DonorEth_by_status (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "disabled-madonna",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "celtic-communications",
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>All Ethnicities</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>Asian</th>\n",
       "      <th>American Indian/Alaska Native</th>\n",
       "      <th>Pacific Islander</th>\n",
       "      <th>Multiracial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All Donor Types</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413,435</td>\n",
       "      <td>290,985</td>\n",
       "      <td>53,737</td>\n",
       "      <td>51,674</td>\n",
       "      <td>328</td>\n",
       "      <td>10,588</td>\n",
       "      <td>1,891</td>\n",
       "      <td>1,328</td>\n",
       "      <td>2,904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Deceased Donor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239,921</td>\n",
       "      <td>168,019</td>\n",
       "      <td>34,046</td>\n",
       "      <td>29,268</td>\n",
       "      <td>217</td>\n",
       "      <td>5,098</td>\n",
       "      <td>997</td>\n",
       "      <td>588</td>\n",
       "      <td>1,688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Living Donor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173,514</td>\n",
       "      <td>122,966</td>\n",
       "      <td>19,691</td>\n",
       "      <td>22,406</td>\n",
       "      <td>111</td>\n",
       "      <td>5,490</td>\n",
       "      <td>894</td>\n",
       "      <td>740</td>\n",
       "      <td>1,216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Unnamed: 1 All Ethnicities    White   Black Hispanic  \\\n",
       "0  All Donor Types         NaN         413,435  290,985  53,737   51,674   \n",
       "1   Deceased Donor         NaN         239,921  168,019  34,046   29,268   \n",
       "2     Living Donor         NaN         173,514  122,966  19,691   22,406   \n",
       "\n",
       "   Unknown   Asian American Indian/Alaska Native Pacific Islander Multiracial  \n",
       "0      328  10,588                         1,891            1,328       2,904  \n",
       "1      217   5,098                           997              588       1,688  \n",
       "2      111   5,490                           894              740       1,216  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/guillermo/Downloads/Donor___Donor_Ethnicity_by_Donor_Type.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "expanded-hampshire",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Donor Type         object\n",
       "All Ethnicities    object\n",
       "White              object\n",
       "Black              object\n",
       "Hispanic           object\n",
       "Asian              object\n",
       "Multiracial        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Unnamed: 1', 'American Indian/Alaska Native', 'Pacific Islander', 'Unknown'], axis = 1, inplace = True)\n",
    "df.rename(columns={'Unnamed: 0': 'Donor Type'}, inplace = True)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "outer-biology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['All Donor Types', 413435, 290985, 53737, 51674, 10588, 2904],\n",
       "       ['Deceased Donor', 239921, 168019, 34046, 29268, 5098, 1688],\n",
       "       ['Living Donor', 173514, 122966, 19691, 22406, 5490, 1216]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['White'] = df['White'].str.replace(',','').astype(int)\n",
    "df['Black'] = df['Black'].str.replace(',','').astype(int)\n",
    "df['Hispanic'] = df['Hispanic'].str.replace(',','').astype(int)\n",
    "df['Asian'] = df['Asian'].str.replace(',','').astype(int)\n",
    "df['Multiracial'] = df['Multiracial'].str.replace(',','').astype(int)\n",
    "df['All Ethnicities'] = df['All Ethnicities'].str.replace(',', '').astype(int)\n",
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "governing-peace",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/frame.py:4308: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "df1 = df.loc[1:2]\n",
    "df1.drop(['All Ethnicities'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greatest-solomon",
   "metadata": {},
   "source": [
    "# Stacked bar plot of each ethnicity's living donor and deceased donor donations. \n",
    "## obvious disparity in the African American community.. Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "trained-tobacco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "df1.set_index('Donor Type').T.plot(kind = 'bar', stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "dangerous-dance",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Donor Type', ylabel='Black'>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkP7cf/8DmExm/vrXNwkO7lVissZz585xyy23OGYIadToVjIzi2cIOXBgP998828GDCg5LcypUyc4ejSVkSOfYvjwwRw6dBB3d/frUrfCRESkmnn66ZEkJv4fGRnpbN68kfT00wQH9/rVUleejeri4kJeXh7z589lwoSoK2YFsVqt/PjjYZYu/RvR0W+wdOlCxx0dnaUwERGpJo4dO8oPPxwEoFatWvj6duf771NITT3CsGGDmDt3JgcP7ufVVydRv34DcnNzsVqtAGRnZ9GokYm9e7/hzJlsIiNfYtiwQWRlZRIR8QLHjx+lYcNGPPro4xiNRry9G3PPPfdy6NDB61K7wkREpJpISzvJ3LmzKCgooLCwkC+/3Ebnzl1Ys2Y9K1euZeLEKO66625mzIjGaDTSoUNHPvnkYwCSkz+gS5euPPjgQ6xfv4WVK9eycuVabr3VxLx5i2je/A4efvgRPv30Y+x2O+fPn+P771No3brNdaldZ3OJiFQTDz3Uje+//47hwwfj4uKCn99j/OlPgb+5/MsvRzJz5jRWr16B2dz4mvd2f+KJwcTFLWLo0Cew2awMG/YMzZtfn4s2dQW8OE1XwP+ProCvWa52BXxVX2dSXZT3CngdmYiI/ELxL/ya80u/utCYiYiIOE1hIiIiTlOYiMhN7SYdNi6V3W4DfvuutlejMBGRm5bR6E5u7gUFyn/Z7XaKigo5dy4Ld/da5VpXA/AictNq0MDE2bOZ5OScq+pSqg0XF1dq1/bCy6teudZTmIjITcvV1cittzap6jJuCOrmEhERpylMRETEaQoTERFxmsJEREScpjARERGnVWiYvPnmmwQHBxMcHExMTAwAkyZNIiAggNDQUEJDQ/n44+Lpk3fs2IHFYiEgIIAFCxY4trF//3769u1LYGAgU6ZMoaioCIC0tDQGDx5MUFAQzz33HLm5uRW5KyIiUooKC5MdO3bw5ZdfsmnTJhISEvjuu+/4+OOPSUlJ4d1332Xz5s1s3rwZf39/8vLymDx5MnFxcSQlJZGSksK2bdsAiIiIYOrUqWzduhW73U58fDwA06dPZ9CgQSQnJ9O+fXvi4uIqaldEROQaKixMTCYTkZGRuLu74+bmxp133klaWhppaWlMnToVi8XC4sWLsdls7Nu3jxYtWtCsWTOMRiMWi4Xk5GROnTpFXl4eHTt2BCAsLIzk5GQKCwvZvXs3gYGBJdpFRKRqVNhFi61bt3b8/+jRoyQlJbF27Vp27drFjBkz8PT0ZOTIkaxfvx5PT09MJpNjebPZTHp6OhkZGSXaTSYT6enpnD17Fi8vL4xGY4n28ihtXn4RZ5hMdaq6BJFKV+FXwP/www+MHDmSiRMn8oc//IGlS5c6nhs6dCgJCQkEBQVdsZ7BYLjqfDmltZeHbo51/eiXZ0m6OZbciK51c6wKHYDfs2cPw4YN4+WXX6ZPnz4cPHiQrVu3Op632+3/vbG9N1lZWY72jIwMzGbzFe2ZmZmYzWYaNmxITk4OVqu1RLuIiFSNCguTn376idGjRxMbG0twcDBQHB6zZ8/m/PnzFBYW8t577+Hv70+HDh1ITU3l2LFjWK1WEhMT8fX1pWnTpnh4eLBnzx4AEhIS8PX1xc3NDR8fH5KSkkq0i4hI1aiwe8DPnDmTDRs20Lx5c0fbgAEDsNlsrFmzhqKiIgICAnjllVcA2LlzJ9HR0eTn5+Pn58ekSZMwGAwcOHCAqKgocnNzadeuHdHR0bi7u3Pq1CkiIyPJzs6mSZMmzJ8/n3r1yj7Lpbq5rh/dA/5/dA94uVFdq5urwsKkulOYXD8Kk/9RmMiNqkrHTERE5OagMBEREacpTERExGkKExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJEREScpjARERGnKUxERMRpChMREXGawkRERJymMBEREacpTERExGkKExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJEREScVqFh8uabbxIcHExwcDAxMTEA7NixA4vFQkBAAAsWLHAsu3//fvr27UtgYCBTpkyhqKgIgLS0NAYPHkxQUBDPPfccubm5AFy4cIERI0bQo0cPBg8eTGZmZkXuioiIlKLCwmTHjh18+eWXbNq0iYSEBL777jsSExOZPHkycXFxJCUlkZKSwrZt2wCIiIhg6tSpbN26FbvdTnx8PADTp09n0KBBJCcn0759e+Li4gBYuHAhPj4+fPjhh/Tv359Zs2ZV1K6IiMg1VFiYmEwmIiMjcXd3x83NjTvvvJOjR4/SokULmjVrhtFoxGKxkJyczKlTp8jLy6Njx44AhIWFkZycTGFhIbt37yYwMLBEO8Dnn3+OxWIBICQkhO3bt1NYWFhRuyMiIqWosDBp3bq1IxyOHj1KUlISBoMBk8nkWMZsNpOenk5GRkaJdpPJRHp6OmfPnsXLywuj0ViiHSixjtFoxMvLizNnzlTU7oiISCmMv3fFoqIixy/50vzwww+MHDmSiRMnYjQaSU1NLfG8wWDAbrdfsV5p7b/FxaXs2diokVeZlxUpD5OpTlWXIFLpyhQma9asYfDgwY7Hhw8fJiIigk2bNpW63p49exg3bhyTJ08mODiYXbt2kZWV5Xg+IyMDs9mMt7d3ifbMzEzMZjMNGzYkJycHq9WKq6urox2Kj2qysrJo3LgxRUVF5OTkUL9+/TLveHZ2DjbblWEl5adfniVlZl6s6hJErjsXF0Opf4SXKUzWrVtH/fr1CQ4O5u9//zvLli3j+eefL3Wdn376idGjR7NgwQIeeughADp06EBqairHjh3j9ttvJzExkb59+9K0aVM8PDzYs2cP999/PwkJCfj6+uLm5oaPjw9JSUlYLBZHO4Cfnx8JCQmMGjWKpKQkfHx8cHNzK+v7InLDqlO3FrU89LMAkJdfyMULeVVdxk3BYL9aX9KvZGZm8tRTT+Hp6YmLiwvR0dG0bNmy1HVmzpzJhg0baN68uaNtwIAB3HHHHURHR5Ofn4+fnx+TJk3CYDBw4MABoqKiyM3NpV27dkRHR+Pu7s6pU6eIjIwkOzubJk2aMH/+fOrVq8e5c+eIjIzkxIkT1KlTh9jYWG6//fYy77iOTK4fk6kOe2KeqeoyqoX7Jyyv8iMTk6kOgyasqdIaqou1MYOr/PO4UVzryKTUMDl37pzj/6dPn2bEiBHMmDHDMbBenm6l6kZhcv0oTP5HYVK9KEyuH6e6ubp06XLFQPioUaOA4oHw/fv3X6cyRUSkJis1TA4cOOD4v91ux2AwYLVasdlsGp8QERGHMp1L+/XXXxMaGgrAkSNHePTRR/nmm28qtDAREak5yhQmc+fOJTo6Gii+GPGtt95yPBYRESlTmBQWFnLPPfc4Ht9zzz0UFBRUWFEiIlKzlClMateuzfbt2x2Pd+7ciaenZ4UVJSIiNUuZLlqcMmUKo0ePxmg0YjAYMBgMLFmypKJrExGRGqJMYdKhQwc+//xzDh06hKurKy1btsTd3b2iaxMRkRqiTGFSUFDAtm3bHDemSklJ4fjx44wfP75CixMRkZqhTGEyfvx4Tpw4QWZmJu3atWPv3r107ty5omsTEZEaokwD8Pv372fjxo08/vjjTJ48mXXr1nHxoqYoEBGRYmUKE7PZjNFo5I477uDQoUO0atWKy5cvV3RtIiJSQ5QpTDw9PdmyZQtt27blww8/5ODBgyUmgRQRkZtbmcJk6tSp7N+/n4cffhgXFxeGDBnC8OHDK7o2ERGpIco0AN+yZUsmTJgAwMKFCyuyHhERqYFKDROLxVLqylu2bLmuxYiISM1UaphMnTq1xOOTJ09Sq1YtcnJyuOOOOyqyLhERqUFKDROj0cikSZMwm82MGDGC2bNn07JlS44dO8brr79eWTWKiEg1V2qYzJ07lxdffJGzZ8/y/PPPs3r1ajp16kRqaiovvvgigYGBlVWniIhUY6WGyaVLl+jRowcAy5cvp1OnTkDxgLzBYKj46kREpEYo9dRgV1dXx//r1q1b4jmFiYiI/KzUMPllYPze8MjJySEkJISTJ08CMGnSJAICAggNDSU0NJSPP/4YgB07dmCxWAgICGDBggWO9ffv30/fvn0JDAxkypQpFBUVAZCWlsbgwYMJCgriueeec0xCKSIila/UMDl48CD33Xcf9913X4n/d+rUiUOHDl1z43v37mXgwIEcPXrU0ZaSksK7777L5s2b2bx5M/7+/uTl5TF58mTi4uJISkoiJSWFbdu2ARAREcHUqVPZunUrdrud+Ph4AKZPn86gQYNITk6mffv2xMXFOfE2iIiIM0odM/n5qOH3io+PZ9q0aY4LHi9dukRaWhpTp04lLS0Nf39/xowZw759+2jRogXNmjUDiq9vSU5OplWrVuTl5dGxY0cAwsLCWLx4Mf3792f37t0sXbrU0T5kyBAiIiKcqldERH6fUsOkadOmTm181qxZJR5nZ2fTpUsXZsyYgaenJyNHjmT9+vV4enpiMpkcy5nNZtLT08nIyCjRbjKZSE9P5+zZs3h5eWE0Gku0i4hI1SjTdCrXS7NmzRxHEwBDhw4lISGBoKCgK5Y1GAzY7fZytZdHo0Ze5VpepKxMpjpVXYL8gj6PylGpYXLw4EGOHj3quD7FbrdjNBrx9vYmKyvLsVxGRgZms/mK9szMTMxmMw0bNiQnJwer1Yqrq6ujvTyys3Ow2a4MJSk//bCWlJlZtff60edRUlV/HjcKFxdDqX+El2nW4OvFbrcze/Zszp8/T2FhIe+99x7+/v506NCB1NRUjh07htVqJTExEV9fX5o2bYqHhwd79uwBICEhAV9fX9zc3PDx8SEpKalEu4iIVI1KPTJp27YtI0aMYODAgRQVFREQEEBISAgAc+bMYezYseTn5+Pn5+fo+oqNjSUqKorc3FzatWtHeHg4ANOmTSMyMpJly5bRpEkT5s+fX5m7IiIiv2CwX20A4iagbq7rx2Sqw56YZ6q6jGrh/gnLq7xbxWSqw6AJa6q0hupibczgKv88bhTVqptLRERuTAoTERFxmsJEREScpjARERGnKUxERMRpChMREXGawkRERJymMBEREacpTERExGkKExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJEREScpjARERGnKUxERMRpChMREXGawkRERJymMBEREacpTERExGkKExERcVqFhklOTg4hISGcPHkSgB07dmCxWAgICGDBggWO5fbv30/fvn0JDAxkypQpFBUVAZCWlsbgwYMJCgriueeeIzc3F4ALFy4wYsQIevToweDBg8nMzKzI3RARkWuosDDZu3cvAwcO5OjRowDk5eUxefJk4uLiSEpKIiUlhW3btgEQERHB1KlT2bp1K3a7nfj4eACmT5/OoEGDSE5Opn379sTFxQGwcOFCfHx8+PDDD+nfvz+zZs2qqN0QEZEyqLAwiY+PZ9q0aZjNZgD27dtHixYtaNasGUajEYvFQnJyMqdOnSIvL4+OHTsCEBYWRnJyMoWFhezevZvAwMAS7QCff/45FosFgJCQELZv305hYWFF7YqIiFyDsaI2/OujhYyMDEwmk+Ox2WwmPT39inaTyUR6ejpnz57Fy8sLo9FYov3X2zIajXh5eXHmzBm8vb3LXF+jRl6/e99ESmMy1anqEuQX9HlUjgoLk1+z2+1XtBkMhnK3/xYXl/IdZGVn52CzXfkaUn76YS0pM/Nilb6+Po+SqvrzuFG4uBhK/SO80s7m8vb2Jisry/E4IyMDs9l8RXtmZiZms5mGDRuSk5OD1Wot0Q7FRzU/r1NUVEROTg7169evrF0REZFfqbQw6dChA6mpqRw7dgyr1UpiYiK+vr40bdoUDw8P9uzZA0BCQgK+vr64ubnh4+NDUlJSiXYAPz8/EhISAEhKSsLHxwc3N7fK2hUREfmVSuvm8vDwYM6cOYwdO5b8/Hz8/PwICgoCIDY2lqioKHJzc2nXrh3h4eEATJs2jcjISJYtW0aTJk2YP38+AC+88AKRkZEEBwdTp04dYmNjK2s3RETkKgz2qw1O3AQ0ZnL9mEx12BPzTFWXUS3cP2F5lffRm0x1GDRhTZXWUF2sjRlc5Z/HjaLajJmIiMiNS2EiIiJOU5iIiIjTFCYiIuI0hYmIiDhNYSIiIk5TmIiIiNMUJiIi4jSFiYiIOE1hIiIiTlOYiIiI0xQmIiLiNIWJiIg4TWEiIiJOU5iIiIjTFCYiIuI0hYmIiDhNYSIiIk5TmIiIiNMUJiIi4jSFiYiIOM1YFS8aHh5OdnY2RmPxy8+YMYPjx4+zbNkyCgsLGTZsGIMHDwZgx44dREdHk5+fT48ePRg/fjwA+/fvJyoqipycHHx8fJg+fbpjeyIiUrkq/cjEbrdz5MgRNm/e7PjXuHFjFixYwNq1a9m8eTPvvfcehw8fJi8vj8mTJxMXF0dSUhIpKSls27YNgIiICKZOncrWrVux2+3Ex8dX9q6IiMh/VXqYHDlyBIPBwLPPPkuvXr1499132bFjB126dKF+/fp4enoSGBhIcnIy+/bto0WLFjRr1gyj0YjFYiE5OZlTp06Rl5dHx44dAQgLCyM5Obmyd0VERP6r0sPkwoULPPTQQyxdupSVK1eybt060tLSMJlMjmXMZjPp6elkZGSUqd1kMpGenl6p+yEiIv9T6YMMnTp1olOnTgB4enrSr18/oqOjGTVqVInlDAYDdrv9ivVLay+PRo28yrW8SFmZTHWqugT5BX0elaPSw+Tf//43hYWFPPTQQ0DxGErTpk3JyspyLJORkYHZbMbb27tM7ZmZmZjN5nLVkZ2dg812ZShJ+emHtaTMzItV+vr6PEqq6s/jRuHiYij1j/BK7+a6ePEiMTEx5Ofnk5OTw6ZNm5g3bx47d+7kzJkzXL58mY8++ghfX186dOhAamoqx44dw2q1kpiYiK+vL02bNsXDw4M9e/YAkJCQgK+vb2XvioiI/FelH5l0796dvXv30rt3b2w2G4MGDeL+++9n/PjxhIeHU1hYSL9+/fjjH/8IwJw5cxg7diz5+fn4+fkRFBQEQGxsLFFRUeTm5tKuXTvCw8Mre1dEROS/DParDUDcBNTNdf2YTHXYE/NMVZdRLdw/YXmVd6uYTHUYNGFNldZQXayNGVzln8eNotp1c4mIyI1HYSIiIk5TmIiIiNMUJiIi4jSFiYiIOE1hIiIiTlOYiIiI0xQmIiLiNIWJiIg4TWEiIiJOU5iIiIjTFCYiIuI0hYmIiDhNYSIiIk5TmIiIiNMUJiIi4jSFiYiIOE1hIiIiTlOYiIiI0xQmIiLiNIWJiIg4TWEiIiJOq9FhsmXLFnr27Im/vz9r1qyp6nJERG5axqou4PdKT09nwYIFbNy4EXd3dwYMGMCDDz5Iq1atqro0EZGbTo0Nkx07dtClSxfq168PQGBgIMnJyYwZM6ZM67u4GCqwupuPe91GVV1CtVEdvlu3NrilqkuoNqrD53EjuNb7WGPDJCMjA5PJ5HhsNpvZt29fmddvoB+26+reUXOruoRqo1Ejr6ougcWTeld1CdVGdfg8bgY1dszEbrdf0WYw6C8QEZGqUGPDxNvbm6ysLMfjjIwMzGZzFVYkInLzqrFh0rVrV3bu3MmZM2e4fPkyH330Eb6+vlVdlojITanGjpl4e3szfvx4wsPDKSwspF+/fvzxj3+s6rJERG5KBvvVBh9ERETKocZ2c4mISPWhMBEREacpTERExGkKExERcZrCpJo7efIk7du3JzQ0lNDQUAIDAxk3blyJa2yqq6+//pqhQ4de0R4ZGcmjjz5KaGgoFosFi8XCpk2bqqBCqQy/9T34z3/+w5QpU35zvWs9X1YbN26kc+fOhIaG0qtXLwIDA5k7dy5Wq9Xpbcv/1NhTg28mZrOZzZs3A8VX/s+fP59x48axdu3aKq7s9xs3bhxhYWEAnDhxgkGDBuHt7U3Xrl2ruDKpLPfeey/33nvv736+PB577DHmzJkDQG5uLqNHj2bJkiW8+OKL12X7oiOTGsdgMDB27Fh++OEHDhw4AMBbb71Fnz596NWrFzExMY6pZlauXElgYCA9e/Zk3rx5AGRlZfH8888TFhZG37592bFjB1A8C/PTTz/Nn//8Z7p3705sbCwABw4c4M9//jNhYWEMHDiQo0ePArB9+3b69etH7969GTNmDGfPngXgyy+/JDg4mLCwMOLj48u0T82aNSM8PNwRjqmpqQwdOhSLxcITTzzhmHMtMjKSmTNnMnDgQB577DE2bNgAwOXLl3n55ZcJCQnBYrGQkJAAFP9F+vN25s+f78zbLhXg5yOWAwcOEBIS4mj/7LPPGDVqVIkjmqFDhxITE8MTTzyBv78/27ZtA+D06dMMGTIEi8XCyy+/XKYLl2+55RZeeukl/vGPf2C320v9/owfP57hw4fj7+/Pa6+95tjGX/7yF3r27InFYmHOnDlYrVZOnjxJUFAQAwcOZNiwYdftfaopdGRSA7m7u9OiRQuOHDlCRkYGKSkprF+/HoPBQEREBO+//z4tW7Zk7dq1bNiwgdq1a/PMM8+QkpLCihUr6Nu3L48//jgZGRkMGjSIhIQEEhMTCQkJoU+fPly8eBE/Pz+GDx/OqlWreOqpp+jRowdJSUl8++231K1blzfeeIPVq1dTr1491q1bR2xsLNOmTSMyMpJVq1Zx5513lquLok2bNo6uroiICEaMGEFAQADffvstL7zwAlu3bgWKf3msXbuWQ4cOER4eTt++fVmyZAkNGjQgMTGRM2fO0L9/f9q2bQsUh2RSUhJGo77q1VXbtm1xcXHh0KFDtGnThsTERHr16nXFcoWFhbz33nt8+umnLFq0CD8/P2bNmkWPHj0YPHgwH3/8MYmJiWV6zdatW3Pu3DnOnDnDihUrfvP7880335CYmIirq6sjKE6fPs2nn37Kxo0bMRqNjB07lnXr1uHn50dqairLly/n9ttvv67vUU2gn7AaymAwUKtWLXbu3Mm+ffscXUZ5eXncdtttZGVl0b17d+rUqQMUH6VA8dT9R44cYfHixQAUFRVx4sQJnn76ab766itWrFjBDz/8QGFhIZcvX8bPz48ZM2bwxRdf0L17dwIDA9m+fTs//fQT4eHhANhsNurVq8fBgwcxm83ceeedAPTp04dFixaVeZ9q1apFbm4ux48fJyAgAICOHTtSr149jhw5AsDDDz+MwWCgTZs2nDt3DoCvvvqK2bNnA9CwYUMef/xxdu3ahZeXF+3atVOQ1AChoaF88MEHNGvWjF27djF79my+/fbbEss88sgjwP+CAOBf//oX0dHRAPj7+1O3bt0yvd7Pk8J6eHiU+v3p1KkTXl7Fsw43a9aM8+fP89VXXxEcHEytWrUA6Nu3LwkJCfj5+dGoUaObMkhAYVIjFRQUkJqaSqtWrfjqq6948skneeqppwC4cOECrq6urF+/vsQ66enp1K5dG5vNxqpVqxz3gUlPT+fWW29lzpw5nDhxgpCQEP70pz+xY8cO7HY7QUFBdOrUic8++4xVq1axbds2Hn30Ue677z7+8pe/AJCfn09ubi5paWnYbDbHa7q6upZ5nw4ePMidd96J3W6/YkZou93uGCz18PAASs4QXdryP//AS/UWEhLCk08+Sdu2benWrZvjc/6lq332rq6uV51B/FoOHjxI48aN8fLyKtP37efXtdvtJb7jPysqKgJu7u+bxkxqGJvNxpIlS+jQoQPNmzenS5cubN68mdzcXIqKihg9ejRbt27Fx8eH7du3O9pffvllUlJS6NKli2Ns4vDhw/Tq1YvLly/zr3/9i6effpoePXrw008/kZ6ejs1m48UXX2Tfvn0MGDCAF154ge+//54OHTrw7bffkpqaCkBcXBwxMTHcddddZGdnO8ZyPvjggzLt09GjR1m7di0DBw7Ey8uLZs2a8dFHHwHw7bffkpWVRevWrX9z/S5dujjC88yZM3zyySd07tz5d7/HUvm8vb1p0qQJb7311lW7uH5L165d2bJlCwDbtm3jwoUL11zn4sWLLFq0iMGDBwPl//506dKFDz74gLy8PIqKitiwYQNdunQpc803Kh2Z1AAZGRmEhoYCxWFy991388YbbwDFZ6n8PEhutVp55JFH6NOnDwaDgSFDhjBgwABsNhv+/v507dqVO++8k1dffRWLxQJATEwMXl5ejBw5kgkTJlC3bl0aNWpE+/btOXnyJKNGjWLKlCnExcXh6upKZGQkJpOJ2bNn8+KLL2Kz2fD29mbevHm4ubkxf/58IiIiMBqNtGvX7jf3afHixaxatQqDwYCrqysTJ07kvvvuA2DevHm89tprLFmyBDc3N5YsWYK7u/tvbmv06NG89tprWCwWrFYro0aN4p577uHgwYPX6yMQJ/373/+mU6dOjscWi4Xg4OASy4SGhrJgwQIefPDBMm938uTJTJw4kfj4eNq2bfub3VyffvopoaGhGAwGrFYrgYGBPPPMM0D5vz/du3dn//799O3bl6KiIh555BGGDBnC6dOny1z3jUgTPYpIjbV69Wq6du1Kq1at+O6775g6dSobN26s6rJuSjoyEZEaq0WLFrz00ku4uLjg4eHB66+/XtUl3bR0ZCIiIk7TALyIiDhNYSIiIk5TmIiIiNM0AC9yFSdPnsTf3582bdoAxadku7m5ER4eTu/evSu1lgsXLjjmqLp06RLp6em0bNkSKL7OYuLEiZVaj8jVKExEfkOtWrUcszUDnDp1imHDhlG7dm0CAwMrrY66des66vj66695/fXXS9QlUh2om0ukjJo2bcq4ceNYsWIFUHwl9SuvvOKYbTYmJsYxrca9997LkiVLGDBgAI899phjbjSApUuXOmacHTduHJmZmUDxzLhjxoyhZ8+evPPOO2Wq6f3332fAgAGOx2lpaXTr1o2CggLatWvH3LlzCQsLIygoyDGrAMA///lPwsLC6N27N8OGDePHH3909u2Rm5zCRKQc2rZty6FDhwCYOXMm9evXZ8uWLWzYsIGDBw/y9ttvA8XzpzVo0IB169axePFi3njjDfLz89mwYQNffPEF69evZ8uWLbRu3ZrIyEjH9uvWrUtSUtJVbyZ1NUFBQRw/fpzDhw8DxSHRp08f3N3dsVqt1KtXj40bN7Jw4UImT57MmTNn2LVrFwkJCaxZs4aEhASeeeYZxo4de53fKbnZKExEyuHn2Zqh+J4uQ4YMwWAw4O7uzoABA9i+fbtj2ccffxyAe+65h4KCAi5dusT27dsJCwvD09MTgPDwcL766isKCgoA8PHxKVc97u7u9O/fn/j4eKxWK5s2beKJJ55wPD9kyBCgOATbtGnD7t27+fzzzzl27BgDBgwgNDSUefPmcf78ecdMvCK/h8ZMRMrhP//5T4lB+V+y2WyObi64cpbbq82I/Ot1fg6Z8njiiSfo378/nTt3pnXr1iWmQP/lzM02mw1XV1dsNhuhoaFEREQ42jMyMqhXr165X1vkZzoyESmj1NRU4uLiGD58OADdunVjzZo12O12CgoKiI+Pv+Zth7t168bGjRu5dOkSAO+88w4PPPBAqRNZXsttt91Gx44dmT17NgMHDizx3M93Dfzuu+9ITU3lgQce4OGHH+aDDz4gIyMDgH/84x88+eSTv/v1RUBHJiK/KS8vzzFb889zP7300ks8+uijAERFRTFz5kwsFguFhYU88sgjjBo1qtRt9uvXj59++on+/ftjs9lo0aKF4xbJzggLC+P111/Hz8+vRPv/+3//j/j4eGw2GwsWLKBevXo88sgjPPvsswwfPhyDwYCXlxdvvvlmifuEiJSX5uYSqeFsNhszZszgtttuY8SIEY72u+66i507d9KwYcMqrE5uFurmEqnBcnJyePDBBzlx4oRjsF2kKujIREREnKYjExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJERESc9v8B2RLfgAk4rQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = df1, x = 'Donor Type', y = 'Black', hue = 'Black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "eleven-aurora",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Donor Type', ylabel='White'>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = df1, x = 'Donor Type', y = 'White', hue = 'White')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "certain-boards",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Donor Type', ylabel='Hispanic'>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+pKenl2kXEanNLjYF/c0330KTJqWzAN9zT2c+/3wDxcXFPP30UFq2LB1SaNmyFdnZR7Db7axfv45+/Z4AwN+/Ey+99E8ArFYbZ88WYrVaOXv2LB4el+4MrMrOTObPn09RUREJCQmOtl69ejFo0CB69+5NSUkJwcHBhIeXTmSWkJDA8OHDKSoqIjAw0NH1lZSURHx8PAUFBbRt25bo6GgAxo8fT2xsLK+99hrNmjVjxowZVbUrIiKXxQ03tHD8/9wU9MnJbzJy5DCys4/QpImJzz5by9GjR3F3dyck5CGgtEvqrbfe4N577+P48WO4u7uzfPn7bNiwjvr1GzB8+PMAPPRQBJ98kka3bl3Jzz/NhAlTLlntVRYm8fHxxMfHX/C5vn3Pn/fm7rvv5sMPPzyvvU2bNixbtuy8dh8fHxYuXOh8oSIiNczevT8zZsxIhg0byXXX/YMhQ54lNvZ5PDzqcP/9D7J79y7HshaLhcmTx1NSYiU6+imOHz/GsWNHqV+/AW+/vYStWzcTFxfDBx+s4q233uDmm2/htdfmc+DAL4wc+Qw33ngTTZs2c7pmXcYgIlKD/HEK+qKiIm666WbefnsJr7/+Ft7eTfHxuRaAM2fOMHr0cKxWKwkJr2A0GvHyaoirqytBQaW9O3fc4c/Zs2c4fvwYn3++gbCwCAwGA9dd50vbtu347393lVdOhSlMRERqiAtNQV9YeJbnnhvKmTMFWCwWPvhgKfffHwzAyy+Pw8enOS+/nIC7uzsA7u7u3HHHXaxd+ykAO3d+R506dfDyakjLlq3YuHE9UHrXxj17dtOqVetLUrumoBcRqSEuNAV9t25RPPXU0wwa9CQlJSUEBYUQHBzKDz98z+efb+Af/7iBJ58sHToovdprDrGx/2T69CmsWJGC0Whk4sRpuLi4MGLE8yQmTqFfv0dxdXVh0KBnHHdydJamoBenaQr632gK+trlQlPQV/f3TGqKyk5BrzMTEZHfKf2FX3t+6dcUGjMRERGnKUxERMRpChMRuaJdocPG5bLbbUDl5jtUmIjIFctodKeg4JQC5X/sdjslJRZOnMjD3b1OpdbVALyIXLGuvtrE8eO55OefqO5SagwXF1fq1vXE09OrUuspTETkiuXqaqRJE+enEhF1c4mIyCWgMBEREaepm0vkb6Z+gzrU8XCr7jJqhMIiC6dPFVZ3GVcEhYnI30wdDzf6jFlc3WXUCEsS+3IahcnloG4uERFxmsJEREScpjARERGnKUxERMRpChMREXGawkRERJymMBEREacpTERExGkKExERcZrCREREnKYwERERp1VpmLz66quEhYURFhZGYmIiAJmZmURERBAcHMzMmTMdy+7evZvu3bsTEhLC2LFjKSkpAeDw4cP07duX0NBQhg4dSkFBAQCnTp1i0KBBdO3alb59+5Kbm1uVuyIiIuWosjDJzMzkiy++YOXKlaSmprJr1y7S0tKIi4sjOTmZ9PR0du7cyYYNGwCIiYlh3LhxrF69GrvdTkpKCgATJ06kT58+ZGRk0K5dO5KTkwGYNWsWfn5+fPLJJ/Ts2ZMpU6ZU1a6IiMifqLIwMZlMxMbG4u7ujpubGy1atGDfvn34+vrSvHlzjEYjERERZGRkcOjQIQoLC+nQoQMAUVFRZGRkYLFY2Lp1KyEhIWXaAdavX09ERAQA4eHhbNy4EYvFUlW7IyIi5aiyMGnVqpUjHPbt20d6ejoGgwGTyeRYxmw2k52dTU5OTpl2k8lEdnY2x48fx9PTE6PRWKYdKLOO0WjE09OTY8eOVdXuiIhIOar8fiY//vgjgwcP5sUXX8RoNJKVlVXmeYPBgN1uP2+98tovxsWl4tnYuLFnhZcVqQyTqX51lyC/o+NxeVRpmGzbto0RI0YQFxdHWFgYW7ZsIS8vz/F8Tk4OZrMZb2/vMu25ubmYzWYaNWpEfn4+VqsVV1dXRzuUntXk5eXRtGlTSkpKyM/Pp2HDhhWu7ejRfGy288NKKk8/rGXl5p6u1u3reJRV3cfj78LFxVDuH+FV1s3166+/MmzYMJKSkggLCwOgffv2ZGVlsX//fqxWK2lpaQQEBODj44OHhwfbtm0DIDU1lYCAANzc3PDz8yM9Pb1MO0BgYCCpqakApKen4+fnh5ubblUqIlIdquzMZP78+RQVFZGQkOBo69WrFwkJCQwfPpyioiICAwMJDQ0FICkpifj4eAoKCmjbti3R0dEAjB8/ntjYWF577TWaNWvGjBkzAHjuueeIjY0lLCyM+vXrk5SUVFW7IiIif8Jgv9DAxBVA3VyXjslUn22JA6u7jBrh9jFvVnu3islUX/eA/58liX2r/Xj8XVRbN5eIiFw5FCYiIuI0hYmIiDhNYSIiIk5TmIiIiNMUJiIi4jSFiYiIOK1CYVJSUsKHH34IlE518s4772Cz2aq0MBERqT0qFCYTJkxg/fr1pSu4uLBt2zamTp1alXWJiEgtUqHpVLZv305aWhoAjRs3Zvbs2URGRlZpYSIiUntU6MzEYrFQXFzseHzulroiIiJQwTOT++67jwEDBhAZGYnBYCAtLY3AwMCqrk1ERGqJCoXJmDFjWLx4MWvXrsVoNBIUFESvXr2qujYREaklKhQmrq6uREdHO6aFFxER+b1yw6R379689957dOzYscztcu12OwaDga+//rrKCxQRkZqv3DCZPXs2gONKLhERkQspN0zO3W/dx8eHLVu2cPLkSX5/Ly0fH5+qrU5ERGqFCg/Ab968GV9fX0ebwWAgODi4ygoTEZHao0Jh8tVXX/HJJ59w1VVXVXU9IiJSC1XoS4vXXHONgkRERC6qQmcmt912G6NGjaJLly7UqVPH0a5uLhERgQqGyTfffAPABx984GjTmImIiJxToTBZuHBhVdchIiK1WIXCZN++fSxatIgzZ85gt9ux2Wzs37+fpUuXVnV9IiJSC1RoAH706NFYLBa++eYbfHx8+Omnn2jdunVV1yYiIrVEhcKkoKCAiRMn0rlzZwICAnj77bfZtWtXVdcmIiK1RIXCpGHDhgD4+vry448/0qBBA922V0REHCoUJr6+vkyZMoXbbruNRYsWsXDhwjI3yypPfn4+4eHhHDx4EICXXnqJ4OBgIiMjiYyMZM2aNQBkZmYSERFBcHAwM2fOdKy/e/duunfvTkhICGPHjnXcmOvw4cP07duX0NBQhg4dSkFBQaV2XERELp0K3wPez8+Ptm3b8uijj7J582YmTZr0p+t9++239O7dm3379jnadu7cyaJFi1i1ahWrVq0iKCiIwsJC4uLiSE5OJj09nZ07d7JhwwYAYmJiGDduHKtXr8Zut5OSkgLAxIkT6dOnDxkZGbRr147k5OS/sPsiInIpVChM6taty5133smaNWswm80kJCRw++23/+l6KSkpjB8/3jFh5JkzZzh8+DDjxo0jIiKCOXPmYLPZ2LFjB76+vjRv3hyj0UhERAQZGRkcOnSIwsJCOnToAEBUVBQZGRlYLBa2bt1KSEhImXYREakeFbo0eM2aNcTFxdG6dWtsNhtjx45l1qxZ+Pv7l7velClTyjw+evQo/v7+TJo0iXr16jF48GCWLVtGvXr1MJlMjuXMZjPZ2dnk5OSUaTeZTGRnZ3P8+HE8PT0xGo1l2iujcWPPSi0vUlEmU/3qLkF+R8fj8qhQmMycOZNFixZx4403ArBr1y7i4+NZuXJlpTbWvHlz5s2b53jcv39/UlNTCQ0NPW9Zg8FQZrr7irRXxtGj+dhs57+OVJ5+WMvKzT1drdvX8Siruo/H34WLi6HcP8Ir1M1Vp04dR5AA3HzzzZX+5Q2wZ88eVq9e7Xhst9sxGo14e3uTl5fnaM/JycFsNp/Xnpubi9lsplGjRuTn52O1Wsu0i4hI9ahQmNx111288cYbnDlzhqKiIt5//31atWrFyZMnOXHiRIU3ZrfbmTp1KidPnsRisfD+++8TFBRE+/btycrKYv/+/VitVtLS0ggICMDHxwcPDw+2bdsGQGpqKgEBAbi5ueHn50d6enqZdhERqR4V6uZ69913sVqtzJgxo0z7qlWrMBgM7N69u0Iba9OmDYMGDaJ3796UlJQQHBxMeHg4AAkJCQwfPpyioiICAwMdXV9JSUnEx8dTUFBA27ZtiY6OBmD8+PHExsby2muv0axZs/NqExGRy8dgv9AAxBVAYyaXjslUn22JA6u7jBrh9jFvVnsfvclUnz5jFldrDTXFksS+1X48/i4uyZhJXl4ea9euBWDq1KlER0fz/fffX5oKRUSk1qtQmMTGxnLgwAE2bdrE5s2b6datG5MnT67q2kREpJaoUJicOHGCJ554go0bNxIeHk5UVBRnz56t6tpERKSWqFCYWCwWLBYLn3/+OZ06deLs2bOcOXOmqmsTEZFaokJh8sADD3D33Xdz9dVX065dO3r27Om4CktERKRClwaPGDGCRx99FG9vb6D0ct02bdpUaWEiIlJ7lBsmq1atIjIykrfffvu85zZt2sSTTz5ZZYWJiEjtUW6Y7N+/H4AffvjhshQjIiK1U7lhMmLECACmTZt2WYoREZHaqdwwGTJkSLkrv/7665e0GBERqZ3KDZNzN58CmDNnjuNMRURE5PfKDZNHHnnE8f8FCxaUeSwiInJOhb5nApW/+ZSIiFw5KhwmIiIiF1NuN9fvb3xltVo5efJkmVvmNmzYsKrqEhGRWqTcMPH39y9zz/W77rrL8VxlboolIiJ/b+WGie5ZIiIiFaExExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJEREScpjARERGnKUxERMRpChMREXFalYZJfn4+4eHhHDx4EIDMzEwiIiIIDg5m5syZjuV2795N9+7dCQkJYezYsZSUlABw+PBh+vbtS2hoKEOHDqWgoACAU6dOMWjQILp27Urfvn3Jzc2tyt0QEZE/UWVh8u2339K7d2/27dsHQGFhIXFxcSQnJ5Oens7OnTvZsGEDADExMYwbN47Vq1djt9tJSUkBYOLEifTp04eMjAzatWtHcnIyALNmzcLPz49PPvmEnj17MmXKlKraDRERqYAqC5OUlBTGjx+P2WwGYMeOHfj6+tK8eXOMRiMRERFkZGRw6NAhCgsL6dChAwBRUVFkZGRgsVjYunWr426P59oB1q9fT0REBADh4eFs3LgRi8VSVbsiIiJ/otyJHp3xx7OFnJwcTCaT47HZbCY7O/u8dpPJRHZ2NsePH8fT0xOj0Vim/Y+vZTQa8fT05NixY3h7e1fV7oiISDmqLEz+6Pf3QTnn99PbV7T9YlxcKneS1bixZ6WWF6kok6l+dZcgv6PjcXlctjDx9vYmLy/P8TgnJwez2Xxee25uLmazmUaNGpGfn4/VasXV1dXRDqVnNXl5eTRt2pSSkhLy8/MrfaOuo0fzsdnODyypPP2wlpWbe7pat6/jUVZ1H4+/CxcXQ7l/hF+2S4Pbt29PVlYW+/fvx2q1kpaWRkBAAD4+Pnh4eLBt2zYAUlNTCQgIwM3NDT8/P9LT08u0AwQGBpKamgpAeno6fn5+uLm5Xa5dERGRP7hsZyYeHh4kJCQwfPhwioqKCAwMJDQ0FICkpCTi4+MpKCigbdu2REdHAzB+/HhiY2N57bXXaNasGTNmzADgueeeIzY2lrCwMOrXr09SUtLl2g0REbkAg/1CgxNXAHVzXTomU322JQ6s7jJqhNvHvFnt3SomU336jFlcrTXUFEsS+1b78fi7qDHdXCIi8velMBEREacpTERExGkKExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJEREScpjARERGnKUxERMRpChMREXGawkRERJymMBEREacpTERExGkKExERcZrCREREnKYwERERpylMRETEaQoTERFxmsJEREScpjARERGnKUxERMRpChMREXGawkRERJymMBEREacZq2Oj0dHRHD16FKOxdPOTJk3il19+4bXXXsNisfDEE0/Qt29fADIzM5k2bRpFRUV07dqVUaNGAbB7927i4+PJz8/Hz8+PiRMnOl5PREQur8t+ZmK329m7dy+rVq1y/GvatCkzZ85kyZIlrFq1ivfff5+ffvqJwsJC4uLiSE5OJj09nZ07d7JhwwYAYmJiGDduHKtXr8Zut5OSknK5d0VERP7nsofJ3r17MRgMPP300zz88MMsWrSIzMxM/P39adiwIfXq1SMkJISMjAx27NiBr68vzZs3x2g0EhERQUZGBocOHaKwsJAOHToAEBUVRUZGxuXeFRER+Z/LHianTp3i7rvvZt68ebzzzjssXbqUw4cPYzKZHMuYzWays7PJycmpULvJZCI7O/uy7oeIiPzmsg8ydOzYkY4dOwJQr149evTowbRp0xgyZEiZ5QwGA3a7/bz1y2uvjMaNPSu1vEhFmUz1q7sE+R0dj8vjsofJV199hcVi4e677wZKx1B8fHzIy8tzLJOTk4PZbMbb27tC7bm5uZjN5krVcfRoPjbb+aEklacf1rJyc09X6/Z1PMqq7uPxd+HiYij3j/DL3s11+vRpEhMTKSoqIj8/n5UrVzJ9+nQ2bdrEsWPHOHv2LJ9++ikBAQG0b9+erKws9u/fj9VqJS0tjYCAAHx8fPDw8GDbtm0ApKamEhAQcLl3RURE/ueyn5l06dKFb7/9lm7dumGz2ejTpw+33347o0aNIjo6GovFQo8ePbj11lsBSEhIYPjw4RQVFREYGEhoaCgASUlJxMfHU1BQQNu2bYmOjr7cuyIiIv9jsF9oAOIKoG6uS8dkqs+2xIHVXUaNcPuYN6u9W8Vkqk+fMYurtYaaYkli32o/Hn8XNa6bS0RE/n4UJiIi4jSFiYiIOE1hIiIiTlOYiIiI0xQmIiLiNIWJiIg4TWEiIiJOU5iIiIjTFCYiIuI0hYmIiDhNYSIiIk5TmIiIiNMUJiIi4jSFiYiIOE1hIiIiTlOYiIiI0xQmIiLiNIWJiIg4TWEiIiJOU5iIiIjTFCYiIuI0hYmIiDhNYSIiIk5TmIiIiNMUJiIi4jSFiYiIOK1Wh8lHH33EQw89RFBQEIsXL67uckRErljG6i7gr8rOzmbmzJmsWLECd3d3evXqxV133UXLli2ruzQRkStOrQ2TzMxM/P39adiwIQAhISFkZGTw7LPPVmh9FxdDFVZ35XFv0Li6S6gxasJnq8nVV1V3CTVGTTgefwd/9j7W2jDJycnBZDI5HpvNZnbs2FHh9a/WD9sldcuQf1V3CTVG48ae1V0Cc17qVt0l1Bg14XhcCWrtmIndbj+vzWDQXyAiItWh1oaJt7c3eXl5jsc5OTmYzeZqrEhE5MpVa8OkU6dObNq0iWPHjnH27Fk+/fRTAgICqrssEZErUq0dM/H29mbUqFFER0djsVjo0aMHt956a3WXJSJyRTLYLzT4ICIiUgm1tptLRERqDoWJiIg4TWEiIiJOU5iIiIjTFCY13MGDB2nXrh2RkZFERkYSEhLCiBEjynzHpqb6z3/+Q//+/c9rj42N5b777iMyMpKIiAgiIiJYuXJlNVQol8PFPgffffcdY8eOveh6f/Z8Ra1YsYI777yTyMhIHn74YUJCQvjXv/6F1Wp1+rXlN7X20uAridlsZtWqVUDpN/9nzJjBiBEjWLJkSTVX9teNGDGCqKgoAA4cOECfPn3w9vamU6dO1VyZXC633HILt9xyy19+vjLuv/9+EhISACgoKGDYsGHMnTuXkSNHXpLXF52Z1DoGg4Hhw4fz448/8v333wPwxhtv8Mgjj/Dwww+TmJjomGrmnXfeISQkhIceeojp06cDkJeXxzPPPENUVBTdu3cnMzMTKJ2FecCAATz66KN06dKFpKQkAL7//nseffRRoqKi6N27N/v27QNg48aN9OjRg27duvHss89y/PhxAL744gvCwsKIiooiJSWlQvvUvHlzoqOjHeGYlZVF//79iYiI4LHHHnPMuRYbG8vkyZPp3bs3999/P8uXLwfg7NmzjB49mvDwcCIiIkhNTQVK/yI99zozZsxw5m2XKnDujOX7778nPDzc0f7ZZ58xZMiQMmc0/fv3JzExkccee4ygoCA2bNgAwJEjR+jXrx8RERGMHj26Ql9cvuqqq3j++ed57733sNvt5X5+Ro0axVNPPUVQUBATJkxwvMbrr7/OQw89REREBAkJCVitVg4ePEhoaCi9e/fmiSeeuGTvU22hM5NayN3dHV9fX/bu3UtOTg47d+5k2bJlGAwGYmJi+PDDD7n++utZsmQJy5cvp27dugwcOJCdO3cyf/58unfvzgMPPEBOTg59+vQhNTWVtLQ0wsPDeeSRRzh9+jSBgYE89dRTLFiwgCeffJKuXbuSnp7O9u3badCgAa+88grvvvsuXl5eLF26lKSkJMaPH09sbCwLFiygRYsWleqiaN26taOrKyYmhkGDBhEcHMz27dt57rnnWL16NVD6y2PJkiX88MMPREdH0717d+bOncvVV19NWloax44do2fPnrRp0wYoDcn09HSMRn3Ua6o2bdrg4uLCDz/8QOvWrUlLS+Phhx8+bzmLxcL777/PunXrmD17NoGBgUyZMoWuXbvSt29f1qxZQ1paWoW22apVK06cOMGxY8eYP3/+RT8/33zzDWlpabi6ujqC4siRI6xbt44VK1ZgNBoZPnw4S5cuJTAwkKysLN58802uvfbaS/oe1Qb6CaulDAYDderUYdOmTezYscPRZVRYWMg111xDXl4eXbp0oX79+kDpWQqUTt2/d+9e5syZA0BJSQkHDhxgwIABbN68mfnz5/Pjjz9isVg4e/YsgYGBTJo0ic8//5wuXboQEhLCxo0b+fXXX4mOjgbAZrPh5eXFnj17MJvNtGjRAoBHHnmE2bNnV3if6tSpQ0FBAb/88gvBwcEAdOjQAS8vL/bu3QvAPffcg8FgoHXr1pw4cQKAzZs3M3XqVAAaNWrEAw88wJYtW/D09KRt27YKklogMjKSjz/+mObNm7NlyxamTp3K9u3byyxz7733Ar8FAcCXX37JtGnTAAgKCqJBgwYV2t65SWE9PDzK/fx07NgRT8/SWYebN2/OyZMn2bx5M2FhYdSpUweA7t27k5qaSmBgII0bN74igwQUJrVScXExWVlZtGzZks2bN/P444/z5JNPAnDq1ClcXV1ZtmxZmXWys7OpW7cuNpuNBQsWOO4Dk52dTZMmTUhISODAgQOEh4fz4IMPkpmZid1uJzQ0lI4dO/LZZ5+xYMECNmzYwH333cdtt93G66+/DkBRUREFBQUcPnwYm83m2Karq2uF92nPnj20aNECu91+3ozQdrvdMVjq4eEBlJ0hurzlz/3AS80WHh7O448/Tps2bejcubPjOP/ehY69q6vrBWcQ/zN79uyhadOmeHp6Vujzdm67dru9zGf8nJKSEuDK/rxpzKSWsdlszJ07l/bt23Pdddfh7+/PqlWrKCgooKSkhGHDhrF69Wr8/PzYuHGjo3306NHs3LkTf39/x9jETz/9xMMPP8zZs2f58ssvGTBgAF27duXXX38lOzsbm83GyJEj2bFjB7169eK5557jv//9L+3bt2f79u1kZWUBkJycTGJiIjfeeCNHjx51jOV8/PHHFdqnffv2sWTJEnr37o2npyfNmzfn008/BWD79u3k5eXRqlWri67v7+/vCM9jx46xdu1a7rzzzr/8Hsvl5+3tTbNmzXjjjTcu2MV1MZ06deKjjz4CYMOGDZw6depP1zl9+jSzZ8+mb9++QOU/P/7+/nz88ccUFhZSUlLC8uXL8ff3r3DNf1c6M6kFcnJyiIyMBErD5KabbuKVV14BSq9SOTdIbrVauffee3nkkUcwGAz069ePXr16YbPZCAoKolOnTrRo0YJ//vOfREREAJCYmIinpyeDBw9mzJgxNGjQgMaNG9OuXTsOHjzIkCFDGDt2LMnJybi6uhIbG4vJZGLq1KmMHDkSm82Gt7c306dPx83NjRkzZhATE4PRaKRt27YX3ac5c+awYMECDAYDrq6uvPjii9x2220ATJ8+nQkTJjB37lzc3NyYO3cu7u7uF32tYcOGMWHCBCIiIrBarQwZMoSbb76ZPXv2XKpDIE766quv6Nixo+NxREQEYWFhZZaJjIxk5syZ3HXXXRV+3bi4OF588UVSUlJo06bNRbu51q1bR2RkJAaDAavVSkhICAMHDgQq//np0qULu3fvpnv37pSUlHDvvffSr18/jhw5UuG6/4400aOI1FrvvvsunTp1omXLluzatYtx48axYsWK6i7riqQzExGptXx9fXn++edxcXHBw8ODl19+ubpLumLpzERERJymAXgREXGawkRERJymMBEREadpAF7kAg4ePEhQUBCtW7cGSi/JdnNzIzo6mm7dul3WWk6dOuWYo+rMmTNkZ2dz/fXXA6Xfs3jxxRcvaz0iF6IwEbmIOnXqOGZrBjh06BBPPPEEdevWJSQk5LLV0aBBA0cd//nPf3j55ZfL1CVSE6ibS6SCfHx8GDFiBPPnzwdKv0n9wgsvOGabTUxMdEyrccsttzB37lx69erF/fff75gbDWDevHmOGWdHjBhBbm4uUDoz7rPPPstDDz3EwoULK1TThx9+SK9evRyPDx8+TOfOnSkuLqZt27b861//IioqitDQUMesAgAffPABUVFRdOvWjSeeeIKff/7Z2bdHrnAKE5FKaNOmDT/88AMAkydPpmHDhnz00UcsX76cPXv28NZbbwGl86ddffXVLF26lDlz5vDKK69QVFTE8uXL+fzzz1m2bBkfffQRrVq1IjY21vH6DRo0ID09/YI3k7qQ0NBQfvnlF3766SegNCQeeeQR3N3dsVqteHl5sWLFCmbNmkVcXBzHjh1jy5YtpKamsnjxYlJTUxk4cCDDhw+/xO+UXGkUJiKVcG62Zii9p0u/fv0wGAy4u7vTq1cvNm7c6Fj2gQceAODmm2+muLiYM2fOsHHjRqKioqhXrx4A0dHRbN68meLiYgD8/PwqVY+7uzs9e/YkJSUFq9XKypUreeyxxxzP9+vXDygNwdatW7N161bWr1/P/v376dWrF5GRkUyfPp2TJ086ZuIV+Ss0ZiJSCd99912ZQfnfs9lsjm4uOH+W2wvNiPzHdc6FTGU89thj9OzZkzvvvJNWrVqVmQL99zM322w2XF1dsdlsREZGEhMT42jPycnBy8ur0tsWOUdnJiIVlJWVRXJyMk899RQAnTt3ZvHixdjtdoqLi0lJSfnT2w537tyZFStWcObMGQAWLlzIHXfcUe5Eln/mmmuuoUOHDkydOpXevXuXee7cXQN37dpFVlYWd9xxB/fccw8ff/wxOTk5ALz33ns8/vjjf3n7IqAzE5GLKiwsdMzWfG7up+eff5777rsPgPj4eCZPnkxERAQWi4V7772XIUOGlPuaPXr04Ndff6Vnz57YbDZ8fX0dt0h2RlRUFC+//DKBgYFl2r/++mtSUlKw2WzMnDkTLy8v7r33Xp5++mmeeuopDAYDnp6evPrqq2XuEyJSWZqbS6SWs9lsTJo0iWuuuYZBgwY52m+88UY2bdpEo0aNqrE6uVKom0ukFsvPz+euu+7iwIEDjsF2keqgMxMREXGazkxERMRpChMREXGawkRERJymMBEREacpTERExGkKExERcdr/BzVylAT25QztAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = df1, x = 'Donor Type', y = 'Hispanic', hue = 'Hispanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "postal-scholarship",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Donor Type', ylabel='Asian'>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gYCaT6brt1+PgULqDo7J4cp/8j57cJ1WR9tPibmY/rZQn9yUkJJCamkpAQADnz5/n0qVLmEwm0tLS7NOkpqZisVho0KABWVlZFBUV4ejoaG8HsFgspKWl0ahRIwoLC8nKyqJevXrlUbKIiBhQLn0aH3zwAfHx8Xz22WeMGzeORx55hFmzZuHq6srevXsBiIuLw8vLC2dnZzw9PUlISCjWDuDt7U1cXBxwOYg8PT1xdnYuj5JFRMSACh3lNioqioiICLKzs2nTpg0hISEATJ06lfDwcBYvXkzjxo2ZN28eAOPHjyc8PJx+/fpRu3ZtoqKiKrJcERH5DZPtNn/ySFn0aQybsLIMK7p1rYocrj4NqZLM5trsjXyussuoEjre5NDoJfVp6I5wERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDHMyMlF+fj45OTnYbDZ7W7169cqrJhERqaJKDI1ly5Yxd+5cCgoKALDZbJhMJg4dOlTuxYmISNVSYmisWLGCjz76iPvuu68i6hERkSqsxD4Ns9mswBAREcBAaHTr1o1Vq1aRnJxMZmam/T8REal+Sjw9tWTJEvLz85kxY4a9zWifxoIFC9i4cSMmk4lBgwbx9NNPs2PHDmbNmkVeXh5+fn6EhoYCcOjQISIiIsjKysLT05Pp06fj5OTEmTNnCAsLIz09nebNmxMVFUWtWrVuYpNFROT3KvFI48CBAxw+fLjYf0YCY/fu3ezatYt169axZs0aVqxYweHDh5k0aRLR0dEkJCRw8OBBtm3bBkBYWBhTpkxh48aN2Gw2YmJiAJg+fTrDhg0jMTGRtm3bEh0dfZObLCIiv1eJoZGfn8/mzZuJi4sjLi6ONWvWMH/+/BIX3KlTJ5YvX46TkxPp6ekUFRVx4cIFmjVrRtOmTXFycsLf35/ExEROnz5Nbm4u7du3ByAwMJDExEQKCgrYs2cPPj4+xdpFRKRylHh6KjQ0lJMnT5KamkqbNm3Yv38/nTp1MrRwZ2dnFi5cyPvvv4+vry8pKSmYzWb7+xaLheTk5KvazWYzycnJnDt3Djc3N5ycnIq1i4hI5SgxNA4dOsSmTZuYNm0aTz/9NDabjenTpxtewbhx4xg1ahSjR4/m+PHjV71vMpmK3TRopL00GjZ0K9X0cmNmc+3KLkFESlCe+2mJoWGxWHBycuKee+7h6NGj+Pn5kZOTU+KCf/rpJ/Lz87n33nupWbMmffr0ITExEUdHR/s0KSkpWCwW3N3dSUtLs7enpqZisVho0KABWVlZFBUV4ejoaG8vjfT0LKzWq8PHKH1JFpeaerGySxC5ivbT4m5mP3VwMN3wx3aJfRp33HEH69evx8PDg88//5wjR44YuuT21KlTREREkJ+fT35+Pl988QXBwcEkJSVx4sQJioqKiI+Px8vLiyZNmuDq6srevXsBiIuLw8vLC2dnZzw9PUlISCjWLiIilaPEI40pU6bw6aefEhYWxurVqxkxYoT9Mtkb8fb2Zv/+/QwYMABHR0f69OlDv379aNCgAWPHjiUvLw9vb298fX0BiIqKIiIiguzsbNq0aUNISAgAU6dOJTw8nMWLF9O4cWPmzZt3k5ssIiK/l8l2rY6D20hZnJ4aNmFlGVZ061oVOVynp6RKMptrszfyucouo0roOOG9cj09dd0jjfHjx7NgwQL8/f2v+f769et/d1EiInJrum5ojBo1Crh8ekpERARu0BHetm1b4PJNeo0bN6ZTp05kZmaye/du7r333gorUEREqo4Sr5567bXXePfdd/npp5+YMWMGp0+fZvLkyRVRm4iIVDElhsbBgweZNm0amzdvZuDAgcyaNYvTp09XRG0iIlLFlBgaNpsNBwcHvvnmGzp37gxg6OY+ERG5/ZQYGn/4wx8YNWoUp06d4qGHHuLVV1/Fw8OjImoTEZEqpsSb+2bNmsXmzZvp2LEjjo6OtGrVii+++KIiahMRkSqmxNC444476NmzJ5988gkrV67k0qVLPPnkkxVRm4iIVDE3DI1jx47x4Ycfsn79epo0aUJubi5btmyhdm0NDiYiUh1dt09j1KhRjBgxAhcXF5YvX058fDy1atVSYIiIVGPXDY1Dhw7Rpk0bWrVqxT333AOU/lkWIiJye7luaGzdupWgoCDi4+Pp3r0748aNIy8vryJrExGRKua6oeHk5ISfnx8rVqxgzZo1WCwWcnNz6dOnDx999FFF1igiIlVEifdpALRs2ZKIiAi++uornn32WWJiYsq7LhERqYIMhcYVNWvWZMiQIaxdu7a86hERkSqsVKEhIiLVm0JDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoaVa2j87W9/o1+/fvTr14/IyEgAduzYgb+/P3369GH+/Pn2aQ8dOkRQUBA+Pj5MnjyZwsJCAM6cOcPw4cPx9fVlzJgxZGdnl2fJIiJyA+UWGjt27ODrr79m7dq1xMXF8f333xMfH8+kSZOIjo4mISGBgwcPsm3bNgDCwsKYMmUKGzduxGaz2Z/ZMX36dIYNG0ZiYiJt27YlOjq6vEoWEZESlFtomM1mwsPDcXFxwdnZmRYtWnD8+HGaNWtG06ZNcXJywt/fn8TERE6fPk1ubi7t27cHIDAwkMTERAoKCtizZw8+Pj7F2kVEpHKUW2i0atXKHgLHjx8nISEBk8mE2Wy2T2OxWEhOTiYlJaVYu9lsJjk5mXPnzuHm5oaTk1OxdhERqRxO5b2CH3/8kRdeeIGJEyfi5OREUlJSsfdNJhM2m+2q+W7UXhoNG7qVrmC5IbO5dmWXICIlKM/9tFxDY+/evYwbN45JkybRr18/du/eTVpamv39lJQULBYL7u7uxdpTU1OxWCw0aNCArKwsioqKcHR0tLeXRnp6Flbr1eFjlL4ki0tNvVjZJYhcRftpcTeznzo4mG74Y7vcTk+dPXuWF198kaioKPr16wdAu3btSEpK4sSJExQVFREfH4+XlxdNmjTB1dWVvXv3AhAXF4eXlxfOzs54enqSkJBQrF1ERCpHuR1pLF26lLy8PGbPnm1vCw4OZvbs2YwdO5a8vDy8vb3x9fUFICoqioiICLKzs2nTpg0hISEATJ06lfDwcBYvXkzjxo2ZN29eeZUsIiIlMNmu1XFwGymL01PDJqwsw4puXasih+v0lFRJZnNt9kY+V9llVAkdJ7x3a56eEhGR249CQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGlXtoZGVl0b9/f06dOgXAjh078Pf3p0+fPsyfP98+3aFDhwgKCsLHx4fJkydTWFgIwJkzZxg+fDi+vr6MGTOG7Ozs8i5ZRESuo1xDY//+/QwdOpTjx48DkJuby6RJk4iOjiYhIYGDBw+ybds2AMLCwpgyZQobN27EZrMRExMDwPTp0xk2bBiJiYm0bduW6Ojo8ixZRERuoFxDIyYmhqlTp2KxWAA4cOAAzZo1o2nTpjg5OeHv709iYiKnT58mNzeX9u3bAxAYGEhiYiIFBQXs2bMHHx+fYu0iIlI5nMpz4W+88Uax1ykpKZjNZvtri8VCcnLyVe1ms5nk5GTOnTuHm5sbTk5OxdpFRKRylGto/JbNZruqzWQylbq9NBo2dCvV9HJjZnPtyi5BREpQnvtphYaGu7s7aWlp9tcpKSlYLJar2lNTU7FYLDRo0ICsrCyKiopwdHS0t5dGenoWVuvV4WOUviSLS029WNkliFxF+2lxN7OfOjiYbvhju0IvuW3Xrh1JSUmcOHGCoqIi4uPj8fLyokmTJri6urJ3714A4uLi8PLywtnZGU9PTxISEoq1i4hI5ajQIw1XV1dmz57N2LFjycvLw9vbG19fXwCioqKIiIggOzubNm3aEBISAsDUqVMJDw9n8eLFNG7cmHnz5lVkySIi8isVEhpbtmyx/3+XLl1Yt27dVdN4eHiwevXqq9qbNGnCihUryrU+ERExRneEi4iIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBh2S4TG+vXr6du3L71792blypWVXY6ISLXlVNkFlCQ5OZn58+cTGxuLi4sLwcHBPPzww7Rs2bKySxMRqXaqfGjs2LGDzp07U69ePQB8fHxITEzkpZdeMjS/g4Pppmu4s36tm17G7aIs/p4i5cGlTsPKLqHKuJn9tKR5q3xopKSkYDab7a8tFgsHDhwwPH/9MvjCX/jXATe9jNtFw4ZulV2CyDXdP/qtyi6hyijP/bTK92nYbLar2kwm/doVEakMVT403N3dSUtLs79OSUnBYrFUYkUiItVXlQ+Nrl27snPnTjIyMsjJyWHTpk14eXlVdlkiItVSle/TcHd3JzQ0lJCQEAoKChg0aBAPPPBAZZclIlItmWzX6jQQERG5hip/ekpERKoOhYaIiBim0BAREcMUGiIiYphCo4o4deoUbdu2JSAggICAAHx8fBg3blyxe1Sqqm+//ZYnn3zyqvbw8HB69uxJQEAA/v7++Pv7s3bt2kqoUCrC9T4H3333HZMnT77ufCW9b1RsbCydOnUiICCAxx9/HB8fH9566y2KiopuetnyP1X+ktvqxGKx8NlnnwGX74SfN28e48aNY9WqVZVc2e83btw4AgMDATh58iTDhg3D3d2drl27VnJlUlHuv/9+7r///t/9fmk88sgjzJ49G4Ds7GxefPFFFi1axMsvv1wmyxcdaVRZJpOJsWPH8uOPP3L48GEAlixZwsCBA3n88ceJjIy0D7Hy4Ycf4uPjQ9++fZkzZw4AaWlp/PnPfyYwMJCgoCB27NgBXB41+Nlnn+WJJ56gV69eREVFAXD48GGeeOIJAgMDGTp0KMePHwdg+/btDBo0iAEDBvDSSy9x7tw5AL7++mv69etHYGAgMTExhrapadOmhISE2EMwKSmJJ598En9/f4YMGWIfUyw8PJyZM2cydOhQHnnkEdasWQNATk4Or776Kv3798ff35+4uDjg8i/MK8uZN2/ezfzZpRxcOQI5fPgw/fv3t7d/+eWXjB49utgRypNPPklkZCRDhgyhd+/ebNu2DYBffvmFESNG4O/vz6uvvmroBt9atWrxyiuv8NFHH2Gz2W74+QkNDeWZZ56hd+/eTJs2zb6Md955h759++Lv78/s2bMpKiri1KlT+Pr6MnToUEaOHFlmf6dbhY40qjAXFxeaNWvGsWPHSElJ4eDBg6xevRqTyURYWBjr1q2jefPmrFq1ijVr1lCzZk2ee+45Dh48yNKlSwkKCuLRRx8lJSWFYcOGERcXR3x8PP3792fgwIFcvHgRb29vnnnmGZYtW8bTTz+Nn58fCQkJ7Nu3jzp16jB37lyWL19O3bp1+fjjj4mKimLq1KmEh4ezbNkyWrRoUapTC61bt7afogoLC+P555+nT58+7Nu3j/Hjx7Nx40bg8pfEqlWrOHr0KCEhIQQFBbFo0SLq169PfHw8GRkZDB48GA8PD+ByGCYkJODkpI90VeXh4YGDgwNHjx6ldevWxMfH8/jjj181XUFBAZ988glbtmxhwYIFeHt788Ybb+Dn58fw4cPZvHkz8fHxhtbZqlUrMjMzycjIYOnSpdf9/Pz73/8mPj4eR0dHeyD88ssvbNmyhdjYWJycnBg7diwff/wx3t7eJCUl8d5773H33XeX6d/oVqA9rIozmUzUqFGDnTt3cuDAAfupntzcXO666y7S0tLo1asXtWvXBi4fdcDlIeWPHTvGwoULASgsLOTkyZM8++yz7Nq1i6VLl/Ljjz9SUFBATk4O3t7ezJgxg6+++opevXrh4+PD9u3bOXv2LCEhIQBYrVbq1q3LkSNHsFgstGjRAoCBAweyYMECw9tUo0YNsrOz+fnnn+nTpw8A7du3p27duhw7dgyAbt26YTKZaN26NZmZmQDs2rWLN998E4AGDRrw6KOPsnv3btzc3GjTpo0C4xYQEBDAhg0baNq0Kbt37+bNN99k3759xabp0aMH8L8vfIBvvvmGWbNmAdC7d2/q1KljaH1XBjd1dXW94eenQ4cOuLldHhm2adOmnD9/nl27dtGvXz9q1KgBQFBQEHFxcXh7e9OwYcNqGRig0KjS8vPzSUpKomXLluzatYunnnqKp59+GoALFy7g6OjI6tWri82TnJxMzZo1sVqtLFu2zP4ckuTkZO68805mz57NyZMn6d+/P4899hg7duzAZrPh6+tLhw4d+PLLL1m2bBnbtm2jZ8+ePPjgg7zzzjsA5OXlkZ2dzZkzZ7BarfZ1Ojo6Gt6mI0eO0KJFC2w221UjGNtsNnunpaurK1B8ROMbTX9lx5aqrX///jz11FN4eHjQvXt3+7/zr13r397R0fGaI16X5MiRIzRq1Ag3NzdDn7cr67XZbMU+41cUFhYC1fvzpj6NKspqtbJo0SLatWvHH/7wBzp37sxnn31GdnY2hYWFvPjii2zcuBFPT0+2b99ub3/11Vc5ePAgnTt3tvcd/Oc//+Hxxx8nJyeHb775hmeffRY/Pz/Onj1LcnIyVquVl19+mQMHDhAcHMz48eP54YcfaNeuHfv27SMpKQmA6OhoIiMj+dOf/kR6erq9r2XDhg2Gtun48eOsWrWKoUOH4ubmRtOmTdm0aRMA+/btIy0tjVatWl13/s6dO9tDMiMjgy+++IJOnTr97r+xVDx3d3caN27MkiVLrnlq6nq6du3K+vXrAdi2bRsXLlwocZ6LFy+yYMEChg8fDpT+89O5c2c2bNhAbm4uhYWFrFmzhs6dOxuu+XalI40qJCUlhYCAAOByaNx7773MnTsXuHxVyJXO6qKiInr06MHAgQMxmUyMGDGC4OBgrFYrvXv3pmvXrrRo0YLXXnsNf39/ACIjI3Fzc+OFF15gwoQJ1KlTh4YNG9K2bVtOnTrF6NGjmTx5MtHR0Tg6OhIeHo7ZbObNN9/k5Zdfxmq14u7uzpw5c3B2dmbevHmEhYXh5OREmzZtrrtNCxcuZNmyZZhMJhwdHZk4cSIPPvggAHPmzGHatGksWrQIZ2dnFi1ahIuLy3WX9eKLLzJt2jT8/f0pKipi9OjR3HfffRw5cqSs/gnkJv3zn/+kQ4cO9tf+/v7069ev2DQBAQHMnz+fhx9+2PByJ02axMSJE4mJicHDw+O6p6e2bNlCQEAAJpOJoqIifHx8eO6554DSf3569erFoUOHCAoKorCwkB49ejBixAh++eUXw3XfjjRgoYhUecuXL6dr1660bNmS77//nilTphAbG1vZZVVLOtIQkSqvWbNmvPLKKzg4OODq6srrr79e2SVVWzrSEBERw9QRLiIihik0RETEMIWGiIgYpo5wqdZOnTpF7969ad26NXD5UmdnZ2dCQkIYMGBAhdZy4cIF+xhMly5dIjk5mebNmwOX71OYOHFihdYjci0KDan2atSoYR9dGOD06dOMHDmSmjVr4uPjU2F11KlTx17Ht99+y+uvv16sLpGqQKenRH6jSZMmjBs3jqVLlwKX7yz+y1/+Yh8dNTIy0j6cxP3338+iRYsIDg7mkUcesY/9BfD3v//dPkLquHHjSE1NBS6P5PrSSy/Rt29fVqxYYaimdevWERwcbH995swZunfvTn5+Pm3atOGtt94iMDAQX19f+132AJ9++imBgYEMGDCAkSNH8tNPP93sn0eqOYWGyDV4eHhw9OhRAGbOnEm9evVYv349a9as4ciRI7z//vvA5fHB6tevz8cff8zChQuZO3cueXl5rFmzhq+++orVq1ezfv16WrVqRXh4uH35derUISEh4ZoPLboWX19ffv75Z/7zn/8Al8Ng4MCBuLi4UFRURN26dYmNjeXtt99m0qRJZGRksHv3buLi4li5ciVxcXE899xzjB07toz/UlLdKDREruHK6MJw+ZkiI0aMwGQy4eLiQnBwMNu3b7dP++ijjwJw3333kZ+fz6VLl9i+fTuBgYHccccdAISEhLBr1y7y8/MB8PT0LFU9Li4uDB48mJiYGIqKili7di1Dhgyxvz9ixAjgcti1bt2aPXv2sHXrVk6cOEFwcDABAQHMmTOH8+fP20eOFfk91Kchcg3fffddsc7xX7NarfbTU3D1qKzXGsH3t/NcCZPSGDJkCIMHD6ZTp060atWq2NDcvx5p2Gq14ujoiNVqJSAggLCwMHt7SkoKdevWLfW6Ra7QkYbIbyQlJREdHc0zzzwDQPfu3Vm5ciU2m438/HxiYmJKfFxt9+7diY2N5dKlSwCsWLGChx566IYDMpbkrrvuon379rz55psMHTq02HtXnkL3/fffk5SUxEMPPUS3bt3YsGEDKSkpAHz00Uc89dRTv3v9IqAjDRFyc3PtowtfGdvolVdeoWfPngBEREQwc+ZM/P39KSgooEePHowePfqGyxw0aBBnz55l8ODBWK1WmjVrZn+07s0IDAzk9ddfx9vbu1j7v/71L2JiYrBarcyfP5+6devSo0cPRo0axTPPPIPJZMLNzY2//e1vxZ5TIVJaGntK5BZhtVqZMWMGd911F88//7y9/U9/+hM7d+6kQYMGlVidVBc6PSVyC8jKyuLhhx/m5MmT9k5vkcqgIw0RETFMRxoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEsP8PUq+XO1rIBWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = df1, x = 'Donor Type', y = 'Asian', hue = 'Asian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "searching-first",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Donor Type', ylabel='Multiracial'>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = df1, x = 'Donor Type', y = 'Multiracial', hue = 'Multiracial')"
   ]
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}