Donor_by_eth_by_year (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "economic-consumption",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "applicable-canada",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>All Ethnicities</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Unknown</th>\n",
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       "      <th>Multiracial</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>All Donors</td>\n",
       "      <td>To Date</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>328.0</td>\n",
       "      <td>10,588</td>\n",
       "      <td>1,891</td>\n",
       "      <td>1,328</td>\n",
       "      <td>2,904</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,740</td>\n",
       "      <td>3,290</td>\n",
       "      <td>573</td>\n",
       "      <td>654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18,317</td>\n",
       "      <td>12,378</td>\n",
       "      <td>2,372</td>\n",
       "      <td>2,676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>564</td>\n",
       "      <td>112</td>\n",
       "      <td>53</td>\n",
       "      <td>162</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19,259</td>\n",
       "      <td>13,057</td>\n",
       "      <td>2,414</td>\n",
       "      <td>2,818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632</td>\n",
       "      <td>80</td>\n",
       "      <td>42</td>\n",
       "      <td>216</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17,566</td>\n",
       "      <td>11,810</td>\n",
       "      <td>2,304</td>\n",
       "      <td>2,538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>555</td>\n",
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       "      <th>245</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1992</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1991</td>\n",
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       "<p>250 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0 Unnamed: 1  Unnamed: 2 All Ethnicities    White   Black  \\\n",
       "0    All Donors    To Date         NaN         413,435  290,985  53,737   \n",
       "1           NaN       2021         NaN           4,740    3,290     573   \n",
       "2           NaN       2020         NaN          18,317   12,378   2,372   \n",
       "3           NaN       2019         NaN          19,259   13,057   2,414   \n",
       "4           NaN       2018         NaN          17,566   11,810   2,304   \n",
       "..          ...        ...         ...             ...      ...     ...   \n",
       "245         NaN       1992         NaN              21       17       3   \n",
       "246         NaN       1991         NaN              11        7       3   \n",
       "247         NaN       1990         NaN               5        5       0   \n",
       "248         NaN       1989         NaN               0        0       0   \n",
       "249         NaN       1988         NaN               0        0       0   \n",
       "\n",
       "    Hispanic  Unknown   Asian American Indian/Alaska Native Pacific Islander  \\\n",
       "0     51,674    328.0  10,588                         1,891            1,328   \n",
       "1        654      0.0     133                            30               15   \n",
       "2      2,676      0.0     564                           112               53   \n",
       "3      2,818      0.0     632                            80               42   \n",
       "4      2,538      0.0     555                            91               50   \n",
       "..       ...      ...     ...                           ...              ...   \n",
       "245        1      0.0       0                             0                0   \n",
       "246        1      0.0       0                             0                0   \n",
       "247        0      0.0       0                             0                0   \n",
       "248        0      0.0       0                             0                0   \n",
       "249        0      0.0       0                             0                0   \n",
       "\n",
       "    Multiracial  \n",
       "0         2,904  \n",
       "1            45  \n",
       "2           162  \n",
       "3           216  \n",
       "4           218  \n",
       "..          ...  \n",
       "245           0  \n",
       "246           0  \n",
       "247           0  \n",
       "248           0  \n",
       "249           0  \n",
       "\n",
       "[250 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/guillermo/Downloads/Donor___Donor_Ethnicity_by_Organ,_Donation_Year.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "christian-prototype",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-96a9f82b2f3e>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1['White'] = df1['White'].str.replace(',','').astype(int)\n",
      "<ipython-input-13-96a9f82b2f3e>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1['Black'] = df1['Black'].str.replace(',','').astype(int)\n",
      "<ipython-input-13-96a9f82b2f3e>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1['Hispanic'] = df1['Hispanic'].str.replace(',','').astype(int)\n",
      "<ipython-input-13-96a9f82b2f3e>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1['Asian'] = df1['Asian'].str.replace(',','').astype(int)\n",
      "<ipython-input-13-96a9f82b2f3e>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1['Multiracial'] = df1['Multiracial'].str.replace(',','').astype(int)\n",
      "<ipython-input-13-96a9f82b2f3e>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df1['All Ethnicities'] = df1['All Ethnicities'].str.replace(',', '').astype(int)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Organ</th>\n",
       "      <th>Dates</th>\n",
       "      <th>All Ethnicities</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Multiracial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All Donors</td>\n",
       "      <td>To Date</td>\n",
       "      <td>413435</td>\n",
       "      <td>290985</td>\n",
       "      <td>53737</td>\n",
       "      <td>51674</td>\n",
       "      <td>10588</td>\n",
       "      <td>2904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2021</td>\n",
       "      <td>4740</td>\n",
       "      <td>3290</td>\n",
       "      <td>573</td>\n",
       "      <td>654</td>\n",
       "      <td>133</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>2020</td>\n",
       "      <td>18317</td>\n",
       "      <td>12378</td>\n",
       "      <td>2372</td>\n",
       "      <td>2676</td>\n",
       "      <td>564</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>2019</td>\n",
       "      <td>19259</td>\n",
       "      <td>13057</td>\n",
       "      <td>2414</td>\n",
       "      <td>2818</td>\n",
       "      <td>632</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>2018</td>\n",
       "      <td>17566</td>\n",
       "      <td>11810</td>\n",
       "      <td>2304</td>\n",
       "      <td>2538</td>\n",
       "      <td>555</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>2017</td>\n",
       "      <td>16467</td>\n",
       "      <td>11221</td>\n",
       "      <td>2127</td>\n",
       "      <td>2275</td>\n",
       "      <td>529</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>2016</td>\n",
       "      <td>15946</td>\n",
       "      <td>10843</td>\n",
       "      <td>2122</td>\n",
       "      <td>2204</td>\n",
       "      <td>500</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>2015</td>\n",
       "      <td>15068</td>\n",
       "      <td>10163</td>\n",
       "      <td>2029</td>\n",
       "      <td>2084</td>\n",
       "      <td>515</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>2014</td>\n",
       "      <td>14415</td>\n",
       "      <td>9828</td>\n",
       "      <td>1944</td>\n",
       "      <td>1947</td>\n",
       "      <td>437</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>2013</td>\n",
       "      <td>14258</td>\n",
       "      <td>9648</td>\n",
       "      <td>2021</td>\n",
       "      <td>1942</td>\n",
       "      <td>416</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>2012</td>\n",
       "      <td>14010</td>\n",
       "      <td>9539</td>\n",
       "      <td>1981</td>\n",
       "      <td>1836</td>\n",
       "      <td>429</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>2011</td>\n",
       "      <td>14149</td>\n",
       "      <td>9635</td>\n",
       "      <td>1995</td>\n",
       "      <td>1892</td>\n",
       "      <td>400</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>2010</td>\n",
       "      <td>14504</td>\n",
       "      <td>9849</td>\n",
       "      <td>2088</td>\n",
       "      <td>1961</td>\n",
       "      <td>405</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Organ    Dates  All Ethnicities   White  Black  Hispanic  Asian  \\\n",
       "0   All Donors  To Date           413435  290985  53737     51674  10588   \n",
       "1                  2021             4740    3290    573       654    133   \n",
       "2                  2020            18317   12378   2372      2676    564   \n",
       "3                  2019            19259   13057   2414      2818    632   \n",
       "4                  2018            17566   11810   2304      2538    555   \n",
       "5                  2017            16467   11221   2127      2275    529   \n",
       "6                  2016            15946   10843   2122      2204    500   \n",
       "7                  2015            15068   10163   2029      2084    515   \n",
       "8                  2014            14415    9828   1944      1947    437   \n",
       "9                  2013            14258    9648   2021      1942    416   \n",
       "10                 2012            14010    9539   1981      1836    429   \n",
       "11                 2011            14149    9635   1995      1892    400   \n",
       "12                 2010            14504    9849   2088      1961    405   \n",
       "\n",
       "    Multiracial  \n",
       "0          2904  \n",
       "1            45  \n",
       "2           162  \n",
       "3           216  \n",
       "4           218  \n",
       "5           194  \n",
       "6           167  \n",
       "7           179  \n",
       "8           155  \n",
       "9           131  \n",
       "10          135  \n",
       "11          148  \n",
       "12          121  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"Unnamed: 1\": \"Dates\", 'Unnamed: 0': 'Organ'}, inplace = True)\n",
    "df.drop(['Unnamed: 2', 'Unknown', 'American Indian/Alaska Native', 'Pacific Islander'], axis=1, inplace = True)\n",
    "df = df.fillna(\"\")\n",
    "df1 = df[0:13]\n",
    "df1['White'] = df1['White'].str.replace(',','').astype(int)\n",
    "df1['Black'] = df1['Black'].str.replace(',','').astype(int)\n",
    "df1['Hispanic'] = df1['Hispanic'].str.replace(',','').astype(int)\n",
    "df1['Asian'] = df1['Asian'].str.replace(',','').astype(int)\n",
    "df1['Multiracial'] = df1['Multiracial'].str.replace(',','').astype(int)\n",
    "df1['All Ethnicities'] = df1['All Ethnicities'].str.replace(',', '').astype(int)\n",
    "df1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "round-toner",
   "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": [
    "plot1 = df1.loc[2:12]\n",
    "plot1.drop(['All Ethnicities', 'Organ', 'Dates'], axis = 1, inplace = True)\n",
    "plot1.values\n",
    "years = ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "robust-terminology",
   "metadata": {},
   "source": [
    "# Plot chart of donations by ethnicity by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "postal-drama",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plt.subplots(figsize =(10,7))\n",
    "eth_labels = ['White', ]\n",
    "plt.plot(np.flip(years),np.flip(plot1.values), label = ['Multiracial', 'Asian', 'Hispanic', 'African American', 'White'], marker = \"o\")\n",
    "# label the legend\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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
}