USviolence.ipynb

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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Victim's name</th>\n",
       "      <th>Victim's age</th>\n",
       "      <th>Victim's gender</th>\n",
       "      <th>Victim's race</th>\n",
       "      <th>URL of image of victim</th>\n",
       "      <th>Date of Incident (month/day/year)</th>\n",
       "      <th>Street Address of Incident</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Symptoms of mental illness?</th>\n",
       "      <th>Unarmed/Did Not Have an Actual Weapon</th>\n",
       "      <th>Alleged Weapon (Source: WaPo and Review of Cases Not Included in WaPo Database)</th>\n",
       "      <th>Alleged Threat Level (Source: WaPo)</th>\n",
       "      <th>Fleeing (Source: WaPo)</th>\n",
       "      <th>Body Camera (Source: WaPo)</th>\n",
       "      <th>WaPo ID (If included in WaPo database)</th>\n",
       "      <th>Off-Duty Killing?</th>\n",
       "      <th>Geography (via Trulia methodology based on zipcode population density: http://jedkolko.com/wp-content/uploads/2015/05/full-ZCTA-urban-suburban-rural-classification.xlsx )</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kevin Lee Catlett</td>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>900 McArthur Street</td>\n",
       "      <td>Johnson City</td>\n",
       "      <td>TN</td>\n",
       "      <td>37601.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Drug or alcohol use</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>attack</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wade Russell Meisberger</td>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>https://fatalencounters.org/wp-content/uploads...</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>West Beech St and South Church St</td>\n",
       "      <td>Hazleton</td>\n",
       "      <td>PA</td>\n",
       "      <td>18201.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>attack</td>\n",
       "      <td>Car</td>\n",
       "      <td>No</td>\n",
       "      <td>5958.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nick Costales</td>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>https://fatalencounters.org/wp-content/uploads...</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>9501 Monte Vista Ave</td>\n",
       "      <td>Montclair</td>\n",
       "      <td>CA</td>\n",
       "      <td>91763.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>attack</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brittany S. Teichroeb</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>TX-191 and TX-250 Loop</td>\n",
       "      <td>Midland</td>\n",
       "      <td>TX</td>\n",
       "      <td>79706.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>John Parks</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>https://fatalencounters.org/wp-content/uploads...</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>Court Avenue and Audubon Drive</td>\n",
       "      <td>Paducah</td>\n",
       "      <td>KY</td>\n",
       "      <td>42001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Unclear</td>\n",
       "      <td>undetermined</td>\n",
       "      <td>undetermined</td>\n",
       "      <td>Car</td>\n",
       "      <td>No</td>\n",
       "      <td>5953.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8258</th>\n",
       "      <td>Tyree Bell</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>http://content.omaha.com/media/maps/ps/2013/ja...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>3727 N. 42nd St.</td>\n",
       "      <td>Omaha</td>\n",
       "      <td>NE</td>\n",
       "      <td>68111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8259</th>\n",
       "      <td>Abel Gurrola</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>http://www.bakersfieldnow.com/news/local/A-26-...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>720 Terrace Way</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>CA</td>\n",
       "      <td>93304.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>Christopher Tavares</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>http://www.krdo.com/image/view/-/17980228/medR...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>Highway 50 and North Elizabeth Street</td>\n",
       "      <td>Pueblo</td>\n",
       "      <td>CO</td>\n",
       "      <td>81008.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8261</th>\n",
       "      <td>Andrew L. Closson</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>http://www.superiortelegram.com/sites/default/...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>U.S. Highway 53</td>\n",
       "      <td>Gordon</td>\n",
       "      <td>WI</td>\n",
       "      <td>54838.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Drug or alcohol use</td>\n",
       "      <td>Allegedly Armed</td>\n",
       "      <td>gun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rural</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8262</th>\n",
       "      <td>Andrew Layton</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>http://bloximages.chicago2.vip.townnews.com/ma...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>410 S Riverfront Drive</td>\n",
       "      <td>Mankato</td>\n",
       "      <td>MN</td>\n",
       "      <td>56001.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Unarmed/Did Not Have an Actual Weapon</td>\n",
       "      <td>unarmed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8263 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Victim's name Victim's age Victim's gender Victim's race  \\\n",
       "0           Kevin Lee Catlett           62            Male  Unknown race   \n",
       "1     Wade Russell Meisberger           48            Male         White   \n",
       "2               Nick Costales           34            Male      Hispanic   \n",
       "3       Brittany S. Teichroeb           26          Female         White   \n",
       "4                  John Parks           57            Male         White   \n",
       "...                       ...          ...             ...           ...   \n",
       "8258               Tyree Bell           31            Male         Black   \n",
       "8259             Abel Gurrola           26            Male      Hispanic   \n",
       "8260      Christopher Tavares           21            Male      Hispanic   \n",
       "8261        Andrew L. Closson           21            Male         White   \n",
       "8262            Andrew Layton           26            Male         White   \n",
       "\n",
       "                                 URL of image of victim  \\\n",
       "0                                                   NaN   \n",
       "1     https://fatalencounters.org/wp-content/uploads...   \n",
       "2     https://fatalencounters.org/wp-content/uploads...   \n",
       "3                                                   NaN   \n",
       "4     https://fatalencounters.org/wp-content/uploads...   \n",
       "...                                                 ...   \n",
       "8258  http://content.omaha.com/media/maps/ps/2013/ja...   \n",
       "8259  http://www.bakersfieldnow.com/news/local/A-26-...   \n",
       "8260  http://www.krdo.com/image/view/-/17980228/medR...   \n",
       "8261  http://www.superiortelegram.com/sites/default/...   \n",
       "8262  http://bloximages.chicago2.vip.townnews.com/ma...   \n",
       "\n",
       "     Date of Incident (month/day/year)             Street Address of Incident  \\\n",
       "0                           2020-06-30                    900 McArthur Street   \n",
       "1                           2020-06-29      West Beech St and South Church St   \n",
       "2                           2020-06-29                   9501 Monte Vista Ave   \n",
       "3                           2020-06-29                 TX-191 and TX-250 Loop   \n",
       "4                           2020-06-29         Court Avenue and Audubon Drive   \n",
       "...                                ...                                    ...   \n",
       "8258                        2013-01-01                       3727 N. 42nd St.   \n",
       "8259                        2013-01-01                        720 Terrace Way   \n",
       "8260                        2013-01-01  Highway 50 and North Elizabeth Street   \n",
       "8261                        2013-01-01                        U.S. Highway 53   \n",
       "8262                        2013-01-01                 410 S Riverfront Drive   \n",
       "\n",
       "              City State  Zipcode  ... Symptoms of mental illness?  \\\n",
       "0     Johnson City    TN  37601.0  ...         Drug or alcohol use   \n",
       "1         Hazleton    PA  18201.0  ...                          No   \n",
       "2        Montclair    CA  91763.0  ...                          No   \n",
       "3          Midland    TX  79706.0  ...                          No   \n",
       "4          Paducah    KY  42001.0  ...                          No   \n",
       "...            ...   ...      ...  ...                         ...   \n",
       "8258         Omaha    NE  68111.0  ...                         Yes   \n",
       "8259   Bakersfield    CA  93304.0  ...                          No   \n",
       "8260        Pueblo    CO  81008.0  ...                          No   \n",
       "8261        Gordon    WI  54838.0  ...         Drug or alcohol use   \n",
       "8262       Mankato    MN  56001.0  ...                          No   \n",
       "\n",
       "      Unarmed/Did Not Have an Actual Weapon  \\\n",
       "0                           Allegedly Armed   \n",
       "1                           Allegedly Armed   \n",
       "2                           Allegedly Armed   \n",
       "3                           Allegedly Armed   \n",
       "4                                   Unclear   \n",
       "...                                     ...   \n",
       "8258                        Allegedly Armed   \n",
       "8259                        Allegedly Armed   \n",
       "8260                        Allegedly Armed   \n",
       "8261                        Allegedly Armed   \n",
       "8262  Unarmed/Did Not Have an Actual Weapon   \n",
       "\n",
       "     Alleged Weapon (Source: WaPo and Review of Cases Not Included in WaPo Database)  \\\n",
       "0                                                   gun                                \n",
       "1                                                   gun                                \n",
       "2                                                   gun                                \n",
       "3                                                   gun                                \n",
       "4                                          undetermined                                \n",
       "...                                                 ...                                \n",
       "8258                                                gun                                \n",
       "8259                                                gun                                \n",
       "8260                                                gun                                \n",
       "8261                                                gun                                \n",
       "8262                                            unarmed                                \n",
       "\n",
       "     Alleged Threat Level (Source: WaPo) Fleeing (Source: WaPo)  \\\n",
       "0                                 attack                    NaN   \n",
       "1                                 attack                    Car   \n",
       "2                                 attack                    NaN   \n",
       "3                                    NaN                    NaN   \n",
       "4                           undetermined                    Car   \n",
       "...                                  ...                    ...   \n",
       "8258                                 NaN                    NaN   \n",
       "8259                                 NaN                    NaN   \n",
       "8260                                 NaN                    NaN   \n",
       "8261                                 NaN                    NaN   \n",
       "8262                                 NaN                    NaN   \n",
       "\n",
       "     Body Camera (Source: WaPo) WaPo ID (If included in WaPo database)  \\\n",
       "0                           NaN                                    NaN   \n",
       "1                            No                                 5958.0   \n",
       "2                           NaN                                    NaN   \n",
       "3                           NaN                                    NaN   \n",
       "4                            No                                 5953.0   \n",
       "...                         ...                                    ...   \n",
       "8258                        NaN                                    NaN   \n",
       "8259                        NaN                                    NaN   \n",
       "8260                        NaN                                    NaN   \n",
       "8261                        NaN                                    NaN   \n",
       "8262                        NaN                                    NaN   \n",
       "\n",
       "     Off-Duty Killing?  \\\n",
       "0                  NaN   \n",
       "1                  NaN   \n",
       "2                  NaN   \n",
       "3                  NaN   \n",
       "4                  NaN   \n",
       "...                ...   \n",
       "8258               NaN   \n",
       "8259               NaN   \n",
       "8260               NaN   \n",
       "8261               NaN   \n",
       "8262               NaN   \n",
       "\n",
       "     Geography (via Trulia methodology based on zipcode population density: http://jedkolko.com/wp-content/uploads/2015/05/full-ZCTA-urban-suburban-rural-classification.xlsx )  \\\n",
       "0                                                   NaN                                                                                                                           \n",
       "1                                                   NaN                                                                                                                           \n",
       "2                                                   NaN                                                                                                                           \n",
       "3                                                   NaN                                                                                                                           \n",
       "4                                                   NaN                                                                                                                           \n",
       "...                                                 ...                                                                                                                           \n",
       "8258                                              Urban                                                                                                                           \n",
       "8259                                              Urban                                                                                                                           \n",
       "8260                                           Suburban                                                                                                                           \n",
       "8261                                              Rural                                                                                                                           \n",
       "8262                                           Suburban                                                                                                                           \n",
       "\n",
       "       ID  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  \n",
       "...   ...  \n",
       "8258  2.0  \n",
       "8259  3.0  \n",
       "8260  4.0  \n",
       "8261  5.0  \n",
       "8262  6.0  \n",
       "\n",
       "[8263 rows x 27 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data source: https://mappingpoliceviolence.org/aboutthedata \n",
    "#23/07/2020\n",
    "import pandas as pd\n",
    "df = pd.read_excel('MPVDatasetDownload.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Victim's race</th>\n",
       "      <th>Date of Incident (month/day/year)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2020-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2020-06-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8258</th>\n",
       "      <td>Black</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8259</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8261</th>\n",
       "      <td>White</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8262</th>\n",
       "      <td>White</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8263 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Victim's race Date of Incident (month/day/year)\n",
       "0     Unknown race                        2020-06-30\n",
       "1            White                        2020-06-29\n",
       "2         Hispanic                        2020-06-29\n",
       "3            White                        2020-06-29\n",
       "4            White                        2020-06-29\n",
       "...            ...                               ...\n",
       "8258         Black                        2013-01-01\n",
       "8259      Hispanic                        2013-01-01\n",
       "8260      Hispanic                        2013-01-01\n",
       "8261         White                        2013-01-01\n",
       "8262         White                        2013-01-01\n",
       "\n",
       "[8263 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select specific columns\n",
    "#df.columns\n",
    "dfrace = df[['Victim\\'s race']]\n",
    "#dfrace\n",
    "\n",
    "dfdate = df[['Victim\\'s race','Date of Incident (month/day/year)']]\n",
    "#dfdate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Race</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8258</th>\n",
       "      <td>Black</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8259</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8261</th>\n",
       "      <td>White</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8262</th>\n",
       "      <td>White</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8263 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Race       Date  Year\n",
       "0     Unknown race 2020-06-30  2020\n",
       "1            White 2020-06-29  2020\n",
       "2         Hispanic 2020-06-29  2020\n",
       "3            White 2020-06-29  2020\n",
       "4            White 2020-06-29  2020\n",
       "...            ...        ...   ...\n",
       "8258         Black 2013-01-01  2013\n",
       "8259      Hispanic 2013-01-01  2013\n",
       "8260      Hispanic 2013-01-01  2013\n",
       "8261         White 2013-01-01  2013\n",
       "8262         White 2013-01-01  2013\n",
       "\n",
       "[8263 rows x 3 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Race = list(df['Victim\\'s race'])\n",
    "Date = list(df['Date of Incident (month/day/year)'])\n",
    "Year = list(pd.DatetimeIndex(df['Date of Incident (month/day/year)']).year)\n",
    "dfs = pd.DataFrame({'Race': Race,\n",
    "                  'Date': Date,\n",
    "                  'Year': Year})\n",
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8258</th>\n",
       "      <td>Black</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8259</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8261</th>\n",
       "      <td>White</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8262</th>\n",
       "      <td>White</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8263 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Race       Date  Year\n",
       "0     Unknown race 2020-06-30  2020\n",
       "1            White 2020-06-29  2020\n",
       "2         Hispanic 2020-06-29  2020\n",
       "3            White 2020-06-29  2020\n",
       "4            White 2020-06-29  2020\n",
       "...            ...        ...   ...\n",
       "8258         Black 2013-01-01  2013\n",
       "8259      Hispanic 2013-01-01  2013\n",
       "8260      Hispanic 2013-01-01  2013\n",
       "8261         White 2013-01-01  2013\n",
       "8262         White 2013-01-01  2013\n",
       "\n",
       "[8263 rows x 3 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There's a problem with \"Unknown race\": sometimes it's \"Unknown Race\". \n",
    "#That will cause problems with our grouping so let's fix that.\n",
    "for i, row in dfs.iterrows():\n",
    "    ifor_val = 'Unkown race'\n",
    "    if dfs.at[i, 'Race'] == 'Unknown Race':\n",
    "        ifor_val = dfs.at[i, 'Race'].replace('Race','race')\n",
    "        dfs.at[i, 'Race'] = ifor_val\n",
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001A6BE443F08>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group data by race\n",
    "#df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])\n",
    "gr = dfs.groupby(dfs['Race'])\n",
    "gr\n",
    "\n",
    "#Group data by date\n",
    "gd = dfs.groupby(dfs['Date'])\n",
    "gd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>Victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>2075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>3614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Victims\n",
       "Race                     \n",
       "Asian                 128\n",
       "Black                2075\n",
       "Hispanic             1418\n",
       "Native American       117\n",
       "Pacific Islander       46\n",
       "Unknown race          865\n",
       "White                3614"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kills by race (2013-2020)\n",
    "dfdr= gr.count()\n",
    "dfdr = dfdr.rename(columns={'Year': 'Victims'}) #Rename columns\n",
    "dfdr = dfdr['Victims']\n",
    "pd.DataFrame(dfdr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2013</th>\n",
       "      <th>Asian</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2014</th>\n",
       "      <th>Asian</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2015</th>\n",
       "      <th>Asian</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2016</th>\n",
       "      <th>Asian</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2017</th>\n",
       "      <th>Asian</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2018</th>\n",
       "      <th>Asian</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2019</th>\n",
       "      <th>Asian</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2020</th>\n",
       "      <th>Asian</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific Islander</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown race</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Victims\n",
       "Year Race                     \n",
       "2013 Asian                  18\n",
       "     Black                 291\n",
       "     Hispanic              170\n",
       "     Native American         4\n",
       "     Pacific Islander        2\n",
       "     Unknown race          195\n",
       "     White                 426\n",
       "2014 Asian                  16\n",
       "     Black                 277\n",
       "     Hispanic              182\n",
       "     Native American        10\n",
       "     Pacific Islander        5\n",
       "     Unknown race           85\n",
       "     White                 475\n",
       "2015 Asian                  29\n",
       "     Black                 305\n",
       "     Hispanic              195\n",
       "     Native American        13\n",
       "     Pacific Islander        4\n",
       "     Unknown race           13\n",
       "     White                 544\n",
       "2016 Asian                  14\n",
       "     Black                 279\n",
       "     Hispanic              194\n",
       "     Native American        23\n",
       "     Pacific Islander        6\n",
       "     Unknown race           23\n",
       "     White                 532\n",
       "2017 Asian                  12\n",
       "     Black                 276\n",
       "     Hispanic              225\n",
       "     Native American        28\n",
       "     Pacific Islander        6\n",
       "     Unknown race           41\n",
       "     White                 505\n",
       "2018 Asian                  12\n",
       "     Black                 257\n",
       "     Hispanic              187\n",
       "     Native American        21\n",
       "     Pacific Islander       10\n",
       "     Unknown race          165\n",
       "     White                 490\n",
       "2019 Asian                  17\n",
       "     Black                 260\n",
       "     Hispanic              182\n",
       "     Native American        13\n",
       "     Pacific Islander        9\n",
       "     Unknown race          212\n",
       "     White                 407\n",
       "2020 Asian                  10\n",
       "     Black                 130\n",
       "     Hispanic               83\n",
       "     Native American         5\n",
       "     Pacific Islander        4\n",
       "     Unknown race          131\n",
       "     White                 235"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is what we want: Deaths by race by year\n",
    "gy = pd.DataFrame(dfs.groupby(['Year','Race'])['Race'].count())\n",
    "gy = gy.rename(columns={'Race': 'Victims'}) #Rename columns\n",
    "gy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resize\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.rcParams['figure.figsize']= (20,10)\n",
    "#plt.rcParams['font.size']= 12\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (20, 10),\n",
    "         'axes.labelsize': 15,\n",
    "         'axes.titlesize':40,\n",
    "          'font.size': 10,\n",
    "         'xtick.labelsize':15,\n",
    "         'ytick.labelsize':15}\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Deaths')"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot deaths from 2013 to 2020\n",
    "ax = dfdr.plot.bar(rot=0, title = 'US citizens killed by police from 2013 to 2020')\n",
    "#ax.title.set_size(40)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Deaths\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get population for each year\n",
    "#Source: https://www.kff.org/other/state-indicator/distribution-by-raceethnicity/\n",
    "df2013 = pd.read_excel('population-2013.xlsx')\n",
    "#df2013.set_index([pd.Index([2013])]) #In case we want to use the year as an Index\n",
    "year = 2013\n",
    "df2013['Year'] = year\n",
    "\n",
    "df2014 = pd.read_excel('population-2014.xlsx')\n",
    "year = 2014\n",
    "df2014['Year'] = year\n",
    "\n",
    "df2015 = pd.read_excel('population-2015.xlsx')\n",
    "year = 2015\n",
    "df2015['Year'] = year\n",
    "\n",
    "df2016 = pd.read_excel('population-2016.xlsx')\n",
    "year = 2016\n",
    "df2016['Year'] = year\n",
    "\n",
    "df2017 = pd.read_excel('population-2017.xlsx')\n",
    "year = 2017\n",
    "df2017['Year'] = year\n",
    "\n",
    "df2018 = pd.read_excel('population-2018.xlsx')\n",
    "#df2018.set_index([pd.Index([2018])])\n",
    "year = 2018\n",
    "df2018['Year'] = year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "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>Location</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>American Indian/Alaska Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Native Hawaiian/Other Pacific Islander</th>\n",
       "      <th>Two Or More Races</th>\n",
       "      <th>Total</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>192379500</td>\n",
       "      <td>37122600</td>\n",
       "      <td>52764900</td>\n",
       "      <td>2008700</td>\n",
       "      <td>15505700</td>\n",
       "      <td>448800</td>\n",
       "      <td>7275400</td>\n",
       "      <td>307505500</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>192419600</td>\n",
       "      <td>37599400</td>\n",
       "      <td>54059600</td>\n",
       "      <td>2006500</td>\n",
       "      <td>16156500</td>\n",
       "      <td>481900</td>\n",
       "      <td>7488800</td>\n",
       "      <td>310212200</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>192593600</td>\n",
       "      <td>37923800</td>\n",
       "      <td>55241300</td>\n",
       "      <td>1986900</td>\n",
       "      <td>16755300</td>\n",
       "      <td>466400</td>\n",
       "      <td>7810600</td>\n",
       "      <td>312777900</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>192537500</td>\n",
       "      <td>38081700</td>\n",
       "      <td>56144400</td>\n",
       "      <td>2041500</td>\n",
       "      <td>17004500</td>\n",
       "      <td>514600</td>\n",
       "      <td>8142200</td>\n",
       "      <td>314466400</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>192336100</td>\n",
       "      <td>38408000</td>\n",
       "      <td>57560600</td>\n",
       "      <td>2039400</td>\n",
       "      <td>17651200</td>\n",
       "      <td>502500</td>\n",
       "      <td>8524700</td>\n",
       "      <td>317022500</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>192117000</td>\n",
       "      <td>38655700</td>\n",
       "      <td>58483600</td>\n",
       "      <td>2082800</td>\n",
       "      <td>17844800</td>\n",
       "      <td>519500</td>\n",
       "      <td>8795000</td>\n",
       "      <td>318498500</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Location      White     Black  Hispanic  \\\n",
       "0  United States  192379500  37122600  52764900   \n",
       "0  United States  192419600  37599400  54059600   \n",
       "0  United States  192593600  37923800  55241300   \n",
       "0  United States  192537500  38081700  56144400   \n",
       "0  United States  192336100  38408000  57560600   \n",
       "0  United States  192117000  38655700  58483600   \n",
       "\n",
       "   American Indian/Alaska Native     Asian  \\\n",
       "0                        2008700  15505700   \n",
       "0                        2006500  16156500   \n",
       "0                        1986900  16755300   \n",
       "0                        2041500  17004500   \n",
       "0                        2039400  17651200   \n",
       "0                        2082800  17844800   \n",
       "\n",
       "   Native Hawaiian/Other Pacific Islander  Two Or More Races      Total  Year  \n",
       "0                                  448800            7275400  307505500  2013  \n",
       "0                                  481900            7488800  310212200  2014  \n",
       "0                                  466400            7810600  312777900  2015  \n",
       "0                                  514600            8142200  314466400  2016  \n",
       "0                                  502500            8524700  317022500  2017  \n",
       "0                                  519500            8795000  318498500  2018  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Union all for the population\n",
    "dfPopulation = pd.concat([df2013, df2014, df2015, df2016, df2017, df2018])\n",
    "dfPopulation = pd.DataFrame(dfPopulation)\n",
    "dfPopulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>Location</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>Location</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>Location</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>Location</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>Location</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>Location</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>White</td>\n",
       "      <td>192379500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>White</td>\n",
       "      <td>192419600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>White</td>\n",
       "      <td>192593600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>White</td>\n",
       "      <td>192537500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>White</td>\n",
       "      <td>192336100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>White</td>\n",
       "      <td>192117000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>Black</td>\n",
       "      <td>37122600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>Black</td>\n",
       "      <td>37599400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>Black</td>\n",
       "      <td>37923800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016</td>\n",
       "      <td>Black</td>\n",
       "      <td>38081700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017</td>\n",
       "      <td>Black</td>\n",
       "      <td>38408000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>Black</td>\n",
       "      <td>38655700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>52764900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>54059600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>55241300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>56144400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>57560600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>58483600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>American Indian/Alaska Native</td>\n",
       "      <td>2008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014</td>\n",
       "      <td>American Indian/Alaska Native</td>\n",
       "      <td>2006500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015</td>\n",
       "      <td>American Indian/Alaska Native</td>\n",
       "      <td>1986900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016</td>\n",
       "      <td>American Indian/Alaska Native</td>\n",
       "      <td>2041500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017</td>\n",
       "      <td>American Indian/Alaska Native</td>\n",
       "      <td>2039400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>American Indian/Alaska Native</td>\n",
       "      <td>2082800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Asian</td>\n",
       "      <td>15505700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014</td>\n",
       "      <td>Asian</td>\n",
       "      <td>16156500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2015</td>\n",
       "      <td>Asian</td>\n",
       "      <td>16755300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2016</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17004500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2017</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17651200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2018</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17844800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>Native Hawaiian/Other Pacific Islander</td>\n",
       "      <td>448800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014</td>\n",
       "      <td>Native Hawaiian/Other Pacific Islander</td>\n",
       "      <td>481900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2015</td>\n",
       "      <td>Native Hawaiian/Other Pacific Islander</td>\n",
       "      <td>466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2016</td>\n",
       "      <td>Native Hawaiian/Other Pacific Islander</td>\n",
       "      <td>514600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2017</td>\n",
       "      <td>Native Hawaiian/Other Pacific Islander</td>\n",
       "      <td>502500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018</td>\n",
       "      <td>Native Hawaiian/Other Pacific Islander</td>\n",
       "      <td>519500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>Two Or More Races</td>\n",
       "      <td>7275400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2014</td>\n",
       "      <td>Two Or More Races</td>\n",
       "      <td>7488800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2015</td>\n",
       "      <td>Two Or More Races</td>\n",
       "      <td>7810600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2016</td>\n",
       "      <td>Two Or More Races</td>\n",
       "      <td>8142200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017</td>\n",
       "      <td>Two Or More Races</td>\n",
       "      <td>8524700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018</td>\n",
       "      <td>Two Or More Races</td>\n",
       "      <td>8795000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>Total</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2014</td>\n",
       "      <td>Total</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2015</td>\n",
       "      <td>Total</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2016</td>\n",
       "      <td>Total</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2017</td>\n",
       "      <td>Total</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2018</td>\n",
       "      <td>Total</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year                                variable          value\n",
       "0   2013                                Location  United States\n",
       "1   2014                                Location  United States\n",
       "2   2015                                Location  United States\n",
       "3   2016                                Location  United States\n",
       "4   2017                                Location  United States\n",
       "5   2018                                Location  United States\n",
       "6   2013                                   White      192379500\n",
       "7   2014                                   White      192419600\n",
       "8   2015                                   White      192593600\n",
       "9   2016                                   White      192537500\n",
       "10  2017                                   White      192336100\n",
       "11  2018                                   White      192117000\n",
       "12  2013                                   Black       37122600\n",
       "13  2014                                   Black       37599400\n",
       "14  2015                                   Black       37923800\n",
       "15  2016                                   Black       38081700\n",
       "16  2017                                   Black       38408000\n",
       "17  2018                                   Black       38655700\n",
       "18  2013                                Hispanic       52764900\n",
       "19  2014                                Hispanic       54059600\n",
       "20  2015                                Hispanic       55241300\n",
       "21  2016                                Hispanic       56144400\n",
       "22  2017                                Hispanic       57560600\n",
       "23  2018                                Hispanic       58483600\n",
       "24  2013           American Indian/Alaska Native        2008700\n",
       "25  2014           American Indian/Alaska Native        2006500\n",
       "26  2015           American Indian/Alaska Native        1986900\n",
       "27  2016           American Indian/Alaska Native        2041500\n",
       "28  2017           American Indian/Alaska Native        2039400\n",
       "29  2018           American Indian/Alaska Native        2082800\n",
       "30  2013                                   Asian       15505700\n",
       "31  2014                                   Asian       16156500\n",
       "32  2015                                   Asian       16755300\n",
       "33  2016                                   Asian       17004500\n",
       "34  2017                                   Asian       17651200\n",
       "35  2018                                   Asian       17844800\n",
       "36  2013  Native Hawaiian/Other Pacific Islander         448800\n",
       "37  2014  Native Hawaiian/Other Pacific Islander         481900\n",
       "38  2015  Native Hawaiian/Other Pacific Islander         466400\n",
       "39  2016  Native Hawaiian/Other Pacific Islander         514600\n",
       "40  2017  Native Hawaiian/Other Pacific Islander         502500\n",
       "41  2018  Native Hawaiian/Other Pacific Islander         519500\n",
       "42  2013                       Two Or More Races        7275400\n",
       "43  2014                       Two Or More Races        7488800\n",
       "44  2015                       Two Or More Races        7810600\n",
       "45  2016                       Two Or More Races        8142200\n",
       "46  2017                       Two Or More Races        8524700\n",
       "47  2018                       Two Or More Races        8795000\n",
       "48  2013                                   Total      307505500\n",
       "49  2014                                   Total      310212200\n",
       "50  2015                                   Total      312777900\n",
       "51  2016                                   Total      314466400\n",
       "52  2017                                   Total      317022500\n",
       "53  2018                                   Total      318498500"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We need this in another structure so we can join with our current tables. Time to melt.\n",
    "dfmelt = pd.melt(dfPopulation,['Year'])\n",
    "dfmelt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Total Population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Year Total Population\n",
       "0  2013        307505500\n",
       "1  2014        310212200\n",
       "2  2015        312777900\n",
       "3  2016        314466400\n",
       "4  2017        317022500\n",
       "5  2018        318498500"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's start cleaning everything. \n",
    "\n",
    "#Totals table\n",
    "dfTotal = dfmelt[dfmelt['variable']=='Total'].reset_index() #Reset index \n",
    "dfTotal = dfTotal.rename(columns={'value': 'Total Population'}) #Rename columns\n",
    "dfTotal = dfTotal[['Year','Total Population']] #Select just the columns we want\n",
    "\n",
    "#Race table\n",
    "dfRace = dfmelt[(dfmelt['variable']!='Total') & (dfmelt['variable']!='Location')]\n",
    "dfRace = dfRace.rename(columns={'variable': 'Race','value': 'Population'}) #Rename columns\n",
    "\n",
    "dfTotal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Year</th>\n",
       "      <th>Race</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>White</td>\n",
       "      <td>192379500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>White</td>\n",
       "      <td>192419600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>White</td>\n",
       "      <td>192593600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>White</td>\n",
       "      <td>192537500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>White</td>\n",
       "      <td>192336100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>White</td>\n",
       "      <td>192117000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>Black</td>\n",
       "      <td>37122600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>Black</td>\n",
       "      <td>37599400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>Black</td>\n",
       "      <td>37923800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016</td>\n",
       "      <td>Black</td>\n",
       "      <td>38081700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017</td>\n",
       "      <td>Black</td>\n",
       "      <td>38408000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>Black</td>\n",
       "      <td>38655700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>52764900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>54059600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>55241300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>56144400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>57560600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>58483600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2008700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2006500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015</td>\n",
       "      <td>Native American</td>\n",
       "      <td>1986900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2041500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2039400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2082800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>Asian</td>\n",
       "      <td>15505700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014</td>\n",
       "      <td>Asian</td>\n",
       "      <td>16156500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2015</td>\n",
       "      <td>Asian</td>\n",
       "      <td>16755300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2016</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17004500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2017</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17651200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2018</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17844800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>448800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>481900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2015</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2016</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>514600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2017</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>502500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>519500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>7275400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2014</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>7488800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>7810600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>8142200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2017</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>8524700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>8795000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year              Race Population\n",
       "6   2013             White  192379500\n",
       "7   2014             White  192419600\n",
       "8   2015             White  192593600\n",
       "9   2016             White  192537500\n",
       "10  2017             White  192336100\n",
       "11  2018             White  192117000\n",
       "12  2013             Black   37122600\n",
       "13  2014             Black   37599400\n",
       "14  2015             Black   37923800\n",
       "15  2016             Black   38081700\n",
       "16  2017             Black   38408000\n",
       "17  2018             Black   38655700\n",
       "18  2013          Hispanic   52764900\n",
       "19  2014          Hispanic   54059600\n",
       "20  2015          Hispanic   55241300\n",
       "21  2016          Hispanic   56144400\n",
       "22  2017          Hispanic   57560600\n",
       "23  2018          Hispanic   58483600\n",
       "24  2013   Native American    2008700\n",
       "25  2014   Native American    2006500\n",
       "26  2015   Native American    1986900\n",
       "27  2016   Native American    2041500\n",
       "28  2017   Native American    2039400\n",
       "29  2018   Native American    2082800\n",
       "30  2013             Asian   15505700\n",
       "31  2014             Asian   16156500\n",
       "32  2015             Asian   16755300\n",
       "33  2016             Asian   17004500\n",
       "34  2017             Asian   17651200\n",
       "35  2018             Asian   17844800\n",
       "36  2013  Pacific Islander     448800\n",
       "37  2014  Pacific Islander     481900\n",
       "38  2015  Pacific Islander     466400\n",
       "39  2016  Pacific Islander     514600\n",
       "40  2017  Pacific Islander     502500\n",
       "41  2018  Pacific Islander     519500\n",
       "42  2013      Unknown race    7275400\n",
       "43  2014      Unknown race    7488800\n",
       "44  2015      Unknown race    7810600\n",
       "45  2016      Unknown race    8142200\n",
       "46  2017      Unknown race    8524700\n",
       "47  2018      Unknown race    8795000"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We'll have to rename everything accordingly. \n",
    "#I don't think I'm mapping this right, because \"Unkown Race\" might fall arbitrarily under different categories.\n",
    "#I might leave these 3 out of the analysis, but I'll do this exercise anyway.\n",
    "\n",
    "#\"American Indian/Alaska Native\" to \"Native American\"\n",
    "for i, row in dfRace.iterrows():\n",
    "    ifor_val = 'Native American'\n",
    "    if dfRace.at[i, 'Race'] == 'American Indian/Alaska Native':\n",
    "        ifor_val = dfRace.at[i, 'Race'].replace('American Indian/Alaska Native','Native American')\n",
    "        dfRace.at[i, 'Race'] = ifor_val\n",
    "\n",
    "#\"Native Hawaiian/Other Pacific Islander\" to \"Pacific Islander\"\n",
    "for i, row in dfRace.iterrows():\n",
    "    ifor_val = 'Pacific Islander'\n",
    "    if dfRace.at[i, 'Race'] == 'Native Hawaiian/Other Pacific Islander':\n",
    "        ifor_val = dfRace.at[i, 'Race'].replace('Native Hawaiian/Other Pacific Islander','Pacific Islander')\n",
    "        dfRace.at[i, 'Race'] = ifor_val\n",
    "        \n",
    "#\"Two Or More Races\" to \"Unknown race\"\n",
    "for i, row in dfRace.iterrows():\n",
    "    ifor_val = 'Unknown race'\n",
    "    if dfRace.at[i, 'Race'] == 'Two Or More Races':\n",
    "        ifor_val = dfRace.at[i, 'Race'].replace('Two Or More Races','Unknown race')\n",
    "        dfRace.at[i, 'Race'] = ifor_val   \n",
    "        \n",
    "dfRace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Race</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>White</td>\n",
       "      <td>192379500</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>Black</td>\n",
       "      <td>37122600</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>52764900</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2008700</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>Asian</td>\n",
       "      <td>15505700</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>448800</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>7275400</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>White</td>\n",
       "      <td>192419600</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>Black</td>\n",
       "      <td>37599400</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>54059600</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2006500</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>Asian</td>\n",
       "      <td>16156500</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>481900</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>7488800</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>White</td>\n",
       "      <td>192593600</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>Black</td>\n",
       "      <td>37923800</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>55241300</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>Native American</td>\n",
       "      <td>1986900</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>Asian</td>\n",
       "      <td>16755300</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>466400</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>7810600</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>White</td>\n",
       "      <td>192537500</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>Black</td>\n",
       "      <td>38081700</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>56144400</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2041500</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17004500</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>514600</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>8142200</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017</td>\n",
       "      <td>White</td>\n",
       "      <td>192336100</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017</td>\n",
       "      <td>Black</td>\n",
       "      <td>38408000</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>57560600</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2017</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2039400</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2017</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17651200</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2017</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>502500</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2017</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>8524700</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2018</td>\n",
       "      <td>White</td>\n",
       "      <td>192117000</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2018</td>\n",
       "      <td>Black</td>\n",
       "      <td>38655700</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2018</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>58483600</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2018</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2082800</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2018</td>\n",
       "      <td>Asian</td>\n",
       "      <td>17844800</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2018</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>519500</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2018</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>8795000</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year              Race  Population  Total Population\n",
       "0   2013             White   192379500         307505500\n",
       "1   2013             Black    37122600         307505500\n",
       "2   2013          Hispanic    52764900         307505500\n",
       "3   2013   Native American     2008700         307505500\n",
       "4   2013             Asian    15505700         307505500\n",
       "5   2013  Pacific Islander      448800         307505500\n",
       "6   2013      Unknown race     7275400         307505500\n",
       "7   2014             White   192419600         310212200\n",
       "8   2014             Black    37599400         310212200\n",
       "9   2014          Hispanic    54059600         310212200\n",
       "10  2014   Native American     2006500         310212200\n",
       "11  2014             Asian    16156500         310212200\n",
       "12  2014  Pacific Islander      481900         310212200\n",
       "13  2014      Unknown race     7488800         310212200\n",
       "14  2015             White   192593600         312777900\n",
       "15  2015             Black    37923800         312777900\n",
       "16  2015          Hispanic    55241300         312777900\n",
       "17  2015   Native American     1986900         312777900\n",
       "18  2015             Asian    16755300         312777900\n",
       "19  2015  Pacific Islander      466400         312777900\n",
       "20  2015      Unknown race     7810600         312777900\n",
       "21  2016             White   192537500         314466400\n",
       "22  2016             Black    38081700         314466400\n",
       "23  2016          Hispanic    56144400         314466400\n",
       "24  2016   Native American     2041500         314466400\n",
       "25  2016             Asian    17004500         314466400\n",
       "26  2016  Pacific Islander      514600         314466400\n",
       "27  2016      Unknown race     8142200         314466400\n",
       "28  2017             White   192336100         317022500\n",
       "29  2017             Black    38408000         317022500\n",
       "30  2017          Hispanic    57560600         317022500\n",
       "31  2017   Native American     2039400         317022500\n",
       "32  2017             Asian    17651200         317022500\n",
       "33  2017  Pacific Islander      502500         317022500\n",
       "34  2017      Unknown race     8524700         317022500\n",
       "35  2018             White   192117000         318498500\n",
       "36  2018             Black    38655700         318498500\n",
       "37  2018          Hispanic    58483600         318498500\n",
       "38  2018   Native American     2082800         318498500\n",
       "39  2018             Asian    17844800         318498500\n",
       "40  2018  Pacific Islander      519500         318498500\n",
       "41  2018      Unknown race     8795000         318498500"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge with total population\n",
    "\n",
    "dfPopulation = dfRace.merge(dfTotal, left_on='Year', right_on='Year')\n",
    "\n",
    "#And turn the population to int32, because they're objects\n",
    "dfPopulation['Population'] = dfPopulation['Population'].astype(int)\n",
    "dfPopulation['Total Population'] = dfPopulation['Total Population'].astype(int)\n",
    "\n",
    "dfPopulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>Race</th>\n",
       "      <th>Year</th>\n",
       "      <th>Victims</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2013</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Black</td>\n",
       "      <td>2013</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2013</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>White</td>\n",
       "      <td>2013</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2014</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>Black</td>\n",
       "      <td>2014</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2014</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2014</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>White</td>\n",
       "      <td>2014</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>Black</td>\n",
       "      <td>2015</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2015</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2015</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2015</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>White</td>\n",
       "      <td>2015</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2016</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>Black</td>\n",
       "      <td>2016</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2016</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2016</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2016</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>White</td>\n",
       "      <td>2016</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>Black</td>\n",
       "      <td>2017</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2017</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2017</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2017</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>White</td>\n",
       "      <td>2017</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>Black</td>\n",
       "      <td>2018</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2018</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2018</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2018</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>White</td>\n",
       "      <td>2018</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2019</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>Black</td>\n",
       "      <td>2019</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2019</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2019</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2019</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>White</td>\n",
       "      <td>2019</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>Black</td>\n",
       "      <td>2020</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2020</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>Native American</td>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>Pacific Islander</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>Unknown race</td>\n",
       "      <td>2020</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>White</td>\n",
       "      <td>2020</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    level_0  index              Race  Year  Victims\n",
       "0         0      0             Asian  2013       18\n",
       "1         1      1             Black  2013      291\n",
       "2         2      2          Hispanic  2013      170\n",
       "3         3      3   Native American  2013        4\n",
       "4         4      4  Pacific Islander  2013        2\n",
       "5         5      5      Unknown race  2013      195\n",
       "6         6      6             White  2013      426\n",
       "7         7      7             Asian  2014       16\n",
       "8         8      8             Black  2014      277\n",
       "9         9      9          Hispanic  2014      182\n",
       "10       10     10   Native American  2014       10\n",
       "11       11     11  Pacific Islander  2014        5\n",
       "12       12     12      Unknown race  2014       85\n",
       "13       13     13             White  2014      475\n",
       "14       14     14             Asian  2015       29\n",
       "15       15     15             Black  2015      305\n",
       "16       16     16          Hispanic  2015      195\n",
       "17       17     17   Native American  2015       13\n",
       "18       18     18  Pacific Islander  2015        4\n",
       "19       19     19      Unknown race  2015       13\n",
       "20       20     20             White  2015      544\n",
       "21       21     21             Asian  2016       14\n",
       "22       22     22             Black  2016      279\n",
       "23       23     23          Hispanic  2016      194\n",
       "24       24     24   Native American  2016       23\n",
       "25       25     25  Pacific Islander  2016        6\n",
       "26       26     26      Unknown race  2016       23\n",
       "27       27     27             White  2016      532\n",
       "28       28     28             Asian  2017       12\n",
       "29       29     29             Black  2017      276\n",
       "30       30     30          Hispanic  2017      225\n",
       "31       31     31   Native American  2017       28\n",
       "32       32     32  Pacific Islander  2017        6\n",
       "33       33     33      Unknown race  2017       41\n",
       "34       34     34             White  2017      505\n",
       "35       35     35             Asian  2018       12\n",
       "36       36     36             Black  2018      257\n",
       "37       37     37          Hispanic  2018      187\n",
       "38       38     38   Native American  2018       21\n",
       "39       39     39  Pacific Islander  2018       10\n",
       "40       40     40      Unknown race  2018      165\n",
       "41       41     41             White  2018      490\n",
       "42       42     42             Asian  2019       17\n",
       "43       43     43             Black  2019      260\n",
       "44       44     44          Hispanic  2019      182\n",
       "45       45     45   Native American  2019       13\n",
       "46       46     46  Pacific Islander  2019        9\n",
       "47       47     47      Unknown race  2019      212\n",
       "48       48     48             White  2019      407\n",
       "49       49     49             Asian  2020       10\n",
       "50       50     50             Black  2020      130\n",
       "51       51     51          Hispanic  2020       83\n",
       "52       52     52   Native American  2020        5\n",
       "53       53     53  Pacific Islander  2020        4\n",
       "54       54     54      Unknown race  2020      131\n",
       "55       55     55             White  2020      235"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We reset the index of gy so we can merge and calculate the rate\n",
    "#I don't know if there's a more efficient way of resetting indexes\n",
    "#gy.reset_index(level=0, inplace=True)\n",
    "#gy.reset_index(level=0, inplace=True)\n",
    "gy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Year</th>\n",
       "      <th>Victims</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>2013</td>\n",
       "      <td>426</td>\n",
       "      <td>192379500</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black</td>\n",
       "      <td>2013</td>\n",
       "      <td>291</td>\n",
       "      <td>37122600</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013</td>\n",
       "      <td>170</td>\n",
       "      <td>52764900</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2013</td>\n",
       "      <td>18</td>\n",
       "      <td>15505700</td>\n",
       "      <td>307505500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>White</td>\n",
       "      <td>2014</td>\n",
       "      <td>475</td>\n",
       "      <td>192419600</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Black</td>\n",
       "      <td>2014</td>\n",
       "      <td>277</td>\n",
       "      <td>37599400</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2014</td>\n",
       "      <td>182</td>\n",
       "      <td>54059600</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2014</td>\n",
       "      <td>16</td>\n",
       "      <td>16156500</td>\n",
       "      <td>310212200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>White</td>\n",
       "      <td>2015</td>\n",
       "      <td>544</td>\n",
       "      <td>192593600</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Black</td>\n",
       "      <td>2015</td>\n",
       "      <td>305</td>\n",
       "      <td>37923800</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2015</td>\n",
       "      <td>195</td>\n",
       "      <td>55241300</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "      <td>16755300</td>\n",
       "      <td>312777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>White</td>\n",
       "      <td>2016</td>\n",
       "      <td>532</td>\n",
       "      <td>192537500</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Black</td>\n",
       "      <td>2016</td>\n",
       "      <td>279</td>\n",
       "      <td>38081700</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2016</td>\n",
       "      <td>194</td>\n",
       "      <td>56144400</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2016</td>\n",
       "      <td>14</td>\n",
       "      <td>17004500</td>\n",
       "      <td>314466400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>White</td>\n",
       "      <td>2017</td>\n",
       "      <td>505</td>\n",
       "      <td>192336100</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Black</td>\n",
       "      <td>2017</td>\n",
       "      <td>276</td>\n",
       "      <td>38408000</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2017</td>\n",
       "      <td>225</td>\n",
       "      <td>57560600</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>17651200</td>\n",
       "      <td>317022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>White</td>\n",
       "      <td>2018</td>\n",
       "      <td>490</td>\n",
       "      <td>192117000</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Black</td>\n",
       "      <td>2018</td>\n",
       "      <td>257</td>\n",
       "      <td>38655700</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2018</td>\n",
       "      <td>187</td>\n",
       "      <td>58483600</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>17844800</td>\n",
       "      <td>318498500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Race  Year  Victims  Population  Total Population\n",
       "0      White  2013      426   192379500         307505500\n",
       "1      Black  2013      291    37122600         307505500\n",
       "2   Hispanic  2013      170    52764900         307505500\n",
       "4      Asian  2013       18    15505700         307505500\n",
       "7      White  2014      475   192419600         310212200\n",
       "8      Black  2014      277    37599400         310212200\n",
       "9   Hispanic  2014      182    54059600         310212200\n",
       "11     Asian  2014       16    16156500         310212200\n",
       "14     White  2015      544   192593600         312777900\n",
       "15     Black  2015      305    37923800         312777900\n",
       "16  Hispanic  2015      195    55241300         312777900\n",
       "18     Asian  2015       29    16755300         312777900\n",
       "21     White  2016      532   192537500         314466400\n",
       "22     Black  2016      279    38081700         314466400\n",
       "23  Hispanic  2016      194    56144400         314466400\n",
       "25     Asian  2016       14    17004500         314466400\n",
       "28     White  2017      505   192336100         317022500\n",
       "29     Black  2017      276    38408000         317022500\n",
       "30  Hispanic  2017      225    57560600         317022500\n",
       "32     Asian  2017       12    17651200         317022500\n",
       "35     White  2018      490   192117000         318498500\n",
       "36     Black  2018      257    38655700         318498500\n",
       "37  Hispanic  2018      187    58483600         318498500\n",
       "39     Asian  2018       12    17844800         318498500"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, let's merge\n",
    "\n",
    "dfEverything = dfPopulation.merge(gy, left_on=['Year', 'Race'], right_on=['Year', 'Race'])\n",
    "\n",
    "#We'll limit this analysis to white, black, hispanic and asian races, since we can't be sure about the others' demographics\n",
    "dfEverything = dfEverything[(dfEverything['Race']=='White') | (dfEverything['Race']=='Black') | (dfEverything['Race']=='Hispanic') | (dfEverything['Race']=='Asian')]\n",
    "\n",
    "#Select only the columns we're interested in and order them appropiately\n",
    "dfEverything = dfEverything[['Race','Year','Victims','Population','Total Population']]\n",
    "dfEverything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Year</th>\n",
       "      <th>Victims</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Rate per race</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>2013</td>\n",
       "      <td>426</td>\n",
       "      <td>192379500</td>\n",
       "      <td>307505500</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black</td>\n",
       "      <td>2013</td>\n",
       "      <td>291</td>\n",
       "      <td>37122600</td>\n",
       "      <td>307505500</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2013</td>\n",
       "      <td>170</td>\n",
       "      <td>52764900</td>\n",
       "      <td>307505500</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2013</td>\n",
       "      <td>18</td>\n",
       "      <td>15505700</td>\n",
       "      <td>307505500</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>White</td>\n",
       "      <td>2014</td>\n",
       "      <td>475</td>\n",
       "      <td>192419600</td>\n",
       "      <td>310212200</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Black</td>\n",
       "      <td>2014</td>\n",
       "      <td>277</td>\n",
       "      <td>37599400</td>\n",
       "      <td>310212200</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2014</td>\n",
       "      <td>182</td>\n",
       "      <td>54059600</td>\n",
       "      <td>310212200</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2014</td>\n",
       "      <td>16</td>\n",
       "      <td>16156500</td>\n",
       "      <td>310212200</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>White</td>\n",
       "      <td>2015</td>\n",
       "      <td>544</td>\n",
       "      <td>192593600</td>\n",
       "      <td>312777900</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Black</td>\n",
       "      <td>2015</td>\n",
       "      <td>305</td>\n",
       "      <td>37923800</td>\n",
       "      <td>312777900</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2015</td>\n",
       "      <td>195</td>\n",
       "      <td>55241300</td>\n",
       "      <td>312777900</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "      <td>16755300</td>\n",
       "      <td>312777900</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>White</td>\n",
       "      <td>2016</td>\n",
       "      <td>532</td>\n",
       "      <td>192537500</td>\n",
       "      <td>314466400</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Black</td>\n",
       "      <td>2016</td>\n",
       "      <td>279</td>\n",
       "      <td>38081700</td>\n",
       "      <td>314466400</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2016</td>\n",
       "      <td>194</td>\n",
       "      <td>56144400</td>\n",
       "      <td>314466400</td>\n",
       "      <td>3.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2016</td>\n",
       "      <td>14</td>\n",
       "      <td>17004500</td>\n",
       "      <td>314466400</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>White</td>\n",
       "      <td>2017</td>\n",
       "      <td>505</td>\n",
       "      <td>192336100</td>\n",
       "      <td>317022500</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Black</td>\n",
       "      <td>2017</td>\n",
       "      <td>276</td>\n",
       "      <td>38408000</td>\n",
       "      <td>317022500</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2017</td>\n",
       "      <td>225</td>\n",
       "      <td>57560600</td>\n",
       "      <td>317022500</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>17651200</td>\n",
       "      <td>317022500</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>White</td>\n",
       "      <td>2018</td>\n",
       "      <td>490</td>\n",
       "      <td>192117000</td>\n",
       "      <td>318498500</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Black</td>\n",
       "      <td>2018</td>\n",
       "      <td>257</td>\n",
       "      <td>38655700</td>\n",
       "      <td>318498500</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2018</td>\n",
       "      <td>187</td>\n",
       "      <td>58483600</td>\n",
       "      <td>318498500</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>17844800</td>\n",
       "      <td>318498500</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Race  Year  Victims  Population  Total Population  Rate per race\n",
       "0      White  2013      426   192379500         307505500           2.21\n",
       "1      Black  2013      291    37122600         307505500           7.84\n",
       "2   Hispanic  2013      170    52764900         307505500           3.22\n",
       "4      Asian  2013       18    15505700         307505500           1.16\n",
       "7      White  2014      475   192419600         310212200           2.47\n",
       "8      Black  2014      277    37599400         310212200           7.37\n",
       "9   Hispanic  2014      182    54059600         310212200           3.37\n",
       "11     Asian  2014       16    16156500         310212200           0.99\n",
       "14     White  2015      544   192593600         312777900           2.82\n",
       "15     Black  2015      305    37923800         312777900           8.04\n",
       "16  Hispanic  2015      195    55241300         312777900           3.53\n",
       "18     Asian  2015       29    16755300         312777900           1.73\n",
       "21     White  2016      532   192537500         314466400           2.76\n",
       "22     Black  2016      279    38081700         314466400           7.33\n",
       "23  Hispanic  2016      194    56144400         314466400           3.46\n",
       "25     Asian  2016       14    17004500         314466400           0.82\n",
       "28     White  2017      505   192336100         317022500           2.63\n",
       "29     Black  2017      276    38408000         317022500           7.19\n",
       "30  Hispanic  2017      225    57560600         317022500           3.91\n",
       "32     Asian  2017       12    17651200         317022500           0.68\n",
       "35     White  2018      490   192117000         318498500           2.55\n",
       "36     Black  2018      257    38655700         318498500           6.65\n",
       "37  Hispanic  2018      187    58483600         318498500           3.20\n",
       "39     Asian  2018       12    17844800         318498500           0.67"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rate per 1,000,000\n",
    "dfEverything['Rate per race'] = (dfEverything['Victims'] / dfEverything['Population'])*1000000\n",
    "dfEverything = dfEverything.round(2)\n",
    "dfEverything\n",
    "\n",
    "#Rate per US population\n",
    "#dfEverything['Rate US population'] = dfEverything['Victims'] / dfEverything['Total Population']\n",
    "#dfEverything.round({'Rate per race': 2, 'Rate US population': 2})\n",
    "#dfEverything.round(decimals=2)\n",
    "#dfEverything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Race</th>\n",
       "      <th>Year</th>\n",
       "      <th>Victims</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Rate per race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>2013</td>\n",
       "      <td>426</td>\n",
       "      <td>192379500</td>\n",
       "      <td>307505500</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black</td>\n",
       "      <td>2013</td>\n",
       "      <td>291</td>\n",
       "      <td>37122600</td>\n",
       "      <td>307505500</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>White</td>\n",
       "      <td>2014</td>\n",
       "      <td>475</td>\n",
       "      <td>192419600</td>\n",
       "      <td>310212200</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Black</td>\n",
       "      <td>2014</td>\n",
       "      <td>277</td>\n",
       "      <td>37599400</td>\n",
       "      <td>310212200</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>White</td>\n",
       "      <td>2015</td>\n",
       "      <td>544</td>\n",
       "      <td>192593600</td>\n",
       "      <td>312777900</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Black</td>\n",
       "      <td>2015</td>\n",
       "      <td>305</td>\n",
       "      <td>37923800</td>\n",
       "      <td>312777900</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>White</td>\n",
       "      <td>2016</td>\n",
       "      <td>532</td>\n",
       "      <td>192537500</td>\n",
       "      <td>314466400</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Black</td>\n",
       "      <td>2016</td>\n",
       "      <td>279</td>\n",
       "      <td>38081700</td>\n",
       "      <td>314466400</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>White</td>\n",
       "      <td>2017</td>\n",
       "      <td>505</td>\n",
       "      <td>192336100</td>\n",
       "      <td>317022500</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Black</td>\n",
       "      <td>2017</td>\n",
       "      <td>276</td>\n",
       "      <td>38408000</td>\n",
       "      <td>317022500</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>White</td>\n",
       "      <td>2018</td>\n",
       "      <td>490</td>\n",
       "      <td>192117000</td>\n",
       "      <td>318498500</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Black</td>\n",
       "      <td>2018</td>\n",
       "      <td>257</td>\n",
       "      <td>38655700</td>\n",
       "      <td>318498500</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Race  Year  Victims  Population  Total Population  Rate per race\n",
       "0   White  2013      426   192379500         307505500           2.21\n",
       "1   Black  2013      291    37122600         307505500           7.84\n",
       "7   White  2014      475   192419600         310212200           2.47\n",
       "8   Black  2014      277    37599400         310212200           7.37\n",
       "14  White  2015      544   192593600         312777900           2.82\n",
       "15  Black  2015      305    37923800         312777900           8.04\n",
       "21  White  2016      532   192537500         314466400           2.76\n",
       "22  Black  2016      279    38081700         314466400           7.33\n",
       "28  White  2017      505   192336100         317022500           2.63\n",
       "29  Black  2017      276    38408000         317022500           7.19\n",
       "35  White  2018      490   192117000         318498500           2.55\n",
       "36  Black  2018      257    38655700         318498500           6.65"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We'll just compare White and Black \n",
    "dfEverything = dfEverything[(dfEverything['Race']=='White') | (dfEverything['Race']=='Black')]\n",
    "dfEverything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Year</th>\n",
       "      <th>Race</th>\n",
       "      <th>Rate per race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>White</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>Black</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>White</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>Black</td>\n",
       "      <td>7.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>White</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>Black</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>White</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>Black</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017</td>\n",
       "      <td>White</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017</td>\n",
       "      <td>Black</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2018</td>\n",
       "      <td>White</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2018</td>\n",
       "      <td>Black</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year   Race  Rate per race\n",
       "0   2013  White           2.21\n",
       "1   2013  Black           7.84\n",
       "7   2014  White           2.47\n",
       "8   2014  Black           7.37\n",
       "14  2015  White           2.82\n",
       "15  2015  Black           8.04\n",
       "21  2016  White           2.76\n",
       "22  2016  Black           7.33\n",
       "28  2017  White           2.63\n",
       "29  2017  Black           7.19\n",
       "35  2018  White           2.55\n",
       "36  2018  Black           6.65"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For a proper bar chart we only need the rates\n",
    "dfRates = dfEverything[['Year','Race', 'Rate per race']]\n",
    "dfRates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Race</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>7.84</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>7.37</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>8.04</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>7.33</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>7.19</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6.65</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race  Black  White\n",
       "Year              \n",
       "2013   7.84   2.21\n",
       "2014   7.37   2.47\n",
       "2015   8.04   2.82\n",
       "2016   7.33   2.76\n",
       "2017   7.19   2.63\n",
       "2018   6.65   2.55"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's reshape this so we can plot it.\n",
    "dfRates = dfRates.pivot('Year', 'Race', 'Rate per race')\n",
    "dfRates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'US citizens killed by police per 1,000,000 population'"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finally, let's get our plot\n",
    "ax = dfRates.plot.bar(rot=0, title = \"US citizens killed by police per 1 million population\", color=['red', 'cyan'])\n",
    "\n",
    "#Put the rates on top of the bars    \n",
    "for p in ax.patches:                 \n",
    "    ax.annotate(np.round(p.get_height(),decimals=2), (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
    "\n",
    "ax.set_ylabel(\"Rates\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}