neighborhood-rates.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare COVID rates per neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# increase the size and quality of any plots we produce\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams[\"figure.dpi\"] = 300\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   modzcta                             modzcta_name  percentpositivity_7day  \\\n0    10001               Chelsea/NoMad/West Chelsea                    2.12   \n1    10002                Chinatown/Lower East Side                    6.34   \n2    10003  East Village/Gramercy/Greenwich Village                    1.63   \n3    10004                       Financial District                    3.04   \n4    10005                       Financial District                    3.75   \n\n   people_tested  people_positive  median_daily_test_rate adequately_tested  \\\n0           1225               26                   822.1               Yes   \n1           3027              192                   663.8               Yes   \n2           3625               59                  1424.7               Yes   \n3            263                8                  1312.2               Yes   \n4            427               16                   879.3               Yes   \n\n                daterange  \n0  December 6-December 12  \n1  December 6-December 12  \n2  December 6-December 12  \n3  December 6-December 12  \n4  December 6-December 12  ",
      "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>modzcta</th>\n      <th>modzcta_name</th>\n      <th>percentpositivity_7day</th>\n      <th>people_tested</th>\n      <th>people_positive</th>\n      <th>median_daily_test_rate</th>\n      <th>adequately_tested</th>\n      <th>daterange</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10001</td>\n      <td>Chelsea/NoMad/West Chelsea</td>\n      <td>2.12</td>\n      <td>1225</td>\n      <td>26</td>\n      <td>822.1</td>\n      <td>Yes</td>\n      <td>December 6-December 12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10002</td>\n      <td>Chinatown/Lower East Side</td>\n      <td>6.34</td>\n      <td>3027</td>\n      <td>192</td>\n      <td>663.8</td>\n      <td>Yes</td>\n      <td>December 6-December 12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10003</td>\n      <td>East Village/Gramercy/Greenwich Village</td>\n      <td>1.63</td>\n      <td>3625</td>\n      <td>59</td>\n      <td>1424.7</td>\n      <td>Yes</td>\n      <td>December 6-December 12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10004</td>\n      <td>Financial District</td>\n      <td>3.04</td>\n      <td>263</td>\n      <td>8</td>\n      <td>1312.2</td>\n      <td>Yes</td>\n      <td>December 6-December 12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10005</td>\n      <td>Financial District</td>\n      <td>3.75</td>\n      <td>427</td>\n      <td>16</td>\n      <td>879.3</td>\n      <td>Yes</td>\n      <td>December 6-December 12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 91
    }
   ],
   "source": [
    "# load data for most recent week\n",
    "all_neighborhoods = pd.read_csv('./neighborhoods-data/covid-rates-nyc-zip-12.csv', sep='\\t')\n",
    "all_neighborhoods.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 top neighborhoods for essential workers \n",
    "# neighborhood 0 is all other \n",
    "essential_dict = {\n",
    "    'Canarsie': 1,\n",
    "    'Flatlands':1,\n",
    "    'Jamaica':2,\n",
    "    'Hollis':2,\n",
    "    'Queens Village':3,\n",
    "    'Cambria Heights':3,\n",
    "    'Washington Heights':4, \n",
    "    'Inwood':4, \n",
    "    'Marble Hill':4,\n",
    "    'Castle Hill':5,\n",
    "    'Clason Point':5, \n",
    "    'Parkchester':5 \n",
    "}\n",
    "\n",
    "#output df\n",
    "output_df = pd.DataFrame(columns = ['neighborhood', 'zipcode', 'positive_rate', 'test_rate', 'month']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_neighborhood_data(month, df, source_data):\n",
    "    #go through rows and add to output df \n",
    "    for index, row in source_data.iterrows():\n",
    "        neighborhood_names = row['modzcta_name'].split('/')\n",
    "        recorded = False\n",
    "\n",
    "        for name in neighborhood_names:\n",
    "            if not recorded:\n",
    "                # find out which neighborhood (0-5) belongs in\n",
    "                code = essential_dict.get(name,0)\n",
    "\n",
    "                # extract positive rate, test rate, month, and zipcode \n",
    "                zipcode = row['modzcta']\n",
    "                pos_rate = row['percentpositivity_7day']\n",
    "                test_rate = row['median_daily_test_rate']\n",
    "\n",
    "                # add new row with info to output df\n",
    "                df = df.append({'neighborhood':code,'zipcode':zipcode,'positive_rate':pos_rate,'test_rate':test_rate,'month':month},ignore_index = True)\n",
    "                recorded = True\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    neighborhood zipcode  positive_rate  test_rate month\n172            0   11691           7.42      496.6    12\n173            0   11692           7.58      504.1    12\n174            0   11693           4.06      639.2    12\n175            0   11694           4.07      789.2    12\n176            0   11697           6.36      854.8    12",
      "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>neighborhood</th>\n      <th>zipcode</th>\n      <th>positive_rate</th>\n      <th>test_rate</th>\n      <th>month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>172</th>\n      <td>0</td>\n      <td>11691</td>\n      <td>7.42</td>\n      <td>496.6</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>0</td>\n      <td>11692</td>\n      <td>7.58</td>\n      <td>504.1</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>0</td>\n      <td>11693</td>\n      <td>4.06</td>\n      <td>639.2</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>0</td>\n      <td>11694</td>\n      <td>4.07</td>\n      <td>789.2</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>0</td>\n      <td>11697</td>\n      <td>6.36</td>\n      <td>854.8</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 94
    }
   ],
   "source": [
    "m = '12'\n",
    "file_name = './neighborhoods-data/covid-rates-nyc-zip-' + m + '.csv'\n",
    "source = pd.read_csv(file_name, sep='\\t')\n",
    "output_df = get_neighborhood_data(m, output_df, source)\n",
    "\n",
    "output_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "              positive_rate   test_rate\nneighborhood                           \n0                  5.143952  641.482036\n1                  5.665000  445.450000\n2                  5.920000  498.000000\n3                  5.500000  553.600000\n4                  5.630000  580.000000\n5                  5.730000  483.250000",
      "text/html": "<div>\n<style>\n    .dataframe thead tr:only-child th {\n        text-align: right;\n    }\n\n    .dataframe thead th {\n        text-align: left;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>positive_rate</th>\n      <th>test_rate</th>\n    </tr>\n    <tr>\n      <th>neighborhood</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.143952</td>\n      <td>641.482036</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.665000</td>\n      <td>445.450000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5.920000</td>\n      <td>498.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5.500000</td>\n      <td>553.600000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.630000</td>\n      <td>580.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.730000</td>\n      <td>483.250000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 95
    }
   ],
   "source": [
    "# group by neighborhood code and month \n",
    "avg_rates = output_df.groupby('neighborhood').mean()\n",
    "\n",
    "avg_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
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     "output_type": "execute_result",
     "data": {
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     "metadata": {},
     "execution_count": 106
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     "output_type": "display_data",
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kTgPrAxOWZN5mZtbzlHkA2yIknRERX17MaGsAV0iqzeeiiLhe0p8kbUpqLpsG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\nxnwAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "#plot differences \n",
    "ax1 = avg_rates.plot.bar(y='positive_rate',rot=0)\n",
    "ax1.set_ylim(5,6)\n",
    "ax1.set_ylabel('Positive Test Rate % ')\n",
    "ax1.set_title('Neighborhoods w/ Essential Workers Have Higher Positive COVID Rates')\n",
    "\n",
    "ax2 = avg_rates.plot.bar(y='test_rate',rot=0)\n",
    "ax2.set_ylim(400,700)\n",
    "ax2.set_ylabel('Tests per 100k People ')\n",
    "ax2.set_title('Neighborhoods w/ Essential Workers Have Lower Testing Rates')\n",
    "\n",
    "#axes = avg_rates.plot.bar( rot=0, subplots=True)\n",
    "#axes[1].legend(loc=2)"
   ]
  },
  {
   "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": "3.6.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}