Food_Bank_Data_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import datetime\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "# Plotly Libraries\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "food_bank_2018 = pd.read_csv(r'C:\\Users\\Jairo\\Desktop\\Stanford_Data\\2021_Stanford_Datathon\\Clean_States_food_bank_data\\States_Food_Bank_Data_2020_2018')\n",
    "food_bank_2017 = pd.read_csv(r'C:\\Users\\Jairo\\Desktop\\Stanford_Data\\2021_Stanford_Datathon\\Clean_States_food_bank_data\\States_Food_Bank_Data_2019_2017')\n",
    "food_bank_2016 = pd.read_csv(r'C:\\Users\\Jairo\\Desktop\\Stanford_Data\\2021_Stanford_Datathon\\Clean_States_food_bank_data\\States_Food_Bank_Data_2018_2016')\n",
    "food_bank_2015 = pd.read_csv(r'C:\\Users\\Jairo\\Desktop\\Stanford_Data\\2021_Stanford_Datathon\\Clean_States_food_bank_data\\States_Food_Bank_Data_2017_2015')\n",
    "food_bank_2014 = pd.read_csv(r'C:\\Users\\Jairo\\Desktop\\Stanford_Data\\2021_Stanford_Datathon\\Clean_States_food_bank_data\\States_Food_Bank_Data_2016_2014')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>State Name</th>\n",
       "      <th>State</th>\n",
       "      <th>2014 Food Insecurity Rate %</th>\n",
       "      <th># of Food Insecure Persons in 2014</th>\n",
       "      <th>% Low Threshold in state</th>\n",
       "      <th>Low Threshold Type</th>\n",
       "      <th>% High Threshold in state</th>\n",
       "      <th>High Threshold Type</th>\n",
       "      <th>% FI ≤ Low Threshold</th>\n",
       "      <th>% FI Btwn Thresholds</th>\n",
       "      <th>% FI &gt; High Threshold</th>\n",
       "      <th>2014 Child Food Insecurity Rate %</th>\n",
       "      <th># of Food Insecure Children in 2014</th>\n",
       "      <th>% food insecure Children in HH w/HH Incomes Below 185 FPL in 2014</th>\n",
       "      <th>% food insecure Children in HH w/HH Incomes Above 185 FPL in 2014</th>\n",
       "      <th>2014 Cost Per Meal</th>\n",
       "      <th>2014 Weighted Annual Food Budget Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>18.8</td>\n",
       "      <td>911440.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>53.3</td>\n",
       "      <td>16.3</td>\n",
       "      <td>30.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>292330.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>4.663670e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>14.4</td>\n",
       "      <td>105770.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>44.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>40.4</td>\n",
       "      <td>20.4</td>\n",
       "      <td>38080.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.33</td>\n",
       "      <td>6.218100e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>17.1</td>\n",
       "      <td>1150650.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>185.0</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>434840.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2.78</td>\n",
       "      <td>5.637670e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>19.1</td>\n",
       "      <td>567250.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>SNAP</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Other Nutrition Program</td>\n",
       "      <td>53.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>30.2</td>\n",
       "      <td>26.3</td>\n",
       "      <td>185660.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.726680e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>13.9</td>\n",
       "      <td>5401770.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>200.0</td>\n",
       "      <td>SNAP, Other Nutrition Programs</td>\n",
       "      <td>78.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>2099120.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.04</td>\n",
       "      <td>2.896128e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  FIPS  State Name State  2014 Food Insecurity Rate %  \\\n",
       "0           0     1     Alabama    AL                         18.8   \n",
       "1           1     2      Alaska    AK                         14.4   \n",
       "2           2     4     Arizona    AZ                         17.1   \n",
       "3           3     5    Arkansas    AR                         19.1   \n",
       "4           4     6  California    CA                         13.9   \n",
       "\n",
       "   # of Food Insecure Persons in 2014   % Low Threshold in state  \\\n",
       "0                             911440.0                     130.0   \n",
       "1                             105770.0                     130.0   \n",
       "2                            1150650.0                     185.0   \n",
       "3                             567250.0                     130.0   \n",
       "4                            5401770.0                     200.0   \n",
       "\n",
       "               Low Threshold Type  % High Threshold in state  \\\n",
       "0                            SNAP                      185.0   \n",
       "1                            SNAP                      185.0   \n",
       "2  SNAP, Other Nutrition Programs                      185.0   \n",
       "3                            SNAP                      185.0   \n",
       "4  SNAP, Other Nutrition Programs                      200.0   \n",
       "\n",
       "              High Threshold Type  % FI ≤ Low Threshold  % FI Btwn Thresholds  \\\n",
       "0         Other Nutrition Program                  53.3                  16.3   \n",
       "1         Other Nutrition Program                  44.5                  15.1   \n",
       "2  SNAP, Other Nutrition Programs                  67.0                   NaN   \n",
       "3         Other Nutrition Program                  53.4                  16.5   \n",
       "4  SNAP, Other Nutrition Programs                  78.6                   NaN   \n",
       "\n",
       "   % FI > High Threshold  2014 Child Food Insecurity Rate %  \\\n",
       "0                   30.4                               26.4   \n",
       "1                   40.4                               20.4   \n",
       "2                   33.0                               26.8   \n",
       "3                   30.2                               26.3   \n",
       "4                   21.4                               22.9   \n",
       "\n",
       "   # of Food Insecure Children in 2014  \\\n",
       "0                             292330.0   \n",
       "1                              38080.0   \n",
       "2                             434840.0   \n",
       "3                             185660.0   \n",
       "4                            2099120.0   \n",
       "\n",
       "   % food insecure Children in HH w/HH Incomes Below 185 FPL in 2014  \\\n",
       "0                                               73.0                   \n",
       "1                                               58.0                   \n",
       "2                                               68.0                   \n",
       "3                                               72.0                   \n",
       "4                                               68.0                   \n",
       "\n",
       "   % food insecure Children in HH w/HH Incomes Above 185 FPL in 2014  \\\n",
       "0                                               27.0                   \n",
       "1                                               42.0                   \n",
       "2                                               32.0                   \n",
       "3                                               28.0                   \n",
       "4                                               32.0                   \n",
       "\n",
       "   2014 Cost Per Meal  2014 Weighted Annual Food Budget Shortfall  \n",
       "0                2.90                                4.663670e+08  \n",
       "1                3.33                                6.218100e+07  \n",
       "2                2.78                                5.637670e+08  \n",
       "3                2.72                                2.726680e+08  \n",
       "4                3.04                                2.896128e+09  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_bank_2014.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ShortFall Graph for the Past 5 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_states_shortfall_2014 = food_bank_2014[['State Name', '2014 Weighted Annual Food Budget Shortfall']]\n",
    "df_states_shortfall_2015 = food_bank_2015['2015 Weighted Annual Food Budget Shortfall']\n",
    "df_states_shortfall_2016 = food_bank_2016['2016 Weighted Annual Food Budget Shortfall']\n",
    "df_states_shortfall_2017 = food_bank_2017['2017 Weighted Annual Food Budget Shortfall']\n",
    "df_states_shortfall_2018 = food_bank_2018['2018 Weighted Annual Food Budget Shortfall']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_states_shortfall = pd.concat([df_states_shortfall_2014,\n",
    "                                 df_states_shortfall_2015,\n",
    "                                df_states_shortfall_2016,\n",
    "                                df_states_shortfall_2017,\n",
    "                                df_states_shortfall_2018], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
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       "      <th>State Name</th>\n",
       "      <th>2014 Weighted Annual Food Budget Shortfall</th>\n",
       "      <th>2015 Weighted Annual Food Budget Shortfall</th>\n",
       "      <th>2016 Weighted Annual Food Budget Shortfall</th>\n",
       "      <th>2017 Weighted Annual Food Budget Shortfall</th>\n",
       "      <th>2018 Weighted Annual Food Budget Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4.663670e+08</td>\n",
       "      <td>4.506700e+08</td>\n",
       "      <td>4.066390e+08</td>\n",
       "      <td>4.041800e+08</td>\n",
       "      <td>4.237450e+08</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>6.218100e+07</td>\n",
       "      <td>6.274300e+07</td>\n",
       "      <td>5.852500e+07</td>\n",
       "      <td>5.772100e+07</td>\n",
       "      <td>5.793000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>5.637670e+08</td>\n",
       "      <td>5.430840e+08</td>\n",
       "      <td>5.062750e+08</td>\n",
       "      <td>4.823580e+08</td>\n",
       "      <td>4.652590e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2.726680e+08</td>\n",
       "      <td>2.677570e+08</td>\n",
       "      <td>2.467760e+08</td>\n",
       "      <td>2.469390e+08</td>\n",
       "      <td>2.443640e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2.896128e+09</td>\n",
       "      <td>2.750484e+09</td>\n",
       "      <td>2.473458e+09</td>\n",
       "      <td>2.381146e+09</td>\n",
       "      <td>2.385968e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   State Name  2014 Weighted Annual Food Budget Shortfall  \\\n",
       "0     Alabama                                4.663670e+08   \n",
       "1      Alaska                                6.218100e+07   \n",
       "2     Arizona                                5.637670e+08   \n",
       "3    Arkansas                                2.726680e+08   \n",
       "4  California                                2.896128e+09   \n",
       "\n",
       "   2015 Weighted Annual Food Budget Shortfall  \\\n",
       "0                                4.506700e+08   \n",
       "1                                6.274300e+07   \n",
       "2                                5.430840e+08   \n",
       "3                                2.677570e+08   \n",
       "4                                2.750484e+09   \n",
       "\n",
       "   2016 Weighted Annual Food Budget Shortfall  \\\n",
       "0                                4.066390e+08   \n",
       "1                                5.852500e+07   \n",
       "2                                5.062750e+08   \n",
       "3                                2.467760e+08   \n",
       "4                                2.473458e+09   \n",
       "\n",
       "   2017 Weighted Annual Food Budget Shortfall  \\\n",
       "0                                4.041800e+08   \n",
       "1                                5.772100e+07   \n",
       "2                                4.823580e+08   \n",
       "3                                2.469390e+08   \n",
       "4                                2.381146e+09   \n",
       "\n",
       "   2018 Weighted Annual Food Budget Shortfall  \n",
       "0                                4.237450e+08  \n",
       "1                                5.793000e+07  \n",
       "2                                4.652590e+08  \n",
       "3                                2.443640e+08  \n",
       "4                                2.385968e+09  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_states_shortfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shortfall Amount in Dollars</th>\n",
       "      <th>Year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.386714e+10</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.317681e+10</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.142669e+10</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.086625e+10</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.045611e+10</td>\n",
       "      <td>2018</td>\n",
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      "text/plain": [
       "   Shortfall Amount in Dollars  Year\n",
       "0                 2.386714e+10  2014\n",
       "1                 2.317681e+10  2015\n",
       "2                 2.142669e+10  2016\n",
       "3                 2.086625e+10  2017\n",
       "4                 2.045611e+10  2018"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "US_shortfall = []\n",
    "US_shortfall.append(df_states_shortfall['2014 Weighted Annual Food Budget Shortfall'].sum(axis=0))\n",
    "US_shortfall.append(df_states_shortfall['2015 Weighted Annual Food Budget Shortfall'].sum(axis=0))\n",
    "US_shortfall.append(df_states_shortfall['2016 Weighted Annual Food Budget Shortfall'].sum(axis=0))\n",
    "US_shortfall.append(df_states_shortfall['2017 Weighted Annual Food Budget Shortfall'].sum(axis=0))\n",
    "US_shortfall.append(df_states_shortfall['2018 Weighted Annual Food Budget Shortfall'].sum(axis=0))\n",
    "\n",
    "US_ShortFall = pd.DataFrame(data={'Shortfall Amount in Dollars': US_shortfall,\n",
    "                                 'Year':['2014','2015','2016','2017','2018']\n",
    "                                 })\n",
    "\n",
    "US_ShortFall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
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\"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"United States Weighted Annual Food Budget Shortfall\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Year\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Shortfall Amount in Dollars\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('b915f1c3-dbec-427a-ad71-126473ea855e');\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(US_ShortFall,x='Year',y='Shortfall Amount in Dollars',title=\"United States Weighted Annual Food Budget Shortfall\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite the shortfall amount yearly seems to decreacre thi spast 5 years.This is still billions of dollars that Food Banks needed to have meals for people in the United States. To put into purpective, that's X amount of adults not having a meal every day and Y aount of children not geetting a meal.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meals For the Past 5 years not distributed due to Shortfall "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9988000000000006"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_meal = []\n",
    "avg_meal.append(food_bank_2014['2014 Cost Per Meal'].sum(axis=0) / 50)\n",
    "avg_meal.append(food_bank_2015['2015 Cost Per Meal'].sum(axis=0) / 50)\n",
    "avg_meal.append(food_bank_2016['2016 Cost Per Meal'].sum(axis=0) / 50)\n",
    "avg_meal.append(food_bank_2017['2017 Cost Per Meal'].sum(axis=0) / 50)\n",
    "avg_meal.append(food_bank_2018['2018 Cost Per Meal'].sum(axis=0) / 50)\n",
    "\n",
    "US_avg_meal_price = pd.DataFrame(data={'Avg. Meal Price': avg_meal,\n",
    "                                 'Year':['2014','2015','2016','2017','2018']\n",
    "                                 })\n",
    "\n",
    "US_avg_meal_price.loc[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "meals_per_year_missing = US_ShortFall['Shortfall Amount in Dollars'].sum(axis=0)/(US_avg_meal_price['Avg. Meal Price'].sum(axis=0) / 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36026052959.70599"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meals_per_year_missing.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is 360_944_575_541 Meals For the Past 5 years not distributed due to Shortfall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Amount of Children with food insecurity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>USA Total Amount of Food Insecure Children</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16192020.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14784730.0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13780920.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13411620.0</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12377330.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   USA Total Amount of Food Insecure Children  Year\n",
       "0                                  16192020.0  2014\n",
       "1                                  14784730.0  2015\n",
       "2                                  13780920.0  2016\n",
       "3                                  13411620.0  2017\n",
       "4                                  12377330.0  2018"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children = []\n",
    "children.append(food_bank_2014['# of Food Insecure Children in 2014'].sum(axis=0))\n",
    "children.append(food_bank_2015['# of Food Insecure Children in 2015'].sum(axis=0) )\n",
    "children.append(food_bank_2016['# of Food Insecure Children in 2016'].sum(axis=0))\n",
    "children.append(food_bank_2017['# of Food Insecure Children in 2017'].sum(axis=0) )\n",
    "children.append(food_bank_2018['# of Food Insecure Children in 2018'].sum(axis=0))\n",
    "\n",
    "children_food_insecure = pd.DataFrame(data={'USA Total Amount of Food Insecure Children': children,\n",
    "                                 'Year':['2014','2015','2016','2017','2018']\n",
    "                                 })\n",
    "\n",
    "children_food_insecure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Meals Per Year Not Distributed Due to SHortfall in the past 5 years) / (USA Total Amount of Foof Insecure Children in th ePast 5 years) = Meals Not Given Per Children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "510.67014918228534"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meals_per_year_missing.sum(axis=0)/ children_food_insecure['USA Total Amount of Food Insecure Children'].sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70546620.0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children_food_insecure['USA Total Amount of Food Insecure Children'].sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adults with Food Insecurity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>USA Total Amount of Food Insecure Adults</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47539790.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44584020.0</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42467360.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41133950.0</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39922630.0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   USA Total Amount of Food Insecure Adults  Year\n",
       "0                                47539790.0  2014\n",
       "1                                44584020.0  2015\n",
       "2                                42467360.0  2016\n",
       "3                                41133950.0  2017\n",
       "4                                39922630.0  2018"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults = []\n",
    "adults.append(food_bank_2014['# of Food Insecure Persons in 2014 '].sum(axis=0) )\n",
    "adults.append(food_bank_2015['# of Food Insecure Persons in 2015 '].sum(axis=0) )\n",
    "adults.append(food_bank_2016['# of Food Insecure Persons in 2016 '].sum(axis=0))\n",
    "adults.append(food_bank_2017['# of Food Insecure Persons in 2017'].sum(axis=0) )\n",
    "adults.append(food_bank_2018['# of Food Insecure Persons in 2018'].sum(axis=0))\n",
    "\n",
    "adults_food_insecure = pd.DataFrame(data={'USA Total Amount of Food Insecure Adults': adults,\n",
    "                                 'Year':['2014','2015','2016','2017','2018']\n",
    "                                 })\n",
    "\n",
    "adults_food_insecure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167.05972104835777"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meals_per_year_missing.sum(axis=0)/ adults_food_insecure['USA Total Amount of Food Insecure Adults'].sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amount of Meals not given to both adults and children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125.87967037823279"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meals_per_year_missing.sum(axis=0)/ (adults_food_insecure['USA Total Amount of Food Insecure Adults'].sum(axis=0) +\n",
    "                                     children_food_insecure['USA Total Amount of Food Insecure Children'].sum(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percentage of Adult Food Insecurity per state per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>State Name</th>\n",
       "      <th>2014 Food Insecurity Rate %</th>\n",
       "      <th>2015 Food Insecurity Rate %</th>\n",
       "      <th>2016 Food Insecurity Rate %</th>\n",
       "      <th>2017 Food Insecurity Rate %</th>\n",
       "      <th>2018 Food Insecurity Rate %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>18.8</td>\n",
       "      <td>17.7</td>\n",
       "      <td>16.5</td>\n",
       "      <td>16.3</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>14.4</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13.4</td>\n",
       "      <td>13.6</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>17.1</td>\n",
       "      <td>15.8</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>19.1</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>13.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>11.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State Name  2014 Food Insecurity Rate %  2015 Food Insecurity Rate %  \\\n",
       "0     Alabama                         18.8                         17.7   \n",
       "1      Alaska                         14.4                         13.9   \n",
       "2     Arizona                         17.1                         15.8   \n",
       "3    Arkansas                         19.1                         18.4   \n",
       "4  California                         13.9                         12.5   \n",
       "\n",
       "   2016 Food Insecurity Rate %  2017 Food Insecurity Rate %  \\\n",
       "0                         16.5                         16.3   \n",
       "1                         13.4                         13.6   \n",
       "2                         14.9                         14.0   \n",
       "3                         17.2                         17.3   \n",
       "4                         11.7                         11.0   \n",
       "\n",
       "   2018 Food Insecurity Rate %  \n",
       "0                         17.0  \n",
       "1                         12.9  \n",
       "2                         13.1  \n",
       "3                         17.3  \n",
       "4                         10.8  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_states_food_insecurity_2014 = food_bank_2014[['State Name', '2014 Food Insecurity Rate %']]\n",
    "df_states_food_insecurity_2015 = food_bank_2015['2015 Food Insecurity Rate %']\n",
    "df_states_food_insecurity_2016 = food_bank_2016['2016 Food Insecurity Rate %']\n",
    "df_states_food_insecurity_2017 = food_bank_2017['2017 Food Insecurity Rate %']\n",
    "df_states_food_insecurity_2018 = food_bank_2018['2018 Food Insecurity Rate %']\n",
    "\n",
    "df_states_adult_food_insecurity = pd.concat([df_states_food_insecurity_2014,\n",
    "                                 df_states_food_insecurity_2015,\n",
    "                                df_states_food_insecurity_2016,\n",
    "                                df_states_food_insecurity_2017,\n",
    "                                df_states_food_insecurity_2018], axis = 1)\n",
    "df_states_adult_food_insecurity.head()"
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         "type": "category"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          5.938888888888888,
          23.16111111111111
         ],
         "type": "linear"
        }
       }
      },
      "image/png": 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    "fig = go.Figure()\n",
    "fig.add_trace(go.Box(y=df_states_adult_food_insecurity['2014 Food Insecurity Rate %'], name='2014',\n",
    "                marker_color = 'indianred'))\n",
    "fig.add_trace(go.Box(y=df_states_adult_food_insecurity['2015 Food Insecurity Rate %'], name = '2015',\n",
    "                marker_color = 'lightseagreen'))\n",
    "fig.add_trace(go.Box(y=df_states_adult_food_insecurity['2016 Food Insecurity Rate %'], name='2016',\n",
    "                marker_color = 'blue'))\n",
    "fig.add_trace(go.Box(y=df_states_adult_food_insecurity['2017 Food Insecurity Rate %'], name = '2017',\n",
    "                marker_color = 'purple'))\n",
    "fig.add_trace(go.Box(y=df_states_adult_food_insecurity['2018 Food Insecurity Rate %'], name='2018',\n",
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    "\n",
    "fig.update_layout(title_text=\"Food Insecurity Rate %\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percentage of Children in Food Insecurity in all states each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>2014 Child Food Insecurity Rate %</th>\n",
       "      <th>2015 Child Food Insecurity Rate %</th>\n",
       "      <th>2016 Child Food Insecurity Rate %</th>\n",
       "      <th>2017 Child Food Insecurity Rate %</th>\n",
       "      <th>2018 Child Food Insecurity Rate %</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>26.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.3</td>\n",
       "      <td>23.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>20.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18.6</td>\n",
       "      <td>18.7</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>26.8</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>26.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>22.9</td>\n",
       "      <td>20.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   State Name  2014 Child Food Insecurity Rate %  \\\n",
       "0     Alabama                               26.4   \n",
       "1      Alaska                               20.4   \n",
       "2     Arizona                               26.8   \n",
       "3    Arkansas                               26.3   \n",
       "4  California                               22.9   \n",
       "\n",
       "   2015 Child Food Insecurity Rate %  2016 Child Food Insecurity Rate %  \\\n",
       "0                               24.1                               22.5   \n",
       "1                               19.6                               18.6   \n",
       "2                               24.0                               22.7   \n",
       "3                               25.0                               23.2   \n",
       "4                               20.7                               19.0   \n",
       "\n",
       "   2017 Child Food Insecurity Rate %  2018 Child Food Insecurity Rate %  \n",
       "0                               22.3                               23.1  \n",
       "1                               18.7                               18.1  \n",
       "2                               21.3                               19.0  \n",
       "3                               23.6                               23.1  \n",
       "4                               18.1                               15.2  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
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   ],
   "source": [
    "df_states_food_insecurity_2014 = food_bank_2014[['State Name', '2014 Child Food Insecurity Rate %']]\n",
    "df_states_food_insecurity_2015 = food_bank_2015['2015 Child Food Insecurity Rate %']\n",
    "df_states_food_insecurity_2016 = food_bank_2016['2016 Child Food Insecurity Rate %']\n",
    "df_states_food_insecurity_2017 = food_bank_2017['2017 Child Food Insecurity Rate %']\n",
    "df_states_food_insecurity_2018 = food_bank_2018['2018 Child Food Insecurity Rate %']\n",
    "\n",
    "df_states_children_food_insecurity = pd.concat([df_states_food_insecurity_2014,\n",
    "                                 df_states_food_insecurity_2015,\n",
    "                                df_states_food_insecurity_2016,\n",
    "                                df_states_food_insecurity_2017,\n",
    "                                df_states_food_insecurity_2018], axis = 1)\n",
    "df_states_children_food_insecurity.head()"
   ]
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jpiiMueDCI+rLgpiaseFcXf1D+AjRHiKXhI9MwidcwgfRbWzKs7O7bDpre65kHz5t5xxpO2eI9hC5JHxkEj7hEj6IbkGf7MOn7ZwjbW+j0x4il4SPTMInXMIH0S3ok3342NjnHCF88pTwCZfwQXQL+hA+FnV7fJrOGaI9RC4JH5mET7iED6Jb0IfwqeDzKZL264Aee/yJJH341q+ZQ1NTZuOqPerL0kV/ffVGs+PAstl89Ifqy9I1L7n0mCkKYy659Jj6siCmZmwefdSYyy4LM1cInxM0ffx2FfvQ1yM/OpKkh79y0ByamjLfWLpKfVm66AtPhM/93/uB+rJ0zYsuPmqKwpiLLj6qviyIqRmbRx8N+/DQM8+MviidgfAx/tFjzMnho73b1CWHumRyqCtcDnUhuo1NGT6nnGLMuee2e845hE/24dN2zpG2c4ZoD5FLwkcm4RMu4YPoNjZl+Jx2mt/hrV275OHT9jEwxjSfsqKk6c1D9qkxqmd7l5J1+Picc6TtnCHaQ+SS8JFJ+IRL+CC6jY1G+LR9DEzbSUqr5+qqC5/VtYPOE5vGIOvwiYH2ELkkfGQSPuESPjgIv3roXvPKV58XTa3bERuN8LGxPwamLYSqX3ednT3m2dhtCB8h2hsDl4SPTMInXMIHB+HuK/aYoiii+eBhndmOTQrhUw2dcg9Q9dCU65CWK3zKIy+uM79LIXyEaG8MXBI+MgmfcAkfHJTXfuT6Rs96/gtMURTmbXO7Wi+rdRtiox0+dryUh6ns8KmLl7rwqTt01vZa3H4hfIRobwhcEj4yCZ9wCR/U8pWvPs8URaEaNm3GRjN86t4RLd3jU/fze5dXou71IXyEaA+RS8JHJuETLuGDWhI+wwufpo+Bkb7Gp+7F0IRPQsQciHvf94Fo3jW/aA5NTZk7/utbol7v331jU33DMQwJn3AJH9SS8Bneu7qaDj21vaurejmfPUETk7POt8SHQPgIiTkQd5+IlZQlfLBNwge1JHwGHz4+HwNjTPPn+FTfzl5qh1H1o2Tq9hZJIHyExByIMnzueteCWV+6Oim//ntvIHzQS8LHbe+Ff2reffoVUfzEGw6o357UzDl8Tj2VT272hfAREnMgyvC55/0fNPdd97GkvP33f5/wQS8JH7fX/NIHzHwxH8WPvC7dJ3ctcw4fztXlD+EjJOZAED7pSPiES/g0u/nNR5ze+zcPmfli3iw9c0/j5Ta/+Yj67UjRXMOHs7P3B+EjJOZAED7pSPiES/iE+8D6d4+Hz7P2qC9LF80xfKB/CB8hMQeC8ElHwidcwidcwkcm4QM+ED5CYg4E4ZOOhE+4hE+4hI9Mwgd8IHyExBwIwicdCZ9wCZ9wCR+ZhA/4QPgIiTkQhE86Ej7hEj7hEj4ycwyfxx993KzNr5kvXPaFWtfm15zmCuEjJOZAED7pSPiES/iES/jIzDV8Qj4O4cozr4y+LF2B8BEScyAIn3QkfMIlfMIlfGTmHD4LpyyYa8+9ttUPnfMhwkd7AbpOzIEgfNKR8AmX8AmX8JGZc/gsnrboPNxV9XO7PicOn7mF3rbTTdSdR6vplBUldWdtd50SY2x8Onh5bQgfITEHgvBJR8InXMInXMJHJuEznPCpxkoZKk1nY68723oZM00nOy1Z6u2Ler4uwkdIzIEgfNKR8AmX8AmX8JFJ+AwnfGwmJmfN3uWVrf+3hVD16z7hMzY+bdY3NqMsqzGEj5iYA0H4pCPhEy7hEy7hI5Pw0QmfauiUe4CqoVJ3SMsYv/CJvbfHGMJHTMyBIHzSkfAJl/AJl/CRSfgMP3zseFldO1gbPhOTs60/W0fsvT3GED5iYg4E4ZOOhE+4hE+4hI9Mwme44TO30DspaGLu8XEdIpNC+AiJORCETzoSPuESPuESPjIJn+GFT130lMR4jU9dQMWC8BEScyAIn3QkfMIlfMIlfGQSPsN7V1c/e2rsEKpeznU9g9rbYwzhIybmQBA+6Uj4hEv4hEv4yCR8Bh8+rs/Zsff+NH2OT/Xt7KV1b3cfxN4eYwgfMTEHgvBJR8InXMInXMJHZs7hs/vU3XxysyeEj5CYA0H4pCPhEy7hEy7hIzPn8OFcXf4QPkJiDgThE+7/+/vHolqGz9cPfy/q9Wqvp2FI+IRL+MjMNXyazsDedNb2XCF8hMQcCMIn3L+47wGz48By0l582yH19TQMCZ9wCR+ZOYYP9A/hIyTmQBA+4VbD5+cTlPBBHwkfmYQP+ED4CIk5EIRPuGX4TNxys9l9++1J+dovfZ7wQS8JH5mED/hA+AiJORCET7iETzoSPuESPjIJH/CB8BEScyAIn3AJn3QkfMIlfGTmGD6PPvqoueyyy4LMFcJHSMyBIHzCJXzSkfAJl/CRmWv4FEXRt2eeeWb0ZekKhI+QmANB+IRL+KQj4RMu4SMz5/A55ZRTzLnnntvqOeecQ/hoL0DXiTkQhE+4hE86Ej7hEj4ycw6f0047zevw1q5du8ThM7fQ23a6CfuUFMY0n7KixHXWdmPMtutvOi9YCISPkJgDQfiES/ikI+ETLuEjk/AZTvhUQ6Q8d1fT2dhd5+JyRc3E5Oy2WLL/L4XwERJzIAifcAmfdCR8wiV8ZBI+wwkfm4nJWbN3eWXr/20hVP16Xfj4/nwohI+QmANB+IRL+KQj4RMu4SOT8NEJn2qolHuAqmdWdx3ScoXPUm+fGRuf3oqp2GdqJ3yExBwIwidcwicdCZ9wCR+ZhM/ww8eOl9W1g7XhMzE52/qz9nWUrxPiNT6JEXMgCJ9wCZ90JHzCJXxkEj7DDZ+5hd5JQRNjj4/98ztnFqPGD+EjJOZAED7hEj7pSPiES/jIJHyGFz510VMieY1PubenimuPUSiEj5CYA0H4hEv4pCPhEy7hI5PwGd67upr2wLS9q6t6OdceH/tdXby4OSFiDgThEy7hk46ET7iEj0zCZ/DhUx7KsrX3yDR9jk/17eyl1TCyf0fM6DGG8BETcyAIn3AJn3QkfMIlfGTmHD6nnnoqn9zsCeEjJOZAED7hEj7pSPiES/jIzDl8OFeXP4SPkJgDQfiES/ikI+ETLuEjM9fw8TnEVWeuED5CYg4E4RMu4ZOOhE+4hI/MHMMH+ofwERJzIAifcAmfdCR8wiV8ZBI+4APhIyTmQJThc/ub3mzueOvbhufb3t7qoelpwofw8ZLwCZfwkUn4gA+Ej5CYA1GGT8oSPoRPm4RPuISPTMIHfCB8hMQciDJ87r78CnPP+z+YlLe/4b8QPoSPl4RPuISPTMIHfCB8hMQcCF7jEy7hk46ET7iEj0zCB3wgfITEHAjCJ1zCJx0Jn3AJH5mED/hA+AiJORCET7iETzoSPuESPjIJH/CB8BEScyAIn3AJH5nnnX80mr/4nCdNURjzi895Mur1aq+jYUj4yCR8wAfCR0jMgSB8wiV8ZJ5+ujFFka6nnx531lKV8JFJ+IAPhI+QmANB+IRL+Mgsw+e884+a1/z7dDzv/KOED3pL+IAP2YfP3ELPjI1Pb7nU23fSZSYmZ53fjzkQhE+4hI/MMnze8vYnzAUXHknGt7z9CcIHvSV8wIfsw2fnzOLWv9c3Ns3Y+LRZXTu49bW5hZ6ZW+ht/d/+fsyBIHzCJXxkEj5pSPjIJHzAh+zDx2ZictbsXV7Z+n9bCMUcCMInXMJHJuGThoSPTMIHfCB8LKqhU+4BWt/Y3Pr+Um/ftr1E3/n+49FcXzgePvde80Fz/0c+npRl+Hzrvm9Gvc2x3Lvxf82OA8vmFbesmMvvuCMp/9OXvmB2HFg2lx46pL6eXJbh89bZJ8w7LkrHt87+JHy015HLT8/cZJan90fxo6/9hJkv5s3lT3t3tOtcnt5v/vpP033sxfRVv/GbpigK8+GPXq++LC5BH8KnwtxCb1vUrK4drA2ficnZrf8fPfZkNDcuf7c5NDVlHvzj/2m+df31SXnnH/yBOTQ1ZX50+NtRb3MsP/Wtb5odB5bNq9ZWzdXfuDspp77yRbPjwLK5fP0O9fXk8owzjofPrl1Pmne+Kx137Tr+1vgzzog7azFduWDFzBfzSXvHdXeqr6dheP75rzFFUZj9N9ygviwuQR/C5wRzC71tQWOM3x6fmLtAOdQVLoe6ZHKoK9xPvvkmM1/Mm/f/mw+YD73qfw/E//WqDwd51bOvNvPFvPnin9ymvp6GIYe6wAfCx9RHTwmv8SF8CB/Cp8kyfP785deaAxf+ZVJeM/ZBwicxQZ/sw2fnzOK2PTg2vKuL8CF8CJ8mCZ90JHzAh6zDpzyUZWvv/eFzfAgfwofwcUn4pCPhAz5kHT4xiDkQhE+4hI9MwidcwicdCR/wgfAREnMgCJ9wCR+ZhE+4hE86Ej7gA+EjJOZAED7hEj4yCZ9wCZ90JHzAB8JHSMyBIHzCJXxkEj7hEj7pSPiAD4SPkJgDQfiES/jIJHzCJXzSkfABHwgfITEHgvAJl/CRSfiES/ikI+EDPhA+QmIOBOETbhk+v7Zyg3ntlz6flC/7q88SPoQP4VPjbXc9ENWXjk+YoijMe//Hn0S93pi3GfQhfITEHAjCJ9wyfFKW8CF8CJ/t/vXf3GmKokjal5z9sqi3GfQhfITEHAjCJ9wyfM767H5z/hf+KilfvHoj4UP4ED41luHzlKc8xZxxxj9Lyqc97Z8SPiMK4SMk5kAQPuHyGh+ZhE+4hE+4Zfg8/RnPMBdceElSTr5uivAZUQgfITEHgvAJl/CRSfiES/iES/iABoSPkJgDQfiES/jIJHzCJXzCJXxAA8JHSMyBIHzCJXxkEj7hEj7hEj6gAeEjJOZAED7hEj4yCZ9wCZ9wCR/QgPAREnMgCJ9wCR+ZhE+4hE+4hA9oQPgIiTkQhE+4hI9Mwidcwidcwgc0IHyExByIMnzuml8063vem5S3/94bCB/Ch/CpMeXwef/YBwgfwgcsCB8hMQeiDJ+UJXwIH8Jnu4RPuIQPaED4CIk5EPfseW80737XvDk0NWVuf/N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   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Box(y=df_states_children_food_insecurity['2014 Child Food Insecurity Rate %'], name='2014',\n",
    "                marker_color = 'indianred'))\n",
    "fig.add_trace(go.Box(y=df_states_children_food_insecurity['2015 Child Food Insecurity Rate %'], name = '2015',\n",
    "                marker_color = 'lightseagreen'))\n",
    "fig.add_trace(go.Box(y=df_states_children_food_insecurity['2016 Child Food Insecurity Rate %'], name='2016',\n",
    "                marker_color = 'blue'))\n",
    "fig.add_trace(go.Box(y=df_states_children_food_insecurity['2017 Child Food Insecurity Rate %'], name = '2017',\n",
    "                marker_color = 'purple'))\n",
    "fig.add_trace(go.Box(y=df_states_children_food_insecurity['2018 Child Food Insecurity Rate %'], name='2018',\n",
    "                marker_color = 'black'))\n",
    "\n",
    "fig.update_layout(title_text=\"Child Food Insecurity Rate % Per Year In Each State\")\n",
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