ACS Male Population by Age US.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from MyCreds.mycreds import USCensusAPI\n",
    "import requests\n",
    "import pandas as pd\n",
    "import ast\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "def get_variable_table_df(year):\n",
    "    variable_table_url = f'https://api.census.gov/data/{year}/acs/acs1/variables.html'\n",
    "    v_table = pd.read_html(variable_table_url)\n",
    "    variable_df = pd.DataFrame(v_table[0])\n",
    "    variable_df['Label'].replace({\"!!\": \" \", \":\": \"\"}, regex=True, inplace=True)\n",
    "\n",
    "    return variable_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def get_male_by_age_index(variable_table):\n",
    "    start_index = variable_table[variable_table['Label'] == 'Estimate Total Male'].index[0]\n",
    "    end_index = variable_table[variable_table['Label'] == 'Estimate Total Male 85 years and over'].index[0]\n",
    "\n",
    "    return start_index, end_index + 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def get_variable_names(variable_table, indeces):\n",
    "    total_male_by_age_variables = \",\".join(variable_table.iloc[indeces[0]: indeces[1]]['Name'].values)\n",
    "    return total_male_by_age_variables"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def get_query_url(year, variables):\n",
    "    # API Reference: https://www.census.gov/data/developers/guidance/api-user-guide.Example_API_Queries.html\n",
    "    # Data Dictionary: https://api.census.gov/data.html\n",
    "    host = 'https://api.census.gov/data'\n",
    "    year = f'/{year}'\n",
    "    dataset_acronym = '/acs/acs1'\n",
    "    g = '?get='\n",
    "    location = '&for=us:*'\n",
    "    usr_key = f\"&key={USCensusAPI.api_key}\"\n",
    "\n",
    "    query_url = f\"{host}{year}{dataset_acronym}{g}{variables}{location}{usr_key}\"\n",
    "\n",
    "    return query_url"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def get_query_text(query_url):\n",
    "    response = requests.get(query_url)\n",
    "    return response.text"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def get_values_from_response(response_text):\n",
    "    values = [int(i) for i in ast.literal_eval(response_text)[1][:-1]]\n",
    "    return values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "def get_labels(variable_df, indeces):\n",
    "    labels = [i.replace(\"!!\", \" \").replace(\":\", \"\") for i in variable_df.iloc[indeces[0]:indeces[1]]['Label'].values]\n",
    "    return labels"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "def create_year_pop_dataframe(year, labels, values):\n",
    "    df = pd.DataFrame({year: {labels[i]: values[i] for i in range(len(labels))}}).reindex(labels)\n",
    "    return df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "def create_male_pop_by_age_df(year):\n",
    "    v_table = get_variable_table_df(year)\n",
    "    male_by_age_indeces = get_male_by_age_index(v_table)\n",
    "    variables = get_variable_names(v_table, male_by_age_indeces)\n",
    "    query_url = get_query_url(year, variables)\n",
    "    response_text = get_query_text(query_url)\n",
    "    vals = get_values_from_response(response_text)\n",
    "    labels = get_labels(v_table, male_by_age_indeces)\n",
    "    df = create_year_pop_dataframe(year, labels, vals)\n",
    "    return df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/15 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0891a64397ab417581c14f0364aa47d1"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = [i for i in range(2005, 2020)]\n",
    "male_pop_by_age_df = pd.DataFrame(columns=['Population Label'])\n",
    "for year in tqdm(years):\n",
    "    try:\n",
    "        y_df = create_male_pop_by_age_df(year).reset_index().rename({'index': 'Population Label'}, axis=1)\n",
    "        male_pop_by_age_df = pd.merge(male_pop_by_age_df, y_df, how='outer', on='Population Label')\n",
    "    except IndexError:\n",
    "        next"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "                         Population Label       2005       2006       2007  \\\n0                     Estimate Total Male  141274964  147434940  148639222   \n1       Estimate Total Male Under 5 years   10356773   10421888   10578358   \n2        Estimate Total Male 5 to 9 years    9945226   10098366   10087957   \n3      Estimate Total Male 10 to 14 years   10674272   10587454   10513768   \n4      Estimate Total Male 15 to 17 years    6415594    6641247    6679175   \n5     Estimate Total Male 18 and 19 years    3642423    4504703    4573863   \n6            Estimate Total Male 20 years    1946123    2346861    2358876   \n7            Estimate Total Male 21 years    1926099    2268506    2269700   \n8      Estimate Total Male 22 to 24 years    5861680    6205974    6187367   \n9      Estimate Total Male 25 to 29 years    9657845   10356272   10505697   \n10     Estimate Total Male 30 to 34 years    9666002    9893240    9800936   \n11     Estimate Total Male 35 to 39 years   10206343   10675120   10625293   \n12     Estimate Total Male 40 to 44 years   11121959   11273754   11110082   \n13     Estimate Total Male 45 to 49 years   10898786   11283628   11345000   \n14     Estimate Total Male 50 to 54 years    9652987   10037576   10289260   \n15     Estimate Total Male 55 to 59 years    8254342    8762788    8782255   \n16    Estimate Total Male 60 and 61 years    2597744    2697559    3134244   \n17     Estimate Total Male 62 to 64 years    3606637    3773329    3860246   \n18    Estimate Total Male 65 and 66 years    2002360    2096170    2199526   \n19     Estimate Total Male 67 to 69 years    2658450    2745928    2811172   \n20     Estimate Total Male 70 to 74 years    3739824    3835107    3897991   \n21     Estimate Total Male 75 to 79 years    3099604    3168881    3182523   \n22     Estimate Total Male 80 to 84 years    2072317    2210627    2234634   \n23  Estimate Total Male 85 years and over    1271574    1549962    1611299   \n\n         2008       2009       2010       2011       2012       2013  \\\n0   149863485  151375321  152089450  153267861  154436243  155627698   \n1    10707049   10853263   10295148   10258117   10166500   10109150   \n2    10154505   10273948   10357528   10384771   10493216   10516217   \n3    10428924   10532166   10694165   10636801   10601990   10622312   \n4     6563046    6487137    6636724    6539994    6432763    6402435   \n5     4678168    4696946    4715886    4680974    4613289    4556876   \n6     2343397    2381015    2408504    2488989    2463952    2450726   \n7     2239549    2285493    2309243    2389475    2444471    2398385   \n8     6228461    6383204    6367407    6435552    6590360    6808517   \n9    10713487   11006461   10540760   10640680   10760625   10867688   \n10    9817992   10060873   10012444   10251662   10451315   10635488   \n11   10553094   10297457   10055878    9822749    9761486    9831950   \n12   10873215   10582197   10438720   10485486   10506617   10382817   \n13   11349805   11308794   11170409   10951047   10710557   10477997   \n14   10539798   10674127   10929686   11039046   11051752   11042453   \n15    8935709    9098584    9528114    9775709    9973838   10227740   \n16    3446968    3390303    3480255    3540293    3632437    3722503   \n17    3868507    4282178    4714934    5030270    4975812    5000227   \n18    2401977    2492871    2501196    2575371    2967347    3274963   \n19    2893564    3029709    3367779    3487306    3589046    3613546   \n20    3991339    4088428    4286621    4412234    4655827    4907097   \n21    3172759    3168175    3172839    3226282    3276860    3390122   \n22    2260017    2258021    2310925    2330345    2366793    2378028   \n23    1702155    1743971    1794285    1884708    1949390    2010461   \n\n         2014       2015       2016       2017       2018       2019  \n0   156890101  158167834  159061631  160402504  161118151  161588973  \n1    10117103   10112826   10130841   10138447   10047966    9938937  \n2    10511796   10507658   10487494   10223398   10095699   10033518  \n3    10596279   10533944   10579748   10854851   11000943   10987313  \n4     6390741    6470980    6480400    6456448    6394674    6361859  \n5     4550073    4495739    4511323    4547414    4578048    4541794  \n6     2416850    2378536    2355103    2354931    2334371    2318283  \n7     2375223    2357032    2318893    2284991    2261277    2257008  \n8     6830222    6833725    6731345    6640221    6526051    6439169  \n9    11070617   11290711   11496946   11720804   11869477   11817829  \n10   10765544   10839950   10901776   11023521   11111452   11281470  \n11   10001134   10235171   10377670   10664164   10790784   10892040  \n12   10273647   10073616    9837971    9807571    9900261   10028675  \n13   10309499   10315271   10375001   10369440   10253794   10079567  \n14   11049751   10957719   10726369   10521449   10266496   10075795  \n15   10333822   10479885   10557089   10561492   10515549   10440265  \n16    3811110    3939947    3971452    4106765    4134525    4168435  \n17    5174218    5290469    5461772    5606148    5745886    5882735  \n18    3217874    3232330    3301033    3381427    3437352    3538792  \n19    4010030    4357791    4609409    4596961    4618529    4652319  \n20    5125062    5304173    5498831    5912776    6221547    6529918  \n21    3523217    3633694    3717778    3934609    4195815    4367764  \n22    2393854    2419737    2460143    2492574    2591562    2671396  \n23    2042435    2106930    2173244    2202102    2226093    2284092  ",
      "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>Population Label</th>\n      <th>2005</th>\n      <th>2006</th>\n      <th>2007</th>\n      <th>2008</th>\n      <th>2009</th>\n      <th>2010</th>\n      <th>2011</th>\n      <th>2012</th>\n      <th>2013</th>\n      <th>2014</th>\n      <th>2015</th>\n      <th>2016</th>\n      <th>2017</th>\n      <th>2018</th>\n      <th>2019</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Estimate Total Male</td>\n      <td>141274964</td>\n      <td>147434940</td>\n      <td>148639222</td>\n      <td>149863485</td>\n      <td>151375321</td>\n      <td>152089450</td>\n      <td>153267861</td>\n      <td>154436243</td>\n      <td>155627698</td>\n      <td>156890101</td>\n      <td>158167834</td>\n      <td>159061631</td>\n      <td>160402504</td>\n      <td>161118151</td>\n      <td>161588973</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Estimate Total Male Under 5 years</td>\n      <td>10356773</td>\n      <td>10421888</td>\n      <td>10578358</td>\n      <td>10707049</td>\n      <td>10853263</td>\n      <td>10295148</td>\n      <td>10258117</td>\n      <td>10166500</td>\n      <td>10109150</td>\n      <td>10117103</td>\n      <td>10112826</td>\n      <td>10130841</td>\n      <td>10138447</td>\n      <td>10047966</td>\n      <td>9938937</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Estimate Total Male 5 to 9 years</td>\n      <td>9945226</td>\n      <td>10098366</td>\n      <td>10087957</td>\n      <td>10154505</td>\n      <td>10273948</td>\n      <td>10357528</td>\n      <td>10384771</td>\n      <td>10493216</td>\n      <td>10516217</td>\n      <td>10511796</td>\n      <td>10507658</td>\n      <td>10487494</td>\n      <td>10223398</td>\n      <td>10095699</td>\n      <td>10033518</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Estimate Total Male 10 to 14 years</td>\n      <td>10674272</td>\n      <td>10587454</td>\n      <td>10513768</td>\n      <td>10428924</td>\n      <td>10532166</td>\n      <td>10694165</td>\n      <td>10636801</td>\n      <td>10601990</td>\n      <td>10622312</td>\n      <td>10596279</td>\n      <td>10533944</td>\n      <td>10579748</td>\n      <td>10854851</td>\n      <td>11000943</td>\n      <td>10987313</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Estimate Total Male 15 to 17 years</td>\n      <td>6415594</td>\n      <td>6641247</td>\n      <td>6679175</td>\n      <td>6563046</td>\n      <td>6487137</td>\n      <td>6636724</td>\n      <td>6539994</td>\n      <td>6432763</td>\n      <td>6402435</td>\n      <td>6390741</td>\n      <td>6470980</td>\n      <td>6480400</td>\n      <td>6456448</td>\n      <td>6394674</td>\n      <td>6361859</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Estimate Total Male 18 and 19 years</td>\n      <td>3642423</td>\n      <td>4504703</td>\n      <td>4573863</td>\n      <td>4678168</td>\n      <td>4696946</td>\n      <td>4715886</td>\n      <td>4680974</td>\n      <td>4613289</td>\n      <td>4556876</td>\n      <td>4550073</td>\n      <td>4495739</td>\n      <td>4511323</td>\n      <td>4547414</td>\n      <td>4578048</td>\n      <td>4541794</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Estimate Total Male 20 years</td>\n      <td>1946123</td>\n      <td>2346861</td>\n      <td>2358876</td>\n      <td>2343397</td>\n      <td>2381015</td>\n      <td>2408504</td>\n      <td>2488989</td>\n      <td>2463952</td>\n      <td>2450726</td>\n      <td>2416850</td>\n      <td>2378536</td>\n      <td>2355103</td>\n      <td>2354931</td>\n      <td>2334371</td>\n      <td>2318283</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Estimate Total Male 21 years</td>\n      <td>1926099</td>\n      <td>2268506</td>\n      <td>2269700</td>\n      <td>2239549</td>\n      <td>2285493</td>\n      <td>2309243</td>\n      <td>2389475</td>\n      <td>2444471</td>\n      <td>2398385</td>\n      <td>2375223</td>\n      <td>2357032</td>\n      <td>2318893</td>\n      <td>2284991</td>\n      <td>2261277</td>\n      <td>2257008</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Estimate Total Male 22 to 24 years</td>\n      <td>5861680</td>\n      <td>6205974</td>\n      <td>6187367</td>\n      <td>6228461</td>\n      <td>6383204</td>\n      <td>6367407</td>\n      <td>6435552</td>\n      <td>6590360</td>\n      <td>6808517</td>\n      <td>6830222</td>\n      <td>6833725</td>\n      <td>6731345</td>\n      <td>6640221</td>\n      <td>6526051</td>\n      <td>6439169</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Estimate Total Male 25 to 29 years</td>\n      <td>9657845</td>\n      <td>10356272</td>\n      <td>10505697</td>\n      <td>10713487</td>\n      <td>11006461</td>\n      <td>10540760</td>\n      <td>10640680</td>\n      <td>10760625</td>\n      <td>10867688</td>\n      <td>11070617</td>\n      <td>11290711</td>\n      <td>11496946</td>\n      <td>11720804</td>\n      <td>11869477</td>\n      <td>11817829</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Estimate Total Male 30 to 34 years</td>\n      <td>9666002</td>\n      <td>9893240</td>\n      <td>9800936</td>\n      <td>9817992</td>\n      <td>10060873</td>\n      <td>10012444</td>\n      <td>10251662</td>\n      <td>10451315</td>\n      <td>10635488</td>\n      <td>10765544</td>\n      <td>10839950</td>\n      <td>10901776</td>\n      <td>11023521</td>\n      <td>11111452</td>\n      <td>11281470</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Estimate Total Male 35 to 39 years</td>\n      <td>10206343</td>\n      <td>10675120</td>\n      <td>10625293</td>\n      <td>10553094</td>\n      <td>10297457</td>\n      <td>10055878</td>\n      <td>9822749</td>\n      <td>9761486</td>\n      <td>9831950</td>\n      <td>10001134</td>\n      <td>10235171</td>\n      <td>10377670</td>\n      <td>10664164</td>\n      <td>10790784</td>\n      <td>10892040</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Estimate Total Male 40 to 44 years</td>\n      <td>11121959</td>\n      <td>11273754</td>\n      <td>11110082</td>\n      <td>10873215</td>\n      <td>10582197</td>\n      <td>10438720</td>\n      <td>10485486</td>\n      <td>10506617</td>\n      <td>10382817</td>\n      <td>10273647</td>\n      <td>10073616</td>\n      <td>9837971</td>\n      <td>9807571</td>\n      <td>9900261</td>\n      <td>10028675</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Estimate Total Male 45 to 49 years</td>\n      <td>10898786</td>\n      <td>11283628</td>\n      <td>11345000</td>\n      <td>11349805</td>\n      <td>11308794</td>\n      <td>11170409</td>\n      <td>10951047</td>\n      <td>10710557</td>\n      <td>10477997</td>\n      <td>10309499</td>\n      <td>10315271</td>\n      <td>10375001</td>\n      <td>10369440</td>\n      <td>10253794</td>\n      <td>10079567</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Estimate Total Male 50 to 54 years</td>\n      <td>9652987</td>\n      <td>10037576</td>\n      <td>10289260</td>\n      <td>10539798</td>\n      <td>10674127</td>\n      <td>10929686</td>\n      <td>11039046</td>\n      <td>11051752</td>\n      <td>11042453</td>\n      <td>11049751</td>\n      <td>10957719</td>\n      <td>10726369</td>\n      <td>10521449</td>\n      <td>10266496</td>\n      <td>10075795</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Estimate Total Male 55 to 59 years</td>\n      <td>8254342</td>\n      <td>8762788</td>\n      <td>8782255</td>\n      <td>8935709</td>\n      <td>9098584</td>\n      <td>9528114</td>\n      <td>9775709</td>\n      <td>9973838</td>\n      <td>10227740</td>\n      <td>10333822</td>\n      <td>10479885</td>\n      <td>10557089</td>\n      <td>10561492</td>\n      <td>10515549</td>\n      <td>10440265</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Estimate Total Male 60 and 61 years</td>\n      <td>2597744</td>\n      <td>2697559</td>\n      <td>3134244</td>\n      <td>3446968</td>\n      <td>3390303</td>\n      <td>3480255</td>\n      <td>3540293</td>\n      <td>3632437</td>\n      <td>3722503</td>\n      <td>3811110</td>\n      <td>3939947</td>\n      <td>3971452</td>\n      <td>4106765</td>\n      <td>4134525</td>\n      <td>4168435</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Estimate Total Male 62 to 64 years</td>\n      <td>3606637</td>\n      <td>3773329</td>\n      <td>3860246</td>\n      <td>3868507</td>\n      <td>4282178</td>\n      <td>4714934</td>\n      <td>5030270</td>\n      <td>4975812</td>\n      <td>5000227</td>\n      <td>5174218</td>\n      <td>5290469</td>\n      <td>5461772</td>\n      <td>5606148</td>\n      <td>5745886</td>\n      <td>5882735</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Estimate Total Male 65 and 66 years</td>\n      <td>2002360</td>\n      <td>2096170</td>\n      <td>2199526</td>\n      <td>2401977</td>\n      <td>2492871</td>\n      <td>2501196</td>\n      <td>2575371</td>\n      <td>2967347</td>\n      <td>3274963</td>\n      <td>3217874</td>\n      <td>3232330</td>\n      <td>3301033</td>\n      <td>3381427</td>\n      <td>3437352</td>\n      <td>3538792</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Estimate Total Male 67 to 69 years</td>\n      <td>2658450</td>\n      <td>2745928</td>\n      <td>2811172</td>\n      <td>2893564</td>\n      <td>3029709</td>\n      <td>3367779</td>\n      <td>3487306</td>\n      <td>3589046</td>\n      <td>3613546</td>\n      <td>4010030</td>\n      <td>4357791</td>\n      <td>4609409</td>\n      <td>4596961</td>\n      <td>4618529</td>\n      <td>4652319</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Estimate Total Male 70 to 74 years</td>\n      <td>3739824</td>\n      <td>3835107</td>\n      <td>3897991</td>\n      <td>3991339</td>\n      <td>4088428</td>\n      <td>4286621</td>\n      <td>4412234</td>\n      <td>4655827</td>\n      <td>4907097</td>\n      <td>5125062</td>\n      <td>5304173</td>\n      <td>5498831</td>\n      <td>5912776</td>\n      <td>6221547</td>\n      <td>6529918</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Estimate Total Male 75 to 79 years</td>\n      <td>3099604</td>\n      <td>3168881</td>\n      <td>3182523</td>\n      <td>3172759</td>\n      <td>3168175</td>\n      <td>3172839</td>\n      <td>3226282</td>\n      <td>3276860</td>\n      <td>3390122</td>\n      <td>3523217</td>\n      <td>3633694</td>\n      <td>3717778</td>\n      <td>3934609</td>\n      <td>4195815</td>\n      <td>4367764</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Estimate Total Male 80 to 84 years</td>\n      <td>2072317</td>\n      <td>2210627</td>\n      <td>2234634</td>\n      <td>2260017</td>\n      <td>2258021</td>\n      <td>2310925</td>\n      <td>2330345</td>\n      <td>2366793</td>\n      <td>2378028</td>\n      <td>2393854</td>\n      <td>2419737</td>\n      <td>2460143</td>\n      <td>2492574</td>\n      <td>2591562</td>\n      <td>2671396</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Estimate Total Male 85 years and over</td>\n      <td>1271574</td>\n      <td>1549962</td>\n      <td>1611299</td>\n      <td>1702155</td>\n      <td>1743971</td>\n      <td>1794285</td>\n      <td>1884708</td>\n      <td>1949390</td>\n      <td>2010461</td>\n      <td>2042435</td>\n      <td>2106930</td>\n      <td>2173244</td>\n      <td>2202102</td>\n      <td>2226093</td>\n      <td>2284092</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_pop_by_age_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "male_pop_by_age_df.to_csv('data_extract/male_pop_by_age_2005-2019.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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