NC_UC_2019_demog.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.049290Z",
     "start_time": "2021-01-26T21:12:04.038580Z"
    }
   },
   "outputs": [],
   "source": [
    "## Import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "import plotly.express as px\n",
    "from capstone_functions import *\n",
    "from plotly_year_functions import *\n",
    "\n",
    "## Set options for displaying Pandas DataFrames\n",
    "pd.options.display.max_columns = 50\n",
    "pd.options.display.max_rows = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.104259Z",
     "start_time": "2021-01-26T21:12:04.052793Z"
    }
   },
   "outputs": [
    {
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       "      <td>37688</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05000US37179</td>\n",
       "      <td>Union County, NC</td>\n",
       "      <td>239859</td>\n",
       "      <td>0</td>\n",
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       "      <td>5475</td>\n",
       "      <td>698</td>\n",
       "      <td>5731</td>\n",
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       "      <td>362</td>\n",
       "      <td>8939</td>\n",
       "      <td>122</td>\n",
       "      <td>8046</td>\n",
       "      <td>883</td>\n",
       "      <td>2077</td>\n",
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       "      <td>4320</td>\n",
       "      <td>959</td>\n",
       "      <td>2292</td>\n",
       "      <td>648</td>\n",
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       "          geoid              name  B01001001  B01001001, Error  B01001002  \\\n",
       "0       01000US     United States  328239523                 0  161588973   \n",
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       "2  05000US37179  Union County, NC     239859                 0     117578   \n",
       "\n",
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       "\n",
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       "\n",
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       "2               700       4320               959       2292               648   \n",
       "\n",
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       "0    5319290             26937    7618630             33692    5392000   \n",
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       "2       3007               768       4990               947       3139   \n",
       "\n",
       "   B01001047, Error  B01001048  B01001048, Error  B01001049  B01001049, Error  \n",
       "0             29716    3709078             23709    4074137             26417  \n",
       "1              5242     116777              4792     117196              5060  \n",
       "2               598       2242               530       1846               530  \n",
       "\n",
       "[3 rows x 100 columns]"
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Import and preview age demographics dataset\n",
    "age_df = pd.read_csv('Data/NC_UC_2019_demog/age_demog_2019.csv')\n",
    "age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.142983Z",
     "start_time": "2021-01-26T21:12:04.107948Z"
    }
   },
   "outputs": [
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       "      <td>146356</td>\n",
       "      <td>199672</td>\n",
       "      <td>130484</td>\n",
       "      <td>175237</td>\n",
       "      <td>264193</td>\n",
       "      <td>181367</td>\n",
       "      <td>116777</td>\n",
       "      <td>117196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05000US37179</td>\n",
       "      <td>Union County, NC</td>\n",
       "      <td>239859</td>\n",
       "      <td>117578</td>\n",
       "      <td>6556</td>\n",
       "      <td>7489</td>\n",
       "      <td>11211</td>\n",
       "      <td>6620</td>\n",
       "      <td>3655</td>\n",
       "      <td>1932</td>\n",
       "      <td>2816</td>\n",
       "      <td>2892</td>\n",
       "      <td>5475</td>\n",
       "      <td>5731</td>\n",
       "      <td>7105</td>\n",
       "      <td>9715</td>\n",
       "      <td>8980</td>\n",
       "      <td>9121</td>\n",
       "      <td>8451</td>\n",
       "      <td>3098</td>\n",
       "      <td>3594</td>\n",
       "      <td>1532</td>\n",
       "      <td>2719</td>\n",
       "      <td>3938</td>\n",
       "      <td>2529</td>\n",
       "      <td>...</td>\n",
       "      <td>923</td>\n",
       "      <td>122281</td>\n",
       "      <td>7135</td>\n",
       "      <td>8229</td>\n",
       "      <td>9994</td>\n",
       "      <td>6097</td>\n",
       "      <td>4356</td>\n",
       "      <td>1656</td>\n",
       "      <td>1116</td>\n",
       "      <td>4010</td>\n",
       "      <td>5749</td>\n",
       "      <td>5892</td>\n",
       "      <td>8176</td>\n",
       "      <td>8883</td>\n",
       "      <td>10090</td>\n",
       "      <td>8939</td>\n",
       "      <td>8046</td>\n",
       "      <td>2077</td>\n",
       "      <td>4320</td>\n",
       "      <td>2292</td>\n",
       "      <td>3007</td>\n",
       "      <td>4990</td>\n",
       "      <td>3139</td>\n",
       "      <td>2242</td>\n",
       "      <td>1846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          geoid              name  B01001001  B01001002  B01001003  B01001004  \\\n",
       "0       01000US     United States  328239523  161588973    9938937   10033518   \n",
       "1     04000US37    North Carolina   10488084    5094327     308544     315965   \n",
       "2  05000US37179  Union County, NC     239859     117578       6556       7489   \n",
       "\n",
       "   B01001005  B01001006  B01001007  B01001008  B01001009  B01001010  \\\n",
       "0   10987313    6361859    4541794    2318283    2257008    6439169   \n",
       "1     346689     200951     158488      73745      77086     214451   \n",
       "2      11211       6620       3655       1932       2816       2892   \n",
       "\n",
       "   B01001011  B01001012  B01001013  B01001014  B01001015  B01001016  \\\n",
       "0   11817829   11281470   10892040   10028675   10079567   10075795   \n",
       "1     352172     333734     315351     322114     337091     327413   \n",
       "2       5475       5731       7105       9715       8980       9121   \n",
       "\n",
       "   B01001017  B01001018  B01001019  B01001020  B01001021  B01001022  \\\n",
       "0   10440265    4168435    5882735    3538792    4652319    6529918   \n",
       "1     332743     131308     180801     110628     145759     221189   \n",
       "2       8451       3098       3594       1532       2719       3938   \n",
       "\n",
       "   B01001023  ...  B01001025  B01001026  B01001027  B01001028  B01001029  \\\n",
       "0    4367764  ...    2284092  166650550    9465898    9656919   10436166   \n",
       "1     138247  ...      67098    5393757     288039     299578     339998   \n",
       "2       2529  ...        923     122281       7135       8229       9994   \n",
       "\n",
       "   B01001030  B01001031  B01001032  B01001033  B01001034  B01001035  \\\n",
       "0    6087175    4362696    2162759    2117048    6174413   11415470   \n",
       "1     194208     159419      71652      63307     188887     362854   \n",
       "2       6097       4356       1656       1116       4010       5749   \n",
       "\n",
       "   B01001036  B01001037  B01001038  B01001039  B01001040  B01001041  \\\n",
       "0   11063706   10836219   10157911   10318659   10389086   11043795   \n",
       "1     352903     341829     333367     355133     343563     367738   \n",
       "2       5892       8176       8883      10090       8939       8046   \n",
       "\n",
       "   B01001042  B01001043  B01001044  B01001045  B01001046  B01001047  \\\n",
       "0    4493986    6438897    3916612    5319290    7618630    5392000   \n",
       "1     146356     199672     130484     175237     264193     181367   \n",
       "2       2077       4320       2292       3007       4990       3139   \n",
       "\n",
       "   B01001048  B01001049  \n",
       "0    3709078    4074137  \n",
       "1     116777     117196  \n",
       "2       2242       1846  \n",
       "\n",
       "[3 rows x 51 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_cols = []\n",
    "for col in age_df.columns:\n",
    "    if 'Error' in col:\n",
    "        error_cols.append(col)\n",
    "        \n",
    "age_df.drop(columns=error_cols, inplace=True)\n",
    "age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.164576Z",
     "start_time": "2021-01-26T21:12:04.146527Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release    {'id': 'acs2019_1yr', 'name': 'ACS 2019 1-year...\n",
       "tables     {'B01001': {'columns': {'B01001001': {'indent'...\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_meta = pd.read_json('Data/NC_UC_2019_demog/age_metadata.json', typ='series')\n",
    "age_meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.176935Z",
     "start_time": "2021-01-26T21:12:04.168446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Total:',\n",
       " 'Male:',\n",
       " 'Under 5 years',\n",
       " '5 to 9 years',\n",
       " '10 to 14 years',\n",
       " '15 to 17 years',\n",
       " '18 and 19 years',\n",
       " '20 years',\n",
       " '21 years',\n",
       " '22 to 24 years',\n",
       " '25 to 29 years',\n",
       " '30 to 34 years',\n",
       " '35 to 39 years',\n",
       " '40 to 44 years',\n",
       " '45 to 49 years',\n",
       " '50 to 54 years',\n",
       " '55 to 59 years',\n",
       " '60 and 61 years',\n",
       " '62 to 64 years',\n",
       " '65 and 66 years',\n",
       " '67 to 69 years',\n",
       " '70 to 74 years',\n",
       " '75 to 79 years',\n",
       " '80 to 84 years',\n",
       " '85 years and over',\n",
       " 'Female:',\n",
       " 'Under 5 years',\n",
       " '5 to 9 years',\n",
       " '10 to 14 years',\n",
       " '15 to 17 years',\n",
       " '18 and 19 years',\n",
       " '20 years',\n",
       " '21 years',\n",
       " '22 to 24 years',\n",
       " '25 to 29 years',\n",
       " '30 to 34 years',\n",
       " '35 to 39 years',\n",
       " '40 to 44 years',\n",
       " '45 to 49 years',\n",
       " '50 to 54 years',\n",
       " '55 to 59 years',\n",
       " '60 and 61 years',\n",
       " '62 to 64 years',\n",
       " '65 and 66 years',\n",
       " '67 to 69 years',\n",
       " '70 to 74 years',\n",
       " '75 to 79 years',\n",
       " '80 to 84 years',\n",
       " '85 years and over']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = []\n",
    "for key in age_meta['tables']['B01001']['columns'].keys():\n",
    "    col_names.append(age_meta['tables']['B01001']['columns'][key]['name'])\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.209437Z",
     "start_time": "2021-01-26T21:12:04.179938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Male:</th>\n",
       "      <th>Under 5 years</th>\n",
       "      <th>5 to 9 years</th>\n",
       "      <th>10 to 14 years</th>\n",
       "      <th>15 to 17 years</th>\n",
       "      <th>18 and 19 years</th>\n",
       "      <th>20 years</th>\n",
       "      <th>21 years</th>\n",
       "      <th>22 to 24 years</th>\n",
       "      <th>25 to 29 years</th>\n",
       "      <th>30 to 34 years</th>\n",
       "      <th>35 to 39 years</th>\n",
       "      <th>40 to 44 years</th>\n",
       "      <th>45 to 49 years</th>\n",
       "      <th>50 to 54 years</th>\n",
       "      <th>55 to 59 years</th>\n",
       "      <th>60 and 61 years</th>\n",
       "      <th>62 to 64 years</th>\n",
       "      <th>65 and 66 years</th>\n",
       "      <th>67 to 69 years</th>\n",
       "      <th>70 to 74 years</th>\n",
       "      <th>75 to 79 years</th>\n",
       "      <th>80 to 84 years</th>\n",
       "      <th>85 years and over</th>\n",
       "      <th>Female:</th>\n",
       "      <th>Under 5 years</th>\n",
       "      <th>5 to 9 years</th>\n",
       "      <th>10 to 14 years</th>\n",
       "      <th>15 to 17 years</th>\n",
       "      <th>18 and 19 years</th>\n",
       "      <th>20 years</th>\n",
       "      <th>21 years</th>\n",
       "      <th>22 to 24 years</th>\n",
       "      <th>25 to 29 years</th>\n",
       "      <th>30 to 34 years</th>\n",
       "      <th>35 to 39 years</th>\n",
       "      <th>40 to 44 years</th>\n",
       "      <th>45 to 49 years</th>\n",
       "      <th>50 to 54 years</th>\n",
       "      <th>55 to 59 years</th>\n",
       "      <th>60 and 61 years</th>\n",
       "      <th>62 to 64 years</th>\n",
       "      <th>65 and 66 years</th>\n",
       "      <th>67 to 69 years</th>\n",
       "      <th>70 to 74 years</th>\n",
       "      <th>75 to 79 years</th>\n",
       "      <th>80 to 84 years</th>\n",
       "      <th>85 years and over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>161588973</td>\n",
       "      <td>9938937</td>\n",
       "      <td>10033518</td>\n",
       "      <td>10987313</td>\n",
       "      <td>6361859</td>\n",
       "      <td>4541794</td>\n",
       "      <td>2318283</td>\n",
       "      <td>2257008</td>\n",
       "      <td>6439169</td>\n",
       "      <td>11817829</td>\n",
       "      <td>11281470</td>\n",
       "      <td>10892040</td>\n",
       "      <td>10028675</td>\n",
       "      <td>10079567</td>\n",
       "      <td>10075795</td>\n",
       "      <td>10440265</td>\n",
       "      <td>4168435</td>\n",
       "      <td>5882735</td>\n",
       "      <td>3538792</td>\n",
       "      <td>4652319</td>\n",
       "      <td>6529918</td>\n",
       "      <td>4367764</td>\n",
       "      <td>2671396</td>\n",
       "      <td>2284092</td>\n",
       "      <td>166650550</td>\n",
       "      <td>9465898</td>\n",
       "      <td>9656919</td>\n",
       "      <td>10436166</td>\n",
       "      <td>6087175</td>\n",
       "      <td>4362696</td>\n",
       "      <td>2162759</td>\n",
       "      <td>2117048</td>\n",
       "      <td>6174413</td>\n",
       "      <td>11415470</td>\n",
       "      <td>11063706</td>\n",
       "      <td>10836219</td>\n",
       "      <td>10157911</td>\n",
       "      <td>10318659</td>\n",
       "      <td>10389086</td>\n",
       "      <td>11043795</td>\n",
       "      <td>4493986</td>\n",
       "      <td>6438897</td>\n",
       "      <td>3916612</td>\n",
       "      <td>5319290</td>\n",
       "      <td>7618630</td>\n",
       "      <td>5392000</td>\n",
       "      <td>3709078</td>\n",
       "      <td>4074137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>5094327</td>\n",
       "      <td>308544</td>\n",
       "      <td>315965</td>\n",
       "      <td>346689</td>\n",
       "      <td>200951</td>\n",
       "      <td>158488</td>\n",
       "      <td>73745</td>\n",
       "      <td>77086</td>\n",
       "      <td>214451</td>\n",
       "      <td>352172</td>\n",
       "      <td>333734</td>\n",
       "      <td>315351</td>\n",
       "      <td>322114</td>\n",
       "      <td>337091</td>\n",
       "      <td>327413</td>\n",
       "      <td>332743</td>\n",
       "      <td>131308</td>\n",
       "      <td>180801</td>\n",
       "      <td>110628</td>\n",
       "      <td>145759</td>\n",
       "      <td>221189</td>\n",
       "      <td>138247</td>\n",
       "      <td>82760</td>\n",
       "      <td>67098</td>\n",
       "      <td>5393757</td>\n",
       "      <td>288039</td>\n",
       "      <td>299578</td>\n",
       "      <td>339998</td>\n",
       "      <td>194208</td>\n",
       "      <td>159419</td>\n",
       "      <td>71652</td>\n",
       "      <td>63307</td>\n",
       "      <td>188887</td>\n",
       "      <td>362854</td>\n",
       "      <td>352903</td>\n",
       "      <td>341829</td>\n",
       "      <td>333367</td>\n",
       "      <td>355133</td>\n",
       "      <td>343563</td>\n",
       "      <td>367738</td>\n",
       "      <td>146356</td>\n",
       "      <td>199672</td>\n",
       "      <td>130484</td>\n",
       "      <td>175237</td>\n",
       "      <td>264193</td>\n",
       "      <td>181367</td>\n",
       "      <td>116777</td>\n",
       "      <td>117196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Union County, NC</td>\n",
       "      <td>117578</td>\n",
       "      <td>6556</td>\n",
       "      <td>7489</td>\n",
       "      <td>11211</td>\n",
       "      <td>6620</td>\n",
       "      <td>3655</td>\n",
       "      <td>1932</td>\n",
       "      <td>2816</td>\n",
       "      <td>2892</td>\n",
       "      <td>5475</td>\n",
       "      <td>5731</td>\n",
       "      <td>7105</td>\n",
       "      <td>9715</td>\n",
       "      <td>8980</td>\n",
       "      <td>9121</td>\n",
       "      <td>8451</td>\n",
       "      <td>3098</td>\n",
       "      <td>3594</td>\n",
       "      <td>1532</td>\n",
       "      <td>2719</td>\n",
       "      <td>3938</td>\n",
       "      <td>2529</td>\n",
       "      <td>1496</td>\n",
       "      <td>923</td>\n",
       "      <td>122281</td>\n",
       "      <td>7135</td>\n",
       "      <td>8229</td>\n",
       "      <td>9994</td>\n",
       "      <td>6097</td>\n",
       "      <td>4356</td>\n",
       "      <td>1656</td>\n",
       "      <td>1116</td>\n",
       "      <td>4010</td>\n",
       "      <td>5749</td>\n",
       "      <td>5892</td>\n",
       "      <td>8176</td>\n",
       "      <td>8883</td>\n",
       "      <td>10090</td>\n",
       "      <td>8939</td>\n",
       "      <td>8046</td>\n",
       "      <td>2077</td>\n",
       "      <td>4320</td>\n",
       "      <td>2292</td>\n",
       "      <td>3007</td>\n",
       "      <td>4990</td>\n",
       "      <td>3139</td>\n",
       "      <td>2242</td>\n",
       "      <td>1846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name      Male:  Under 5 years  5 to 9 years  10 to 14 years  \\\n",
       "0     United States  161588973        9938937      10033518        10987313   \n",
       "1    North Carolina    5094327         308544        315965          346689   \n",
       "2  Union County, NC     117578           6556          7489           11211   \n",
       "\n",
       "   15 to 17 years  18 and 19 years  20 years  21 years  22 to 24 years  \\\n",
       "0         6361859          4541794   2318283   2257008         6439169   \n",
       "1          200951           158488     73745     77086          214451   \n",
       "2            6620             3655      1932      2816            2892   \n",
       "\n",
       "   25 to 29 years  30 to 34 years  35 to 39 years  40 to 44 years  \\\n",
       "0        11817829        11281470        10892040        10028675   \n",
       "1          352172          333734          315351          322114   \n",
       "2            5475            5731            7105            9715   \n",
       "\n",
       "   45 to 49 years  50 to 54 years  55 to 59 years  60 and 61 years  \\\n",
       "0        10079567        10075795        10440265          4168435   \n",
       "1          337091          327413          332743           131308   \n",
       "2            8980            9121            8451             3098   \n",
       "\n",
       "   62 to 64 years  65 and 66 years  67 to 69 years  70 to 74 years  \\\n",
       "0         5882735          3538792         4652319         6529918   \n",
       "1          180801           110628          145759          221189   \n",
       "2            3594             1532            2719            3938   \n",
       "\n",
       "   75 to 79 years  80 to 84 years  85 years and over    Female:  \\\n",
       "0         4367764         2671396            2284092  166650550   \n",
       "1          138247           82760              67098    5393757   \n",
       "2            2529            1496                923     122281   \n",
       "\n",
       "   Under 5 years  5 to 9 years  10 to 14 years  15 to 17 years  \\\n",
       "0        9465898       9656919        10436166         6087175   \n",
       "1         288039        299578          339998          194208   \n",
       "2           7135          8229            9994            6097   \n",
       "\n",
       "   18 and 19 years  20 years  21 years  22 to 24 years  25 to 29 years  \\\n",
       "0          4362696   2162759   2117048         6174413        11415470   \n",
       "1           159419     71652     63307          188887          362854   \n",
       "2             4356      1656      1116            4010            5749   \n",
       "\n",
       "   30 to 34 years  35 to 39 years  40 to 44 years  45 to 49 years  \\\n",
       "0        11063706        10836219        10157911        10318659   \n",
       "1          352903          341829          333367          355133   \n",
       "2            5892            8176            8883           10090   \n",
       "\n",
       "   50 to 54 years  55 to 59 years  60 and 61 years  62 to 64 years  \\\n",
       "0        10389086        11043795          4493986         6438897   \n",
       "1          343563          367738           146356          199672   \n",
       "2            8939            8046             2077            4320   \n",
       "\n",
       "   65 and 66 years  67 to 69 years  70 to 74 years  75 to 79 years  \\\n",
       "0          3916612         5319290         7618630         5392000   \n",
       "1           130484          175237          264193          181367   \n",
       "2             2292            3007            4990            3139   \n",
       "\n",
       "   80 to 84 years  85 years and over  \n",
       "0         3709078            4074137  \n",
       "1          116777             117196  \n",
       "2            2242               1846  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_df.columns = ['geoid', 'name'] + col_names\n",
    "age_df.drop(columns=['geoid', 'Total:'], inplace=True)\n",
    "age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.217613Z",
     "start_time": "2021-01-26T21:12:04.211840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name',\n",
       " 'male',\n",
       " 'under_5_years',\n",
       " '5_to_9_years',\n",
       " '10_to_14_years',\n",
       " '15_to_17_years',\n",
       " '18_and_19_years',\n",
       " '20_years',\n",
       " '21_years',\n",
       " '22_to_24_years',\n",
       " '25_to_29_years',\n",
       " '30_to_34_years',\n",
       " '35_to_39_years',\n",
       " '40_to_44_years',\n",
       " '45_to_49_years',\n",
       " '50_to_54_years',\n",
       " '55_to_59_years',\n",
       " '60_and_61_years',\n",
       " '62_to_64_years',\n",
       " '65_and_66_years',\n",
       " '67_to_69_years',\n",
       " '70_to_74_years',\n",
       " '75_to_79_years',\n",
       " '80_to_84_years',\n",
       " '85_years_and_over',\n",
       " 'female',\n",
       " 'under_5_years',\n",
       " '5_to_9_years',\n",
       " '10_to_14_years',\n",
       " '15_to_17_years',\n",
       " '18_and_19_years',\n",
       " '20_years',\n",
       " '21_years',\n",
       " '22_to_24_years',\n",
       " '25_to_29_years',\n",
       " '30_to_34_years',\n",
       " '35_to_39_years',\n",
       " '40_to_44_years',\n",
       " '45_to_49_years',\n",
       " '50_to_54_years',\n",
       " '55_to_59_years',\n",
       " '60_and_61_years',\n",
       " '62_to_64_years',\n",
       " '65_and_66_years',\n",
       " '67_to_69_years',\n",
       " '70_to_74_years',\n",
       " '75_to_79_years',\n",
       " '80_to_84_years',\n",
       " '85_years_and_over']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = []\n",
    "for col in age_df.columns:\n",
    "    col = col.lower()\n",
    "    col = col.replace(':', '')\n",
    "    col = col.replace(' ', '_')\n",
    "    col_names.append(col)\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.246068Z",
     "start_time": "2021-01-26T21:12:04.222389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>male</th>\n",
       "      <th>under_5_years</th>\n",
       "      <th>5_to_9_years</th>\n",
       "      <th>10_to_14_years</th>\n",
       "      <th>15_to_17_years</th>\n",
       "      <th>18_and_19_years</th>\n",
       "      <th>20_years</th>\n",
       "      <th>21_years</th>\n",
       "      <th>22_to_24_years</th>\n",
       "      <th>25_to_29_years</th>\n",
       "      <th>30_to_34_years</th>\n",
       "      <th>35_to_39_years</th>\n",
       "      <th>40_to_44_years</th>\n",
       "      <th>45_to_49_years</th>\n",
       "      <th>50_to_54_years</th>\n",
       "      <th>55_to_59_years</th>\n",
       "      <th>60_and_61_years</th>\n",
       "      <th>62_to_64_years</th>\n",
       "      <th>65_and_66_years</th>\n",
       "      <th>67_to_69_years</th>\n",
       "      <th>70_to_74_years</th>\n",
       "      <th>75_to_79_years</th>\n",
       "      <th>80_to_84_years</th>\n",
       "      <th>85_years_and_over</th>\n",
       "      <th>female</th>\n",
       "      <th>under_5_years</th>\n",
       "      <th>5_to_9_years</th>\n",
       "      <th>10_to_14_years</th>\n",
       "      <th>15_to_17_years</th>\n",
       "      <th>18_and_19_years</th>\n",
       "      <th>20_years</th>\n",
       "      <th>21_years</th>\n",
       "      <th>22_to_24_years</th>\n",
       "      <th>25_to_29_years</th>\n",
       "      <th>30_to_34_years</th>\n",
       "      <th>35_to_39_years</th>\n",
       "      <th>40_to_44_years</th>\n",
       "      <th>45_to_49_years</th>\n",
       "      <th>50_to_54_years</th>\n",
       "      <th>55_to_59_years</th>\n",
       "      <th>60_and_61_years</th>\n",
       "      <th>62_to_64_years</th>\n",
       "      <th>65_and_66_years</th>\n",
       "      <th>67_to_69_years</th>\n",
       "      <th>70_to_74_years</th>\n",
       "      <th>75_to_79_years</th>\n",
       "      <th>80_to_84_years</th>\n",
       "      <th>85_years_and_over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Union County, NC</td>\n",
       "      <td>117578</td>\n",
       "      <td>6556</td>\n",
       "      <td>7489</td>\n",
       "      <td>11211</td>\n",
       "      <td>6620</td>\n",
       "      <td>3655</td>\n",
       "      <td>1932</td>\n",
       "      <td>2816</td>\n",
       "      <td>2892</td>\n",
       "      <td>5475</td>\n",
       "      <td>5731</td>\n",
       "      <td>7105</td>\n",
       "      <td>9715</td>\n",
       "      <td>8980</td>\n",
       "      <td>9121</td>\n",
       "      <td>8451</td>\n",
       "      <td>3098</td>\n",
       "      <td>3594</td>\n",
       "      <td>1532</td>\n",
       "      <td>2719</td>\n",
       "      <td>3938</td>\n",
       "      <td>2529</td>\n",
       "      <td>1496</td>\n",
       "      <td>923</td>\n",
       "      <td>122281</td>\n",
       "      <td>7135</td>\n",
       "      <td>8229</td>\n",
       "      <td>9994</td>\n",
       "      <td>6097</td>\n",
       "      <td>4356</td>\n",
       "      <td>1656</td>\n",
       "      <td>1116</td>\n",
       "      <td>4010</td>\n",
       "      <td>5749</td>\n",
       "      <td>5892</td>\n",
       "      <td>8176</td>\n",
       "      <td>8883</td>\n",
       "      <td>10090</td>\n",
       "      <td>8939</td>\n",
       "      <td>8046</td>\n",
       "      <td>2077</td>\n",
       "      <td>4320</td>\n",
       "      <td>2292</td>\n",
       "      <td>3007</td>\n",
       "      <td>4990</td>\n",
       "      <td>3139</td>\n",
       "      <td>2242</td>\n",
       "      <td>1846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name    male  under_5_years  5_to_9_years  10_to_14_years  \\\n",
       "2  Union County, NC  117578           6556          7489           11211   \n",
       "\n",
       "   15_to_17_years  18_and_19_years  20_years  21_years  22_to_24_years  \\\n",
       "2            6620             3655      1932      2816            2892   \n",
       "\n",
       "   25_to_29_years  30_to_34_years  35_to_39_years  40_to_44_years  \\\n",
       "2            5475            5731            7105            9715   \n",
       "\n",
       "   45_to_49_years  50_to_54_years  55_to_59_years  60_and_61_years  \\\n",
       "2            8980            9121            8451             3098   \n",
       "\n",
       "   62_to_64_years  65_and_66_years  67_to_69_years  70_to_74_years  \\\n",
       "2            3594             1532            2719            3938   \n",
       "\n",
       "   75_to_79_years  80_to_84_years  85_years_and_over  female  under_5_years  \\\n",
       "2            2529            1496                923  122281           7135   \n",
       "\n",
       "   5_to_9_years  10_to_14_years  15_to_17_years  18_and_19_years  20_years  \\\n",
       "2          8229            9994            6097             4356      1656   \n",
       "\n",
       "   21_years  22_to_24_years  25_to_29_years  30_to_34_years  35_to_39_years  \\\n",
       "2      1116            4010            5749            5892            8176   \n",
       "\n",
       "   40_to_44_years  45_to_49_years  50_to_54_years  55_to_59_years  \\\n",
       "2            8883           10090            8939            8046   \n",
       "\n",
       "   60_and_61_years  62_to_64_years  65_and_66_years  67_to_69_years  \\\n",
       "2             2077            4320             2292            3007   \n",
       "\n",
       "   70_to_74_years  75_to_79_years  80_to_84_years  85_years_and_over  \n",
       "2            4990            3139            2242               1846  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_df.columns = col_names\n",
    "age_df.drop(index=[0,1], inplace=True)\n",
    "age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.280877Z",
     "start_time": "2021-01-26T21:12:04.250644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>gender_code</th>\n",
       "      <th>GenZ</th>\n",
       "      <th>Millennial</th>\n",
       "      <th>GenX</th>\n",
       "      <th>Boomer</th>\n",
       "      <th>Greatest-Silent</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>11295</td>\n",
       "      <td>28026</td>\n",
       "      <td>18101</td>\n",
       "      <td>23332</td>\n",
       "      <td>4948</td>\n",
       "      <td>85702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender_code   GenZ  Millennial   GenX  Boomer  Greatest-Silent  Total\n",
       "2           M  11295       28026  18101   23332             4948  85702"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a slice with columns corresponding to voting age males only\n",
    "vote_age_males = age_df.copy()\n",
    "vote_age_males = vote_age_males.iloc[:, 6:25]\n",
    "\n",
    "\n",
    "## Calculate the total number of voting age males\n",
    "vote_age_males['Total'] = vote_age_males.sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Gen Z\n",
    "vote_age_males['GenZ'] = vote_age_males[[\n",
    "    '18_and_19_years',\n",
    "    '20_years',\n",
    "    '21_years', \n",
    "    '22_to_24_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Millennials\n",
    "vote_age_males['Millennial'] = vote_age_males[[\n",
    "    '25_to_29_years',\n",
    "    '30_to_34_years',\n",
    "    '35_to_39_years',\n",
    "    '40_to_44_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Generation X\n",
    "vote_age_males['GenX'] = vote_age_males[[\n",
    "    '45_to_49_years',\n",
    "    '50_to_54_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Baby Boomers\n",
    "vote_age_males['Boomer'] = vote_age_males[[\n",
    "    '55_to_59_years',\n",
    "    '60_and_61_years',\n",
    "    '62_to_64_years',\n",
    "    '65_and_66_years',\n",
    "    '67_to_69_years',\n",
    "    '70_to_74_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Greatest and Silent generations\n",
    "vote_age_males['Greatest-Silent'] = vote_age_males[[\n",
    "    '75_to_79_years',\n",
    "    '80_to_84_years',\n",
    "    '85_years_and_over'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Add gender_code column\n",
    "vote_age_males['gender_code'] = 'M'\n",
    "\n",
    "vote_age_males = vote_age_males[[\n",
    "    'gender_code',\n",
    "    'GenZ',\n",
    "    'Millennial',\n",
    "    'GenX',\n",
    "    'Boomer',\n",
    "    'Greatest-Silent',\n",
    "    'Total'\n",
    "]]\n",
    "vote_age_males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.315029Z",
     "start_time": "2021-01-26T21:12:04.283281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_code</th>\n",
       "      <th>GenZ</th>\n",
       "      <th>Millennial</th>\n",
       "      <th>GenX</th>\n",
       "      <th>Boomer</th>\n",
       "      <th>Greatest-Silent</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>11138</td>\n",
       "      <td>28700</td>\n",
       "      <td>19029</td>\n",
       "      <td>24732</td>\n",
       "      <td>7227</td>\n",
       "      <td>90826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender_code   GenZ  Millennial   GenX  Boomer  Greatest-Silent  Total\n",
       "2           F  11138       28700  19029   24732             7227  90826"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a slice with columns corresponding to voting age females only\n",
    "vote_age_females = age_df.copy()\n",
    "vote_age_females = vote_age_females.iloc[:, 30:]\n",
    "\n",
    "## Calculate the total number of voting age females\n",
    "vote_age_females['Total'] = vote_age_females.sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Gen Z\n",
    "vote_age_females['GenZ'] = vote_age_females[[\n",
    "    '18_and_19_years',\n",
    "    '20_years',\n",
    "    '21_years', \n",
    "    '22_to_24_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Millennials\n",
    "vote_age_females['Millennial'] = vote_age_females[[\n",
    "    '25_to_29_years',\n",
    "    '30_to_34_years',\n",
    "    '35_to_39_years',\n",
    "    '40_to_44_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Generation X\n",
    "vote_age_females['GenX'] = vote_age_females[[\n",
    "    '45_to_49_years',\n",
    "    '50_to_54_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Baby Boomers\n",
    "vote_age_females['Boomer'] = vote_age_females[[\n",
    "    '55_to_59_years',\n",
    "    '60_and_61_years',\n",
    "    '62_to_64_years',\n",
    "    '65_and_66_years',\n",
    "    '67_to_69_years',\n",
    "    '70_to_74_years'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Group and sum Greatest and Silent generations\n",
    "vote_age_females['Greatest-Silent'] = vote_age_females[[\n",
    "    '75_to_79_years',\n",
    "    '80_to_84_years',\n",
    "    '85_years_and_over'\n",
    "]].sum(axis=1)\n",
    "\n",
    "\n",
    "## Add gender_code column\n",
    "vote_age_females['gender_code'] = 'F'\n",
    "\n",
    "vote_age_females = vote_age_females[[\n",
    "    'gender_code',\n",
    "    'GenZ',\n",
    "    'Millennial',\n",
    "    'GenX',\n",
    "    'Boomer',\n",
    "    'Greatest-Silent',\n",
    "    'Total'\n",
    "]]\n",
    "vote_age_females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.331064Z",
     "start_time": "2021-01-26T21:12:04.317182Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>GenZ</th>\n",
       "      <th>Millennial</th>\n",
       "      <th>GenX</th>\n",
       "      <th>Boomer</th>\n",
       "      <th>Greatest-Silent</th>\n",
       "      <th>Total</th>\n",
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       "    <tr>\n",
       "      <th>gender_code</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>11138</td>\n",
       "      <td>28700</td>\n",
       "      <td>19029</td>\n",
       "      <td>24732</td>\n",
       "      <td>7227</td>\n",
       "      <td>90826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>11295</td>\n",
       "      <td>28026</td>\n",
       "      <td>18101</td>\n",
       "      <td>23332</td>\n",
       "      <td>4948</td>\n",
       "      <td>85702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>22433</td>\n",
       "      <td>56726</td>\n",
       "      <td>37130</td>\n",
       "      <td>48064</td>\n",
       "      <td>12175</td>\n",
       "      <td>176528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GenZ  Millennial   GenX  Boomer  Greatest-Silent   Total\n",
       "gender_code                                                           \n",
       "F            11138       28700  19029   24732             7227   90826\n",
       "M            11295       28026  18101   23332             4948   85702\n",
       "All          22433       56726  37130   48064            12175  176528"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Concatenate both gender dataframes\n",
    "age_gender_df = pd.concat([vote_age_females, vote_age_males],\n",
    "                          ignore_index=True)\n",
    "age_gender_df.set_index('gender_code', inplace=True)\n",
    "\n",
    "## Add a row that sums the total number of voters in each group\n",
    "age_gender_df.loc['All'] = age_gender_df.sum(axis=0)\n",
    "age_gender_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:04.987955Z",
     "start_time": "2021-01-26T21:12:04.333416Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_method_4</th>\n",
       "      <th>vote_method_5</th>\n",
       "      <th>vote_bin</th>\n",
       "      <th>pri_vote_bin</th>\n",
       "      <th>birth_age_adj</th>\n",
       "      <th>gen_grp</th>\n",
       "      <th>party_grp</th>\n",
       "      <th>gender_code</th>\n",
       "      <th>race_grp</th>\n",
       "      <th>birth_reg_other</th>\n",
       "      <th>drivers_lic</th>\n",
       "      <th>city_grp</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Early</td>\n",
       "      <td>Early</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Boomer</td>\n",
       "      <td>Rep</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
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       "      <td>License</td>\n",
       "      <td>Indian Trail</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Vote</td>\n",
       "      <td>No Vote</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Boomer</td>\n",
       "      <td>Other</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>License</td>\n",
       "      <td>Waxhaw</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Early</td>\n",
       "      <td>Early</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>47.0</td>\n",
       "      <td>GenX</td>\n",
       "      <td>Dem</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>License</td>\n",
       "      <td>Waxhaw</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Vote</td>\n",
       "      <td>No Vote</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Boomer</td>\n",
       "      <td>Rep</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>License</td>\n",
       "      <td>Monroe</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Early</td>\n",
       "      <td>Early</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>53.0</td>\n",
       "      <td>GenX</td>\n",
       "      <td>Dem</td>\n",
       "      <td>F</td>\n",
       "      <td>Black</td>\n",
       "      <td>South</td>\n",
       "      <td>License</td>\n",
       "      <td>Matthews</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  vote_method_4 vote_method_5 vote_bin pri_vote_bin  birth_age_adj gen_grp  \\\n",
       "0         Early         Early        Y            Y           65.0  Boomer   \n",
       "1       No Vote       No Vote        N            N           57.0  Boomer   \n",
       "2         Early         Early        Y            N           47.0    GenX   \n",
       "3       No Vote       No Vote        N            N           72.0  Boomer   \n",
       "4         Early         Early        Y            Y           53.0    GenX   \n",
       "\n",
       "  party_grp gender_code race_grp birth_reg_other drivers_lic      city_grp  \\\n",
       "0       Rep           M    White           South     License  Indian Trail   \n",
       "1     Other           M    White       Northeast     License        Waxhaw   \n",
       "2       Dem           F    White         Midwest     License        Waxhaw   \n",
       "3       Rep           F    White       Northeast     License        Monroe   \n",
       "4       Dem           F    Black           South     License      Matthews   \n",
       "\n",
       "   year  \n",
       "0  2020  \n",
       "1  2020  \n",
       "2  2020  \n",
       "3  2020  \n",
       "4  2020  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Import and preview UC gen elecs dataset\n",
    "gen_elecs_df = pd.read_csv('Data/UC_gen_elecs.gz')\n",
    "df_2020 = gen_elecs_df.loc[gen_elecs_df['year']==2020]\n",
    "df_2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:05.009831Z",
     "start_time": "2021-01-26T21:12:04.989942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gen_grp</th>\n",
       "      <th>reg_voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boomer</td>\n",
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       "      <td>GenX</td>\n",
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       "      <th>2</th>\n",
       "      <td>GenZ</td>\n",
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       "      <th>3</th>\n",
       "      <td>Greatest-Silent</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Millennial</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "           gen_grp  reg_voters\n",
       "0           Boomer       44171\n",
       "1             GenX       53717\n",
       "2             GenZ       19834\n",
       "3  Greatest-Silent       11748\n",
       "4       Millennial       38600"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_df = df_2020.groupby(\n",
    "    ['gen_grp']\n",
    ").size().to_frame().reset_index()\n",
    "gen_df.rename(columns={0:'reg_voters'}, inplace=True)\n",
    "gen_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:05.016782Z",
     "start_time": "2021-01-26T21:12:05.011964Z"
    }
   },
   "outputs": [],
   "source": [
    "age_gender_df.reset_index(inplace=True)\n",
    "all_gen_slice = age_gender_df.loc[age_gender_df['gender_code']=='All'].copy()\n",
    "# gen_df['total_2019'] = 0\n",
    "\n",
    "# for grp in gen_df['gen_grp'].unique():\n",
    "#     gen_df['total_2019'].loc[gen_df['gen_grp']==grp] = all_gen_slice[grp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:05.021782Z",
     "start_time": "2021-01-26T21:12:05.019011Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_gen_total_2019(grp):\n",
    "    return all_gen_slice[grp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:05.031133Z",
     "start_time": "2021-01-26T21:12:05.024047Z"
    }
   },
   "outputs": [],
   "source": [
    "gen_df['total_2019'] = gen_df['gen_grp'].apply(get_gen_total_2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T21:02:01.554903Z",
     "start_time": "2021-01-27T21:02:01.549192Z"
    }
   },
   "outputs": [],
   "source": [
    "gen_df['unreg_vote_age'] = gen_df['total_2019'] - gen_df['reg_voters']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-27T21:02:01.946250Z",
     "start_time": "2021-01-27T21:02:01.932019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gen_grp</th>\n",
       "      <th>reg_voters</th>\n",
       "      <th>total_2019</th>\n",
       "      <th>unreg_vote_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boomer</td>\n",
       "      <td>44171</td>\n",
       "      <td>48064</td>\n",
       "      <td>3893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GenX</td>\n",
       "      <td>53717</td>\n",
       "      <td>37130</td>\n",
       "      <td>-16587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GenZ</td>\n",
       "      <td>19834</td>\n",
       "      <td>22433</td>\n",
       "      <td>2599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Greatest-Silent</td>\n",
       "      <td>11748</td>\n",
       "      <td>12175</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Millennial</td>\n",
       "      <td>38600</td>\n",
       "      <td>56726</td>\n",
       "      <td>18126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "           gen_grp  reg_voters  total_2019  unreg_vote_age\n",
       "0           Boomer       44171       48064            3893\n",
       "1             GenX       53717       37130          -16587\n",
       "2             GenZ       19834       22433            2599\n",
       "3  Greatest-Silent       11748       12175             427\n",
       "4       Millennial       38600       56726           18126"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
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     "end_time": "2021-01-26T21:12:05.051477Z",
     "start_time": "2021-01-26T21:12:05.045791Z"
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     "execution_count": 70,
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     "output_type": "execute_result"
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   "source": [
    "len(df_2020)"
   ]
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   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T21:12:05.088269Z",
     "start_time": "2021-01-26T21:12:05.054325Z"
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     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(df_2020.loc[df_2020['vote_bin']=='Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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