07 Capstone 1 Choropleth Map USA .ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This Notebook will  <li>draw the Chorogrid Map of the United States showing variation of submissions differening from the population estimates  <li> draw the normalized distribution of these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from chorogrid import Colorbin, Chorogrid\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the data and slice for the state abbreviation and variation from population estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('FCC_pop_comments_perc2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>diff_perc_pop_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OR</td>\n",
       "      <td>-0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>VA</td>\n",
       "      <td>-0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WA</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MA</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NJ</td>\n",
       "      <td>-0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   abbreviation  diff_perc_pop_comments\n",
       "37           OR                   -0.89\n",
       "46           VA                   -0.72\n",
       "47           WA                   -0.55\n",
       "21           MA                   -0.46\n",
       "30           NJ                   -0.41"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['abbreviation','diff_perc_pop_comments']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the values for the color range and fenceposts for the values that will trigger the different colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the fenceposts for the colors and specify the color range\n",
    "\n",
    "mycolors = ['#b35806', '#f1a340', '#fee0b6', '#D3D3D3','#d8daeb', '#998ec3', '#542788']\n",
    "\n",
    "mybin = Colorbin(df['diff_perc_pop_comments'], mycolors, proportional=True, decimals=None)\n",
    "mybin.set_decimals(2)\n",
    "mybin.recalc(fenceposts=True)\n",
    "\n",
    "mybin.fenceposts = [-2.00,-0.70,-0.36,-0.1,0.1,0.36,.70,2.00]\n",
    "#mybin.recalc(fenceposts=True)\n",
    "\n",
    "mybin.recalc(fenceposts=False) # if this were true, the fenceposts would be changed back to what they were originally\n",
    "#mybin.count_bins()\n",
    "\n",
    "mybin.calc_complements(0.5, '#e0e0e0', '#101010')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the values to the parameters that will be used by the Chorogrid function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = list(df['abbreviation'])\n",
    "colors_by_state = mybin.colors_out\n",
    "font_colors_by_state = mybin.complements\n",
    "legend_colors = mybin.colors_in\n",
    "legend_labels = mybin.labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the design and description files for creating the grid map of the USA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abbrev                                                                   AK\n",
      "full_name                                                            Alaska\n",
      "long_abbrev                                                           Alas.\n",
      "FIPS                                                                      2\n",
      "pop                                                                  710231\n",
      "sqmi                                                                 663267\n",
      "map_path                  m 135.58488,358.02208 -0.24846,65.59232 1.2422...\n",
      "map_fill_default                                                          2\n",
      "map_label_x                                                         99.7626\n",
      "map_label_y                                                         398.173\n",
      "map_label_text_anchor                                                middle\n",
      "map_label_line_path                                                     NaN\n",
      "altmap_path               m 151.26632,459.09682 -0.31386,83.24785 1.5692...\n",
      "square_x                                                                  0\n",
      "square_y                                                                  0\n",
      "altsquare_x                                                               0\n",
      "altsquare_y                                                               0\n",
      "hex_x                                                                     1\n",
      "hex_y                                                                     0\n",
      "althex_x                                                                  0\n",
      "althex_y                                                                  0\n",
      "fourhex_x                                                                 2\n",
      "fourhex_y                                                                 1\n",
      "fourhex_contour                                              ababcdcdedefaf\n",
      "fourhex_label_offset_x                                                 0.25\n",
      "fourhex_label_offset_y                                                  0.5\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "_ = pd.read_csv('usa_states.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abbrev                       Postal abbreviation for 50 states and D.C.\n",
      "full_name                    Full name\n",
      "long_abbrev                  Abbreviation, based on but not identical to recommendations of Associated Press\n",
      "FIPS                         Federal Information Processing Standards\n",
      "pop                          Population in 2013\n",
      "sqmi                         Area in square miles\n",
      "\n",
      "map_path                     SVG path for geographic map\n",
      "map_fill_default             Number, 1-4, so that no states sharing a border will have same fill\n",
      "map_label_x                  X-coordinate for map label, e.g. state name\n",
      "map_label_y                  Y-coordinate for map label\n",
      "map_label_text_anchor        Text anchor (start, middle, end) for label\n",
      "map_label_line_path          Path for line connecting state and label, if applicable\n",
      "\n",
      "altmap_path                  Alternate SVG path, without labels\n",
      "\n",
      "square_x                     Horizontal position of square grid\n",
      "square_y                     Vertical position of square grid\n",
      "\n",
      "altsquare_x                  Alternate horizontal position of square grid\n",
      "altsquare_y                  Alternate vertical position of square grid\n",
      "\n",
      "hex_x                        Horizontal position of hex grid\n",
      "hex_y                        Vertical position of hex grid\n",
      "\n",
      "fourhex_x                    Horizontal position of topmost, then leftmost, hex in four-hex multihex layout\n",
      "fourhex_y                    Vertical position of topmost, then leftmost, hex in four-hex multihex layout\n",
      "fourhex_contour              Contour of four-hex layout: a=up&right, b=down&right, c=down, d=down&left, e=up&left, f=up\n",
      "fourhex_label_offset_x       Horizontal offset of label, in terms of hex width\n",
      "fourhex_label_offset_y       Vertical offset of label, in terms of hex width\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('usa_states_column_descriptions.txt') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot out the grid map of the USA with the variations in color indicating how far from the mean of 0 the values for each state are. The hue intensities indicate less than 1, more than 1 and more than 2 standard deviations from the mean. Gray indicates very close to the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
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   "outputs": [
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     "output_type": "display_data"
    }
   ],
   "source": [
    "cg = Chorogrid('usa_states.csv', states, colors_by_state)\n",
    "cg.set_title('Net Neutratlity Fillings \\n as Variation of % from State Proportions', font_dict={'font-size': 19})\n",
    "cg.set_legend(legend_colors, legend_labels, title='% Difference from State')\n",
    "cg.draw_squares(x_column='altsquare_x', y_column='altsquare_y', spacing_dict={'margin_right': 150},\n",
    "                font_colors=font_colors_by_state)\n",
    "cg.done(show=True,save_filename='chorogrid proportions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot out the normalized distribution of the values from the data, the markers for the standard deviations and the annotations for the extreme values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Variation from Population Proportion')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSctaccG6EMLpwMeBt8YYP774uRjjE2KMH8j//Rbg7BBCNd8kJEma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\nBD/MqNInEPoEKPQJgEgTaJzw72OMaao434mnzXe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z5pcyFEWqZp81Y2zjVPmG44pRKscq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e2893e1668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "%matplotlib inline\n",
    "\n",
    "mu = df['diff_perc_pop_comments'].mean() # 0.000196\n",
    "variance = df['diff_perc_pop_comments'].var() #0.1292\n",
    "sigma = math.sqrt(variance) #0.359\n",
    "x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
    "\n",
    "\n",
    "plt.figure( figsize=(12,5), dpi=80)\n",
    "plt.plot(x,mlab.normpdf(x, mu, sigma))\n",
    "plt.title('Distribution of Differences \\n in FCC Comments filing to State Populations')\n",
    "\n",
    "\n",
    "df.sort_values('diff_perc_pop_comments', inplace=True)\n",
    "plt.plot([0, 0], [0, 4.0], linewidth=1)\n",
    "plt.plot([0.7, 0.7], [0, 4.0], linewidth=1,linestyle='--',alpha=0.3,color='red')\n",
    "plt.plot([-0.7, -0.7], [0, 4.0], linewidth=1,linestyle='--',alpha=0.3,color='red')\n",
    "plt.plot([0.36, 0.36], [0, 4.0], linewidth=1,linestyle='--',alpha=0.3,color='pink')\n",
    "plt.plot([-0.36, -0.36], [0, 4.0], linewidth=1,linestyle='--',alpha=0.3,color='pink')\n",
    "plt.ylim(0, 4.0)\n",
    "plt.xlim(-2.0, 2.0)\n",
    "plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)\n",
    "plt.grid(False)\n",
    "plt.annotate('Texas', xy=(1.65, 0.4), xytext=(1.65, 1),arrowprops=dict(facecolor='black', shrink=0.1))\n",
    "plt.annotate('California', xy=(1.1, 0.4), xytext=(1.1, 1),arrowprops=dict(facecolor='black', shrink=0.1))\n",
    "plt.annotate('Oregon', xy=(-.85, 0.4), xytext=(-.85, 1.2),arrowprops=dict(facecolor='black', shrink=0.1))\n",
    "plt.annotate('Virginia', xy=(-.72, 0.4), xytext=(-.8, .9),arrowprops=dict(facecolor='black', shrink=0.1))\n",
    "\n",
    "ax = sns.distplot(df['diff_perc_pop_comments'],bins=42)\n",
    "plt.xlabel('Variation from Population Proportion')\n",
    "#ax.fill_between(x2,y2, color=\"blue\", alpha=0.3)\n",
    "\n"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}