notebooks/24 - Selecting subsets.ipynb

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   "source": [
    "# Selecting subsets\n",
    "\n",
    "Selecting subsets of data to analyze can give deeper insights.\n",
    "\n",
    "When dealing with large datasets, selecting subsets of data to analyze can provide more focused and meaningful insights than analyzing the entire dataset.\n",
    "\n",
    "This approach allows researchers to identify patterns and trends within specific subgroups and gain a deeper understanding of the data. Additionally, selecting subsets of data can help to reduce the amount of noise and irrelevant information in the analysis, making it easier to draw accurate conclusions.\n",
    "\n",
    "Whether analyzing data for scientific research or business intelligence, selecting the correct subset of data can be a crucial step in unlocking deeper insights and driving successful outcomes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How To"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data/housing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "         ...  \n",
       "20635    False\n",
       "20636    False\n",
       "20637    False\n",
       "20638    False\n",
       "20639    False\n",
       "Name: longitude, Length: 20640, dtype: bool"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.longitude < -122"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
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       "      <th>20573</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>38.56</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>5.6790</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20581</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>38.83</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>3.1534</td>\n",
       "      <td>123400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20582</th>\n",
       "      <td>-122.16</td>\n",
       "      <td>38.90</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>3.7574</td>\n",
       "      <td>92700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20585</th>\n",
       "      <td>-122.04</td>\n",
       "      <td>38.68</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0486</td>\n",
       "      <td>83600.0</td>\n",
       "      <td>INLAND</td>\n",
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       "    <tr>\n",
       "      <th>20586</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>38.69</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>2.7955</td>\n",
       "      <td>96300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3967 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20573    -122.05     38.56                20.0       1005.0           168.0   \n",
       "20581    -122.21     38.83                20.0       1138.0           221.0   \n",
       "20582    -122.16     38.90                33.0       1221.0           236.0   \n",
       "20585    -122.04     38.68                26.0       1113.0           222.0   \n",
       "20586    -122.03     38.69                23.0       1796.0           380.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20573       457.0       157.0         5.6790            225000.0   \n",
       "20581       459.0       209.0         3.1534            123400.0   \n",
       "20582       488.0       199.0         3.7574             92700.0   \n",
       "20585       689.0       234.0         3.0486             83600.0   \n",
       "20586       939.0       330.0         2.7955             96300.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "0            NEAR BAY  \n",
       "1            NEAR BAY  \n",
       "2            NEAR BAY  \n",
       "3            NEAR BAY  \n",
       "4            NEAR BAY  \n",
       "...               ...  \n",
       "20573          INLAND  \n",
       "20581          INLAND  \n",
       "20582          INLAND  \n",
       "20585          INLAND  \n",
       "20586          INLAND  \n",
       "\n",
       "[3967 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "df[df.longitude < -122]"
   ]
  },
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   "execution_count": 6,
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    {
     "data": {
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       "(20640, 10)"
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     "execution_count": 6,
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   "source": [
    "df.shape"
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   "execution_count": 13,
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       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
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       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "      <th>20573</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>38.56</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>5.6790</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>INLAND</td>\n",
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       "    <tr>\n",
       "      <th>20581</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>38.83</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>3.1534</td>\n",
       "      <td>123400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20582</th>\n",
       "      <td>-122.16</td>\n",
       "      <td>38.90</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>3.7574</td>\n",
       "      <td>92700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20585</th>\n",
       "      <td>-122.04</td>\n",
       "      <td>38.68</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0486</td>\n",
       "      <td>83600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20586</th>\n",
       "      <td>-122.03</td>\n",
       "      <td>38.69</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1796.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>2.7955</td>\n",
       "      <td>96300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2718 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20573    -122.05     38.56                20.0       1005.0           168.0   \n",
       "20581    -122.21     38.83                20.0       1138.0           221.0   \n",
       "20582    -122.16     38.90                33.0       1221.0           236.0   \n",
       "20585    -122.04     38.68                26.0       1113.0           222.0   \n",
       "20586    -122.03     38.69                23.0       1796.0           380.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20573       457.0       157.0         5.6790            225000.0   \n",
       "20581       459.0       209.0         3.1534            123400.0   \n",
       "20582       488.0       199.0         3.7574             92700.0   \n",
       "20585       689.0       234.0         3.0486             83600.0   \n",
       "20586       939.0       330.0         2.7955             96300.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "0            NEAR BAY  \n",
       "1            NEAR BAY  \n",
       "2            NEAR BAY  \n",
       "3            NEAR BAY  \n",
       "4            NEAR BAY  \n",
       "...               ...  \n",
       "20573          INLAND  \n",
       "20581          INLAND  \n",
       "20582          INLAND  \n",
       "20585          INLAND  \n",
       "20586          INLAND  \n",
       "\n",
       "[2718 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.ocean_proximity.isin([\"NEAR BAY\", \"INLAND\"]) & (df.longitude < -122)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEAR BAY', 'INLAND', 'NEAR OCEAN', '<1H OCEAN'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset = df[df.ocean_proximity.isin([\"NEAR BAY\", \"INLAND\"]) | (df.longitude < -122)]\n",
    "df_subset.ocean_proximity.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude\n",
       "0        -122.23     37.88\n",
       "1        -122.22     37.86\n",
       "2        -122.24     37.85\n",
       "3        -122.25     37.85\n",
       "4        -122.25     37.85\n",
       "...          ...       ...\n",
       "20635    -121.09     39.48\n",
       "20636    -121.21     39.49\n",
       "20637    -121.22     39.43\n",
       "20638    -121.32     39.43\n",
       "20639    -121.24     39.37\n",
       "\n",
       "[20640 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, [\"longitude\", \"latitude\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>496 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude\n",
       "5      -122.25     37.85\n",
       "6      -122.25     37.84\n",
       "7      -122.25     37.84\n",
       "8      -122.26     37.84\n",
       "9      -122.25     37.84\n",
       "..         ...       ...\n",
       "496    -122.26     37.85\n",
       "497    -122.27     37.85\n",
       "498    -122.27     37.85\n",
       "499    -122.27     37.85\n",
       "500    -122.27     37.85\n",
       "\n",
       "[496 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[5:500, [\"longitude\", \"latitude\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20635       845.0       330.0         1.5603             78100.0   \n",
       "20636       356.0       114.0         2.5568             77100.0   \n",
       "20637      1007.0       433.0         1.7000             92300.0   \n",
       "20638       741.0       349.0         1.8672             84700.0   \n",
       "20639      1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "0            NEAR BAY  \n",
       "1            NEAR BAY  \n",
       "2            NEAR BAY  \n",
       "3            NEAR BAY  \n",
       "4            NEAR BAY  \n",
       "...               ...  \n",
       "20635          INLAND  \n",
       "20636          INLAND  \n",
       "20637          INLAND  \n",
       "20638          INLAND  \n",
       "20639          INLAND  \n",
       "\n",
       "[20640 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index(\"latitude\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([37.88, 37.86, 37.85, 37.85, 37.85, 37.85, 37.84, 37.84, 37.84,\n",
       "              37.84,\n",
       "              ...\n",
       "              39.29, 39.33, 39.26, 39.19, 39.27, 39.48, 39.49, 39.43, 39.43,\n",
       "              39.37],\n",
       "             dtype='float64', name='latitude', length=20640)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
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       "      <th>median_house_value</th>\n",
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       "    <tr>\n",
       "      <th>latitude</th>\n",
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       "      <th>37.85</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "latitude                                                               \n",
       "37.85       -122.24                52.0       1467.0           190.0   \n",
       "37.85       -122.25                52.0       1274.0           235.0   \n",
       "37.85       -122.25                52.0       1627.0           280.0   \n",
       "37.85       -122.25                52.0        919.0           213.0   \n",
       "37.85       -122.26                52.0       2202.0           434.0   \n",
       "\n",
       "          population  households  median_income  median_house_value  \\\n",
       "latitude                                                              \n",
       "37.85          496.0       177.0         7.2574            352100.0   \n",
       "37.85          558.0       219.0         5.6431            341300.0   \n",
       "37.85          565.0       259.0         3.8462            342200.0   \n",
       "37.85          413.0       193.0         4.0368            269700.0   \n",
       "37.85          910.0       402.0         3.2031            281500.0   \n",
       "\n",
       "         ocean_proximity  \n",
       "latitude                  \n",
       "37.85           NEAR BAY  \n",
       "37.85           NEAR BAY  \n",
       "37.85           NEAR BAY  \n",
       "37.85           NEAR BAY  \n",
       "37.85           NEAR BAY  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[37.85, :].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-122.24</td>\n",
       "      <td>496.0</td>\n",
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       "    <tr>\n",
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       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
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       "      <th>20636</th>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
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       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>1007.0</td>\n",
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       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "      <td>741.0</td>\n",
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       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "      <td>1387.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       latitude  longitude  population\n",
       "0         37.88    -122.23       322.0\n",
       "1         37.86    -122.22      2401.0\n",
       "2         37.85    -122.24       496.0\n",
       "3         37.85    -122.25       558.0\n",
       "4         37.85    -122.25       565.0\n",
       "...         ...        ...         ...\n",
       "20635     39.48    -121.09       845.0\n",
       "20636     39.49    -121.21       356.0\n",
       "20637     39.43    -121.22      1007.0\n",
       "20638     39.43    -121.32       741.0\n",
       "20639     39.37    -121.24      1387.0\n",
       "\n",
       "[20640 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"longitude\", \"population\"]].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
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       "      <td>489.0</td>\n",
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       "      <td>647.0</td>\n",
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       "      <th>37.84</th>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
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       "      <th>37.84</th>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
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       "    <tr>\n",
       "      <th>37.86</th>\n",
       "      <td>663.0</td>\n",
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       "      <td>590.0</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>768.0</td>\n",
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       "      <td>755.0</td>\n",
       "      <td>3.2619</td>\n",
       "      <td>309600.0</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>920.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>815.0</td>\n",
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       "      <td>182300.0</td>\n",
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       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>400.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>2.2431</td>\n",
       "      <td>172700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>300.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1.9028</td>\n",
       "      <td>150800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>495 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          total_bedrooms  population  households  median_income  \\\n",
       "latitude                                                          \n",
       "37.85              213.0       413.0       193.0         4.0368   \n",
       "37.84              489.0      1094.0       514.0         3.6591   \n",
       "37.84              687.0      1157.0       647.0         3.1200   \n",
       "37.84              665.0      1206.0       595.0         2.0804   \n",
       "37.84              707.0      1551.0       714.0         3.6912   \n",
       "...                  ...         ...         ...            ...   \n",
       "37.86              663.0      1316.0       590.0         5.3794   \n",
       "37.85              768.0      1508.0       755.0         3.2619   \n",
       "37.85              920.0      1800.0       815.0         2.7054   \n",
       "37.85              400.0       719.0       326.0         2.2431   \n",
       "37.85              300.0       675.0       255.0         1.9028   \n",
       "\n",
       "          median_house_value  \n",
       "latitude                      \n",
       "37.85               269700.0  \n",
       "37.84               299200.0  \n",
       "37.84               241400.0  \n",
       "37.84               226700.0  \n",
       "37.84               261100.0  \n",
       "...                      ...  \n",
       "37.86               376900.0  \n",
       "37.85               309600.0  \n",
       "37.85               182300.0  \n",
       "37.85               172700.0  \n",
       "37.85               150800.0  \n",
       "\n",
       "[495 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[5:500, 3:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Columns and Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>39.43</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>741.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.37</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>1387.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude  population\n",
       "latitude                       \n",
       "37.88       -122.23       322.0\n",
       "37.86       -122.22      2401.0\n",
       "37.85       -122.24       496.0\n",
       "37.85       -122.25       558.0\n",
       "37.85       -122.25       565.0\n",
       "...             ...         ...\n",
       "39.48       -121.09       845.0\n",
       "39.49       -121.21       356.0\n",
       "39.43       -121.22      1007.0\n",
       "39.43       -121.32       741.0\n",
       "39.37       -121.24      1387.0\n",
       "\n",
       "[20640 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"longitude\", \"population\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37.88</th>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.86</th>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.85</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.48</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.49</th>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.43</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.43</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.37</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          housing_median_age  total_rooms  total_bedrooms  households  \\\n",
       "latitude                                                                \n",
       "37.88                   41.0        880.0           129.0       126.0   \n",
       "37.86                   21.0       7099.0          1106.0      1138.0   \n",
       "37.85                   52.0       1467.0           190.0       177.0   \n",
       "37.85                   52.0       1274.0           235.0       219.0   \n",
       "37.85                   52.0       1627.0           280.0       259.0   \n",
       "...                      ...          ...             ...         ...   \n",
       "39.48                   25.0       1665.0           374.0       330.0   \n",
       "39.49                   18.0        697.0           150.0       114.0   \n",
       "39.43                   17.0       2254.0           485.0       433.0   \n",
       "39.43                   18.0       1860.0           409.0       349.0   \n",
       "39.37                   16.0       2785.0           616.0       530.0   \n",
       "\n",
       "          median_income  median_house_value ocean_proximity  \n",
       "latitude                                                     \n",
       "37.88            8.3252            452600.0        NEAR BAY  \n",
       "37.86            8.3014            358500.0        NEAR BAY  \n",
       "37.85            7.2574            352100.0        NEAR BAY  \n",
       "37.85            5.6431            341300.0        NEAR BAY  \n",
       "37.85            3.8462            342200.0        NEAR BAY  \n",
       "...                 ...                 ...             ...  \n",
       "39.48            1.5603             78100.0          INLAND  \n",
       "39.49            2.5568             77100.0          INLAND  \n",
       "39.43            1.7000             92300.0          INLAND  \n",
       "39.43            1.8672             84700.0          INLAND  \n",
       "39.37            2.3886             89400.0          INLAND  \n",
       "\n",
       "[20640 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"longitude\", \"population\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
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       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37.88</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.86</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.84</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.84</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.84</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.48</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.49</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.43</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.43</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.37</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20595 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "latitude                                                               \n",
       "37.88       -122.23                41.0        880.0           129.0   \n",
       "37.86       -122.22                21.0       7099.0          1106.0   \n",
       "37.84       -122.25                52.0       2535.0           489.0   \n",
       "37.84       -122.25                52.0       3104.0           687.0   \n",
       "37.84       -122.26                42.0       2555.0           665.0   \n",
       "...             ...                 ...          ...             ...   \n",
       "39.48       -121.09                25.0       1665.0           374.0   \n",
       "39.49       -121.21                18.0        697.0           150.0   \n",
       "39.43       -121.22                17.0       2254.0           485.0   \n",
       "39.43       -121.32                18.0       1860.0           409.0   \n",
       "39.37       -121.24                16.0       2785.0           616.0   \n",
       "\n",
       "          population  households  median_income  median_house_value  \\\n",
       "latitude                                                              \n",
       "37.88          322.0       126.0         8.3252            452600.0   \n",
       "37.86         2401.0      1138.0         8.3014            358500.0   \n",
       "37.84         1094.0       514.0         3.6591            299200.0   \n",
       "37.84         1157.0       647.0         3.1200            241400.0   \n",
       "37.84         1206.0       595.0         2.0804            226700.0   \n",
       "...              ...         ...            ...                 ...   \n",
       "39.48          845.0       330.0         1.5603             78100.0   \n",
       "39.49          356.0       114.0         2.5568             77100.0   \n",
       "39.43         1007.0       433.0         1.7000             92300.0   \n",
       "39.43          741.0       349.0         1.8672             84700.0   \n",
       "39.37         1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "         ocean_proximity  \n",
       "latitude                  \n",
       "37.88           NEAR BAY  \n",
       "37.86           NEAR BAY  \n",
       "37.84           NEAR BAY  \n",
       "37.84           NEAR BAY  \n",
       "37.84           NEAR BAY  \n",
       "...                  ...  \n",
       "39.48             INLAND  \n",
       "39.49             INLAND  \n",
       "39.43             INLAND  \n",
       "39.43             INLAND  \n",
       "39.37             INLAND  \n",
       "\n",
       "[20595 rows x 9 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([37.85], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude  housing_median_age   total_rooms  total_bedrooms  \\\n",
       "count  20640.000000        20640.000000  20640.000000    20433.000000   \n",
       "mean    -119.569704           28.639486   2635.763081      537.870553   \n",
       "std        2.003532           12.585558   2181.615252      421.385070   \n",
       "min     -124.350000            1.000000      2.000000        1.000000   \n",
       "25%     -121.800000           18.000000   1447.750000      296.000000   \n",
       "50%     -118.490000           29.000000   2127.000000      435.000000   \n",
       "75%     -118.010000           37.000000   3148.000000      647.000000   \n",
       "max     -114.310000           52.000000  39320.000000     6445.000000   \n",
       "\n",
       "         population    households  median_income  median_house_value  \n",
       "count  20640.000000  20640.000000   20640.000000        20640.000000  \n",
       "mean    1425.476744    499.539680       3.870671       206855.816909  \n",
       "std     1132.462122    382.329753       1.899822       115395.615874  \n",
       "min        3.000000      1.000000       0.499900        14999.000000  \n",
       "25%      787.000000    280.000000       2.563400       119600.000000  \n",
       "50%     1166.000000    409.000000       3.534800       179700.000000  \n",
       "75%     1725.000000    605.000000       4.743250       264725.000000  \n",
       "max    35682.000000   6082.000000      15.000100       500001.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>longitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3967.000000</td>\n",
       "      <td>3967.000000</td>\n",
       "      <td>3967.000000</td>\n",
       "      <td>3927.000000</td>\n",
       "      <td>3967.000000</td>\n",
       "      <td>3967.000000</td>\n",
       "      <td>3967.000000</td>\n",
       "      <td>3967.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-122.408072</td>\n",
       "      <td>33.831107</td>\n",
       "      <td>2461.945551</td>\n",
       "      <td>497.507003</td>\n",
       "      <td>1203.207714</td>\n",
       "      <td>468.308546</td>\n",
       "      <td>4.103604</td>\n",
       "      <td>244230.881775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.423863</td>\n",
       "      <td>13.270992</td>\n",
       "      <td>1710.886064</td>\n",
       "      <td>341.984939</td>\n",
       "      <td>842.230630</td>\n",
       "      <td>325.648578</td>\n",
       "      <td>2.048455</td>\n",
       "      <td>127889.888450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-122.470000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>287.000000</td>\n",
       "      <td>699.500000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>2.708300</td>\n",
       "      <td>140900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-122.300000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2104.000000</td>\n",
       "      <td>417.000000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>3.729200</td>\n",
       "      <td>226500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-122.160000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>3010.000000</td>\n",
       "      <td>612.500000</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>575.500000</td>\n",
       "      <td>5.001450</td>\n",
       "      <td>331500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-122.010000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>18634.000000</td>\n",
       "      <td>3226.000000</td>\n",
       "      <td>8276.000000</td>\n",
       "      <td>3589.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         longitude  housing_median_age   total_rooms  total_bedrooms  \\\n",
       "count  3967.000000         3967.000000   3967.000000     3927.000000   \n",
       "mean   -122.408072           33.831107   2461.945551      497.507003   \n",
       "std       0.423863           13.270992   1710.886064      341.984939   \n",
       "min    -124.350000            2.000000      8.000000        1.000000   \n",
       "25%    -122.470000           23.000000   1440.000000      287.000000   \n",
       "50%    -122.300000           34.000000   2104.000000      417.000000   \n",
       "75%    -122.160000           46.000000   3010.000000      612.500000   \n",
       "max    -122.010000           52.000000  18634.000000     3226.000000   \n",
       "\n",
       "        population   households  median_income  median_house_value  \n",
       "count  3967.000000  3967.000000    3967.000000         3967.000000  \n",
       "mean   1203.207714   468.308546       4.103604       244230.881775  \n",
       "std     842.230630   325.648578       2.048455       127889.888450  \n",
       "min       8.000000     1.000000       0.499900        14999.000000  \n",
       "25%     699.500000   270.000000       2.708300       140900.000000  \n",
       "50%    1019.000000   395.000000       3.729200       226500.000000  \n",
       "75%    1479.000000   575.500000       5.001450       331500.000000  \n",
       "max    8276.000000  3589.000000      15.000100       500001.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"longitude\"] < -122].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Select data from a specific population size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[...]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Pandas Documentation Subsetting](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)"
   ]
  }
 ],
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