data_analysis_acs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis ACS PUMS Income dataset\n",
    "This notebook provides all code for the data analysis images from \"Bridging Fairness and Privacy: An Experimental Analysis of Bias Assessment in Federated Learning\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as gp\n",
    "import matplotlib.pyplot as plt\n",
    "current_path = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1737629"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folderpath = os.path.join(current_path, 'acs_data')\n",
    "file_list = os.listdir(folderpath)\n",
    "file_list.remove('ACS-2022-complete.csv')\n",
    "list_state_df = [pd.read_csv(os.path.join(folderpath, file)) for file in file_list]\n",
    "df_all_states = pd.concat(list_state_df, ignore_index=True)\n",
    "df_all_states = df_all_states.drop('State',axis=1)\n",
    "df_all_states.head()\n",
    "len(df_all_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "1    904275\n",
       "2    833354\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_states['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.47631629076172\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "1    904275\n",
       "2    833354\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(1589519 / len(df_all_states) * 100)\n",
    "df_all_states['Sex'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total population sizes per state\n",
    "entire_population = {\n",
    " 'AL': 5039877,\n",
    " 'AK': 732441,\n",
    " 'AZ': 7233988,\n",
    " 'AR': 3011524,\n",
    " 'CA': 39237836,\n",
    " 'CO': 5826185,\n",
    " 'CT': 3596298,\n",
    " 'DE': 1004523,\n",
    " 'FL': 22229952,\n",
    " 'GA': 10725135,\n",
    " 'HI': 1411151,\n",
    " 'ID': 1900923,\n",
    " 'IL': 12538344,\n",
    " 'IN': 6805985,\n",
    " 'IA': 3193079,\n",
    " 'KS': 2910341,\n",
    " 'KY': 4509394,\n",
    " 'LA': 4622982,\n",
    " 'ME': 1354522,\n",
    " 'MD': 6185278,\n",
    " 'MA': 6911743,\n",
    " 'MI': 9989642,\n",
    " 'MN': 5726298,\n",
    " 'MS': 2951996,\n",
    " 'MO': 6147027,\n",
    " 'MT': 1100185,\n",
    " 'NE': 1962071,\n",
    " 'NV': 3241676,\n",
    " 'NH': 1377088,\n",
    " 'NJ': 9288994,\n",
    " 'NM': 2097022,\n",
    " 'NY': 19377858,\n",
    " 'NC': 10600823,\n",
    " 'ND': 800394,\n",
    " 'OH': 11642640,\n",
    " 'OK': 4007177,\n",
    " 'OR': 4241507,\n",
    " 'PA': 12964056,\n",
    " 'RI': 1071140,\n",
    " 'SC': 5347796,\n",
    " 'SD': 901165,\n",
    " 'TN': 7056933,\n",
    " 'TX': 30077954,\n",
    " 'UT': 3350582,\n",
    " 'VT': 647156,\n",
    " 'VA': 8711177,\n",
    " 'WA': 7705281,\n",
    " 'WV': 1780573,\n",
    " 'WI': 5895908,\n",
    " 'WY': 576850,\n",
    " 'PR': 3193694\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Size</th>\n",
       "      <th>Population</th>\n",
       "      <th>Size Percentage</th>\n",
       "      <th>#Males</th>\n",
       "      <th>#Females</th>\n",
       "      <th>#Whites</th>\n",
       "      <th>#Non_whites</th>\n",
       "      <th>Mean Age</th>\n",
       "      <th>#&gt;50k</th>\n",
       "      <th>#&lt;50k</th>\n",
       "      <th>%&gt;50k</th>\n",
       "      <th>female_&gt;50k</th>\n",
       "      <th>male_&gt;50k</th>\n",
       "      <th>white_&gt;50k</th>\n",
       "      <th>non_white_&gt;50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GA</td>\n",
       "      <td>55896</td>\n",
       "      <td>10725135</td>\n",
       "      <td>0.521168</td>\n",
       "      <td>29051</td>\n",
       "      <td>26845</td>\n",
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       "      <td>42.94</td>\n",
       "      <td>23644</td>\n",
       "      <td>32252</td>\n",
       "      <td>42.299986</td>\n",
       "      <td>9610</td>\n",
       "      <td>14034</td>\n",
       "      <td>15975</td>\n",
       "      <td>7669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HI</td>\n",
       "      <td>7880</td>\n",
       "      <td>1411151</td>\n",
       "      <td>0.558409</td>\n",
       "      <td>4139</td>\n",
       "      <td>3741</td>\n",
       "      <td>1996</td>\n",
       "      <td>5884</td>\n",
       "      <td>43.94</td>\n",
       "      <td>3470</td>\n",
       "      <td>4410</td>\n",
       "      <td>44.035533</td>\n",
       "      <td>1432</td>\n",
       "      <td>2038</td>\n",
       "      <td>967</td>\n",
       "      <td>2503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CT</td>\n",
       "      <td>20487</td>\n",
       "      <td>3596298</td>\n",
       "      <td>0.569669</td>\n",
       "      <td>10487</td>\n",
       "      <td>10000</td>\n",
       "      <td>14777</td>\n",
       "      <td>5710</td>\n",
       "      <td>44.17</td>\n",
       "      <td>10797</td>\n",
       "      <td>9690</td>\n",
       "      <td>52.701713</td>\n",
       "      <td>4642</td>\n",
       "      <td>6155</td>\n",
       "      <td>8544</td>\n",
       "      <td>2253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NJ</td>\n",
       "      <td>50354</td>\n",
       "      <td>9288994</td>\n",
       "      <td>0.542082</td>\n",
       "      <td>26078</td>\n",
       "      <td>24276</td>\n",
       "      <td>30964</td>\n",
       "      <td>19390</td>\n",
       "      <td>44.40</td>\n",
       "      <td>27135</td>\n",
       "      <td>23219</td>\n",
       "      <td>53.888470</td>\n",
       "      <td>11550</td>\n",
       "      <td>15585</td>\n",
       "      <td>18192</td>\n",
       "      <td>8943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LA</td>\n",
       "      <td>21008</td>\n",
       "      <td>4622982</td>\n",
       "      <td>0.454425</td>\n",
       "      <td>10619</td>\n",
       "      <td>10389</td>\n",
       "      <td>13831</td>\n",
       "      <td>7177</td>\n",
       "      <td>43.64</td>\n",
       "      <td>8048</td>\n",
       "      <td>12960</td>\n",
       "      <td>38.309216</td>\n",
       "      <td>2940</td>\n",
       "      <td>5108</td>\n",
       "      <td>6121</td>\n",
       "      <td>1927</td>\n",
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       "  State   Size  Population  Size Percentage  #Males  #Females  #Whites  \\\n",
       "0    GA  55896    10725135         0.521168   29051     26845    33710   \n",
       "1    HI   7880     1411151         0.558409    4139      3741     1996   \n",
       "2    CT  20487     3596298         0.569669   10487     10000    14777   \n",
       "3    NJ  50354     9288994         0.542082   26078     24276    30964   \n",
       "4    LA  21008     4622982         0.454425   10619     10389    13831   \n",
       "\n",
       "   #Non_whites  Mean Age  #>50k  #<50k      %>50k  female_>50k  male_>50k  \\\n",
       "0        22186     42.94  23644  32252  42.299986         9610      14034   \n",
       "1         5884     43.94   3470   4410  44.035533         1432       2038   \n",
       "2         5710     44.17  10797   9690  52.701713         4642       6155   \n",
       "3        19390     44.40  27135  23219  53.888470        11550      15585   \n",
       "4         7177     43.64   8048  12960  38.309216         2940       5108   \n",
       "\n",
       "   white_>50k  non_white_>50k  \n",
       "0       15975            7669  \n",
       "1         967            2503  \n",
       "2        8544            2253  \n",
       "3       18192            8943  \n",
       "4        6121            1927  "
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# create dataframe per state\n",
    "df_per_state = pd.DataFrame(columns=['State', 'Size', 'Population', 'Size Percentage', '#Males', '#Females', '#Whites', '#Non_whites', 'Mean Age',  '#>50k', '#<50k', '%>50k', 'female_>50k', 'male_>50k', 'white_>50k', 'non_white_>50k'])\n",
    "\n",
    "# loop through all states\n",
    "for df in list_state_df:\n",
    "    state = df.loc[0, 'State']\n",
    "    \n",
    "    # obtain number of total individuals, males, females, whites, non-whites\n",
    "    size = len(df)\n",
    "    population = entire_population[state]\n",
    "    size_percentage = (size / population) * 100\n",
    "    males = df['Sex'].value_counts()[1]\n",
    "    females = df['Sex'].value_counts()[2]\n",
    "    whites = df['Race'].value_counts()[1]\n",
    "    non_whites = size - whites\n",
    "\n",
    "    assert size == males + females, \"Sizes don't add up\"\n",
    "\n",
    "    # calculate percentage that earns above/below 50k\n",
    "    below_50k = df.groupby('Income').size()['<=50K']\n",
    "    above_50k = df.groupby('Income').size()['>50K']\n",
    "    percentage = (above_50k / (above_50k + below_50k)) * 100\n",
    "\n",
    "    # count females and males with income above/below 50k\n",
    "    count_sex = df[['Sex', 'Income']].value_counts(ascending=True).reset_index(name='count')\n",
    "    female_above_50k = count_sex.loc[(count_sex['Sex'] == 2 ) & (count_sex['Income'] == '>50K')]['count'].sum()\n",
    "    male_above_50k = count_sex.loc[(count_sex['Sex'] == 1 ) & (count_sex['Income'] == '>50K')]['count'].sum()\n",
    "    \n",
    "    # count non-whites and whites with income above/below 50k\n",
    "    count_race = df[['Race', 'Income']].value_counts(ascending=True).reset_index(name='count')\n",
    "    non_white_above_50k = count_race.loc[(count_race['Race'].isin([2, 3, 4, 5, 6, 7, 8, 9])) & (count_race['Income'] == '>50K')]['count'].sum()\n",
    "    white_above_50k = count_race.loc[(count_race['Race'] == 1) & (count_race['Income'] == '>50K')]['count'].sum()\n",
    "    \n",
    "    # calculate mean age per state\n",
    "    mean_age = df.loc[:, 'Age'].mean().round(2)\n",
    "\n",
    "    # create dataframe with all relevant variables\n",
    "    new_row = {'State': state, 'Size': size, 'Population': population, 'Size Percentage':size_percentage, '#Males':males, '#Females':females, '#Whites':whites, '#Non_whites':non_whites, 'Mean Age':mean_age, '#>50k': above_50k, '#<50k': below_50k, 'female_>50k':female_above_50k, 'male_>50k':male_above_50k, 'white_>50k': white_above_50k, 'non_white_>50k': non_white_above_50k,'%>50k':percentage}\n",
    "    df_per_state.loc[len(df_per_state)] = new_row\n",
    "\n",
    "df_per_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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    "# code for total size per state\n",
    "fig = gp.Figure(data=gp.Choropleth(\n",
    "    locations=df_per_state['State'], # Spatial coordinates\n",
    "    z = df_per_state['Size'].astype(float), # Data to be color-coded\n",
    "    locationmode = 'USA-states', # set of locations match entries in `locations`\n",
    "    colorscale = 'mint',\n",
    "    colorbar_title = \"<b>State dataset size</b>\",\n",
    "    marker_line_color='white', # line markers between states\n",
    "    zmax = 200000,\n",
    "    zmin = 0.0\n",
    " \n",
    "    \n",
    "))\n",
    "fig.add_scattergeo(\n",
    "    locations=df_per_state['State'],    ###codes for states,\n",
    "    locationmode='USA-states',\n",
    "    text=df_per_state['State'],\n",
    "    mode='text')\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = '2022 ACS dataset - Data Size by State',\n",
    "    geo_scope='usa', # limite map scope to USA\n",
    "    autosize=False,\n",
    "                width=900,\n",
    "                height=550,\n",
    "    font=dict(size=13)\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n"
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  {
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   "metadata": {},
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    "# code for percentage dataset size of total population\n",
    "fig = gp.Figure(data=gp.Choropleth(\n",
    "    locations=df_per_state['State'], # Spatial coordinates\n",
    "    z = df_per_state['Size Percentage'].astype(float), # Data to be color-coded\n",
    "    locationmode = 'USA-states', # set of locations match entries in `locations`\n",
    "    colorscale = 'mint',\n",
    "    colorbar_title = \"<b>Data set size to population (%)</b>\",\n",
    "    marker_line_color='white', # line markers between states\n",
    "    zmax = 0.7,\n",
    "    zmin = 0.4\n",
    " \n",
    "    \n",
    "))\n",
    "fig.add_scattergeo(\n",
    "    locations=df_per_state['State'],    ###codes for states,\n",
    "    locationmode='USA-states',\n",
    "    text=df_per_state['State'],\n",
    "    mode='text')\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = '2022 ACS dataset - Percentage dataset to population by State',\n",
    "    geo_scope='usa', # limite map scope to USA\n",
    "    autosize=False,\n",
    "                width=900,\n",
    "                height=550,\n",
    "    font=dict(size=13)\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
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    "# code for total size per state\n",
    "fig = gp.Figure(data=gp.Choropleth(\n",
    "    locations=df_per_state['State'], # Spatial coordinates\n",
    "    z = df_per_state['%>50k'].astype(float), # Data to be color-coded\n",
    "    locationmode = 'USA-states', # set of locations match entries in `locations`\n",
    "    colorscale = 'dense',\n",
    "    colorbar_title = \"<b>Percentage that earns >50k</b>\",\n",
    "    marker_line_color='white', # line markers between states\n",
    "    zmax = 100,\n",
    "    zmin = 0.0\n",
    " \n",
    "    \n",
    "))\n",
    "fig.add_scattergeo(\n",
    "    locations=df_per_state['State'],    ###codes for states,\n",
    "    locationmode='USA-states',\n",
    "    text=df_per_state['State'],\n",
    "    mode='text')\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = '2022 ACS dataset - Percentage that earns >50k per State',\n",
    "    geo_scope='usa', # limite map scope to USA\n",
    "    autosize=False,\n",
    "                width=900,\n",
    "                height=550,\n",
    "    font=dict(size=13)\n",
    ")\n",
    "\n",
    "fig.show()\n"
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            "zerolinecolor": "white"
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           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          "title": {
           "x": 0.05
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          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
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           "title": {
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "font": {
          "size": 30
         },
         "text": "Percentage of Non-White vs. White individuals with >50K"
        },
        "width": 1200,
        "xaxis": {
         "linecolor": "gray",
         "linewidth": 0,
         "showline": true,
         "ticktext": [
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         "title": {
          "font": {
           "size": 25
          },
          "text": "<b>Population</b>"
         }
        },
        "yaxis": {
         "linecolor": "gray",
         "linewidth": 0,
         "showline": true,
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          "KY",
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         "title": {
          "font": {
           "size": 25
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          "text": "<b>State</b>"
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        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state = df_per_state['State']\n",
    "size = df_per_state['Size']\n",
    "x_W = df_per_state['#Whites'] / size\n",
    "x_NW = (df_per_state['#Non_whites'] * -1) / size\n",
    "x_Pos_W = df_per_state['white_>50k'] / size\n",
    "x_Pos_NW = (df_per_state['non_white_>50k']* -1) / size \n",
    "\n",
    "\n",
    "# Creating instance of the figure\n",
    "fig = gp.Figure()\n",
    " \n",
    "# Adding Male data to the figure\n",
    "fig.add_trace(gp.Bar(y= state, x = x_W, \n",
    "                     name = 'White', \n",
    "                     showlegend=False,\n",
    "                     orientation = 'h',marker=dict(color='brown')))\n",
    "\n",
    "fig.add_trace(gp.Bar(y=state,\n",
    "               x=x_Pos_W,\n",
    "               orientation='h',\n",
    "               hoverinfo='x',\n",
    "               name = 'White with income >50K',      \n",
    "               opacity=0.5,\n",
    "               marker=dict(color='orange')\n",
    "               ))\n",
    "  \n",
    "# Adding Female data to the figure\n",
    "fig.add_trace(gp.Bar(y = state, x = x_NW,\n",
    "                     name = 'Non-White', \n",
    "                     showlegend=False,\n",
    "                     orientation = 'h'))\n",
    "\n",
    "fig.add_trace(gp.Bar(y=state,\n",
    "               x=x_Pos_NW,\n",
    "               orientation='h',\n",
    "               hoverinfo='x',\n",
    "               name = 'Non-White with income >50K',      \n",
    "               opacity=0.5,\n",
    "               marker=dict(color='darkgreen')\n",
    "               ))\n",
    "\n",
    "# Updating the layout for our graph\n",
    "fig.update_layout(title = 'Percentage of Non-White vs. White individuals with >50K',\n",
    "                 title_font_size = 30, barmode = 'overlay',\n",
    "                 bargap = 0.0, bargroupgap = 0,\n",
    "                 xaxis = dict(tickvals = [-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1],\n",
    "                              ticktext = ['100%', '75%', '50%', '25%','0%','25%', '50%', '75%','100%'],\n",
    "                              title = '<b>Population</b>',\n",
    "                              title_font_size = 25),\n",
    "                 yaxis =    dict(title = '<b>State</b>',tickvals = state.tolist(),\n",
    "                              title_font_size = 25),\n",
    "                autosize=False,\n",
    "                width=1200,\n",
    "                height=800,\n",
    "                font=dict(size=14),       \n",
    "                plot_bgcolor='rgba(0,0,0,0)'\n",
    "                )\n",
    "\n",
    "  \n",
    "fig.update_xaxes(showline=True, linewidth=0, linecolor='gray')\n",
    "fig.update_yaxes(showline=True, linewidth=0, linecolor='gray')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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          "xaxis": {
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           "automargin": true,
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           "zerolinewidth": 2
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        },
        "title": {
         "font": {
          "size": 25
         },
         "text": "Percentage of Female vs. Male individuals with >50K"
        },
        "width": 1200,
        "xaxis": {
         "linecolor": "gray",
         "linewidth": 0,
         "showline": true,
         "ticktext": [
          "100%",
          "75%",
          "50%",
          "25%",
          "0%",
          "25%",
          "50%",
          "75%",
          "100%"
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         "title": {
          "font": {
           "size": 25
          },
          "text": "<b>Population</b>"
         }
        },
        "yaxis": {
         "linecolor": "gray",
         "linewidth": 0,
         "showline": true,
         "tickvals": [
          "GA",
          "HI",
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          "NH",
          "WV",
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          "AL",
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          "CO",
          "NC",
          "SC",
          "FL",
          "PA",
          "SD",
          "NE",
          "ME",
          "CA",
          "AL",
          "NM",
          "ID",
          "DE",
          "VA",
          "NV",
          "OR",
          "KY",
          "IL",
          "TX",
          "MO",
          "PR",
          "KS",
          "NY",
          "IN"
         ],
         "title": {
          "font": {
           "size": 25
          },
          "text": "<b>State</b>"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state = df_per_state['State'] # states\n",
    "size = df_per_state['Size'] # dataset size by state\n",
    "x_M = df_per_state['#Males'] / size # percentage males\n",
    "x_F = (df_per_state['#Females'] * -1) / size # percentage females \n",
    "x_Pos_M = df_per_state['male_>50k'] / size # percentage males with income >50K\n",
    "x_Pos_F = (df_per_state['female_>50k']* -1) / size # percentage females with income >50K\n",
    "\n",
    "\n",
    "# Creating instance of the figure\n",
    "fig = gp.Figure()\n",
    " \n",
    "# Adding Male data to the figure\n",
    "fig.add_trace(gp.Bar(y= state, x = x_M, \n",
    "                     name = 'Male', \n",
    "                     showlegend=False,\n",
    "                     orientation = 'h',marker=dict(color='brown')))\n",
    "\n",
    "fig.add_trace(gp.Bar(y=state,\n",
    "               x=x_Pos_M,\n",
    "               orientation='h',\n",
    "               hoverinfo='x',\n",
    "               #showlegend=False,\n",
    "               name = 'Male with income >50K',      \n",
    "               opacity=0.7,\n",
    "               marker=dict(color='orange')\n",
    "               ))\n",
    "  \n",
    "# Adding Female data to the figure\n",
    "fig.add_trace(gp.Bar(y = state, x = x_F,\n",
    "                     name = 'Female', \n",
    "                     showlegend=False,\n",
    "                     orientation = 'h'))\n",
    "\n",
    "fig.add_trace(gp.Bar(y=state,\n",
    "               x=x_Pos_F,\n",
    "               orientation='h',\n",
    "               hoverinfo='x',\n",
    "               #showlegend=False,\n",
    "               name = 'Female with income >50K',      \n",
    "               opacity=0.7,\n",
    "               marker=dict(color='darkgreen')\n",
    "               ))\n",
    "\n",
    "# Updating the layout for our graph\n",
    "fig.update_layout(title = 'Percentage of Female vs. Male individuals with >50K',\n",
    "                 title_font_size = 25, barmode = 'overlay',\n",
    "                 bargap = 0.0, bargroupgap = 0,\n",
    "                 xaxis = dict(tickvals = [-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1],\n",
    "                              ticktext = ['100%', '75%', '50%', '25%','0%','25%', '50%', '75%','100%'],\n",
    "                              title = '<b>Population</b>',\n",
    "                              title_font_size = 25),\n",
    "                 yaxis =    dict(title = '<b>State</b>',tickvals = state.tolist(),\n",
    "                              title_font_size = 25),\n",
    "                autosize=False,\n",
    "                width=1200,\n",
    "                height=800,\n",
    "                font=dict(size=14),       \n",
    "                plot_bgcolor='rgba(0,0,0,0)'\n",
    "                 )\n",
    "\n",
    "fig.update_xaxes(showline=True, linewidth=0, linecolor='gray')\n",
    "fig.update_yaxes(showline=True, linewidth=0, linecolor='gray')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17542/178175665.py:70: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data for binary classifications\n",
    "sex = {\n",
    "    'Category': ['Males', 'Females'],\n",
    "    'Count': [895612, 824542]\n",
    "}\n",
    "\n",
    "white_nonwhite = {\n",
    "    'Category': ['Whites', 'Non-Whites'],\n",
    "    'Count': [1152609, 567545]\n",
    "}\n",
    "\n",
    "black_nonblack = {\n",
    "    'Category': ['Blacks', 'Non-Blacks'],\n",
    "    'Count': [141530, 1578624]\n",
    "}\n",
    "\n",
    "# Data for multi-class race categories\n",
    "race = {\n",
    "    'Category': [\n",
    "        'White', 'Two or More', 'Black', 'Asian', 'Other',\n",
    "        'American Indian', 'Islander',\n",
    "        'Tribes', 'Alaska Native'\n",
    "    ],\n",
    "    'Count': [\n",
    "        1152609, 183537, 141530, 115655, 104439, 16052, 2821, 2544, 967\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Creating the figure and subplots\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 15))\n",
    "\n",
    "# Font sizes\n",
    "title_fontsize = 20\n",
    "label_fontsize = 18\n",
    "tick_fontsize = 16\n",
    "\n",
    "# Common y-axis limit\n",
    "y_limit = [0, 1650000]\n",
    "\n",
    "# Plotting binary classifications (Males vs. Females)\n",
    "axes[0, 0].bar(sex['Category'], sex['Count'])\n",
    "axes[0, 0].set_ylabel('Count', fontsize=label_fontsize)\n",
    "axes[0, 0].set_xlabel('Class', fontsize=label_fontsize)\n",
    "axes[0, 0].set_title('Male vs Female', fontsize=title_fontsize)\n",
    "axes[0, 0].tick_params(axis='both', which='major', labelsize=tick_fontsize)\n",
    "axes[0, 0].set_ylim(y_limit)\n",
    "\n",
    "# Plotting binary classifications (Whites vs. Non-Whites)\n",
    "axes[0, 1].bar(white_nonwhite['Category'], white_nonwhite['Count'])\n",
    "axes[0, 1].set_ylabel('Count', fontsize=label_fontsize)\n",
    "axes[0, 1].set_xlabel('Class', fontsize=label_fontsize)\n",
    "axes[0, 1].set_title('Whites vs Non-Whites', fontsize=title_fontsize)\n",
    "axes[0, 1].tick_params(axis='both', which='major', labelsize=tick_fontsize)\n",
    "axes[0, 1].set_ylim(y_limit)\n",
    "\n",
    "# Plotting binary classifications (Blacks vs. Non-Blacks)\n",
    "axes[1, 0].bar(black_nonblack['Category'], black_nonblack['Count'])\n",
    "axes[1, 0].set_ylabel('Count', fontsize=label_fontsize)\n",
    "axes[1, 0].set_xlabel('Class', fontsize=label_fontsize)\n",
    "axes[1, 0].set_title('Blacks vs Non-Blacks', fontsize=title_fontsize)\n",
    "axes[1, 0].tick_params(axis='both', which='major', labelsize=tick_fontsize)\n",
    "axes[1, 0].set_ylim(y_limit)\n",
    "\n",
    "# Plotting multi-class race categories\n",
    "axes[1, 1].bar(race['Category'], race['Count'])\n",
    "axes[1, 1].set_ylabel('Count', fontsize=label_fontsize)\n",
    "axes[1, 1].set_xlabel('Class', fontsize=label_fontsize)\n",
    "axes[1, 1].set_title('Multi-Class Race Categories', fontsize=title_fontsize)\n",
    "axes[1, 1].tick_params(axis='both', which='major', labelsize=tick_fontsize)\n",
    "axes[1, 1].set_xticklabels(race['Category'], rotation=45, ha='right', fontsize=tick_fontsize)\n",
    "axes[1, 1].set_ylim(y_limit)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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