TN_City_Demographics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_colwidth = 100\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_age_data(dataset):\n",
    "    age_cats = [\"HC03_VC08\", \"HC03_VC09\", \"HC03_VC10\", \"HC03_VC11\", \"HC03_VC12\", \"HC03_VC13\", \n",
    "            \"HC03_VC14\", \"HC03_VC15\", \"HC03_VC16\", \"HC03_VC17\", \"HC03_VC18\", \"HC03_VC19\", \"HC03_VC20\"]\n",
    "    \n",
    "    age_labels = [\"Under 5 Years\", \"5 to 9 Years\", \"10 to 14 Years\", \"15 to 19 Years\", \"20 to 24 Years\",\n",
    "             \"25 to 34 Years\", \"35 to 44 Years\", \"45 to 54 Years\", \"55 to 59 Years\", \"60 to 64 Years\",\n",
    "             \"65 to 74 Years\", \"75 to 84 Years\", \"85 Years and Over\"]\n",
    "    \n",
    "    ages = {}\n",
    "    dataset = dataset.transpose()\n",
    "    for i in range(0,len(age_cats)):\n",
    "        #ages.append(dataset[dataset.index == i][1])\n",
    "        ages[age_labels[i]] = dataset[dataset.index == age_cats[i]][1][0]\n",
    "        \n",
    "    ages = pd.DataFrame(ages, index=[0])\n",
    "    ages = ages[[\"Under 5 Years\", \"5 to 9 Years\", \"10 to 14 Years\", \"15 to 19 Years\", \"20 to 24 Years\",\n",
    "             \"25 to 34 Years\", \"35 to 44 Years\", \"45 to 54 Years\", \"55 to 59 Years\", \"60 to 64 Years\",\n",
    "             \"65 to 74 Years\", \"75 to 84 Years\", \"85 Years and Over\"]]\n",
    "    \n",
    "    return ages\n",
    "\n",
    "ages_C = get_age_data(pd.read_csv(\"ACS_16_5YR_DP05_with_ann.csv\"))\n",
    "ages_N = get_age_data(pd.read_csv(\"ACS_16_5YR_DP05_with_ann_N.csv\"))\n",
    "ages_CR = get_age_data(pd.read_csv(\"ACS_16_5YR_DP05_with_ann_CR.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3, figsize=(12,10))\n",
    "sns.barplot(orient=\"h\", data=ages_C, ax=ax[1]).set_title(\"Clarksville\")\n",
    "sns.barplot(orient=\"h\", data=ages_N, ax=ax[2]).set_title(\"Nashville\")\n",
    "sns.barplot(orient=\"h\", data=ages_CR, ax=ax[0]).set_title(\"Crossville\")\n",
    "ax[0].set_xlim(0, 22)\n",
    "ax[1].set_xlim(0, 22)\n",
    "ax[2].set_xlim(0, 22)\n",
    "ax[1].set(yticklabels=[])\n",
    "ax[2].set(yticklabels=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age Breakdown (Percentages) by City\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crossville</th>\n",
       "      <th>Clarksville</th>\n",
       "      <th>Nashville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Under 5 Years</th>\n",
       "      <td>8.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 to 9 Years</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 to 14 Years</th>\n",
       "      <td>5.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 to 19 Years</th>\n",
       "      <td>6.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20 to 24 Years</th>\n",
       "      <td>7.3</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25 to 34 Years</th>\n",
       "      <td>12.3</td>\n",
       "      <td>20.3</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35 to 44 Years</th>\n",
       "      <td>10.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45 to 54 Years</th>\n",
       "      <td>11.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55 to 59 Years</th>\n",
       "      <td>4.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60 to 64 Years</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65 to 74 Years</th>\n",
       "      <td>10.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75 to 84 Years</th>\n",
       "      <td>6.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85 Years and Over</th>\n",
       "      <td>2.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Crossville Clarksville Nashville\n",
       "Under 5 Years            8.9         9.3       7.1\n",
       "5 to 9 Years             7.0         7.7       6.2\n",
       "10 to 14 Years           5.9         6.8       5.3\n",
       "15 to 19 Years           6.5         6.3       5.7\n",
       "20 to 24 Years           7.3        10.8       7.9\n",
       "25 to 34 Years          12.3        20.3      19.4\n",
       "35 to 44 Years          10.5        12.5      13.8\n",
       "45 to 54 Years          11.5        10.5      12.5\n",
       "55 to 59 Years           4.1         4.2       6.1\n",
       "60 to 64 Years           5.7         3.8       5.2\n",
       "65 to 74 Years          10.6         4.8       6.3\n",
       "75 to 84 Years           6.9         2.4       3.2\n",
       "85 Years and Over        2.9         0.6       1.3"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages_table = ages_CR.transpose()\n",
    "ages_table[\"Clarksville\"] = ages_C.transpose()\n",
    "ages_table[\"Nashville\"] = ages_N.transpose()\n",
    "ages_table.columns = [\"Crossville\", \"Clarksville\", \"Nashville\"]\n",
    "print(\"Age Breakdown (Percentages) by City\")\n",
    "ages_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_race(dataset):\n",
    "    race_cats = [\"HC03_VC88\", \"HC03_VC94\", \"HC03_VC95\", \"HC03_VC96\", \"HC03_VC97\", \"HC03_VC98\", \"HC03_VC100\"]\n",
    "    race_labels = [\"Hispanic or Latino\", \"White\", \"Black\", \"American Indian\", \"Asian\", \"Pacific Islander\", \n",
    "               \"Multiracial\"]\n",
    "    \n",
    "    races = {}\n",
    "    dataset = dataset.transpose()\n",
    "    \n",
    "    for i in range(0,len(race_cats)):\n",
    "        races[race_labels[i]] = dataset[dataset.index == race_cats[i]][1][0]\n",
    "        \n",
    "    races = pd.DataFrame(races, index=[0])\n",
    "    races = races[[\"White\", \"Hispanic or Latino\", \"Black\", \"Asian\", \"Pacific Islander\", \"American Indian\",  \n",
    "               \"Multiracial\"]]\n",
    "    \n",
    "    return races\n",
    "\n",
    "race_C = get_race(pd.read_csv(\"ACS_16_5YR_DP05_with_ann.csv\"))\n",
    "race_N = get_race(pd.read_csv(\"ACS_16_5YR_DP05_with_ann_N.csv\"))\n",
    "race_CR = get_race(pd.read_csv(\"ACS_16_5YR_DP05_with_ann_CR.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 70)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3,1, figsize=(14,12))\n",
    "sns.barplot(data=race_CR, ax=ax[0]).set_title(\"Crossville\")\n",
    "sns.barplot(data=race_C, ax=ax[1]).set_title(\"Clarksville\")\n",
    "sns.barplot(data=race_N, ax=ax[2]).set_title(\"Nashville\")\n",
    "ax[0].set_xlabel(\"\")\n",
    "ax[1].set_xlabel(\"\")\n",
    "ax[0].set_ylim(0, 70)\n",
    "ax[1].set_ylim(0, 70)\n",
    "ax[1].set_ylim(0, 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Racial Demographic Percentages by City\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crossville</th>\n",
       "      <th>Clarksville</th>\n",
       "      <th>Nashville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>88.1</td>\n",
       "      <td>59.4</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic or Latino</th>\n",
       "      <td>8.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>1.7</td>\n",
       "      <td>22.1</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>0.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Indian</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiracial</th>\n",
       "      <td>1.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Crossville Clarksville Nashville\n",
       "White                    88.1        59.4      55.7\n",
       "Hispanic or Latino        8.6        10.8      10.3\n",
       "Black                     1.7        22.1      27.9\n",
       "Asian                     0.1         2.3       3.5\n",
       "Pacific Islander          0.0         0.5       0.1\n",
       "American Indian           0.3         0.6       0.2\n",
       "Multiracial               1.3         4.1       2.1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_table = race_CR.transpose()\n",
    "race_table[\"Clarksville\"] = race_C.transpose()\n",
    "race_table[\"Nashville\"] = race_N.transpose()\n",
    "race_table.columns = [\"Crossville\", \"Clarksville\", \"Nashville\"]\n",
    "print(\"Racial Demographic Percentages by City\")\n",
    "race_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_occupation(dataset):\n",
    "    job_cats = [\"HC03_VC50\", \"HC03_VC51\", \"HC03_VC52\", \"HC03_VC53\", \"HC03_VC54\", \"HC03_VC55\", \"HC03_VC56\",\n",
    "                \"HC03_VC57\", \"HC03_VC58\", \"HC03_VC59\", \"HC03_VC60\", \"HC03_VC61\", \"HC03_VC62\"]\n",
    "    job_labels = [\"Agriculture / Forestry / Fishing / etc\", \"Construction\", \"Manufacturing\", \"Wholesale Trade\", \n",
    "                 \"Retail Trade\", \"Transporation / Warehouse\", \"Information\", \"Finance, Insurance, Real Estate\",\n",
    "                 \"Professional, Admin., Mgmt., and Sci.\", \"Education and Healthcare\",\n",
    "                \"Arts, Entrtnmnt., and Food\", \"Other\", \"Public Administration\"]\n",
    "    \n",
    "    jobs = {}\n",
    "    dataset = dataset.transpose()\n",
    "    \n",
    "    for i in range(0,len(job_cats)):\n",
    "        jobs[job_labels[i]] = dataset[dataset.index == job_cats[i]][1][0]\n",
    "        \n",
    "    jobs = pd.DataFrame(jobs, index=[0])\n",
    "    jobs = jobs[[\"Agriculture / Forestry / Fishing / etc\", \"Construction\", \"Manufacturing\", \"Wholesale Trade\", \n",
    "                 \"Retail Trade\", \"Transporation / Warehouse\", \"Information\", \"Finance, Insurance, Real Estate\",\n",
    "                 \"Professional, Admin., Mgmt., and Sci.\", \"Education and Healthcare\",\n",
    "                \"Arts, Entrtnmnt., and Food\", \"Other\", \"Public Administration\"]]\n",
    "    \n",
    "    return jobs\n",
    "\n",
    "jobs_C = get_occupation(pd.read_csv(\"ACS_16_5YR_DP03_with_ann.csv\"))\n",
    "jobs_N = get_occupation(pd.read_csv(\"ACS_16_5YR_DP03_with_ann_N.csv\"))\n",
    "jobs_CR = get_occupation(pd.read_csv(\"ACS_16_5YR_DP03_with_ann_CR.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 25)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3, figsize=(12,10))\n",
    "sns.barplot(orient=\"h\", data=jobs_CR, ax=ax[0]).set_title(\"Crossville\")\n",
    "sns.barplot(orient=\"h\", data=jobs_C, ax=ax[1]).set_title(\"Clarksville\")\n",
    "sns.barplot(orient=\"h\", data=jobs_N, ax=ax[2]).set_title(\"Nashville\")\n",
    "ax[1].set(yticklabels=[])\n",
    "ax[2].set(yticklabels=[])\n",
    "ax[0].set_xlim(0, 25)\n",
    "ax[1].set_xlim(0, 25)\n",
    "ax[2].set_xlim(0, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occupation Percentages by City\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crossville</th>\n",
       "      <th>Clarksville</th>\n",
       "      <th>Nashville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agriculture / Forestry / Fishing / etc</th>\n",
       "      <td>3.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Construction</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>14.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesale Trade</th>\n",
       "      <td>3.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail Trade</th>\n",
       "      <td>13.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transporation / Warehouse</th>\n",
       "      <td>2.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance, Insurance, Real Estate</th>\n",
       "      <td>3.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional, Admin., Mgmt., and Sci.</th>\n",
       "      <td>7.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education and Healthcare</th>\n",
       "      <td>21.9</td>\n",
       "      <td>22.2</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arts, Entrtnmnt., and Food</th>\n",
       "      <td>11.4</td>\n",
       "      <td>11.3</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>6.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Administration</th>\n",
       "      <td>4.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Crossville Clarksville Nashville\n",
       "Agriculture / Forestry / Fishing / etc        3.7         0.3       0.2\n",
       "Construction                                  6.3         5.4       6.0\n",
       "Manufacturing                                14.0        12.1       7.3\n",
       "Wholesale Trade                               3.1         1.4       2.3\n",
       "Retail Trade                                 13.7        13.4      10.8\n",
       "Transporation / Warehouse                     2.7         5.0       4.4\n",
       "Information                                   1.4         1.5       3.1\n",
       "Finance, Insurance, Real Estate               3.8         4.3       7.2\n",
       "Professional, Admin., Mgmt., and Sci.         7.2         9.3      13.0\n",
       "Education and Healthcare                     21.9        22.2      24.1\n",
       "Arts, Entrtnmnt., and Food                   11.4        11.3      12.1\n",
       "Other                                         6.5         4.2       5.1\n",
       "Public Administration                         4.2         9.5       4.4"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs_table = jobs_CR.transpose()\n",
    "jobs_table[\"Clarksville\"] = jobs_C.transpose()\n",
    "jobs_table[\"Nashville\"] = jobs_N.transpose()\n",
    "jobs_table.columns = [\"Crossville\", \"Clarksville\", \"Nashville\"]\n",
    "print(\"Occupation Percentages by City\")\n",
    "jobs_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_education(dataset):\n",
    "    edu_cats_male = [\"HC04_EST_VC43\", \"HC04_EST_VC47\", \"HC04_EST_VC51\", \"HC04_EST_VC55\", \"HC04_EST_VC59\",\n",
    "             \"HC04_EST_VC67\", \"HC04_EST_VC71\"]\n",
    "    edu_cats_female = [\"HC06_EST_VC43\", \"HC06_EST_VC47\", \"HC06_EST_VC51\", \"HC06_EST_VC55\", \"HC06_EST_VC59\",\n",
    "             \"HC06_EST_VC67\", \"HC06_EST_VC71\"] \n",
    "    edu_labels = [\"White (non-Hispanic)\", \"Black\", \"American Indian\", \"Asian\", \"Pacific Islander\", \n",
    "               \"Multiracial\", \"Hispanic or Latino\"]\n",
    "    \n",
    "    edu_male = {}\n",
    "    edu_female = {}\n",
    "    dataset = dataset.transpose()\n",
    "    \n",
    "    for i in range(0,len(edu_cats_male)):\n",
    "        edu_male[edu_labels[i]] = dataset[dataset.index == edu_cats_male[i]][1][0]\n",
    "        edu_female[edu_labels[i]] = dataset[dataset.index == edu_cats_female[i]][1][0]\n",
    "        \n",
    "    edu_male = pd.DataFrame(edu_male, index=[0])\n",
    "    edu_male = edu_male[edu_labels]\n",
    "    edu_female = pd.DataFrame(edu_female, index=[0])\n",
    "    edu_female = edu_female[edu_labels]\n",
    "    \n",
    "    edu = edu_male.transpose()\n",
    "    edu[\"female\"] = edu_female.transpose()\n",
    "    edu.columns = [\"male\", \"female\"]\n",
    "    edu = pd.DataFrame(edu.unstack().reset_index())\n",
    "    edu.columns = [\"sex\", \"race\", \"percent\"]\n",
    "    edu[\"percent\"] = edu[\"percent\"].str.replace('-', '0',)\n",
    "    edu[\"percent\"] = pd.to_numeric(edu[\"percent\"])\n",
    "    \n",
    "    \n",
    "    return edu\n",
    "\n",
    "edu_CR = get_education(pd.read_csv(\"ACS_16_5YR_S1501_with_ann_CR.csv\"))\n",
    "edu_C = get_education(pd.read_csv(\"ACS_16_5YR_S1501_with_ann.csv\"))\n",
    "edu_N = get_education(pd.read_csv(\"ACS_16_5YR_S1501_with_ann_N.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 60)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x792 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "fig, ax = plt.subplots(3,1, figsize=(14,11))\n",
    "sns.barplot(x=\"race\", y=\"percent\", hue=\"sex\", data=edu_CR, ax=ax[0]).set_title(\"Crossville\")\n",
    "sns.barplot(x=\"race\", y=\"percent\", hue=\"sex\", data=edu_C, ax=ax[1]).set_title(\"Clarksville\")\n",
    "sns.barplot(x=\"race\", y=\"percent\", hue=\"sex\", data=edu_N, ax=ax[2]).set_title(\"Nashville\")\n",
    "ax[0].set_xlabel(\"\")\n",
    "ax[1].set_xlabel(\"\")\n",
    "ax[0].set_ylim(0, 60)\n",
    "ax[1].set_ylim(0, 60)\n",
    "ax[2].set_ylim(0, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_income(dataset):\n",
    "    income_cats = [\"HC03_VC75\", \"HC03_VC76\", \"HC03_VC77\", \"HC03_VC78\", \"HC03_VC79\", \"HC03_VC80\", \"HC03_VC81\",\n",
    "                \"HC03_VC82\", \"HC03_VC83\", \"HC03_VC84\"]\n",
    "    income_labels = [\"Less than 10K\", \"10K to 15K\", \"15K to 25K\", \"25K to 35K\", \"35K to 45K\", \"50K to 75K\", \n",
    "                 \"75K to 100K\", \"100K to 150K\", \"150K to 200K\", \"200K or More\"]\n",
    "    \n",
    "    incomes = {}\n",
    "    dataset = dataset.transpose()\n",
    "    \n",
    "    for i in range(0,len(income_cats)):\n",
    "        incomes[income_labels[i]] = dataset[dataset.index == income_cats[i]][1][0]\n",
    "        \n",
    "    incomes = pd.DataFrame(incomes, index=[0])\n",
    "    incomes = incomes[income_labels]\n",
    "    \n",
    "    return incomes\n",
    "\n",
    "\n",
    "incomes_CR = get_income(pd.read_csv(\"ACS_16_5YR_DP03_with_ann_CR.csv\"))\n",
    "incomes_C = get_income(pd.read_csv(\"ACS_16_5YR_DP03_with_ann.csv\"))\n",
    "incomes_N = get_income(pd.read_csv(\"ACS_16_5YR_DP03_with_ann_N.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 22)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x792 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3,1, figsize=(14,11))\n",
    "sns.barplot(data=incomes_CR, ax=ax[0]).set_title(\"Crossville\")\n",
    "sns.barplot(data=incomes_C, ax=ax[1]).set_title(\"Clarksville\")\n",
    "sns.barplot(data=incomes_N, ax=ax[2]).set_title(\"Nashville\")\n",
    "ax[0].set_xlabel(\"\")\n",
    "ax[0].set_ylim(0, 22)\n",
    "ax[1].set_ylim(0, 22)\n",
    "ax[2].set_ylim(0, 22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income Breakdowns (Percentages) by City\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crossville</th>\n",
       "      <th>Clarksville</th>\n",
       "      <th>Nashville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Less than 10K</th>\n",
       "      <td>15.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10K to 15K</th>\n",
       "      <td>10.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15K to 25K</th>\n",
       "      <td>21.3</td>\n",
       "      <td>9.9</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25K to 35K</th>\n",
       "      <td>12.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35K to 45K</th>\n",
       "      <td>12.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50K to 75K</th>\n",
       "      <td>13.4</td>\n",
       "      <td>23.5</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75K to 100K</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100K to 150K</th>\n",
       "      <td>5.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150K to 200K</th>\n",
       "      <td>3.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200K or More</th>\n",
       "      <td>1.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Crossville Clarksville Nashville\n",
       "Less than 10K       15.5         8.0       7.3\n",
       "10K to 15K          10.2         4.1       5.1\n",
       "15K to 25K          21.3         9.9      10.8\n",
       "25K to 35K          12.5        11.8      11.3\n",
       "35K to 45K          12.8        17.3      15.6\n",
       "50K to 75K          13.4        23.5      18.7\n",
       "75K to 100K          4.0        12.7      11.9\n",
       "100K to 150K         5.1         9.0      11.1\n",
       "150K to 200K         3.1         2.3       4.1\n",
       "200K or More         1.9         1.2       4.1"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomes_table = incomes_CR.transpose()\n",
    "incomes_table[\"Clarksville\"] = incomes_C.transpose()\n",
    "incomes_table[\"Nashville\"] = incomes_N.transpose()\n",
    "incomes_table.columns = [\"Crossville\", \"Clarksville\", \"Nashville\"]\n",
    "print(\"Income Breakdowns (Percentages) by City\")\n",
    "incomes_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_poverty(dataset):\n",
    "    poverty_cats = [\"HC03_EST_VC27\", \"HC03_EST_VC19\", \"HC03_EST_VC20\", \"HC03_EST_VC21\", \"HC03_EST_VC22\",\n",
    "             \"HC03_EST_VC24\", \"HC03_EST_VC26\"]\n",
    "    poverty_labels = [\"White (non-Hispanic)\", \"Black\", \"American Indian\", \"Asian\", \"Pacific Islander\", \n",
    "               \"Multiracial\", \"Hispanic or Latino\"]\n",
    "    \n",
    "    poverty = {}\n",
    "    dataset = dataset.transpose()\n",
    "    \n",
    "    for i in range(0,len(poverty_cats)):\n",
    "        poverty[poverty_labels[i]] = dataset[dataset.index == poverty_cats[i]][1][0]\n",
    "        \n",
    "    poverty = pd.DataFrame(poverty, index=[0])\n",
    "    poverty = poverty[[\"White (non-Hispanic)\", \"Hispanic or Latino\", \"Black\", \"Asian\", \"Pacific Islander\", \n",
    "               \"American Indian\", \"Multiracial\"]]\n",
    "    \n",
    "    return poverty\n",
    "\n",
    "poverty_CR = get_poverty(pd.read_csv(\"ACS_16_5YR_S1701_with_ann_CR.csv\"))\n",
    "poverty_C = get_poverty(pd.read_csv(\"ACS_16_5YR_S1701_with_ann.csv\"))\n",
    "poverty_N = get_poverty(pd.read_csv(\"ACS_16_5YR_S1701_with_ann_N.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 60)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x792 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3,1, figsize=(14,11))\n",
    "sns.barplot(data=poverty_CR, ax=ax[0]).set_title(\"Crossville\")\n",
    "sns.barplot(data=poverty_C, ax=ax[1]).set_title(\"Clarksville\")\n",
    "sns.barplot(data=poverty_N, ax=ax[2]).set_title(\"Nashville\")\n",
    "ax[0].set_xlabel(\"\")\n",
    "ax[0].set_ylim(0, 60)\n",
    "ax[1].set_ylim(0, 60)\n",
    "ax[2].set_ylim(0, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Poverty Breakdown by Race (Percentages) by City\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Crossville</th>\n",
       "      <th>Clarksville</th>\n",
       "      <th>Nashville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>White (non-Hispanic)</th>\n",
       "      <td>26.8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic or Latino</th>\n",
       "      <td>57.1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>-</td>\n",
       "      <td>2.9</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Indian</th>\n",
       "      <td>12.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiracial</th>\n",
       "      <td>74.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Crossville Clarksville Nashville\n",
       "White (non-Hispanic)       26.8        13.4      11.4\n",
       "Hispanic or Latino         57.1        20.3      31.4\n",
       "Black                       0.0        25.1      26.3\n",
       "Asian                       0.0        12.4      14.4\n",
       "Pacific Islander              -         2.9      47.6\n",
       "American Indian            12.0        17.8      15.7\n",
       "Multiracial                74.8        15.1      21.6"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty_table = poverty_CR.transpose()\n",
    "poverty_table[\"Clarksville\"] = poverty_C.transpose()\n",
    "poverty_table[\"Nashville\"] = poverty_N.transpose()\n",
    "poverty_table.columns = [\"Crossville\", \"Clarksville\", \"Nashville\"]\n",
    "print(\"Poverty Breakdown by Race (Percentages) by City\")\n",
    "poverty_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
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}