Pessimism.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Are the Swedish party leaders becoming more pessimistic?\n",
    "\n",
    "This is an analysis and re-interpretation of an article in Dagens Nyheter (DN 21/6-2018), where they claim that the leaders of the parties in the Swedish parliment has become more pessimistic in their speeches since 2014. The article is based on a textual analysis of the party leaders speeches and identifies/count words that are associated with a pessimistic world view.\n",
    "\n",
    "We start to import some Python modules that we need. The first one is part of the standard library, whereas the other four a open source projects, commonly used in data analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "I have manually entered the values of the relative pessimism from the online version of the DN article. Instead of the date for the speeches at Almedalen (which occurs on different days for the different parties), I have simply put 1:st of July. I have also added an index colum, which runs from 1 to 19 (the number of sample points). I can then import the data into this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "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>Idx</th>\n",
       "      <th>S</th>\n",
       "      <th>MP</th>\n",
       "      <th>V</th>\n",
       "      <th>SD</th>\n",
       "      <th>M</th>\n",
       "      <th>KD</th>\n",
       "      <th>L</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datum</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>2014-01-15</th>\n",
       "      <td>1</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-18</th>\n",
       "      <td>2</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-18</th>\n",
       "      <td>4</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>5</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>6</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>7</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-14</th>\n",
       "      <td>8</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>9</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-15</th>\n",
       "      <td>10</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>11</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>13</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>15</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-11</th>\n",
       "      <td>16</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-13</th>\n",
       "      <td>18</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>19</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Idx      S     MP      V     SD      M     KD      L      C\n",
       "Datum                                                                  \n",
       "2014-01-15    1  0.140  0.110  0.087  0.095  0.126  0.116  0.127  0.098\n",
       "2014-06-18    2  0.144  0.095  0.147  0.166  0.136  0.074  0.138  0.055\n",
       "2014-07-01    3  0.107  0.179  0.159  0.122  0.095  0.130  0.139  0.123\n",
       "2014-10-18    4  0.121  0.087  0.135  0.186  0.064  0.084  0.118  0.112\n",
       "2015-01-14    5  0.107  0.128  0.113  0.151  0.148  0.148  0.103  0.110\n",
       "2015-06-10    6  0.165  0.118  0.137  0.148  0.146  0.167  0.147  0.108\n",
       "2015-07-01    7  0.121  0.120  0.105  0.176  0.105  0.125  0.168  0.139\n",
       "2015-10-14    8  0.156  0.116  0.111  0.129  0.157  0.137  0.177  0.133\n",
       "2016-01-13    9  0.149  0.089  0.146  0.138  0.103  0.072  0.144  0.096\n",
       "2016-06-15   10  0.153  0.114  0.089  0.147  0.102  0.108  0.160  0.155\n",
       "2016-07-01   11  0.118  0.111  0.149  0.171  0.167  0.142  0.138  0.164\n",
       "2016-10-12   12  0.133  0.120  0.141  0.163  0.163  0.091  0.130  0.101\n",
       "2017-01-11   13  0.173  0.116  0.141  0.129  0.158  0.084  0.161  0.129\n",
       "2017-06-14   14  0.125  0.131  0.101  0.149  0.140  0.129  0.139  0.167\n",
       "2017-07-01   15  0.102  0.109  0.170  0.246  0.171  0.151  0.152  0.157\n",
       "2017-10-11   16  0.180  0.130  0.087  0.128  0.190  0.108  0.203  0.143\n",
       "2018-01-17   17  0.131  0.150  0.147  0.144  0.193  0.131  0.179  0.146\n",
       "2018-06-13   18  0.148  0.203  0.132  0.169  0.146  0.116  0.122  0.119\n",
       "2018-07-01   19  0.119  0.152  0.107  0.167  0.157  0.136  0.103  0.149"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('pessimism.dat', parse_dates=True, index_col=\"Datum\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will now create a linear regression model to identify any trends in the relative pessimism. As my independent variable I will choose the index column, i.e. I will replace the dates with an ordinal number. There are of course many ways to do this, but I think this is the closest to what they have done in the DN article. \n",
    "\n",
    "From the regression models I will specifially save the slope, the p-value of the slope, the correlation coefficient and the p-value of the regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "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>Rows</th>\n",
       "      <th>S</th>\n",
       "      <th>MP</th>\n",
       "      <th>V</th>\n",
       "      <th>SD</th>\n",
       "      <th>M</th>\n",
       "      <th>KD</th>\n",
       "      <th>L</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Slope</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.003732</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>0.000944</td>\n",
       "      <td>0.003093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p-val (slope)</td>\n",
       "      <td>0.646239</td>\n",
       "      <td>0.063884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231819</td>\n",
       "      <td>0.004587</td>\n",
       "      <td>0.569974</td>\n",
       "      <td>0.400382</td>\n",
       "      <td>0.005338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R2</td>\n",
       "      <td>0.012681</td>\n",
       "      <td>0.187705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082945</td>\n",
       "      <td>0.385025</td>\n",
       "      <td>0.019359</td>\n",
       "      <td>0.041931</td>\n",
       "      <td>0.374753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p-value (model)</td>\n",
       "      <td>0.646239</td>\n",
       "      <td>0.063884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231819</td>\n",
       "      <td>0.004587</td>\n",
       "      <td>0.569974</td>\n",
       "      <td>0.400382</td>\n",
       "      <td>0.005338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rows         S        MP    V        SD         M        KD  \\\n",
       "0            Slope  0.000454  0.002232  0.0  0.001609  0.003732  0.000675   \n",
       "1    p-val (slope)  0.646239  0.063884  1.0  0.231819  0.004587  0.569974   \n",
       "2               R2  0.012681  0.187705  0.0  0.082945  0.385025  0.019359   \n",
       "3  p-value (model)  0.646239  0.063884  1.0  0.231819  0.004587  0.569974   \n",
       "\n",
       "          L         C  \n",
       "0  0.000944  0.003093  \n",
       "1  0.400382  0.005338  \n",
       "2  0.041931  0.374753  \n",
       "3  0.400382  0.005338  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {}\n",
    "results = {}\n",
    "saved_results = OrderedDict()\n",
    "saved_results[\"Rows\"] = [\"Slope\", \"p-val (slope)\", \"R2\", \"p-value (model)\"] \n",
    "for party in \"S MP V SD M KD L C\".split():\n",
    "    models[party] = smf.ols(\"{} ~ Idx\".format(party), data=df)\n",
    "    results[party] = models[party].fit()\n",
    "    saved_results[party] = [results[party].params[1], results[party].pvalues[1],\n",
    "                           results[party].rsquared, results[party].f_pvalue]\n",
    "pd.DataFrame.from_dict(saved_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the slope is small for all parties. And only for M, the slope is significant at the 95% confidence interval, although it is a matter of judgement. The correlation coefficients are also weak, indicating that there is no strong correlation between time and pessimism. \n",
    "\n",
    "To obtain a feeling for the magnitude of the slope, we can do some simple calculations for the most extreme party (M) and for the average party (SD)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tIncrease from 2014\tPessimism in 2022\n",
      "M\t              1.63\t           0.2449\n",
      "SD\t              1.21\t           0.1989\n"
     ]
    }
   ],
   "source": [
    "m_trend=results['M'].params[0] + results['M'].params[1]*df['Idx']\n",
    "m_trend_2022=results['M'].params[0] + results['M'].params[1]*(19*2)\n",
    "sd_trend=results['SD'].params[0] + results['SD'].params[1]*df['Idx']\n",
    "sd_trend_2022=results['SD'].params[0] + results['SD'].params[1]*(19*2)\n",
    "print(\"\\tIncrease from 2014\\tPessimism in 2022\")\n",
    "print(\"M\\t{0:18.2f}\\t{1:17.4f}\".format(m_trend[-1] / m_trend[0], m_trend_2022))\n",
    "print(\"SD\\t{0:18.2f}\\t{1:17.4f}\".format(sd_trend[-1] / sd_trend[0], sd_trend_2022))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "\n",
    "The code below closley reproduces the plot for Socialdemokraterna (S) as presented in the DN article. It shows the scatter of the pessimism and the trend line of the regression model. From a layman's view it appears that the black trendline indicates that the pessimism has increased between 2014 and 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAHQCAYAAAA233itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3f0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width, height = plt.figaspect(1.6)\n",
    "f = plt.figure(figsize=(width*0.8,height*0.8), dpi=400)\n",
    "f.gca().plot(df['Idx'], df['S'], color='r')\n",
    "f.gca().plot(df['Idx'], results['S'].params[0] + results['S'].params[1]*df['Idx'], color='k', linewidth=5)\n",
    "f.gca().fill_between(df['Idx'], 0, df['S'], color='r')\n",
    "f.gca().spines['top'].set_visible(False)\n",
    "f.gca().spines['right'].set_visible(False)\n",
    "f.gca().tick_params(top='off', right='off')\n",
    "f.gca().set_xticks(df['Idx'][::4])\n",
    "f.gca().set_xticklabels(df.index.year[::4], rotation=45)\n",
    "f.gca().set_ylabel(\"Relativ pessimism\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same plot, but the aspect ratio is different. The slope of the trend line seems to be smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAFkCAYAAABvkjJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4iEiIiKp+f8BnT36yw1nO30AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cabd3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width, height = plt.figaspect(0.4)\n",
    "f = plt.figure(dpi=400)\n",
    "f.gca().plot(df['Idx'], df['S'], color='r')\n",
    "f.gca().plot(df['Idx'], results['S'].params[0] + results['S'].params[1]*df['Idx'], color='k', linewidth=5)\n",
    "f.gca().fill_between(df['Idx'], 0, df['S'], color='r')\n",
    "f.gca().spines['top'].set_visible(False)\n",
    "f.gca().spines['right'].set_visible(False)\n",
    "f.gca().tick_params(top='off', right='off')\n",
    "f.gca().set_xticks(df['Idx'][::4])\n",
    "f.gca().set_xticklabels(df.index.year[::4])\n",
    "f.gca().set_ylabel(\"Relativ pessimism\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this finally shows the plot as a simple scatter plot, similarly to how it was presented in the online version of the DN article. It is hard to judge by eye if there has been an increase in the pessimism. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAFkCAYAAABvkjJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3e3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(dpi=400)\n",
    "f.gca().plot(df['Idx'], df['S'], color='r')\n",
    "f.gca().spines['top'].set_visible(False)\n",
    "f.gca().spines['right'].set_visible(False)\n",
    "f.gca().tick_params(top='off', right='off')\n",
    "f.gca().set_xticks(df['Idx'][::4])\n",
    "f.gca().set_xticklabels(df.index.year[::4])\n",
    "f.gca().set_ylabel(\"Relativ pessimism\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Moderaterna\n",
    "\n",
    "Below I have plotted the relative pessimism for M and I have marked to date when they elected a new chair (or rather the first date after his inauguration). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAFkCAYAAABvkjJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d34bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(dpi=400)\n",
    "f.gca().plot(df['Idx'], df['M'], color='b')\n",
    "f.gca().plot((df['Idx'][-4], df['Idx'][-4]), (0.07,0.19), '--k')\n",
    "f.gca().spines['top'].set_visible(False)\n",
    "f.gca().spines['right'].set_visible(False)\n",
    "f.gca().tick_params(top='off', right='off')\n",
    "f.gca().set_xticks(df['Idx'][::4])\n",
    "f.gca().set_xticklabels(df.index.year[::4])\n",
    "f.gca().set_ylabel(\"Relativ pessimism\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will create a new regression model for M using only the samples with their old chair. This is to illustrate that the baseline for the two party chairs might be different and hence it is not correct to use a single regression model for the two of them. Then I will also exclude the last point from that data as well, just to prove a point about sample size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             slope\t p-value of slope\n",
      "           0.00363\t          0.05054\n",
      "           0.00319\t          0.12352\n"
     ]
    }
   ],
   "source": [
    "model_akb0 = smf.ols(\"M ~ Idx\", data=df[:-4])\n",
    "results_akb0 = model_akb0.fit()\n",
    "model_akb1 = smf.ols(\"M ~ Idx\", data=df[:-5])\n",
    "results_akb1 = model_akb1.fit()\n",
    "print(\"{0:>18}\\t{1:>17}\".format(\"slope\",\"p-value of slope\"))\n",
    "print(\"{0:18.5f}\\t{1:17.5f}\".format(results_akb0.params[1], results_akb0.pvalues[1]))\n",
    "print(\"{0:18.5f}\\t{1:17.5f}\".format(results_akb1.params[1], results_akb1.pvalues[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py35]",
   "language": "python",
   "name": "conda-env-py35-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}