{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Census API Example\n",
    "This example only works with an api key: https://www.census.gov/developers/.\n",
    "You can run it a limited number of times without a key; comment out block 2 and remove &key={api_key} from the data_url string in block 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pull data from Population Estimates Vintage 2018\n",
    "%matplotlib inline\n",
    "import requests,pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('census_key.txt') as key:\n",
    "    api_key=key.read().strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "year='2018'\n",
    "dsource='pep'\n",
    "dname='components'\n",
    "cols='GEONAME,NATURALINC,DOMESTICMIG,INTERNATIONALMIG'\n",
    "state='42'\n",
    "county='017,029,045,091,101'\n",
    "#state='34'\n",
    "#county='005,007,015,021'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://api.census.gov/data/2018/pep/components'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_url = f'https://api.census.gov/data/{year}/{dsource}/{dname}'\n",
    "base_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[\"GEONAME\",\"NATURALINC\",\"DOMESTICMIG\",\"INTERNATIONALMIG\",\"state\",\"county\"],\n",
      "[\"Bucks County, Pennsylvania\",\"-178\",\"-605\",\"862\",\"42\",\"017\"],\n",
      "[\"Chester County, Pennsylvania\",\"1829\",\"-887\",\"1374\",\"42\",\"029\"],\n",
      "[\"Delaware County, Pennsylvania\",\"1374\",\"-2513\",\"1579\",\"42\",\"045\"],\n",
      "[\"Montgomery County, Pennsylvania\",\"1230\",\"-1987\",\"2315\",\"42\",\"091\"],\n",
      "[\"Philadelphia County, Pennsylvania\",\"8617\",\"-11796\",\"8904\",\"42\",\"101\"]]\n"
     ]
    }
   ],
   "source": [
    "data_url = f'{base_url}?get={cols}&for=county:{county}&in=state:{state}&key={api_key}'\n",
    "response=requests.get(data_url)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>GEONAME</th>\n",
       "      <th>Natural Increase</th>\n",
       "      <th>Net Domestic Mig</th>\n",
       "      <th>Net Foreign Mig</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42017</th>\n",
       "      <td>Bucks County, Pennsylvania</td>\n",
       "      <td>-178</td>\n",
       "      <td>-605</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42029</th>\n",
       "      <td>Chester County, Pennsylvania</td>\n",
       "      <td>1829</td>\n",
       "      <td>-887</td>\n",
       "      <td>1374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42045</th>\n",
       "      <td>Delaware County, Pennsylvania</td>\n",
       "      <td>1374</td>\n",
       "      <td>-2513</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42091</th>\n",
       "      <td>Montgomery County, Pennsylvania</td>\n",
       "      <td>1230</td>\n",
       "      <td>-1987</td>\n",
       "      <td>2315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42101</th>\n",
       "      <td>Philadelphia County, Pennsylvania</td>\n",
       "      <td>8617</td>\n",
       "      <td>-11796</td>\n",
       "      <td>8904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 GEONAME  Natural Increase  Net Domestic Mig  \\\n",
       "fips                                                                           \n",
       "42017         Bucks County, Pennsylvania              -178              -605   \n",
       "42029       Chester County, Pennsylvania              1829              -887   \n",
       "42045      Delaware County, Pennsylvania              1374             -2513   \n",
       "42091    Montgomery County, Pennsylvania              1230             -1987   \n",
       "42101  Philadelphia County, Pennsylvania              8617            -11796   \n",
       "\n",
       "       Net Foreign Mig  \n",
       "fips                    \n",
       "42017              862  \n",
       "42029             1374  \n",
       "42045             1579  \n",
       "42091             2315  \n",
       "42101             8904  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=response.json()\n",
    "df=pd.DataFrame(data[1:], columns=data[0]).\\\n",
    "    rename(columns={\"NATURALINC\": \"Natural Increase\", \"DOMESTICMIG\": \"Net Domestic Mig\", \"INTERNATIONALMIG\":\"Net Foreign Mig\"})\n",
    "df['fips']=df.state+df.county\n",
    "df.set_index('fips',inplace=True)\n",
    "df.drop(columns=['state','county'],inplace=True)\n",
    "df=df.astype(dtype={'Natural Increase':'int64','Net Domestic Mig':'int64','Net Foreign Mig':'int64'},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fips\n",
       "42017      79\n",
       "42029    2316\n",
       "42045     440\n",
       "42091    1558\n",
       "42101    5725\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total population change 2017-2018\n",
    "df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels=df['GEONAME'].str.split(' ',expand=True)[0]\n",
    "ax=df.plot.bar(rot=0, title='Components of Population Change 2017-18')\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_xlabel('')"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}