data visualization assignment 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data = pandas.read_csv('politics and outlook.csv', low_memory = False )\n",
    "# added in W1_F2A into the csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = my_data.copy()\n",
    "columns = ['W1_A1', 'W1_A4', 'W1_A10', 'W1_B4', 'W1_F2', 'W1_F6']\n",
    "for x in columns:\n",
    "    data[x] = data[x].replace(-1, numpy.nan)    #change all missing values to NaN\n",
    "    pandas.to_numeric(data[x])    #convert data inside to numeric to help with msthematical functions if necessary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### reversed values such that higher = more interest/more optimistic ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How interested are you in what's going on in government and politics?\n",
      "1.0    198\n",
      "2.0    354\n",
      "3.0    658\n",
      "4.0    679\n",
      "5.0    389\n",
      "NaN     16\n",
      "Name: A1_FREQ, dtype: int64\n",
      "      A1_FREQ  Cum_prop\n",
      "1.0  0.086312  0.086312\n",
      "2.0  0.154316  0.240628\n",
      "3.0  0.286835  0.527463\n",
      "4.0  0.295990  0.823452\n",
      "5.0  0.169573  0.993025\n",
      "NaN  0.006975  1.000000\n"
     ]
    },
    {
     "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": [
    "print(\"How interested are you in what's going on in government and politics?\")\n",
    "recode1 = {1:5, 2:4, 3:3, 4:2, 5:1}    #a dictionary to reverse what the numbers represent\n",
    "data['A1_FREQ'] = data['W1_A1'].map(recode1)    #recode and give the variable a new name\n",
    "count1 = data['A1_FREQ'].value_counts(dropna = False).sort_index(ascending = True)   #sort = False to make sure values displayed in original order\n",
    "#instead of by the count value, include dropna = False for NaN values to be counted\n",
    "perc1 = data['A1_FREQ'].value_counts(normalize = True, dropna = False).sort_index(ascending = True)   #normalize for fraction\n",
    "perc1a = perc1.to_frame()    #convert series to dataframe to be able to add column\n",
    "perc1a['Cum_prop'] = perc1a.cumsum()    #add new column to the table\n",
    "print(count1)\n",
    "print(perc1a)\n",
    "\n",
    "perc1.plot.bar()\n",
    "plt.title(\"How interested are you in what's going on in government and politics?\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1 for \"not interested at all\", 5 for \"extremely interested\"', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you remember whether or not you voted in that election? 1, yes\n",
      "1.0     502\n",
      "2.0    1783\n",
      "NaN       9\n",
      "Name: A4_REV, dtype: int64\n",
      "       A4_REV  Cum_prop\n",
      "1.0  0.218832  0.218832\n",
      "2.0  0.777245  0.996077\n",
      "NaN  0.003923  1.000000\n"
     ]
    },
    {
     "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": [
    "print(\"Do you remember whether or not you voted in that election? 1, yes\")\n",
    "recode2 = {1:2, 2:1}    #a dictionary to reverse what the numbers represent\n",
    "data['A4_REV'] = data['W1_A4'].map(recode2)    #recode and give the variable a new name\n",
    "count2 = data['A4_REV'].value_counts(dropna = False).sort_index(ascending = True)\n",
    "perc2 = data['A4_REV'].value_counts(sort = False, normalize = True, dropna = False).sort_index(ascending = True)\n",
    "print(count2)\n",
    "perc2a = perc2.to_frame()\n",
    "perc2a['Cum_prop'] = perc2a.cumsum()\n",
    "print(perc2a)\n",
    "\n",
    "perc2.plot.bar()\n",
    "plt.title(\"Do you remember whether or not you voted in that election?\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1 for \"no\", 2 for \"yes\"', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How often, if ever, do you discuss politics with your family or friends? 1, never. 5, everyday\n",
      "1.0    349\n",
      "2.0    796\n",
      "3.0    557\n",
      "4.0    344\n",
      "5.0    233\n",
      "NaN     15\n",
      "Name: W1_A10, dtype: int64\n",
      "       W1_A10  Cum_prop\n",
      "1.0  0.152136  0.152136\n",
      "2.0  0.346992  0.499128\n",
      "3.0  0.242807  0.741935\n",
      "4.0  0.149956  0.891892\n",
      "5.0  0.101569  0.993461\n",
      "NaN  0.006539  1.000000\n"
     ]
    },
    {
     "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": [
    "print(\"How often, if ever, do you discuss politics with your family or friends? 1, never. 5, everyday\")\n",
    "count3 = data['W1_A10'].value_counts(dropna = False).sort_index(ascending = True)   #sort = False to make sure values displayed in original order\n",
    "#instead of by the count value, include dropna = False for NaN values to be counted\n",
    "perc3 = data['W1_A10'].value_counts(normalize = True, dropna = False).sort_index(ascending = True)   #normalize for fraction\n",
    "perc3a = perc3.to_frame()    #convert series to dataframe to be able to add column\n",
    "perc3a['Cum_prop'] = perc3a.cumsum()    #add new column to the table\n",
    "print(count3)\n",
    "print(perc3a)\n",
    "\n",
    "perc3.plot.bar()\n",
    "plt.title(\"How often, if ever, do you discuss politics with your family or friends?\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1 for never, 5 for everyday', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generally speaking, how angry do you feel about the way things are going in the country these days? 1, extremely angry\n",
      "1.0    243\n",
      "2.0    437\n",
      "3.0    731\n",
      "4.0    555\n",
      "5.0    301\n",
      "NaN     27\n",
      "Name: W1_B4, dtype: int64\n",
      "        W1_B4  Cum_prop\n",
      "1.0  0.105929  0.105929\n",
      "2.0  0.190497  0.296425\n",
      "3.0  0.318657  0.615083\n",
      "4.0  0.241935  0.857018\n",
      "5.0  0.131212  0.988230\n",
      "NaN  0.011770  1.000000\n"
     ]
    },
    {
     "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": [
    "print(\"Generally speaking, how angry do you feel about the way things are going in the country these days? 1, extremely angry\")\n",
    "#data['B4_FREQ'] = data['W1_B4'].map(recode1)\n",
    "count4 = data['W1_B4'].value_counts(dropna = False).sort_index(ascending = True)   #sort = False to make sure values displayed in original order\n",
    "#instead of by the count value, include dropna = False for NaN values to be counted\n",
    "perc4 = data['W1_B4'].value_counts(normalize = True, dropna = False).sort_index(ascending = True)   #normalize for fraction\n",
    "perc4a = perc4.to_frame()    #convert series to dataframe to be able to add column\n",
    "perc4a['Cum_prop'] = perc4a.cumsum()    #add new column to the table\n",
    "print(count4)\n",
    "print(perc4a)\n",
    "\n",
    "perc4.plot.bar()\n",
    "plt.title(\"Generally speaking, how angry do you feel about the way things are going in the country these days?\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1 extremely angry, 5 not angry at all', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And when you think about the future of the United States as a whole, are you generally optimistic, pessimistic, or neither optimistic nor pessimistic? 1, optimistic, 3, pessimistic\n",
      "1.0    565\n",
      "2.0    823\n",
      "3.0    851\n",
      "NaN     55\n",
      "Name: F2_REV, dtype: int64\n",
      "1.0    851\n",
      "2.0    823\n",
      "3.0    565\n",
      "NaN     55\n",
      "Name: W1_F2, dtype: int64\n",
      "       F2_REV  Cum_prop\n",
      "1.0  0.246295  0.246295\n",
      "2.0  0.358762  0.605057\n",
      "3.0  0.370968  0.976024\n",
      "NaN  0.023976  1.000000\n"
     ]
    },
    {
     "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": [
    "print(\"And when you think about the future of the United States as a whole, are you generally optimistic, pessimistic, or neither optimistic nor pessimistic? 1, optimistic, 3, pessimistic\")\n",
    "recode5 = {1:3, 2:2, 3:1}    #a dictionary to reverse what the numbers represent\n",
    "data['F2_REV'] = data['W1_F2'].map(recode5)\n",
    "count5 = data['F2_REV'].value_counts(sort = False, dropna = False).sort_index(ascending = True)\n",
    "count5a = data['W1_F2'].value_counts(sort = False, dropna = False).sort_index(ascending = True)   #to confirm the values\n",
    "perc5 = data['F2_REV'].value_counts(sort = False, normalize = True, dropna = False).sort_index(ascending = True)\n",
    "print(count5)\n",
    "print(count5a)\n",
    "#print(data['F2_REV'].head(n=30)) values are correct\n",
    "perc5a = perc5.to_frame()\n",
    "perc5a['Cum_prop'] = perc5a.cumsum()\n",
    "print(perc5a)\n",
    "\n",
    "perc5.plot.bar()\n",
    "plt.title(\"And when you think about the future of the United States as a whole, are you generally optimistic, pessimistic, or neither optimistic nor pessimistic?\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1, pessimistic. 2, neither. 3, optimistic', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### TEST ###\n",
    "# print(\"Future of America (detailed)\")\n",
    "# sub1 = data['F2_REV']\n",
    "# sub2 = data['W1_F2A']\n",
    "# print(sub2.head (n=10))\n",
    "# print(sub1.head(n=10))\n",
    "# #sub1 = sub1.fillna\n",
    "# # def foa(row):\n",
    "# #     if row['F2_REV'] == 2:\n",
    "# #         return 4\n",
    "# # sub1['detailed'] = sub1.apply (lambda row: foa(row))\n",
    "# # axis = 1 tells python to apply function to each row\n",
    "# #lambda creates functions without a name\n",
    "# #print(sub1.head(n=10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "### to make a scale from 1 to 7 combining F2 and F2A ###\n",
    "# print(\"Future of America (detailed)\")\n",
    "sub1 = data['F2_REV']    #create a new subcategory of F2\n",
    "sub2 = data['W1_F2A'].replace(' ', 0)    #new sub of F2A plus fill up space(which was NOT EMPTY)\n",
    "#changed the replace to 0 because it could not be detected in the if...\n",
    "sub2 = pandas.to_numeric(sub2)\n",
    "# print(sub1.head(n=11))\n",
    "# print(sub2.head(n=11))\n",
    "\n",
    "for i in range(len(sub2)):\n",
    "#    if sub2[i] == None or sub1[i] == None:    #dk why but this does not work\n",
    "    if sub2[i] == -1:    #replace all the missing values\n",
    "        sub2[i] = None\n",
    "    elif sub1[i] == 1:\n",
    "        sub2[i] = sub2[i]\n",
    "    elif sub1[i] == 2:\n",
    "        sub2[i] = 4\n",
    "    elif sub1[i] == 3:\n",
    "        sub2[i] = sub2[i] + 4\n",
    "    else:\n",
    "        sub2[i] = sub1[i]    #to make sure when sub1 gets NaN, sub2 also gets NaN\n",
    "\n",
    "\n",
    "# print(sub1)\n",
    "# print(sub1)\n",
    "# print(sub2.head(n = 26))\n",
    "# print(sub1[1])\n",
    "#sub2 = sub2.fillna(\"NaN\")   does not work\n",
    "#print(sub2.head(n=10))\n",
    "# axis = 1 tells python to apply function to each row\n",
    "#lambda creates functions without a name\n",
    "#print(sub1.head(n=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are you extremely, moderately, or slightly [optimistic/pessimistic]?(combining both F2 and F2A)\n",
      "1.0    148\n",
      "2.0    291\n",
      "3.0    123\n",
      "4.0    823\n",
      "5.0    206\n",
      "6.0    490\n",
      "7.0    148\n",
      "NaN     65\n",
      "Name: W1_F2A, dtype: int64\n",
      "       W1_F2A  Cum_prop\n",
      "1.0  0.064516  0.064516\n",
      "2.0  0.126853  0.191369\n",
      "3.0  0.053618  0.244987\n",
      "4.0  0.358762  0.603749\n",
      "5.0  0.089799  0.693548\n",
      "6.0  0.213601  0.907149\n",
      "7.0  0.064516  0.971665\n",
      "NaN  0.028335  1.000000\n"
     ]
    },
    {
     "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": [
    "print(\"Are you extremely, moderately, or slightly [optimistic/pessimistic]?(combining both F2 and F2A)\")\n",
    "data5a = sub2.replace(-1, numpy.nan)\n",
    "#print(data5a.head(n = 27))\n",
    "count5b = data5a.value_counts(sort = False, dropna = False).sort_index(ascending = True)   #to confirm the values\n",
    "perc5b = data5a.value_counts(sort = False, normalize = True, dropna = False).sort_index(ascending = True)\n",
    "print(count5b)\n",
    "#print(data['F2_REV'].head(n=30)) values are correct\n",
    "perc5c = perc5b.to_frame()\n",
    "perc5c['Cum_prop'] = perc5c.cumsum()\n",
    "print(perc5c)\n",
    "\n",
    "perc5b.plot.bar()\n",
    "plt.title(\"Optimism on the future of the United States as a whole\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1, extremely pessimistic. 7, extremely optimistic', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How far along the road to your American Dream do you think you will ultimately get on a 10-point scale where 1 is not far at all and 10 nearly there?\n",
      "1.0      70\n",
      "2.0      59\n",
      "3.0      98\n",
      "4.0     152\n",
      "5.0     362\n",
      "6.0     336\n",
      "7.0     440\n",
      "8.0     350\n",
      "9.0     165\n",
      "10.0    203\n",
      "NaN      59\n",
      "Name: W1_F6, dtype: int64\n",
      "         W1_F6  Cum_prop\n",
      "1.0   0.030514  0.030514\n",
      "2.0   0.025719  0.056234\n",
      "3.0   0.042720  0.098954\n",
      "4.0   0.066260  0.165214\n",
      "5.0   0.157803  0.323017\n",
      "6.0   0.146469  0.469486\n",
      "7.0   0.191805  0.661290\n",
      "8.0   0.152572  0.813862\n",
      "9.0   0.071927  0.885789\n",
      "10.0  0.088492  0.974281\n",
      "NaN   0.025719  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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3MH9fGAiRDtZFVetpJ2AS8GTFctpI85tZLSnpSKWmSfNI+8Uf8zTK8/WvWPAd18Y6+30s+F7e7aSTVatmfu8hndTa7Wt9PQ6WnQRsK2kUpObEwLtIL5I3bEU6ns8oTP9l0s2CjdVBr1E1aHf8bqW8rTVL+ybSul0WeG9xfls4n3Qz6zOFsAOByyJiVh5uHLvLx5/GcH+PP6+K1JTqD8w//7+f9CT8AmC0pOWUXh1YjX6e/4eY3zZ+5O3iYdI2+lQhTmP/e/X4mJu5/VzSA6R9+UXSk+BW1xoNAzlHd7KdVGmcc8vXJb8qDig19986x3ulUDaRblp2cv1Qpa/7fr+O65I+oNTs81HmXwO+lc7WSyeUv8vnhnI5Fiuu24omkL+KQrPFiPgTaf1vkYMGuq8/GxHXFKb/POlGfX/PjVNI6+nHeRkvXRq/Jel4d2qrifTnGj2fU9Zj4f3lWVKrna1K8VcltSI5LiJO6mTm+lLZuiUiphY/pOZeZSuS7kiVPUS689FwNfBmpe5ztyXd0XuAdIdgW9LMLc789vEdyxvdRNIj3m+TDuabkl46BHhDKUlVeaumuxbpjsyKpMeZW+bpXl4xzaJGz0XNlku5Z6MF2n/G/BfkG3k0LnweLsV7mVQLH6hy+9Ny/o3K6u+ZfxJofP6ddKHcTtWy6HTb+RnpUfvxpJ1oU+CQUhkby2hOaVrl4b5qVcbG+FZOBvaQtJKktUkX0sWebCq3lYho3A3tbw+SHW3j2T75+xKlroVXIB0I5xbGFT1eGn6hRXhxP1mFtJ+Xt6HGybq8HXUyDyuRmgU3JelzpAv335OeBm/G/BtHVftx1f7Qan9vK19wrMzC81Q1j6uQeoMsL6f/y+Mby+l7pDuJ55CaRGxGmj8qyvtEcSDmdwjUbF22mt9O97W+HgfLLsrxGjcrDiI1v7qklEez6YsFjyPd0u743XFaFj72NrwTeDup5UdHNwoj4jnSsXP/fDHxPtINjbbHHzo/vnXqamDzvB9sSzrPTyE96X5fDnsR+FNN+fVS1T7Vcj+TtCzpyfVGpKZU7yOd5yaQbnq10+9zdIfbSZXK6xKqjwPDSC0bymU7FBheUXHoRF/3/T4f15W62J9EeoK3P+m8sSmppdaAzgkV5SrvayuWxk9gwWU3oRS/6lpnDqlTueL0+ruvl7dhGNi58Szgs6Sbd1cAj0m6sPCeWWObbVrhH8A1emN/OZ2Ft8n/ZOH9ZRSpfnJ5+9lKutEb4WOkO1Jlq7Hgxn0tqenA+/OnsSNfnYfvJT1BuLMfZViP1HRjr4h4tdYuabcm8VveQSjYCfg30nsDr67wihp4WWO+my2XqR3m39DYORZ4Qqf04urKfZxWfzyav/cFplWM76Tr/apl3nbbkfQGUlva8RHxo0YESf9eStNYRquWyrhqB2Vr5TGq75A0yv1oxbiis0gXxfuSDv7zWPBOf3FbebXc+S7LSoXpP5e/l2RBzU6iHW3jSi+tNzpt+EdFlBGS3tLhnfR2HiWdmBfqNCSbXhruZB4eITWPaGUsqbnAlxoBktbpYNp12pF0wXFdKbxqHh8lPcX4fpNpPZi/xwJnRcSr/5OXL9q6rbiv3V0IL+9rAzoORsSLkk4DDpZ0NGl+f5BvRBTzaDb9KJThORbed2DBfWwou5y0fx4t6bnisbCNk0nNusaQWqvMZMEn0MV1VOz9r3x86+vxp+yanHar/Dk1Il6S9EfS+X8d4C/RoqfOARho2QfDFsDapCbnrx4jVOpgqIWBnqPbbSdVitclrc65T5Cu/U4knQ8XEp11JFHW6b4/EB8hPS35cLH1j1InRE80TdU3jWX3dhasCDVaetyav8eTmt41lG+0V13rrAo0Wk50uq8Pikht834C/CQvzx1Irxb9glQBa8zfGqSnkFX6e43emNf/Jd2gKCv3Tv4C6frkqYq4lbrRG+G1wK65GSAA+fdueRwAudnR30gnzA2Z31zgalKzwu3ofxOCxoIt7gxLkF7AG4iq6b6V1Ba2lemkOwpji4FK/7OzNoXl0qFZ+fPRUvjudFaBbtwtfWPLWM39mXSwfkv5aWf+lC+SO9XJtrMU6SK13PXpvqXhm0kv4JabvY2lM82W0bXAmpLK6/wTpIrDba0mmpuOnEu6O78f6UXU4g57Q867XM6PkdZtYznMyfHKFYuOet1pYTfSXaFvkO4uFz+NMtX1n1uXAxsA9zXZjvrzf3m/IzVFbnZjBdJ+XN5+PtWPvPpF6Q+NjyadSDtp1no56UnGtCbLqbGt9mq+biRdPLXb1+o4Dv6EdDL9JelY8NPS+GtJT0xGFqY/jLT//K2wTd0LrCpp5UK89eisKdBAj5+1iIj/I10QHyfpCx2muYu0j/w3ueOY0oVtYx2U113j3Nloenpv/i4ff3bppByki6W5uRzLFKZ7NencvzXda0I4kGPnYK37qmuN4aTKTyflGdA5uoPtpMo/Sefc8nXJAsO5Av1H0lO7m6rKV5q/Tpd1p/v+QCxNep/t1Ztikt5PPc3KG64nVSzK16t7kipIfwKIiJml5TazFH+P4hPCfM2yZp4+dL6vD0S/9peIeDwifkFqWtzYT/9MeqJ4QNOEA7tGn0nqmr9qf7m5VL6/RMQGEXF95dQqdOPJ1rdIj92ukvR90kb5ZdJC+GYp7tWknfnhiGjU5ieTLvZWIvUQ0x+3kU4G35H0MmnBd3QyauP3pLsaZ0n6Aemx+TdI73s0rbhGxMuSvkaqsZ9DauazBqlZ453M7062I5H+p+EbpO5eTyNdeKxLam7wJOnCp5XGnZFDJJ1JWj43R4f/LRYRT0n6b+BEpW6hf5vzXYN0kpwcEef1ZZ6ytttORDwp6QbgS5Jmkw5K+zH/0XijjE9IOhb4iqSnSSeOTUmP/jvRWEZfkvRb4OV8EjiD9CTmQklfIVV6P0lqznhgdPY/VCcxvynUAk0zIuIxST8E/lfSv0hNFt5Gag57HbmdeUSEpF+QmnrcQTpY7Eq6UTEQ+5AOaMdExDPlkfmibm9JX893ogbiWNKJ8I95XU0nXXhtQLqju1D3wx04h/Suwc+VugO/kfQ/fDuS2lffTqq8fFnSkaSet95PuqDohjWUugVejHRc2zyXT8BuETGvg2l8LZfzD5JOIJ0UhpNOQutGRONPPy8H9pH0T9LLvh8mNaPoqoiYLuk84Jv55D6FtD/sUoo34ONgRDwg6RLS3fZLIuL+UpRjSTderpT0ddKdx4NJ71QUL6Z/STrenJv3t5VJdzU7aYZ9B+k8sJ+kx0gXFNNrupjrk4g4Np/jjpM0LCKO6SDZSaT3exdqehQR0yT9HBifn6L8mfSU5avAzxsXHRExW9K1pOPUI6QbTXuSWpV0Uu5GN94fBabE/Hc+r2F+89g+v0LQh7z7e+wc0LmzD/5M2nZPzNvxMqSOCx4h3WxomEO6Kz9WUuMG4z0R8WgN5+im20mViHhc0nHAkfmc+3tSr4ONc27xuuSLpIv5KySdTrrxtHKOPywixuV4t5KeZH+M9PTl6RYVxU73/YG4HDgcOEPSz/K0v0qbpusNkkaTOmdoXC9uKKlx7pkUEc/mJ/hfJf2J8QOk5fh+0nXO5/qwrS0H/EbST0jvk3+PdJw9Czrf1weo2XXUQiSdSrpBcD3pePJWUq+Pv8vlfVrS/wI/lvRr0k3rp0kd7DwXET+m/9foIekQ4OLcuucC0r62Kukcel9E/LBQ1q1JzRV3iIjObgpF+9489qV57zuVPaqQHvn9nnTR9q9cqM0q0u+c05e77P4HbXo0KsSt7I0wr4DrSC+4zSJdrH+6PF0K3eh28iHdvb2d1BRhGumuwBm06SUph++Z5+150gHybBbu3rOyPE2W8+GkSuVzpCY4/0FqR7tQr20V0/s66QDRuEszspDPt0txm83PLqQT4lOk5nAzSAflpt2z5nSTadLDUCfbTi7Pb0k72sOkR+m7snBPUsNIlZSHcvkmM78L6fFtyjiM1MzhYdJJIgrjVs/r7pG8Lm8G9ux0G8rTmE66yKgaJ9LNgemkx9Wzc1mWL8VboVCOx0gVt6rl0HR5l6Y3gnRiPb1FnM8Up1+1vdKktzbSfjKrFDacdJK8J8/rw6S7nsXesval+THoDEq9j5Feov0/0r7RWH6/AlbJ499IaiYzN29Dl5KaLS2wXdC8R8qF8myyrKLweTGvp+tIF00j+rhfNLrRfaAwT1cWtzvSxcr5pGPA46ST0aZU9yQ1q0l5y/t+02Vfird0XqaPkfbdiczv/arPx8E2eX08T3fXJuPXJ3WH/iTp2HgD+a8CSvF2Jz1hmZfLswMd9EaY0x5IajL5Em32h8KyLW5bC2xDNO9VbhsW3p8XyoP0zuorwP90UPZhpGPrL5uMX4J03Lw3b7f35uElSvHWJL0v9wTpGPtdKs6xLcrx2Ry32ONgo6fC51i4N7Zmy6y8bXeyb/bl2Dm5lLbVubOT40fV+mus5w8Uwt5Pav0zj1TROKwxzYrt+Na8rsrLo+05mlJvhJ1uJy22re+w4Dl3y1yuz5fivo10vHqYdCyYRTpu7FKIsxrphuPTeRot90062PdbrJdOt53Pkc5X80g3lj5QtZ00SXsGC54Xip+RpbgHkm7sNDqe6KjXO+bvFweTOp2bS7oOvgxYp6/7Os33s6pzyALLgRbXURVp92H+3588n5fxsSx83bMH6SbqvLxd3wj8Z2H8QK7RtyBdDzye08/M2+gW7fbXdh/lhPY6oNQ7119IXc2e3S6+9UZ+rH078JmIOL3X5TF7LZF0Lqkit270792ORZqk7Ul3iz8QEVf1ujw2NNW1nUj6KOlJwVYR8cd28W1gNP/Prj8TEaf1tjTW0I1mhDYI8gv9h5CeAjxFukt0JGkn+3UPi2ZNSFqT9CT2G6QnE/1pamm2SMrNMTcmNT39oitafZPfSVuXdLf4Jle0rMpAthNJ7yE9IbyR9GTg3aTXG25g4c6AzBYZrmy9ds0jvbOxN6kp1uOk5nfjIuLZXhbMmvo06f2bO0h/cNrJ+zpmllxPaqJ4Jul9EuubrzK/CWddndzY689AtpNnSL1LHkL6n6+HSU+1/jfcjMoWYW5GaGZmZmZm1gXd6PrdzPpJ0k6SpkuaIWlcxfhPSro5f/4saaN2aSWtKOlKSXfm78H4Y1czMzOzRZ6fbJkNEfk/Qe4gdZs9i9TT0ccj4tZCnC2B2yJ1s7szqfer97RKq/QHsI9FxFG5EjY8Ir7cqiwrr7xyjBw5sgtzaWb2+vXXv/71kYgY0etymNnQ4Xe2zIaOzYAZEXE3gKTzSX9i+WplKyL+XIh/A6n75XZpxzD/P2TOJHWv2rKyNXLkSKZOrfw7DDMza0LSve1jmdmixM0IzYaONYDin7TOovRnzSX7k/5vrF3aVSNiNqQ/IwVWqZqYpAMkTZU0de7cuf0ovpmZmZkVubJlNnSoIqyyna+kbUmVrcYTqo7TNhMRp0bE6IgYPWKEW8GYmZmZDZQrW2ZDxyxgrcLwmsCD5UiS3gmcBoyJiEc7SDtH0uo57eqk7njNzMzMrMtc2TIbOqYAoyStI2lJYCwwsRhB0puBC4G9IuKODtNOBPbJv/cBLu7iPJiZmZlZ5g4yzIaIiHhJ0qHAFcAwYEJETJN0UB5/CulPkVcCTpIE8FJu+leZNk/6KOACSfsD9wEfHdQZMzMzM1tEuet3M1vI6NGjw70Rmpn1jaS/RsToXpfDzIYONyM0MzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wBxlmZmbZyHGX9TvtzKN2rbEkZmb2euAnW2ZmZmZmZl3gypaZmZmZmVkXuLJlZmZmZmbWBa5smZmZmZmZdYErW2ZmZmZmZl3gypaZmZmZmVkXuLJlZmZmZmbWBa5smZmZmZmZdYErW2ZmZmZmZl3gypaZmZmZmVkXuLJlZmZmZmbWBa5smZmZmZmZdYErW2ZmZmZmZl3gypaZmZmZmVkXuLJlNoRI2knSdEkzJI2rGL+BpOslPS/piEL4+pL+Xvg8JenwPG68pAcK43YZzHkyMzMzW1Qt3usCmFkiaRhwIrA9MAuYImliRNxaiPYYcBiwezFtREwHNi5M5wHgokKUYyPimC4W38zMzKg9JtwAACAASURBVMxK/GTLbOjYDJgREXdHxAvA+cCYYoSIeDgipgAvtpjOdsBdEXFv94pqZmZmZu24smU2dKwB3F8YnpXD+mos8PNS2KGSbpY0QdLwqkSSDpA0VdLUuXPn9iNbMzMzMytyZcts6FBFWPRpAtKSwAeBXxaCTwbWIzUznA38oCptRJwaEaMjYvSIESP6kq2ZmZmZVXBly2zomAWsVRheE3iwj9PYGbgpIuY0AiJiTkS8HBGvAD8lNVc0MzMzsy5zZcts6JgCjJK0Tn5CNRaY2MdpfJxSE0JJqxcGPwTcMqBSmpmZmVlH3Buh2RARES9JOhS4AhgGTIiIaZIOyuNPkbQaMBVYHngld+++YUQ8JWlpUk+GB5YmfbSkjUlNEmdWjDczMzOzLnBly2wIiYhJwKRS2CmF3w+RmhdWpX0WWKkifK+ai2lmZmZmHXAzQjMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMumDxXhfA7PVM0huBlyPihV6XxayvRo67rN9pZx61a40lMTMze23yky2zGkk6RtJm+feuwGPAE5J2623JzMzMzGywubJlVq9PArfk318D9gQ+CHy3ZyUyMzMzs55wM0Kzei0dEc9KWglYNyJ+DSBp7R6Xy8zMzMwGmStbZvW6Q9IngbcAVwJIWhmY19NSmZmZmdmgc2XLrF4HAz8CXgT2y2E7Ar/rWYnMzMzMrCdc2TKrUURMAbYshZ0LnNtJekk7kSprw4DTIuKo0vgNgJ8BmwBfiYhjCuNmAk8DLwMvRcToHL4i8AtgJDAT+K+IeLzvc2dm3eKeH83MXp/cQYZZzSRtL+l0SZfk4dGS3t9BumHAicDOwIbAxyVtWIr2GHAYcAzVto2IjRsVrWwccFVEjAKuysNmZmZm1mWubJnVSNLngJOBO4GtcvA84NsdJN8MmBERd+f/5TofGFOMEBEP56dnL/ahWGOAM/PvM4Hd+5DWzMzMzPrJlS2zeh0OfCA3/3slh90OrN9B2jWA+wvDs3JYpwL4naS/SjqgEL5qRMwGyN+rVCWWdICkqZKmzp07tw/ZmpmZmVkVV7bM6rUc8ytMkb+XAF7oIK0qwqIirJn3RsQmpGaIh0jaql2CBTKKODUiRkfE6BEjRvQlqZmZmZlVcGXLrF5/YOF3og4Drukg7SxgrcLwmsCDnWYcEQ/m74eBi0jNEgHmSFodIH8/3Ok0zczMzKz/XNkyq9fngA/lngGXkzQd+CjwxQ7STgFGSVpH0pLAWGBiJ5lKWkbSco3fwA7ALXn0RGCf/Hsf4OIO58XMzMzMBsBdv5vVKCJmS9oU2BRYm9Sk8C8R8UrrlBARL0k6FLiC1PX7hIiYJumgPP4USasBU4HlgVckHU7quXBl4CJJkPbr8yLi8jzpo4ALJO0P3Eeq/JkNWe4G3czMXi9c2TKrWUQE8BfgL5K2Bf6D1Lywk7STgEmlsFMKvx8iNS8sewrYqMk0HwW266jwZmZmZlYbNyM0q5GkayW9N//+Mqn79p9LOrK3JTMzMzOzwebKllm93gHckH9/BtgG2Bw4qFcFMjMzM7PecDNCs3otBoSk9QBFxG0Akob3tlhmZmZmNthc2TKr13XACcDqpO7XyRWvR3pZKDMzMzMbfG5GaFavfYEngJuB8TlsA+BHPSqPmZmZmfWIn2yZ1Sj3/HdkKaz//VibmZmZ2WuWK1tmNcp/RrwvsDGwbHFcROzdizKZmZmZWW+4smVWrzNJ/3d1CTCnx2UxMzMzsx5yZcusXjsB60TEE70uiJmZmZn1ljvIMKvXfcBSvS6EmZmZmfWen2yZ1ess4GJJP6LUjDAiru5NkczMzMysF1zZMqvXofn7u6XwANYd5LKYmZmZWQ+5smVWo4hYp9dlMDMzM7OhwZUts5pJWhzYElgDmAVcHxEv9bZUZmZmZjbYXNkyq5GkDUjdvr8RuB9YC3hO0m4RcVtPC2dmZmZmg8q9EZrV6yTgVGCtiNgiItYETsnhZmZmZrYIcWXLrF4bAz+MiCiEHZfDzczMzGwR4sqWWb0eBLYuhb0vh5uZmZnZIsSVLbN6HQlMlHS+pO9LOh+YmMPbkrSTpOmSZkgaVzF+A0nXS3pe0hGF8LUkXSPpNknTJH2+MG68pAck/T1/dqlhPs3MzMysDVe2zGoUEROBTYBbgOXy97sj4uJ2aSUNA04EdgY2BD4uacNStMeAw4BjSuEvAV+KiLcBmwOHlNIeGxEb58+kfsyamZmZmfWReyM0q1lE3CHpO8DKwCOl97da2QyYERF3A+SnYmOAWwvTfhh4WNKupTxnA7Pz76cl3Ubqev5WzMzMzKwn/GTLrEaSVpB0NjAPeAiYJ+lsSSt2kHwNUnfxDbNyWF/LMBJ4F3BjIfhQSTdLmiBpeJN0B0iaKmnq3Llz+5qtmZmZmZW4smVWr5+R/mPrXaRmhO8ClgImdJBWFWGdPhVLE5CWBX4NHB4RT+Xgk4H1SD0izgZ+UJU2Ik6NiNERMXrEiBF9ydbMzMzMKrgZoVm9tgVWj4h5efg2SfvSWW+Es0h/gtywZofpAJC0BKmidW5EXNgIj4g5hTg/BS7tdJpmZmZm1n9+smVWr+nAyFLYm3N4O1OAUZLWkbQkMJbUk2FbkgScDtwWET8sjVu9MPghUqcdZmZmZtZlfrJlVq+rgN/l97buJz2p2hM4W9J+jUgRsVCzwoh4SdKhwBXAMGBCREyTdFAef4qk1YCpwPLAK5IOJ/Vc+E5gL+Cfkv6eJ3lk7nnwaEkbk5okzgQO7MJ8m5mZmVmJK1tm9doCmJG/t8hhdwFb5g+kSk/lO1y5cjSpFHZK4fdDpOaFZddR/c4XEbFX58U3MzMzs7q4smVWo4jYttdlMDMzM7Ohwe9smdVI0ojcIyCShkn6lKS9JHlfMzMzM1vE+ALQrF6XAqPy7+8CRwBfokl362ZmZmb2+uVmhGb1eivQ6KDik6T3tJ4BpgFf6FWhzMzMzGzwubJlVq+XgSUlvRV4MiLuy00Il+1xuczMzMxskLmyZVav3wIXACsB5+ewDYEHelYiMzMzM+sJV7bM6vVpYB/gReDsHLYyML5XBTIzMzOz3nBly6xGEfE8cGpuOrgqMDsiJve2VGZmZmbWC+6N0KxGklaQdB7wHOnPjZH0QUnf7m3JzMzMzGywubJlVq9TgCeBtYEXctj1wMd6ViIzMzMz6wk3IzSr13bAmyLiRUkBEBFzJa3S43KZmZmZ2SDzky2zej1J6hDjVZLeDMzuTXHMzMzMrFdc2TKr12nAryVtCywmaQvgTFLzQjMzMzNbhLgZoVm9vk/qHONEYAlgAvAT4Ee9LJSZmZmZDT5XtsxqImkYqXJ1QEQc1+vymJmZmVlvuRmhWU0i4mVgB+CVXpfFzMzMzHrPlS2zeh0LfEPSkr0uiJmZmZn1lpsRmtXrc8BqwBclzQWiMSIi3tyzUpmZmZnZoPOTLbN67Ql8ANgx/96r8GlL0k6SpkuaIWlcxfgNJF0v6XlJR3SSVtKKkq6UdGf+Hj6QGTQzMzOzzvjJllmNIuLa/qbNHWycCGwPzAKmSJoYEbcWoj0GHAbs3oe044CrIuKoXAkbB3y5v+U0MzMzs874yZbZ0LEZMCMi7o6IF4DzgTHFCBHxcERMAV7sQ9oxpP/6In/vjpmZmZl1nStbZkPHGsD9heFZOWygaVeNiNkA+XuVqglIOkDSVElT586d26eCm5mZmdnCXNkyGzpUERYVYXWnTZEjTo2I0RExesSIEX1JamZmZmYVXNkyGzpmAWsVhtcEHqwh7RxJqwPk74cHWE4zMzMz64A7yDCrkaQVgSOAjYFli+MiYqs2yacAoyStAzwAjAU+0WHWrdJOBPYBjsrfF3c4TTMzMzMbAFe2zOp1HrAUcAHwbF8SRsRLkg4FrgCGARMiYpqkg/L4UyStBkwFlgdekXQ4sGFEPFWVNk/6KOACSfsD9wEfHfBcmpmZmVlbrmyZ1WtLYEREPN+fxBExCZhUCjul8PshUhPBjtLm8EeB7fpTHjMzMzPrP7+zZVavm2lSGTIzMzOzRYufbJnV62rgckk/Ax4qjoiICb0pkpmZmZn1gitbZvV6H6lnwO1L4QG4smVmZma2CHFly6xGEbFtr8tgZmZmZkODK1tmNZM0HNgNWIPUDfslEfF4b0tlZmZmZoPNHWSY1UjSFsBdwEHAO4EDgbtyuJmZmZktQvxky6xexwEHR8T5jQBJHwOOBzbtWanMzIaYkeMu63famUftWmNJzMy6x0+2zOr1VtIfGhf9CnhLD8piZmZmZj3kypZZve4ExpbCPkpqWmhmZmZmixA3IzSr1+HApZIOA+4FRgKjgP/sZaHMzMzMbPC5smVWo4j4s6T1gF2BNwGXAJMi4rHelszMzMzMBpsrW2Y1y928n9PrcpiZmZlZb7myZTZAki6PiJ3y7z8CURUvIrYa1IKZmZmZWU+5smU2cGcVfp/Ws1KYmZmZ2ZDiypbZAEXEeYXB2yPixnIcSZsNYpHMzMzMbAhw1+9m9bqySfjlg1oKMzMzM+s5P9kyq4GkxQCln1L+3bAe8FJPCmZmZmZmPePKllk9XiJ1jCEWrli9Anxn0EtkZmZmZj3lZoRm9ViH9ATrfmDdwmcdYPmIGN/JRCTtJGm6pBmSxlWMl6Tj8/ibJW2Sw9eX9PfC5ylJh+dx4yU9UBi3Sz2zbGZmZmat+MmWWQ0i4l5Jw4B7gIci4vm+TiOnPxHYHpgFTJE0MSJuLUTbGRiVP+8BTgbeExHTgY0L03kAuKiQ7tiIOKbvc2ZmZmZm/eUnW2Y1iYiXSU+y+rtfbQbMiIi7I+IF4HxgTCnOGOCsSG4AVpC0einOdsBdEXFvP8thZmZmZjVwZcusXt8ATpa0tqRhkhZrfDpIuwapGWLDrBzW1zhjgZ+Xwg7NzQ4nSBpelbmkAyRNlTR17ty5HRTXzMzMzFpxZcusXqcBewN3Ay8AL5I6zHixg7SqCIu+xJG0JPBB4JeF8SeT3ifbGJgN/KAq84g4NSJGR8ToESNGdFBcMzMzM2vF72yZ1WudAaSdBaxVGF4TeLCPcXYGboqIOY2A4m9JPwUuHUAZzczMzKxDrmyZ1ajxnlRuNrgqMCciXukw+RRglKR1SB1cjAU+UYozkdQk8HxSBxlPRsTswviPU2pCKGn1QpwPAbf0YZbM7HVs5LjLBpR+5lG71lQSM7PXJzcjNKuRpOUlnQU8R6owzZN0pqR/a5c2Il4CDgWuAG4DLoiIaZIOknRQjjaJ1ERxBvBT4OBC3kuTejK8sDTpoyX9U9LNwLbAFwY0k2ZmZmbWET/ZMqvX8cAywDuAe4G1SX9ofDywT7vEETGJVKEqhp1S+B3AIU3SPgusVBG+V+fFNzMzM7O6uLJlVq+dgHVzxQfgDkmfAu7qYZnMzMzMrAfcjNCsXs8B5a78Vgb6/CfHZmZmZvba5idbZvU6DbhS0g+Z34zwC8CpPS2VmZmZmQ06V7bM6vUdUlfsnwDelH8fDUzoZaHstWsgvcW5pzgzM7PecmXLrEa5A4sJuHJlZmZmtsjzO1tmNZO0n6QrJU3L3/tLUq/LZWZmZmaDy0+2zGok6WhgDHAc89/ZOgJYH/ifHhbNzMzMzAaZK1tm9doX2CQiZjUCJF0K3IQrW2ZmZmaLFDcjNKvX0/lTDnuqB2UxMzMzsx7yky2zeh0HXCjpKGAWsBbw38CxktZtRIqIu3tUPjMzMzMbJK5smdXrR/l721L4dsDx+XcAwwatRGZmZmbWE65smdUoItw018zMzMwAV7bMukLSm4E1gFkRcX+vy2NmZmZmg8934c1qJGl1SdcCM4ALgbsk/UHSm3pcNDMzMzMbZK5smdXrZOAfwPCIWB0YDvwNOKWnpTIzMzOzQedmhGb1+g9g9Yh4ESAi/iXpf4AHelssMzMzMxtsfrJlVq/HgQ1LYesDT/SgLGZmZmbWQ36yZVavo4HfSzoduBdYG/gU8NWelsrMzMzMBp2fbJnVKCJ+CnwMWBnYLX9/PCJO7SS9pJ0kTZc0Q9K4ivGSdHwef7OkTQrjZkr6p6S/S5paCF9R0pWS7szfwwc8o2ZmZmbWlp9smdVE0jDgDmDDiLi6n+lPBLYHZgFTJE2MiFsL0XYGRuXPe0gdcrynMH7biHikNOlxwFURcVSuwI0DvtzX8pmZmZlZ3/jJlllNIuJl4GXgDf2cxGbAjIi4OyJeAM4HxpTijAHOiuQGYAVJq7eZ7hjgzPz7TGD3fpbPzMzMzPrAlS2zeh0HXCBpa0nrSVq38ekg7RpA8Q+QZ+WwTuME8DtJf5V0QCHOqhExGyB/r1KVuaQDJE2VNHXu3LkdFNfMzMzMWnEzQrN6nZC/ty+FBzCsTVpVhEUf4rw3Ih6UtApwpaTbI+IPbfKcP5H0XtmpAKNHjy7na2ZmZmZ95CdbZjWKiMWafNpVtCA9pVqrMLwm8GCncSKi8f0wcBGpWSLAnEZTw/z9cF/ny8zMzMz6zpUtsxpIWlrSdyVNlDRe0lL9mMwUYJSkdSQtCYwFJpbiTAT2zr0Sbg48GRGzJS0jablclmWAHYBbCmn2yb/3AS7uR9nMzMzMrI/cjNCsHicAmwK/BfYAVgI+15cJRMRLkg4FriA1OZwQEdMkHZTHnwJMAnYBZgDPkv7DC2BV4CJJkPbr8yLi8jzuKNJ7ZPsD9wEf7e9MmpmZmVnnXNkyq8fOwCb5KdOPgT/Qx8oWQERMIlWoimGnFH4HcEhFuruBjZpM81Fgu76WxczMzMwGxs0IzeqxTKHHv/uBf+txeczMzMysx/xky6wei0valvm9BZaH6c8fHZuZmZnZa5crW2b1eBiYUBh+tDQcQCf/tWVD1Mhxl/U77cyjdq2xJGZmZvZa4cqWWQ0iYmSvy2BmZmZmQ4vf2TIzMzMzM+sCV7bMzMzMzMy6wJUtMzMzMzOzLnBly8zMzMzMrAtc2TIzMzMzM+sCV7bMzMzMzMy6wJUtMzMzMzOzLnBly8zMzMzMrAtc2TIzMzMzM+sCV7bMzMzMzMy6wJUtMzMzMzOzLli81wUws9eekeMu63famUftWmNJzMzMzIYuP9kyMzMzMzPrAle2zMzMzMzMusCVLbMhRNJOkqZLmiFpXMV4STo+j79Z0iY5fC1J10i6TdI0SZ8vpBkv6QFJf8+fXQZznszMzMwWVX5ny2yIkDQMOBHYHpgFTJE0MSJuLUTbGRiVP+8BTs7fLwFfioibJC0H/FXSlYW0x0bEMYM1L2ZmZmbmypbZULIZMCMi7gaQdD4wBihWtsYAZ0VEADdIWkHS6hExG5gNEBFPS7oNWKOU9jVvIB1zgDvnMDMfR8xscLkZodnQsQZwf2F4Vg7rUxxJI4F3ATcWgg/NzQ4nSBpelbmkAyRNlTR17ty5/ZsDMzMzM3uVK1tmQ4cqwqIvcSQtC/waODwinsrBJwPrARuTnn79oCrziDg1IkZHxOgRI0b0texmZmZmVuLKltnQMQtYqzC8JvBgp3EkLUGqaJ0bERc2IkTEnIh4OSJeAX5Kaq5oZmZmZl3mypbZ0DEFGCVpHUlLAmOBiaU4E4G9c6+EmwNPRsRsSQJOB26LiB8WE0havTD4IeCW7s2CmZmZmTW4gwyzISIiXpJ0KHAFMAyYEBHTJB2Ux58CTAJ2AWYAzwKfysnfC+wF/FPS33PYkRExCTha0sak5oYzgQMHaZbMzMzMFmmubJkNIblyNKkUdkrhdwCHVKS7jur3uYiIvWouppmZmZl1wM0IzczMzMzMusCVLTMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wZcvMzMzMzKwLXNkyMzMzMzPrAle2zMzMzMzMusCVLTMzMzMzsy5wZctsCJG0k6TpkmZIGlcxXpKOz+NvlrRJu7SSVpR0paQ78/fwwZofMzMzs0WZK1tmQ4SkYcCJwM7AhsDHJW1YirYzMCp/DgBO7iDtOOCqiBgFXJWHzczMzKzLXNkyGzo2A2ZExN0R8QJwPjCmFGcMcFYkNwArSFq9TdoxwJn595nA7t2eETMzMzMDRUSvy2BmgKQ9gJ0i4tN5eC/gPRFxaCHOpcBREXFdHr4K+DIwsllaSU9ExAqFaTweEQs1JZR0AOlpGcD6wPQBzM7KwCMDSO98h37enudFI2/Pc9+sHREj6iyMmb22Ld7rApjZq1QRVr4b0ixOJ2lbiohTgVP7kqYZSVMjYnQd03K+QzNvz/Oikbfn2cxsYNyM0GzomAWsVRheE3iwwzit0s7JTQ3J3w/XWGYzMzMza8KVLbOhYwowStI6kpYExgITS3EmAnvnXgk3B56MiNlt0k4E9sm/9wEu7vaMmJmZmZmbEZoNGRHxkqRDgSuAYcCEiJgm6aA8/hRgErALMAN4FvhUq7R50kcBF0jaH7gP+OggzE4tzRGd75DO2/O8aOTteTYzGwB3kGFmZmZmZtYFbkZoZmZmZmbWBa5smZmZmZmZdYErW2ZmZmZmZl3gypaZ9YmkQyVNlfS8pDPaxP2CpIckPSlpgqSlBpDvUpJOl3SvpKcl/U3SzoORd57eOZJmS3pK0h2SPj1YeedpjpL0nKRzBitfSZNzns/kT9M/uu5C3mMl3SbpX5LukvS+budbmM/G52VJP24Rv868R0qaJOnxPM0TJFV2YlVzvm+TdHWe1gxJH2oRd0D5tjp2SNpO0u2SnpV0jaS1W0xnRUkX5W3jXkmf6Es5zMwGkytbZtZXDwLfBia0iiRpR2AcsB0wElgX+MYA8l0cuB/YGvg34KukXhZHDkLeAN8DRkbE8sAHgW9Levcg5Q1wIqmL/0pdzPfQiFg2f9YfjLwlbQ98n9Tb5nLAVsDd3c63MJ/LAqsC84BfNilj3cv7JNJ/4K0ObEzazg/uZr65MncxcCmwInAAcI6kt3Yp38pjh6SVgQtJ+/SKwFTgFy2mcyLwAmkdfRI4WdLb+1gWM7NB4cqWmfVJRFwYEb8BHm0TdR/g9IiYFhGPA98C9h1Avv+KiPERMTMiXomIS4F7gIUqPHXnnfOfFhHPNwbzZ73ByFvSWOAJ4KoW0WrPtw/qzvsbwDcj4oa8rh+IiAcGId+iPUiVnz82GV933usAF0TEcxHxEHA5UFWBqDPfDYA3AcdGxMsRcTXwJ2CvbuTb4tjxYWBaRPwyIp4DxgMbSdqgPA1JywAfAb4aEc9ExHWk/xKsKnM57UxJR0i6OT+d+4WkN0gaLulSSXPzk8VLJa1ZSDdZ0rck/Sk/Vf9driCambXlypaZdcvbgX8Uhv8BrCpppTomLmlV4K3AtIrRXclb0kmSngVuB2aT/vesq3lLWh74JvClNlG7tby/J+mRfKG5TbfzljQMGA2MyM3aZuUmdW/sZr4V9gHOiub/j1J33j8CxkpaWtIawM6kClc381WTsHd0Od+W046IfwF3UV3ZfCvwckTcUSpLp0+2/gvYiVS5fSepwrgY8DNgbeDNpCeaJ5TSfYL0pHUVYEngiA7zM7NFnCtbZtYtywJPFoYbv5cb6IQlLQGcC5wZEbcPVt4RcXCexvtIzZ6er4hWd97fIj1RuL9NvG7M85dJzcXWIP3R6yWSqp7m1Zn3qsASpCdL7yM1qXsX8P+6nO+rJL2Z1IzvzBbR6s77WlKF4SlgFqkp3W+6nO/tpKd3/y1pCUk7kOZ76S7n227ajelXTbsvcascHxEPRsRjwCXAxhHxaET8OiKejYinge+QlkPRzyLijoiYB1xA2i7NzNpyZcvMuuUZYPnCcOP30wOZqKTFgLNJ72wcOph5A+TmVtcBawKf7WbekjYGPgAc20H02uc5Im6MiKcj4vmIOJPUxGyXLuc9L3//OCJmR8QjwA8HId+ivYHrIuKeFnHqXM+LAVeQKvDLACsDw0nvrXUt34h4Edgd2BV4iPT09AJSZa9r+XYw7cb0q6bdl7hVHir8fhZYNj9N/EnubOMp4A/ACvkpa9N0HeZnZos4V7bMrFumARsVhjcC5kREu3e9mpIk4HTS04+P5IvFQcm7wuJUv7NVZ97bkDojuE/SQ6SmSx+RdFOX820mqG56Vlve+X2gWTmvdro1z3vT+qlW3XmvCKwFnJArto+SmrVVVTBrneeIuDkito6IlSJiR9KTzL90O99W087vZa1HdRPhO4DFJY0qlaUqbqe+BKwPvCd3gLNVoygDmKaZGeDKlpn1kaTFJb0BGAYMyy+YV3VRfRawv6QNJQ0nNQM7Y4DZnwy8DdgtN+dppta8Ja2i1BX5spKG5Z7ZPg5c3eW8TyVddG6cP6cAlwE7djlfJK0gacfG+pX0SdJF6BXdzptU0fhcXu7DgcNJPeZ1O18kbUlqNlnZC2E38s5P7+4BPpuX9Qqkd8b+URG97vX8zryOl5Z0BKk3xKrpDTjfFseOi4B3SPpIHv814OaqJsL5fa4LgW9KWkbSe4ExpKfd/bUc6YnqE5JWBL4+gGmZmS0oIvzxxx9/Ov6QegqL0mc86cXyZ4A3F+J+EZjD/2/v3oOtrMo4jn9/CQIKiDdqchRNJW9DDI2SzahN4qVRi2mUCqUh0kHMaWrELuYYlojdBTGlZkIDTMjMa4zS4CkVMKQa1CHAPxBGLnI7CAgo+vTHWjvftvvss/fBfQ6e8/vM7BnOu9611vOudx/mfWat9Z60D2U60GMf+h2Q+9qd+yl9Lm+Hvo8kPD1D/wAACI5JREFU7alpzu29AFyVyxrad4Wxn9ke/eZrXkxantUMLALOa6e+u5Nehd5MWr41BejZHmMNTANmVDje6GseDDQBW4FNpGSvfzv0+7Pc5w5gLnBCo66XFv7vyGXDSHvIduVxOLZQ7wZgbuHnw0j72XYCq4GRNfa/ChhW/vtEeiNjU77eFcDYHFu3fF4TcGWh3mjSMtN9/p32xx9/Ov9HEbWs1DAzMzMzM7N6eBmhmZmZmZlZAzjZMjMzMzMzawAnW2ZmZmZmZg3gZMvMzMzMzKwBnGyZmZmZmZk1gJMtMzMzMzOzBnCyZWbWCkmXS3qyo+MoJ2mcpA2Sdkg6vKPjaY2kkHRC/vc9km5p7347C0nH5uuq9AfF29JepxsjM7P9gZMtM+uSJF0r6XlJeyTdU+3ciJgVEee3U2gASFolaViV8u7AL4HzI6J3RGxucDzv68P9/kLSCEkLJL0hqalC+WBJS3L5EkmDOyDM95WkJklXdnQcZmZdgZMtM+uq1gK3AL/r6EDa6MNAT+CltlTubEnTPtgC3A7cVl4g6UDgYWAmcChwL/BwPt5h9ud7tz/HZmbWEZxsmVmXFBEPRsRDQKszQpJGS3om/1uSfiXpNUnbJC2VdFoL9Zok/VjSs5K2S3pS0hGF8s9LeklScz735Hx8BnAM8GheIvidsnYHAsvzj82S5ufjkyWtkfR6noU5q1BngqQHJM2U9DowukK8F0n6V66/RtKEQvHfC/3tkHRmhfpnSFqYr2edpKltSUwkHS9pvqTNkjZJmiWpX6F8laTxeey3SZotqWeh/Prc/1pJY6r1FRF/jYg5pOS73GeAbsDtEbEnIqYAAj7bQtz3SLpT0uP5fj8n6fhC+UmS5knaImm5pBGFshbHvjCr+HVJq4H5Zf1eJmlJ2bHrJD1UIcaJwFnA1HwfpxaKh0laKWlrvg4V6o2RtCyXPSFpQKEsJH1D0kpgZT52saR/5+/CAkmDKo2ZmVln52TLzKw+5wNnAwOBfsCXqJ6wjQS+BvQHDgTGw/8Spj8A3wKOBP5CSq4OjIhRwGrgkrxE8KfFBiNiBXBq/rFfRJQe/hcDg4HDgPuAPxaTEOALwAM57lkVYt0JfDWXXwSMkzQ8l51d6K93RCysUP9t4NvAEcCZwLnANVXGpiUCJgEfBU4GjgYmlJ0zArgQOA4YRE4eJV1IGuPzgBOBFpdi1uBUYGlEROHYUt4d+0q+AtxMmgl7GZiY4zoYmEe6L/3zeb+WVGqr2tiXnEMajwvKjj8CHFdK1rMrgBnlwUXED4CngWvzfby2UHwxcDrwCdL4XpBjHw7cAHyR9F19mvTdLRoODAVOkTSENGM8FjgcmAY8IqlHeTxmZp2dky0zs/q8BfQBTgIUEcsiYl2V86dHxIqI2AXMISVDkJK0xyNiXkS8Bfwc6AV8uq2BRcTMiNgcEXsj4hdAD+DjhVMWRsRDEfFOjqe8flNEvJDLl5IeqM+po/8lEbEo97+K9JBdc/1COy/ncdkTERtJe9PK25kSEWsjYgvwKO+O6wjSmL8YETt5b5JWj97AtrJj20j3vyUPRsQ/ImIvKaEtxXUxsCoipufx+SfwJ+BSqHnsJ0TEzvJ7FxF7gNmkBIucwB0LPFbf5XJbRDRHxGrgqULsY4FJ+bu+F7gVGFyc3crlW3JsVwHTIuK5iHg7Iu4F9gCfqjMeM7MPPCdbZmZ1iIj5wFTgTmCDpN9I6lulyvrCv98gPcBDmrV5pdDuO8Aa4Ki2xpaXji3LS+uagUNIs0wla1qpP1TSU5I2StoGXF1Wv7X+B0p6TNL6vFTx1nrqF9rpL+l+Sa/mdmZWaKfauBav8xXabgdQfm/7Atur1GkprgHA0Lysrjnfn8uBj0DNY1/t/t0LjMxL/0YBc3ISVo9qsU8uxL2FNPtY/K4WYxsAXFd2rUeT7o2ZWZfiZMvMrE4RMSUiPklaTjYQuL4NzawlPZQCaS8Y6YH01VI39TSmtD/ru6SZnUMjoh9pFkaF01pr8z7SkrSjI+IQ4O5C/VriuQv4D3BiRPQlLT1T9SoVTcr9DcrtXFFHO+tI41hyTBv6L3kJGFTcu0RastiWl5KsAf4WEf0Kn94RMS6XVxv7khbvQUQsAt4k7ccaSYUlhLW0UyX2sWWx94qIBS20uQaYWHb+QRFRvvTQzKzTc7JlZl2SpG55P9MBwAGSeqqGN6lJOj3PQnQn7bPZTdqrVK85wEWSzs1tXUdaalV6gN0AfKyO9voAe4GNQDdJN/HeWZla2tgSEbslnUF6aC/ZCLzTSkx9gNeBHZJOAsZVObe1OHaQXsZxFPUls3OA0ZJOkXQQ8MNqJ0s6IH8PugEfyt+D7rm4iXRvvymph6TS/qb5FZpqzWPAQEmjJHXPn9ML+6yqjX2tfk+add0bEc9UOa/e79bdwPdL+8skHSLpsirn/xa4Ov+eSNLB+QUg1ZZfmpl1Sk62zKyruhHYBXyPNHOyKx9rTV/Sw+RW0hK1zaT9VnWJiOW53zuATcAlpBdivJlPmQTcmJdhja+hySeAucCKHNduWlk2WME1wI8kbQduIiUupXjfIL3s4dkcU6X9N+NJScJ20hjNrrP/kpuBIaSZuceBB2utGBFzSa9yn096QUVridEo0r2/izQrtIsUO/leDCe9uKIZGAMML9yjmkXEdtLLVb5MmtVcD/yEtK8Oqox9HWYAp1F9VgtgMnBpfrPglBpi/3OO9f68rPNF4HNVzn+etG9rKun35GUqvP3SzKwr0P+/ZMnMzMw+iCT1Al4DhkTEyo6Ox8zMPLNlZmbWWYwDFjvRMjPbf/gvvZuZmX3ASVpFeqFG+d/mMjOzDuRlhGZmZmZmZg3gZYRmZmZmZmYN4GTLzMzMzMysAZxsmZmZmZmZNYCTLTMzMzMzswZwsmVmZmZmZtYA/wWPBt9Z7zwy5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"How far along the road to your American Dream do you think you will ultimately get on a 10-point scale where 1 is not far at all and 10 nearly there?\")\n",
    "count6 = data['W1_F6'].value_counts(sort = False, dropna = False).sort_index(ascending = True)\n",
    "perc6 = data['W1_F6'].value_counts(sort = False, normalize = True, dropna = False).sort_index(ascending = True)\n",
    "print(count6)\n",
    "perc6a = perc6.to_frame()\n",
    "perc6a['Cum_prop'] = perc6a.cumsum()\n",
    "print(perc6a)\n",
    "\n",
    "perc6.plot.bar()\n",
    "plt.title(\"How far along the road to your American Dream do you think you will ultimately get on a 10-point scale?\", fontsize=16)\n",
    "plt.ylabel('Proportion responses',fontsize=12)\n",
    "plt.xlabel('1 is not far at all and 10 nearly there', fontsize = 12)\n",
    "plt.xticks(rotation = 0, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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