Food Access dataframe.ipynb

{
 "cells": [
  {
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   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Health food data for group project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>GROCPTH14</th>\n",
       "      <th>FFRPTH14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>0.072209</td>\n",
       "      <td>0.649878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>0.144920</td>\n",
       "      <td>0.659634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>Barbour</td>\n",
       "      <td>0.185963</td>\n",
       "      <td>0.818239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>Bibb</td>\n",
       "      <td>0.222163</td>\n",
       "      <td>0.222163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>Blount</td>\n",
       "      <td>0.103952</td>\n",
       "      <td>0.363832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  State   County  GROCPTH14  FFRPTH14\n",
       "0    AL  Autauga   0.072209  0.649878\n",
       "1    AL  Baldwin   0.144920  0.659634\n",
       "2    AL  Barbour   0.185963  0.818239\n",
       "3    AL     Bibb   0.222163  0.222163\n",
       "4    AL   Blount   0.103952  0.363832"
      ]
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     "execution_count": 3,
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   "source": [
    "food_access = pd.read_csv('/Users/staufferkm/Desktop/gitrepos/vanderbilt-data-essentials-cohort-2/data/grocery_fastfood.csv')\n",
    "food_access.head()"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>County</th>\n",
       "      <th>GROCPTH14</th>\n",
       "      <th>FFRPTH14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>TN</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.172122</td>\n",
       "      <td>0.847368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>TN</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>0.128681</td>\n",
       "      <td>0.514723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>TN</td>\n",
       "      <td>Benton</td>\n",
       "      <td>0.247755</td>\n",
       "      <td>0.495509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>TN</td>\n",
       "      <td>Bledsoe</td>\n",
       "      <td>0.071782</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>TN</td>\n",
       "      <td>Blount</td>\n",
       "      <td>0.126643</td>\n",
       "      <td>0.506574</td>\n",
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      "text/plain": [
       "     State    County  GROCPTH14  FFRPTH14\n",
       "2428    TN  Anderson   0.172122  0.847368\n",
       "2429    TN   Bedford   0.128681  0.514723\n",
       "2430    TN    Benton   0.247755  0.495509\n",
       "2431    TN   Bledsoe   0.071782  0.000000\n",
       "2432    TN    Blount   0.126643  0.506574"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "tn_food_access = food_access.query('State == \"TN\"')\n",
    "tn_food_access.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>GROCPTH14</th>\n",
       "      <th>FFRPTH14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2428</th>\n",
       "      <td>TN</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.172122</td>\n",
       "      <td>0.847368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>TN</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>0.128681</td>\n",
       "      <td>0.514723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>TN</td>\n",
       "      <td>Benton</td>\n",
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       "      <td>0.495509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>TN</td>\n",
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       "      <th>2432</th>\n",
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       "     state    county  GROCPTH14  FFRPTH14\n",
       "2428    TN  Anderson   0.172122  0.847368\n",
       "2429    TN   Bedford   0.128681  0.514723\n",
       "2430    TN    Benton   0.247755  0.495509\n",
       "2431    TN   Bledsoe   0.071782  0.000000\n",
       "2432    TN    Blount   0.126643  0.506574"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "tn_food_access.columns = ['state', 'county', 'GROCPTH14', 'FFRPTH14']\n",
    "tn_food_access.head()"
   ]
  },
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   "execution_count": 6,
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      "\n",
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      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>GROCPTH14</th>\n",
       "      <th>FFRPTH14</th>\n",
       "      <th>fast_grocery_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>TN</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>0.172122</td>\n",
       "      <td>0.847368</td>\n",
       "      <td>4.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>TN</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>0.128681</td>\n",
       "      <td>0.514723</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>TN</td>\n",
       "      <td>Benton</td>\n",
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       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>TN</td>\n",
       "      <td>Bledsoe</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>TN</td>\n",
       "      <td>Blount</td>\n",
       "      <td>0.126643</td>\n",
       "      <td>0.506574</td>\n",
       "      <td>4.000000</td>\n",
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      "text/plain": [
       "     state    county  GROCPTH14  FFRPTH14  fast_grocery_ratio\n",
       "2428    TN  Anderson   0.172122  0.847368            4.923077\n",
       "2429    TN   Bedford   0.128681  0.514723            4.000000\n",
       "2430    TN    Benton   0.247755  0.495509            2.000000\n",
       "2431    TN   Bledsoe   0.071782  0.000000            0.000000\n",
       "2432    TN    Blount   0.126643  0.506574            4.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tn_food_access['fast_grocery_ratio'] = tn_food_access.FFRPTH14 / tn_food_access.GROCPTH14\n",
    "tn_food_access.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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