71.333328    69.547737   \n",
       "2007.0   87.954544  84.856758       71.818176    65.567451   \n",
       "2012.0   87.162163  87.269371       69.361702    63.488697   \n",
       "2017.0   91.304352  85.648148       76.543213    67.063728   \n",
       "\n",
       "degree5  Less than high school  \n",
       "year5                           \n",
       "1972.0               39.827374  \n",
       "1977.0               37.187721  \n",
       "1982.0               39.844357  \n",
       "1987.0               44.380402  \n",
       "1992.0               48.183254  \n",
       "1997.0               40.149628  \n",
       "2002.0               45.147678  \n",
       "2007.0               43.650795  \n",
       "2012.0               42.821159  \n",
       "2017.0               48.195877  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.sort_values(by=2017, axis=1, ascending=False, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"Suppose an admitted Communist wanted to make a speech in your community. \n",
    "Should he be allowed to speak, or not?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"allowed\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Highest degree', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('spkcom_vs_year_by_degree.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are always some people whose ideas are considered bad or dangerous by other people. For instance, somebody who is against all churches and religion . . .\n",
    "\n",
    "If some people in your community suggested that a book he wrote against churches and religion should be taken out of your public library, would you favor removing this book, or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>param</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>stderr</th>\n",
       "      <th>conf_inf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.020020</td>\n",
       "      <td>1.155318e-139</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>[0.018460663867511762, 0.02158008662287882]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bachelor</th>\n",
       "      <td>0.002794</td>\n",
       "      <td>3.329208e-01</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>[-0.00286207320822676, 0.008450756301191506]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduate</th>\n",
       "      <td>-0.001364</td>\n",
       "      <td>7.914382e-01</td>\n",
       "      <td>0.005156</td>\n",
       "      <td>[-0.011469777270686371, 0.008742661551919181]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High school</th>\n",
       "      <td>0.011170</td>\n",
       "      <td>8.864263e-24</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>[0.008992540105935186, 0.013347869961416787]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Junior college</th>\n",
       "      <td>0.000880</td>\n",
       "      <td>8.409281e-01</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>[-0.0077151502305467376, 0.009475530326046725]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than high school</th>\n",
       "      <td>0.014161</td>\n",
       "      <td>1.835754e-18</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>[0.010995416516936413, 0.017327331504967836]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          param         pvalue    stderr  \\\n",
       "all                    0.020020  1.155318e-139  0.000796   \n",
       "Bachelor               0.002794   3.329208e-01  0.002886   \n",
       "Graduate              -0.001364   7.914382e-01  0.005156   \n",
       "High school            0.011170   8.864263e-24  0.001111   \n",
       "Junior college         0.000880   8.409281e-01  0.004385   \n",
       "Less than high school  0.014161   1.835754e-18  0.001615   \n",
       "\n",
       "                                                             conf_inf  \n",
       "all                       [0.018460663867511762, 0.02158008662287882]  \n",
       "Bachelor                 [-0.00286207320822676, 0.008450756301191506]  \n",
       "Graduate                [-0.011469777270686371, 0.008742661551919181]  \n",
       "High school              [0.008992540105935186, 0.013347869961416787]  \n",
       "Junior college         [-0.0077151502305467376, 0.009475530326046725]  \n",
       "Less than high school    [0.010995416516936413, 0.017327331504967836]  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvarname = 'year'\n",
    "yvarname = 'libath'\n",
    "gvarname = 'degree5'\n",
    "yvalue = 2\n",
    "\n",
    "run_subgroups(gss, xvarname, yvarname, gvarname, yvalue)"
   ]
  },
  {
   "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>degree5</th>\n",
       "      <th>Graduate</th>\n",
       "      <th>Bachelor</th>\n",
       "      <th>Junior college</th>\n",
       "      <th>High school</th>\n",
       "      <th>Less than high school</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year5</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>1972.0</th>\n",
       "      <td>87.628868</td>\n",
       "      <td>88.461533</td>\n",
       "      <td>82.692307</td>\n",
       "      <td>69.604721</td>\n",
       "      <td>43.528694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977.0</th>\n",
       "      <td>93.617020</td>\n",
       "      <td>85.097191</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>67.314651</td>\n",
       "      <td>43.555241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>94.715446</td>\n",
       "      <td>83.842796</td>\n",
       "      <td>78.787880</td>\n",
       "      <td>64.505676</td>\n",
       "      <td>39.176830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>95.312500</td>\n",
       "      <td>90.072205</td>\n",
       "      <td>76.521736</td>\n",
       "      <td>67.649399</td>\n",
       "      <td>48.803825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>92.068962</td>\n",
       "      <td>87.027916</td>\n",
       "      <td>82.159630</td>\n",
       "      <td>67.964348</td>\n",
       "      <td>48.275860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>90.837700</td>\n",
       "      <td>83.617744</td>\n",
       "      <td>74.242424</td>\n",
       "      <td>71.018456</td>\n",
       "      <td>49.007446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>95.092026</td>\n",
       "      <td>85.046730</td>\n",
       "      <td>88.387093</td>\n",
       "      <td>72.154472</td>\n",
       "      <td>52.479340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>93.197281</td>\n",
       "      <td>84.196182</td>\n",
       "      <td>75.510201</td>\n",
       "      <td>73.533188</td>\n",
       "      <td>53.894077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>92.229729</td>\n",
       "      <td>89.776955</td>\n",
       "      <td>85.281387</td>\n",
       "      <td>76.361130</td>\n",
       "      <td>50.859951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>90.816322</td>\n",
       "      <td>88.632874</td>\n",
       "      <td>80.241936</td>\n",
       "      <td>77.505829</td>\n",
       "      <td>63.592232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "degree5   Graduate   Bachelor  Junior college  High school  \\\n",
       "year5                                                        \n",
       "1972.0   87.628868  88.461533       82.692307    69.604721   \n",
       "1977.0   93.617020  85.097191       87.000000    67.314651   \n",
       "1982.0   94.715446  83.842796       78.787880    64.505676   \n",
       "1987.0   95.312500  90.072205       76.521736    67.649399   \n",
       "1992.0   92.068962  87.027916       82.159630    67.964348   \n",
       "1997.0   90.837700  83.617744       74.242424    71.018456   \n",
       "2002.0   95.092026  85.046730       88.387093    72.154472   \n",
       "2007.0   93.197281  84.196182       75.510201    73.533188   \n",
       "2012.0   92.229729  89.776955       85.281387    76.361130   \n",
       "2017.0   90.816322  88.632874       80.241936    77.505829   \n",
       "\n",
       "degree5  Less than high school  \n",
       "year5                           \n",
       "1972.0               43.528694  \n",
       "1977.0               43.555241  \n",
       "1982.0               39.176830  \n",
       "1987.0               48.803825  \n",
       "1992.0               48.275860  \n",
       "1997.0               49.007446  \n",
       "2002.0               52.479340  \n",
       "2007.0               53.894077  \n",
       "2012.0               50.859951  \n",
       "2017.0               63.592232  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series_all, table = summarize(gss, xvarname, yvarname, gvarname, yvalue)\n",
    "table.sort_values(by=2017, axis=1, ascending=False, inplace=True)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize(series_all, table)\n",
    "plt.title('')\n",
    "\n",
    "title = \"\"\"If people object to a book by someone who is opposed to \n",
    "churches and religion, should it be removed from a public library, or not?\n",
    "\"\"\"\n",
    "plt.title(title, loc='left', fontdict=dict(fontsize=14))\n",
    "plt.ylabel('Percent saying \"not removed\"')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "x = y = 1.02\n",
    "plt.legend(title='Highest degree', bbox_to_anchor=(x, y), loc='upper left', ncol=1)   \n",
    "plt.tight_layout()\n",
    "plt.savefig('libath_vs_year_by_degree.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}