02_polviews_soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Political Alignment Case Study\n",
    "\n",
    "Allen Downey\n",
    "\n",
    "[MIT License](https://en.wikipedia.org/wiki/MIT_License)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "This is the second in a series of notebooks that make up a case study in exploratory data analysis.\n",
    "\n",
    "In this notebook, we:\n",
    "\n",
    "1. Look at responses to the variable `polviews`, which represent political alignment on a 7-point scale from liberal to conservative.\n",
    "\n",
    "2. Compare the distribution of responses in 1974 and 1990.\n",
    "\n",
    "3. Plot the mean and standard deviation of responses over time as a way of quantifying changes in political alignment and polarization.\n",
    "\n",
    "4. Use local regression to plot a smooth line through noisy data.\n",
    "\n",
    "5. Use cross tabulation to compute the fraction of respondents in each category over time.\n",
    "\n",
    "6. Plot the results using a custom color palette.\n",
    "\n",
    "As an exercise, you will look at changes in political party affiliation over the same period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "If you are running this notebook in Colab, the following cell downloads the `empiricaldist` library.\n",
    "\n",
    "If you are running in another environment, you will need to install it yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we're running in Colab, set up the environment\n",
    "\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "if IN_COLAB:\n",
    "    !pip install empiricaldist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything we need is installed, the following cell should run without error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from empiricaldist import Pmf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell defines a function I use to decorate the axes in plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decorate(**options):\n",
    "    \"\"\"Decorate the current axes.\n",
    "    Call decorate with keyword arguments like\n",
    "    decorate(title='Title',\n",
    "             xlabel='x',\n",
    "             ylabel='y')\n",
    "    The keyword arguments can be any of the axis properties\n",
    "    https://matplotlib.org/api/axes_api.html\n",
    "    \"\"\"\n",
    "    plt.gca().set(**options)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data\n",
    "\n",
    "In the previous notebook, we downloaded GSS data, loaded and cleaned it, resampled it to correct for stratified sampling, and then saved the data in an HDF5 file, which is much faster to load.  In this and the following notebooks, we'll download the HDF5 file and load it.\n",
    "\n",
    "The following cell downloads the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data file\n",
    "\n",
    "import os\n",
    "\n",
    "datafile = 'gss_eda.3.hdf5'\n",
    "if not os.path.exists(datafile):\n",
    "    !wget https://github.com/AllenDowney/PoliticalAlignmentCaseStudy/raw/master/gss_eda.3.hdf5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file contains three DataFrames containing resamples of the GSS data.  We'll work with the first resampling, `gss0`, to get started; at the end of this notebook, we'll see the other two as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64814, 165)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss = pd.read_hdf(datafile, 'gss0')\n",
    "gss.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Political alignment\n",
    "\n",
    "The people surveyed as part of the GSS were asked about their \"political alignment\", which is where they place themselves on a spectrum from liberal to conservative.\n",
    "\n",
    "The variable `polviews` contains responses to the [following question](https://gssdataexplorer.norc.org/projects/52787/variables/178/vshow):\n",
    "\n",
    "> We hear a lot of talk these days about liberals and conservatives. \n",
    "I'm going to show you a seven-point scale on which the political views that people might hold are arranged from extremely liberal--point 1--to extremely conservative--point 7. Where would you place yourself on this scale?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the valid responses:\n",
    "\n",
    "```\n",
    "1\tExtremely liberal\n",
    "2\tLiberal\n",
    "3\tSlightly liberal\n",
    "4\tModerate\n",
    "5\tSlightly conservative\n",
    "6\tConservative\n",
    "7\tExtremely conservative\n",
    "```\n",
    "\n",
    "To see how the responses have changed over time, we'll inspect them at the beginning and end of the observation period.\n",
    "\n",
    "First I'll select the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "polviews = gss['polviews']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then compute a Boolean Series that's `True` for responses from 1974."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "year74 = (gss['year'] == 1974)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can select the responses from 1974."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "polviews74 = polviews[year74]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the previous notebook, we'll use `values` to plot the values in the series and their frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def values(series):\n",
    "    \"\"\"Count the values and sort.\n",
    "    \n",
    "    series: pd.Series\n",
    "    \n",
    "    returns: series mapping from values to frequencies\n",
    "    \"\"\"\n",
    "    return series.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the responses from 1974."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     31\n",
       "2.0    201\n",
       "3.0    211\n",
       "4.0    538\n",
       "5.0    223\n",
       "6.0    181\n",
       "7.0     30\n",
       "Name: polviews, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values(polviews74)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here are the responses from 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     89\n",
       "2.0    269\n",
       "3.0    265\n",
       "4.0    891\n",
       "5.0    310\n",
       "6.0    342\n",
       "7.0     92\n",
       "Name: polviews, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year18 = (gss['year'] == 2018)\n",
    "polviews18 = polviews[year18]\n",
    "values(polviews18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PMFs\n",
    "\n",
    "To visualize these distributions, we'll use the Probability Mass Function (PMF), which is similar to a histogram.  The difference is that the PMF is \"normalized\", which means that it shows the percentage of people who gave each response, rather than the number.\n",
    "\n",
    "I use the `Pmf` class from `empiricaldist` to compute them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from empiricaldist import Pmf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution from 1974:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmf74 = Pmf.from_seq(polviews74)\n",
    "pmf74.bar(label='1974', color='C0', alpha=0.7)\n",
    "\n",
    "decorate(xlabel='Political view on a 7-point scale',\n",
    "         ylabel='Fraction of population',\n",
    "         title='Distribution of political views')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the distribution from 1974:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmf18 = Pmf.from_seq(polviews18)\n",
    "pmf18.bar(label='2018', color='C1', alpha=0.7)\n",
    "\n",
    "decorate(xlabel='Political view on a 7-point scale',\n",
    "         ylabel='Fraction of population',\n",
    "         title='Distribution of political views')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In both cases, the most common response is `4`, which is the code for \"moderate\".  And few respondents describe themselves as \"extremely\" liberal or conservative.\n",
    "\n",
    "So maybe we're not so polarized after all.\n",
    "\n",
    "To make it easier to compare the distributions, I'll plot them side by side.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmf74.bar(label='1974', width=-0.45, align='edge', alpha=0.7)\n",
    "\n",
    "pmf18.bar(label='2018', width=0.45, align='edge', alpha=0.7)\n",
    "\n",
    "decorate(xlabel='Political view on a 7-point scale',\n",
    "         ylabel='Fraction of population',\n",
    "         title='Distribution of political views')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the changes in the distribution more clearly.  It looks like the number of people at the extremes (1 and 7) has increased, and the fraction of liberal (2) and slightly liberal (3) has decreased."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** To summarize these changes, we can compare the mean and standard deviation of `polviews` in 1974 and 2018.\n",
    "\n",
    "The mean of the responses measures the balance of people in the population with liberal or conservative leanings.  If the mean increases over time, that might indicate a shift in the population toward conservatism.\n",
    "\n",
    "The standard deviation measures the dispersion of views in the population; if it increases over time, that might indicate an increase in polarization.\n",
    "\n",
    "Compute the mean and standard deviation of `polviews74` and `polviews18`.\n",
    "\n",
    "What do they indicate about changes over this interval?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.978092, 1.3324163)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "polviews74.mean(), polviews74.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.088574, 1.4323136)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "polviews18.mean(), polviews18.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# The mean has increased slightly, which suggests a shift toward conservatism.\n",
    "\n",
    "# The standard deviation has increased, which suggests increasting polarization.\n",
    "\n",
    "# But in both cases it is hard to interpret how big, or meaningful, these\n",
    "# changes are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series\n",
    "\n",
    "At this point we have looked at the endpoints, 1974 and 2018, but we don't know what happened in between.\n",
    "\n",
    "To see how the distribution changes over time, we can group by year and compute the mean of `polviews` during each year.\n",
    "\n",
    "First I'll use `groupby` to group the respondents by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f83eb6cd8d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gss_by_year = gss.groupby('year')\n",
    "gss_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `DataFrameGroupBy` value that represents a collection of groups.  We can loop through the groups and display the number of respondents in each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1972 1613\n",
      "1973 1504\n",
      "1974 1484\n",
      "1975 1490\n",
      "1976 1499\n",
      "1977 1530\n",
      "1978 1532\n",
      "1980 1468\n",
      "1982 1860\n",
      "1983 1599\n",
      "1984 1473\n",
      "1985 1534\n",
      "1986 1470\n",
      "1987 1819\n",
      "1988 1481\n",
      "1989 1537\n",
      "1990 1372\n",
      "1991 1517\n",
      "1993 1606\n",
      "1994 2992\n",
      "1996 2904\n",
      "1998 2832\n",
      "2000 2817\n",
      "2002 2765\n",
      "2004 2812\n",
      "2006 4510\n",
      "2008 2023\n",
      "2010 2044\n",
      "2012 1974\n",
      "2014 2538\n",
      "2016 2867\n",
      "2018 2348\n"
     ]
    }
   ],
   "source": [
    "for year, group in gss_by_year:\n",
    "    print(year, len(group))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many ways the `DataFrameGroupBy` behaves like a DataFrame.  We can use the bracket operator to select a column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7f83eb4439d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polviews_by_year = gss_by_year['polviews']\n",
    "polviews_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A column from a `DataFrameGroupBy` is a `SeriesGroupBy`.  If we invoke `mean` on it, the results is a series that contains the mean of `polviews` for each year of the survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_series = polviews_by_year.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_series.plot(color='C2', label='polviews')\n",
    "decorate(xlabel='Year', \n",
    "         ylabel='Mean (7 point scale)',\n",
    "         title='Mean of polviews')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The standard deviation quantifies the spread of the distribution, which is one way to measure polarization.\n",
    "\n",
    "Plot standard deviation of `polviews` for each year of the survey from 1972 to 2018.\n",
    "\n",
    "Does it show evidence of increasing polarization?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_series = gss_by_year['polviews'].std()\n",
    "std_series.plot(color='C4', label='polviews')\n",
    "decorate(ylabel='Standard deviation (7 point scale)',\n",
    "         title='Standard deviation of polviews')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local regression\n",
    "\n",
    "In the previous section we plotted mean and standard deviation of `polviews` over time.  Both plots are quite noisy.\n",
    "\n",
    "We can use [local regression](https://en.wikipedia.org/wiki/Local_regression) to compute a smooth line through these data points.  \n",
    "\n",
    "The following function takes a Pandas Series and uses and algorithm called LOWESS to compute a smooth line.  LOWESS stands for \"locally weighted scatterplot smoothing\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
    "\n",
    "def make_lowess(series):\n",
    "    \"\"\"Use LOWESS to compute a smooth line.\n",
    "    \n",
    "    series: pd.Series\n",
    "    \n",
    "    returns: pd.Series\n",
    "    \"\"\"\n",
    "    y = series.values\n",
    "    x = series.index.values\n",
    "\n",
    "    smooth = lowess(y, x)\n",
    "    index, data = np.transpose(smooth)\n",
    "\n",
    "    return pd.Series(data, index=index) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the following function to plot data points and the smoothed line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_series_lowess(series, color):\n",
    "    \"\"\"Plots a series of data points and a smooth line.\n",
    "    \n",
    "    series: pd.Series\n",
    "    color: string or tuple\n",
    "    \"\"\"\n",
    "    series.plot(linewidth=0, marker='o', color=color, alpha=0.5)\n",
    "    smooth = make_lowess(series)\n",
    "    smooth.plot(label='_', color=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure shows the mean of `polviews` and a smooth line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0JkqDNxCtiRKtjVJSUJLu0FrUniSjubFEUiueBLXJzI4G/heYa2b/D+i8bTgSt8J+hcw4fgaR3Ajb9mwjkhvJ+A4S7UkyW6q3kJeTd0hZZ2/SFEmneB7UbZxX4TtmtgA4Cng2qVFJh9HRBpJtz32z/Lx8ojXRg51BoGM0aYp0VPF0kjgDWOHuu919kZn1IbgP9UrSoxNJsPYkGc2NJamkDjnxNfH9Aoi9E74nLBPpcNpz36wjNmlKx6QOOYF4noMyj5nV0N0bzCyu56dEMk1jkon9ZnrxmIvjTjIdrUlTOiY9YxiIJ9GsM7Ov8VGt6RpgXfJCEkkuJRnJdF3xGcPmxNPEdxUwAdgMbAJOB2YmMygRka6soz5jmGjx9OLbDlyaglhEpIPQDfzkUoecwBFrUGZ2t5lFzKy7mZWZ2ftmNj3eE5hZtpktM7Onmlk30cyWmlmdmf1jk3X1ZrY8fM2J93zSdTR4A/vr9tPgmuA5lXQDP/nUIScQzz2oc939JjO7mKCJ75+ABcBjcZ5jFrAKiDSzrhL4v8CNzazb5+4nx3kO6SI2V2/m5S0vs3jLYl7Z+grR2igA3bK6kZudS252LjnZOeRk5ZCTnYOZkUUWZoZhZFnWwZ89u/ckkhOhT04f+nTvE/zM6XPw4d2m5T279cTMjhBh56cb+Kmhe6XxJaju4c9pwO/cfUe8/0nNbBjwGeBO4Iam6919fbidvgJLs6prq3lt22ss3rKYl7e+zIboBgCO6XUMxQXFjIiM4ED9AWobaqmpr6G2PvhZU1/DgfoDNHgDjuPuOE6DN9BAA+7O3gN7Wbd3Hbtrd7P7wG721e07bCzdsroRyYmQ1z2P3G65B5PgwZ/hMkCd13Gg/gAH/AB1DcFyndcFyw1BWdPluoY6srOyDx6ve1b3Q47fPbs7OVk55GbnHlxuPG+vbr04fsDxjB80nr49+ib13ySdN/Db2rSoJsmOKZ4E9Rcz+zuwD7jGzAYC++M8/r3ATUCfNsTWw8zKgTrgLnf/36YbmNlMwg4bBQUFbTiFZKq1H67lkZWP8NTap6htqKVnt54UHVPEpYWXMiF/AqOOGpXw2syB+gNEa6NBwgpf0QPBM1OxZbtrdwfJsKH2YHLcfWA3tfW1HGg4AED3rO50y+pGN+tG9+xgOdeCxNLNutEtq9uhy+H2jUmrtr6W2obagz8P1B9gf91+ovXRj6+vr2Vf3T7qvR6ATxz1CU455hTGHzOeU4455WPJpL3SNaJGW+cf07xlHVc8nSRuNrMfAFF3rzezvcBFR9rPzD4LbHf3CjOb3IbYCtx9i5kdC8w3szfdfW2T2GYDswGKioq8uYNIx+HuLNm6hNKVpby0+SVys3O5aPRFXDDqAk4aeBI52TlxHaet35a7Z3enf8/+9O/Zv70fJeVq62tZ8cEKKt6roPy9cp5+92l+//bvARiaNzRIWIOChDUiMqJdyT1dN/Db2rSoJsmOK64Hbt19Z8zyHoLRJI7kTOBCM5tGMNFhxMwec/e4Oli4+5bw5zozW0gwvNLaw+4kHdKB+gP89d2/8sjKR3h759v079Gf606+jksKL2l1c1VX/back53DuEHjGDdoHFeccAX1DfWs3rmape8tpeK9Cl7c/CJz1gZ9jfr36H+wdnXKMacw5ugxZGdlx32u9j7s3FZtbVrUM0UdV9JGhHD3W4BbAMIa1I3xJqdwBt+97l5jZgMIkt3dyYpV0mNXzS7+8PYf+O2q31K1r4rRR4/m9gm3M+3YaeRm57bpmPq2HMjOymZs/7GM7T+W6WOn4+68G333YMKqeK+CuRvmApDXPY+TBp7EyYNOZtygcZww4AR6de912OOn4wZ+W5sWNchvx5XyIYvM7Hag3N3nmNmpwJ+BvsDnzOy77n48cBzwYNh5IovgHtTKVMcqybFx90YeW/kYf17zZ/bV7ePTQz7NHWfewYT8Ce2+r6Rvy80zM4496liOPepY/vGTwRMdW6q3UPFeBcu2L2PZ9mX8fPnPAci2bAr7FTJu0LggaQ0cxzG9j0ln+EDbmxb1TFHHZTHD7DW/gVmZu5ccqSzdioqKvLy8PN1hyGG8XvU6pStKKassI8uymDZqGl8Z+5WEfhO/f/n9H/u23Pj+mpOvSdh5OqNdNbt4o+oNlm1fxvKq5bxZ9Sb764P+UPm98w/WsMYNGsfoo0e3qlkwUdSLL/OZWYW7FyXkWC0lKDPrAfQieOZpMtD41TYCPOPuxyUigERRgspcL21+iQffeJBl25fRJ6cPl3zyEr503JcY1GtQws8Vew8q9ttyZ78HlQwHGg6wesfqgzWs5duXU7WvCgiaBU8ceCJnDDmD80eez5C8IWmOVjJFqhLULOB6IJ9gHL7GBBUF/sfd70tEAImiBJV53J3Zb8zmvuX3MTRvKJeNvYyLR198xPsbjfRtObO4O5urNx9MVku3L2XNh2sAGD9oPNNGTePckecm/TksyWwpSVAxJ/uqu/8sESdLJiWozFJbX8t3Fn+Hv6z7C5879nN8Z8J34u4mDqoJdRQboxt5Zv0zPL3uadbtWkc368YZ+WcwbdQ0iguK6d29d7pDlBRLaYIKTzgBGElMpwp3fyQRASSKElTm2Ll/J9cvuJ6l25dy3cnXMfPEma3u/KB7SR2Lu/P2zrf567t/5Zl3n2Hrnq30yO7BpOGTmDZqGmcNPatVX1Ck40pkgopnyvdHgU8Ay4H6sNiBjEpQkhne3fUu15Zdy3t73uOeifdw/qjz23Qc9cbrWMzsYNfzWeNn8XrV6zy97mmeW/8cf1v/N/rk9OGcEedwwagLOPWYU9PSwUI6nni6mRcBYz2eqpZ0aa9ufZXrF15P96zu/Oq8X3HyoLaP9atnVzquLMs62Nvvm6d9k1e2vsJf1/2VZ999lj+98ycG9hzIeSPP49yR53LigBOVrKRF8SSot4DBwNYkxyId2J/f+TO3v3w7IyIjuK/kPob1Gdau4+nZlc6he1Z3zhp6FmcNPYv9dftZtGkRz7z7DE+sfoLHVj1Gvx79mDx8MsXDizl9yOn06NYj3SFLBomnk8QC4GTgVaCmsdzdL0xuaK2je1Dp4e78fPnPefCNB5mQP4EfTvohfXLaMjbwx6k3Xue1u3Y3L25+kfmV83lh8wvsObCHnt16MiF/AsUFxUwcOpGjexyd7jClDVLdi29Sc+XuvigRASSKElTq1TXU8b0l3+PJd57k4tEX861Pf4vuWd2PvKNIjNr6Wl7b9hoLNi5gQeUCtu/bTrZlM/6Y8UwZPoUpw6e0u0YuqZPyXnwdgRJUau2v2883nv8GCzcuZOaJM7nu5Os0mZ+0W4M3sPKDlcyvnM+CjQsOPmf1yb6fZMrwKRQXFHNcv+P0u5bBUvWg7ovufpaZ7SbotXdwFeDu3twMuWmjBJU6u2p2cV3Zdbxe9Tq3nH4L//x//jndIUknVRmtZMHGBcyvnM/yquU0eAODew9m0rBJTBo2idOGnNbmgYUlOVSDaoYSVGps27ONq+ZeReXuSu46+y7OHXluukOSLmLH/h0s2riIBRsXsGTrEvbV7aNnt558esinmTR8EhOHTWRAzwHpDrPLS8eDuicBZ4dvn3f3NxJx8kRSgkq+NTvXcNW8q9hzYA8/Lf4ppw4+Nd0hSRe1v24/r217jUWbFrFw40Le2/seACcMOIGJwyYyefhkCvsWqikwDVLdSWIWcCXwp7DoYmB2pg1/pASVXEvfW8p186+jR3YPfjH1F+pNJxmjcRSLhRsX8vym53nz/TdxnGN6HRM0BQ6fxGmDT1MX9hRJdYJ6A/h0OJMuZtYbeNndT0xEAImiBJU8z61/jlteuIX8vHweOOcBhuYNTXdIElJX/I97f9/7vLDpBRZtWsTiLYsPNgWePuR0Jg+bzMRhExnYa2C6w+y0Up2g3gROdff94fsewGvufkIiAkgUJajEc3ceWfkIPyr/EScNPImfFf9Mz6ZkEA2oe2Q19TVBU+DGRSzatIite4LxBj7V/1NMGj5JTYFJkOoEdQMwg2DmWwMuAn7t7vcmIoBEUYJKrPqGeu4pv4ffrPoN54w4h++f9X01kWQYDajbOo1NgYs2LWLRxkUHmwLVKzCxUjpYrLv/t5ktBM4Ki/7F3Zcl4uTSeqlo0tlft5+bX7iZssoyLht7GTcW3UiWZSX0HNJ+GlC3dWIHtJ154syDTYELNy5kzto5PLH6iYO9AicPn8zZw85Wr8A0i2csvkYGNPDRxIWSYrFNOoN7DyZaE6V0RWlCm3R27N/BV+d/lTer3uSbp36T6WOnJ+S4kngaULd9BvQcwMVjLubiMRdTU1/Dq1tfPdgrcP7G+RjGCQNPYPKwyUwaPokxR49RU2CKxdPE923gn4AnCZLT54E/uPv3kh9e/LpCE1+ym3Qqo5VcPe9q3tv7HnedfRdTR0xt9zEleXQPKjncndU7V7Nw40IWbVzEWx+8BcDQvKFMHj6ZKcOnMP6Y8RrWqwWpvge1ChgX00miJ7DU3Y+L6wRm2UA5sNndP9tk3UTgXuBE4FJ3/2PMuhnAbeHb77l76eHO0xUS1G0v3sbg3oMPaW5r8Aa27dnG985q3/eF5duX87X5X8Nxflb8s3ZNlSGpo158yVe1t4rnNz1/8AHhmvoaIjkRzh52NlOGT+GsoWdp5uAYKb0HBawHegD7w/e5wNpWnGMWsApobmikSuD/AjfGFppZP+A/CeaicqDCzOa4+85WnLfTSVaTzt/W/41bX7iVwb0Hc//U+xkRGdHeUCVFGu+pSPIM7DWQL3zyC3zhk19g74G9vLzlZeZvnM/zm57n6XVP0z2rO6cNOY3i4cVMHj6ZQb0GpTvkTiOeBFUDrDCzuQTJ4hzgRTP7KYC7f62lHc1sGPAZ4E7ghqbr3X19uF1Dk1XnAXPdfUe4fi5wPvC7OOLttBI9R5K789BbD3Hv0nsZN2gcP5nyE/r26JvIkEU6lV7de1EyooSSESXUNdSxfPvyYBT2jQu4Y8kd3LHkDj7V/1NMKQhGYR999Gjdt2qHeJr4Zhxu/eGa3szsj8B/AX2AG5s28cVs92vgqcYmPjO7EejReJ/LzL4F7HP3HzbZbyYwE6CgoOCUDRs2HPazdAaJatI50HCAO5fcyZPvPMkFIy/gjrPuUPdakTZyd9Z+uPZgsnrz/TcBGJY3jCkFUygeXsy4QeO6xOzBqe5mfth7Py0xs88C2929wswmt3b35kJpJrbZwGwI7kG1OsgOKBFNOtW11dy46EZe2vISV55wJdeNu07dyEXawcwY3Xc0o/uO5soTr2T73u0s3LiQBRsX8PjfH+fRlY/SN7cvk4dPpqSghDPyz9AXwji0ppt5a50JXGhm0wjuYUXM7DF3j6ff8iZgcsz7YcDChEfYBW3bs41ryq5h3Yfr+O6E7/IPY/4h3SGJdDqDeg3iksJLuKTwEvYc2MOLm1+krLKMuRvm8uc1f6ZXt16cNfQsSgpKOHvY2QmbhbqzScl0G2ENqjVNfP2ACmB8uMlS4JTGe1LN6Qq9+Npr1QeruLbsWvbV7eNHk3/EhPwJ6Q5JpEupra/l1W2vUlZZxoLKBXyw/wO6ZXXj9MGnU1xQTHFBcYd/OLjDzQcVm6DM7Hag3N3nmNmpBEMo9SXoJbjN3Y8P9/lX4NbwEHe6+8OHO4cS1OE9v+l5blx0I0flHsX9Jfczpu+YdIck0qU1eANvVL1BWWUZZZVlbNy9EcM4aeBJFBcUU1JQQkGkIKUxJeIed6pm1P0TwRQb/+vu1Yk4WTIpQbXsib8/wfdf/T6FfQv5ecnPNZKzSIZxd9758B3mV85nfuV8Vu1YBcDoo0czdcTUIFEkeVDbRD34naoEtRl4GSgG5hF08X7a3WsTceJEU4L6uAZv4McVP+bXK37NxGETuWfiPfTq3ivdYYnIEWyu3syCygXMq5zHsu3LaPAGhuYNZWrBVEpGlHDSwJMS3rEpUSPVpKoX33Z3/0cz60MwvNGVwGwzewr4nbs/l4gAJDn21+3n1hdvZe6GuXyx8IvcfNrNdMtKZp8YEUmUoXlDmT52OtPHTueDfR+wcONCyirL+M3ff0PpylIG9BxA8fCgGfDUIacmZNilTBx8+HB/sZjxuTAAABYwSURBVBzA3XcDjwKPhp0XLgFuBpSgMlTsgK83Ft3IV8Z+pdM9LKghfqSr6N+z/8GRLHbX7uaFTS9QVlnGX9b9hd+//Xv65PRh8rDJlIwoYUL+BHp269mm82Ti4MOHa+J73t0npjieNutITXzJ/OO6ftd6rp53NVX7qjrtgK8aJFUkaCVZsnUJ8zbMY+Gmheyq2UWP7B5B9/URJUwcNpFITnMjzDWvQ92D6mg6SoJK5h/XivcqmLVgFtmWzU+Lf8pJA09KUNSZRRP1iRyqrqGOivcqmLdhHvMr57N93/ag+/qQ05laMJUpw6fQv2f/Ix6nI/XiO8vdXzxMEBGgwN3fSkQg7dVRElSy/rj+dd1fue2l2xiaN5T7p97P8D7DExFuRkrmqO4iHV2DN/DW+28xb8M85lXOY+PujWRZFuMHjT/YI7DpvaZESlUniS+Y2d3AswQPzVYRjAgxGpgCjAC+noggupJE34h0d3711q/4ydKfcMoxp/CTKT/hqNyjEhFqxsrEtnKRTJFlWZw48EROHHgi/37Kv/P2zreZVzmPeRvmcderd3HXq3dxwoATKCko4ZwR56T8WavWOGwTn5n1Bf6RYNiiIcA+gqkznj5c7SodumINKnbA12mjpnHHmXeQk52T6JAzju5BibTN+l3rmVc5j7INZQcnYhzTdwxTC6YydcTUhMwarHtQzegoCSpRf1yra6v5+qKvs3jLYmaeOJPrTr6u0/XUOxz14hNpn63VWymrLGNe5TyWvrcUxynoU8DUEVOZWjCVTw34VJv+pihBNaOjJCho/x/XxgFf3/3wXb796W+3eT4oERGA9/e9z4KNC5i3YR6vbn2V7tndef6Lz9OjW49WH0sJqhkdKUG1x+odq7lm3jXsrdurAV9FJOF21ezi7Z1vc+rgU9u0fyITlCYB6kCWbF3CjGdngEHpBaVKTiKScEflHtXm5JRocY19Y2YTgJGx27v7I0mKSZrx9Lqnue2l2xgZGckvpv4iqd1ERUQywRETlJk9CnwCWA7Uh8UOKEGlgLtTuqKUH1X8iKJjivhJ8U9a9XR4I3UqEJGOJp4aVBEw1jvLzaoOpMEbuOe1e3hs1WOcN/I8vn/W99vUjTy25+Dg3oOJ1kQpXVGqbtkiktHiSVBvAYOBrUmORWLU1Ndw6wu38tyG55h+3HS+ceo32jy8flllGZGcyMFnrxp/llWWKUGJHIFaH9InngQ1AFhpZq8CNY2F7n5h0qLq4nbV7GLWgllUvFfBjUU3MuP4Ge06XiYOoy/SEaj1Ib3iSVDfSXYQ8pFte7Zx9byrWR9dz90T7+aCURe0+5gaGkikbdT6kF5HTFDuvigVgQi8vfNtrp53NXsP7OWBqQ9w+pDTE3LckoISSleUAhwyeoUe8BU5PLU+pFc8vfjOAH4GHAfkANnAHndvfVcyadFr215j1vxZ9OzWk1+f/+uEfjsr7FfIjONnHNKOfvGYi/UNsIvTvZUjU+tDesXTxHcfcCnwB4IefV8BxsR7AjPLBsqBze7+2Sbrcgm6q58CfAB80d3Xm9lIgkFpV4ebLnH3q+I9Z0fz7LvPcuuLtzK8z3AemPoAQ/KGJPwchf0K9cdHDtK9lfio9SG94npQ193XmFm2u9cDD5vZ4lacYxZBsmmuxnU5sNPdR5vZpcAPgC+G69a6+8mtOE+H9OjKR7n7tbsZP2g8Py3+aaefKkMyg+6txKcjtj50pppxPAlqr5nlAMvD+aG2Ar3jObiZDQM+A9wJ3NDMJhfxUSeMPwL3WRcZkrvBG/jv8v+mdGUpUwumctfEu8jNzk13WNJF6N5K/DpS60NnqxnH82DNZeF21wF7gOHAF+I8/r3ATUBDC+uHAhsB3L0O2AU0zks8ysyWmdkiMzu7uZ3NbKaZlZtZeVVVVZwhpd+B+gPc/MLNlK4s5dLCS/nhpB8qOUlK5eflU11bfUiZ7q10fLE14yzLIpIbIZIToayyLN2htckRE5S7bwAMGOLu33X3G9x9zZH2M7PPAtvdveJwmzV3SoJaWoG7jyOoef02nGK+aWyz3b3I3YsGDhx4pJAywt4De7m27FqeefcZZo2fxa2n30p2Vna6w5IupqSghGhtlGhNlAZvIFoTJVobpaSgJN2hSTtsqd5CXk7eIWUduWYcTy++zwE/JOjBN8rMTgZuj+NB3TOBC81sGsFU8REze8zdp8dss4mgRrbJzLoBRwE7wmGVagDcvcLM1gKfJOhskRHa0s67c/9Ori27lhUfrOD2CbfrRqukTUe8tyJH1tl6Hcb7oO5pwEIAd18e9rI7LHe/BbgFwMwmAzc2SU4Ac4AZwMsEU8vPd3c3s4EEiarezI4l6DW4Lo5YU6It7bzb9mxj5tyZbN69mR9P/jHFBcUpjlrkUB3p3orEp7P1OoznHlSdu+9K1AnN7HYza6x9/Qrob2ZrCJrybg7LJwJvmNnrBJ0nrnL3HYmKob1a2867btc6LnvmMqr2VvHAOQ8oOYlIUjTWjCO5Ebbt2UYkN9JhO0hAnIPFmtmXgGwzGwN8DWhNN3PcfSEf1cC+HVO+H/inZrZ/EniyNedIpdb0gHqz6k2uKbuGLMviofMe4rj+x6UqTBHpgjpTzTieBPVV4D8I7gn9DvgbcEcyg8p08bbzLt6ymOsXXE+/Hv2Yfc5sCiIFqQ41o3Wm5zVEJPHiGYtvL0GC+o/kh9MxxNPO+9z65/jmC99k1FGjeHDqgwzs1TF6GaZKZ3teQ6Q5+hLWPi0mKDObc7gdu/J0G0fqAfXnd/7Md17+DicOOJH7Su7T6BDN0EgG0tnpS1j7Ha4G9WmCh2h/B7xC888sdVkttfM+suIR7im/hwn5E/jx5B/Tq3uvNESX+TSSgXR2+hLWfofrxTcYuBX4FPAT4BzgfXdfpCk4Ps7d+fnyn3NP+T2cM+Icflb8MyWnw9BIBtLZdbaHZtOhxQTl7vXu/qy7zwDOANYAC83sqymLroNo8AZ+8NoPeOD1B/j86M9z98S7ycnOSXdYGU0jGUhnpy9h7XfY56DMLNfM/gF4DLgW+Cnwp1QE1lHUNdTxrZe+xW9W/Ybpx03nuxO+S7esuAaJ79I62/MaIk3pS1j7WTCqUDMrzEoJmveeAR5397dSGVhrFRUVeXl5akdCqq2v5abnb6KssoxrTrqGq066ii4yGLuIxKEr9uIzswp3L0rIsQ6ToBoIRi+HYADXg6sAz7QZdVOdoPbV7eP6BdezeMtibjr1Ji4be1nKzi0ikqkSmaBabIty93iGQeqS9hzYw7Vl17L0vaUa9FVEJEl0s6SVdtXs4pp517DigxXcdfZdTDt2WrpDEhHplJSgWmHH/h3829x/Y+2Ha/nR5B/pZqeISBIpQcWpam8VVz53JZuqN/Gz4p9x5tAz0x2SiEinpgQVh63VW7niuSuo2lfFL6b+glMHn5rukEREOj0lqCPYGN3IFc9dwe7a3cw+ZzYnDzo53SGJiHQJSlCH8e6ud7nib1dQ21DLL8/7JWP7j013SCIiXYYSVAvWfriWK567ggZv4KHzHmJM3zHpDklEpEtRgmrGOzvf4YrnriDLsnj4vIc59uhjW7V/V3x6XEQk0fQwbhOrd6zm8r9dTjfrxkPnPdSm5FS6opRoTfSQOWBW71idpIhFRDonJagYqz5YxeXPXU5Odg4Pn/8wo44a1epjxM4Bk2VZRHIjRHIilFWWJSFiEZHOK+kJysyyzWyZmT3VzLpcM3vCzNaY2StmNjJm3S1h+WozOy/Zca54fwWXP3c5vbr14uHzH6YgUtCm42gOGBGRxEhFDWoWsKqFdZcDO919NPBj4AcAZjYWuBQ4HjgfuN/MspMV4JtVb3Llc1cSyYnw8PkPM7zP8DYfS3PAiIgkRlITlJkNAz4D/LKFTS4CSsPlPwIlFsxXcRHBFB817v4uwWSJpyUjxuXblzNz7kyOyj2Kh857iKF5Q9t1PM0BIyKSGMmuQd0L3AQ0tLB+KLARwN3rgF1A/9jy0Kaw7BBmNtPMys2svKqqqk0BLtm6hH49+vHw+Q8npJajifhERBIjad3MzeyzwHZ3rzCzyS1t1kyZH6b80AL32cBsCOaDakuc/3biv/Hl475Mn5w+bdm9WYX9CpWQRETaKZk1qDOBC81sPfA4UGxmjzXZZhMwHMDMugFHATtiy0PDgKT0MjCzhCYnERFJjKQlKHe/xd2HuftIgg4P8919epPN5gAzwuV/DLfxsPzSsJffKGAM8GqyYhURkcyT8pEkzOx2oNzd5wC/Ah41szUENadLAdx9hZn9HlgJ1AHXunt9qmMVEZH0saDC0vEVFRV5eXl5usMQEenSzKzC3YsScSyNJCEiIhlJCUpERDKSEpSIiGQkJSgREclIXXo+KM3bJCKSubpsDUrzNomIZLYum6A0b5OISGbrsglK8zaJiGS2LpugNG+TiEhm67IJSvM2iYhkti6boDRvk4hIZuvS3cw1b5OISObqsjUoERHJbEpQIiKSkZSgREQkIylBiYhIRlKCEhGRjKQEJSIiGUkJSkREMpISlIiIZKSkJSgz62Fmr5rZ62a2wsy+28w2I8yszMzeMLOFZjYsZl29mS0PX3OSFaeIiGSmZI4kUQMUu3u1mXUHXjSzZ9x9Scw2PwQecfdSMysG/gu4LFy3z91PTmJ8IiKSwZJWg/JA43Dh3cOXN9lsLNA4AdMC4KJkxSMiIh1LUu9BmVm2mS0HtgNz3f2VJpu8DnwhXL4Y6GNm/cP3Pcys3MyWmNnnkxmniIhknqQmKHevD5vphgGnmdmnmmxyIzDJzJYBk4DNQF24rsDdi4AvAfea2SeaHt/MZoZJrLyqqip5H0RERFIuJaOZu/uHZrYQOB94K6Z8C/APAGaWB3zB3XfFrMPd14X7jgPWNjnubGA2QFFRUdPmw3ZbvWM1ZZVlbKneQn5ePiUFJRr9XEQkRZLZi2+gmR0dLvcEpgJ/b7LNADNrjOEW4KGwvK+Z5TZuA5wJrExWrM1ZvWM1pStKidZEGdx7MNGaKKUrSlm9Y3UqwxAR6bKS2cQ3BFhgZm8ArxHcg3rKzG43swvDbSYDq83sbeAY4M6w/Dig3MxeJ+g8cZe7pzRBlVWWEcmJEMmNkGVZRHIjRHIilFWWHXlnERFpt6Q18bn7GwTNck3Lvx2z/Efgj81ssxg4IVmxxWNL9RYG9x58SFleTh5bqrekKSIRka5FI0m0ID8vn+ra6kPKqmuryc/LT1NEIiJdixJUC0oKSojWRonWRGnwBqI1UaK1UUoKStIdmohIl6AE1YLCfoXMOH4GkdwI2/ZsI5IbYcbxM9SLT0QkRVLSzbyjKuxXqIQkIpImqkGJiEhGUoISEZGMpAQlIiIZSQlKREQykhKUiIhkJHNP+BiraWFmVcCGdMeRQgOA99MdRIbTNYqPrlN8dJ3iU+jufRJxoE7TzdzdB6Y7hlQys/JwOhJpga5RfHSd4qPrFB8zK0/UsdTEJyIiGUkJSkREMpISVMc1O90BdAC6RvHRdYqPrlN8EnadOk0nCRER6VxUgxIRkYykBCUiIhlJCSpDmNlDZrbdzN6KKTvJzF42szfN7C9mFgnLu5tZaVi+ysxuidnnfDNbbWZrzOzmdHyWZGrldcoxs4fD8tfNbHLMPqeE5WvM7KdmZmn4OElhZsPNbEH4u7HCzGaF5f3MbK6ZvRP+7BuWW3gN1pjZG2Y2PuZYM8Lt3zGzGen6TMnQhuv0f8Lfsxozu7HJsTrt/7s2XKcvh79Hb5jZYjM7KeZYrbtO7q5XBryAicB44K2YsteASeHyvwJ3hMtfAh4Pl3sB64GRQDawFjgWyAFeB8am+7Ol8TpdCzwcLg8CKoCs8P2rwKcBA54BLkj3Z0vgNRoCjA+X+wBvA2OBu4Gbw/KbgR+Ey9PCa2DAGcArYXk/YF34s2+43Dfdny+N12kQcCpwJ3BjzHE69f+7NlynCY2/J8AFMb9Prb5OqkFlCHd/HtjRpLgQeD5cngt8oXFzoLeZdQN6ArVAFDgNWOPu69y9FngcuCjZsadSK6/TWKAs3G878CFQZGZDgIi7v+zB/5xHgM8nO/ZUcfet7r40XN4NrAKGEvwulIablfLRZ74IeMQDS4Cjw2t0HjDX3Xe4+06Ca3t+Cj9KUrX2Orn7dnd/DTjQ5FCd+v9dG67T4vD3BWAJMCxcbvV1UoLKbG8BF4bL/wQMD5f/COwBtgKVwA/dfQfBL83GmP03hWWdXUvX6XXgIjPrZmajgFPCdUMJrk2jTnudzGwkMA54BTjG3bdC8EeHoEYALf/edJnfpzivU0t0nVq+TpcT1M6hDddJCSqz/StwrZlVEFSta8Py04B6IB8YBXzdzI4laKJpqis8R9DSdXqI4D9BOXAvsBioo4tcJzPLA54Ernf36OE2babMD1PeqbTiOrV4iGbKuvx1MrMpBAnqm41FzWx22OvUacbi64zc/e/AuQBm9kngM+GqLwHPuvsBYLuZvQQUEXw7GR5ziGHAltRFnB4tXSd3rwP+vXE7M1sMvAPs5KNmB+iE18nMuhP8MfmNu/8pLH7PzIa4+9awCW97WL6J5n9vNgGTm5QvTGbcqdbK69SSlq5fp9Ha62RmJwK/JLi3+0FY3OrrpBpUBjOzQeHPLOA24IFwVSVQHPa+6k1wY/vvBJ0FxpjZKDPLAS4F5qQ+8tRq6TqZWa/w+mBm5wB17r4ybI7YbWZnhL33vgL8v/REn3jhZ/oVsMrd/ztm1RygsSfeDD76zHOAr4S/T2cAu8Jr9DfgXDPrG/bQOjcs6xTacJ1a0qn/37X2OplZAfAn4DJ3fztm+9Zfp3T3ENHrYE+Z3xHcUzpA8E3jcmAWQY+Zt4G7+GjkjzzgD8AKYCXwjZjjTAu3Xwv8R7o/V5qv00hgNcFN3XnAiJjjFBHcu1oL3Ne4T2d4AWcRNJ28ASwPX9OA/gSdRt4Jf/YLtzfg5+G1eBMoijnWvwJrwte/pPuzpfk6DQ5/56IEHW42EXS26dT/79pwnX5J0ErRuG15zLFadZ001JGIiGQkNfGJiEhGUoISEZGMpAQlIiIZSQlKREQykhKUiIhkJCUokSQKny160cwuiCm7xMyeTWdcIh2BupmLJJmZfYrgubVxBCM6LwfOd/e17ThmNw9GyhDptJSgRFLAzO4mGOC3N7Db3e8I51e6lmDqgcXAde7eYGazCaYU6Qk84e63h8fYBDxIMKL4ve7+hzR8FJGU0Vh8IqnxXWApwUC2RWGt6mJggrvXhUnpUuC3BHPs7AinU1lgZn9095Xhcfa4+5np+AAiqaYEJZIC7r7HzJ4Aqt29xsymEkx+Vx4MdUZPPpqK4J/N7HKC/5/5BPNaNSaoJ1IbuUj6KEGJpE5D+IJg/LuH3P1bsRuY2RiCsQVPc/cPzewxoEfMJntSEqlIBlAvPpH0mAdcYmYDAMysfzgKdATYDURjZrUV6ZJUgxJJA3d/08y+C8wLpwk5AFxFMLniSoKR1tcBL6UvSpH0Ui8+ERHJSGriExGRjKQEJSIiGUkJSkREMpISlIiIZCQlKBERyUhKUCIikpGUoEREJCP9f1djt/KJnDnlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_series = gss_by_year['polviews'].mean()\n",
    "plot_series_lowess(mean_series, 'C2')\n",
    "decorate(ylabel='Mean (7 point scale)',\n",
    "         title='Mean of polviews',\n",
    "         xlabel='Year',\n",
    "         xlim=[1972, 2020])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One reason the PMFs for 1974 and 2018 did not look very different is that the mean seems to have gone up (more conservative) and then down again (more liberal).\n",
    "\n",
    "Generally, it looks like the U.S. has been trending toward liberal for the last 20 years, or more, at least in the sense of how people describe themselves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Use `plot_series_lowess` to plot the standard deviation of `polviews` with a smooth line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "std_series = gss_by_year['polviews'].std()\n",
    "plot_series_lowess(std_series, color='C4')\n",
    "decorate(ylabel='Standard deviation (7 point scale)',\n",
    "         title='Standard deviation of polviews',\n",
    "         xlabel='Year',\n",
    "         xlim=[1972, 2020])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross tabulation\n",
    "\n",
    "In the previous sections, we treated `polviews` as a numerical quantity, so we were able to compute means and standard deviations.\n",
    "\n",
    "But the responses are really categorical, which means that each value represents a discrete category, like \"liberal\" or \"conservative\".  \n",
    "\n",
    "In this section, we'll treat `polviews` as a categorical variable.  Specifically, we'll compute the number of respondents in each category for each year, and plot changes over time.\n",
    "\n",
    "Pandas provides a function called `crosstab` that computes a [cross tabulation](https://en.wikipedia.org/wiki/Contingency_table).\n",
    "\n",
    "It takes two Series as arguments and returns a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = gss['year']\n",
    "column = gss['polviews']\n",
    "\n",
    "xtab = pd.crosstab(year, column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the first few lines from the result."
   ]
  },
  {
   "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>polviews</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>1974</th>\n",
       "      <td>31</td>\n",
       "      <td>201</td>\n",
       "      <td>211</td>\n",
       "      <td>538</td>\n",
       "      <td>223</td>\n",
       "      <td>181</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>56</td>\n",
       "      <td>184</td>\n",
       "      <td>207</td>\n",
       "      <td>540</td>\n",
       "      <td>204</td>\n",
       "      <td>162</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>31</td>\n",
       "      <td>198</td>\n",
       "      <td>175</td>\n",
       "      <td>564</td>\n",
       "      <td>209</td>\n",
       "      <td>206</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>37</td>\n",
       "      <td>181</td>\n",
       "      <td>214</td>\n",
       "      <td>594</td>\n",
       "      <td>243</td>\n",
       "      <td>164</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>21</td>\n",
       "      <td>140</td>\n",
       "      <td>255</td>\n",
       "      <td>559</td>\n",
       "      <td>265</td>\n",
       "      <td>187</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "polviews  1.0  2.0  3.0  4.0  5.0  6.0  7.0\n",
       "year                                       \n",
       "1974       31  201  211  538  223  181   30\n",
       "1975       56  184  207  540  204  162   45\n",
       "1976       31  198  175  564  209  206   34\n",
       "1977       37  181  214  594  243  164   42\n",
       "1978       21  140  255  559  265  187   25"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtab.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It contains one row for each value of `year` and one column for each value of `polviews`.  Reading the first row, we see that in 1974, 31 people gave response 1, \"extremely liberal\", 201 people gave response 2, \"liberal\", and so on.\n",
    "\n",
    "The number of respondents varies from year to year, so we need to \"normalize\" the results, which means computing for each year the *fraction* of respondents in each category, rather than the count.\n",
    "\n",
    "`crosstab` takes an optional argument that normalizes each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtab_norm = pd.crosstab(year, column, normalize='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what that looks like for the 7-point scale."
   ]
  },
  {
   "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>polviews</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>1974</th>\n",
       "      <td>0.021908</td>\n",
       "      <td>0.142049</td>\n",
       "      <td>0.149117</td>\n",
       "      <td>0.380212</td>\n",
       "      <td>0.157597</td>\n",
       "      <td>0.127915</td>\n",
       "      <td>0.021201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>0.040057</td>\n",
       "      <td>0.131617</td>\n",
       "      <td>0.148069</td>\n",
       "      <td>0.386266</td>\n",
       "      <td>0.145923</td>\n",
       "      <td>0.115880</td>\n",
       "      <td>0.032189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>0.021877</td>\n",
       "      <td>0.139732</td>\n",
       "      <td>0.123500</td>\n",
       "      <td>0.398024</td>\n",
       "      <td>0.147495</td>\n",
       "      <td>0.145378</td>\n",
       "      <td>0.023994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>0.025085</td>\n",
       "      <td>0.122712</td>\n",
       "      <td>0.145085</td>\n",
       "      <td>0.402712</td>\n",
       "      <td>0.164746</td>\n",
       "      <td>0.111186</td>\n",
       "      <td>0.028475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.014463</td>\n",
       "      <td>0.096419</td>\n",
       "      <td>0.175620</td>\n",
       "      <td>0.384986</td>\n",
       "      <td>0.182507</td>\n",
       "      <td>0.128788</td>\n",
       "      <td>0.017218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "polviews       1.0       2.0       3.0       4.0       5.0       6.0       7.0\n",
       "year                                                                          \n",
       "1974      0.021908  0.142049  0.149117  0.380212  0.157597  0.127915  0.021201\n",
       "1975      0.040057  0.131617  0.148069  0.386266  0.145923  0.115880  0.032189\n",
       "1976      0.021877  0.139732  0.123500  0.398024  0.147495  0.145378  0.023994\n",
       "1977      0.025085  0.122712  0.145085  0.402712  0.164746  0.111186  0.028475\n",
       "1978      0.014463  0.096419  0.175620  0.384986  0.182507  0.128788  0.017218"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtab_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the results easier to interpret, I'm going to replace the numeric codes 1-7 with strings.  First I'll make a dictionary that maps from numbers to strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recode the 7 point scale with words\n",
    "d7 = {1: 'Extremely liberal', \n",
    "      2: 'Liberal', \n",
    "      3: 'Slightly liberal', \n",
    "      4: 'Moderate', \n",
    "      5: 'Slightly conservative', \n",
    "      6: 'Conservative', \n",
    "      7: 'Extremely conservative'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can use the `replace` function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "polviews7 = gss['polviews'].replace(d7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `values` to confirm that the values in `polviews7` are strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Conservative               8495\n",
       "Extremely conservative     1770\n",
       "Extremely liberal          1699\n",
       "Liberal                    6299\n",
       "Moderate                  21444\n",
       "Slightly conservative      8864\n",
       "Slightly liberal           6981\n",
       "Name: polviews, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values(polviews7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we make the cross tabulation again, we can see that the column names are strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>polviews</th>\n",
       "      <th>Conservative</th>\n",
       "      <th>Extremely conservative</th>\n",
       "      <th>Extremely liberal</th>\n",
       "      <th>Liberal</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Slightly conservative</th>\n",
       "      <th>Slightly liberal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>1974</th>\n",
       "      <td>0.127915</td>\n",
       "      <td>0.021201</td>\n",
       "      <td>0.021908</td>\n",
       "      <td>0.142049</td>\n",
       "      <td>0.380212</td>\n",
       "      <td>0.157597</td>\n",
       "      <td>0.149117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>0.115880</td>\n",
       "      <td>0.032189</td>\n",
       "      <td>0.040057</td>\n",
       "      <td>0.131617</td>\n",
       "      <td>0.386266</td>\n",
       "      <td>0.145923</td>\n",
       "      <td>0.148069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>0.145378</td>\n",
       "      <td>0.023994</td>\n",
       "      <td>0.021877</td>\n",
       "      <td>0.139732</td>\n",
       "      <td>0.398024</td>\n",
       "      <td>0.147495</td>\n",
       "      <td>0.123500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>0.111186</td>\n",
       "      <td>0.028475</td>\n",
       "      <td>0.025085</td>\n",
       "      <td>0.122712</td>\n",
       "      <td>0.402712</td>\n",
       "      <td>0.164746</td>\n",
       "      <td>0.145085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.128788</td>\n",
       "      <td>0.017218</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>0.096419</td>\n",
       "      <td>0.384986</td>\n",
       "      <td>0.182507</td>\n",
       "      <td>0.175620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "polviews  Conservative  Extremely conservative  Extremely liberal   Liberal  \\\n",
       "year                                                                          \n",
       "1974          0.127915                0.021201           0.021908  0.142049   \n",
       "1975          0.115880                0.032189           0.040057  0.131617   \n",
       "1976          0.145378                0.023994           0.021877  0.139732   \n",
       "1977          0.111186                0.028475           0.025085  0.122712   \n",
       "1978          0.128788                0.017218           0.014463  0.096419   \n",
       "\n",
       "polviews  Moderate  Slightly conservative  Slightly liberal  \n",
       "year                                                         \n",
       "1974      0.380212               0.157597          0.149117  \n",
       "1975      0.386266               0.145923          0.148069  \n",
       "1976      0.398024               0.147495          0.123500  \n",
       "1977      0.402712               0.164746          0.145085  \n",
       "1978      0.384986               0.182507          0.175620  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtab_norm = pd.crosstab(year, polviews7, normalize='index')\n",
    "xtab_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are almost ready to plot the results, but first we need some colors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Color palettes\n",
    "\n",
    "Seaborn provides a variety of color palettes,  [which you can read about here](https://seaborn.pydata.org/tutorial/color_palettes.html).\n",
    "\n",
    "To represent political views, I'll use a diverging palette from blue to red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = sns.color_palette('RdBu_r', 7)\n",
    "sns.palplot(palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The middle color is white, which won't work when we plot it, so I will replace it with a purple color from another palette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muted = sns.color_palette('muted', 7)\n",
    "purple = muted[4]\n",
    "sns.palplot(muted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the modified diverging palette  with purple in the middle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette[3] = purple\n",
    "sns.palplot(palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll make a \"color map\", which is a dictionary that maps from the responses to the corresponding colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['Extremely liberal', \n",
    "           'Liberal', \n",
    "           'Slightly liberal', \n",
    "           'Moderate', \n",
    "           'Slightly conservative', \n",
    "           'Conservative',\n",
    "           'Extremely conservative']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extremely liberal (0.16339869281045752, 0.4449826989619377, 0.6975009611687812)\n",
      "Liberal (0.4206843521722413, 0.6764321414840445, 0.8186851211072664)\n",
      "Slightly liberal (0.7614763552479815, 0.8685121107266436, 0.9245674740484429)\n",
      "Moderate (0.5843137254901961, 0.4235294117647059, 0.7058823529411765)\n",
      "Slightly conservative (0.9824682814302192, 0.8006920415224915, 0.7061130334486739)\n",
      "Conservative (0.8945790080738178, 0.5038062283737026, 0.39976931949250294)\n",
      "Extremely conservative (0.7284890426758939, 0.15501730103806227, 0.1973856209150327)\n"
     ]
    }
   ],
   "source": [
    "color_map = dict(zip(columns, palette))\n",
    "\n",
    "for key, value in color_map.items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "To plot the results, I use the following function, which takes a `DataFrame` and plots each column using `plot_series_lowess`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_columns_lowess(table, columns, colors):\n",
    "    \"\"\"Plot the columns in a DataFrame.\n",
    "    \n",
    "    table: DataFrame with a cross tabulation\n",
    "    columns: list of column names, in the desired order\n",
    "    colors: mapping from column names to colors\n",
    "    \"\"\"\n",
    "    for col in columns:\n",
    "        series = table[col]\n",
    "        plot_series_lowess(series, colors[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function sets the position of the figure legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anchor_legend(x, y):\n",
    "    \"\"\"Place the upper left corner of the legend box.\n",
    "    \n",
    "    x: x coordinate\n",
    "    y: y coordinate\n",
    "    \"\"\"\n",
    "    plt.legend(bbox_to_anchor=(x, y), loc='upper left', ncol=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the 7 categories plotted as a function of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_columns_lowess(xtab_norm, columns, color_map)\n",
    "decorate(xlabel='Year',\n",
    "         ylabel='Proportion',\n",
    "         title='Fraction of people with each political view',\n",
    "         xlim=[1972, 2020])\n",
    "\n",
    "anchor_legend(1.02, 1.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way of looking at the results suggests that changes in political alignment during this period have generally been slow and small.\n",
    "\n",
    "The fraction of self-described moderates has not changed substantially.\n",
    "\n",
    "The fraction of conservatives increased, but seems to be decreasing now; the number of liberals seems to be increasing.\n",
    "\n",
    "The fraction of people at the extremes has increased, but it is hard to see clearly in this figure.\n",
    "\n",
    "We can get a better view by plotting just the extremes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns2 = ['Extremely liberal', 'Extremely conservative']\n",
    "\n",
    "plot_columns_lowess(xtab_norm, columns2, color_map)\n",
    "decorate(xlabel='Year',\n",
    "         ylabel='Proportion',\n",
    "         title='Fraction of people with extreme political views',\n",
    "         xlim=[1970, 2020])\n",
    "\n",
    "anchor_legend(1.02, 1.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This figure shows that the fraction of people who describe themselves as \"extreme\" has increased from about 2.5% to about 4%.\n",
    "\n",
    "In relative terms, that's a big increase.  But in absolute terms these tails of the distribution are still small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Let's do a similar analysis with `partyid`, which encodes responses to the question:\n",
    "\n",
    ">Generally speaking, do you usually think of yourself as a Republican, Democrat, Independent, or what?\n",
    "\n",
    "The valid responses are:\n",
    "\n",
    "```\n",
    "0\tStrong democrat\n",
    "1\tNot str democrat\n",
    "2\tInd,near dem\n",
    "3\tIndependent\n",
    "4\tInd,near rep\n",
    "5\tNot str republican\n",
    "6\tStrong republican\n",
    "7\tOther party\n",
    "```\n",
    "\n",
    "You can [read the codebook for `partyid` here](https://gssdataexplorer.norc.org/projects/52787/variables/141/vshow).\n",
    "\n",
    "Here are the steps I suggest:\n",
    "\n",
    "1) If you have not already saved this notebook, you should do that first.  If you are running on Colab, select \"Save a copy in Drive\" from the File menu.\n",
    "\n",
    "2) Now, before you modify this notebook, make *another* copy and give it an appropriate name.\n",
    "\n",
    "3) Search and replace `polviews` with `partyid` (use \"Edit->Find and replace\").\n",
    "\n",
    "4) Run the notebook from the beginning and see what other changes you have to make.\n",
    "\n",
    "You will have to make changes in `d7` and `columns`.  Otherwise you might get a message like \n",
    "\n",
    "`TypeError: '<' not supported between instances of 'float' and 'str'`\n",
    "\n",
    "Also, you might have to drop \"Other party\" or change the color palette.\n",
    "\n",
    "And you should change the titles of the figures.\n",
    "\n",
    "\n",
    "What changes in party affiliation do you see over the last 50 years?  Are things going in the directions you expected?\n",
    "\n",
    "Write a headline (or a couple) that describe the most substantial changes you see."
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}